With the recent popularity of smart terminals, the demand for high-data-rate transmission is growing rapidly, which brings a new challenge for the traditional cellular networks. Both device-to-device (D2D) communication and small cells are effective to improve the transmission efficiency of local communication. In this paper, we apply D2D communication into a small cell network system (SNets) and study about the optimization problem of resource allocation for D2D communication. The optimization problem includes system scheduling and resource allocation, which is exponentially complex and the optimal solution is infeasible to achieve. Therefore, in this paper, the optimization problem is decomposed into several smaller problems and a hierarchical scheme is proposed to obtain the solution. The proposed hierarchical scheme consists of three steps: D2D communication groups formation, the estimation of sub-channels needed by each D2D communication group and specific resource allocation. From numerical simulation results, we find that the proposed resource allocation scheme is effective in improving the spectral efficiency and reducing the outage probability of D2D communication.

Spectrum sensing is a key component of cognitive radio. The prediction of the primary user status in a low signal-to-noise ratio is an important factor in spectrum sensing. However, because of noise uncertainty, secondary users have difficulty distinguishing between the primary signal and an unauthorized signal when an unauthorized user exists in a cognitive radio network. To resolve the sensitivity to the noise uncertainty problem, we propose an entropy-based spectrum sensing scheme to detect the primary signal accurately in the presence of an unauthorized signal. The proposed spectrum sensing uses the conditional entropy between the primary signal and the unauthorized signal. The ability to detect the primary signal is thus robust against noise uncertainty, which leads to superior sensing performance in a low signal-to-noise ratio. Simulation results show that the proposed spectrum sensing scheme outperforms the conventional entropy-based spectrum sensing schemes in terms of the primary user detection probability.

Market means of spectrum trading have been utilized as a vital method of spectrum sharing and access in future cognitive radio system. In this paper, we consider the spectrum trading with multiple primary carrier providers (PCP) leasing the spectrum to multiple secondary carrier providers (SCP) for a short period of time. Several factors including the price of the resource, duration of leasing, and the spectrum quality guides the proposed model. We formulate three trading policies based on the game theory for dynamic spectrum access in a LTE based cognitive radio system (CRS). In the first, we consider utility function based resource sharing (UFRS) without any knowledge of past transaction. In the second policy, each SCP deals with PCP using a non-cooperative resource sharing (NCRS) method which employs optimal strategy based on reinforcement learning. In variation of second policy, third policy adopts a Nash bargaining while incorporating a recommendation entity in resource sharing (RERS). The simulation results suggest overall increase in throughput while maintaining higher spectrum efficiency and fairness.

The Cognitive Radio (CR) paradigm in tactical ad hoc networks is an important element of future military communications for network-centric warfare. This paper presents a novel Cognitive Link State Routing protocol for CR-based tactical ad hoc networks. The proposed scheme provides prompt and reliable routes for Primary User (PU) activity through procedures that incorporate two main functions: PU-aware power adaptation and channel switching. For the PU-aware power adaptation, closer multipoint relay nodes are selected to prevent network partition and ensure successful PU communication. The PU-aware channel switching is proactively conducted using control messages to switch to a new available channel based on a common channel list. Our simulation study based on the ns-3 simulator demonstrates that the proposed routing scheme delivers significantly improved performance in terms of average end-to-end delay, jitter, and packet delivery ratio.

Future wireless network aims to integrate different radio access networks (RANs) to provide a seamless access and service continuity. In this paper, a new resource denotation method is proposed in the WLAN and LTE heterogeneous networks based on a concept of spectral bandwidth mapping. This method simplifies the denotation of system resources and makes it possible to calculate system residual capacity, upon which an economic model-based network selection algorithm is designed in both under-loaded and over-loaded scenarios in the heterogeneous networks. The simulation results show that this algorithm achieves better performance than the utility function-based access selection (UFAS) method proposed in [12] in increasing system capacity and system revenue, achieving load balancing and reducing the new call blocking probability in the heterogeneous networks.

Considering the localization estimation issue in mixed line-of-sight (LOS)/non-LOS(NLOS) environments based on received signal strength (RSS) measurements in wireless sensor networks, a grid-based correlation method based on the relationship between distance and RSS is proposed in this paper. The Maximum-Likelihood (ML) estimator is appended to further improve the localization accuracy. Furthermore, in order to reduce computation load and enhance performance, an improved recursively version with NLOS mitigation is also proposed. The most advantages of the proposed localization algorithm is that, it does not need any prior knowledge of the propagation model parameters and therefore does not need any offline calibration effort to calibrate the model parameters in harsh environments, which makes it more convenient for rapid implementation in practical applications. The simulation and experimental results evidence that the proposed localization algorithm exhibits good localization performance and flexibilities for different devices.

We propose a new time-division half-duplex estimate-and-forward (EF) relaying strategy suitable for a relay with mobility. We reconfigure EF relaying to guarantee a strong relay-destination link which is required to achieve a high rate using EF relaying. Based on the reconfigured model, we optimize the relaying strategy to attain a high rate irrespective of the relay position with preserving the total transmit bandwidth and energy. The proposed relaying strategy achieves high communication reliability for any relay position, which differs from conventional EF and decode-and-forward (DF) relaying schemes.

Different from the existing works on coverage problems in wireless sensor networks (WSNs), this paper considers the exposure-path prevention problem by using the percolation theory in three dimensional (3D) WSNs, which can be implemented in intruder detecting applications. In this paper, to avoid the loose bounds of critical density, a bond percolation-based scheme is proposed to put the exposure-path problem into a 3D uniform lattice. Within this scheme, the tighter bonds of critical density for omnidirectional and directional sensor networks under random sensor deployment-a 3D Poisson process are derived. Extensive simulation results show that our scheme generates tighter bounds of critical density with no exposure path in 3D WSNs.

In cognitive radio networks (CRNs), hybrid overlay and underlay sharing transmission mode is an effective technique for improving the efficiency of radio spectrum. Unlike existing works in the literature, where only one secondary user (SU) uses overlay and underlay modes, the different transmission modes should be allocated to different SUs, according to their different quality of services (QoS), to achieve the maximal efficiency of radio spectrum. However, hybrid sharing mode allocation for heterogeneous services is still a challenge in CRNs. In this paper, we propose a new resource allocation method for hybrid sharing transmission mode of overlay and underlay (HySOU), to achieve more potential resources for SUs to access the spectrum without interfering with the primary users. We formulate the HySOU resource allocation as a mixed-integer programming problem to optimize the total system throughput, satisfying heterogeneous QoS. To decrease the algorithm complexity, we divide the problem into two sub-problems: subchannel allocation and power allocation. Cutset is used to achieve the optimal subchannel allocation, and the optimal power allocation is obtained by Lagrangian dual function decomposition and subgradient algorithm. Simulation results show that the proposed algorithm further improves spectrum utilization with a simultaneous fairness guarantee, and the achieved HySOU diversity gain is a satisfactory improvement.

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

In the combustion engineering, to decrease pollution and increase production efficiency, and to optimally keep solid burning material amount constant in a burner online, it needs a smart method to detect the amount variation of the burning materials in a high temperature environment. This paper presents an online machine vision system for automatically measuring and detecting the burning material amount inside a burner or a boiler. In the camera-protecting box of the system, a sub-system for cooling is constructed by using the cooling water circulation techqique. In addition, the key and intelligent step in the system is to detect the pile profile of the variable burning material, and the algorithm for the pile profile tracing was studied based on the combination of the gey level (color) discontinuity and similarity based image segmentation methods, the discontinuity based sub-algorithm is made on the quaternion convolution, and the similarity based sub-algorithm is designed according to the region growing with multi-scale clustering. The results of the two sub-algoritms are fused to delineate the final pile profile, and the algorithm has been tested and applied in different industrial burners and boilers. The experiements show that the proposed algorithm works satisfactorily.

To solve the problem of face recognition with complex changes and further improve the efficiency, a new near-infrared face recognition algorithm which fuses E-GV-LBP and FKNN algorithm is proposed. Firstly, it transforms near infrared face image by Gabor wavelet. Then, it extracts LBP coding feature that contains space, scale and direction information. Finally, this paper introduces an improved FKNN algorithm which is based on spatial domain. The proposed approach has brought face recognition more quickly and accurately. The experiment results show that the new algorithm has improved the recognition accuracy and computing time under the near-infrared light and other complex changes. In addition, this method can be used for face recognition under visible light as well.

Multiview plus depth (MVD) videos are widely used in free-viewpoint TV systems. The best-known technique to determine depth information is based on stereo vision. In this paper, we propose a novel local stereo matching algorithm which is radiometric invariant. The key idea is to use a combined matching cost of intensity and gradient based similarity measure. In addition, we realize an adaptive cost aggregation scheme by constructing an adaptive support window for each pixel, which can solve the boundary and low texture problems. In the disparity refinement process, we propose a four-step post-processing technique to handle outliers and occlusions. Moreover, we conduct stereo reconstruction tests to verify the performance of the algorithm more intuitively. Experimental results show that the proposed method is effective and robust against local radiometric distortion. It has an average error of 5.93% on the Middlebury benchmark and is compatible to the state-of-art local methods.

Three-dimensional video coding is one of the main challenges restricting the widespread applications of 3D video and free viewpoint video. In this paper, a novel fractal coding algorithm with motion-vector-field-based motion estimation for depth map sequence is proposed. We firstly add pre-search restriction to rule the improper domain blocks out of the matching search process so that the number of blocks involved in the search process can be restricted to a smaller size. Some improvements for motion estimation including initial search point prediction, threshold transition condition and early termination condition are made based on the feature of fractal coding. The motion-vector-field-based adaptive hexagon search algorithm on the basis of center-biased distribution characteristics of depth motion vector is proposed to accelerate the search. Experimental results show that the proposed algorithm can reach optimum levels of quality and save the coding time. The PSNR of synthesized view is increased by 0.56 dB with 36.97% bit rate decrease on average compared with H.264 Full Search. And the depth encoding time is saved by up to 66.47%. Moreover, the proposed fractal depth map sequence codec outperforms the recent alternative codecs by improving the H.264/AVC, especially in much bitrate saving and encoding time reduction.

In this paper, we investigate physical layer security for multiple decode-and-forward (DF) relaying underlay cognitive radio networks (CRNs) with fixed transmit power at the secondary network against passive eavesdropping attacks. We propose a simple relay selection scheme to improve wireless transmission security based on the instantaneous channel information of all legitimate users and the statistical information about the eavesdropper channels. The closed-form expressions of the probability of non-zero secrecy capacity and the secrecy outage probability (SOP) are derived over independent and non-identically distributed Rayleigh fading environments. Furthermore, we conduct the asymptotic analysis to evaluate the secrecy diversity order performance and prove that full diversity is achieved by using the proposed relay selection. Finally, numerical results are presented to verify the theoretical analysis and depict that primary interference constrain has a significant impact on the secure performance and a proper transmit power for the second transmitters is preferred to be energy-efficient and improve the secure performance.

Block cipher ARIA was first proposed by some South Korean experts in 2003, and later, it was established as a Korean Standard block cipher algorithm by Korean Agency for Technology and Standards. In this paper, we focus on the security evaluation of ARIA block cipher against the recent zero-correlation linear cryptanalysis. In addition, Partial-sum technique and FFT (Fast Fourier Transform) technique are used to speed up the cryptanalysis, respectively. We first introduce some 4-round linear approximations of ARIA with zero-correlation, and then present some key-recovery attacks on 6/7-round ARIA-128/256 with the Partial-sum technique and FFT technique. The key-recovery attack with Partial-sum technique on 6-round ARIA-128 needs $2^{123.6}$ known plaintexts (KPs), $2^{121}$ encryptions and $2^{90.3}$ bytes memory, and the attack with FFT technique requires $2^{124.1}$ KPs, $2^{121.5}$ encryptions and $2^{90.3}$ bytes memory. Moreover, applying Partial-sum technique, we can attack 7-round ARIA-256 with $2^{124.6}$ KPs, $2^{203.5}$ encryptions and $2^{152}$ bytes memory and 7-round ARIA-256 employing FFT technique, requires $2^{124.7}$ KPs, $2^{209.5}$ encryptions and $2^{152}$ bytes memory. Our results are the first zero-correlation linear cryptanalysis results on ARIA.

Advanced Encryption Standard (AES) is widely used for protecting wireless sensor network (WSN). At the Workshop on Cryptographic Hardware and Embedded Systems (CHES) 2012, G$\acute{e}$rard et al. proposed an optimized collision attack and break a practical implementation of AES. However, the attack needs at least 256 averaged power traces and has a high computational complexity because of its byte wise operation. In this paper, we propose a novel double sieve collision attack based on bitwise collision detection, and an improved version with an error-tolerant mechanism. Practical attacks are successfully conducted on a software implementation of AES in a low-power chip which can be used in wireless sensor node. Simulation results show that our attack needs 90% less time than the work published by G$\acute{e}$rard et al. to reach a success rate of 0.9.

Recently many companies began to realize their visions for the sustainable growth with the advent of CSV(Creating Shared Values). Michael E. Porter, a Harvard Professor, claims that placing social value creation at the core of business strategy has the potential to uncover big opportunities for individual companies and that shared value can play a significant role in increasing competitive advantages while fostering social prosperity. In consequence, the various researches have illustrated how to get the opportunity for competitive advantages from building a social value proposition into corporate strategy, and considerable studies have been promoted heavily from the managerial perspective. However, due to the lack of capability converging information technology with business strategy, any research effort to identify technological or Internet-related issues and to link the issues to CSV does not exist. With Korean being a Internet leading country, the demands of researches analyzing core technology, information, and service utilizing Internet are rapidly growing. The study aims to find out Internet-related enablers for CSV. This paper describes the concepts and features of CSV, identifies emerging Internet-related issues toward the opportunity for competitive advantage, and then depicts the rigorous research endeavors in the areas of Internet information, technology, and services. As a result, 11 papers presented and selected as the outstanding papers at APIC-IST 2014 handle the issues to be brought together, which include: Wireless and Sensor Network, Image Processing and HCI, Big Data and Business Intelligence, Security & Privacy in Internet, SNS & Communication, Smart-Learning and e-Learning, and Internet Business Strategy. The study finally recommends indispensible terms for substantially vitalizing CSV.

In wearable sensor systems (WSSs), sensor nodes are deployed around human body parts such as the arms, the legs, the stomach, and the back. These sensors have limited lifetimes because they are battery-operated. Thus, transmission power control (TPC) is needed to save the energy of sensor nodes. The TPC should control the transmission power level (TPL) of sensor nodes based on current channel conditions. However, previous TPC algorithms did not precisely estimate the channel conditions. Therefore, we propose a new TPC algorithm that uses an accelerometer to directly measure the current channel condition. Based on the directly measured channel condition, the proposed algorithm adaptively adjusts the transmission interval of control packets for updating TPL. The proposed algorithm is efficient because the power consumption of the accelerometer is much lower than that of control packet transmissions. To evaluate the effectiveness of our approach, we implemented the proposed algorithm in real sensor devices and compared its performance against diverse TPC algorithms. Through the experimental results, we proved that the proposed TPC algorithm outperformed other TPC algorithms in all channel environments.

We propose a distributed medium access control protocol for cognitive radio networks to opportunistically utilize multiple channels. Under the proposed protocol, cognitive radio nodes forecast and rank channel availability observing primary users' activities on the channels for a period of time by time series analyzing using smoothing models for seasonal data by Winters' method. The proposed approach protects primary users, mitigates channel access delay, and increases network performance. We analyze the optimal time to sense channels to avoid conflict with the primary users. We simulate and compare the proposed protocol with the existing protocol. The results show that the proposed approach utilizes channels more efficiently.

With the increase in a number of access technologies and data rates, a continuous connection among different networks is demand of the future wireless networks. In the last decade, user connectivity among different access networks remained a challenging job. Therefore, in this article, we proposed a user-centric and user-perspective based network selection mechanism for fast handover management in heterogeneous wireless networks. The proposed scheme selects the most appropriate network among available networks on the basis of resources i.e. cost, data rate, and link quality. Initially, we load the Media Independent Information Service (MIIS) with the information of cost and data rate provided by different network operators. Similarly, Mobile Node (MN) is also loaded with the user preferred cost and data rate for different applications. The MN obtains the information of cost and data rate from MIIS server upon a predefined threshold, and make a decision for handover according to its current cost and data rate. Furthermore, we employ an optimal threshold mechanism for initiation of the handover execution phase to minimize false handover indications. The proposed scheme is based on a survey for network selection and its implementation in C programming language to validate its performance and accuracy. The simulation result shows that the proposed scheme performs superior then the schemes present in the current literature.

In this paper, we propose a novel approach for trajectory recovery. Our system uses a triangulation procedure for skeletonization and graph theory to extract the trajectory. Skeletonization extracts the polyline skeleton according to the polygonal contours of the handwritten characters, and as a result, the junction becomes clear and the characters that are touching each other are separated. The approach for the trajectory recovery is based on graph theory to find the optimal path in the graph that has the best representation of the trajectory. An undirected graph model consisting of one or more strokes is constructed from a polyline skeleton. By using the polyline skeleton, our approach accelerates the process to search for an optimal path. In order to evaluate the performance, we built our own dataset, which includes testing and ground-truth. The dataset consist of thousands of handwritten characters and word images, which are extracted from five handwritten documents. To show the relative advantage of our skeletonization method, we first compare the results against those from Zhang-Suen, a state-of-the-art skeletonization method. For the trajectory recovery, we conduct a comparison using the Root Means Square Error (RMSE) and Dynamic Time Warping (DTW) in order to measure the error between the ground truth and the real output. The comparison reveals that our approach has better performance for both the skeletonization stage and the trajectory recovery stage. Moreover, the processing time comparison proves that our system is faster than the existing systems.

Image projectors can turn any surface into a display. Integrating a surface projection with a user interface transforms it into an interactive display with many possible applications. Hand gesture interfaces are often used with projector-camera systems. Hand detection through color image processing is affected by the surrounding environment. The lack of illumination and color details greatly influences the detection process and drops the recognition success rate. In addition, there can be interference from the projection system itself due to image projection. In order to overcome these problems, a gesture interface based on depth images is proposed for projected surfaces. In this paper, a depth camera is used for hand recognition and for effectively extracting the area of the hand from the scene. A hand detection and finger tracking method based on depth images is proposed. Based on the proposed method, a touch interface for the projected surface is implemented and evaluated.

Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers' opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well.

Natural Language Question Answering (NLQA) and Prescriptive Analytics (PA) have been identified as innovative, emerging technologies in 2015 by the Gartner group. These technologies require knowledge bases that consist of data that has been extracted from unstructured texts. Every business requires a knowledge base for business analytics as it can enhance companies' competitiveness in their industry. Most intelligent or analytic services depend a lot upon on knowledge bases. However, building a qualified knowledge base is very time consuming and requires a considerable amount of effort, especially if it is to be manually created. Another problem that occurs when creating a knowledge base is that it will be outdated by the time it is completed and will require constant updating even when it is ready in use. For these reason, it is more advisable to create a computerized knowledge base. This research focuses on building a computerized knowledge base for business using a supervised learning and rule-based method. The method proposed in this paper is based on information extraction, but it has been specialized and modified to extract information related only to a business. The business knowledge base created by our system can also be used for advanced functions such as presenting the hierarchy of technologies and products, and the relations between technologies and products. Using our method, these relations can be expanded and customized according to business requirements.

Recently, there have been fast-growing social network services based on the Internet environment and web technology development, the prevalence of smartphones, etc. Social networks also allow the users to convey the information and news so that they have a great influence on the public opinion formed by social interaction among users as well as the spread of information. On the other hand, these social networks also serve as perfect environments for rampant malware. Malware is rapidly being spread because relationships are formed on trust among the users. In this paper, an effective patch strategy is proposed to deal with malicious worms based on social networks. A graph is formed to analyze the structure of a social network, and subgroups are formed in the graph for the distributed patch strategy. The weighted directions and activities between the nodes are taken into account to select reliable key nodes from the generated subgroups, and the Incremental PageRanking algorithm reflecting dynamic social network features (addition/deletion of users and links) is used for deriving the high influential key nodes. With the patch based on the derived key nodes, the proposed method can prevent worms from spreading over social networks.

In reading news stories online, people are exposed to others' comments to a news story. In spite of popularity of comments to a news story online, the understanding of why and who posts a comment is still lacking. To complement scarcity and have a better understanding of comment-posting online, this study examined psychological factors which affect the likelihood of posting a comment to a news story online. In particular, three variables were considered: The first variable was communication efficacy, that is, an individual's belief about communication practices and systems, under the supposition that the people who have greater levels of communication efficacy are more likely to post a comment. The second variable was perception of public opinion, to test that when and if people judge their position of the issue as favored by the majority, they tend to post a comment. And finally, the tone of existing comments was included, to test if the tone of comments affects the willingness to post a comment. The results showed that firstly, people at a high level of communication efficacy are more likely to post a comment compared to people at the low level of communication efficacy. Secondly, the perception of public opinion partly influenced the willingness to post a comment. Especially, when people believe communication systems contribute to develop society and also when judged that their opinion is the majority's opinion, they are more likely to post a comment. Finally, the tone of the comments influenced the willingness to post a comment only on the condition that people are confident of the communication practice and are exposed to emotional comments.

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

IT is the key enabler of digital economy. Appropriate usage of IT can provide a strategic competitive advantage to a firm in a dynamic competitive environment. However, there has been a continuing debate on whether IT can actually enhance the productivity of firms. This concept is called IT productivity paradox. In this study, we analyzed the causality among appropriate indicators to demonstrate the real impact of IT on productivity. The 12,100 sample data from 2011 were used for analysis. As expected, the results indicated that mobile device usage, website adoption, e-commerce, open source, cloud computing, and green computing positively influence IT productivity. This unprecedented large-scale analysis can provide clarification regarding the ambiguous causal mechanism between IT usage and productivity.