Empathy is central to social interaction, yet how it is externally expressed in virtual reality (VR) communication remains underexplored. In this study, we examined how directionality-aware cues of empathy, such as mimicry, eye contact, and body proximity, relate to cognitive and emotional empathy. We designed high- and low-empathy scenarios and recruited participants with acting experience to ensure clear emotional expressions. Our findings indicate that facial mimicry patterns differ by empathy type: cognitive empathy involves subtle, speech-related muscle movements, whereas emotional empathy is associated with more intense affective expressions. Interestingly, we also found that while facial expressions and lower-body mimicry tend to emerge unconsciously, upper-body mimicry occurs more consciously, suggesting distinct pathways of empathic embodiment. We also observed that vocal intensity mimicry and pitch variability serve as important indicators of empathy, and a consistent hand approach is closely linked to empathy. Additionally, emotional empathy fosters longer eye contact, whereas cognitive empathy stabilizes gaze and head movements. Finally, we constructed machine learning models to predict empathy from these external expressions. Our best classifier achieved an accuracy of 0.756 for cognitive empathy and 0.704 for emotional empathy, indicating the feasibility of objective assessment. These findings provide a deeper understanding of how empathy is manifested in VR communication and support the development of empathy-aware virtual agents and training systems.

When giving input with a button, users follow one of two strategies: (1) react to the output from the computer or (2) proactively act in anticipation of the output from the computer. We propose a technique to quantify reactiveness and proactiveness to determine the degree and characteristics of each input strategy. The technique proposed in this study uses only screen recordings and does not require instrumentation beyond the input logs. The likelihood distribution of the time interval between the button inputs and system outputs, which is uniquely determined for each input strategy, is modeled. Then the probability that each observed input/output pair originates from a specific strategy is estimated along with the parameters of the corresponding likelihood distribution. In two empirical studies, we show how to use the technique to answer questions such as how to design animated transitions and how to predict a player’s score in real-time games.

Playtesting is widely performed in the game industry to gauge the difficulty of a game. A large number of test participants with different skills must be recruited for reliable test results, resulting in high costs. Automated playtesting based on player simulation is expected to reduce playtesting costs. Still, it has not yet been widely applied due to the lack of a method that realistically simulates players' gameplays with different skills. Based on a cognitive model of sensorimotor coordination that explains the human button input process, we propose a novel automated playtesting technique that predicts the game difficulty experienced by players with different skills in moving-target acquisition (MTA) games. The model has free parameters representing the inherent skills of players. Once the parameters are obtained for a specific population (e.g., seniors), it is possible to estimate the game difficulty at the population level in multiple games. We applied the technique to two simple MTA games and showed that it could predict the relative difference in game difficulties experienced by players with different skills.

Phase-change memory (PCM) is a non-volatile memory device with favorable characteristics such as persistence, byte-addressability, and lower latency when compared to flash memory. However, it comprises memory cells that have limited lifetime and higher access latency than DRAM. The row buffer size of a PCM is preferred to be larger than 128B to fill the latency gap between two memories and to reduce the metadata overhead incurred by wear leveling. As the cache line size in a general-purpose processor is 64B, a read-modify-write (RMW) module is required to be placed between the processor and the PCM, which in turn induces a performance degradation. To reduce such an overhead and enhance the reliability of a device, this paper presents a new RMW architecture. The proposed model introduces a DRAM cache in the RMW module, which minimizes redundant read operations for write operations by pre-fetching the entire transaction unit instead of merely caching the 64B requested data. Furthermore, a typeless merge operation is performed with the proposed cache by gathering multiple commands accessing consecutive addresses, irrespective of whether they are READ or WRITE. Simulation results indicate that the proposed method enhances the speed by 3.2 times and the reliability by 49 percent as compared to the baseline model.

Event-based social networks (EBSNs) are increasingly popular since they provide platforms on which online and offline activities are combined. Despite the increasing interest in EBSNs, little research has paid attention to the privacy issues coming from the unique features of EBSNs; the on-site information of users is highly relevant to real lives. In this paper, we try to investigate privacy leakages in Meetup, one of the most popular EBSN service. More specifically, we answer what private information can be inferred from the site's publicly available data. To this end, we conduct a measurement study by crawling webpages from Meetup containing 240K groups, 8.9M users, 27M group affiliations and 78M topical interests. By analyzing the dataset, we find that LGBT status of users, which is one of the most sensitive privacy information, can be predicted with 93% accuracy. Finally we discuss the cause of the privacy leakage on EBSNs and its possible ensuing damages.

Online games have been played mainly in desktop computers over wired networks because of high speed and intensive computation requirements. The advances in mobile devices and ever increasing wireless link bandwidth motivate us to study whether players can enjoy online gaming over broadband wireless networks such as mobile Worldwide Inter-operability for Microwave Access WiMAX. In this paper, we carry out comprehensive measurements of the World of Warcraft WoW over the mobile WiMAX in Seoul, Korea, and analyze the network performance focusing on two aspects: 1 network layer dynamics such as round trip time, jitter, and packet loss and 2 WiMAX link layer statistics such as the radio signal strength, handovers, and piggyback mechanism. From the empirical data, we set up performance models and evaluate the performance of WoW over WiMAX. We also discuss how to improve the service quality of online gaming over WiMAX.Copyright © 2013 John Wiley & Sons, Ltd.

Pinterest, a popular social curating service where people collect, organize, and share content (pins in Pinterest), has gained great attention in recent years. Despite the increasing interest in Pinterest, little research has paid attention to how people collect, manage, and share pins in Pinterest. In this paper, to shed insight on such issues, we study the following questions. How do people collect and manage pins by their tastes in Pinterest? What factors do mainly drive people to share their pins in Pinterest? How do the characteristics of users (e.g., gender, popularity, country) or properties of pins (e.g., category, topic) play roles in propagating pins in Pinterest? To answer these questions, we have conducted a measurement study on patterns of pin curating and sharing in Pinterest. By keeping track of all the newly posted and shared pins in each category (e.g., animal, kids, women's fashion) from June 5 to July 18, 2013, we built 350 K pin propagation trees for 3 M users. With the dataset, we investigate: (1) how users collect and curate pins, (2) how users share their pins and why, and (3) how users are related by shared pins of interest. Our key finding is that pin propagation in Pinterest is mostly driven by pin's properties like its topic, not by user's characteristics like her number of followers. We further show that users in the same community in the interest graph (i.e., representing the relations among users) of Pinterest share pins (i) in the same category with 94% probability and (ii) of the same URL where pins come from with 89% probability. Finally, we explore the implications of our findings for predicting how pins are shared in Pinterest.

While accurate and complete modeling of the Internet topology at the Autonomous System (AS) level is critical for future protocol design, performance evaluation, simulation and analysis, still it remains a challenge to construct its accurate representation. In this paper, we collect BGP route announcements of ASes from Looking glass (LG) servers. By querying LG servers, we build an AS topology estimate of around 116 K AS links, from which we discover 11 K new AS links and 686 new ASes. We conclude that collecting BGP traces from LG servers can help enhance the current view of the AS topology from the BGP collector projects (e.g., RouteViews).

According to the seven degrees of freedom (DOFs) human arm model composed of the shoulder, elbow, and wrist joints, positioning of the wrist in space and orientating the palm is a task requiring only six DOFs. Due to this redundancy, a given task can be completed by multiple arm configurations, and there is no unique mathematical solution to the inverse kinematics. The redundancy of a wearable robotic system (exoskeleton) that interacts with the human is expected to be resolved in the same way as that of the human arm. A unique solution to the system's redundancy was introduced by combining both kinematic and dynamic criteria. The redundancy of the arm is expressed mathematically by defining the swivel angle: the rotation angle of the plane including the upper and lower arm around a virtual axis connecting the shoulder and wrist joints which are fixed in space. Two different swivel angles were generated based on kinematic and dynamic constraints. The kinematic criterion is to maximize the projection of the longest principle axis of the manipulability ellipsoid for the human arm on the vector connecting the wrist and the virtual target on the head region. The dynamic criterion is to minimize the mechanical work done in the joint space for each two consecutive points along the task space trajectory. These two criteria were then combined linearly with different weight factors for estimating the swivel angle. Post processing of experimental data collected with a motion capturing system indicated that by using the proposed synthesis of redundancy resolution criteria, the error between the predicted swivel angle and the actual swivel angle adopted by the motor control system was less then five degrees. This result outperformed the prediction based on a single criteria.

We conduct comprehensive measurements on the current practice of content bundling to understand the structural patterns of torrents and the participant behaviors of swarms on one of the largest BitTorrent portals: The Pirate Bay. From the datasets of the 120K torrents and 14.8M peers, we investigate what constitutes torrents and how users participate in swarms from the perspective of bundling, across different content categories: Movie, TV, Porn, Music, Application, Game and E-book. In particular, we focus on: (1) how prevalent content bundling is, (2) how and what files are bundled into torrents, (3) what motivates publishers to bundle files, and (4) how peers access the bundled files. We find that over 72% of BitTorrent torrents contain multiple files, which indicates that bundling is widely used for file sharing. We reveal that profit-driven BitTorrent publishers who promote their own web sites for financial gains like advertising tend to prefer to use the bundling. We also observe that most files (94%) in a bundle torrent are selected by users and the bundle torrents are more popular than the single (or non-bundle) ones on average. Overall, there are notable differences in the structural patterns of torrents and swarm characteristics (i) across different content categories and (ii) between single and bundle torrents.

We analyze multi-relational social interaction networks in a large-scale commercial Massively Multiplayer Online Role-Playing Game(MMORPG). Our work is based on data from AION, currently the world's second most-played MMORPG with 3.4 million subscribers as of mid 2010, created and serviced by NCSoft, Inc. We construct and characterize six distinct interactivity networks (Friend, Private Messaging, Party invitation, Trade, Mail, and Shop), each representing diverse player interaction types.

"Real money trading" or "Gold farming" refers to a set of illicit practices for gathering and distributing virtual goods in online games for real money. Unlike previous work, we use network-wide economic interactions among in-game characters as a lens to monitor, detect and identify gold farming networks. Our work is based on a set of real in-game trade activity logs collected for one month in year 2010 from the world's second largest MMORPG called AION (with 3.4 million subscribers). This is the first work that empirically (i) shows that "free money network" is a promising measure/approximation for detecting and characterizing gold farming networks, and (ii) measures the size of the free money net and in-game virtual economy in a large-scale MMORPG in terms of the cash flow.

Despite the increasing interest in content bundling in BitTorrent systems, there are still few empirical studies on the bundling practice in real BitTorrent communities. In this paper, we conduct comprehensive measurements on one of the largest BitTorrent portals: The Pirate Bay. From the torrents data set collected for 38 days from April to May, 2010, we study how prevalent bundling is and how many files are bundled in a torrent, across different types of contents shared: Movie, Porn, TV, Music, Application, E-book, and Game.

Recent research on Internet traffic classification has yield a number of data mining techniques for distinguishing types of traffic, but no systematic analysis on "Why" some algorithms achieve high accuracies. In pursuit of empirically grounded answers to the "Why" question, which is critical in understanding and establishing a scientific ground for traffic classification research, this paper reveals the three sources of the discriminative power in classifying the Internet application traffic: (i) ports, (ii) the sizes of the first one-two (for UDP flows) or four-five (for TCP flows) packets, and (iii) discretization of those features. We find that C4.5 performs the best under any circumstances, as well as the reason why; because the algorithm discretizes input features during classification operations. We also find that the entropy-based Minimum Description Length discretization on ports and packet size features substantially improve the classification accuracy of every machine learning algorithm tested (by as much as 59.8%!) and make all of them achieve >93% accuracy on average without any algorithm-specific tuning processes. Our results indicate that dealing with the ports and packet size features as discrete nominal intervals, not as continuous numbers, is the essential basis for accurate traffic classification (i.e., the features should be discretized first), regardless of classification algorithms to use.

As mobile Internet environments are becoming dominant, how to revamp P2P operations for mobile hosts is gaining more and more attention. In this paper, we carry out empirical traffic measurement of BitTorrent service in various settings (static, bus and subway) in commercial WiMAX networks. To this end, we analyze the connectivity among peers, the download throughput/stability, and the signaling overhead of mobile WiMAX hosts in comparison to a wired (Ethernet) host. We find out the drawbacks of BitTorrent operations in mobile Internet are characterized by lower connection ratio, unstable connections amongst peers, and higher control message overhead.

While the Internet was designed with host-oriented networking applications, recent Internet statistics show that content-oriented traffic has become more and more dominant. Even though content-oriented networking, which tries to resolve this discordance, has received increasing attention, there have been few comprehensive and quantitative studies on how to realize a content-oriented networking architecture. In this paper, we focus on the design alternatives of the content-oriented networking architecture and evaluate their performance: (i) how to locate contents, (ii) how to cache contents, and (iii) how to deliver contents. There are two major infrastructure alternatives in substantiating these mechanisms: a tree and a distributed hash table (DHT).We carry out comprehensive simulation experiments to compare these alternatives in terms of content transfer latency, cache effectiveness, and failure resilience.

Recent research on Internet traffic classification algorithms has yield a flurry of proposed approaches for distinguishing types of traffic, but no systematic comparison of the various algorithms. This fragmented approach to traffic classification research leaves the operational community with no basis for consensus on what approach to use when, and how to interpret results. In this work we critically revisit traffic classification by conducting a thorough evaluation of three classification approaches, based on transport layer ports, host behavior, and flow features. A strength of our work is the broad range of data against which we test the three classification approaches: seven traces with payload collected in Japan, Korea, and the US. The diverse geographic locations, link characteristics and application traffic mix in these data allowed us to evaluate the approaches under a wide variety of conditions. We analyze the advantages and limitations of each approach, evaluate methods to overcome the limitations, and extract insights and recommendations for both the study and practical application of traffic classification. We make our software, classifiers, and data available for researchers interested in validating or extending this work.

Timely worldwide distribution of biosequence and bioinformatics data depends on high performance networking and advances in Internet transport methods. The Bio-Mirror project focuses on providing up-to-date distribution of this rapidly growing and changing data. It offers FTP, Web and Rsync access to many high-volume databanks from several sites around the world. Experiments with data grids and other methods offer future improvements in biology data distribution.

Broadcasting protocols offer an efficient and scalable method to provide video-on-demand. We present a new broadcasting protocol that assumes that some portions of the video are already preloaded in the set-top-box (STB), and at the same time, requires the user to wait for a fixed-delay before viewing. As a result, there is a trade-off between the size of the consumed local storage, user waiting time, and bandwidth consumption. This trade-off makes this protocol very flexible, in that we can control the consumption of one resource by adjusting the use of the other two. Also, the performance of the proposed protocol is not very far from the theoretical minimum. We also present a heuristic version of the protocol, in which the performance is improved a little.