Web-based social engineering (SE) attacks manipulate users to perform specific actions, such as downloading malware and exposing personal information. Aiming to effectively lure users, some SE attacks, which we call multi-step SE attacks, constitute a sequence of web pages starting from a landing page and require browser interactions at each web page. Also, different browser interactions executed on a web page often branch to multiple sequences to redirect users to different SE attacks. Although common systems analyze only landing pages or conduct browser interactions limited to a specific attack, little effort has been made to follow such sequences of web pages to collect multi-step SE attacks. We propose STRAYSHEEP, a system to automatically crawl a sequence of web pages and detect diverse multi-step SE attacks. We evaluate the effectiveness of STRAYSHEEP's three modules (landing-page-collection, web-crawling, and SE-detection) in terms of the rate of collected landing pages leading to SE attacks, efficiency of web crawling to reach more SE attacks, and accuracy in detecting the attacks. Our experimental results indicate that STRAYSHEEP can lead to 20% more SE attacks than Alexa top sites and search results of trend words, crawl five times more efficiently than a simple crawling module, and detect SE attacks with 95.5% accuracy. We demonstrate that STRAYSHEEP can collect various SE attacks, not limited to a specific attack. We also clarify attackers' techniques for tricking users and browser interactions, redirecting users to attacks.

The 2019 Typhoon Hagibis (No. 19) caused widespread destruction in eastern Japan. During the disaster, many tweets including rescue request hashtags such as #救助 (meaning #Rescue) and #救助要請 (meaning #Rescue_request) were posted on Twitter. An official disaster information account of the Nagano Prefectural Government asked the public to provide information in the form of damage reports and rescue requests using the hashtag #台風19号長野県被害 (#Typhoon_No.19_Nagano_Prefecture_damage). As a result, many tweets were posted using this hashtag. Moreover, the account contacted the posters of tweets requesting rescue and delivered the information to the Fire Department. In this study, we analyze the circumstances of the above tweets.

Computation methods using custom circuits are frequently employed to improve the throughput and power efficiency of computing systems. Hardware development, however, can incur significant development costs because designs at the register-transfer level (RTL) with a hardware description language (HDL) are time-consuming. This paper proposes a hardware and software co-design environment, named Mulvery, which is designed for non-professional hardware designer We focus on the similarities between functional reactive programming (FRP) and dataflow in computation. This study provides an idea to design hardware with a dynamic typing language, such as Ruby, using FRP and provides the proof-of-concept of the method. Mulvery, which is a hardware and software co-design tool based on our method, reduces development costs. Mulvery exhibited high performance compared with software processing techniques not equipped with hardware knowledge. According to the experiment, the method allows us to design hardware without degradation of performance. The sample application applied a Laplacian filter to an image with a size of 128×128 and processed a convolution operation within one clock.

Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.

Multiple human tracking is widely used in various fields such as marketing and surveillance. The typical approach associates human detection results between consecutive frames using the features and bounding boxes (position+size) of detected humans. Some methods use an omnidirectional camera to cover a wider area, but ID switch often occurs in association with detections due to following two factors: i) The feature is adversely affected because the bounding box includes many background regions when a human is captured from an oblique angle. ii) The position and size change dramatically between consecutive frames because the distance metric is non-uniform in an omnidirectional image. In this paper, we propose a novel method that accurately tracks humans with an association metric for omnidirectional images. The proposed method has two key points: i) For feature extraction, we introduce local rectification, which reduces the effect of background regions in the bounding box. ii) For distance calculation, we describe the positions in a world coordinate system where the distance metric is uniform. In the experiments, we confirmed that the Multiple Object Tracking Accuracy (MOTA) improved 3.3 in the LargeRoom dataset and improved 2.3 in the SmallRoom dataset.

Device-to-device (D2D) content delivery reduces the energy consumption of frequent content retrieval in future content-centric cellular networks based on proximal content delivery. Compared with unicast, multicast may be more efficient since it serves the content requests of multiple users simultaneously. The serving efficiency mainly depends on the selection of multicast transmitter, which has not been well addressed. In this letter, we consider the match degree between the multicast content of transmitter and the required content of receiver based on social relationship between transceivers. By integrating the effects of communication environments and match degree into the selection procedure, a multicast UE selection scheme is proposed to improve the number of benefited receivers from D2D multicast. Simulation results show that the proposed scheme can efficiently improve the performance of D2D multicast content delivery under different communication environments.

Communication networks are now an essential infrastructure of society. Many services are constructed across multiple network domains. Therefore, the reliability of multi-domain networks should be evaluated to assess the sustainability of our society, but there is no known method for evaluating it. One reason is the high computation complexity; i.e., network reliability evaluation is known to be #P-complete, which has prevented the reliability evaluation of multi-domain networks. The other reason is intra-domain privacy; i.e., network providers never disclose the internal data required for reliability evaluation. This paper proposes a novel method that computes the lower and upper bounds of reliability in a distributed manner without requiring privacy disclosure. Our method is solidly based on graph theory, and is supported by a simple protocol that secures intra-domain privacy. Experiments on real datasets show that our method can successfully compute the reliability for 14-domain networks in one second. The reliability is bounded with reasonable errors; e.g., bound gaps are less than 0.1% for reliable networks.

Objective interestingness measures play a vital role in association rule mining of a large-scaled database because they are used for extracting, filtering, and ranking the patterns. In the past, several measures have been proposed but their similarities or relations are not sufficiently explored. This work investigates sixty-one objective interestingness measures on the pattern of A → B, to analyze their similarity and dissimilarity as well as their relationship. Three-probability patterns, P(A), P(B), and P(AB), are enumerated in both linear and exponential scales and each measure's values of those conditions are calculated, forming synthesis data for investigation. The behavior of each measure is explored by pairwise comparison based on these three-probability patterns. The relationship among the sixty-one interestingness measures has been characterized with correlation analysis and association rule mining. In the experiment, relationships are summarized using heat-map and association rule mined. As the result, selection of an appropriate interestingness measure can be realized using the generated heat-map and association rules.

Depression is a major mental health problem in Thailand. The depression rates have been rapidly increasing. Over 1.17 million Thai people suffer from this mental illness. It is important that a reliable depression screening tool is made available so that depression could be early detected. Given Facebook is the most popular social network platform in Thailand, it could be a large-scale resource to develop a depression detection tool. This research employs techniques to develop a depression detection algorithm for the Thai language on Facebook where people use it as a tool for sharing opinions, feelings, and life events. To establish the reliable result, Thai Mental Health Questionnaire (TMHQ), a standardized psychological inventory that measures major mental health problems including depression. Depression scale of the TMHQ comprises of 20 items, is used as the baseline for concluding the result. Furthermore, this study also aims to do factor analysis and reduce the number of depression items. Data was collected from over 600 Facebook users. Descriptive statistics, Exploratory Factor Analysis, and Internal consistency were conducted. Results provide the optimized version of the TMHQ-depression that contain 9 items. The 9 items are categorized into four factors which are suicidal ideation, sleep problems, anhedonic, and guilty feelings. Internal consistency analysis shows that this short version of the TMHQ-depression has good to excellent reliability (Cronbach's alpha >.80). The findings suggest that this optimized TMHQ-depression questionnaire holds a good psychometric property and can be used for depression detection.

In small cell deployments, the combined usage of user association and inter-cell interference coordination (ICIC) is inevitable. This paper investigates the joint optimization of user association and ICIC in the downlink. We first formulate the joint optimization problem as a utility maximization problem. We then employ the logarithmic utility function known as the proportional fair criteria. The optimum user association and the ICIC are derived by solving a convex optimization problem based on the average spectral efficiencies of all users. We propose an iterative algorithm to obtain the optimum solution to this problem. We evaluate the performance of the proposed algorithm for the small cell deployments and shows that the proposed algorithm works well. We also compare the performance of the proposed algorithm based on utility maximization user association with the CRE, and show the superiority of the utility maximization. Furthermore, we show that intra-tier ICIC and inter-tier ICIC can effectively improve the throughput performance according to the conditions. It is also shown that the combined usage of inter-tier ICIC and intra-tier ICIC enhances the throughput performance compared to schemes employing either the inter- or intra-tier ICIC scheme.

In the theory of orthogonal arrays, an orthogonal array (OA) is called schematic if its rows form an association scheme with respect to Hamming distances. In this paper, we study the Hamming distances of any two rows in an OA, construct some schematic OAs of strength two and give the positive solution to the open problem for classifying all schematic OAs. Some examples are given to illustrate our methods.

It is of great importance to recommend collaborators for scholars in academic social networks, which can benefit more scientific research results. Facing the problem of data sparsity of co-author recommendation in academic social networks, a novel recommendation algorithm named HeteroRWR (Heterogeneous Random Walk with Restart) is proposed. Different from the basic Random Walk with Restart (RWR) model which only walks in homogeneous networks, HeteroRWR implements multiple random walks in a heterogeneous network which integrates a citation network and a co-authorship network to mine the k mostly valuable co-authors for target users. By introducing the citation network, HeteroRWR algorithm can find more suitable candidate authors when the co-authorship network is extremely sparse. Candidate recommenders will not only have high topic similarities with target users, but also have good community centralities. Analyses on the convergence and time efficiency of the proposed approach are presented. Extensive experiments have been conducted on DBLP and CiteSeerX datasets. Experimental results demonstrate that HeteroRWR outperforms state-of-the-art baseline methods in terms of precision and recall rate even in the case of incorporating an incomplete citation dataset.

A travel route recommendation service that recommends a sequence of points of interest for tourists traveling in an unfamiliar city is a very useful tool in the field of location-based social networks. Although there are many web services and mobile applications that can help tourists to plan their trips by providing information about sightseeing attractions, travel route recommendation services are still not widely applied. One reason could be that most of the previous studies that addressed this task were based on the orienteering problem model, which mainly focuses on the estimation of a user-location relation (for example, a user preference). This assumes that a user receives a reward by visiting a point of interest and the travel route is recommended by maximizing the total rewards from visiting those locations. However, a location-location relation, which we introduce as a transition pattern in this paper, implies useful information such as visiting order and can help to improve the quality of travel route recommendations. To this end, we propose a travel route recommendation method by combining location and transition knowledge, which assigns rewards for both locations and transitions.

Recently, social relation analysis receives an increasing amount of attention from text to image data. However, social relation analysis from video is an important problem, which is lacking in the current literature. There are still some challenges: 1) it is hard to learn a satisfactory mapping function from low-level pixels to high-level social relation space; 2) how to efficiently select the most relevant information from noisy and unsegmented video. In this paper, we present an Attentive Sequences Recurrent Network model, called ASRN, to deal with the above challenges. First, in order to explore multiple clues, we design a Multiple Feature Attention (MFA) mechanism to fuse multiple visual features (i.e. image, motion, body, and face). Through this manner, we can generate an appropriate mapping function from low-level video pixels to high-level social relation space. Second, we design a sequence recurrent network based on Global and Local Attention (GLA) mechanism. Specially, an attention mechanism is used in GLA to integrate global feature with local sequence feature to select more relevant sequences for the recognition task. Therefore, the GLA module can better deal with noisy and unsegmented video. At last, extensive experiments on the SRIV dataset demonstrate the performance of our ASRN model.

According to the official TDOAS 2009~2017 survey, the penetration rate of social media in Taiwan has reached a record 96.8%, while the Internet access rate is as high as 99.7%. However, people using government online services access to relevant information has continued to decline over the years, from 50.8% in 2009 to 35.4% in 2017. At the same time, the proportion of e-transaction users has also dropped simultaneously from 30.3% to 27.7%. In particular, only 1.1% of them are interested in government online forums, while the remaining 97.2% are more willing to engage in social media as a source of personal reference. The study aims to explore why are users not interested in accessing e-government services? Are they affected by the popularity of social networking applications? What are the key factors for users to continue to use e-government service? The research framework was adapted from expectation confirmation theory and model (ECT/ECM), technology acceptance model (TAM) with trust theories, in validating attitude measures for a better understanding of continuance intention of using e-government service. In terms of measurement, the assessment used the structural equation modeling method (SEM) to explore the views and preferences of 400 college students on e-government service. The study results identified that perceived usefulness not only plays a full mediating role, it is expected to be the most important ex-post factor influencing user's intention to continue using e-government service. It also clarifies that the intent to continue to use e-government services is not related to use any alternative means such as social media application.

The Cyber-Physical Integrated Society (CPIS) is being formed with the fusion of cyber-space and the real-world. In this paper, we will discuss Data-Driven Decision-Making (DDDM) support systems to solve social problems in the CPIS. First, we introduce a Web of Resources (WoR) that uses Web booking log data for destination data management. Next, we introduce an Internet of Persons (IoP) system to visualize individual and group flows of people by analyzing collected Wi-Fi usage log data. Specifically, we present examples of how WoR and IoP visualize flows of groups of people that can be shared across different industries, including telecommunications carriers and railway operators, and policy decision support for local, short-term events. Finally, the importance of data-driven training of human resources to support DDDM in the future CPIS is discussed.

Social networks often demonstrate hierarchical community structure with communities embedded in other ones. Most existing hierarchical community detection methods need one or more tunable parameters to control the resolution levels, and the obtained dendrograms, a tree describing the hierarchical community structure, are extremely complex to understand and analyze. In the paper, we propose a parameter-free hierarchical community detection method based on micro-community and minimum spanning tree. The proposed method first identifies micro-communities based on link strength between adjacent vertices, and then, it constructs minimum spanning tree by successively linking these micro-communities one by one. The hierarchical community structure of social networks can be intuitively revealed from the merging order of these micro-communities. Experimental results on synthetic and real-world networks show that our proposed method exhibits good accuracy and efficiency performance and outperforms other state-of-the-art methods. In addition, our proposed method does not require any pre-defined parameters, and the output dendrogram is simple and meaningful for understanding and analyzing the hierarchical community structure of social networks.

This paper surveys advances in biomedical processor SoC technology for healthcare application and reviews state-of-the-art architecture and circuits used in SoC integration. Particularly, this paper categorizes and describes techniques for improving power efficiency in communication, computation, and sensing. Additionally, it surveys accuracy enhancement techniques for bio-signal measurement and recognition. Finally, we have discussed the potential new directions for development as well as research.

There are different types of social ties among people, and recognizing specialized types of relationship, such as family or friend, has important significance. It can be applied to personal credit, criminal investigation, anti-terrorism and many other business scenarios. So far, some machine learning algorithms have been used to establish social relationship inferencing models, such as Decision Tree, Support Vector Machine, Naive Bayesian and so on. Although these algorithms discover family members in some context, they still suffer from low accuracy, parameter sensitive, and weak robustness. In this work, we develop a Novel Family Relationship Recognition (NFRR) algorithm on telecom dataset for identifying one's family members from its contact list. In telecom dataset, all attributes are divided into three series, temporal, spatial and behavioral. First, we discover the most probable places of residence and workplace by statistical models, then we aggregate data and select the top-ranked contacts as the user's intimate contacts. Next, we establish Relational Spectrum Matrix (RSM) of each user and its intimate contacts to form communication feature. Then we search the user's nearest neighbors in labelled training set and generate its Specialized Family Spectrum (SFS). Finally, we decide family relationship by comparing the similarity between RSM of intimate contacts and the SFS. We conduct complete experiments to exhibit effectiveness of the proposed algorithm, and experimental results also show that it has a lower complexity.

Spectral graph theory gives an algebraic approach to the analysis of the dynamics of a network by using the matrix that represents the network structure. However, it is not easy for social networks to apply the spectral graph theory because the matrix elements cannot be given exactly to represent the structure of a social network. The matrix element should be set on the basis of the relationship between persons, but the relationship cannot be quantified accurately from obtainable data (e.g., call history and chat history). To get around this problem, we utilize the universality of random matrices with the feature of social networks. As such a random matrix, we use the normalized Laplacian matrix for a network where link weights are randomly given. In this paper, we first clarify that the universality (i.e., the Wigner semicircle law) of the normalized Laplacian matrix appears in the eigenvalue frequency distribution regardless of the link weight distribution. Then, we analyze the information propagation speed by using the spectral graph theory and the universality of the normalized Laplacian matrix. As a result, we show that the worst-case speed of the information propagation changes up to twice if the structure (i.e., relationship among people) of a social network changes.