Query: Enabling Technologies for the Internet of Health Things

Relevant passages:
Privacy and Security in Internet of Things and Wearable Devices <=SEP=> Enter the nascent era of Internet of Things (IoT) and wearable devices, where small embedded devices loaded with sensors collect information from its surroundings, process it, and relay it to remote locations for further analysis. Albeit looking harmless, these nascent technologies raise security and privacy concerns. We pose the question of the possibility and effects of compromising such devices. Concentrating on the design flow of IoT and wearable devices, we discuss some common design practices and their implications on security and privacy. Two representatives from each category, the Google Nest Thermostat and the Nike+ Fuelband, are selected as examples on how current industry practices of security as an afterthought or an add-on affect the resulting device and the potential consequences to the user's security and privacy. We then discuss design flow enhancements, through which security mechanisms can efficiently be added into a device, vastly differing from traditional practices.

Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios <=SEP=> Connectivity is probably the most basic building block of the IoT paradigm. Up to now, the two main approaches to provide data access to things have been based on either multihop mesh networks using short-range communication technologies in the unlicensed spectrum, or long-range legacy cellular technologies, mainly 2G/GSM/GPRS, operating in the corresponding licensed frequency bands. Recently, these reference models have been challenged by a new type of wireless connectivity, characterized by low-rate, long-range transmission technologies in the unlicensed sub-gigahertz frequency bands, used to realize access networks with star topology referred to as low-power WANs (LPWANs). In this article, we introduce this new approach to provide connectivity in the IoT scenario, discussing its advantages over the established paradigms in terms of efficiency, effectiveness, and architectural design, particularly for typical smart city applications.

Identity Authentication and Capability Based Access Control (IACAC) for the Internet of Things <=SEP=> In the last few years the Internet of Things (IoT) has seen widespread application and can be found in each field. Authentication and access control are important and critical functionalities in the context of IoT to enable secure communication between devices. Mobility, dynamic network topology and weak physical security of low power devices in IoT networks are possible sources for security vulnerabilities. It is promising to make an authentication and access control attack resistant and lightweight in a resource constrained and distributed IoT environment. This paper presents the Identity Authentication and Capability based Access Control (IACAC) model with protocol evaluation and performance analysis. To protect IoT from man-in-the-middle, replay and denial of service (Dos) attacks, the concept of capability for access control is introduced. The novelty of this model is that, it presents an integrated approach of authentication and access control for IoT devices. The results of other related study have also been analyzed to validate and support our findings. Finally, the proposed protocol is evaluated by using security protocol verification tool and verification results shows that IACAC is secure against aforementioned attacks. This paper also discusses performance analysis of the protocol in terms of computational time compared to other Journal of Cyber Security and Mobility, Vol. 1, 309–348. c © 2013 River Publishers. All rights reserved. 310 P.N. Mahalle et al. existing solutions. Furthermore, this paper addresses challenges in IoT and security attacks are modelled with the use cases to give an actual view of IoT networks.

The Internet of Things: A survey <=SEP=> This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details. 2010 Elsevier B.V. All rights reserved.

Stealthy Dopant-Level Hardware Trojans <=SEP=> In recent years, hardware Trojans have drawn the attention of governments and industry as well as the scientific community. One of the main concerns is that integrated circuits, e.g., for military or criticalinfrastructure applications, could be maliciously manipulated during the manufacturing process, which often takes place abroad. However, since there have been no reported hardware Trojans in practice yet, little is known about how such a Trojan would look like, and how difficult it would be in practice to implement one. In this paper we propose an extremely stealthy approach for implementing hardware Trojans below the gate level, and we evaluate their impact on the security of the target device. Instead of adding additional circuitry to the target design, we insert our hardware Trojans by changing the dopant polarity of existing transistors. Since the modified circuit appears legitimate on all wiring layers (including all metal and polysilicon), our family of Trojans is resistant to most detection techniques, including fine-grain optical inspection and checking against “golden chips”. We demonstrate the effectiveness of our approach by inserting Trojans into two designs — a digital post-processing derived from Intel’s cryptographically secure RNG design used in the Ivy Bridge processors and a side-channel resistant SBox implementation — and by exploring their detectability and their effects on security.

ERP in action - Challenges and benefits for management control in SME context <=SEP=> Article history: Received 31 January 2011 Received in revised form 7 February 2012 Accepted 2 March 2012 ERP systems have fundamentally re-shaped the way business data is collected, stored, disseminated and used throughout the world. However, the existing research in accounting has provided only relatively few empirical findings on the implications for management control when companies implement ERP systems as the technological platform. Especially scarce are the findings concerning the production phase, after implementation, when the information processes, related work practices and the new information contents can be seen as established. In this paper we explored and theorized the benefits, challenges and problems for management control when an ERP system is in use, four years after the implementation. Our findings also illustrate why and under what circumstances these challenges and benefits may exist. For a holistic view of the organization our findings, based on a qualitative case study, are constructed from the viewpoints of people at different levels and functions of the organization. Top management expected a new strategic control system, but due to the many challenges it ended up with merely financial accounting based control. At the operational level, serious challenges lead to inadequate usage of the ERP system. Management control produces the financial basic data and must contend with many practical problems caused by ERP implementation. © 2012 Elsevier Inc. All rights reserved.

Process discovery from event data: Relating models and logs through abstractions <=SEP=> Event data are collected in logistics, manufacturing, finance, healthcare, customer relationship management, e-learning, e-government, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for a particular case (i.e., process instances). Process mining techniques are able to exploit such data. In this article, we focus on process discovery. However, process mining also includes conformance checking, performance analysis, decision mining, organizational mining, predictions, recommendations, etc. These techniques help to diagnose problems and improve processes. All process mining techniques involve both event data and process models. Therefore, a typical first step is to automatically learn a control-flow model from the event data. This is very challenging, but in recent years many powerful discovery techniques have been developed. It is not easy to compare these techniques since they use different representations and make different assumptions. Users often need to resort to trying different algorithms in an ad-hoc manner. Developers of new techniques are often trying to solve specific instances of a more general problem. Therefore, we aim to unify existing approaches by focusing on log and model abstractions. These abstractions link observed and modeled behavior: Concrete behaviors recorded in event logs are related to possible behaviors represented by process models. Hence, such behavioral abstractions provide an “interface” between both. We discuss four discovery approaches involving three abstractions and different types of process models (Petri nets, block-structured models, and declarative models). The goal is to provide a comprehensive understanding of process discovery and show how to develop new techniques. Examples illustrate the different approaches and pointers to software are given. The discussion on abstractions and process representations is also used to reflect on the gap between process mining literature and commercial process mining tools. This facilitates users to select an appropriate process discovery technique. Moreover, structuring the role of internal abstractions and representations helps to broaden the view and facilitates the creation of new discovery approaches. ∗Process and Data Science (PADS), RWTH Aachen University, Aachen, Germany

End-to-End Conversion of HTML Tables for Populating a Relational Database <=SEP=> Automating the conversion of human-readable HTML tables into machine-readable relational tables will enable end-user query processing of the millions of data tables found on the web. Theoretically sound and experimentally successful methods for index-based segmentation, extraction of category hierarchies, and construction of a canonical table suitable for direct input to a relational database are demonstrated on 200 heterogeneous web tables. The methods are scalable: the program generates the 198 Access compatible CSV files in ~0.1s per table (two tables could not be indexed).

Reinforcement Learning with Perturbed Rewards <=SEP=> Recent studies have shown the vulnerability of reinforcement learning (RL) models in noisy settings. The sources of noises differ across scenarios. For instance, in practice, the observed reward channel is often subject to noise (e.g., when observed rewards are collected through sensors), and thus observed rewards may not be credible as a result. Also, in applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors. In this paper, we consider noisy RL problems where observed rewards by RL agents are generated with a reward confusion matrix. We call such observed rewards as perturbed rewards. We develop an unbiased reward estimator aided robust RL framework that enables RL agents to learn in noisy environments while observing only perturbed rewards. Our framework draws upon approaches for supervised learning with noisy data. The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards. We prove the convergence and sample complexity of our approach. Extensive experiments on different DRL platforms show that policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines. For instance, the state-of-the-art PPO algorithm is able to obtain 67.5% and 46.7% improvements in average on five Atari games, when the error rates are 10% and 30% respectively.

Current Trends in Educational Technology Research <=SEP=> Educational technology research has moved through several stages or “ages”, focusing at the beginning on the content to be learned, then on the format of instructional messages and, finally on the interaction between computers and students. The present paper reviews the research in technology-based learning environments in order to give both a historical perspective on educational technology research and a view of the current state of this discipline. We conclude that: 1) trends in educational technology research were forged by the evolution of learning theories and the technological changes, 2) a clear shift from the design of instruction to the design of learning environments can be noticed; 3) there is a positive effect of educational technology on learning, but the size of the effect varies considerably; 4) learning is much more dependent on the activity of the learner than on the quantity of information and processing opportunities provided by the environment.

A COMPRESSED SENSING VIEW OF UNSUPERVISED TEXT EMBEDDINGS , BAG-OF-n-GRAMS , AND LSTM S <=SEP=> Low-dimensional vector embeddings, computed using LSTMs or simpler techniques, are a popular approach for capturing the “meaning” of text and a form of unsupervised learning useful for downstream tasks. However, their power is not theoretically understood. The current paper derives formal understanding by looking at the subcase of linear embedding schemes. Using the theory of compressed sensing we show that representations combining the constituent word vectors are essentially information-preserving linear measurements of Bag-of-n-Grams (BonG) representations of text. This leads to a new theoretical result about LSTMs: low-dimensional embeddings derived from a low-memory LSTM are provably at least as powerful on classification tasks, up to small error, as a linear classifier over BonG vectors, a result that extensive empirical work has thus far been unable to show. Our experiments support these theoretical findings and establish strong, simple, and unsupervised baselines on standard benchmarks that in some cases are state of the art among word-level methods. We also show a surprising new property of embeddings such as GloVe and word2vec: they form a good sensing matrix for text that is more efficient than random matrices, the standard sparse recovery tool, which may explain why they lead to better representations in practice.

Employee turnover prediction and retention policies design: a case study <=SEP=> This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design & test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper. 2010 Mathematics Subject Classification.

Safety and Efficacy of Selective Neurectomy of the Gastrocnemius Muscle for Calf Reduction in 300 Cases <=SEP=> Liposuction alone is not always sufficient to correct the shape of the lower leg, and muscle reduction may be necessary. To assess the outcomes of a new technique of selective neurectomy of the gastrocnemius muscle to correct calf hypertrophy. Between October 2007 and May 2010, 300 patients underwent neurectomy of the medial and lateral heads of the gastrocnemius muscle at the Department of Cosmetic and Plastic Surgery, the Second People’s Hospital of Guangdong Province (Guangzhou, China) to correct the shape of their lower legs. Follow-up data from these 300 patients were analyzed retrospectively. Cosmetic results were evaluated independently by the surgeon, the patient, and a third party. Preoperative and postoperative calf circumferences were compared. The Fugl-Meyer motor function assessment was evaluated 3 months after surgery. The average reduction in calf circumference was 3.2 ± 1.2 cm. The Fugl-Meyer scores were normal in all patients both before and 3 months after surgery. A normal calf shape was achieved in all patients. Six patients complained of fatigue while walking and four of scar pigmentation, but in all cases, this resolved within 6 months. Calf asymmetry was observed in only two patients. The present series suggests that neurectomy of the medial and lateral heads of the gastrocnemius muscle may be safe and effective for correcting the shape of the calves. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Eastern meditative techniques and hypnosis: a new synthesis. <=SEP=> In this article major ancient Buddhist meditation techniques, samatha, vipassana, Zen, and ton-len, will be described in reference to contemporary clinical hypnosis. In so doing, the Eastern healing framework out of which these techniques emerged is examined in comparison with and in contrast to its Western counterpart. A growing body of empirical literature shows that meditation and hypnosis have many resemblances despite the distinct differences in underlying philosophy and technical methodologies. Although not all meditation techniques "fit" the Western culture, each has much to offer to clinicians who are familiar with hypnosis.

Architecture for an Intelligent Tutoring System that Considers Learning Styles <=SEP=> In this paper we propose the architecture of an Intelligent Tutoring System that considers the student's learning style and the competency-based education. We also describe the processes that have been implemented so far. Our architecture presents innovations in the representation of the tutor module and in the knowledge module; the tutor module incorporates a selector agent, which will choose the content to show, considering the teaching strategies that support the student's learning style.

Real-time tracking on adaptive critic design with uniformly ultimately bounded condition <=SEP=> In this paper, we proposed a new nonlinear tracking controller based on heuristic dynamic programming (HDP) with the tracking filter. Specifically, we integrate a goal network into the regular HDP design and provide the critic network with detailed internal reward signal to help the value function approximation. The architecture is explicitly explained with the tracking filter, goal network, critic network and action network, respectively. We provide the stability analysis of our proposed controller with Lyapunov approach. It is shown that the filtered tracking errors and the weights estimation errors in neural networks are all uniformly ultimately bounded (UUB) under certain conditions. Finally, we compare our proposed approach with regular HDP approach in virtual reality (VR)/Simulink environment to justify the improved control performance.

EEG-based BCI and video games: a progress report <=SEP=> This paper presents a systematic review of electroencephalography (EEG)-based brain–computer interfaces (BCIs) used in the video games, a vibrant field of research that touches upon all relevant questions concerning the future directions of BCI. The paper examines the progress of BCI research with regard to games and shows that gaming applications offer numerous advantages by orienting BCI to concerns and expectations of a gaming application. Different BCI paradigms are investigated, and future directions are discussed.

A CMOS hysteresis undervoltage lockout with current source inverter structure <=SEP=> This paper describes a simple architecture and low power consumption undervoltage lockout (UVLO) circuit with hysteretic threshold. The UVLO circuit monitors the supply voltage and determines whether or not the supply voltage satisfies a predetermined condition. The under voltage lockout circuit is designed based on CSMC 0.5um CMOS technology, utilizing a relatively few amount of circuitry. It is realized with a current source inverter. The threshold voltage is determined by the W/L ratio of current source inverter and resistor in reference generator. The hysteresis is realized by using a feedback circuit to overcome the bad disturbance and noise rejection of the single threshold. Hysteretic threshold range is 40mV. The quiescent current is about 1uA at 3V supply voltage,while the power of circuit consumes only 3uW.

A High Torque Density Vernier PM Machines for Hybrid Electric Vehicle Applications <=SEP=> This paper proposes a novel vernier permanent magnet (VPM) machine for hybrid electric vehicle (HEV) applications. The main features of the proposed machine are concentrated armature windings with short and non- overlapping end-winding, and the permanent magnets (PMs) are on both sides of rotor and stator. Unlike conventional motors, this machine operates with flux modulation principles, and the electromagnetic torque is produced by the interaction between the armature magneto motive force (MMF) and the exciting fields by stator and rotor PMs. In this paper, the electromagnetic performance is simulated by the finite element analysis (FEA). The results show that the torque density is about 33% higher than an existing interior permanent magnet (IPM) machines.

A High Speed Architecture for Galois/Counter Mode of Operation (GCM) <=SEP=> In this paper we present a fully pipelined high speed hardware architecture for Galois/Counter Mode of Operation (GCM) by analyzing the data dependencies in the GCM algorithm at the architecture level. We show that GCM encryption circuit and GCM authentication circuit have similar critical path delays resulting in an efficient pipeline structure. The proposed GCM architecture yields a throughput of 34 Gbps running at 271 MHz using a 0.18 μm CMOS standard cell library.

Small-world networks and functional connectivity in Alzheimer's disease. <=SEP=> We investigated whether functional brain networks are abnormally organized in Alzheimer's disease (AD). To this end, graph theoretical analysis was applied to matrices of functional connectivity of beta band-filtered electroencephalography (EEG) channels, in 15 Alzheimer patients and 13 control subjects. Correlations between all pairwise combinations of EEG channels were determined with the synchronization likelihood. The resulting synchronization matrices were converted to graphs by applying a threshold, and cluster coefficients and path lengths were computed as a function of threshold or as a function of degree K. For a wide range of thresholds, the characteristic path length L was significantly longer in the Alzheimer patients, whereas the cluster coefficient C showed no significant changes. This pattern was still present when L and C were computed as a function of K. A longer path length with a relatively preserved cluster coefficient suggests a loss of complexity and a less optimal organization. The present study provides further support for the presence of "small-world" features in functional brain networks and demonstrates that AD is characterized by a loss of small-world network characteristics. Graph theoretical analysis may be a useful approach to study the complexity of patterns of interrelations between EEG channels.

Semantic MEDLINE: An advanced information management application for biomedicine <=SEP=> Semantic MEDLINE integrates information retrieval, advanced natural language processing, automatic summarization, and visualization into a single Web portal. The application is intended to help manage the results of PubMed searches by condensing core semantic content in the citations retrieved. Output is presented as a connected graph of semantic relations, with links to the original MEDLINE citations. The ability to connect salient information across documents helps users keep up with the research literature and discover connections which might otherwise go unnoticed. Semantic MEDLINE can make an impact on biomedicine by supporting scientific discovery and the timely translation of insights from basic research into advances in clinical practice and patient care. Marcelo Fiszman has an M.D. from the State University of Rio de Janeiro and a Ph.D. in biomedical informatics from the University of Utah. He was awarded a postdoctoral fellowship in biomedical informatics at the National Library of Medicine (NLM) and is currently a research scientist there. His work focuses on natural language processing algorithms that exploit symbolic, rule-based techniques for semantic interpretation of biomedical text. He is also interested in using extracted semantic information for automatic abstraction summarization and literaturebased discovery. These efforts underpin Semantic MEDLINE, which is currently under development at NLM. This innovative biomedical information management application combines document retrieval, semantic interpretation, automatic summarization, and knowledge visualization into a single application.

The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs <=SEP=> Despite an increasing interest in understanding human perception in social media through the automatic analysis of users' personality, existing attempts have explored user profiles and text blog data only. We approach the study of personality impressions in social media from the novel perspective of crowdsourced impressions, social attention, and audiovisual behavioral analysis on slices of conversational vlogs extracted from YouTube. Conversational vlogs are a unique case study to understand users in social media, as vloggers implicitly or explicitly share information about themselves that words, either written or spoken cannot convey. In addition, research in vlogs may become a fertile ground for the study of video interactions, as conversational video expands to innovative applications. In this work, we first investigate the feasibility of crowdsourcing personality impressions from vlogging as a way to obtain judgements from a variate audience that consumes social media video. Then, we explore how these personality impressions mediate the online video watching experience and relate to measures of attention in YouTube. Finally, we investigate on the use of automatic nonverbal cues as a suitable lens through which impressions are made, and we address the task of automatic prediction of vloggers' personality impressions using nonverbal cues and machine learning techniques. Our study, conducted on a dataset of 442 YouTube vlogs and 2210 annotations collected in Amazon's Mechanical Turk, provides new findings regarding the suitability of collecting personality impressions from crowdsourcing, the types of personality impressions that emerge through vlogging, their association with social attention, and the level of utilization of nonverbal cues in this particular setting. In addition, it constitutes a first attempt to address the task of automatic vlogger personality impression prediction using nonverbal cues, with promising results.

Packet Injection Attack and Its Defense in Software-Defined Networks <=SEP=> Software-defined networks (SDNs) are novel networking architectures that decouple the network control and forwarding functions from the data plane. Unlike traditional networking, the control logic of SDNs is implemented in a logically centralized controller which provides a global network view and open programming interface to the applications. While SDNs have become a hot topic among both academia and industry in recent years, little attention has been paid on the security aspect. In this paper, we introduce a novel attack, namely, packet injection attack, in SDNs. By maliciously injecting manipulated packets into SDNs, attackers can affect the services and networking applications in the control plane, and largely consume the resources in the data plane. The consequences could be the disruption of applications built on the top of the topology manager service and rest API, as well as a huge consumption of network resources, such as the bandwidth of the OpenFlow channel. To defend against the packet injection attack, we present PacketChecker, a lightweight extension module on SDN controllers to effectively detect and mitigate the flooding of falsified packets. We implement a prototype of PacketChecker in floodlight controller and conduct experiments to evaluate the efficiency of the defense mechanism. The evaluation shows that the PacketChecker module can effectively mitigate the attack with a minor overhead to the SDN controller.

Detecting and Classifying Crimes from Arabic Twitter Posts using Text Mining Techniques <=SEP=> Crime analysis has become a critical area for helping law enforcement agencies to protect civilians. As a result of a rapidly increasing population, crime rates have increased dramatically, and appropriate analysis has become a timeconsuming effort. Text mining is an effective tool that may help to solve this problem to classify crimes in effective manner. The proposed system aims to detect and classify crimes in Twitter posts that written in the Arabic language, one of the most widespread languages today. In this paper, classification techniques are used to detect crimes and identify their nature by different classification algorithms. The experiments evaluate different algorithms, such as SVM, DT, CNB, and KNN, in terms of accuracy and speed in the crime domain. Also, different features extraction techniques are evaluated, including rootbased stemming, light stemming, n-gram. The experiments revealed the superiority of n-gram over other techniques. Specifically, the results indicate the superiority of SVM with trigram over other classifiers, with a 91.55% accuracy. Keywords—Crimes; text mining; classification; features extraction techniques; arabic posts; twitter

Tracking down software bugs using automatic anomaly detection <=SEP=> This paper introduces DIDUCE, a practical and effective tool that aids programmers in detecting complex program errors and identifying their root causes. By instrumenting a program and observing its behavior as it runs, DIDUCE dynamically formulates hypotheses of invariants obeyed by the program. DIDUCE hypothesizes the strictest invariants at the beginning, and gradually relaxes the hypothesis as violations are detected to allow for new behavior. The violations reported help users to catch software bugs as soon as they occur. They also give programmers new visibility into the behavior of the programs such as identifying rare corner cases in the program logic or even locating hidden errors that corrupt the program's results.We implemented the DIDUCE system for Java programs and applied it to four programs of significant size and complexity. DIDUCE succeeded in identifying the root causes of programming errors in each of the programs quickly and automatically. In particular, DIDUCE is effective in isolating a timing-dependent bug in a released JSSE (Java Secure Socket Extension) library, which would have taken an experienced programmer days to find. Our experience suggests that detecting and checking program invariants dynamically is a simple and effective methodology for debugging many different kinds of program errors across a wide variety of application domains.

Causal Interventions for Fairness <=SEP=> Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help private companies comply with non-discrimination laws or avoid negative publicity, we believe it is often too little, too late. By the time the training data is collected, individuals in disadvantaged groups have already suffered from discrimination and lost opportunities due to factors out of their control. In the present work we focus instead on interventions such as a new public policy, and in particular, how to maximize their positive effects while improving the fairness of the overall system. We use causal methods to model the effects of interventions, allowing for potential interference–each individual’s outcome may depend on who else receives the intervention. We demonstrate this with an example of allocating a budget of teaching resources using a dataset of schools in New York City.

Atanassov's Intuitionistic Fuzzy Programming Method for Heterogeneous Multiattribute Group Decision Making With Atanassov's Intuitionistic Fuzzy Truth Degrees <=SEP=> The aim of this paper is to develop a new Atanassov's intuitionistic fuzzy (A-IF) programming method to solve heterogeneous multiattribute group decision-making problems with A-IF truth degrees in which there are several types of attribute values such as A-IF sets (A-IFSs), trapezoidal fuzzy numbers, intervals, and real numbers. In this method, preference relations in comparisons of alternatives with hesitancy degrees are expressed by A-IFSs. Hereby, A-IF group consistency and inconsistency indices are defined on the basis of preference relations between alternatives. To estimate the fuzzy ideal solution (IS) and weights, a new A-IF programming model is constructed on the concept that the A-IF group inconsistency index should be minimized and must be not larger than the A-IF group consistency index by some fixed A-IFS. An effective method is developed to solve the new derived model. The distances of the alternatives to the fuzzy IS are calculated to determine their ranking order. Moreover, some generalizations or specializations of the derived model are discussed. Applicability of the proposed methodology is illustrated with a real supplier selection example.

Individuals’ Stress Assessment Using Human-Smartphone Interaction Analysis <=SEP=> The increasing presence of stress in people’ lives has motivated much research efforts focusing on continuous stress assessment methods of individuals, leveraging smartphones and wearable devices. These methods have several drawbacks, i.e., they use invasive external devices, thus increasing entry costs and reducing user acceptance, or they use some of privacy-related information. This paper presents an approach for stress assessment that leverages data extracted from smartphone sensors, and that is not invasive concerning privacy. Two different approaches are presented. One, based on smartphone gestures analysis, e.g., ‘tap’, ‘scroll’, ‘swipe’ and ‘text writing’, and evaluated in laboratory settings with 13 participants (F-measure 79-85 percent within-subject model, 70-80 percent global model); the second one based on smartphone usage analysis and tested in-the-wild with 25 participants (F-measure 77-88 percent within-subject model, 63-83 percent global model). Results show how these two methods enable an accurate stress assessment without being too intrusive, thus increasing ecological validity of the data and user acceptance.

Word Segmentation in Sanskrit Using Path Constrained Random Walks <=SEP=> In Sanskrit, the phonemes at the word boundaries undergo changes to form new phonemes through a process called as sandhi. A fused sentence can be segmented into multiple possible segmentations. We propose a word segmentation approach that predicts the most semantically valid segmentation for a given sentence. We treat the problem as a query expansion problem and use the path-constrained random walks framework to predict the correct segments.