Computers — Open Access Journal

This paper presents an experimental study on modeling machine emotion elicitation in a socially intelligent service, the typing tutor. The aim of the study is to evaluate the extent to which the machine emotion elicitation can influence the affective state (valence and arousal) of the learner during a tutoring session. The tutor provides continuous real-time emotion elicitation via graphically rendered emoticons, as an emotional feedback to learner’s performance. Good performance is rewarded by the positive emoticon, based on the notion of positive reinforcement. Facial emotion recognition software is used to analyze the affective state of the learner for later evaluation. Experimental results show the correlation between the positive emoticon and the learner’s affective state is significant for all 13 (100%) test participants on the arousal dimension and for 9 (69%) test participants on both affective dimensions. The results also confirm our hypothesis and show that the machine emotion elicitation is significant for 11 (85%) of 13 test participants. We conclude that the machine emotion elicitation with simple graphical emoticons has a promising potential for the future development of the tutor. Full article

Real-time collaborative editing applications are drastically different from typical client–server applications in that every participant has a copy of the shared document. In this type of environment, each participant acts as both a client and a server replica. In this article, we elaborate on how to adapt Byzantine fault tolerance (BFT) mechanisms to enhance the trustworthiness of such applications. It is apparent that traditional BFT algorithms cannot be used directly because it would dictate that all updates submitted by participants be applied sequentially, which would defeat the purpose of collaborative editing. The goal of this study is to design and implement an efficient BFT solution by exploiting the application semantics and by doing a threat analysis of these types of applications. Our solution can be considered as a form of optimistic BFT in that local states maintained by each participant may diverge temporarily. The states of the participants are made consistent with each other by a periodic synchronization mechanism. Full article

The opportunities for wearable technologies go well beyond always-available information displays or health sensing devices. The concept of the cyborg introduced by Clynes and Kline, along with works in various fields of research and the arts, offers a vision of what technology integrated with the body can offer. This paper identifies different categories of research aimed at augmenting humans. The paper specifically focuses on three areas of augmentation of the human body and its sensorimotor capabilities: physical morphology, skin display, and somatosensory extension. We discuss how such digital extensions relate to the malleable nature of our self-image. We argue that body-borne devices are no longer simply functional apparatus, but offer a direct interplay with the mind. Finally, we also showcase some of our own projects in this area and shed light on future challenges. Full article

Security has been an issue of contention in healthcare. The lack of familiarity and poor implementation of security in healthcare leave the patients’ data vulnerable to attackers. The main issue is assessing how we can provide security in an RPM infrastructure. The findings in literature show there is little empirical evidence on proper implementation of security. Therefore, there is an urgent need in addressing cybersecurity issues in medical devices. Through the review of relevant literature in remote patient monitoring and use of a Microsoft threat modelling tool, we identify and explore current vulnerabilities and threats in IEEE 11073 standard devices to propose a new security framework for remote patient monitoring devices. Additionally, current RPM devices have a limitation on the number of people who can share a single device, therefore, we propose the use of NFC for identification in Remote Patient Monitoring (RPM) devices for multi-user environments where we have multiple people sharing a single device to reduce errors associated with incorrect user identification. We finally show how several techniques have been used to build the proposed framework. Full article

The problem of production flow in manufacturing systems is analyzed. The machines can be operated by workers or by robots, since breakdowns and human factors destabilize the production processes that robots are preferred to perform. The problem is how to determine the real difference in work efficiency between humans and robots. We present an analysis of the production efficiency and reliability of the press shop lines operated by human operators or industrial robots. This is a problem from the field of Operations Research for which the Discrete Event Simulation (DES) method has been used. Three models have been developed, including the manufacturing line before and after robotization, taking into account stochastic parameters of availability and reliability of the machines, operators, and robots. We apply the OEE (Overall Equipment Effectiveness) indicator to present how the availability, reliability, and quality parameters influence the performance of the workstations, especially in the short run and in the long run. In addition, the stability of the simulation model was analyzed. This approach enables a better representation of real manufacturing processes. Full article

Biomedical sensors (BMSs) monitor the heterogeneous vital signs of patients. They have diverse Quality of Service (QoS) requirements including reduced collision, delay, loss, and energy consumption in the transmission of data, which are non-constrained, delay-constrained, reliabilityconstrained, and critical. In this context, this paper proposes a traffic priority-aware adaptive slot allocation-based medium access control (TraySL-MAC) protocol. Firstly, a reduced contention adaptive slot allocation algorithm is presented to minimize contention rounds. Secondly, a low threshold vital signs criticality-based adaptive slot allocation algorithm is developed for high priority data. Thirdly, a high threshold vital signs criticality-based adaptive slot allocation algorithm is designed for low priority data. Simulations are performed to comparatively evaluate the performance of the proposed protocol with state-of-the-art MAC protocols. From the analysis of the results, it is evident that the proposed protocol is beneficial in terms of lower packet delivery delay and energy consumption, and higher throughput in realistic biomedical environments. Full article

Non-volatile memories (NVMs) offer superior density and energy characteristics compared to the conventional memories; however, NVMs suffer from severe reliability issues that can easily eclipse their energy efficiency advantages. In this paper, we survey architectural techniques for improving the soft-error reliability of NVMs, specifically PCM (phase change memory) and STT-RAM (spin transfer torque RAM). We focus on soft-errors, such as resistance drift and write disturbance, in PCM and read disturbance and write failures in STT-RAM. By classifying the research works based on key parameters, we highlight their similarities and distinctions. We hope that this survey will underline the crucial importance of addressing NVM reliability for ensuring their system integration and will be useful for researchers, computer architects and processor designers. Full article

Personalized prompting research has shown the significant learning benefit of prompting. The current paper outlines and examines a personalized prompting approach aimed at eliminating performance differences on the basis of a number of learner characteristics (capturing learning strategies and traits). The learner characteristics of interest were the need for cognition, work effort, computer self-efficacy, the use of surface learning, and the learner’s confidence in their learning. The approach was tested in two e-modules, using similar assessment forms (experimental n = 413; control group n = 243). Several prompts which corresponded to the learner characteristics were implemented, including an explanation prompt, a motivation prompt, a strategy prompt, and an assessment prompt. All learning characteristics were significant correlates of at least one of the outcome measures (test performance, errors, and omissions). However, only the assessment prompt increased test performance. On this basis, and drawing upon the testing effect, this prompt may be a particularly promising option to increase performance in e-learning and similar personalized systems. Full article

As one of the most reliable technologies, network intrusion detection system (NIDS) allows the monitoring of incoming and outgoing traffic to identify unauthorised usage and mishandling of attackers in computer network systems. To this extent, this paper investigates the experimental performance of Snort-based NIDS (S-NIDS) in a practical network with the latest technology in various network scenarios including high data speed and/or heavy traffic and/or large packet size. An effective testbed is designed based on Snort using different muti-core processors, e.g., i5 and i7, with different operating systems, e.g., Windows 7, Windows Server and Linux. Furthermore, considering an enterprise network consisting of multiple virtual local area networks (VLANs), a centralised parallel S-NIDS (CPS-NIDS) is proposed with the support of a centralised database server to deal with high data speed and heavy traffic. Experimental evaluation is carried out for each network configuration to evaluate the performance of the S-NIDS in different network scenarios as well as validating the effectiveness of the proposed CPS-NIDS. In particular, by analysing packet analysis efficiency, an improved performance of up to 10% is shown to be achieved with Linux over other operating systems, while up to 8% of improved performance can be achieved with i7 over i5 processors. Full article

In many non-deterministic search algorithms, particularly those analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. The BA is a population-based metaheuristic search algorithm inspired by bees seeking nectar/pollen. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 well-known benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much more quickly. Full article

Wearable devices monitoring food intake through passive sensing is slowly emerging to complement self-reporting of users’ caloric intake and eating behaviors. Though the ultimate goal for the passive sensing of eating is to become a reliable gold standard in dietary assessment, it is currently showing promise as a means of validating self-report measures. Continuous food-intake monitoring allows for the validation and refusal of users’ reported data in order to obtain more reliable user information, resulting in more effective health intervention services. Recognizing the importance and strength of wearable sensors in food intake monitoring, there has been a variety of approaches proposed and studied in recent years. While existing technologies show promise, many challenges and opportunities discussed in this survey, still remain. This paper presents a meticulous review of the latest sensing platforms and data analytic approaches to solve the challenges of food-intake monitoring, ranging from ear-based chewing and swallowing detection systems that capture eating gestures to wearable cameras that identify food types and caloric content through image processing techniques. This paper focuses on the comparison of different technologies and approaches that relate to user comfort, body location, and applications for medical research. We identify and summarize the forthcoming opportunities and challenges in wearable food intake monitoring technologies. Full article

Conventional wearable sensors are mainly used to detect the physiological and activity information of individuals who wear them, but fail to perceive the information of the surrounding environment. This paper presents a wearable thermal sensing system to detect and perceive the information of surrounding human subjects. The proposed system is developed based on a pyroelectric infrared sensor. Such a sensor system aims to provide surrounding information to blind people and people with weak visual capability to help them adapt to the environment and avoid collision. In order to achieve this goal, a low-cost, low-data-throughput binary sampling and analyzing scheme is proposed. We also developed a conditioning sensing circuit with a low-noise signal amplifier and programmable system on chip (PSoC) to adjust the amplification gain. Three statistical features in information space are extracted to recognize static humans and human scenarios in indoor environments. The results demonstrate that the proposed wearable thermal sensing system and binary statistical analysis method are efficient in static human detection and human scenario perception. Full article

Privacy-Preserving Data Publishing (PPDP) has become a critical issue for companies and organizations that would release their data. k-Anonymization was proposed as a first generalization model to guarantee against identity disclosure of individual records in a data set. Point access methods (PAMs) are not well studied for the problem of data anonymization. In this article, we propose yet another approximation algorithm for anonymization, coined BangA, that combines useful features from Point Access Methods (PAMs) and clustering. Hence, it achieves fast computation and scalability as a PAM, and very high quality thanks to its density-based clustering step. Extensive experiments show the efficiency and effectiveness of our approach. Furthermore, we provide guidelines for extending BangA to achieve a relaxed form of differential privacy which provides stronger privacy guarantees as compared to traditional privacy definitions. Full article

Due to an increase in the number of internet users, electronic commerce has grown significantly during the last decade. Electronic auction (e-auction) is one of the famous e-commerce applications. Even so, security and robustness of e-auction schemes still remain a challenge. Requirements like anonymity and privacy of the $bid$ value are under threat from the attackers. Any auction protocol must not leak the anonymity and the privacy of the $bid$ value of an honest Bidder. Keeping these requirements in mind, we have firstly proposed a controlled traceable blind signature scheme (CTBSS) because e-auction schemes should be able to trace the Bidders. Using CTBSS, a blind sealed-bid electronic auction scheme is proposed (BSEA). We have incorporated the notion of blind signature to e-auction schemes. Moreover, both the schemes are based upon elliptic curve cryptography (ECC), which provides a similar level of security with a comparatively smaller key size than the discrete logarithm problem (DLP) based e-auction protocols. The analysis shows that BSEA fulfills all the requirements of e-auction protocol, and the total computation overhead is lower than the existing schemes. Full article

A brain-computer interface (BCI) is a tool to communicate with a computer via brain signals without the user making any physical movements, thus enabling disabled people to communicate with their environment and with others. P300-based ERP spellers are a widely used spelling visual BCI using the P300 component of event-related potential (ERP). However, they have a technical problem in that at least $\sqrt{2N}$ flashes are required to present N characters. This prevents the improvement of accuracy and restricts the typing speed. To address this issue, we propose a method that uses N100 in addition to P300. We utilize novel stimulus images to detect the user’s gazing position by using N100. By using both P300 and N100, the proposed visual BCI reduces the number of flashes and improves the accuracy of the P300 speller. We also propose using N100 to classify non-control (NC) and intentional control (IC) states. In our experiments, the detection accuracy of N100 was significantly higher than that of P300 and the proposed method exhibited a higher information transfer rate (ITR) than the P300 speller. Full article

This paper concerns a service in which multiple guide robots in an area display arrows to guide individual users to their destinations. It proposes a method of identifying malfunctioning robots and robots that give wrong directions to users. In this method, users’ mobile terminals and robots form a store-carry and forward-type M2M communication network, and a distributed cooperative protocol is used to enable robots to share information and identify malfunctioning robots using the Byzantine algorithm. The robots do not directly communicate with each other, but through users’ mobile terminals. We have introduced the concept of the quasi-synchronous number, so whether a certain robot is malfunctioning can be determined even when items of information held by all of the robots are not synchronized. Using simulation, we have evaluated the proposed method in terms of the rate of identifying malfunctioning robots, the rate of reaching the destination and the average length of time to reach the destination. Full article

Cluster-based pseudo-relevance feedback (PRF) is an effective approach for searching relevant documents for relevance feedback. Standard approach constructs clusters for PRF only on the basis of high similarity between retrieved documents. The standard approach works quite well if the retrieval bias of the retrieval model does not create any effect on the retrievability of documents. In our experiments we observed when a collection contains retrieval bias, then high retrievable documents of clusters are frequently retrieved at top positions for most of the queries, and these drift the relevance feedback away from relevant documents. For reducing (retrieval bias) noise, we enhance the standard cluster construction approach by constructing clusters on the basis of high similarity and retrievability. We call this retrievability and cluster-based PRF. This enhanced approach keeps only those documents in the clusters that are not frequently retrieve due to retrieval bias. Although this approach improves the effectiveness, however, it penalizes high retrievable documents even if these documents are most relevant to the clusters. To handle this problem, in a second approach, we extend the basic retrievability concept by mining frequent neighbors of the clusters. The frequent neighbors approach keeps only those documents in the clusters that are frequently retrieved with other neighbors of clusters and infrequently retrieved with those documents that are not part of the clusters. Experimental results show that two proposed extensions are helpful for identifying relevant documents for relevance feedback and increasing the effectiveness of queries. Full article

The development of a computer-aided diagnosis (CAD) system for differentiation between benign and malignant mammographic masses is a challenging task due to the use of extensive pre- and post-processing steps and ineffective features set. In this paper, a novel CAD system is proposed called DeepCAD, which uses four phases to overcome these problems. The speed-up robust features (SURF) and local binary pattern variance (LBPV) descriptors are extracted from each mass. These descriptors are then transformed into invariant features. Afterwards, the deep invariant features (DIFs) are constructed in supervised and unsupervised fashion through multilayer deep-learning architecture. A fine-tuning step is integrated to determine the features, and the final decision is performed via softmax linear classifier. To evaluate this DeepCAD system, a dataset of 600 region-of-interest (ROI) masses including 300 benign and 300 malignant masses was obtained from two publicly available data sources. The performance of DeepCAD system is compared with the state-of-the-art methods in terms of area under the receiver operating characteristics (AUC) curve. The difference between AUC of DeepCAD and other methods is statistically significant, as it demonstrates a sensitivity (SN) of 92%, specificity (SP) of 84.2%, accuracy (ACC) of 91.5% and AUC of 0.91. The experimental results indicate that the proposed DeepCAD system is reliable for providing aid to radiologists without the need for explicit design. Full article

This paper surveys the available system architectures for cyber-physical systems. Several candidate architectures are examined using a series of essential qualities for cyber-physical systems for healthcare. Next, diagrams detailing the expected functionality of infusion pumps in two of the architectures are analyzed. The STRIDE Threat Model is then used to decompose each to determine possible security issues and how they can be addressed. Finally, a comparison of the major security issues in each architecture is presented to help determine which is most adaptable to meet the security needs of cyber-physical systems in healthcare. Full article