title: Flying-capacitor boost converter

abstract: This paper introduces the flying-capacitor dc-dc boost converter. In the proposed topology, a high voltage gain ratio and improved conversion efficiency are achieved with low inductor core and copper losses. The input current ripple of the new converter is low while its ripple frequency is high, yielding a low-sized input filter. Furthermore, the voltage stresses on the power switches and diodes of the proposed converter are considerably low. It is shown that the proposed converter can be extended to higher stages to achieve a higher voltage gain ratio at enhanced efficiency. The principle of operation of the new converter is proposed, and the comparison with the conventional boost topology is presented. A complete analysis of this converter in the steady state and small signal operation is performed as well. Simulation results and a design example are also given to demonstrate the effectiveness of the proposed power converter.


title: How to Train Your Browser: Preventing XSS Attacks Using Contextual Script Fingerprints

abstract: Cross-Site Scripting (XSS) is one of the most common web application vulnerabilities. It is therefore sometimes referred to as the “buffer overflow of the web.” Drawing a parallel from the current state of practice in preventing unauthorized native code execution (the typical goal in a code injection), we propose a script whitelisting approach to tame JavaScript-driven XSS attacks. Our scheme involves a transparent script interception layer placed in the browser’s JavaScript engine. This layer is designed to detect every script that reaches the browser, from every possible route, and compare it to a list of valid scripts for the site or page being accessed; scripts not on the list are prevented from executing. To avoid the false positives caused by minor syntactic changes (e.g., due to dynamic code generation), our layer uses the concept of contextual fingerprints when comparing scripts. Contextual fingerprints are identifiers that represent specific elements of a script and its execution context. Fingerprints can be easily enriched with new elements, if needed, to enhance the proposed method’s robustness. The list can be populated by the website’s administrators or a trusted third party. To verify our approach, we have developed a prototype and tested it successfully against an extensive array of attacks that were performed on more than 50 real-world vulnerable web applications. We measured the browsing performance overhead of the proposed solution on eight websites that make heavy use of JavaScript. Our mechanism imposed an average overhead of 11.1p on the execution time of the JavaScript engine. When measured as part of a full browsing session, and for all tested websites, the overhead introduced by our layer was less than 0.05p. When script elements are altered or new scripts are added on the server side, a new fingerprint generation phase is required. To examine the temporal aspect of contextual fingerprints, we performed a short-term and a long-term experiment based on the same websites. The former, showed that in a short period of time (10 days), for seven of eight websites, the majority of valid fingerprints stay the same (more than 92p on average). The latter, though, indicated that, in the long run, the number of fingerprints that do not change is reduced. Both experiments can be seen as one of the first attempts to study the feasibility of a whitelisting approach for the web.


title: Simulation of a memristor-based spiking neural network immune to device variations

abstract: We propose a design methodology to exploit adaptive nanodevices (memristors), virtually immune to their variability. Memristors are used as synapses in a spiking neural network performing unsupervised learning. The memristors learn through an adaptation of spike timing dependent plasticity. Neurons' threshold is adjusted following a homeostasis-type rule. System level simulations on a textbook case show that performance can compare with traditional supervised networks of similar complexity. They also show the system can retain functionality with extreme variations of various memristors' parameters, thanks to the robustness of the scheme, its unsupervised nature, and the power of homeostasis. Additionally the network can adjust to stimuli presented with different coding schemes.


title: Ischial Pain and Sitting Disability Due to Ischiogluteal Bursitis: Visual Vignette

abstract: Ischial bursitis or ischiogluteal bursitis is the inflammation of the ischiogluteal bursa due to excessive or inappropriate physical exercise, prolonged sitting, running, repetitive jumping, and kicking. Since ischial bursitis is a rare, infrequently recognized pathology and is difficult to differentiate from the soft tissue disease and tumors (both malignant and benign), herein exemplified is a case with ischiogluteal bursitis whereby the role of magnetic resonance imaging (MRI) in the prompt diagnosis has been highlighted.


title: Reliability and Validity of the NDNQI® Injury Falls Measure

abstract: Although remarkable efforts have been made to improve patient fall reporting through the utilization of standardized definitions, injury falls reporting has rarely been examined. This study used an overall intra-class correlation coefficient (ICC) estimate and factor analysis to assess the reliability and validity of the National Database of Nursing Quality Indicators® (NDNQI®) falls with injury measure. Data were collected from an online Fall Injury Level Survey that was administered to 1,159 NDNQI site coordinators (39.7% response rate; 91% registered nurses [RNs]). Estimated overall ICC was .85. Exploratory factor analysis (EFA) with a Promax rotation (root mean square error of approximation [RMSEA] = 0.053) identified three latent factors: No Injury, Minor Injury, and Moderate/Major Injuries. Final confirmatory factor analysis (CFA) assessment (comparative fit index [CFI] = 0.914, Tucker Lewis Index [TLI] = 0.910, RMSEA = 0.048) confirmed an acceptable model fit. Results provided strong evidence that the NDNQI falls with injury measure is reliable and valid in supporting hospitals' fall prevention efforts and future injurious falls research.


title: Fast accurate fish detection and recognition of underwater images with Fast R-CNN

abstract: This paper aims at detecting and recognizing fish species from underwater images by means of Fast R-CNN (Regions with Convolutional Neural and Networks) features. Encouraged by powerful recognition results achieved by Convolutional Neural Networks (CNNs) on generic VOC and ImageNet dataset, we apply this popular deep ConvNets to domain-specific underwater environment which is more complicated than overland situation, using a new dataset of 24277 ImageCLEF fish images belonging to 12 classes. The experimental results demonstrate the promising performance of our networks. Fast R-CNN improves mean average precision (mAP) by 11.2% relative to Deformable Parts Model (DPM) baseline-achieving a mAP of 81.4%, and detects 80× faster than previous R-CNN on a single fish image.


title: Raptor Binds the SAIN (Shc and IRS-1 NPXY Binding) Domain of Insulin Receptor Substrate-1 (IRS-1) and Regulates the Phosphorylation of IRS-1 at Ser-636/639 by mTOR

abstract: In normal physiological states mTOR phosphorylates and activates Akt. However, under diabetic-mimicking conditions mTOR inhibits phosphatidylinositol (PI) 3-kinase/Akt signaling by phosphorylating insulin receptor substrate-1 (IRS-1) at Ser-636/639. The molecular basis for the differential effect of mTOR signaling on Akt is poorly understood. Here, it has been shown that knockdown of mTOR, Raptor, and mLST8, but not Rictor and mSin1, suppresses insulin-stimulated phosphorylation of IRS-1 at Ser-636/639 and stabilizes IRS-1 after long term insulin stimulation. This phosphorylation depends on the PI 3-kinase/PDK1 axis but is Akt-independent. At the molecular level, Raptor binds the SAIN (Shc and IRS-1 NPXY binding) domain of IRS-1 and regulates the phosphorylation of IRS-1 at Ser-636/639 by mTOR. IRS-1 lacking the SAIN domain does not interact with Raptor, is not phosphorylated at Ser-636/639, and favorably interacts with PI 3-kinase. Overall, these data provide new insights in the molecular mechanisms by which mTORC1 inhibits PI 3-kinase/Akt signaling at the level of IRS-1 and suggest that mTOR signaling toward Akt is scaffold-dependent.


title: Sample-Based Tree Search with Fixed and Adaptive State Abstractions

abstract: Sample-based tree search (SBTS) is an approach to solving Markov decision problems based on constructing a lookahead search tree using random samples from a generative model of the MDP. It encompasses Monte Carlo tree search (MCTS) algorithms like UCT as well as algorithms such as sparse sampling. SBTS is well-suited to solving MDPs with large state spaces due to the relative insensitivity of SBTS algorithms to the size of the state space. The limiting factor in the performance of SBTS tends to be the exponential dependence of sample complexity on the depth of the search tree. The number of samples required to build a search tree is O((|A|B)d), where |A| is the number of available actions, B is the number of possible random outcomes of taking an action, and d is the depth of the tree. State abstraction can be used to reduce B by aggregating random outcomes together into abstract states. Recent work has shown that abstract tree search often performs substantially better than tree search conducted in the ground state space. ::: ::: This paper presents a theoretical and empirical evaluation of tree search with both fixed and adaptive state abstractions. We derive a bound on regret due to state abstraction in tree search that decomposes abstraction error into three components arising from properties of the abstraction and the search algorithm. We describe versions of popular SBTS algorithms that use fixed state abstractions, and we introduce the Progressive Abstraction Refinement in Sparse Sampling (PARSS) algorithm, which adapts its abstraction during search. We evaluate PARSS as well as sparse sampling with fixed abstractions on 12 experimental problems, and find that PARSS outperforms search with a fixed abstraction and that search with even highly inaccurate fixed abstractions outperforms search without abstraction. These results establish progressive abstraction refinement as a promising basis for new tree search algorithms, and we propose directions for future work within the progressive refinement framework.


Semantic Web of Things for Industry 4.0


Learning Dynamic Embeddings from Temporal Interaction Networks


title: Prior Knowledge for Part Correspondence

abstract: Classical approaches to shape correspondence base their computation purely on the properties, in particular geometric similarity, of the shapes in question. Their performance still falls far short of that of humans in challenging cases where corresponding shape parts may differ significantly in geometry or even topology. We stipulate that in these cases, shape correspondence by humans involves recognition of the shape parts where prior knowledge on the parts would play a more dominant role than geometric similarity. We introduce an approach to part correspondence which incorporates prior knowledge imparted by a training set of pre-segmented, labeled models and combines the knowledge with content-driven analysis based on geometric similarity between the matched shapes. First, the prior knowledge is learned from the training set in the form of per-label classifiers. Next, given two query shapes to be matched, we apply the classifiers to assign a probabilistic label to each shape face. Finally, by means of a joint labeling scheme, the probabilistic labels are used synergistically with pairwise assignments derived from geometric similarity to provide the resulting part correspondence. We show that the incorporation of knowledge is especially effective in dealing with shapes exhibiting large intra-class variations. We also show that combining knowledge and content analyses outperforms approaches guided by either attribute alone.


title: Modeling and control of a pair of robot fingers with saddle joint under orderless actuations

abstract: A new robot hand dynamics model with rolling constraints and with a saddle joint at one finger is proposed, where two saddle-joint actuations are considered to be orderless. Spinning motion around the opposition axis connecting two center points of each finger-tip contact area with an object is faithfully treated, and a viscosity model for damping rotational motion of the object is proposed. A class of control signals without referring to object kinematics or using external sensing is proposed. Finally, numerical simulation results show the stability of motion of the overall closed-loop dynamics supplied with the proposed control input.


title: Cyclic tempogram—A mid-level tempo representation for musicsignals

abstract: The extraction of local tempo and beat information from audio recordings constitutes a challenging task, particularly for music that reveals significant tempo variations. Furthermore, the existence of various pulse levels such as measure, tactus, and tatum often makes the determination of absolute tempo problematic. In this paper, we present a robust mid-level representation that encodes local tempo information. Similar to the well-known concept of cyclic chroma features, where pitches differing by octaves are identified, we introduce the concept of cyclic tempograms, where tempi differing by a power of two are identified. Furthermore, we describe how to derive cyclic tempograms from music signals using two different methods for periodicity analysis and finally sketch some applications to tempo-based audio segmentation.


title: Researchers misunderstand confidence intervals and standard error bars

abstract: La Trobe UniversityLittle is known about researchersO understanding of conÞdence intervals (CIs) and standarderror (SE ) bars. Authors of journal articles in psychology, behavioral neuroscience, andmedicine were invited to visit a Web site where they adjusted a Þgure until they judged 2means, with error bars, to be just statistically signiÞcantly different (p ! .05). Results from473 respondents suggest that many leading researchers have severe misconceptions abouthow error bars relate to statistical signiÞcance, do not adequately distinguish CIs and SE bars,and do not appreciate the im portance of w hether the 2 m eans are independent or com e from arepeated m easures design. B etter guidelines for researchers and less am biguous graphical con-ventions are needed before the advantages of C Is for research com m unication can be realized.Keywords: conÞdence intervals, statistical cognition, standard error, error bars, statisticalreform


title: Coverage and Capacity Analysis of Sigfox, LoRa, GPRS, and NB-IoT

abstract: In this paper the coverage and capacity of SigFox, LoRa, GPRS, and NB-IoT is compared using a real site deployment covering 8000 km2 in Northern Denmark. Using the existing Telenor cellular site grid it is shown that the four technologies have more than 99 % outdoor coverage, while GPRS is challenged for indoor coverage. Furthermore, the study analyzes the capacity of the four technologies assuming a traffic growth from 1 to 10 IoT device per user. The conclusion is that the 95 %-tile uplink failure rate for outdoor users is below 5 % for all technologies. For indoor users only NB-IoT provides uplink and downlink connectivity with less than 5 % failure rate, while SigFox is able to provide an unacknowledged uplink data service with about 12 % failure rate. Both GPRS and LoRa struggle to provide sufficient indoor coverage and capacity.


title: Impacto nas Redes de MT da Ligação de Fontes de Energia Renovável

abstract: Over the last decades, the connection of Distributed Generation (DG) sources, mainly from renewable ones, to the distribution networks has increased significantly. Distribution networks were not originally designed to receive power injections. Its conventional operation corresponded only to load feeding through a unidirectional energy flow from substations to the different consumers. However, DG sources connection implies power injections at several points of distribution network that not only change the amount of energy flowing in the network but may also change the direction of energy flow under certain circumstances. The DG large scale presence in the distribution network has therefore significant impacts on its operation. Nowadays quality and reliability of electric energy supply are factors that may be positively or negatively influenced by the growth connection of DG sources to the distribution networks. In this dissertation is presented and analyzed the impact of DG sources connection, from renewable ones, to the Medium Voltage (MV) networks. Changes at voltage profiles, fault and steady state currents, power quality, losses and congestions, protection schemes, network stability and operation are studied. In order to verify some of the impacts, power flow studies were performed in a 15 kV MV network through the PSSE University 33 software. The network operation was simulated without DG sources presence and, later with the connection of two wind farms and two photovoltaic power stations at different points. Changes at voltage profiles, active network losses and branches congestions caused by the renewable DG sources connection were analyzed.


title: A force/torque sensor-based technique for robot harvesting of fruits and vegetables

abstract: In the harvest of many fruits and vegetables, it is important to cut the peduncle at a certain length, and not just to pull the fruit away, because this may damage the plant or the fruit during the harvest. In this paper, a method for the detection of the peduncle of close-to-spherical fruits and vegetables in robot harvesting is presented. An experimental set-up, consisting of a compliant three-finger hand, a 6-axis force/torque sensor and a PUMA 560 robot arm is described, and an implementation of the method is shown. >


title: The role of autophagy in cancer development and response to therapy

abstract: Autophagy is a process in which subcellular membranes undergo dynamic morphological changes that lead to the degradation of cellular proteins and cytoplasmic organelles. This process is an important cellular response to stress or starvation. Many studies have shed light on the importance of autophagy in cancer, but it is still unclear whether autophagy suppresses tumorigenesis or provides cancer cells with a rescue mechanism under unfavourable conditions. What is the present state of our knowledge about the role of autophagy in cancer development, and in response to therapy? And how can the autophagic process be manipulated to improve anticancer therapeutics?


title: Discrete Dielectric Reflectarray and Lens for E-Band With Different Feed

abstract: This letter presents the design and results of low-loss discrete dielectric flat reflectarray and lens for E-band. Using two different kinds of feed, 3-D-pyramidal (wideband) horn and 2 $\,\times\,$ 2 planar microstrip array (narrowband) antenna, the radiation performances of the two collimating structures are investigated. The discrete lens is optimized to cover the frequencies 71–86 GHz (71–76- and 81–86-GHz bands), while the discrete reflectarray is optimized to cover the 71–76-GHz band. The presented designs utilize the principle of perforated dielectric substrate using a square lattice of drilled holes of different radii and can be fabricated using standard printed circuit board (PCB) technology. The discrete lens has 41 $\,\times\,$ 41 unit cells and thickness of 6.35 mm, while the reflectarray has 40 $\,\times\,$ 40 unit cells and thickness of 3.24 mm. A good impedance matching ( $\vert { S}_{11} \vert 10 dB) and peak gain of $34 \pm 1~$ dB with maximum aperture efficiency of 44.6% are achieved over 71–86 GHz for the lens case. On the other hand, reflectarray with peak gain of $32 \pm 1~$ dB and aperture efficiency of 41.9% are achieved for 71–76-GHz band.


title: Performances of the AES design in 0.18μm CMOS technology

abstract: The Advanced Encryption Standard (AES) has been studied by designers with the goal to improve its performances in terms of area, power consumption and frequency. In this paper, we present the implementation details of the AES encryption 128-bit, the MixColumns transformation and the SubBytes transformation. The latter can be implemented using a multi-stage PPRM architecture and composite field arithmetic in GF(((22)2)2). In addition, the MixColumns transformation is used in two architectures. The AES algorithm is implemented using 1.8V 0.18μm Complementary Metal Oxide Semiconductor (CMOS) technology. A low power consumption of 24.92 μW at 10 MHz and 23.2 mW at 67 MHz were achieved for the multi-stage PPRM architecture of SubBytes transformation and the AES encryption respectively. Compared to previous works, our AES implementations achieve good performance in term of power consumption.


title: Estimating sentiment orientation in social media for intelligence monitoring and analysis

abstract: This paper presents a computational approach to inferring the sentiment orientation of “social media” content (e.g., blog posts) which focuses on the challenges associated with Web-based analysis. The proposed methodology formulates the task as one of text classification, models the data as a bipartite graph of documents and words, and uses this framework to develop a semi-supervised sentiment classifier that is well-suited for social media domains. In particular, the proposed algorithm is capable of combining prior knowledge regarding the sentiment orientation of a few documents and words with information present in unlabeled data, which is abundant online. We demonstrate the utility of the approach by showing it outperforms several standard methods for the task of inferring the sentiment of online movie reviews, and illustrate its potential for security informatics through a case study involving the estimation of Indonesian public sentiment regarding the July 2009 Jakarta hotel bombings.


title: ICE: Information Credibility Evaluation on Social Media via Representation Learning

abstract: With the rapid growth of social media, rumors are also spreading widely on social media and bring harm to people's daily life. Nowadays, information credibility evaluation has drawn attention from academic and industrial communities. Current methods mainly focus on feature engineering and achieve some success. However, feature engineering based methods require a lot of labor and cannot fully reveal the underlying relations among data. In our viewpoint, the key elements of user behaviors for evaluating credibility are concluded as "who", "what", "when", and "how". These existing methods cannot model the correlation among different key elements during the spreading of microblogs. In this paper, we propose a novel representation learning method, Information Credibility Evaluation (ICE), to learn representations of information credibility on social media. In ICE, latent representations are learnt for modeling user credibility, behavior types, temporal properties, and comment attitudes. The aggregation of these factors in the microblog spreading process yields the representation of a user's behavior, and the aggregation of these dynamic representations generates the credibility representation of an event spreading on social media. Moreover, a pairwise learning method is applied to maximize the credibility difference between rumors and non-rumors. To evaluate the performance of ICE, we conduct experiments on a Sina Weibo data set, and the experimental results show that our ICE model outperforms the state-of-the-art methods.


title: Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change

abstract: Several large technology companies including Apple, Google, and Samsung are entering the expanding market of population health with the introduction of wearable devices. This technology, worn in clothing or accessories, is part of a larger movement often referred to as the “quantified self.” The notion is that by recording and reporting information about behaviors such as physical activity or sleep patterns, these devices can educate and motivate individuals toward better habits and better health. The gap between recording information and changing behavior is substantial, however, and while these devices are increasing in popularity, little evidence suggests that they are bridging that gap. Only 1% to 2% of individuals in the United States have used a wearable device, but annual sales are projected to increase to more than $50 billion by 2018. 1 Some of these devices aim at individuals already motivated to change their health behaviors. Others are being considered by health care organizations, employers, insurers, and clinicians who see promise in using these devices to better engage less motivated individuals. Some of these devices may justify that promise, but less because of their technology and more because of the behavioral change strat


Pharmacist-managed medication therapy adherence clinics: The Malaysian experience


Video Coding for Streaming Media Delivery on the Internet


title: A Secure Electronic Prescription System Using Steganography with Encryption Key Implementation

abstract: Over the years health care has seen major improvement due to the introduction information and communication technology with electronic medical prescription being one the areas benefiting from it. Within the overall context of protection of health care information, privacy of prescription data needs special treatment. This paper presents an e-prescription system that addresses some challenges pertaining to the prescription privacy protection in the process of drug prescription. The developed system uses spread spectrum image steganography algorithm with Advanced Encryption Standard (AES) key implementation to provide a secure means of delivering medical prescription to the parties involved. The architecture for encoding and decoding was implemented with an electronic health record. The software development tools used were PHP and MySQL database management system for front end and backend data management respectively. The designed system demonstration shows that the synergistic combination of steganography and cryptography technologies in medical prescription is capable of providing a secure transmission to properly provide security for patients medical prescription.


title: ALF-200k: Towards Extensive Multimodal Analyses of Music Tracks and Playlists

abstract: In recent years, approaches in music information retrieval have been based on multimodal analyses of music incorporating audio as well as lyrics features. Because most of those approaches are lacking reusable, high-quality datasets, in this work we propose ALF-200k, a publicly available, novel dataset including 176 audio and lyrics features of more than 200,000 tracks and their attribution to more than 11,000 user-created playlists. While the dataset is of general purpose and thus, may be used in experiments for diverse music information retrieval problems, we present a first multimodal study on playlist features and particularly analyze, which type of features are shared within specific playlists and thus, characterize it. We show that while acoustic features act as the major glue between tracks contained in a playlists, also lyrics features are a powerful means to attribute tracks to playlists.


title: Internal control framework for a compliant ERP system

abstract: After the occurrence of numerous worldwide financial scandals, the importance of related issues such as internal control and information security has greatly increased. This study develops an internal control framework that can be applied within an enterprise resource planning (ERP) system. A literature review is first conducted to examine the necessary forms of internal control in information technology (IT) systems. The control criteria for the establishment of the internal control framework are then constructed. A case study is conducted to verify the feasibility of the established framework. This study proposes a 12-dimensional framework with 37 control items aimed at helping auditors perform effective audits by inspecting essential internal control points in ERP systems. The proposed framework allows companies to enhance IT audit efficiency and mitigates control risk. Moreover, companies that refer to this framework and consider the limitations of their own IT management can establish a more robust IT management mechanism.


title: The Functional Anatomy of Time: What and When in the Brain

abstract: This Opinion article considers the implications for functional anatomy of how we represent temporal structure in our exchanges with the world. It offers a theoretical treatment that tries to make sense of the architectural principles seen in mammalian brains. Specifically, it considers a factorisation between representations of temporal succession and representations of content or, heuristically, a segregation into when and what. This segregation may explain the central role of the hippocampus in neuronal hierarchies while providing a tentative explanation for recent observations of how ordinal sequences are encoded. The implications for neuroanatomy and physiology may have something important to say about how self-organised cell assembly sequences enable the brain to exhibit purposeful behaviour that transcends the here and now.


title: Sinus Microbiome Diversity Depletion and Corynebacterium tuberculostearicum Enrichment Mediates Rhinosinusitis

abstract: Persistent mucosal inflammation and microbial infection are characteristics of chronic rhinosinusitis (CRS). Mucosal microbiota dysbiosis is found in other chronic inflammatory diseases; however, the relationship between sinus microbiota composition and CRS is unknown. Using comparative microbiome profiling of a cohort of CRS patients and healthy subjects, we demonstrate that the sinus microbiota of CRS patients exhibits significantly reduced bacterial diversity compared with that of healthy controls. In our cohort of CRS patients, multiple, phylogenetically distinct lactic acid bacteria were depleted concomitant with an increase in the relative abundance of a single species, Corynebacterium tuberculostearicum. We recapitulated the conditions observed in our human cohort in a murine model and confirmed the pathogenic potential of C. tuberculostearicum and the critical necessity for a replete mucosal microbiota to protect against this species. Moreover, Lactobacillus sakei, which was identified from our comparative microbiome analyses as a potentially protective species, defended against C. tuberculostearicum sinus infection, even in the context of a depleted sinus bacterial community. These studies demonstrate that sinus mucosal health is highly dependent on the composition of the resident microbiota as well as identify both a new sino-pathogen and a strong bacterial candidate for therapeutic intervention.


A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases


title: Off the beaten pathway: the complex cross talk between Notch and NF-κB

abstract: The canonical Notch pathway that has been well characterized over the past 25 years is relatively simple compared to the plethora of recently published data suggesting non-canonical signaling mechanisms and cross talk with other pathways. The manner in which other pathways cross talk with Notch signaling appears to be extraordinarily complex and, not surprisingly, context-dependent. While the physiological relevance of many of these interactions remains to be established, there is little doubt that Notch signaling is integrated with numerous other pathways in ways that appear increasingly complex. Among the most intricate cross talks described for Notch is its interaction with the NF-κB pathway, another major cell fate regulatory network involved in development, immunity, and cancer. Numerous reports over the last 11 years have described multiple cross talk mechanisms between Notch and NF-κB in diverse experimental models. This article will provide a brief overview of the published evidence for Notch–NF-κB cross talk, focusing on vertebrate systems.


Role of diet and gut microbiota in management of inflammatory bowel disease in an Asian migrant.


title: Portable option discovery for automated learning transfer in object-oriented Markov decision processes

abstract: We introduce a novel framework for option discovery and learning transfer in complex domains that are represented as object-oriented Markov decision processes (OO-MDPs) [Diuk et al., 2008]. Our framework, Portable Option Discovery (POD), extends existing option discovery methods, and enables transfer across related but different domains by providing an unsupervised method for finding a mapping between object-oriented domains with different state spaces. The framework also includes heuristic approaches for increasing the efficiency of the mapping process. We present the results of applying POD to Pickett and Barto's [2002] Policy-Blocks and MacGlashan's [2013] Option-Based Policy Transfer in two application domains. We show that our approach can discover options effectively, transfer options among different domains, and improve learning performance with low computational overhead.


A General Approach to Prediction and Forecasting Crime Rates with Gaussian Processes


title: Specific thought patterns in chronic cannabis smokers observed during treatment

abstract: Abstract Clinicians report that chronic cannabis users seem to have symptoms, such as mental confusion and memory problems when entering treatment. The present study systematizes observations that were made during treatment of cannabis users during and after cessation of cannabis use. Cognitive symptoms prior to cessation are described in the conceptual framework of cognitive categories in the I.Q. test. Normalization of these cognitive functions during therapy is discussed.


title: Personalized recommendation of social software items based on social relations

abstract: We study personalized recommendation of social software items, including bookmarked web-pages, blog entries, and communities. We focus on recommendations that are derived from the user's social network. Social network information is collected and aggregated across different data sources within our organization. At the core of our research is a comparison between recommendations that are based on the user's familiarity network and his/her similarity network. We also examine the effect of adding explanations to each recommended item that show related people and their relationship to the user and to the item. Evaluation, based on an extensive user survey with 290 participants and a field study including 90 users, indicates superiority of the familiarity network as a basis for recommendations. In addition, an important instant effect of explanations is found - interest rate in recommended items increases when explanations are provided.


title: Upper bounds on the minimum distance of spherical codes

abstract: We use linear programming techniques to obtain new upper bounds on the maximal squared minimum distance of spherical codes with fixed cardinality. Functions Q/sub j/(n,s) are introduced with the property that Q/sub j/(n,s) m if and only if the Levenshtein bound L/sub m/(n,s) on A(n,s)=max{|W|:W is an (n,|W|,s) code} can be improved by a polynomial of degree at least m+1. General conditions on the existence of new bounds are presented. We prove that for fixed dimension n/spl ges/5 there exists a constant k=k(n) such that all Levenshtein bounds L/sub m/(n, s) for m/spl ges/2k-1 can be improved. An algorithm for obtaining new bounds is proposed and discussed.


title: Challenges Towards Protecting VNF With SGX

abstract: Network Function Virtualization (NFV) is an emerging technology to implement network functions in software, which reduces equipment costs (CAPEX) and operational cost (OPEX) through decoupling network functions from network dedicated devices and deploying them on high-volume standard servers and running as virtual instances. However, due to running in a shared and open environment and lacking the protection of proprietary hardware, virtual network functions (VNFs) face more security threats than traditional network functions. Hence, it is crucial to build a trusted execution environment to protect VNFs. In this paper, we first analyze the challenges for VNF security protection. We then propose a lightweight and trusted execution environment for securing VNFs based on SGX and Click. To demonstrate the feasibility of our approach, we implement a DDoS defense function on top of our environment and conduct paramilitary evaluations. Our evaluation results show that our system only introduces manageable performance overhead for protecting VNFs.


title: A Novel Dual Full-Bridge LLC Resonant Converter for CC and CV Charges of Batteries for Electric Vehicles

abstract: This paper introduces a novel concept for implementing constant current (CC) and constant voltage (CV) charges of batteries for electric vehicles using a dual full-bridge LLC (FBLLC) resonant converter, which shares its primary switches. In the proposed concept, the CC and CV charges are implemented using the inherent characteristics of an LLC converter as a current source and a voltage source. One FBLLC resonant converter operates with a fixed-resonant network whereas the other operates with a variable resonant network for the CC and CV charge operations. The proposed converter can achieve zero voltage switching (ZVS) and nearly zero current switching (ZCS) for all of the primary switches in the CC charge operation, and ZVS for all of the primary switches in the CV charge operation, which lead to a high efficiency. In addition, fixed switching frequency operation is possible in both the CC and CV charges thanks to the variable resonant network. A 3.3-kW prototype converter is implemented to verify the feasibility and validity of the proposed converter and a maximum efficiency of 98% was achieved.


title: Decentralized Cloud-SDN Architecture in Smart Grid: A Dynamic Pricing Model

abstract: Smart grids (SG) energy management system and electric vehicle (EV) have gained considerable reputation in recent years. This has been enabled by the high growth of EVs on roads; however, this may lead to a significant impact on the power grids. In order to keep EVs far from causing peaks in power demand and to manage building energy during the day, it is important to perform an intelligent scheduling for EVs charging and discharging service and buildings areas by including different metrics, such as real-time price and demand–supply curve. In this paper, we propose a real-time dynamic pricing model for EVs charging and discharging service and building energy management, in order to reduce the peak loads. Our proposed approach uses a decentralized cloud computing architecture based on software define networking (SDN) technology and network function virtualization (NFV). We aim to schedule user's requests in a real-time way and to supervise communications between microgrids controllers, SG and user entities (i.e., EVs, electric vehicles public supply stations, advance metering infrastructure, smart meters, etc.). We formulate the problem as a linear optimization problem for EV and a global optimization problem for all microgrids. We solve the problems by using different decentralized decision algorithms. To the best of our knowledge, this is the first paper that proposes a pricing model based on decentralized Cloud-SDN architecture in order to solve all the aforementioned issues. The extensive simulations and comparisons with related works proved that our proposed pricing model optimizes the energy load during peak hours, maximizes EVs utility, and maintains the microgrid stability. The simulation is based on real electric load of the city of Toronto.


title: How Distractor Objects Trigger Referential Overspecification: Testing the Effects of Visual Clutter and Distractor Distance

abstract: In two experiments, we investigate to what extent various visual saliency cues in realistic visual scenes cause speakers to overspecify their definite object descriptions with a redundant color attribute. The results of the first experiment demonstrate that speakers are more likely to redundantly mention color when visual clutter is present in a scene as compared to when this is not the case. In the second experiment, we found that distractor type and distractor color affect redundant color use: Speakers are most likely to overspecify if there is at least one distractor object present that has the same type, but a different color than the target referent. Reliable effects of distractor distance were not found. Taken together, our results suggest that certain visual saliency cues guide speakers in determining which objects in a visual scene are relevant distractors, and which not. We argue that this is problematic for algorithms that aim to generate human-like descriptions of objects (such as the Incremental Algorithm), since these generally select properties that help to distinguish a target from all objects that are present in a scene.


title: Backlobe and Sidelobe Suppression of a Q-Band Patch Antenna Array by Using Substrate Integrated Coaxial Line Feeding Technique

abstract: A low profile aperture-coupled patch antenna array with high front-to-back-ratio (FTBR) is designed at Q -band by using substrate integrated coaxial line technique, the intrinsic shielded enclosure of which is capable of constructing an equal-phase and amplitude-tapering feeding network with backlobe suppression. An illustrative example of a 6 $\times$ 6 patch antenna array is designed and fabricated with a 2 $\times$ 2 prior verification module by using multilayer liquid crystal polymer process. Measured results indicate that the array can achieve a gain of 17.6 dBi at 41.5 GHz and the first sidelobe levels is $-$ 24.1 dB in the E -plane and $-$ 20.4 dB in the H -plane. The holistic FTBR is better than 27 dB in both planes.


title: A meta-analysis of mobile commerce adoption and the moderating effect of culture

abstract: This research aims to discover the general factors that influence mobile commerce adoption. A meta-analysis of previous empirical studies on individuals' mobile commerce adoption behavior was conducted and the proposed mobile commerce adoption model was tested using structural equation modeling. Furthermore, moderator analysis was carried out using partition tests by dividing context into subgroups of eastern and western culture, to investigate the moderating effect of culture. Results confirm the proposed model, and moderator analysis indicates that culture does have specific moderating effects on mobile commerce adoption. The findings of this paper will yield theoretical and managerial implications for the future.


title: Real-Time IRC Threat Detection Framework

abstract: Most of the social media platforms generate a massive amount of raw data that is slow-paced. On the other hand, Internet Relay Chat (IRC) protocol, which has been extensively used by hacker community to discuss and share their knowledge, facilitates fast-paced and real-time text communications. Previous studies of malicious IRC behavior analysis were mostly either offline or batch processing. This results in a long response time for data collection, pre-processing, and threat detection. However, since the threats can use the latest vulnerabilities to exploit systems (e.g. zero-day attack) and which can spread fast using IRC channels. Current IRC channel monitoring techniques cannot provide the required fast detection and alerting. In this paper, we present an alternative approach to overcome this limitation by providing real-time and autonomic threat detection in IRC channels. We demonstrate the capabilities of our approach using as an example the shadow brokers' leak exploit (the exploit leveraged by WannaCry ransomware attack) that was captured and detected by our framework.


title: Micro Power Generator for Harvesting Low-Frequency and Nonperiodic Vibrations

abstract: This paper presents a new inertial power generator for scavenging low-frequency nonperiodic vibrations called the Parametric Frequency-Increased Generator (PFIG). The PFIG utilizes three magnetically coupled mechanical structures to initiate high-frequency mechanical oscillations in an electromechanical transducer. The fixed internal displacement and dynamics of the PFIG allow it to operate more effectively than resonant generators when the ambient vibration amplitude is higher than the internal displacement limit of the device. The design, fabrication, and testing of an electromagnetic PFIG are discussed. The developed PFIG can generate a peak power of 163 μW and an average power of 13.6 μW from an input acceleration of 9.8 m/s2 at 10 Hz, and it can operate at frequencies up to 65 Hz, giving it an unprecedented operating bandwidth and versatility. The internal volume of the generator is 2.12 cm3 (3.75 cm3 including the casing). The harvester has a volume figure of merit of 0.068% and a bandwidth figure of merit of 0.375%. These values, although seemingly low, are the highest reported in the literature for a device of this size and operating in the difficult frequency range of ≤ 20 Hz.


title: Moonwalk: NRE Optimization in ASIC Clouds

abstract: Cloud services are becoming increasingly globalized and data-center workloads are expanding exponentially. GPU and FPGA-based clouds have illustrated improvements in power and performance by accelerating compute-intensive workloads. ASIC-based clouds are a promising way to optimize the Total Cost of Ownership (TCO) of a given datacenter computation (e.g. YouTube transcoding) by reducing both energy consumption and marginal computation cost. The feasibility of an ASIC Cloud for a particular application is directly gated by the ability to manage the Non-Recurring Engineering (NRE) costs of designing and fabricating the ASIC, so that it is significantly lower (e.g. 2X) than the TCO of the best available alternative. In this paper, we show that technology node selection is a major tool for managing ASIC Cloud NRE, and allows the designer to trade off an accelerator's excess energy efficiency and cost performance for lower total cost. We explore NRE and cross-technology optimization of ASIC Clouds for four different applications: Bitcoin mining, YouTube-style video transcoding, Litecoin, and Deep Learning. We address these challenges and show large reductions in the NRE, potentially enabling ASIC Clouds to address a wider variety of datacenter workloads. Our results suggest that advanced nodes like 16nm will lead to sub-optimal TCO for many workloads, and that use of older nodes like 65nm can enable a greater diversity of ASIC Clouds.


title: Framework for Analyzing Android I/O Stack Behavior: From Generating theWorkload to Analyzing the Trace

abstract: Abstract: The existing I/O workload generators and trace capturing tools are not adaptable to generating nor capturing the I/O requests of Android apps. The Android platform needs proper tools to capture and replay real world workload in the Android platform to verify the result of benchmark tools. This paper introduces Android Storage Performance Analysis Tool, AndroStep, which is specifically designed for characterizing and analyzing the behavior of the I/O subsystem in Android based devices. The AndroStep consists of Mobibench (workload generator), MOST (Mobile Storage Analyzer), and Mobigen (workload replayer). Mobibench is an Android app that generates a filesystem as well as SQLite database operations. Mobibench can also vary the number of concurrent threads to examining the filesystem scalability to support concurrency, e.g., metadata updates, journal file creation/deletion. MOST captures the trace and extracts key filesystem access characteristics such as access pattern with respect to file types, ratio between random vs. sequential access, ratio between buffered vs. synchronous I/O, fraction of metadata accesses, etc. MOST implements reverse mapping feature (finding an inode for a given block) and retrospective reverse mapping (finding an inode for a deleted file). Mobigen is a trace capturing and replaying tool that is specifically designed to perform the user experiment without actual human intervention. Mobigen records the system calls generated from the user behavior and sanitizes the trace for replayable form. Mobigen can replay this trace on different Android platforms or with different I/O stack configurations. As an example of using AndroStep, we analyzed the performances of twelve Android smartphones and the SQLite performances on five different filesystems. AndroStep makes otherwise time consuming I/O stack analysis extremely versatile. AndroStep makes a significant contribution in terms of shedding light on internal behavior of the Android I/O stack.


title: HEFT: eQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factors

abstract: MOTIVATION ::: Identification of expression Quantitative Trait Loci (eQTL), the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. ::: ::: ::: METHODS ::: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model is a combined multivariate regression and factor analysis, where the complete likelihood of the model is used to derive a ridge estimator for simultaneous factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects; it provides P-values and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of single nucleotide polymorphisms on a standard 8 core 2.6 G desktop. ::: ::: ::: RESULTS ::: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and significantly outperforms all related hidden factor methods for eQTL analysis when there are eQTL with univariate and multivariate (pleiotropic) effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in the human lung for a study that included presumptive hidden factors. HEFT identified all of the cis-eQTL found by other hidden factor methods and 91 additional cis-eQTL. HEFT also identified a number of eQTLs with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer. ::: ::: ::: AVAILABILITY ::: Software is available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx. ::: ::: ::: SUPPLEMENTARY INFORMATION ::: Supplementary data are available at Bioinformatics online.


title: Comparison and evaluation of advanced motion models for vehicle tracking

abstract: The estimation of a vehiclepsilas dynamic state is one of the most fundamental data fusion tasks for intelligent traffic applications. For that, motion models are applied in order to increase the accuracy and robustness of the estimation. This paper surveys numerous (especially curvilinear) models and compares their performance using a tracking tasks which includes the fusion of GPS and odometry data with an Unscented Kalman Filter. For evaluation purposes, a highly accurate reference trajectory has been recorded using an RTK-supported DGPS receiver. With this ground truth data, the performance of the models is evaluated in different scenarios and driving situations.


title: A discussion on IGBT short circuit behavior and fault protection schemes

abstract: An in-depth discussion on the behavior of insulated gate bipolar transistors (IGBTs) under different short circuit conditions is presented, and the effects of various parameters on permissible short circuit time are analyzed. The problem of providing short circuit protection in relation to the special characteristics of the most efficient IGBTs is addressed. The pros and cons of some of the existing protection circuits are discussed, and, based on the recommendations, a protection scheme is implemented to demonstrate that reliable short circuit protection of these types of IGBTs can be achieved without difficulty in a PWM motor drive invertor application. >


A Survey Paper on Crime Prediction Technique Using Data Mining


title: Stability Evaluation of Event Detection Techniques for Twitter

abstract: Twitter continues to gain popularity as a source of up-to-date news and information. As a result, numerous event detection techniques have been proposed to cope with the steadily increasing rate and volume of social media data streams. Although most of these works conduct some evaluation of the proposed technique, comparing their effectiveness is a challenging task. In this paper, we examine the challenges to reproducing evaluation results for event detection techniques. We apply several event detection techniques and vary four parameters, namely time window (15 vs. 30 vs. 60 mins), stopwords (include vs. exclude), retweets (include vs. exclude), and the number of terms that define an event (1...5 terms). Our experiments use real-world Twitter streaming data and show that varying these parameters alone significantly influences the outcomes of the event detection techniques, sometimes in unforeseen ways. We conclude that even minor variations in event detection techniques may lead to major difficulties in reproducing experiments.


title: Eight-Band Antenna With A Small Ground Clearance for LTE Metal-Frame Mobile Phone Applications

abstract: This letter presents a compact eight-band monopole antenna whose size is 70 mm × 7 mm × 6 mm for LTE metal-frame mobile phones. The mainly radiating part of the proposed antenna is the metal frame. There are three gaps with the same size of 1 mm in the frame, and these gaps divide the antenna into two parts. Besides, a match circuit is used to enlarge the bandwidth and make the lower band matched. The advantage of the proposed antenna is that eight bands can be covered with a metal frame under the condition of only a 7 mm ground clearance. A prototype is manufactured and tested. The measured –6 dB impedance bandwidths are 375 MHz (0.675–1.05 GHz) and 1.2 GHz (1.6–2.8 GHz). The LTE700, GSM850, GSM900, GSM1800, GSM1900, UMTS, LTE2300, and LTE2500 bands are covered. The measured efficiencies are 52.7%–78.7% at the lower band and 45.6%–81% at the higher band. The measured gains are 0–2.1 dBi at the lower band and 1.6–3.6 dBi at the higher band.


title: Thermal constrained resource management for mixed ILP-TLP workloads in dark silicon chips

abstract: In dark silicon chips, a significant amount of on-chip resources cannot be simultaneously powered on and need to stay dark, i.e., power gated, in order to avoid thermal emergencies. This paper presents a resource management technique, called DsRem, that selects the number of active cores jointly with their voltage/frequency (v/f) levels, considering the high Instruction Level Parallelism (ILP) or Thread Level Parallelism (TLP) nature of different applications, in order to maximize the overall system performance. DsRem leverages the positioning of dark cores, to efficiently dissipate the heat generated by the active cores. This facilitates increasing the v/f level of the active cores, which leads to further performance improvement. Compared to state-of-the-art thermal-aware task application mapping, DsRem achieves up to 46% performance gain, while avoiding any thermal emergencies. Additionally, DsRem outperforms the boosting technique with 26%.


title: Oral lichenoid drug reaction associated with antihypertensive and hypoglycemic drugs.

abstract: Oral lichen planus and oral lichenoid drug reactions have similar clinical and histologic findings. The onset of oral lichenoid drug reactions appears to correspond to the administration of medications, especially antihypertensive drugs, oral hypoglycemic drugs, antimalarial drugs, gold salts, penicillamine and others. The author reports the case of 58-year-old male patient with oral lichenoid drug reaction, hypertension and diabetes mellitus. The oral manifestation showed radiated white lines with erythematous and erosive areas. The patient experienced pain and a burning sensation when eating spicy food. A tissue biopsy was carried out and revealed the characteristics of lichen planus. The patient was treated with 0.1% fluocinolone acetonide in an orabase as well as the replacement of the oral hypoglycemic and antihypertensive agents. The lesions improved and the burning sensation disappeared in two weeks after treatment. No recurrence was observed in the follow-up after three months.


title: Real-Time Gaze Estimator Based on Driver's Head Orientation for Forward Collision Warning System

abstract: This paper presents a vision-based real-time gaze zone estimator based on a driver's head orientation composed of yaw and pitch. Generally, vision-based methods are vulnerable to the wearing of eyeglasses and image variations between day and night. The proposed method is novel in the following four ways: First, the proposed method can work under both day and night conditions and is robust to facial image variation caused by eyeglasses because it only requires simple facial features and not specific features such as eyes, lip corners, and facial contours. Second, an ellipsoidal face model is proposed instead of a cylindrical face model to exactly determine a driver's yaw. Third, we propose new features-the normalized mean and the standard deviation of the horizontal edge projection histogram-to reliably and rapidly estimate a driver's pitch. Fourth, the proposed method obtains an accurate gaze zone by using a support vector machine. Experimental results from 200 000 images showed that the root mean square errors of the estimated yaw and pitch angles are below 7 under both daylight and nighttime conditions. Equivalent results were obtained for drivers with glasses or sunglasses, and 18 gaze zones were accurately estimated using the proposed gaze estimation method.


title: Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification

abstract: We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error by more than half. Existing approaches, such as the DeepDoc-Classifier, apply standard Convolutional Network architectures with transfer learning from the object recognition domain. The contribution of the paper is threefold: First, it investigates recently introduced very deep neural network architectures (GoogLeNet, VGG, ResNet) using transfer learning (from real images). Second, it proposes transfer learning from a huge set of document images, i.e. 400; 000 documents. Third, it analyzes the impact of the amount of training data (document images) and other parameters to the classification abilities. We use two datasets, the Tobacco-3482 and the large-scale RVL-CDIP dataset. We achieve an accuracy of 91:13% for the Tobacco-3482 dataset while earlier approaches reach only 77:6%. Thus, a relative error reduction of more than 60% is achieved. For the large dataset RVL-CDIP, an accuracy of 90:97% is achieved, corresponding to a relative error reduction of 11:5%.


title: A Nonparametric N-Gram Topic Model with Interpretable Latent Topics

abstract: Most nonparametric topic models such as Hierarchical Dirichlet Processes, when viewed as an infinite-dimensional extension to the Latent Dirichlet Allocation, rely on the bag-of-words assumption. They thus lose the semantic ordering of the words inherent in the text which can give an extra leverage to the computational model. We present a new nonparametric topic model that not only maintains the word order in the topic discovery process, but also generates topical n-gram words leading to more interpretable latent topics in the family of the nonparametric topic models. Our experimental results show an improved performance over the current state-of-the-art topic models in document modeling and generating n-gram words in topics.


title: Generic Advice: On the Combination of AOP with Generative Programming in AspectC++

abstract: Besides object-orientation, generic types or templates and aspect-oriented programming (AOP) gain increasing popularity as they provide additional dimensions of decomposition. Most modern programming languages like Ada, Eiffel, and C++ already have built-in support for templates. For Java and C# similar extensions will be available in the near future. Even though promising, the combination of aspects with generic and generative programming is still a widely unexplored field. This paper presents our extensions to the AspectC++ language, an aspect-oriented C++ derivate. By these extensions aspects can now affect generic code and exploit the potentials of generic code and template metaprogramming in their implementations. This allows aspects to inject template metaprograms transparently into the component code. A case study demonstrates that this feature enables the development of highly expressive and efficient generic aspect implementations in AspectC++. A discussion whether these concepts are applicable in the context of other aspect-oriented language extensions like AspectJ rounds up our contribution.


title: Antenna Grouping based Feedback Compression for FDD-based Massive MIMO Systems

abstract: Recent works on massive multiple-input multiple-output (MIMO) have shown that a potential breakthrough in capacity gains can be achieved by deploying a very large number of antennas at the base station. In order to achieve the performance that massive MIMO systems promise, accurate transmit-side channel state information (CSI) should be available at the base station. While transmit-side CSI can be obtained by employing channel reciprocity in time division duplexing (TDD) systems, explicit feedback of CSI from the user terminal to the base station is needed for frequency division duplexing (FDD) systems. In this paper, we propose an antenna grouping based feedback reduction technique for FDD-based massive MIMO systems. The proposed algorithm, dubbed antenna group beamforming (AGB), maps multiple correlated antenna elements to a single representative value using predesigned patterns. The proposed method modifies the feedback packet by introducing the concept of a header to select a suitable group pattern and a payload to quantize the reduced dimension channel vector. Simulation results show that the proposed method achieves significant feedback overhead reduction over conventional approach performing the vector quantization of whole channel vector under the same target sum rate requirement.


title: Web question answering: is more always better?

abstract: This paper describes a question answering system that is designed to capitalize on the tremendous amount of data that is now available online. Most question answering systems use a wide variety of linguistic resources. We focus instead on the redundancy available in large corpora as an important resource. We use this redundancy to simplify the query rewrites that we need to use, and to support answer mining from returned snippets. Our system performs quite well given the simplicity of the techniques being utilized. Experimental results show that question answering accuracy can be greatly improved by analyzing more and more matching passages. Simple passage ranking and n-gram extraction techniques work well in our system making it efficient to use with many backend retrieval engines.


title: Electron Density Response to Phonon Dynamics in MgB2: An Indicator of Superconducting Properties

abstract: Electron density differences resulting from atom displacement patterns aligned with phonon modes in MgB2 have been calculated using density functional theory (DFT). The extent of phonon anomalies, identified as indicators of the superconducting transition temperature, Tc, under a range of conditions in AlB2-type structures, reduce as boron atoms are displaced from their equilibrium positions along E2g mode directions. The Fermi energy for displacements along the directions of the E2g phonon mode accounts for changes in the covalent B-B bond electronic charge density. We applied differential atom displacements to show that the shifted σ band structure associated with the light effective mass became tangential to the Fermi level and that the Fermi surface undergoes a topological transition at a critical relative displacement of ~0.6% of the boron atoms from equilibrium. The difference in Fermi energies at this critical displacement and at the equilibrium position correspond to the superconducting energy gap. The net volume between tubular σ surfaces in reciprocal space correlated with the depth of the phonon anomaly and, by inference, it is a key to an understanding of superconductivity. This ab initioapproach offers a phenomenological understanding of the factors that determine Tc based on knowledge of the crystal structure.


title: Low-voltage low-power fast-settling CMOS operational transconductance amplifiers for switched-capacitor applications

abstract: This paper presents a new fully differential operational transconductance amplifier (OTA) for low-voltage and fast-settling switched-capacitor circuits in digital CMOS technology. The proposed two-stage OTA is a hybrid class A/AB that combines a folded cascode as the first stage with active current mirrors as the second stage. It employs a hybrid cascode compensation scheme, merged Ahuja and improved Ahuja style compensations, for fast settling.


title: Digital image hashing using local histogram of Oriented Gradients

abstract: The ease with which digital images can be manipulated without severe degradation of quality makes it necessary to be able to verify the authenticity of digital images. One way to establish the image authenticity is by computing a hash sequence from an image. This hash sequence must be robust against non content-altering manipulations, but must be able to show if the content of the image has been tampered with. Furthermore, the hash has to have enough differentiating power such that the hash sequences from two different images are not similar. This paper presents an image hashing system based on local Histogram of Oriented Gradients. The system is shown to have good differentiating power, robust against non content-altering manipulations such as filtering and JPEG compression and is sensitive to content-altering attacks.


title: Agrobacterium-mediated co-transformation of multiple genes in upland cotton

abstract: Two cotton genotypes, Simian 3 (SM 3) and WC, were co-transformed using a mixture of four Agrobacterium tumefaciens cultures of strain LBA4404, each carrying a plasmid harboring the following genes, Bt + sck (for Bacillus thuringenesis protein and modified Cowpea trypsin inhibitor), bar (for glufosinate), keratin, and fibroin. The frequency of callus induction, embryogenesis, and plant regeneration were notably different between the two genotypes. However, there were no differences between the two genotypes for number of plantlets carrying multiple gene copies of different gene combinations as well as transformation frequency for different gene combinations. PCR analysis indicated that more than 80% of plantlets carried the nptII gene for kanamycin resistance. Overall, the co-transformation frequency of two or more genes was about 35%. Southern blot analysis confirmed integration of target genes into the cotton genome, and the number of copies of the transgene(s) varied from one to four. Multiple transgene expression was confirmed by RT-PCR analysis in some transgenic lines. Further analysis of T1 plants demonstrated that multiple transgenes were inherited and expressed in progenies.


title: A robotics toolbox for MATLAB

abstract: The Robotics Toolbox is a software package that allows a MATLAB user to readily create and manipulate datatypes fundamental to robotics such as homogeneous transformations, quaternions and trajectories. Functions provided, for arbitrary serial-link manipulators, include forward and inverse kinematics, Jacobians, and forward and inverse dynamics. This article introduces the Toolbox in tutorial form, with examples chosen to demonstrate a range of capabilities. The complete Toolbox and documentation is freely available via anonymous ftp.


title: MISRA C, for Security's Sake!

abstract: A third of United States new cellular subscriptions in Q1 2016 were for cars. There are now more than 112 million vehicles connected around the world. The percentage of new cars shipped with Internet connectivity is expected to rise from 13% in 2015 to 75% in 2020, and 98% of all vehicles will likely be connected by 2025. Moreover, the news continuously report about "white hat" hackers intruding on car software. For these reasons, security concerns in automotive and other industries have skyrocketed. MISRA C, which is widely respected as a safety-related coding standard, is equally applicable as a security-related coding standard. In this presentation, we will show that security-critical and safety-critical software have the same requirements. We will then introduce the new documents MISRA C:2012 Amendment 1 (Additional security guidelines for MISRA C:2012) and MISRA C:2012 Addendum 2 (Coverage of MISRA C:2012 against ISO/IEC TS 17961:2013 "C Secure Coding Rules"). We will illustrate the relationship between MISRA C, CERT C and ISO/IEC TS 17961, with a particular focus on the objective of preventing security vulnerabilities (and of course safety hazards) as opposed to trying to eradicate them once they have been inserted in the code.


title: Oxytocin Attenuates Amygdala Reactivity to Fear in Generalized Social Anxiety Disorder

abstract: Patients with generalized social anxiety disorder (GSAD) exhibit heightened activation of the amygdala in response to social cues conveying threat (eg, fearful/angry faces). The neuropeptide oxytocin (OXT) decreases anxiety and stress, facilitates social encounters, and attenuates amygdala reactivity to threatening faces in healthy subjects. The goal of this study was to examine the effects of OXT on fear-related amygdala reactivity in GSAD and matched healthy control (CON) subjects. In a functional magnetic resonance imaging study utilizing a double-blind placebo-controlled within-subjects design, we measured amygdala activation to an emotional face matching task of fearful, angry, and happy faces following acute intranasal administration of OXT (24 IU or 40.32 μg) and placebo in 18 GSAD and 18 CON subjects. Both the CON and GSAD groups activated bilateral amygdala to all emotional faces during placebo, with the GSAD group exhibiting hyperactivity specifically to fearful faces in bilateral amygdala compared with the CON group. OXT had no effect on amygdala activity to emotional faces in the CON group, but attenuated the heightened amygdala reactivity to fearful faces in the GSAD group, such that the hyperactivity observed during the placebo session was no longer evident following OXT (ie, normalization). These findings suggest that OXT has a specific effect on fear-related amygdala activity, particularly when the amygdala is hyperactive, such as in GSAD, thereby providing a brain-based mechanism of the impact of OXT in modulating the exaggerated processing of social signals of threat in patients with pathological anxiety.


title: Green Supply-Chain Management: A State-of-the-Art Literature Review

abstract: There is a growing need for integrating environmentally sound choices into supply-chain management research and practice. Perusal of the literature shows that a broad frame of reference for green supply-chain management (GrSCM) is not adequately developed. Regulatory bodies that formulate regulations to meet societal and ecological concerns to facilitate growth of business and economy also suffer from its absence. A succinct classification to help academicians, researchers and practitioners in understanding integrated GrSCM from a wider perspective is needed. Further, sufficient literature is available to warrant such classification. This paper takes an integrated and fresh look into the area of GrSCM. The literature on GrSCM is covered exhaustively from its conceptualization, primarily taking a 'reverse logistics angle'. Using the rich body of available literature, including earlier reviews that had relatively limited perspectives, the literature on GrSCM is classified on the basis of the problem context in supply chain's major influential areas. It is also classified on the basis of methodology and approach adopted. Various mathematical tools/techniques used in literature vis-a-vis the contexts of GrSCM are mapped. A timeline indicating relevant papers is also provided as a ready reference. Finally, the findings and interpretations are summarized, and the main research issues and opportunities are highlighted.


title: E.G.P.: Strategies for affect-controlled action-selection

abstract: Reinforcement learning (RL) agents can benefit from adaptive exploration/exploitation behavior, especially in dynamic environments. We focus on regulating this exploration/exploitation behavior by controlling the action-selection mechanism of RL. Inspired by psychological studies which show that affect influences human decision making, we use artificial affect to influence an agent's action-selection. Two existing affective strategies are implemented and, in addition, a new hybrid method that combines both. These strategies are tested on `maze tasks' in which a RL agent has to find food (rewarded location) in a maze. We use Soar-RL, the new RL-enabled version of Soar, as a model environment. One task tests the ability to quickly adapt to an environmental change, while the other tests the ability to escape a local optimum in order to find the global optimum. We show that artificial affect-controlled action-selection in some cases helps agents to faster adapt to changes in the environment.


title: Beating the hold-out: bounds for K-fold and progressive cross-validation

abstract: The empirical error on a test set, the hold-out estimate, often is a more reliable estimate of generalization error than the observed error on the training set, the training estimate. K-fold cross validation is used in practice with the hope of being more accurate than the hold-out estimate without reducing the number of training examples. We argue that the k-fold estimate does in fact achieve this goal. Specifically, we show that for any nontrivial learning problem and learning algorithm that is insensitive to example ordering, the k-fold estimate is strictly more accurate than a single hold-out estimate on 1/k of the data, for 2 < k < n (k = n is leave-one-out), based on its variance and all higher moments. Previous bounds were termed sanitycheck because they compared the k-fold estimate to the training estimate and, further, restricted the VC dimension and required a notion of hypothesis stability [2]. In order to avoid these dependencies, we consider a k-fold hypothesis that is a randomized combination or average of the k individual hypotheses. We introduce progressive validationas another possible improvement on the hold-out estimate. This estimate of the generalization error is, in many ways, as good as that of a single hold-out, but it uses an average of half as many examples for testing. The procedure also involves a hold-out set, but after an example has been tested, it is added to the training set and the learning algorithm is rerun.


title: Multiple objects tracking in the presence of long-term occlusions

abstract: We present a robust object tracking algorithm that handles spatially extended and temporally long object occlusions. The proposed approach is based on the concept of ''object permanence'' which suggests that a totally occluded object will re-emerge near its occluder. The proposed method does not require prior training to account for differences in the shape, size, color or motion of the objects to be tracked. Instead, the method automatically and dynamically builds appropriate object representations that enable robust and effective tracking and occlusion reasoning. The proposed approach has been evaluated on several image sequences showing either complex object manipulation tasks or human activity in the context of surveillance applications. Experimental results demonstrate that the developed tracker is capable of handling several challenging situations, where the labels of objects are correctly identified and maintained over time, despite the complex interactions among the tracked objects that lead to several layers of occlusions.


title: VideoMAP and VideoSpaceIcon: tools for anatomizing video content

abstract: A new approach to interacting with stored video is proposed. The approach utilizes VideoMAP and VideoSpaceIcon. VideoMAP is the interface that shows the essential video features in an easy to perceive manner. VideoSpaceIcon represents the temporal and spatial characteristics of a video shot as an intuitive icon. A video indexing method supports both tools. These tools allow the user's creativity to directly interact with the essential features of each video by offering spatial and temporal clues. This paper introduces the basic concept and describes prototype versions of the tools as implemented in a video handling system. VideoMAP and VideoSpaceIcon are effective for video handling functions such as video content analysis, video editing, and various video applications which need an intuitive visual interface.


title: Machine learning in digital games: a survey

abstract: Artificial intelligence for digital games constitutes the implementation of a set of algorithms and techniques from both traditional and modern artificial intelligence in order to provide solutions to a range of game dependent problems. However, the majority of current approaches lead to predefined, static and predictable game agent responses, with no ability to adjust during game-play to the behaviour or playing style of the player. Machine learning techniques provide a way to improve the behavioural dynamics of computer controlled game agents by facilitating the automated generation and selection of behaviours, thus enhancing the capabilities of digital game artificial intelligence and providing the opportunity to create more engaging and entertaining game-play experiences. This paper provides a survey of the current state of academic machine learning research for digital game environments, with respect to the use of techniques from neural networks, evolutionary computation and reinforcement learning for game agent control.


title: The sweet-home project: Audio technology in smart homes to improve well-being and reliance

abstract: The Sweet-Home project aims at providing audio-based interaction technology that lets the user have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. This paper presents an overview of the project focusing on the multimodal sound corpus acquisition and labelling and on the investigated techniques for speech and sound recognition. The user study and the recognition performances show the interest of this audio technology.


title: Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming

abstract: This paper extends previous work with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate trial-and-error (reinforcement) learning and execution-time planning into a single process operating alternately on the world and on a learned model of the world. In this paper, I present and show results for two Dyna architectures. The Dyna-PI architecture is based on dynamic programming's policy iteration method and can be related to existing AI ideas such as evaluation functions and universal plans (reactive systems). Using a navigation task, results are shown for a simple Dyna-PI system that simultaneously learns by trial and error, learns a world model, and plans optimal routes using the evolving world model. The Dyna-Q architecture is based on Watkins's Q-learning, a new kind of reinforcement learning. Dyna-Q uses a less familiar set of data structures than does Dyna-PI, but is arguably simpler to implement and use. We show that Dyna-Q architectures are easy to adapt for use in changing environments.


title: Capacitor-couple ESD protection circuit for deep-submicron low-voltage CMOS ASIC

abstract: Capacitor-couple technique used to lower snapback-trigger voltage and to ensure uniform ESD current distribution in deep-submicron CMOS on-chip ESD protection circuit is proposed. The coupling capacitor is realized by a poly layer right under the wire-bonding metal pad without increasing extra layout area to the pad. A timing-original design model has been derived to calculate the capacitor-couple efficiency of this proposed ESD protection circuit. Using this capacitor-couple ESD protection circuit, the thinner gate oxide of CMOS devices in deep-submicron low-voltage CMOS ASIC can be effectively protected.


title: Detection of replay attacks in cyber-physical systems

abstract: A new approach for detecting replay attacks on cyber-physical systems is presented in this paper. The diagnosis system under a replay attack will not trigger an alarm as long as the received sensor readings are plausible. Since an adversary is not able to react on system changes due to the simplicity of the attack and a lack of system knowledge, our goal is to excite the system in non-regular time intervals. Subsequently we can check by means of signal processing whether the received sensor readings are valid. To excite harmonic oscillations, we insert a nonlinear element in the control loop and exploit the theory of describing functions. Since the nonlinear function is a design parameter, we are able to design robust harmonic oscillations. Another advantage of the proposed approach is the possibility of elegant implementation into existing control systems.


Generating Text via Adversarial Training


title: Performance analysis of stacked RFID tags

abstract: Most RFID tag performance analysis is based on the assumption that the tag is stand-alone when interacting with the readers. However, in the real world, RFID tags are often stacked together when deployed, i.e., RFID-labeled carton boxes stacked on the pallet. When multiple RFID tag antennas are placed close to each other, each antenna behaves like a shielding and reflecting object to others and the radiation pattern and other EM properties will be detuned. The stacking impact often leads to performance degradation and in many cases, read and write failure. In this paper, we first derive an RCS model to generalize a loaded antenna as a shielding and reflecting object. We then introduce a normalized mutual impedance matrix of multiple antennas to formulate the coupling effects among stacked antennas. To quantify and visualize the stacking impact in a real world scenario, we also construct a 3-D EM model in Agilent® AMDS consisting of 27 (3 × 3 × 3) stacked carton boxes, each with Alien® tags attached on the surface. Our theoretical calculation agrees with the simulation and shows that both tag radiation pattern and other EM properties will be greatly altered by neighboring tags. Compared with tags in stand-alone scenarios, the degradation at different positions could be very significant (more than 6dB in some directions).


Low Power Ring Oscillator at 180nm CMOS Technology


title: On using machine learning to automatically classify software applications into domain categories

abstract: Software repositories hold applications that are often categorized to improve the effectiveness of various maintenance tasks. Properly categorized applications allow stakeholders to identify requirements related to their applications and predict maintenance problems in software projects. Manual categorization is expensive, tedious, and laborious --- this is why automatic categorization approaches are gaining widespread importance. Unfortunately, for different legal and organizational reasons, the applications' source code is often not available, thus making it difficult to automatically categorize these applications. In this paper, we propose a novel approach in which we use Application Programming Interface (API) calls from third-party libraries for automatic categorization of software applications that use these API calls. Our approach is general since it enables different categorization algorithms to be applied to repositories that contain both source code and bytecode of applications, since API calls can be extracted from both the source code and byte-code. We compare our approach to a state-of-the-art approach that uses machine learning algorithms for software categorization, and conduct experiments on two large Java repositories: an open-source repository containing 3,286 projects and a closed-source repository with 745 applications, where the source code was not available. Our contribution is twofold: we propose a new approach that makes it possible to categorize software projects without any source code using a small number of API calls as attributes, and furthermore we carried out a comprehensive empirical evaluation of automatic categorization approaches.


title: A Coordinated Approach for Practical OS-Level Cache Management in Multi-core Real-Time Systems

abstract: Many modern multi-core processors sport a large shared cache with the primary goal of enhancing the statistic performance of computing workloads. However, due to resulting cache interference among tasks, the uncontrolled use of such a shared cache can significantly hamper the predictability and analyzability of multi-core real-time systems. Software cache partitioning has been considered as an attractive approach to address this issue because it does not require any hardware support beyond that available on many modern processors. However, the state-of-the-art software cache partitioning techniques face two challenges: (1) the memory co-partitioning problem, which results in page swapping or waste of memory, and (2) the availability of a limited number of cache partitions, which causes degraded performance. These are major impediments to the practical adoption of software cache partitioning. In this paper, we propose a practical OS-level cache management scheme for multi-core real-time systems. Our scheme provides predictable cache performance, addresses the aforementioned problems of existing software cache partitioning, and efficiently allocates cache partitions to schedule a given task set. We have implemented and evaluated our scheme in Linux/RK running on the Intel Core i7 quad-core processor. Experimental results indicate that, compared to the traditional approaches, our scheme is up to 39% more memory space efficient and consumes up to 25% less cache partitions while maintaining cache predictability. Our scheme also yields a significant utilization benefit that increases with the number of tasks.


title: Community, consensus, coercion, control: cs*w or how policy mediates mass participation

abstract: When large groups cooperate, issues of conflict and control surface because of differences in perspective. Managing such diverse views is a persistent problem in cooperative group work. The Wikipedian community has responded with an evolving body of policies that provide shared principles, processes, and strategies for collaboration. We employ a grounded approach to study a sample of active talk pages and examine how policies are employed as contributors work towards consensus. Although policies help build a stronger community, we find that ambiguities in policies give rise to power plays. This lens demonstrates that support for mass collaboration must take into account policy and power.


title: Bayesian Nonparametric Inverse Reinforcement Learning for Switched Markov Decision Processes

abstract: In this paper we develop a Bayesian nonparametric Inverse Reinforcement Learning technique for switched Markov Decision Processes (MDP). Similar to switched linear dynamical systems, switched MDP (sMDP) can be used to represent complex behaviors composed of temporal transitions between simpler behaviors each represented by a standard MDP. We use sticky Hierarchical Dirichlet Process as a nonparametric prior on the sMDP model space, and describe a Markov Chain Monte Carlo method to efficiently learn the posterior given the behavior data. We demonstrate the effectiveness of sMDP models for learning, prediction and classification of complex agent behaviors in a simulated surveillance scenario.


title: Heart rate variability is associated with emotion recognition: direct evidence for a relationship between the autonomic nervous system and social cognition.

abstract: Abstract It is well established that heart rate variability (HRV) plays an important role in social communication. Polyvagal theory suggests that HRV may provide a sensitive marker of one's ability to respond and recognize social cues. The aim of the present study was to directly test this hypothesis. Resting-state HRV was collected and performance on the Reading the Mind in the Eyes Test was assessed in 65 volunteers. HRV was positively associated with performance on this emotion recognition task confirming our hypothesis and these findings were retained after controlling for a variety of confounding variables known to influence HRV — sex, BMI, smoking habits, physical activity levels, depression, anxiety, and stress. Our data suggests that increased HRV may provide a novel marker of one's ability to recognize emotions in humans. Implications for understanding the biological basis of emotion recognition, and social impairment in humans are discussed.


title: Counterfactual regret minimization in sequential security games

abstract: Many real world security problems can be modelled as finite zero-sum games with structured sequential strategies and limited interactions between the players. An abstract class of games unifying these models are the normal-form games with sequential strategies (NFGSS). We show that all games from this class can be modelled as well-formed imperfect-recall extensive-form games and consequently can be solved by counterfactual regret minimization. We propose an adaptation of the CFR+ algorithm for NFGSS and compare its performance to the standard methods based on linear programming and incremental game generation. We validate our approach on two security-inspired domains. We show that with a negligible loss in precision, CFR+ can compute a Nash equilibrium with five times less computation than its competitors.


title: Multiagent Systems: Milestones and New Horizons

abstract: Research in multiagent systems (MAS), or distributed artificial intelligence (DAI), dates back to the late 1970s. Initial work in the area focused on distributed interpretation of sensor data, organizational structuring and generic negotiation protocols. But several recent developments have helped reshape the focus of the field. Like the rest of Al, the field has matured from being largely exploratory in nature to focusing on formal theories of negotiation, distributed reasoning, multiagent learning and communication languages. The field is also maturing to the point of developing its first few fielded applications. The recent widespread interest in the Internet, the World Wide Web, and intelligent agent applications have further fueled the need for techniques and mechanisms by which agents representing users can interact effectively with other agents in open, dynamic environments. The development of several new international workshops and conferences has helped focus research in the area. The field is poised at a critical juncture, with stimulating problems and challenges promising some very exciting developments in the next few years.


title: RNA-Seq: a revolutionary tool for transcriptomics

abstract: RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.


title: Consistent-Aware Deep Learning for Person Re-identification in a Camera Network

abstract: In this paper, we propose a consistent-aware deep learning (CADL) framework for person re-identification in a camera network. Unlike most existing person re-identification methods which identify whether two body images are from the same person, our approach aims to obtain the maximal correct matches for the whole camera network. Different from recently proposed camera network based re-identification methods which only consider the consistent information in the matching stage to obtain a global optimal association, we exploit such consistent-aware information under a deep learning framework where both feature representation and image matching are automatically learned with certain consistent constraints. Specifically, we reach the global optimal solution and balance the performance between different cameras by optimizing the similarity and association iteratively. Experimental results show that our method obtains significant performance improvement and outperforms the state-of-the-art methods by large margins.


An unusual prey item for the yellow tail cribo Drymarchon corais Boie 1827, in the Brazilian Savannah


title: Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture

abstract: Learning to represent and generate videos from unlabeled data is a very challenging problem. To generate realistic videos, it is important not only to ensure that the appearance of each frame is real, but also to ensure the plausibility of a video motion and consistency of a video appearance in the time direction. The process of video generation should be divided according to these intrinsic difficulties. In this study, we focus on the motion and appearance information as two important orthogonal components of a video, and propose Flow-and-Texture-Generative Adversarial Networks (FTGAN) consisting of FlowGAN and TextureGAN. In order to avoid a huge annotation cost, we have to explore a way to learn from unlabeled data. Thus, we employ optical flow as motion information to generate videos. FlowGAN generates optical flow, which contains only the edge and motion of the videos to be begerated. On the other hand, TextureGAN specializes in giving a texture to optical flow generated by FlowGAN. This hierarchical approach brings more realistic videos with plausible motion and appearance consistency. Our experiments show that our model generates more plausible motion videos and also achieves significantly improved performance for unsupervised action classification in comparison to previous GAN works. In addition, because our model generates videos from two independent information, our model can generate new combinations of motion and attribute that are not seen in training data, such as a video in which a person is doing sit-up in a baseball ground.


title: SOTA: Towards a General Model for Self-Adaptive Systems

abstract: The increasing complexity and dynamics in which software systems are deployed call for solutions to make such systems autonomic, i.e., capable of dynamically self-adapting their behavior in response to changing situations. To this end, proper models and software engineering tools are required to be available to support the design and development of autonomic systems. In this paper, we introduce a new general model, SOTA, for modeling the adaptation requirements. SOTA, by bringing together the lessons of goal-oriented modeling and of context-aware system modeling, has the potentials for tackling some key issues in the design and development of complex self-adaptive software systems. In particular, SOTA enables: early verification of requirements, identification of knowledge requirements for self-adaptation, and identification of the most suitable self-adaptive patterns.


title: The Effect of Online Piracy Deterrence on Self-Control and Piracy Intention

abstract: As the internet is largely used worldwide, it becomes critical to protect digital contents on the internet from illegal piracy. The purpose of this study is to explore the effect of online piracy deterrence methods punishment and education – on self-control and piracy intention. The gender differences on the relationship of piracy deterrence and intention is also examined. The result of this study shows that both piracy education and punishment have a strong relationship with self-control, and piracy education shows a stronger effect on self-control than piracy punishment. The study also reveals the gender differences in self-control and piracy intention toward piracy deterrence. This research would propose practical guidelines for policy makers and educator to reduce online piracy.


title: A literature review and classification of electronic commerce research

abstract: In this paper, we present a literature review and classification scheme for electronic commerce (EC) research. The former consists of 275 journal articles published between 1993 and 1999 in nine journals that are appropriate outlets for EC research. The results show that an increasing volume of EC research has been conducted for a diverse range of areas. The articles are classified and results of these are presented, based on a scheme that consists of four main categories: application areas, technological issues, support and implementation, and others. A comprehensive list of references is presented. Hopefully, this review will provide a source for anyone interested in EC research and help simulate further interest.