title: Therapeutic use of sleep deprivation in depression

abstract: Abstract Total sleep deprivation (TSD) for one whole night improves depressive symptoms in 40–60% of treatments. The degree of clinical change spans a continuum from complete remission to worsening (in 2–7%). Other side effects are sleepiness and (hypo-) mania. Sleep deprivation (SD) response shows up in the SD night or on the following day. Ten to 15% of patients respond after recovery sleep only. After recovery sleep 50–80% of day 1 responders suffer a complete or partial relapse; but improvement can last for weeks. Sleep seems to lead to relapse although this is not necessarily the case. Treatment effects may be stabilised by antidepressant drugs, lithium, shifting of sleep time or light therapy. The best predictor of a therapeutic effect is a large variability of mood. Current opinion is that partial sleep deprivation (PSD) in the second half of the night is equally effective as TSD. There are, however, indications that TSD is superior. Early PSD (i.e. sleeping between 3:00 and 6:00) has the same effect as late PSD given equal sleep duration. New data cast doubt on the time-honoured conviction that REM sleep deprivation is more effective than non-REM SD. Both may work by reducing total sleep time. SD is an unspecific therapy. The main indication is the depressive syndrome. Some studies show positive effects in Parkinson's disease. It is still unknown how sleep deprivation works.


title: Turn-taking cues in task-oriented dialogue

abstract: Abstract: As interactive voice response systems become more prevalent and provide increasingly more complex functionality, it becomes clear that the challenges facing such systems are not solely in their synthesis and recognition capabilities. Issues such as the coordination of turn exchanges between system and user also play an important role in system usability. In particular, both systems and users have difficulty determining when the other is taking or relinquishing the turn. In this paper, we seek to identify turn-taking cues correlated with human-human turn exchanges which are automatically computable. We compare the presence of potential prosodic, acoustic, and lexico-syntactic turn-yielding cues in prosodic phrases preceding turn changes (smooth switches) vs. turn retentions (holds) vs. backchannels in the Columbia Games Corpus, a large corpus of task-oriented dialogues, to determine which features reliably distinguish between these three. We identify seven turn-yielding cues, all of which can be extracted automatically, for future use in turn generation and recognition in interactive voice response (IVR) systems. Testing Duncan's (1972) hypothesis that these turn-yielding cues are linearly correlated with the occurrence of turn-taking attempts, we further demonstrate that, the greater the number of turn-yielding cues that are present, the greater the likelihood that a turn change will occur. We also identify six cues that precede backchannels, which will also be useful for IVR backchannel generation and recognition; these cues correlate with backchannel occurrence in a quadratic manner. We find similar results for overlapping and for non-overlapping speech.


title: Novel Modified UWB Planar Monopole Antenna With Variable Frequency Band-Notch Function

abstract: A novel modified microstrip-fed ultrawideband (UWB) planar monopole antenna with variable frequency band-notch characteristic is presented. By inserting two slots in the both sides of microstrip feedline on the ground plane, much wider impedance bandwidth can be produced. A modified H-shaped conductor-backed plane with variable dimensions is used in order to generate the frequency band-stop performance and control its characteristics such as band-notch frequency and its bandwidth. The designed antenna has a small size of 22 x 22 mm2 and operates over the frequency band between 3.1 and 14 GHz for VSWR < 2 while showing the band rejection performance in the frequency band of 5.1 to 5.9 GHz.


title: Accurate Assessment of the Bioactivities of Redox-Active Polyphenolics in Cell Culture

abstract: Phenolic compounds are widely known for their roles as antioxidants and anti-inflammatory agents, as well as for their epidemiological association with reduced risks for certain types of diseases. In the present study, we used rabbit peripheral blood mononuclear cells (PBMCs) to evaluate possible artifacts that result from the reactivity of polyphenolics. We evaluated several common methods for cytotoxicity tests using nine polyphenolics, representing several major classes of tannins and their subunits. For three of those phenolics, we investigated whether or not the bioactivities of the phenolics were altered by spontaneous oxidation. Our study showed that many of the nine tested tannins interfered with the 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide (MTT) assay, which is commonly used to measure cell viability. A better method for determining cell viability is the luciferin/luciferase ATP assay, and using that method, we found that several tannins are toxic to PBMCs. We measured TNF-α ...


title: Hashimoto's encephalopathy

abstract: We report a case of Hashimoto’s encephalopathy with atypical and partially reversible MRI findings. T2-weighted MRI images revealed bilaterally symmetric areas of increased signal in the mesial temporal lobes and basal ganglia. Despite clinical and imaging improvement after steroid therapy, some memory deficits and MRI abnormalities persisted.


title: Slick: Secure Middleboxes using Shielded Execution

abstract: Cloud computing offers the economies of scale for computational resources with the ease of management, elasticity, and fault tolerance. To take advantage of these benefits, many enterprises are contemplating to outsource the middlebox processing services in the cloud. However, middleboxes that process confidential and private data cannot be securely deployed in the untrusted environment of the cloud. ::: To securely outsource middleboxes to the cloud, the state-of-the-art systems advocate network processing over the encrypted traffic. Unfortunately, these systems support only restrictive middlebox functionalities, and incur prohibitively high overheads due to the complex computations involved over the encrypted traffic. ::: This motivated the design of Slick --- a secure middlebox framework for deploying high-performance Network Functions (NFs) on untrusted commodity servers. Slick exposes a generic interface based on Click to design and implement a wide-range of NFs using its out-of-the box elements and C++ extensions. Slick leverages SCONE (a shielded execution framework based on Intel SGX) and DPDK to securely process confidential data at line rate. ::: More specifically, Slick provides hardware-assisted memory protection, and configuration and attestation service for seamless and verifiable deployment of middleboxes. ::: We have also added several new features for commonly required functionalities: new specialized Click elements for secure packet processing, secure shared memory packet transfer for NFs chaining, secure state persistence, an efficient on-NIC timer for SGX enclaves, and memory safety against DPDK-specific Iago attacks. Furthermore, we have implemented several SGX-specific optimizations in Slick. Our evaluation shows that Slick achieves near-native throughput and latency.


title: FoodCam: A real-time food recognition system on a smartphone

abstract: We propose a mobile food recognition system, FoodCam, the purposes of which are estimating calorie and nutrition of foods and recording a user's eating habits. In this paper, we propose image recognition methods which are suitable for mobile devices. The proposed method enables real-time food image recognition on a consumer smartphone. This characteristic is completely different from the existing systems which require to send images to an image recognition server. To recognize food items, a user draws bounding boxes by touching the screen first, and then the system starts food item recognition within the indicated bounding boxes. To recognize them more accurately, we segment each food item region by GrubCut, extract image features and finally classify it into one of the one hundred food categories with a linear SVM. As image features, we adopt two kinds of features: one is the combination of the standard bag-of-features and color histograms with ?2 kernel feature maps, and the other is a HOG patch descriptor and a color patch descriptor with the state-of-the-art Fisher Vector representation. In addition, the system estimates the direction of food regions where the higher SVM output score is expected to be obtained, and it shows the estimated direction in an arrow on the screen in order to ask a user to move a smartphone camera. This recognition process is performed repeatedly and continuously. We implemented this system as a standalone mobile application for Android smartphones so as to use multiple CPU cores effectively for real-time recognition. In the experiments, we have achieved the 79.2 % classification rate for the top 5 category candidates for a 100-category food dataset with the ground-truth bounding boxes when we used HOG and color patches with the Fisher Vector coding as image features. In addition, we obtained positive evaluation by a user study compared to the food recording system without object recognition.


title: Gastric cancer biomarkers; A systems biology approach

abstract: Gastric cancer is one of the most fatal cancers in the world. Many efforts in recent years have attempted to find effective proteins in gastric cancer. By using a comprehensive list of proteins involved in gastric cancer, scientists were able to retrieve interaction information. The study of protein-protein interaction networks through systems biology based analysis provides appropriate strategies to discover candidate proteins and key biological pathways. In this study, we investigated dominant functional themes and centrality parameters including betweenness as well as the degree of each topological clusters and expressionally active sub-networks in the resulted network. The results of functional analysis on gene sets showed that neurotrophin signaling pathway, cell cycle and nucleotide excision possess the strongest enrichment signals. According to the computed centrality parameters, HNF4A, TAF1 and TP53 manifested as the most significant nodes in the interaction network of the engaged proteins in gastric cancer. This study also demonstrates pathways and proteins that are applicable as diagnostic markers and therapeutic targets for future attempts to overcome gastric cancer.


title: A Semi-Analytical Model for Macaroni MOSFETs With Application to Vertical Flash Memories

abstract: We present a semianalytical model for the electrostatics and the current–voltage characteristics of Macaroni MOSFETs based on the exact solution of the Poisson equation, which is useful to investigate both the subthreshold and the $\mathrm{\scriptstyle ON}$ -state regimes of these devices. With the addition of a simplified calculation of the tunneling current, the model is exploited to analyze the program performance of Macaroni $\mathrm{\scriptstyle NAND}$ flash memories.


title: Honeypots for employee information security awareness and education training: A conceptual EASY training model

abstract: The increasing pervasiveness of internet-connected systems means that such systems will continue to be exploited for criminal purposes by cybercriminals (including malicious insiders such as employees and vendors). The importance of protecting corporate system and intellectual property, and the escalating complexities of the online environment underscore the need for ongoing information security awareness and education training and the promotion of a culture of security among employees. Two honeypots were deployed at a private university based in Singapore. Findings from the analysis of the honeypot data are presented in this paper. This paper then examines how analysis of honeypot data can be used in employee information security awareness and education training. Adapting the Routine Activity Theory, a criminology theory widely used in the study of cybercrime, this paper proposes a conceptual Engaging Stakeholders, Acceptable Behavior, Simple Teaching method, Yardstick (EASY) training model, and explains how the model can be used to design employee information security awareness and education training. Future research directions are also outlined in this paper.


Developing and Examining a Model of Perceived Quality , Perceived Value and Perceived Risk Affecting Customer Loyalty of Environmentally-Friendly Electronic Products


title: BUSINESS ALIGNMENT IN THE CRM DOMAIN: PREDICTING CRM PERFORMANCE

abstract: In this paper we present a framework that aims to answer the central question why some organizations are successful in the CRM domain, while others are not. The framework is built on two foundations. The first claims that an organization’s CRM performance is positively affected by aligning CRM activities according to five business dimensions: strategy, monitoring and control, organization and processes, employees and culture, and IT. Secondly, it is hypothesized that CRM performance is additionally and positively influenced by integrating four CRM areas: CRM strategy, customer insight, customer contact, and marketing. This twofold hypothesis is tested empirically using data from thirty questionnaires that were completed by an equal number of Dutch marketing, sales and account managers. All respondents had experience with the concept of CRM and represented a large variety of organizations according to size and sector. Several construction methods are applied in order to operationalize the concept of CRM alignment and performance. Bivariate analysis supports the hypothesis. We conclude that the twofold alignment of CRM activities is a critical success factor for organizations embarking on CRM initiatives.


title: Online Variational Inference for the Hierarchical Dirichlet Process

abstract: The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric model that can be used to model mixed-membership data with a potentially infinite number of components. It has been applied widely in probabilistic topic modeling, where the data are documents and the components are distributions of terms that reflect recurring patterns (or “topics”) in the collection. Given a document collection, posterior inference is used to determine the number of topics needed and to characterize their distributions. One limitation of HDP analysis is that existing posterior inference algorithms require multiple passes through all the data—these algorithms are intractable for very large scale applications. We propose an online variational inference algorithm for the HDP, an algorithm that is easily applicable to massive and streaming data. Our algorithm is significantly faster than traditional inference algorithms for the HDP, and lets us analyze much larger data sets. We illustrate the approach on two large collections of text, showing improved performance over online LDA, the finite counterpart to the HDP topic model.


title: B-TREPID: Batteryless tamper-resistant envelope with a PUF and integrity detection

abstract: Protecting embedded devices against physical attacks is a challenging task since the attacker has control of the device in a hostile environment. To address this issue, current countermeasures typically use a battery-backed tamper-respondent envelope that encloses the entire device to create a trusted compartment. However, the battery affects the system's robustness and weight, and also leads to difficulties with the security mechanism while shipping the device. In contrast, we present a batteryless tamper-resistant envelope, which contains a fine mesh of electrodes, and its complementary security concept. An evaluation unit checks the integrity of the sensor mesh by detecting short and open circuits. Additionally, it measures the capacitances of the mesh. Once its preliminary integrity is confirmed, a cryptographic key is derived from the capacitive measurements that represent a PUF, to decrypt and authenticate the firmware of the enclosed host system. We demonstrate the feasibility of our concept, provide details on the layout and electrical properties of the batteryless envelope, and explain the underlying security architecture. Practical results from a set of manufactured envelopes facilitate future research.


title: Query relaxation for entityrelationship search

abstract: Entity-relationship-structured data is becoming more important on the Web. For example, large knowledge bases have been automatically constructed by information extraction from Wikipedia and other Web sources. Entities and relationships can be represented by subject-property-object triples in the RDF model, and can then be precisely searched by structured query languages like SPARQL. Because of their Boolean-match semantics, such queries often return too few or even no results. To improve recall, it is thus desirable to support users by automatically relaxing or reformulating queries in such a way that the intention of the original user query is preserved while returning a sufficient number of ranked results. ::: ::: In this paper we describe comprehensive methods to relax SPARQL-like triplepattern queries in a fully automated manner. Our framework produces a set of relaxations by means of statistical language models for structured RDF data and queries. The query processing algorithms merge the results of different relaxations into a unified result list, with ranking based on any ranking function for structured queries over RDF-data. Our experimental evaluation, with two different datasets about movies and books, shows the effectiveness of the automatically generated relaxations and the improved quality of query results based on assessments collected on the Amazon Mechanical Turk platform.


title: New Video Target Tracking Algorithm Based on KNN

abstract: Compressed sensing is an advanced technique used in visual tracking problem. However, current research uses too many pre-knowledge, and cannot deal with quick object sensitively. Based on the problems, the paper proposes a new real-time visual tracking algorithm based on compressive sensing. On one hand, the algorithm uses the characteristics of compressive sensing, and make sure of the time constraint of the tracking. On the other hand, KNN classifier is used to differentiate the target and the background. Results show that the algorithm can provide the accuracy of the real-time tracking.


title: Multidrug-resistant bacterial microorganisms (MDRO) in end-of-life care: development of recommendations for hospitalized patients using a mixed-methods approach

abstract: Palliative care professionals are frequently confronted with patients colonized or infected with MDRO. One major challenge is how to balance necessary isolation measures and social inclusion as one of the main principles of palliative and end-of-life care. To date, MDRO-specific policies and protocols vary widely between institutions. Aim: provide empirical recommendations on how to deal with hospitalized MDRO patients in end-of-life care. Recommendations were developed based on (i) initial results of face-to-face interviews and focus groups, (ii) impartial experts’ comments and consensus on the draft via online survey and (iii) a face-to-face meeting with consortium members to finalize recommendations. Findings of 158 interviews and six focus groups (39 participants) with patients, family caregivers, staff members and institutional stakeholders contributed to the recommendations. The assessments of 17 experts were considered. In total, 21 recommendations were approved. The recommended strategy in dealing with MDRO at the end of life allows case-based application of protection and isolation measures. MDRO diagnostics and therapy involve screening at admission. The recommendations suggest consideration of required accommodation facilities, provided material as well as staff and time resources. The recommendations further highlight the importance of providing for strategies enabling the patient’s social inclusion and provision of verbal and written information about MDRO for patients and family caregivers, transparent medical documentation, and staff member training. The recommendations summarize the perspectives of individuals and groups affected by MDRO at the end of life and provide practical guidance for clinical routine. Further dissemination and implementation requirements are discussed and should contain the evaluation of the knowledge, views, worries, and anxieties of the target groups.


title: Craniofacial characteristics in children with Angle Class II div. 2 malocclusion combined with extreme deep bite.

abstract: Craniofacial characteristics in two groups of children were compared. In one group (n = 22) the children had Angle Class II division 2 malocclusion combined with extreme deep bite. The other group (n = 25) was composed of children with ideal occlusion. The mean ages of the children were 12.8 and 12.9 years respectively. In the Class II-2 group the distance between gonion and B-point was underdeveloped, causing B-point to have a retruded position in relation to both A-point and cranial base. The Class II-2 children also had a retroclination of the symphysis, which gave the B-point a retruded position in relation to pogonion. As for vertical dimensions, Class II-2 children had a smaller anterior lower facial height than normal. Furthermore, Class II-2 had a discrepancy between the maxillary incisal and molar heights, i.e. a slightly larger incisal height and a slightly smaller molar height. Finally, children with Class II-2 had a high lip line and a very large interincisal angle. Three variables--the sagittal distance between points A and B, the inclination of the symphysis, and the relationship between the maxillary incisal and molar heights--in combination, differentiated nearly 100% correctly between Class II-2 and normal occlusion.


title: Effect of Multilayer Windings With Different Stator Winding Connections on Interior PM Machines for EV Applications

abstract: The interior permanent magnet (IPM) machine equipped with a fractional-slot concentrated winding (FSCW) has met an increasing interest in electric vehicle applications due to its higher power density and efficiency. Torque production is due to both PM and reluctance torques. However, one of the main challenges of FSCWs is their inability to produce a high-quality magnetomotive force (MMF) distribution, yielding undesirable rotor core and magnet eddy-current losses. Literature shows that the reduction of low-order space harmonics significantly reduces these loss components. Moreover, it has been previously shown that by employing a higher number of layers, although causing some reduction in the winding factor of the torque-producing MMF component, both machine saliency and reluctance torque components are improved. Recently, a dual three-phase winding connected in a star/delta connection has also shown promise to concurrently enhance machine torque of a surface-mounted PM machine while significantly reducing both rotor core and magnet losses. In this paper, a multilayer winding configuration and a dual three-phase winding connection are combined and applied to the well-known 12-slot/10-pole IPM machine with v-shaped magnets. The proposed winding layout is compared with a conventional double-layer winding, a dual three-phase double-layer winding, and a four-layer winding. The comparison is carried out using 2-D finite-element analysis. The comparison shows that the proposed winding layout, while providing similar output torque to a conventional double-layer three-phase winding, offers a significant reduction in core and magnet losses, correspondingly a higher efficiency, improves the machine saliency ratio, and maximizes the reluctance toque component.


title: Deep Learning for Efficient Discriminative Parsing

abstract: We propose a new fast purely discriminative algorithm for natural language parsing, based on a “deep” recurrent convolutional graph transformer network (GTN). Assuming a decomposition of a parse tree into a stack of “levels”, the network predicts a level of the tree taking into account predictions of previous levels. Using only few basic text features which leverage word representations from Collobert and Weston (2008), we show similar performance (in F1 score) to existing pure discriminative parsers and existing “benchmark” parsers (like Collins parser, probabilistic context-free grammars based), with a huge speed advantage.


title: Data collection and audit logs of digital forensics in cloud

abstract: Cloud computing has become more important to the users and the service providers, since it is convenient for the users to get the required service from their service providers. As the amount of services given rises, the amount spent on privacy and security of these services too raise. The internet as it is known has been growing insecure in the recent years. With the vast expanse of technology with regard to cloud computing and popularization of the same, the crimes due to these or on these devices will also increase in the near future. The digital environment should be capable of not only preventing the attacks, but it should also be capable enough to deal with the high security threat to defend it. Logs based forensic model approach which is to build a forensic-friendly system has been proposed. A proposed framework for the pursuit of a cloud computing digital investigation is environment that depends on a particular event's “cause and effects”. Search for evidence and to develop hypotheses about such events that at the crime. In the proposed work, Log audit plays a vital role in the forensic framework. Logs model with timestamps can collect the information on the cloud rapidly for forensic resolutions. Hence the complexity involved in these kinds of forensics is reduced.


title: A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application

abstract: The design, analysis, and application of a new recurrent neural network for quadratic programming, called simplified dual neural network, are discussed. The analysis mainly concentrates on the convergence property and the computational complexity of the neural network. The simplified dual neural network is shown to be globally convergent to the exact optimal solution. The complexity of the neural network architecture is reduced with the number of neurons equal to the number of inequality constraints. Its application to k-winners-take-all (KWTA) operation is discussed to demonstrate how to solve problems with this neural network


title: A 900 MHz ladder-type SAW filter duplexer

abstract: A low-cost hybrid-integrated SAW filter duplexer consisting of receiver top filter, transmitter final-stage filter and antenna terminal has been developed. The prototype design was oriented at the GSM system specifications in the 900 MHz-band. Three-stage ladder-type SAW filters have been designed to perform the duplexing function by the use of only one transmission line. The complete duplexer has an insertion loss of 1.2 dB in the Tx path (Tx) and 2.1 dB in the Rx path (Rx). The 3 dB-bandwidth is 40.1 MHz (Tx) and 47.4 MHz (Rx), respectively.


Algorithms and Applications for Spatial Data Mining


THE LINGUISTIC ANALYSIS OF CHINESE EMOTICON


title: N-best list re-ranking using semantic relatedness and syntactic score: An approach for improving speech recognition accuracy in air traffic control

abstract: In this paper, we investigate how we can take advantage of the availability of linguistic knowledge, particularly semantic knowledge, in Air Traffic Control (ATC) to reduce the Word Error Rate (WER) of Automatic Speech Recognition (ASR) systems. To facilitate this, we integrate semantic knowledge into post-processing by performing n-best list re-ranking. We first propose a feature called semantic relatedness. We then use the WER-Sensitive Pairwise Perceptron algorithm which is proposed in previous work to combine the semantic relatedness, syntactic score and speech decoder's confidence score features to perform n-best list re-ranking. We evaluate the proposed approach in terms of WER on the well known ATCOSIM Corpus of Non-prompted Clean Air Traffic Control Speech (ATCOSIM) and our own Air Traffic Control Speech Corpus (ATCSC). The evaluation results show that our proposed approach reduces the WER by 0.31% and 1.53% on the ATCOSIM and ATCSC corpora respectively. Our proposed approach also shows 19.93% WER improvement compared with traditional n-gram model on the ATCSC corpus.


title: A Direct Torque Controlled Induction Motor with Variable Hysteresis Band

abstract: An improved direct torque controlled induction motor drive is reported in this paper. It is established that the conventional direct torque controlled drive has more torque and flux ripples in steady state, which result in poor torque response, acoustic noise and incorrect speed estimations. Hysteresis controllers also make the switching frequency of voltage source inverter a variable quantity. A strategy of variable duty ratio control scheme is proposed to increase switching frequency, and adjust the width of hysteresis bands according to the switching frequency. This technique minimizes torque and current ripples, improves torque response, and reduces switching losses in spite of its simplicity. Simulation results establish the improved performance of the proposed direct torque control method compared to conventional methods.


title: Human Temporal Lobe Activation by Speech and Nonspeech Sounds

abstract: Functional organization of the lateral temporal cortex in humans is not well understood. We recorded blood oxygenation signals from the temporal lobes of normal volunteers using functional magnetic resonance imaging during stimulation with unstructured noise, frequency-modulated (FM) tones, reversed speech, pseudowords and words. For all conditions, subjects performed a materialnonspecific detection response when a train of stimuli began or ceased. Dorsal areas surrounding Heschl’s gyrus bilaterally, particularly the planum temporale and dorsolateral superior temporal gyrus, were more strongly activated by FM tones than by noise, suggesting a role in processing simple temporally encoded auditory information. Distinct from these dorsolateral areas, regions centered in the superior temporal sulcus bilaterally were more activated by speech stimuli than by FM tones. Identical results were obtained in this region using words, pseudowords and reversed speech, suggesting that the speech–tones activation difference is due to acoustic rather than linguistic factors. In contrast, previous comparisons between word and nonword speech sounds showed left-lateralized activation differences in more ventral temporal and temporoparietal regions that are likely involved in processing lexical–semantic or syntactic information associated with words. The results indicate functional subdivision of the human lateral temporal cortex and provide a preliminary framework for understanding the cortical processing of speech sounds.


title: Value co-creation between firms and customers: The role of big data-based cooperative assets

abstract: Big data interconnects firms and customers in promoting value co-creation.Big data can be transformed from resources to valuable cooperative assets.We describes the transformation process of big data-based cooperative assets.The transformation requires heterogeneous resource bases and cooperative capabilities. To better understand how big data interconnects firms and customers in promoting value co-creation, we propose a theoretical framework of big data-based cooperative assets based on evidence of multiple case studies. We identify four types of big data resources and four types of associated digital platforms, and we explore how firms develop the cooperative assets by transforming big data resources via the theoretical lens of service-dominant logic. This study offers a new theoretical perspective on value co-creation and an alternative competitive strategy in the era of big data for firms.


title: Dynamic voltage regulator based on PWM AC chopper converter: topology and control

abstract: This article presents a novel topology of dynamic voltage regulator using transformer and PWM AC chopper. The proposed voltage regulator employs an ac chopper converter to generate compensation voltage and uses the transformer to isolate the power converter from the load. When input voltage sags and swells occur, the output voltage can be regulated by changing the duty ratio of the PWM control signals. The transformer is connected in series with the load. Thus, the load voltage can be kept stable when input voltage fluctuations occur. For this purpose, a feedforward feedback digital controller has been designed to perform the close-loop control. The proposed converter and control strategy present the advantages of fast dynamic response and effective compensation to the voltage fluctuations. Simulations are made to investigate the performances of the proposed converter. The simulation results show that the designed voltage regulator has fast transient response, and can suppress the load voltage fluctuations effectively. The prototype is also setup. The simulation results have been validated by the experiment.


title: Are Pets a Healthy Pleasure? The Influence of Pets on Blood Pressure:

abstract: Pet owners often describe their pets as important and cherished family members who offer solace in times of stress. This article considers evidence suggesting that pets influence human blood pressure. Studies on this topic extend current research testing the hypothesis that having other people around in stressful times can buffer the negative consequences of stress. The existing data suggest that people perceive pets as important, supportive parts of their lives and that the presence of a pet is associated with significant cardiovascular benefits, among both people with normal blood pressure and those with high blood pressure. Studies about pets and blood pressure have examined both naturally occurring and randomly assigned pet ownership but are limited by their focus on responses to short-term, acute stress. Future prospective studies should explore the influence of pets on people at risk for cardiovascular disease and also consider explanatory mechanisms for the pet effect.


title: Reasoning about goal-directed real-time teleo-reactive programs

abstract: The teleo-reactive programming model is a high-level approach to developing real-time systems that supports hierarchical composition and durative actions. The model is different from frameworks such as action systems, timed automata and TLA+, and allows programs to be more compact and descriptive of their intended behaviour. Teleo-reactive programs are particularly useful for implementing controllers for autonomous agents that must react robustly to their dynamically changing environments. In this paper, we develop a real-time logic that is based on Duration Calculus and use this logic to formalise the semantics of teleo-reactive programs. We develop rely/guarantee rules that facilitate reasoning about a program and its environment in a compositional manner. We present several theorems for simplifying proofs of teleo-reactive programs and present a partially mechanised method for proving progress properties of goal-directed agents.


Numerical Simulation of Flow behind Vortex Generators


title: Thermal characterisation of a copper-clip-bonded IGBT module with double-sided cooling

abstract: In pursuing higher power density power electronic equipment, the copper clip bonding concept is one of the double-sided cooling approaches under investigation for power semiconductors. This paper presents a comprehensive thermal assessment of the copper clip concept by undertaking comparative studies on one bespoke copper-clip-bonded IGBT power module and a conventional wire-bonded counterpart. A series of single-sided cooling tests and double-sided cooling tests have been undertaken on these two modules. Reductions are reported in the junction to case thermal resistance of up to 23% for one individual die and around 18% for the parallel operation of two dies in the single-sided cooling tests. Whilst in the double-sided cooling experiments, an additional average 18% thermal improvement is achieved due to the addition of a top fan-cooled heatsink mounted onto the copper clip. The results show clearly that the copper clip technology could provide significant benefits in the heat removal for power semiconductors and enable more power dense equipment.


title: Face occlusion detection based on multi-task convolution neural network

abstract: With the rise of crimes associated with ATM, security reinforcement by surveillance techniques has been in high agenda for both academia and industries. Though cameras are generally installed in ATMs to capture the facial images of users, the function is only limited to recording for follow-up criminal investigations, which could become useless when a criminal's face is occluded. Therefore, face occlusion detection has become very important to prevent crimes connected with ATMs. Traditional approaches to solve the problem typically consist of a succession of steps such as localization, segmentation, feature extraction and recognition. This paper proposes robust and effective facial occlusion detection based on convolutional neural networks (ConvNets) with multi-task learning. Covering of different facial parts, namely, left eye, right eye, nose and mouth, can be predicted by the multi-task CNN. In comparison with previous approaches, CNN is optimal from the system point of view as the design is based on end-to-end principle and the model operates directly on the image pixels. We created a large scale face occlusion database, consisting of over fifty thousand images, with annotated facial parts. Experimental results revealed that the proposed method is extremely effective.


title: Ransomware: The CryptoVirus subverting cloud security

abstract: Cloud computing presents unlimited prospects for Information Technology (IT) industry and business enterprises alike. Rapid advancement brings a dark underbelly of new vulnerabilities and challenges unfolding with alarming regularity. Although cloud technology provides a ubiquitous environment facilitating business enterprises to conduct business across disparate locations, security effectiveness of this platform interspersed with threats which can bring everything that subscribes to the cloud, to a halt raises questions. However advantages of cloud platforms far outweighs drawbacks and study of new challenges helps overcome drawbacks of this technology. One such emerging security threat is of ransomware attack on the cloud which threatens to hold systems and data on cloud network to ransom with widespread damaging implications. This provides huge scope for IT security specialists to sharpen their skillset to overcome this new challenge. This paper covers the broad cloud architecture, current inherent cloud threat mechanisms, ransomware vulnerabilities posed and suggested methods to mitigate it.


title: Leadership Style of Managers in Five-Star Hotels and its Relationship with Employee's Job Satisfaction

abstract: The purpose of the study is to investigate the impact of leadership style on employee job satisfaction in five-star hotels. A questionnaire consisted of 57 items based on five-point likert scale (from strongly disagree to strongly agree) was used to measure both leadership style and job satisfaction from employees’ perspectives. Using random sampling, the researcher distributed 350 questionnaires, 220 of which were completed. The obtained data from the questionnaires are investigated through the SPSS statistical packaged software. The empirical results indicated that two types of leadership styles, namely, democratic and laissezfaire were found to have direct positive significant relationships with employees’ job satisfaction. The findings showed that the dominant leadership style was democratic and employees were moderately satisfied with their job. This implies that democratic leadership is deemed suitable for managing hotels. The study’s results show that different leadership style will have different impacts on employee job satisfaction. An interesting finding is that democratic leadership has a stronger influence on job satisfaction than laissez-faire leadership does. As a result, by adopting the appropriate leadership styles, leaders can affect employee job satisfaction.


title: Making Online Products More Tangible: The Effect of Product Presentation Formats on Product Evaluations

abstract: Although several studies have looked at the effects of online product presentations on consumer decision making, no study thus far has considered a potential key factor in online product evaluations: tangibility. The present study aims at filling this gap by developing and testing a model that relates different online product presentation formats to the three-dimensional concept of product tangibility. We test how the three tangibility dimensions influence perceived diagnosticity and, eventually, online purchase intentions. A between-subjects lab experiment (n = 366) was used to test the hypothesized effects of three common online product presentation formats (pictures vs. 360 spin rotation vs. virtual mirror). The results showed that out of these formats, virtual mirrors were superior in providing a sense of product tangibility, followed by the 360-spin rotation format and static pictures. Furthermore, in terms of predictive validity, two of the three tangibility dimensions significantly increased perceived diagnosticity, which, in turn, positively and strongly affected purchase intentions. Overall, our results add to previous works studying the relationships between online product presentation formats and consumer decision making. Also, they hold value for online practitioners by highlighting the potential benefits of applying technologically advanced product presentation formats such as the virtual mirror.


title: Ferromagnetic neodymium dihydride

abstract: Two polycrystalline samples of neodymium dihydride have been investigated by both ac‐susceptibility and dc‐magnetization measurements. The ac susceptibility displays a sharp peak at 5.8±0.1 K, suggestive of the onset of long‐range ferromagnetic order. The dc magnetization shows the onset of coercivity and remanence at the same temperature. Arrott plots of the magnetization confirm ferromagnetic order at 5.6 K. However, saturation is not observed at 4.2 K in external fields up to 60 kOe.


The State of the Art in Visualizing Group Structures in Graphs


title: Large scale graph mining for web reputation inference

abstract: The explosion of the number of devices and users on the Internet results in massive amounts of data and information. This poses the most complex challenges in security we have ever faced. The detection of malicious domains and Internet protocol (IP) addresses has been a hot topic in cyber security. We present a scalable and effective graph inference system for detecting malicious domains and IP addresses. The goal is to protect Internet users from network threats. Based on the loopy belief propagation algorithm, the system infers every domain or IP reputation, flagging it with high reputation as malicious one. We have evaluated the system with 75 million-node graph constructed from the huge dataset (500 gigabytes). The system attains performance with 86% and 87% area under receiver operating curves for inferring domain and IP reputations respectively. We demonstrate that the graphical solution provides rapid assessment of safe or risky sites on McAfee's data. It provides an automatic tool for web reputation inference in the field and serves as an assisting tool for “first pass” classification and triaging.


title: Tag recommendations in folksonomies

abstract: Collaborative tagging systems allow users to assign keywords--so called "tags"--to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. ::: ::: In this paper we evaluate and compare two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.


Numerical Dispersion and Stability of an Extended FDTD Method Applied to Negative Refractive Index Media Modeling


title: A configuration-based recommender system for supporting e-commerce decisions

abstract: Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers’ decision processes in e-commerce shopping tasks.


Robotics in A 46 . Robotics in Agriculture and Forestry


title: Adding Physical Objects to an Interactive Game Improves Learning and Enjoyment: Evidence from EarthShake

abstract: Can experimenting with three-dimensional (3D) physical objects in mixed-reality environments produce better learning and enjoyment than flat-screen two-dimensional (2D) interaction? We explored this question with EarthShake: a mixed-reality game bridging physical and virtual worlds via depth-camera sensing, designed to help children learn basic physics principles. In this paper, we report on a controlled experiment with 67 children, 4--8 years old, that examines the effect of observing physical phenomena and collaboration (pairs vs. solo). A follow-up experiment with 92 children tests whether adding simple physical control, such as shaking a tablet, improves learning and enjoyment. Our results indicate that observing physical phenomena in the context of a mixed-reality game leads to significantly more learning and enjoyment compared to screen-only versions. However, there were no significant effects of adding simple physical control or having students play in pairs vs. alone. These results and our gesture analysis provide evidence that children's science learning can be enhanced through experiencing physical phenomena in a mixed-reality environment.


title: Methodological Review: Temporal reasoning with medical data-A review with emphasis on medical natural language processing

abstract: Temporal information is crucial in electronic medical records and biomedical information systems. Processing temporal information in medical narrative data is a very challenging area. It lies at the intersection of temporal representation and reasoning (TRR) in artificial intelligence and medical natural language processing (MLP). Some fundamental concepts and important issues in relation to TRR have previously been discussed, mainly in the context of processing structured data in biomedical informatics; however, it is important that these concepts be re-examined in the context of processing narrative data using MLP. Theoretical and methodological TRR studies in biomedical informatics can be classified into three main categories: category 1 applies theories and models from temporal reasoning in AI; category 2 defines frameworks that meet needs from clinical applications; category 3 resolves issues such as temporal granularity and uncertainty. Currently, most MLP systems are not designed with a formal representation of time, and their ability to reason about temporal relations among medical events is limited. Previous work in processing time with clinical narrative data includes processing time in clinical reports, modeling textual temporal expressions in clinical databases, processing time in clinical guidelines, and building time standards for data exchange and integration. In addition to common problems in MLP, there are challenges specific to TRR in medical text, which occur at each level of linguistic structure and analysis. Despite advances in temporal reasoning in biomedical informatics, processing time in medical text deserves more attention. Besides the need for more research in temporal granularity, fuzzy time, temporal contradiction, intermittent events and uncertainty, broad areas for future research include enhancing functions of current MLP systems on processing temporal information, incorporating medical knowledge into temporal reasoning systems, resolving coreference, integrating narrative data with structured data and evaluating these systems.


title: Continuous-action reinforcement learning with fast policy search and adaptive basis function selection

abstract: As an important approach to solving complex sequential decision problems, reinforcement learning (RL) has been widely studied in the community of artificial intelligence and machine learning. However, the generalization ability of RL is still an open problem and it is difficult for existing RL algorithms to solve Markov decision problems (MDPs) with both continuous state and action spaces. In this paper, a novel RL approach with fast policy search and adaptive basis function selection, which is called Continuous-action Approximate Policy Iteration (CAPI), is proposed for RL in MDPs with both continuous state and action spaces. In CAPI, based on the value functions estimated by temporal-difference learning, a fast policy search technique is suggested to search for optimal actions in continuous spaces, which is computationally efficient and easy to implement. To improve the generalization ability and learning efficiency of CAPI, two adaptive basis function selection methods are developed so that sparse approximation of value functions can be obtained efficiently both for linear function approximators and kernel machines. Simulation results on benchmark learning control tasks with continuous state and action spaces show that the proposed approach not only can converge to a near-optimal policy in a few iterations but also can obtain comparable or even better performance than Sarsa-learning, and previous approximate policy iteration methods such as LSPI and KLSPI.


title: Change-point monitoring for the detection of DoS attacks

abstract: This paper presents a simple and robust mechanism, called change-point monitoring (CPM), to detect denial of service (DoS) attacks. The core of CPM is based on the inherent network protocol behavior and is an instance of the sequential change point detection. To make the detection mechanism insensitive to sites and traffic patterns, a nonparametric cumulative sum (CUSUM) method is applied, thus making the detection mechanism robust, more generally applicable, and its deployment much easier. CPM does not require per-flow state information and only introduces a few variables to record the protocol behaviors. The statelessness and low computation overhead of CPM make itself immune to any flooding attacks. As a case study, the efficacy of CPM is evaluated by detecting a SYN flooding attack - the most common DoS attack. The evaluation results show that CPM has short detection latency and high detection accuracy


title: Extracting depth and matte using a color-filtered aperture

abstract: This paper presents a method for automatically extracting a scene depth map and the alpha matte of a foreground object by capturing a scene through RGB color filters placed in the camera lens aperture. By dividing the aperture into three regions through which only light in one of the RGB color bands can pass, we can acquir three shifted views of a scene in the RGB planes of an image in a single exposure. In other words, a captured image has depth-dependent color misalignment. We develop a color alignment measure to estimate disparities between the RGB planes for depth reconstruction. We also exploit color misalignment cues in our matting algorithm in order to disambiguate between the foreground and background regions even where their colors are similar. Based on the extracted depth and matte, the color misalignment in the captured image can be canceled, and various image editing operations can be applied to the reconstructed image, including novel view synthesis, postexposure refocusing, and composition over different backgrounds.


A Remedy called Affordance 70 Affordances for robots : a brief survey


title: Kernel sparse representation with local patterns for face recognition

abstract: In this paper we propose a novel kernel sparse representation classification (SRC) framework and utilize the local binary pattern (LBP) descriptor in this framework for robust face recognition. First we develop a kernel coordinate descent (KCD) algorithm for 11 minimization in the kernel space, which is based on the covariance update technique. Then we extract LBP descriptors from each image and apply two types of kernels (χ2 distance based and Hamming distance based) with the proposed KCD algorithm under the SRC framework for face recognition. Experiments on both the Extended Yale B and the PIE face databases show that the proposed method is more robust against noise, occlusion, and illumination variations, even with small number of training samples.


title: Standard high-resolution pelvic MRI vs. low-resolution pelvic MRI in the evaluation of deep infiltrating endometriosis

abstract: Objective ::: To compare the capabilities of standard pelvic MRI with low-resolution pelvic MRI using fast breath-hold sequences to evaluate deep infiltrating endometriosis (DIE).


title: Broadly Improving User Classification via Communication-Based Name and Location Clustering on Twitter

abstract: Hidden properties of social media users, such as their ethnicity, gender, and location, are often reflected in their observed attributes, such as their first and last names. Furthermore, users who communicate with each other often have similar hidden properties. We propose an algorithm that exploits these insights to cluster the observed attributes of hundreds of millions of Twitter users. Attributes such as user names are grouped together if users with those names communicate with other similar users. We separately cluster millions of unique first names, last names, and userprovided locations. The efficacy of these clusters is then evaluated on a diverse set of classification tasks that predict hidden users properties such as ethnicity, geographic location, gender, language, and race, using only profile names and locations when appropriate. Our readily-replicable approach and publiclyreleased clusters are shown to be remarkably effective and versatile, substantially outperforming state-of-the-art approaches and human accuracy on each of the tasks studied.


title: A Survey on Quality of Experience of HTTP Adaptive Streaming

abstract: Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.


title: Utilizing Focused Relevance Feedback

abstract: We present a novel study of ad hoc retrieval methods utilizing document-level relevance feedback and/or focused relevance feedback; namely, passages marked as (non-)relevant. The first method uses a novel mixture model that integrates relevant and non-relevant information at the language model level. The second method fuses retrieval scores produced by using relevant and non-relevant information separately. Empirical exploration attests to the merits of our methods, and sheds light on the effectiveness of using and integrating relevance feedback for textual units of varying granularities.


title: Automatic segmentation of left ventricular myocardium by deep convolutional and de-convolutional neural networks

abstract: Deep learning has been integrated into several existing left ventricle (LV) endocardium segmentation methods to yield impressive accuracy improvements. However, challenges remain for segmentation of LV epicardium due to its fuzzier appearance and complications from the right ventricular insertion points. Segmenting the myocardium collectively (i.e., endocardium and epicardium together) confers the potential for better segmentation results. In this work, we develop a computational platform based on deep learning to segment the whole LV myocardium simultaneously from a cardiac magnetic resonance (CMR) image. The deep convolutional network is constructed using Caffe platform, which consists of 6 convolutional layers, 2 pooling layers, and 1 de-convolutional layer. A preliminary result with Dice metric of 0.75±0.04 is reported on York MR dataset. While in its current form, our proposed one-step deep learning method cannot compete with state-of-art myocardium segmentation methods, it delivers promising first pass segmentation results.


title: Dynamic Provisioning Modeling for Virtualized Multi-tier Applications in Cloud Data Center

abstract: Dynamic provisioning is a useful technique for handling the virtualized multi-tier applications in cloud environment. Understanding the performance of virtualized multi-tier applications is crucial for efficient cloud infrastructure management. In this paper, we present a novel dynamic provisioning technique for a cluster-based virtualized multi-tier application that employ a flexible hybrid queueing model to determine the number of virtual machines at each tier in a virtualized application. We present a cloud data center based on virtual machine to optimize resources provisioning. Using simulation experiments of three-tier application, we adopt an optimization model to minimize the total number of virtual machines while satisfying the customer average response time constraint and the request arrival rate constraint. Our experiments show that cloud data center resources can be allocated accurately with these techniques, and the extra cost can be effectively reduced.


title: A Deep Architecture for Semantic Parsing

abstract: Many successful approaches to semantic parsing build on top of the syntactic analysis of text, and make use of distributional representations or statistical models to match parses to ontology-specific queries. This paper presents a novel deep learning architecture which provides a semantic parsing system through the union of two neural models of language semantics. It allows for the generation of ontology-specific queries from natural language statements and questions without the need for parsing, which makes it especially suitable to grammatically malformed or syntactically atypical text, such as tweets, as well as permitting the development of semantic parsers for resourcepoor languages.


title: A Theoretic Framework of K-Means-Based Consensus Clustering

abstract: Consensus clustering emerges as a promising solution to find cluster structures from data. As an efficient approach for consensus clustering, the K-means based method has garnered attention in the literature, but the existing research is still preliminary and fragmented. In this paper, we provide a systematic study on the framework of K-means-based Consensus Clustering (KCC). We first formulate the general definition of KCC, and then reveal a necessary and sufficient condition for utility functions that work for KCC, on both complete and incomplete basic partitionings. Experimental results on various real-world data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with substantial missing values.


title: Blob detection and filtering for character segmentation of license plates

abstract: This paper presents a character segmentation method to address automatic number plate recognition problem. The method considered pixel intensity, character appearance, and arrangement of characters altogether to segment character regions. The method firstly discovers candidate blobs of characters by using connected component analysis and appearance-based character detection. A character recognizer is used for removing redundant and noisy blobs. Then, a trained classifier selects character blobs among the candidates by examining arrangement of the blobs. Experimental results show an achievement of 98.3% of segmentation rate, which prove the effectiveness of our method.


title: Remaining useful life estimation based on nonlinear feature reduction and support vector regression

abstract: Abstract Prognostics and health management (PHM) of rotating machines is gaining importance in industry and allows increasing reliability and decreasing machines’ breakdowns. Bearings are one of the most components present in mechanical equipments and one of their most common failures. So, to assess machines’ degradations, fault prognostic of bearings is developed in this paper. The proposed method relies on two steps (an offline step and an online step) to track the health state and predict the remaining useful life (RUL) of the bearings. The offline step is used to learn the degradation models of the bearings whereas the online step uses these models to assess the current health state of the bearings and predict their RUL. During the offline step, vibration signals acquired on the bearings are processed to extract features, which are then exploited to learn models that represent the evolution of the degradations. For this purpose, the isometric feature mapping reduction technique (ISOMAP) and support vector regression (SVR) are used. The method is applied on a laboratory experimental degradations related to bearings. The obtained results show that the method can effectively model the evolution of the degradations and predict the RUL of the bearings.


title: The First Law of Robotics (A Call to Arms)

abstract: Even before the advent of Artificial Intelligence, science fiction writer Isaac Asimov recognized that an agent must place the protection of humans from harm at a higher priority than obeying human orders. Inspired by Asimov, we pose the following fundamental questions: (1) How should one formalize the rich, but informal, notion of "harm"? (2) How can an agent avoid performing harmful actions, and do so in a computationally tractable manner? (3) How should an agent resolve conflict between its goals and the need to avoid harm? (4) When should an agent prevent a human from harming herself? While we address some of these questions in technical detail, the primary goal of this paper is to focus attention on Asimov's concern: society will reject autonomous agents unless we have some credible means of making them safe!


title: The Common Neural Basis of Autobiographical Memory, Prospection, Navigation, Theory of Mind, and the Default Mode: A Quantitative Meta-analysis

abstract: A core brain network has been proposed to underlie a number of different processes, including remembering, prospection, navigation, and theory of mind [Buckner, R. L., & Carroll, D. C. Self-projection and the brain. Trends in Cognitive Sciences,11, 49-57, 2007]. This purported network-medial prefrontal, medial-temporal, and medial and lateral parietal regions-is similar to that observed during default-mode processing and has been argued to represent self-projection [Buckner, R. L., & Carroll, D. C. Self-projection and the brain. Trends in Cognitive Sciences,11, 49-57, 2007] or scene-construction [Hassabis, D., & Maguire, E. A. Deconstructing episodic memory with construction. Trends in Cognitive Sciences,11, 299-306, 2007]. To date, no systematic and quantitative demonstration of evidence for this common network has been presented. Using the activation likelihood estimation (ALE) approach, we conducted four separate quantitative meta-analyses of neuroimaging studies on: (a) autobiographical memory, (b) navigation, (c) theory of mind, and (d) default mode. A conjunction analysis between these domains demonstrated a high degree of correspondence. We compared these findings to a separate ALE analysis of prospection studies and found additional correspondence. Across all domains, and consistent with the proposed network, correspondence was found within the medial-temporal lobe, precuneus, posterior cingulate, retrosplenial cortex, and the temporo-parietal junction. Additionally, this study revealed that the core network extends to lateral prefrontal and occipital cortices. Autobiographical memory, prospection, theory of mind, and default mode demonstrated further reliable involvement of the medial prefrontal cortex and lateral temporal cortices. Autobiographical memory and theory of mind, previously studied as distinct, exhibited extensive functional overlap. These findings represent quantitative evidence for a core network underlying a variety of cognitive domains.


title: Energy Optimization for Cellular-Connected Multi-UAV Mobile Edge Computing Systems with Multi-Access Schemes

abstract: In this paper, a cellular-connected unmanned aerial vehicles (UAV) mobile edge computing system is studied in which the multiple UAVs are served by terrestrial base station (TBS) for computation task offloading. Our goal is to minimize the total UAVs energy consumption, including propulsion energy, computation energy and communication energy, while ensuring that the total number of bits of UAVs are completely computed. For tackling the large amount of bits for computation, we propose a resource partitioning strategy where one portion of tasks is migrated to TBS for computation and the other portion of tasks is locally computed at UAV. For deeply comprehending the impacts of access manners on the system performance, we consider four access schemes in the uplink transmission, i.e., time division multiple access (TDMA), orthogonal frequency division multiple access (OFDMA), One-by-One access and non-orthogonal multiple access (NOMA). The problem of jointly optimizing bit allocation, power allocation, resource partitioning as well as UAV trajectory under TBS's energy budget is formulated and tackled by means of successive convex approximation (SCA) technique. The numerical results show that the proposed schemes save much energy compared with benchmark schemes.


title: Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data

abstract: The urban traffic flow prediction is a significant issue in the intelligent transportation system. In consideration of nonlinear and spatial-temporal features of urban traffic data, we propose a deep hybrid neural network improved by greedy algorithm for urban traffic flow prediction with taxi GPS trace. The proposed deep neural network model first combines the convolutional neural network (CNN), which extracts the spatial features, with the long short term memory (LSTM), which captures the temporal information, to predict urban traffic flow. Then, the proposed model is trained by a greedy policy to short time consumption and improves accuracy when a network goes deeper. Experimental results with real taxis GPS trajectory data from ${Xi'an}$ city show that the improved deep hybrid CNN-LSTM model can achieve higher prediction accuracy and shorter time consumption compared with existing methods.


title: CRISPR interference (CRISPRi) for sequence-specific control of gene expression

abstract: Sequence-specific control of gene expression on a genome-wide scale is an important approach for understanding gene functions and for engineering genetic regulatory systems. We have recently described an RNA-based method, CRISPR interference (CRISPRi), for targeted silencing of transcription in bacteria and human cells. The CRISPRi system is derived from the Streptococcus pyogenes CRISPR (clustered regularly interspaced palindromic repeats) pathway, requiring only the coexpression of a catalytically inactive Cas9 protein and a customizable single guide RNA (sgRNA). The Cas9-sgRNA complex binds to DNA elements complementary to the sgRNA and causes a steric block that halts transcript elongation by RNA polymerase, resulting in the repression of the target gene. Here we provide a protocol for the design, construction and expression of customized sgRNAs for transcriptional repression of any gene of interest. We also provide details for testing the repression activity of CRISPRi using quantitative fluorescence assays and native elongating transcript sequencing. CRISPRi provides a simplified approach for rapid gene repression within 1-2 weeks. The method can also be adapted for high-throughput interrogation of genome-wide gene functions and genetic interactions, thus providing a complementary approach to RNA interference, which can be used in a wider variety of organisms.


title: Variable Selection with Neural Networks

abstract: Abstract In this paper, we present 3 different neural network-based methods to perform variable selection . OCD — Optimal Cell Damage — is a pruning method, which evaluates the usefulness of a variable and prunes the least useful ones (it is related to the Optimal Brain Damage method of Le Cun et al.). Regularization theory proposes to constrain estimators by adding a term to the cost function used to train a neural network. In the Bayesian framework, this additional term can be interpreted as the log prior to the weights distribution. We propose to use two priors (a Gaussian and a Gaussian mixture) and show that this regularization approach allows to select efficient subsets of variables. Our methods are compared to conventional statistical selection procedures and are shown to significantly improve on that.


title: High-dimensional stochastic optimal control using continuous tensor decompositions

abstract: Motion planning and control problems are embedded and essential in almost all robotics applications. These problems are often formulated as stochastic optimal control problems and solved using dynamic programming algorithms. Unfortunately, most existing algorithms that guarantee convergence to optimal solutions suffer from the curse of dimensionality: the run time of the algorithm grows exponentially with the dimension of the state space of the system. We propose novel dynamic programming algorithms that alleviate the curse of dimensionality in problems that exhibit certain low-rank structure. The proposed algorithms are based on continuous tensor decompositions recently developed by the authors. Essentially, the algorithms represent high-dimensional functions (e.g., the value function) in a compressed format, and directly perform dynamic programming computations (e.g., value iteration, policy iteration) in this format. Under certain technical assumptions, the new algorithms guarantee convergence towards optimal solutions with arbitrary precision. Furthermore, the run times of the new algorithms scale polynomially with the state dimension and polynomially with the ranks of the value function. This approach realizes substantial computational savings in "compressible" problem instances, where value functions admit low-rank approximations. We demonstrate the new algorithms in a wide range of problems, including a simulated six-dimensional agile quadcopter maneuvering example and a seven-dimensional aircraft perching example. In some of these examples, we estimate computational savings of up to ten orders of magnitude over standard value iteration algorithms. We further demonstrate the algorithms running in real time on board a quadcopter during a flight experiment under motion capture.


title: Design of a low-cost underactuated finger for positioning or pinching thin objects

abstract: Robotic grippers in industry are expected to have low degrees of freedom (DOFs) with cost and simplicity concerns. However, thin objects (e.g. mobile phone covers, shielding of IC chips) cannot be handled by traditional gripper fingers, since deformation may be caused. To solve this problem, a low-cost underactuated finger with a special four-bar linkage and a flexible joint is proposed in this paper. The special four-bar linkage is designed to obtain the desirable motion trail of the fingertip which is a quasi-horizontal line, while the flexible joints reduce the impact force and achieve compliant force control. In this paper, the design methodology of the four-bar linkage and the flexible joint is introduced, while the kinematic performance is analyzed. An experiment was conducted to verify the design and analysis. Results show that the proposed two DOFs underactuated finger can be applied on a robotic gripper to pinch or position thin objects. Finally, an example for its application is briefly explained to reinforce the concept.


title: Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation

abstract: We study the problem of unsupervised domain adaptation, which aims to adapt classifiers trained on a labeled source domain to an unlabeled target domain. Many existing approaches first learn domain-invariant features and then construct classifiers with them. We propose a novel approach that jointly learn the both. Specifically, while the method identifies a feature space where data in the source and the target domains are similarly distributed, it also learns the feature space discriminatively, optimizing an information-theoretic metric as an proxy to the expected misclassification error on the target domain. We show how this optimization can be effectively carried out with simple gradient-based methods and how hyperparameters can be cross-validated without demanding any labeled data from the target domain. Empirical studies on benchmark tasks of object recognition and sentiment analysis validated our modeling assumptions and demonstrated significant improvement of our method over competing ones in classification accuracies.


title: IncludeOS: A Minimal, Resource Efficient Unikernel for Cloud Services

abstract: The emergence of cloud computing as a ubiquitous platform for elastically scaling services has generated need and opportunity for new types of operating systems. A service that needs to be both elastic and resource efficient needs A) highly specialized components, and B) to run with minimal resource overhead. Classical general purpose operating systems designed for extensive hardware support are by design far from meeting these requirements. In this paper we present IncludeOS, a single tasking library operating system for cloud services, written from scratch in C++. Key features include: extremely small disk-and memory footprint, efficient asynchronous I/O, OS-library where only what your service needs gets included, and only one device driver by default (virtio). As a test case a bootable disk image consisting of a simple DNS server with OS included is shown to require only 158 kb of disk space and to require 5-20% less CPU-time, depending on hardware, compared to the same binary running on Linux.


title: Systematic and optimal design of CMOS two-stage opamps with hybrid cascode compensation

abstract: This paper presents a systematic and optimal design of hybrid cascode compensation method which is used in fully differential two-stage CMOS operational transconductance amplifiers (OTAs). The closed loop analysis results are given to obtain a design procedure. A simple design procedure for the minimum settling time of the hybrid cascode compensation technique for a two-stage class A/AB amplifier is proposed. Optimal design issues of power dissipation are considered to achieve the lowest power consumption for the required settling time. Finally, a design example is presented to show both the usefulness of the hybrid cascode compensation and the proposed design procedure. The proposed design technique can help circuit designers as well as it can be used in computer aided circuit design tools.


title: Administrative change in the Gulf: modernization in Bahrain and Oman:

abstract: States in the Middle East tend to be overlooked by researchers in comparative public administration. However, these states offer potentially useful insights into the nature of administrative change as they defy standard assumptions about pressures for reform. The aim of the article is to provide an account of reform by analysing important contextual factors in Bahrain and Oman. Given the large gap in the literature regarding the Gulf States, this article is supplemented by observations and evidence gathered on regular visits to both countries by the researcher. The analysis reveals systems of public administration highly resistant to international reform trends. Many of these factors are situational; including highly centralized political systems, tradition and strong national and administrative cultures. It is concluded that while reform processes are emerging in these countries, they are slow and evolutionary and are more adapted to the domestic rather than the international context.Points for practitio...


DNA algorithms for computing shortest paths


Why do students complete MOOCs ?


title: HOW TO MAKE LARGE SELFORGANIZING MAPS FOR NONVECTORIAL DATA

abstract: The self-organizing map (SOM) represents an open set of input samples by a topologically organized, finite set of models. In this paper, a new version of the SOM is used for the clustering, organization, and visualization of a large database of symbol sequences (viz. protein sequences). This method combines two principles: the batch computing version of the SOM, and computation of the generalized median of symbol strings.


title: Reviews/Essays Recent Research on Human Learning Challenges Conventional Instructional Strategies

abstract: There has been a recent upsurge of interest in exploring how choices of methods and timing of instruction affect the rate and persistence of learning. The authors review three lines of experimentation—all conducted using educationally relevant materials and time intervals—that call into question important aspects of common instructional practices. First, research reveals that testing, although typically used merely as an assessment device, directly potentiates learning and does so more effectively than other modes of study. Second, recent analysis of the temporal dynamics of learning show that learning is most durable when study time is distributed over much greater periods of time than is customary in educational settings. Third, the interleaving of different types of practice problems (which is quite rare in math and science texts) markedly improves learning. The authors conclude by discussing the frequently observed dissociation between people’s perceptions of which learning procedures are most effecti...


title: A domain-independent statistical methodology for dialog management in spoken dialog systems

abstract: HighlightsDialog systems (DS) allow intuitive interaction through natural language.Dialog managers are usually implemented ad hoc and difficult to adapt to new domains.A statistical methodology is proposed to reduce the effort required to develop and adapt dialog managers.User simulation is also proposed to facilitate the acquisition of the required dialog corpus.A complete implementation of our proposal for different dialog systems and its evaluation are also detailed. This paper proposes a domain-independent statistical methodology to develop dialog managers for spoken dialog systems. Our methodology employs a data-driven classification procedure to generate abstract representations of system turns taking into account the previous history of the dialog. A statistical framework is also introduced for the development and evaluation of dialog systems created using the methodology, which is based on a dialog simulation technique. The benefits and flexibility of the proposed methodology have been validated by developing statistical dialog managers for four spoken dialog systems of different complexity, designed for different languages (English, Italian, and Spanish) and application domains (from transactional to problem-solving tasks). The evaluation results show that the proposed methodology allows rapid development of new dialog managers as well as to explore new dialog strategies, which permit developing new enhanced versions of already existing systems.


title: Cybersecurity Automated Information Extraction Techniques: Drawbacks of Current Methods, and Enhanced Extractors

abstract: We address a crucial element of applied information extraction—accurate identification of basic security entities in text-—by evaluating previous methods and presenting new labelers. Our survey reveals that the previous efforts have not been tested on documents similar to the targeted sources (news articles, blogs, tweets, etc.) and that no sufficiently large publicly available annotated corpus of these documents exists. By assembling a representative test corpus, we perform a quantitative evaluation of previous methods in a realistic setting, revealing an overall lack of recall, and giving insight to the models' beneficial and inhibiting elements. In particular, our results show that many previous efforts overfit to the non-representative test corpora in this domain. Informed by this evaluation, we present three novel cyber entity extractors, which seek to leverage the available labeled data but remain worthwhile on the more diverse documents encountered in the wild. Each new model increases the state of the art in recall, with maximal or near maximal F1 score. Our results establish that the state of the art in cyber entity tagging is characterized by F1 = 0.61.


title: A Variable Splitting Augmented Lagrangian Approach to Linear Spectral Unmixing

abstract: This paper presents a new linear hyperspectral unmixing method of the minimum volume class, termed simplex identification via split augmented Lagrangian (SISAL). Following Craig's seminal ideas, hyperspectral linear unmixing amounts to finding the minimum vol ume simplex containing the hyperspectral vectors. This is a noncon vex optimization problem with convex constraints. In the proposed approach, the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The obtained problem is solved by a sequence of augmented Lagrangian optimizations. The resulting algorithm is very fast and able so solve problems far beyond the reach of the current state-of-the art algorithms. The effectiveness of SISAL is illustrated with simulated data.


Canonical Genetic Signatures of the Adult Human Brain


title: An Adaptive Nonlocal Regularized Shadow Removal Method for Aerial Remote Sensing Images

abstract: Shadows are evident in most aerial images with high resolutions, particularly in urban scenes, and their existence obstructs the image interpretation and the following application, such as classification and target detection. Most current shadow removal methods were proposed for natural images, whereas shadows in remote sensing images show distinct characteristics. We have therefore analyzed the characteristics of shadows in aerial images, and in this paper, we propose a new shadow removal method for aerial images, using nonlocal (NL) operators. In the proposed method, the soft shadow is introduced to replace the traditional binary hard shadow. NL operators are used to regularize the shadow scale and the updated shadow-free image. Furthermore, a spatially adaptive NL regularization is introduced to handle compound shadows. The combination of the soft shadow and NL operators yields satisfying shadow-free results, preserving textures and holding regular color. Different types of shadowed aerial images are employed to verify the proposed method, and the results are compared with two other methods. The experimental results confirm the validity of the proposed method and the advantage of the soft-shadow approach.


Abstract Pier – The Meta-Described Content Management System ∗ ESUG Innovation Technology Awards 2007


title: A Comparative Study of Feature Extraction Algorithms in Customer Reviews

abstract: The paper systematically compares two feature extraction algorithms to mine product features commented on in customer reviews. The first approach [17] identifies candidate features by applying a set of POS patterns and pruning the candidate set based on the log likelihood ratio test. The second approach [11] applies association rule mining for identifying frequent features and a heuristic based on the presence of sentiment terms for identifying infrequent features. We evaluate the performance of the algorithms on five product specific document collections regarding consumer electronic devices. We perform an analysis of errors and discuss advantages and limitations of the algorithms.


title: An autonomous defense against SYN flooding attacks: Detect and throttle attacks at the victim side independently

abstract: Distributed denial of service (DDoS) attacks seriously threaten Internet services yet there is currently no defence against such attacks that provides both early detection, allowing time for counteraction, and an accurate response. Traditional detection methods rely on passively sniffing an attacking signature and are inaccurate in the early stages of an attack. Current counteractions such as traffic filter or rate-limit methods do not accurately distinguish between legitimate and illegitimate traffic and are difficult to deploy. This work seeks to provide a method that detects SYN flooding attacks in a timely fashion and that responds accurately and independently on the victim side. We use the knowledge of network traffic delay distribution and apply an active probing technique (DARB) to identify half-open connections that, suspiciously, may not arise from normal network congestion. This method is suitable for large network areas and is capable of handling bursts of traffic flowing into a victim server. Accurate filtering is ensured by a counteraction method using IP address and time-to-live(TTL) fields. Simulation results show that our active detection method can detect SYN flooding attacks accurately and promptly and that the proposed rate-limit counteraction scheme can efficiently minimize the damage caused by DDoS attacks and guarantee constant services to legitimate users.


title: A hybrid spatial data clustering method for site selection: The data driven approach of GIS mining

abstract: This article applies customer service to be the research background. Spatial data mining method is proposed to solve site selection of the service center. Firstly, a new data model for recording all the information of customer management is given, which transforms the traditional model-driven strategy to data-oriented method. Secondly, a hybrid spatial clustering method named OETTC-MEANS-CLASA algorithm is proposed. It has the advantages of applying k-means algorithm to reduce the result space and using simulated annealing method (CLASA) as result-searching strategy to find more qualified solutions. On the basis of GIS functions, we design deeper analytical function to take spatial obstacle factors, spatial environmental factors, spatial terrain factors, spatial traffic factors and cost factors into account. The result of the experiment declares that the algorithm does better at the both aspects of perform efficiency and result quality.


title: Saliency Detection via Graph-Based Manifold Ranking

abstract: Most existing bottom-up methods measure the foreground saliency of a pixel or region based on its contrast within a local context or the entire image, whereas a few methods focus on segmenting out background regions and thereby salient objects. Instead of considering the contrast between the salient objects and their surrounding regions, we consider both foreground and background cues in a different way. We rank the similarity of the image elements (pixels or regions) with foreground cues or background cues via graph-based manifold ranking. The saliency of the image elements is defined based on their relevances to the given seeds or queries. We represent the image as a close-loop graph with super pixels as nodes. These nodes are ranked based on the similarity to background and foreground queries, based on affinity matrices. Saliency detection is carried out in a two-stage scheme to extract background regions and foreground salient objects efficiently. Experimental results on two large benchmark databases demonstrate the proposed method performs well when against the state-of-the-art methods in terms of accuracy and speed. We also create a more difficult benchmark database containing 5,172 images to test the proposed saliency model and make this database publicly available with this paper for further studies in the saliency field.


title: A high flexible Early-Late Gate bit synchronizer in FPGA-based software defined radios

abstract: The more increasing necessity of integration inside digital systems together with the advantages in terms of portability, reduced time-to-market, better flexibility and versatility, lead towards integrated all-digital FPGA based communication systems. Bit synchronization is a fundamental operation required for the best symbol detection. A high flexible Early-Late Gate implementation is proposed, it is optimized for low resource consumption in FPGA implementations.


title: Label-Based Automatic Alignment of Video with Narrative Sentences

abstract: In this paper we consider videos (e.g. Hollywood movies) and their accompanying natural language descriptions in the form of narrative sentences (e.g. movie scripts without timestamps). We propose a method for temporally aligning the video frames with the sentences using both visual and textual information, which provides automatic timestamps for each narrative sentence. We compute the similarity between both types of information using vectorial descriptors and propose to cast this alignment task as a matching problem that we solve via dynamic programming. Our approach is simple to implement, highly efficient and does not require the presence of frequent dialogues, subtitles, and character face recognition. Experiments on various movies demonstrate that our method can successfully align the movie script sentences with the video frames of movies.


title: Optimal Design of a Permanent Magnet Linear Synchronous Motor With Low Cogging Force

abstract: In this paper, a technique for reducing the cogging force in slotted permanent magnet linear synchronous motors (PMLSMs) is presented. A two-step minimization process that depends on the slotting and end effects is proposed. Taguchi's parameter method, coupled with 2-D and 3-D finite element analysis (FEA), is employed to minimize the cogging force.


title: Computational and statistical analysis of metabolomics data

abstract: Metabolomics is the comprehensive study of small molecule metabolites in biological systems. By assaying and analyzing thousands of metabolites in biological samples, it provides a whole picture of metabolic status and biochemical events happening within an organism and has become an increasingly powerful tool in the disease research. In metabolomics, it is common to deal with large amounts of data generated by nuclear magnetic resonance (NMR) and/or mass spectrometry (MS). Moreover, based on different goals and designs of studies, it may be necessary to use a variety of data analysis methods or a combination of them in order to obtain an accurate and comprehensive result. In this review, we intend to provide an overview of computational and statistical methods that are commonly applied to analyze metabolomics data. The review is divided into five sections. The first two sections will introduce the background and the databases and resources available for metabolomics research. The third section will briefly describe the principles of the two main experimental methods that produce metabolomics data: MS and NMR, followed by the fourth section that describes the preprocessing of the data from these two approaches. In the fifth and the most important section, we will review four main types of analysis that can be performed on metabolomics data with examples in metabolomics. These are unsupervised learning methods, supervised learning methods, pathway analysis methods and analysis of time course metabolomics data. We conclude by providing a table summarizing the principles and tools that we discussed in this review.


title: Player Experience: Articulating Suspense as a Configurative Encounter

abstract: As a complex and hybrid medium, games are at once a ‘text’ that can be read and an activity that demands that players participate in the construction of its structure. This article seeks to articulate the relationship and interactions between games and their players, with a specifi c focus on suspense as a type of player involvement. Previous studies lack a description of the specifi c qualities of game suspense or the participatory aspects that are responsible for triggering the emotion. By examining how the textual and structural characteristics of the game trigger specifi c types of suspense, this article explores the involvement of a player between the virtual world and the actual confi gurative act of play, where personal success and failure are at stake.


title: Visual Tracking via Coarse and Fine Structural Local Sparse Appearance Models

abstract: Sparse representation has been successfully applied to visual tracking by finding the best candidate with a minimal reconstruction error using target templates. However, most sparse representation-based tracking methods only consider holistic rather than local appearance to discriminate between target and background regions, and hence may not perform well when target objects are heavily occluded. In this paper, we develop a simple yet robust tracking algorithm based on a coarse and fine structural local sparse appearance model. The proposed method exploits both partial and structural information of a target object based on sparse coding using the dictionary composed of patches from multiple target templates. The likelihood obtained by averaging and pooling operations exploits consistent appearance of object parts, thereby helping not only locate targets accurately but also handle partial occlusion. To update templates more accurately without introducing occluding regions, we introduce an occlusion detection scheme to account for pixels belonging to the target objects. The proposed method is evaluated on a large benchmark data set with three evaluation metrics. Experimental results demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.


title: Impairments to wound healing

abstract: When unabated, wound healing progresses in an orderly and predictable fashion: inflammation, proliferation, and maturation. Numerous factors, however, play a detrimental role in the wound-healing process. Poor nutritional status, various drugs, radiation, smoking, and hypoxia impair wound healing. A precise understanding of these mechanisms of impairment is necessary when treating acute or chronic wounds.