title: Comparing exercise in Parkinson's disease—the Berlin BIG Study

abstract: Physiotherapy is widely used in Parkinson's disease (PD), but there are few controlled studies compar- ing active interventions. Recently, a technique named ''LSVT 1 BIG'' has been introduced. LSVT 1 BIG is derived from the Lee Silverman Voice Treatment and focuses on in- tensive exercising of high-amplitude movements. In the pres- ent comparative study, 60 patients with mild to moderate PD were randomly assigned to receive either one-to-one training (BIG), group training of Nordic Walking (WALK), or domes- tic nonsupervised exercises (HOME). Patients in training (BIG) and WALK received 16 hours of supervised training within 4 (BIG) or 8 (WALK) weeks. The primary efficacy measure was difference in change in Unified Parkinson's Dis- ease Rating Scale (UPDRS) motor score from baseline to fol- low-up at 16 weeks between groups. UPDRS scores were obtained by blinded video rating. ANCOVA showed signifi- cant group differences for UPDRS-motor score at final assess- ment (P < 0.001). Mean improvement of UPDRS in BIG was 25.05 (SD 3.91) whereas there was a mild deterioration of 0.58 (SD 3.17) in WALK and of 1.68 (SD 5.95) in HOME. LSVT 1 BIG was also superior to WALK and HOME in timed-up-and-go and timed 10 m walking. There were no sig- nificant group differences for quality of life (PDQ39). These results provide evidence that LSVT 1 BIG is an effective tech- nique to improve motor performance in patients with PD. 2010 Movement Disorder Society


title: SDN Based Collaborative Scheme for Mitigation of DDoS Attacks

abstract: Software Defined Networking (SDN) has proved itself to be a backbone in the new network design and is quickly becoming an industry standard. The idea of separation of control plane and data plane is the key concept behind SDN. SDN not only allows us to program and monitor our networks but it also helps in mitigating some key network problems. Distributed denial of service (DDoS) attack is among them. In this paper we propose a collaborative DDoS attack mitigation scheme using SDN. We design a secure controller-to-controller (C-to-C) protocol that allows SDN-controllers lying in different autonomous systems (AS) to securely communicate and transfer attack information with each other. This enables efficient notification along the path of an ongoing attack and effective filtering of traffic near the source of attack, thus saving valuable time and network resources. We also introduced three different deployment approaches i.e., linear, central and mesh in our testbed. Based on the experimental results we demonstrate that our SDN based collaborative scheme is fast and reliable in efficiently mitigating DDoS attacks in real time with very small computational footprints.


title: Capacity for downlink massive MIMO MU-SCMA system

abstract: Sparse code multiple access (SCMA) is a new multiple access technique with reusing non-orthogonal code domain resource. It has massive connectivity serving a large number of users and improves spectral efficiency of radio access. SCMA directly maps the information bits of different users to sparse complex codewords selected from the predefined codebooks, and each codeword represents a transmission layer. SCMA can be combined with MIMO technique to further improve the spectral efficiency. This combined technique is called code-space-multiplexing (CSM) which is the extension of the spatial multiplexing. Under downlink massive MIMO MU-SCMA (massive-CSM) system, the base station, equipped with massive transmit antennas, transmits SCMA codewords for each user. In this paper, by applying random matrix theory, we derive an asymptotic equivalent (AE) of signal to interference plus noise ratio (SINR) as the number of transmit antennas approaches infinity. In addition, some asymptotic results of SINR for two special cases can be obtained in terms of large-scale fading coefficients, sparse parameter and the number of resource blocks. Simulation results show massive-CSM can improve the capacity of MU-SCMA system and has a substantial advantage in terms of higher sum rate performance compared with Massive MIMO MU-OFDMA system. As the number of transmit antennas increases, our analytic expression of SINR closely approaches the theoretical result.


title: Understanding trust as an essential element of trainee supervision and learning in the workplace

abstract: Clinical supervision requires that supervisors make decisions about how much independence to allow their trainees for patient care tasks. The simultaneous goals of ensuring quality patient care and affording trainees appropriate and progressively greater responsibility require that the supervising physician trusts the trainee. Trust allows the trainee to experience increasing levels of participation and responsibility in the workplace in a way that builds competence for future practice. The factors influencing a supervisor’s trust in a trainee are related to the supervisor, trainee, the supervisor–trainee relationship, task, and context. This literature-based overview of these five factors informs design principles for clinical education that support the granting of entrustment. Entrustable professional activities offer promise as an example of a novel supervision and assessment strategy based on trust. Informed by the design principles offered here, entrustment can support supervisors’ accountability for the outcomes of training by maintaining focus on future patient care outcomes.


title: Acoustic novelty detection with adversarial autoencoders

abstract: Novelty detection is the task of recognising events the differ from a model of normality. This paper proposes an acoustic novelty detector based on neural networks trained with an adversarial training strategy. The proposed approach is composed of a feature extraction stage that calculates Log-Mel spectral features from the input signal. Then, an autoencoder network, trained on a corpus of “normal” acoustic signals, is employed to detect whether a segment contains an abnormal event or not. A novelty is detected if the Euclidean distance between the input and the output of the autoencoder exceeds a certain threshold. The innovative contribution of the proposed approach resides in the training procedure of the autoencoder network: instead of using the conventional training procedure that minimises only the Minimum Mean Squared Error loss function, here we adopt an adversarial strategy, where a discriminator network is trained to distinguish between the output of the autoencoder and data sampled from the training corpus. The autoencoder, then, is trained also by using the binary cross-entropy loss calculated at the output of the discriminator network. The performance of the algorithm has been assessed on a corpus derived from the PASCAL CHiME dataset. The results showed that the proposed approach provides a relative performance improvement equal to 0.26% compared to the standard autoencoder. The significance of the improvement has been evaluated with a one-tailed z-test and resulted significant with p < 0.001. The presented approach thus showed promising results on this task and it could be extended as a general training strategy for autoencoders if confirmed by additional experiments.


title: Machine Learning Based Student Grade Prediction: A Case Study

abstract: In higher educational institutes, many students have to struggle hard to complete different courses since there is no dedicated support offered to students who need special attention in the registered courses. Machine learning techniques can be utilized for students' grades prediction in different courses. Such techniques would help students to improve their performance based on predicted grades and would enable instructors to identify such individuals who might need assistance in the courses. In this paper, we use Collaborative Filtering (CF), Matrix Factorization (MF), and Restricted Boltzmann Machines (RBM) techniques to systematically analyze a real-world data collected from Information Technology University (ITU), Lahore, Pakistan. We evaluate the academic performance of ITU students who got admission in the bachelor's degree program in ITU's Electrical Engineering department. The RBM technique is found to be better than the other techniques used in predicting the students' performance in the particular course.


title: Medicinal cannabis for psychiatric disorders: a clinically-focused systematic review

abstract: Background ::: Medicinal cannabis has received increased research attention over recent years due to loosening global regulatory changes. Medicinal cannabis has been reported to have potential efficacy in reducing pain, muscle spasticity, chemotherapy-induced nausea and vomiting, and intractable childhood epilepsy. Yet its potential application in the field of psychiatry is lesser known.


title: Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution

abstract: We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward deep neural network that allows selective execution. Given an input, only a subset of D2NN neurons are executed, and the particular subset is determined by the D2NN itself. By pruning unnecessary computation depending on input, D2NNs provide a way to improve computational efficiency. To achieve dynamic selective execution, a D2NN augments a feed-forward deep neural network (directed acyclic graph of differentiable modules) with controller modules. Each controller module is a sub-network whose output is a decision that controls whether other modules can execute. A D2NN is trained end to end. Both regular and controller modules in a D2NN are learnable and are jointly trained to optimize both accuracy and efficiency. Such training is achieved by integrating backpropagation with reinforcement learning. With extensive experiments of various D2NN architectures on image classification tasks, we demonstrate that D2NNs are general and flexible, and can effectively optimize accuracy-efficiency trade-offs.


title: Syslog processing for switch failure diagnosis and prediction in datacenter networks

abstract: Syslogs on switches are a rich source of information for both post-mortem diagnosis and proactive prediction of switch failures in a datacenter network. However, such information can be effectively extracted only through proper processing of syslogs, e.g., using suitable machine learning techniques. A common approach to syslog processing is to extract (i.e., build) templates from historical syslog messages and then match syslog messages to these templates. However, existing template extraction techniques either have low accuracies in learning the “correct” set of templates, or does not support incremental learning in the sense the entire set of templates has to be rebuilt (from processing all historical syslog messages again) when a new template is to be added, which is prohibitively expensive computationally if used for a large datacenter network. To address these two problems, we propose a frequent template tree (FT-tree) model in which frequent combinations of (syslog) words are identified and then used as message templates. FT-tree empirically extracts message templates more accurately than existing approaches, and naturally supports incremental learning. To compare the performance of FT-tree and three other template learning techniques, we experimented them on two-years' worth of failure tickets and syslogs collected from switches deployed across 10+ datacenters of a tier-1 cloud service provider. The experiments demonstrated that FT-tree improved the estimation/prediction accuracy (as measured by F1) by 155% to 188%, and the computational efficiency by 117 to 730 times.


title: Supercritical fluid extraction and fractionation of essential oils and related products

abstract: Supercritical CO2 extraction of essential oils is one of the most widely discussed applications in the supercritical fluid literature. Nevertheless, a comprehensive overview of the analytical, processing and modeling aspects has never been attempted. This is partly due to the difficulties involved in isolating essential oils from the other products which supercritical CO2 can dissolve. Moreover, only a limited number of studies provide quantitative data on the parameters governing this process. ::: ::: In this review, solubility data on pure compounds belonging to essential oils are analyzed. Processes proposed to isolate and fractionate essential oils by supercritical CO2 and the corresponding modelling aspects are discussed critically.


title: Meta-learning with memory-augmented neural networks

abstract: Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of "one-shot learning." Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models. Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples. We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms.


title: Of Words, Eyes and Brains: Correlating Image-Based Distributional Semantic Models with Neural Representations of Concepts

abstract: Traditional distributional semantic models extract word meaning representations from cooccurrence patterns of words in text corpora. Recently, the distributional approach has been extended to models that record the cooccurrence of words with visual features in image collections. These image-based models should be complementary to text-based ones, providing a more cognitively plausible view of meaning grounded in visual perception. In this study, we test whether image-based models capture the semantic patterns that emerge from fMRI recordings of the neural signal. Our results indicate that, indeed, there is a significant correlation between image-based and brain-based semantic similarities, and that image-based models complement text-based ones, so that the best correlations are achieved when the two modalities are combined. Despite some unsatisfactory, but explained outcomes (in particular, failure to detect differential association of models with brain areas), the results show, on the one hand, that imagebased distributional semantic models can be a precious new tool to explore semantic representation in the brain, and, on the other, that neural data can be used as the ultimate test set to validate artificial semantic models in terms of their cognitive plausibility.


title: How Brand Community Practices Create Value

abstract: Abstract Using social practice theory, this article reveals the process of collective value creation within brand communities. Moving beyond a single case study, the authors examine previously published research in conjunction with data collected in nine brand communities comprising a variety of product categories, and they identify a common set of value-creating practices. Practices have an “anatomy” consisting of (1) general procedural understandings and rules (explicit, discursive knowledge); (2) skills, abilities, and culturally appropriate consumption projects (tacit, embedded knowledge or how-to); and (3) emotional commitments expressed through actions and representations. The authors find that there are 12 common practices across brand communities, organized by four thematic aggregates, through which consumers realize value beyond that which the firm creates or anticipates. They also find that practices have a physiology, interact with one another, function like apprenticeships, endow participants ...


Spline parameterization based nonlinear trajectory optimization along 4D waypoints


title: A Novel Traction Control for EV Based on Maximum Transmissible Torque Estimation

abstract: Controlling an immeasurable state with an indirect control input is a difficult problem faced in traction control of vehicles. Research on motion control of electric vehicles (EVs) has progressed considerably, but traction control has not been so sophisticated and practical because of this difficulty. Therefore, this paper takes advantage of the features of driving motors to estimate the maximum transmissible torque output in real time based on a purely kinematic relationship. An innovative controller that follows the estimated value directly and constrains the torque reference for slip prevention is then proposed. By analysis and comparison with prior control methods, the resulting control design approach is shown to be more effective and more practical, both in simulation and on an experimental EV.


title: Automatic transfer switch (ATS) using programmable logic controller (PLC)

abstract: In the industrial engineering education curriculum, advanced manufacturing technology is so important to provide the students with effective industrial instructions and enrich their practical skills through the continuous development of laboratory industrial resources. Hence, the author has developed an automatic transfer switch (ATS) based on a programmable logic controller (PLC) in the laboratory of the Arab Academy for Science & Technology (AAST) as an industrial case study for the students through the course of industrial automated systems. General concepts and practical considerations of the ATS have been discussed. Steps of designing and implementing the proposed setup, including system requirements, sensors, actuators, hardware configuration, software programming and experimental tests, are demonstrated. The proposed setup has been found so effective not only in increasing students industrial knowledge and skills but also can be used as a stand-alone system in commercial and industrial practical fields.


title: IMPLEMENTING ELLIPTIC CURVE CRYPTOGRAPHY ON PC AND SMART CARD

abstract: Elliptic Curve Cryptography (ECC) is a relatively new branch of public key ::: cryptography. Its main advantage is that it can provide the same level of ::: security as RSA with significantly shorter keys, which is beneficial for a ::: smart card based implementation. It is also important as a possible alternative ::: of RSA. This paper presents the author´s research concerning ECC and smart ::: cards. ::: The authors introduce their ECC prototype implementation that relies on Java ::: Card technology and is capable of running on smart cards. Test results with ::: various cards are attached. It is also analyzed in what extent algorithms with ::: the complexity of ECC can be executed in smart card environment with limited ::: resources.


title: Two models to investigate Medicare fraud within unsupervised databases

abstract: We propose two models to identify fraud, waste and abuse in Medicare. These models are used to flag health care providers. The motivation for these models is based on observed cases of fraud. The paper details the use of clustering algorithms, regression analysis, and various descriptive statistics that are components of these models. Some of the challenges in the struggle to reduce fraud in Medicare are discussed.


title: Educational use of Facebook by undergraduate students in Malaysia higher education: A case study of a private university

abstract: The learning environments in higher education are moving towards the integration of information and communication technology (ICT) such as social media. Recent years have witnessed an increased interest of using social media in higher education and this has created a growing ::: phenomenon for the educational use of social networking sites such as Facebook, to create, engage, and share information among the students. This paper presents a qualitative analysis of the Facebook closed group ::: activities of undergraduate students in a Malaysian private university. The analysis of data shows four main themes of students' education related use of Facebook closed group and issues relating to students’ use ::: of Facebook for educational purposes due to its increasing prevalence in the everyday lives of students. This paper concludes with two main findings of the realities of students' Facebook closed group activity and the role of Facebook in students' experience in higher education.


title: SmartRestaurant: mobile payments in context-aware environment

abstract: Mobility, context-awareness and payment combined provide a customer with a completely new setting of consuming services at any time and any place. We introduce SmartRestaurant service, which allows customers to use mobile devices for ordering and paying lunches from a nearby campus restaurant beforehand. Further, SmartRestaurant provides the restaurant with means of adjusting the sales with production capacity and prior knowledge of upcoming orders. We present a user evaluation of the system in form of a field trial in the real environment of use.


title: The key performance indicators of the BIM implementation process

abstract: Contemporarily some firms in the construction industry are attempting to adopt a BIM method of working. Each of these attempts reflects a varying BIM adoption philosophy and inevitably different BIM technologies, implementation strategies and roadmaps. On the other hand, all these attempts are often motivated to attain competitive advantages for product delivery in the market place. The question of what the best method of adopting BIM has not been answered yet. That is to say, it is required to identify a standard method that will benchmark the different BIM adoption cases by comparing the efficiency gains in these cases: a standard benchmarking method can help the stakeholders to decide on the most appropriate strategies for themselves. ::: ::: This paper explains the live experience of BIM adoption in a KTP (Knowledge Transfer Partnership) project, undertaken between the University of Salford and John McCall Architects practicing in the housing and regeneration fields, with a particular focus on a set of KPIs that have been developed and tested through the action research strategy in the project. Weighting of these KPI’s has been developed from an architectural business perspective


title: Successful Treatment of Keratosis Pilaris Rubra with Pulsed Dye Laser

abstract: BACKGROUND ::: Keratosis pilaris rubra is a common but rarely reported condition characterized by follicular-based hyperkeratotic papules on a background of erythema. It can be embarrassing and symptomatic for patients, particularly adolescent boys. We sought to explore the efficacy of pulsed dye laser (PDL) in the treatment of keratosis pilaris rubra. ::: ::: ::: METHODS ::: Eight patients were treated with PDL for keratosis pilaris rubra. ::: ::: ::: RESULTS ::: All patients reported noticeable improvement after one to four treatments. ::: ::: ::: CONCLUSIONS ::: PDL is an effective, easily accessible, and underused therapy in the treatment of keratosis pilaris rubra.


title: Cultured retinal pericytes stimulate in vitro angiogenesis of endothelial cells through secretion of a fibroblast growth factor-like molecule

abstract: Interaction between cultured endothelial cells (EC) and pericytes (PC) was studied in vitro to clarify the mechanism of diabetic proliferative retinopathy. Conditioned medium (CM) from retinal PC strongly increased the proliferation and moderately stimulated migration of retinal EC. Moreover, CM from PC caused stimulation of angiogenesis of retinal EC and umbilical cord vein EC in vitro at the same extent as basic fibroblast growth factor (bFGF). PC also stimulated angiogenesis by EC in mixed cultures. The angiogenic, proliferative and migration activities in CM from PC were inhibited by an antibody to bFGF. These data suggest that PC play an important role in angiogenesis through secretion of an FGF-like molecule.


title: A Ka-band (26 GHz) circularly polarized 2×2 microstrip patch sub-array with compact feed

abstract: A Ka-Band (26 GHz) 2×2 sub-array with square-shaped microstrip patch antenna elements having two truncated corners for circular polarization (CP) is presented. In addition, the layout for a new compact microstrip feed network for the sub-array is also presented The compact feed network offers a footprint size reduction of near 60% over traditional sub-array at 26 GHz. Experimental data indicates that a truncation amount a = 0.741 mm for an isolated patch element results in a return loss (S11) of −35 dB at 26.3 GHz Furthermore, the measured S11 for the proof-of-concept sub-array with the above elements is better than −10.0 dB at 27.7 GHz. However, the impedance match and the operating frequency can be fine-tuned to 26 GHz by adjusting the feed network dimensions. Lastly, good agreement is observed between the measured and simulated S11 for the sub-array for both right hand and left hand CP. The goal of this effort is utilize the above sub-array as a building block for a larger NxN element array, which would serve as a feed for a reflector antenna for satellite communications.


title: 4W1H in IoT Semantics

abstract: IoT systems are now being deployed worldwide to sense phenomena of interest. The existing IoT systems are often independent, which limits the use of sensor data to only one application. Semantic solutions have been proposed to support the reuse of sensor data across IoT systems and applications. This allows the integration of IoT systems for increased productivity by solving challenges associated with their interoperability and heterogeneity. Several ontologies have been proposed to handle different aspects of sensor data collection in IoT systems, ranging from sensor discovery to applying reasoning on collected sensor data for drawing inferences. In this paper, we study and categorize the existing ontologies based on the fundamental ontological concepts (e.g., sensors, context, location, and so on) required for annotating different aspects of data collection and data access in an IoT application. We identify these fundamental concepts by answering the 4Ws (What, When, Who, Where) and 1H (How) identified using the 4W1H methodology.


title: Diminished surface clustering and increased perinuclear accumulation of large conductance Ca2+-activated K+ channel in mouse myometrium with pregnancy.

abstract: Large conductance Ca2+-activated K+ channels play a critical role in regulating myometrium contractility. Their current density, mRNA, and total protein are greatly diminished in myometrium of late pregnant rats versus nonpregnant animals. Opposite to rats, in mice, channel mRNA and total protein increase in late pregnancy, but current density decreases as in rats. Here, we investigated the mechanism of these differences. Real time PCR and Western blots demonstrate that, in late pregnancy, channel transcript quantities and total protein were diminished in rats but up-regulated in mice. High resolution confocal microscopy of single myocytes showed that, in nonpregnant mice, channels were expressed in clusters at the surface membrane. In late pregnancy, although there was an overall increase in channel protein, its majority was accumulated in perinuclear organelles, and channel clustering practically disappeared from the surface membrane. This contrasts with rat myometrium, where there is a reduction of channel transcripts and overall protein levels including the surface membrane. We conclude that large conductance Ca2+-activated K+ channel surface expression is reduced in both rat and mouse late pregnant myometrium. However, in rats, the main mechanism for the reduced channel expression at the cell surface is a diminished transcription, whereas in mice, it is an altered traffic to the surface.


title: A Survey on Expert Recommendation in Community Question Answering

abstract: Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time, on the other hand, an answerer might face numerous new questions without being able to identify their questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.


title: 0.7–20-GHz Dual-Polarized Bilateral Tapered Slot Antenna for EMC Measurements

abstract: An asymmetric wideband dual-polarized bilateral tapered slot antenna (DBTSA) is proposed in this letter for wireless EMC measurements. The DBTSA is formed by two bilateral tapered slot antennas designed with low cross polarization. With careful design, the achieved DBTSA not only has a wide operating frequency band, but also maintains a single main-beam from 700 MHz to 20 GHz. This is a significant improvement compared to the conventional dual-polarized tapered slot antennas, which suffer from main-beam split in the high-frequency band. The innovative asymmetric configuration of the proposed DBTSA significantly reduces the field coupling between the two antenna elements, so that low cross polarization and high port isolation are obtained across the entire frequency range. All these intriguing characteristics make the proposed DBTSA a good candidate for a dual-polarized sensor antenna for wireless EMC measurements.


title: Generating Conditional Probabilities for Bayesian Networks: Easing the Knowledge Acquisition Problem

abstract: The number of probability distributions required to populate a conditional probability table (CPT) in a Bayesian network, grows exponentially with the number of parent-nodes associated with that table. If the table is to be populated through knowledge elicited from a domain expert then the sheer magnitude of the task forms a considerable cognitive barrier. In this paper we devise an algorithm to populate the CPT while easing the extent of knowledge acquisition. The input to the algorithm consists of a set of weights that quantify the relative strengths of the influences of the parent-nodes on the child-node, and a set of probability distributions the number of which grows only linearly with the number of associated parent-nodes. These are elicited from the domain expert. The set of probabilities are obtained by taking into consideration the heuristics that experts use while arriving at probabilistic estimations. The algorithm is used to populate the CPT by computing appropriate weighted sums of the elicited distributions. We invoke the methods of information geometry to demonstrate how these weighted sums capture the expert's judgemental strategy.


title: Proxemics with multiple dynamic characters in an immersive virtual environment

abstract: An experiment was carried out to examine the impact on electrodermal activity of people when approached by groups of one or four virtual characters at varying distances. It was premised on the basis of proxemics theory that the closer the approach of the virtual characters to the participant, the greater the level of physiological arousal. Physiological arousal was measured by the number of skin conductance responses within a short time period after the approach, and the maximum change in skin conductance level 5 s after the approach. The virtual characters were each either female or a cylinder of human size, and one or four characters approached each subject a total of 12 times. Twelve male subjects were recruited for the experiment. The results suggest that the number of skin conductance responses after the approach and the change in skin conductance level increased the closer the virtual characters approached toward the participants. Moreover, these response variables were inversely correlated with the number of visits, showing a typical adaptation effect. There was some evidence to suggest that the number of characters who simultaneously approached (one or four) was positively associated with the responses. Surprisingly there was no evidence of a difference in response between the humanoid characters and cylinders on the basis of this physiological data. It is suggested that the similarity in this quantitative arousal response to virtual characters and virtual objects might mask a profound difference in qualitative response, an interpretation supported by questionnaire and interview results. Overall the experiment supported the premise that people exhibit heightened physiological arousal the closer they are approached by virtual characters.


Universal Decompositional Semantics on Universal Dependencies


title: Extended Use Case Points Method for Software Cost Estimation

abstract: Software cost estimation is a key open issue for the software industry, which suffers from cost overruns frequently. As the most popular technique for object-oriented software cost estimation, Use Case Points(UCP) method, however, has two major drawbacks: the uncertainty of the cost factors and the abrupt classification. To address these two issues, we propose the Extended Use Case Points (EUCP) method. With a probabilistic cost model constructed from integrating fuzzy set theory and Bayesian Belief Networks(BBNs) with the UCP method, EUCP provides a probability distribution of cost and a refined gradual classification, which mitigate the uncertainty of cost factors and improve the accuracy of classification. In this paper, we provides two case studies to demonstrate the effectiveness of EUCP in the real life.


title: Design and PLC Implementation for Speed Control of DC Motor using Fuzzy Logic

abstract: In this article, a speed control of DC motor is designed and illustrated using fuzzy logic-based programmable logic controller (PLC). The DC motor is an attractive part of electrical equipment in many industrial applications requiring variable speed and load specifications due to its ease of controllability. The designed system is consisted of three main parts including programmable logic controller, pulse width modulation (PWM) bipolar drive and DC motor. In the control section, PLC is used as real time controller and fuzzy logic algorithm is designed based on nonlinear model of DC motor, and its parameters are optimized in MATLAB software. Then, it is implemented using rslogix5000 PLC and programming language ladder for speed control. Finally, with favorable results, the efficiency of the controller is successfully proved under different load conditions. The obtained results demonstrate the efficiency of the PLC intelligent controller in enhancing the accuracy and speed control of DC motor.


title: Detecting Android Malware Leveraging Text Semantics of Network Flows

abstract: The emergence of malicious apps poses a serious threat to the Android platform. Most types of mobile malware rely on network interface to coordinate operations, steal users' private information, and launch attack activities. In this paper, we propose an effective and automatic malware detection method using the text semantics of network traffic. In particular, we consider each HTTP flow generated by mobile apps as a text document, which can be processed by natural language processing to extract text-level features. Then, we use the text semantic features of network traffic to develop an effective malware detection model. In an evaluation using 31 706 benign flows and 5258 malicious flows, our method outperforms the existing approaches, and gets an accuracy of 99.15%. We also conduct experiments to verify that the method is effective in detecting newly discovered malware, and requires only a few samples to achieve a good detection result. When the detection model is applied to the real environment to detect unknown applications in the wild, the experimental results show that our method performs significantly better than other popular anti-virus scanners with a detection rate of 54.81%. Our method also reveals certain malware types that can avoid the detection of anti-virus scanners. In addition, we design a detection system on encrypted traffic for bring-your-own-device enterprise network, home network, and 3G/4G mobile network. The detection model is integrated into the system to discover suspicious network behaviors.


title: [Roux-en-Y limb anastomosed to small biliary duct].

abstract: We describe a simple and safe technique of cholédocho-jejunostomy using a Roux-en-Y jéjunal limb, to repair immediately biliary tract injuries, even when the conduits are thin.


title: Fast detection of arbitrary planar surfaces from unreliable 3D data

abstract: Man-made real-world environments are dominated by planar surfaces many of which constitute behavior-relevant entities. Thus, the ability to perceive planar surfaces is vital for any embodied system operating in such environments, be it human or robotic. In this paper, we present an architecture for detection and estimation of planar surfaces in the scene from calibrated stereo images. They are represented in a behavior-oriented way, focusing on geometrical properties that are relevant for enabling basic interaction between a robot and the planar surfaces it perceives. Ego-motion of the robot is compensated for by transforming the representations into a global coordinate system using the kinematics of the robot. Our architecture is able to detect and estimate arbitrary planar surfaces, regardless of their visual appearance, their geometrical properties other than planarity and their being static or arbitrarily moving. The latter is achieved by processing each frame independently of the others. Stable representations are obtained by establishing spatio-temporal coherence between the single-frame representations of subsequent frames. Based on a RANSAC approach to plane fitting, our method is robust to unreliable 3D data such as obtained by local stereo correlation, for example. In our experiments using the Honda humanoid robot ASIMO, we show that our method is able to provide a robot in real-time with representations of planar surfaces in its environment that are sufficiently accurate for basic interaction.


title: Cooling load calculation of cold storage container for vegetables case study C Campus-UISI, Ngipik

abstract: Campus C UISI-Ngipik has 4 hectares of land and is Green Campus which focuses on gardening, from seeding, planting, treatment, harvesting, and product processing. The agricultural products, i.e. vegetables harvested at Green Campus UISI are perishable products, which are liable to spoil due to their moisture content. When vegetables are harvested, they start to deteriorate. The quality will lose and may be damaged then would be dangerous if consumed. To hold these products, cold storage facility can be used. With cooling, vegetables stored can be categorized as fresh and healthy products. It is important to determine the storage capacity and total cooling load of a cold storage. Therefore, this study deals with different aspect of design of cold storage. The storage capacity is calculated with volume difference calculation method, and the total cooling load is analyzed with cooling load temperature difference (CLTD) method based on ASHRAE handbook. The results show that the cold storage is able to accommodate 259.68 kg (572.5 lb) of leafy greens vegetables at Green Campus UISI and the total cooling load is 189721.8356 Btu/h. This design is intended to serve as a guide for future fabrication need.


Bidirectional Echo State Network-based Reservoir Computing for Cross-domain Authorship Attribution: Notebook for PAN at CLEF 2018


title: Hybrid Beamforming via the Kronecker Decomposition for the Millimeter-Wave Massive MIMO Systems

abstract: Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) seamlessly integrates two wireless technologies, mmWave communications and massive MIMO , which provides spectrums with tens of GHz of total bandwidth and supports aggressive space division multiple access using large-scale arrays. Though it is a promising solution for next-generation systems, the realization of mmWave massive MIMO faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds to thousands of radio frequency chains and analog-to-digital converters matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of the hybrid-beamforming architecture, where MIMO signal processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming , respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients. They render the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the angles-of-arrival (AoA) of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length, and the spatial separation between paths, suggesting different ways of tackling pilot contamination.


title: Face–brain asymmetry in autism spectrum disorders

abstract: The heterogeneity of autism spectrum disorders (ASDs) confounds attempts to identify causes and pathogenesis. Identifiable endophenotypes and reliable biomarkers within ASDs would help to focus molecular research and uncover genetic causes and developmental mechanisms. We used dense surface-modelling techniques to compare the facial morphology of 72 boys with ASD and 128 first-degree relatives to that of 254 unrelated controls. Pattern-matching algorithms were able to discriminate between the faces of ASD boys and those of matched controls (AUC=0.82) and also discriminate between the faces of unaffected mothers of ASD children and matched female controls (AUC=0.76). We detected significant facial asymmetry in boys with ASD (P<0.01), notably depth-wise in the supra- and periorbital regions anterior to the frontal pole of the right hemisphere of the brain. Unaffected mothers of children with ASD display similar significant facial asymmetry, more exaggerated than that in matched controls (P<0.03) and, in particular, show vertical asymmetry of the periorbital region. Unaffected fathers of children with ASD did not show facial asymmetry to a significant degree compared to controls. Two thirds of unaffected male siblings tested were classified unseen as more facially similar to unrelated boys with ASD than to unrelated controls. These unaffected male siblings and two small groups of girls with ASD and female siblings, all show overall directional asymmetry, but without achieving statistical significance in two-tailed t-tests of individual asymmetry of ASD family and matched control groups. We conclude that previously identified right dominant asymmetry of the frontal poles of boys with ASD could explain their facial asymmetry through the direct effect of brain growth. The atypical facial asymmetry of unaffected mothers of children with ASD requires further brain studies before the same explanation can be proposed. An alternative explanation, not mutually exclusive, is a simultaneous and parallel action on face and brain growth by genetic factors. Both possibilities suggest the need for coordinated face and brain studies on ASD probands and their first-degree relatives, especially on unaffected mothers, given that their unusual facial asymmetry suggests an ASD susceptibility arising from maternal genes.


title: An 8mW frequency detector for 10Gb/s half-rate CDR using clock phase selection

abstract: A half-rate single-loop CDR with a new frequency detection scheme is introduced. The proposed frequency detector selects between the clock phases (I and Q) to reduce cycle slipping, hence improving lock time and capture range. This frequency detector, implemented within a 10Gb/s CDR in Fujitsu 65nm CMOS, consumes only 8mW, but improves the capture range by up to 3.6×. The measured capture range with the FD is from 8.675Gb/s to 11Gb/s.


Query Learning with Large Margin Classifiers


title: The Dimensions of Job Characteristics: A Validation Study in a Malaysian Context

abstract: Ths study attempts to examine the psychometric properties of the job characteristics constmct which integrates 5 subscales-skill variety, task identity, task significance, autonomy and feedback using a Malay language version of the Job Diagnostic Survey (IDS). A priori proposition was made that job characteristics could be explained by the 5 aforementioned factors. SPSS version 14 and AMOS 16 were used to analyze the data.The results supported the hypothesis of the study that job characteristics can be measured by the 5 theorized factors. The findngs also showed acceptable internal consistency reliability for the overall and the 5 specific subscales of job characteristics factor.Based on the results, it can be concluded that the Malay translated version of JDS can be a useful instnunent in assessing the job characteristics comtruct.


title: Arabic Sentiment Analysis Using Supervised Classification

abstract: Sentiment analysis is a process during which the polarity (i.e. positive, negative or neutral) of a given text is determined. In general there are two approaches to address this problem, namely, machine learning approach or lexicon based approach. The current paper deals with sentiment analysis in Arabic reviews from a machine learning perspective. Three classifiers were applied on an in-house developed dataset of tweets/comments. In particular, the Naive Bayes, SVM and K-Nearest Neighbor classifiers were run on this dataset. The results show that SVM gives the highest precision while KNN (K=10) gives the highest Recall.


title: A simple prognostic scoring system for typhoid ileal perforation peritonitis

abstract: The increasing awareness of the worse than expected outcome after typhoid ileal perforation (TIP) prompted us to prospectively prognosticate patients with the help of the Jabalpur prognostic score (JPS), a simplified scoring system for peptic perforation peritonitis (PPP). Eighty-two consecutive patients with TIP were studied from May 2005 to August 2008 in the Department of Surgery, NSCB Government Medical College, Jabalpur (MP), India. Six parameters used in the JPS were recorded: age, heart rate, mean blood pressure, serum creatinine, any co-morbid illness and perforation-operation interval. JPS correlated with morbidity and mortality in TIP patients and, as the score increased, so did the morbidity and mortality. Survivors had a significantly lower mean score (3.86 ± 2.23) than non-survivors (7.94 ± 3.6; P < 0.001). Expectedly, TIP patients had worse outcome, stage by stage, than PPP patients. JPS can be easily modified for TIP (JPS-TIP) and can be easily used for its prognostication.


title: Multi-View Unsupervised Feature Selection with Adaptive Similarity and View Weight

abstract: With the advent of multi-view data, multi-view learning has become an important research direction in both machine learning and data mining. Considering the difficulty of obtaining labeled data in many real applications, we focus on the multi-view unsupervised feature selection problem. Traditional approaches all characterize the similarity by fixed and pre-defined graph Laplacian in each view separately and ignore the underlying common structures across different views. In this paper, we propose an algorithm named Multi-view Unsupervised Feature Selection with Adaptive Similarity and View Weight (ASVW) to overcome the above mentioned problems. Specifically, by leveraging the learning mechanism to characterize the common structures adaptively, we formulate the objective function by a common graph Laplacian across different views, together with the sparse $\ell _{2,p}$ -norm constraint designed for feature selection. We develop an efficient algorithm to address the non-smooth minimization problem and prove that the algorithm will converge. To validate the effectiveness of ASVW, comparisons are made with some benchmark methods on real-world datasets. We also evaluate our method in the real sports action recognition task. The experimental results demonstrate the effectiveness of our proposed algorithm.


title: SIW Butler Matrix with Modified Hybrid Coupler for Slot Antenna Array

abstract: This paper presents a substrate integrated waveguide Butler matrix with a modified hybrid coupler, operating from 28 to 32 GHz. The modified hybrid coupler is realized by using 90° hybrid coupler followed by a -45° compensating phase shifter. Thus, 45°/135° output phase differences can be obtained using this type of coupler. Compared with the equal-length unequal-width phase shifter, the compensating phase shifter exhibits better phase characteristics, with only 3° phase error over 28-32 GHz. Because the phase of compensating phase shifter is not realized by taking the phase introduced by the crossover as a reference, it gives great flexibility to the design of compensating phase shifter. Adopting the modified hybrid coupler, the designed Butler matrix features the output phases with peak to peak error of 13° and wideband performance from 28 to 32 GHz. The slot array fed by the Butler matrix can radiate four slanted beams with acceptable measured gains, in the range of 9.7~12 dBi for port 1 excitation and 8.4~11.1 dBi for port 2 excitation. The radiated beams can reach a wide azimuthal coverage between ±61°.


title: Device Modeling and Simulation of the Performance of Cu(In1-x,Gax)Se2 Solar Cells

abstract: Device modeling and simulation studies of a Cu(In1� x,Gax)Se2 (CIGS) thin film solar cell have been carried out. A variety of graded band-gap structures, including space charge region (SCR) grading, back surface region grading, and double grading of the CIGS absorber layer, are examined. The device physics and performance parameters for different band-gap profiles were analyzed. Based on the simulation results, an optimal graded band-gap structure for the CIGS solar cell is proposed. The performance of the optimally graded band-gap cell is superior to that of the uniform bandgap cell. The SCR grading of the CIGS absorber layer improves the open-circuit voltage (Voc) without significantly sacrificing the short-circuit current density (Jsc) compared to the uniform band-gap CIGS. The back surface grading enhances both Voc and Jsc. An optimal graded band-gap profile, such as a double grading consisting of the SCR grading and back surface grading, improves significantly the efficiency up to 19.83% AM1.5G compared to the uniform bandgap profile with 15.42% efficiency. A comparison of the simulation results with published data for the CIGS cells shows an excellent agreement of photo-current density–voltage and quantum efficiency characteristics. � 2003 Elsevier Ltd. All rights reserved.


title: A 14 Bit 1 GS/s RF Sampling Pipelined ADC With Background Calibration

abstract: We discuss a 14 bit 1 GS/s RF sampling pipelined ADC that utilizes correlation-based background calibration to correct the inter-stage gain, settling and memory errors. To improve the sampling linearity and RF sampling performance, the ADC employs input distortion cancellation and a digital background calibration technique to compensate for the non-linear charge injection (kick-back) from the sampling capacitors on the input driver. In addition, an effective dithering technique is embedded in the calibration signal to break the dependence of the calibration's convergence on the input signal amplitude. The ADC is fabricated on a 65 nm CMOS process and has an integrated input buffer. With a 140 MHz and 2 Vpp input signal, the SNR is 69 dB, the SFDR is 86 dB, and the power is 1.2 W.


title: Increasing the breakdown capability of superjunction power MOSFETs at the edge of the active region

abstract: When superjunction power MOSFETs operate near the rim of the safe operating area, avalanche breakdown can occur in the transition region between the active cell array and the edge termination. Numerical device simulations confirmed this and revealed local charge imbalances, created by irregularities in the superjunction doping pattern, as major cause. Based on the simulation results, we proposed optimized transitions of the superjunction doping pattern from the active cell array to the edge termination. Numerical device simulations as well as experiments demonstrated the enhanced breakdown capability of these transition regions.


title: Automatic Classification for Vietnamese News

abstract: This paper proposes an automatic framework to classify Vietnamese news from news sites on the Internet. In this proposed framework, the extracted main content of Vietnamese news is performed automatically by applying the improved performance extraction method from [1]. This information will be classified by using two machine learning methods: Support vector machine and naive bayesian method. Our experiments implemented with Vietnamese news extracted from some sites showed that the proposed classification framework give acceptable results with a rather high accuracy, leading to applying it to real information systems.


title: Molecular Communication: A 10 Year Retrospective

abstract: This paper provides the author’s personal retrospective on molecular communication research. The author has been involved in molecular communication from its conceptualization to the establishment as a scientific discipline and its flourishing today. Based on his personal journey, the author describes the history of molecular communication research and shares what he learned from the journey. The author also discusses what might help for the further development of molecular communication research and points out technical challenges that he would like to see addressed in the next decade.


title: Interval-Valued Multiplicative Intuitionistic Fuzzy Preference Relations

abstract: Inspired by the idea of multiplicative intuitionistic preference relation (Xia MM et al. Preference relations based on intuitionistic multiplicative information, IEEE Transactions on Fuzzy Systems, 2013, 21(1): 113-133), in this paper, a new preference relation called the interval-valued multiplicative intuitionistic preference relation is developed. It is analyzed the basic operations for interval-valued multiplicative intuitionistic preference information and its aggregation techniques. An interval-valued multiplicative intuitionistic group decision making model is presented in which experts provide their preference relation by interval-valued multiplicative intuitionistic fuzzy expressions, and give a real case about talent introduction in Zhejiang University of Finance and Economics to illustrate our methods.


title: Theory-driven design strategies for technologies that support behavior change in everyday life

abstract: In this paper, we propose design strategies for persuasive technologies that help people who want to change their everyday behaviors. Our strategies use theory and prior work to substantially extend a set of existing design goals. Our extensions specifically account for social characteristics and other tactics that should be supported by persuasive technologies that target long-term discretionary use throughout everyday life. We used these strategies to design and build a system that encourages people to lead a physically active lifestyle. Results from two field studies of the system - a three-week trial and a three-month experiment - have shown that the system was successful at helping people maintain a more physically active lifestyle and validate the usefulness of the strategies.


title: No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling

abstract: Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challenges to behavioral cloning algorithms. Furthermore, due to the limitations of automatic metrics on evaluating story quality, reinforcement learning methods with hand-crafted rewards also face difficulties in gaining an overall performance boost. Therefore, we propose an Adversarial REward Learning (AREL) framework to learn an implicit reward function from human demonstrations, and then optimize policy search with the learned reward function. Though automatic eval- uation indicates slight performance boost over state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation shows that our approach achieves significant improvement in generating more human-like stories than SOTA systems.


Genre Classification using Machine Learning Techniques


title: Double-polysilicon self-aligned SiGe HBT architecture based on nonselective epitaxy and polysilicon reflow

abstract: This paper describes a self-aligned SiGe heterojunction bipolar transistor (HBT) based on a standard double-polysilicon architecture and nonselective epitaxial growth (i.e. DPSA-NSEG). Emitter-base self-alignment is realized by polysilicon reflow in a hydrogen ambient after emitter window patterning. The fabricated self-aligned SiGe HBTs, with emitter widths of 0.3–0.4 μm, exhibit 20% lower base resistance and 15% higher maximum oscillation frequency f max than non-self-aligned reference devices. The minimum noise figure of a Ku-band low-noise amplifier is reduced from 0.9 to 0.8 dB by emitter-base self-alignment.


title: Improving sensing performance of the ZnO foam structure with exposed {001} facets by hydrogenation and sensing mechanism at molecule level

abstract: Abstract The ZnO foam structure assembled from nanoflakes with exposed {001} facets has been successfully prepared by heating aqueous Zn(NO3)2-polyvinyl pyrrolidone mixed solution. Response of the ZnO foam structure towards ethanol, acetone and n-Butylamine was observably increased by increasing the amounts of 3-coordinated Zn (Zn3c) atoms on the ZnO (001) surface through hydrogenation. The unsaturated Zn3c atoms are regarded as the reaction active sites and they contribute to generating free electrons, adsorbing oxygen molecules and catalyzing the reaction of the target gas with chemisorbed oxygen. The sensing mechanism will be instructive for the understanding of the gas sensing and heterogeneous catalytic reactions and the design of high performance sensing materials and catalysts. The hydrogenation optimizing surface-structural engineering may be a general strategy to enhancing sensing and catalytic properties.


title: A large scale dataset for classification of vehicles in urban traffic scenes

abstract: Vehicle Classification has been a well-researched topic in the recent past. However, advances in the field have not been corroborated with deployment in Intelligent Traffic Management, due to non-availability of surveillance quality visual data of vehicles in urban traffic junctions. In this paper, we present a dataset aimed at exploring Vehicle Classification and related problems in dense, urban traffic scenarios. We present our on-going effort of collecting a large scale, surveillance quality, dataset of vehicles seen mostly on Indian roads. The dataset is an extensive collection of vehicles under different poses, scales and illumination conditions in addition to a smaller set of Near Infrared spectrum images for night time and low light traffic surveillance. We will make the dataset available for further research in this area. We propose and evaluate few baseline algorithms for the task of vehicle classification on this dataset. We also discuss challenges and potential applications of the data.


title: Quaternion kinematics for the error-state Kalman filter

abstract: This article is an exhaustive revision of concepts and formulas related to quaternions and rotations in 3D space, and their proper use in estimation engines such as the error-state Kalman filter. The paper includes an in-depth study of the rotation group and its Lie structure, with formulations using both quaternions and rotation matrices. It makes special attention in the definition of rotation perturbations, derivatives and integrals. It provides numerous intuitions and geometrical interpretations to help the reader grasp the inner mechanisms of 3D rotation. The whole material is used to devise precise formulations for error-state Kalman filters suited for real applications using integration of signals from an inertial measurement unit (IMU).


title: Self-supervised weed detection in vegetable crops using ground based hyperspectral imaging

abstract: A critical step in treating or eradicating weed infestations amongst vegetable crops is the ability to accurately and reliably discriminate weeds from crops. In recent times, high spatial resolution hyperspectral imaging data from ground based platforms have shown particular promise in this application. Using spectral vegetation signatures to discriminate between crop and weed species has been demonstrated on several occasions in the literature over the past 15 years. A number of authors demonstrated successful per-pixel classification with accuracies of over 80%. However, the vast majority of the related literature uses supervised methods, where training datasets have been manually compiled. In practice, static training data can be particularly susceptible to temporal variability due to physiological or environmental change. A self-supervised training method that leverages prior knowledge about seeding patterns in vegetable fields has recently been introduced in the context of RGB imaging, allowing the classifier to continually update weed appearance models as conditions change. This paper combines and extends these methods to provide a self-supervised framework for hyperspectral crop/weed discrimination with prior knowledge of seeding patterns using an autonomous mobile ground vehicle. Experimental results in corn crop rows demonstrate the system's performance and limitations.


title: Application of Boost Converter to Increase the Speed Range of Dual-Stator Winding Induction Generator in Wind Power Systems

abstract: In this paper, a topology using a dual-stator winding induction generator (DWIG) and a boost converter is proposed for the variable speed wind power application. At low rotor speeds, the generator saturation limits the voltage of the DWIG. Using a boost converter, higher dc voltage can be produced while the DWIG operates at maximum power point tracking even at low-speed and low-voltage conditions. Semiconductor excitation controller (SEC) of the DWIG utilizes control-winding voltage oriented control method to adjust the voltage, considering V/f characteristics. For the proposed topology, the SEC capacity and the excitation capacitor are optimized by analyzing the SEC reactive current considering wind turbine power-speed curve, V/f strategy, and the generator parameters. The method shows that the per-unit capacity of the SEC can be limited to the inverse of DWIG magnetizing reactance per-unit value. The topology is simulated in MATLAB/Simulink platform and experimented with a scaled 1 kW prototype. Both simulation and experimental results demonstrate wide variable speed operation range of the DWIG and verify the optimization.


title: SentiWS – a Publicly Available German-language Resource for Sentiment Analysis

abstract: University of Leipzig, Natural Language Processing Department, Johannisgasse 26, 04081 Leipzig, Germany robert.remus@googlemail.com, {quasthoff, heyer}@informatik.uni-leipzig.de Abstract SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, opinion mining etc. It lists positive and negative sentiment bearing words weighted within the interval of [−1; 1] plus their part of speech tag, and if applicable, their inflections. The current version of SentiWS (v1.8b) contains 1,650 negative and 1,818 positive words, which sum up to 16,406 positive and 16,328 negative word forms, respectively. It not only contains adjectives and adverbs explicitly expressing a sentiment, but also nouns and verbs implicitly containing one. The present work describes the resource’s structure, the three sources utilised to assemble it and the semi-supervised method incorporated to weight the strength of its entries. Furthermore the resource’s contents are extensively evaluated using a German-language evaluation set we constructed. The evaluation set is verified being reliable and its shown that SentiWS provides a beneficial lexical resource for German-language sentiment analysis related tasks to build on.


title: A Compact Quadband CPW-Fed Slot Antenna for M-WiMAX/WLAN Applications

abstract: A novel compact CPW-fed slot antenna for quadband operation is proposed. The antenna is printed on a 25 × 20 mm2 FR4 substrate with thickness of 1.6 mm and relative permittivity of 4.6. Three L-shaped slots and a rectangular slot are fabricated on the substrate to achieve quadband operation of 2.07-2.77, 3.3-3.8, 5.15-5.35, and 5.7-5.89 GHz. As the four resonant frequencies of antenna are excited by certain parts of the patch respectively, one resonant frequency can be flexibly tuned with little effect on the other. Experimental results demonstrate the good broadside radiation patterns for the four bands and suggest that the proposed antenna is suitable for the M-WiMAX and WLAN applications.


title: hClock: hierarchical QoS for packet scheduling in a hypervisor

abstract: Higher consolidation ratios in virtualized datacenters, SSD based storage arrays and converged IO fabric are mandating the need for more flexible and powerful models for physical network adapter bandwidth allocation. Existing solutions such as fair-queuing mechanisms, traffic-shapers and hierarchical allocation techniques are insufficient in terms of dealing with various use cases for a cloud service provider. In this paper, we present the design and implementation of a hierarchical bandwidth allocation algorithm called hClock. hClock supports three controls in a hierarchical manner: (1) minimum bandwidth guarantee, (2) rate limit and (3) shares (a.k.a weight). Our prototype implementation in VMware ESX server hypervisor, built with several optimizations to minimize CPU overhead and increase parallelism, shows that hClock is able to enforce hierarchical controls for a variety of workloads with diverse traffic patterns at scale.


title: Sensing the air we breathe: the opensense Zurich dataset

abstract: Monitoring and managing urban air pollution is a significant challenge for the sustainability of our environment. We quickly survey the air pollution modeling problem, introduce a new dataset of mobile air quality measurements in Zurich, and discuss the challenges of making sense of these data.


title: DCM-232 converter: A PV transformerless three-phase inverter

abstract: Nowadays transformerless PV converters are widely used in the energy conversion stage of a PV plant, because their good characteristics regarding efficiency, size, cost and weight. The main problem in transformerless PV systems is the common mode voltage variations that can generate leakage ground currents (LGC) through the parasitic capacitance formed between the PV panels and ground. These LGC are a risk for the users in a PV system therefore they should be avoided. The LGC has a clear origin on the pulse width modulation (PWM) strategy and the converter structure. In this paper a three-phase inverter with constant common mode voltage (CMV) is proposed. Simulation results are also provided.


title: Facial emotion recognition system for autistic children: a feasible study based on FPGA implementation

abstract: Children with autism spectrum disorder have difficulty in understanding the emotional and mental states from the facial expressions of the people they interact. The inability to understand other people’s emotions will hinder their interpersonal communication. Though many facial emotion recognition algorithms have been proposed in the literature, they are mainly intended for processing by a personal computer, which limits their usability in on-the-move applications where portability is desired. The portability of the system will ensure ease of use and real-time emotion recognition and that will aid for immediate feedback while communicating with caretakers. Principal component analysis (PCA) has been identified as the least complex feature extraction algorithm to be implemented in hardware. In this paper, we present a detailed study of the implementation of serial and parallel implementation of PCA in order to identify the most feasible method for realization of a portable emotion detector for autistic children. The proposed emotion recognizer architectures are implemented on Virtex 7 XC7VX330T FFG1761-3 FPGA. We achieved 82.3 % detection accuracy for a word length of 8 bits.


title: Innovation and certification in aviation software

abstract: Software safety is of critical importance to the ongoing success of the aviation industry. The DO-178 standard is the de-facto standard for safe aviation software across different regulatory contexts. Regulatory usage of DO-178 has coincided with an excellent aviation safety record, and therefore is often considered a success. There is an open question on whether the DO-178 standard prohibits the ability to utilize recent innovative techniques and processes that are being used by the broader software industry. This question is increasingly relevant in a time when rising software complexity, long considered a challenge, is posing a serious threat to the continued success of the industry. Aviation software is particularly subject to this complexity, and is challenged by growing software size, increased importance, and other accidental complexity.


title: Neuroimaging Studies of Word and Pseudoword Reading: Consistencies, Inconsistencies, and Limitations

abstract: Several functional neuroimaging studies have compared words and pseudowords to test different cognitive models of reading. There are difficulties with this approach, however, because cognitive models do not make clear-cut predictions at the neural level. Therefore, results can only be interpreted on the basis of prior knowledge of cognitive anatomy. Furthermore, studies comparing words and pseudowords have produced inconsistent results. The inconsistencies could reflect false-positive results due to the low statistical thresholds applied or confounds from nonlexical aspects of the stimuli. Alternatively, they may reflect true effects that are inconsistent across subjects; dependent on experimental parameters such as stimulus rate or duration; or not replicated across studies because of insufficient statistical power. In this fMRI study, we investigate consistent and inconsistent differences between word and pseudoword reading in 20 subjects, and distinguish between effects associated with increases and decreases in activity relative to fixation. In addition, the interaction of word type with stimulus duration is explored. We find that words and pseudowords activate the same set of regions relative to fixation, and within this system, there is greater activation for pseudowords than words in the left frontal operculum, left posterior inferior temporal gyrus, and the right cerebellum. The only effects of words relative to pseudowords consistent over subjects are due to decreases in activity for pseudowords relative to fixation; and there are no significant interactions between word type and stimulus duration. Finally, we observe inconsistent but highly significant effects of word type at the individual subject level. These results (i) illustrate that pseudowords place increased demands on areas that have previously been linked to lexical retrieval, and (ii) highlight the importance of including one or more baselines to qualify word type effects. Furthermore, (iii) they suggest that inconsistencies observed in the previous literature may result from effects arising from a small number of subjects only.


title: Enhanced semantic arabic Question Answering system based on Khoja stemmer and AWN

abstract: Due to the great amount of information available on the web, Question/Answering systems have become a focus for researchers and users as well. This paper introduces a proposed design for an Arabic Question Answering system based on Query Expansion ontology and an Arabic Stemmer. A set of factoid CLEF and TREC questions used to evaluate the system. Improved results obtained using AWN as a semantic Query Expansion and Khoja stemmer as a stemming system. Two experiments conducted using AWN the first using one level of expansion and the second using two level of expansion. Three measures are performed: Accuracy, Mean Reciprocal Rank, and Answered Questions, and the obtained results are 35.5%, 20.2%, and 65.33% respectively when using one level of expansion. But when using two level of expansion we get 38.77%, 16.2%, and 65.55% respectively.


title: Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases

abstract: In this paper we present an efficient method for visual descriptors retrieval based on compact hash codes computed using a multiple k-means assignment. The method has been applied to the problem of approximate nearest neighbor (ANN) search of local and global visual content descriptors, and it has been tested on different datasets: three large scale public datasets of up to one billion descriptors (BIGANN) and, supported by recent progress in convolutional neural networks (CNNs), also on the CIFAR-10 and MNIST datasets. Experimental results show that, despite its simplicity, the proposed method obtains a very high performance that makes it superior to more complex state-of-the-art methods.


title: Negotiation as a metaphor for distributed problem solving

abstract: Abstract We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques. We present a framework called the contract net that specifies communication and control in a distributed problem solver. Task distribution is viewed as an interactive process, a discussion carried on between a node with a task to be executed and a group of nodes that may be able to execute the task. We describe the kinds of information that must be passed between nodes during the discussion in order to obtain effective problem-solving behavior. This discussion is the origin of the negotiation metaphor: Task distribution is viewed as a form of contract negotiation. We emphasize that protocols for distributed problem solving should help determine the content of the information transmitted, rather than simply provide a means of sending bits from one node to another. The use of the contract net framework is demonstrated in the solution of a simulated problem in area surveillance, of the sort encountered in ship or air traffic control. We discuss the mode of operation of a distributed sensing system, a network of nodes extending throughout a relatively large geographic area, whose primary aim is the formation of a dynamic map of traffic in the area. From the results of this preliminary study we abstract features of the framework applicable to problem solving in general, examining in particular transfer of control. Comparisons with planner, conniver, hearsay-ii , and pup 6 are used to demonstrate that negotiation—the two-way transfer of information—is a natural extension to the transfer of control mechanisms used in earlier problem-solving systems.


title: ORIGINAL ARTICLE A 33-Year Follow-up of Narcotics Addicts

abstract: Background This study examined longitudinal patterns of heroin use, other substance use, health, mental health, employment, criminal involvement, and mortality among heroin addicts. Methods The sample was composed of 581 male heroin addicts admitted to the California Civil Addict Program (CAP) during the years 1962 through 1964; CAP was a compulsory drug treatment program for heroin-dependent criminal offenders. This 33-year follow-up study updates information previously obtained from admission records and 2 face-to-face interviews conducted in 1974-1975 and 1985-1986; in 1996-1997, at the latest follow-up, 284 were dead and 242 were interviewed. Results In 1996-1997, the mean age of the 242 interviewed subjects was 57.4 years. Age, disability, years since first heroin use, and heavy alcohol use were significant correlates of mortality. Of the 242 interviewed subjects, 20.7% tested positive for heroin (with additional 9.5% urine refusal and 14.0% incarceration, for whom urinalyses were unavailable), 66.9% reported tobacco use, 22.1% were daily alcohol drinkers, and many reported illicit drug use (eg, past-year heroin use was 40.5%; marijuana, 35.5%; cocaine, 19.4%; crack, 10.3%; amphetamine, 11.6%). The group also reported high rates of health problems, mental health problems, and criminal justice system involvement. Long-term heroin abstinence was associated with less criminality, morbidity, psychological distress, and higher employment. Conclusions While the number of deaths increased steadily over time, heroin use patterns were remarkably stable for the group as a whole. For some, heroin addiction has been a lifelong condition associated with severe health and social consequences.


title: RAPID COMMUNICATION Prolonged Synaptic Integration in Perirhinal Cortical Neurons

abstract: Layer II/III of rat perirhinal cortex (PR) contains numerous late-spiking (LS) pyramidal neurons. When injected with a depolarizing current step, these LS cells typically delay spiking for one or more seconds from the onset of the current step and then sustain firing for the duration of the step. This pattern of delayed and sustained firing suggested a specific computational role for LS cells in temporal learning. This hypothesis predicts and requires that some layer II/III neurons should also exhibit delayed and sustained spiking in response to a train of excitatory synaptic inputs. Here we tested this prediction using visually guided, whole cell recordings from rat PR brain slices. Most LS cells (19 of 26) exhibited delayed spiking to synaptic stimulation (>1 s latency from the train onset), and the majority of these cells (13 of 19) also showed sustained firing that persisted for the duration of the synaptic train (5–10 s duration). Delayed and sustained firing in response to long synaptic trains has not been previously reported in vertebrate neurons. The data are consistent with our model that a circuit containing late spiking neurons can be used for encoding long time intervals during associative learning.


title: Glaucoma risk index : Automated glaucoma detection from color fundus images

abstract: Abstract Glaucoma as a neurodegeneration of the optic nerve is one of the most common causes of blindness. Because revitalization of the degenerated nerve fibers of the optic nerve is impossible early detection of the disease is essential. This can be supported by a robust and automated mass-screening. We propose a novel automated glaucoma detection system that operates on inexpensive to acquire and widely used digital color fundus images. After a glaucoma specific preprocessing, different generic feature types are compressed by an appearance-based dimension reduction technique. Subsequently, a probabilistic two-stage classification scheme combines these features types to extract the novel Glaucoma Risk Index (GRI) that shows a reasonable glaucoma detection performance. On a sample set of 575 fundus images a classification accuracy of 80% has been achieved in a 5-fold cross-validation setup. The GRI gains a competitive area under ROC (AUC) of 88% compared to the established topography-based glaucoma probability score of scanning laser tomography with AUC of 87%. The proposed color fundus image-based GRI achieves a competitive and reliable detection performance on a low-priced modality by the statistical analysis of entire images of the optic nerve head.


title: Deriving common malware behavior through graph clustering

abstract: Detection of malicious software (malware) continues to be a problem as hackers devise new ways to evade available methods. The proliferation of malware and malware variants requires methods that are both powerful, and fast to execute. This paper proposes a method to derive the common execution behavior of a family of malware instances. For each instance, a graph is constructed that represents kernel objects and their attributes, based on system call traces. The method combines these graphs to develop a supergraph for the family. This supergraph contains a subgraph, called the HotPath, which is observed during the execution of all the malware instances. The proposed method is scalable, identifies previously-unseen malware instances, shows high malware detection rates, and false positive rates close to 0%.


title: Framework for evaluation and selection of the software packages: A hybrid knowledge based system approach

abstract: Evaluation and selection of the software packages is complicated and time consuming decision making process. Selection of inappropriate software package can turn out to be costly and adversely affects business processes and functioning of the organization. In this paper we describe (i) generic methodology for software selection, (ii) software evaluation criteria, and (iii) hybrid knowledge based system (HKBS) approach to assist decision makers in evaluation and selection of the software packages. The proposed HKBS approach employs an integrated rule based and case based reasoning techniques. Rule based reasoning is used to capture user needs of the software package and formulate a problem case. Case based reasoning is used to retrieve and compare candidate software packages with the user needs of the package. This paper also evaluates and compares HKBS approach with the widely used existing software evaluation techniques such as analytic hierarchy process (AHP) and weighted scoring method (WSM).


title: The 2nd competition on counter measures to 2D face spoofing attacks

abstract: As a crucial security problem, anti-spoofing in biometrics, and particularly for the face modality, has achieved great progress in the recent years. Still, new threats arrive inform of better, more realistic and more sophisticated spoofing attacks. The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks. The submitted propositions are evaluated on the Replay-Attack database and the achieved results are presented in this paper.


title: Questioning ubiquitous computing

abstract: Ubiquitous Computing represents the most explicit attempt yet to move computing technology beyond the confines of tool usage towards a pervasive penetration of everyday bye. We outline a critical analysis of this technology based on a close examination of the proposals advancing it. After introducing a framework for understanding modern technology, we develop an interpretation of Ubiquitous Computing concentrating on its guiding principles, technological infrastructure, and scenarios. Next, we examine possible ways in which this technology could transform the human-world relationships that it would mediate. Finally, we analyze the particular kind of technical thinking underlying Ubiquitous Computing by focusing on the explicit and implicit assumptions and just$cations supporting the proposals. By means of this analysis, we arrive at a preliminary characterization of both the potential effects of Ubiquitous Computing on human-world relationships and the kind of technical thinking from which it originates.


The Relationship between Change Management Strategy and Successful Enterprise Resource Planning ( ERP ) Implementations : A Theoretical Perspective


title: Visualization and GPU-accelerated simulation of medical ultrasound from CT images

abstract: We present a fast GPU-based method for simulation of ultrasound images from volumetric CT scans and their visualization. The method uses a ray-based model of the ultrasound to generate view-dependent ultrasonic effects such as occlusions, large-scale reflections and attenuation combined with speckle patterns derived from pre-processing the CT image using a wave-based model of ultrasound propagation in soft tissue. The main applications of the method are ultrasound training and registration of ultrasound and CT images.


title: Application of model predictive control for a thermal process using STM32 microcontroller

abstract: Model Predictive Control (MPC) is increasingly being proposed for real time application to provide flexible device monitoring and it has become an advanced development trend of embedded technologies. In this paper, practical application of predictive algorithm is proposed to control a thermal system in real time using the high performance processor STM32. The control algorithm is obtained by the implementation of the MPC in this industrial standard hardware, using Keil development tools designed for ARM processor-based microcontroller devices. Based on experimental results, a comparative analysis proves the effectiveness and the advantages of the MPC algorithm compared to PI controller.


title: Employee Engagement From a Self‐Determination Theory Perspective

abstract: JOHN P. MEYERThe University of Western OntarioMARYLE`NE GAGNE´Concordia UniversityMaceyandSchneider(2008)drawonnumer-ous theories to explain what engagement isand how it is similar to and different fromrelated constructs in the organizationalbehavior literature. As a result, we now havea better understanding of some of the key‘‘components’’ofengagement.Whatappearsto be missing, however, is a strong unifyingtheory to guide research and practice. Webelieve that such a theory exists in the formof self-determination theory (SDT; Deci R Ryan & Deci, 2000) and its var-ious corollaries, self-concordance theory(SCT; Sheldon & Elliot, 1999), hierarchicaltheory (Vallerand, 1997), and passion theory(Vallerandetal.,2003).AlthoughMaceyandSchneider acknowledged the relevance ofSDT and SCT, we believe that much greateruseof thesetheoriescouldbe madetojustifyand extend their conceptual model.Self-Determination TheorySDT(Deci&Ryan,1985)proposestwoover-arching forms of motivation. Intrinsic moti-vation refers to doing an activity for its ownsake out of enjoyment and interest. Extrinsicmotivation refers to doing an activity forinstrumental reasons. Although extrinsicmotivation is arguably predominant in aworkcontext, ittoo can takedifferent forms.According to SDT, extrinsic motivation canreflectadesiretogainrewardsoravoidpun-ishment (external regulation), boost one’sego or avoid feelings of guilt (introjection),attainavaluedpersonalgoal(identification),or express one’s sense of self (integration).Identification and integration involve a highlevel of volition and, along with intrinsicmotivation, are considered forms of autono-mous regulation. External regulation andintrojectioninvolvemore external influenceand less authenticity and are consideredforms of controlledregulation. Autonomousregulation, which is also at the heart ofSheldon’s concept of self-concordance andVallerand’s characterization of harmoniouspassion, has been demonstrated to lead tohigher levels of performance, persistence,initiative,andcreativity(Ryan&Deci,2000).The concept of autonomous regulationoverlaps considerably with Macey andSchneider’s conceptualization of state en-gagement.Moreover,thebehavioraloutcomesfoundtobeassociatedwithautonomousreg-ulationcorrespondwith what they describedas behavioral engagement.Incontrastto


title: An intelligent task analysis approach for special education based on MIRA

abstract: Abstract This paper describes a novel approach for generating a logical sequence of tasks in the task analysis process of special education. This approach is based on the formalism MIRA (Mīmāṁsā Inspired Representation of Actions), which has the feature of expressing an action as reason, instruction and goal. MIRA also prescribes a set of deduction rules, which helps in the reasoning process of actions. These features are incorporated in this approach and a software tool, namely MIRATaskGen is designed, which facilitates the task analysis process of special education. The software receives various action related inputs along with the start and finish stages and generates a sequence of tasks from the start to the finish. This tool also informs the user, whether the desired goal can be achieved. If the desired goal cannot be achieved, then the sequence of actions from the start to a point of discontinuity is detected.


title: Nanofluidics: enabling processes for biotech

abstract: The advance of micro and nanodevice manufacturing technology enables us to carry out biological and chemical processes in a more efficient manner. In fact, fluidic processes connect the macro and the micro/nano worlds. For devices approaching the size of the fluid molecules, many physical phenomena occur that are not observed in macro flows. In this brief review, we discuss a few selected topics which of are interest for basic research and are important for applications in biotechnology.


title: Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs

abstract: We present a deep convolutional decoder architecture that can generate volumetric 3D outputs in a compute- and memory-efficient manner by using an octree representation. The network learns to predict both the structure of the octree, and the occupancy values of individual cells. This makes it a particularly valuable technique for generating 3D shapes. In contrast to standard decoders acting on regular voxel grids, the architecture does not have cubic complexity. This allows representing much higher resolution outputs with a limited memory budget. We demonstrate this in several application domains, including 3D convolutional autoencoders, generation of objects and whole scenes from high-level representations, and shape from a single image.


title: Multiagent system solutions for distributed computing, communications, and data integration needs in the power industry

abstract: We identify three fundamental issues underlying many problems in power systems today: distributed computing, communications, and data integration. We review the characteristics of intelligent agents and multi-agent systems (MAS) technologies that and argue that MAS offer a modular, extensible, flexible, and integrated approach to address all three of these issues and the problems resulting from them. The MAS design methodology is summarized, and an illustrative MAS application scenario in electric power systems is presented.


title: Silicon Evanescent Amplifiers

abstract: Optical amplifiers are important elements of photonic integrated circuits. We present a hybrid silicon evanescent amplifier utilizing a wafer bonded structure of silicon waveguide and AlGaInAs quantum wells. A chip gain of 13 dB with a power penalty of 0.5 dB at 40 Gb/s data amplification is demonstrated.


Towards Efficient Heap Overflow Discovery


Addressee and Response Selection for Multi-Party Conversation


title: Emerging Guidelines for Communicating with Animation in Mobile User Interfaces

abstract: Animations are a widely used design element in mobile user interfaces; however, it is unclear how these uses of animation support or challenge prior research. Therefore, I examine the current animation guidelines published by Microsoft, Apple, and Google, and then compare these guidelines to seven design principles proposed by previous studies (solidity, exaggeration, reinforcement, attachment, reluctance, smoothness, anticipation). I then examine the animation types (e.g., rotation and fading) used by a specific mobile application. By comparing current practices to previous research, I argue that decades-old design principles can be used to understand and clarify the frequent injunction to design animation that is "realistic" and/or "lively." I also call attention to emerging terms and concepts that are not covered by prior research, providing possible threads for future study.


title: Vitamins and Minerals for Energy, Fatigue and Cognition: A Narrative Review of the Biochemical and Clinical Evidence

abstract: Vitamins and minerals are essential to humans as they play essential roles in a variety of basic metabolic pathways that support fundamental cellular functions. In particular, their involvement in energy-yielding metabolism, DNA synthesis, oxygen transport, and neuronal functions makes them critical for brain and muscular function. These, in turn, translate into effects on cognitive and psychological processes, including mental and physical fatigue. This review is focused on B vitamins (B1, B2, B3, B5, B6, B8, B9 and B12), vitamin C, iron, magnesium and zinc, which have recognized roles in these outcomes. It summarizes the biochemical bases and actions of these micronutrients at both the molecular and cellular levels and connects them with cognitive and psychological symptoms, as well as manifestations of fatigue that may occur when status or supplies of these micronutrients are not adequate.


title: Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

abstract: The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.


A Composite COG Trajectory Planning Method for the Quadruped Robot Walking on Rough Terrain


title: Realization of optical carpets in the Talbot and Talbot-Lau configurations.

abstract: Talbot and Talbot-Lau effects are frequently used in lensless imaging applications with light, ultrasound, x-rays, atoms and molecules--generally in situations where refractive optical elements are non-existent or not suitable. We here show an experimental visualization of the intriguing wave patterns that are associated with near-field interferometry behind a single periodic diffraction grating under plane wave illumination and which are often referred to as Talbot carpets or quantum carpets. We also show the patterns behind two separated diffraction gratings under nearly-monochromatic but spatially incoherent illumination that illustrate the nature of Talbot-Lau carpets.


Improvement on ELM-RBF Neural Network using Genetic Algorithm