title: WFID: Passive Device-free Human Identification Using WiFi Signal

abstract: We present WFID, a passive device-free indoor human identification system with one pair of WiFi signal transmitter and receiver. WFID design is motivated by the observation that PHY layer Channel State Information (CSI) is capable of capturing the frequency diversity of wideband channel, such that the human body curve may be uniquely identified by learning the feature pattern of CSI. Different from many CSI-based techniques focusing on phase shift, we propose a novel feature of subcarrier-amplitude frequency (SAF). Based on this feature, WFID realizes human identification through a linear-kernel SVM. We have implemented a prototype of WFID with a commercial AP and a computer equipped with one Intel 5300 NIC. WFID is evaluated in two typical indoor scenarios. The results confirm that WFID achieves high classification accuracy which is permanent over several days under two typical indoor scenarios, with low computation cost. This reveals the potential for WFID to realize real-time indoor human identification.


title: Vietnam: A Portrait of Its People at War

abstract: The American experience of the Vietnam War is universally known, and much has been written about the brutalization of the US fighting men, the widespread atrocities committed and the use of Agent Orange. Too often "Vietnam" is used to refer to the conflict (and specifically to the American perception of it) rather than to the country and its people. The view from the other side - the Vietcong and North Vietnamese - has been virtually ignored. This work attempts to tell the story of the ordinary people of both North and South Vietnam, of the Vietcong guerrilla fighters and terrorists, North Vietnamese soldiers and cadres, monks, opposition leaders, propaganda chiefs and village secretaries. Constructed as a series of interviews, this book provides an account of dedication and heroism at all levels, but also of brutality and social control, and of the traumatization of a people in the grip of revolution and a terrifying war. Those featured in the book describe aspects of the war: the murderous trek down the Ho Chi Minh trail, the self-immolation of Buddhist monks, the methods of Vietcong assassinatin teams and life under attack from American bombs and napalm. Taken as a whole, the accounts provide a rare insight into the experience of the war from the "other side". There is an afterword by David Chanoff, as well as a list of important dates and events.


title: Selection, preparation, and monitoring: Current approaches to studying the neural control of action

abstract: F.C. Donders Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The NetherlandsImagine an athlete throwing a disc, as shown on the cover ofthis issue. We see him tensing his muscles but how can weknow which brain structures control this powerful action?The ancient Greeks could only observe and measure limited,externalaspectsofthismovement,suchasthetimingofmus-cle ﬂexions and the distance the discus is thrown. Just as theancient Greeks, until recently we had little way to observe thebrainmechanismsthatgiverisetothisfeat.Sincemovementswere considered to be difﬁcult to transfer to the laboratory toprovide quantitative data, the study of movement has foralongtimebeenoneof theless studiedﬁeldsofcognitivepsy-chology. The rise of various techniques to study the humanbrain during task performance initially seemed to aggravatethese problems, as they all require the participant to remainmotionless for an extended period and are highly sensitiveto movement artifacts. As such, the study of movement haslong been a neglectedchild in the realm of cognitive neurosci-ence. Over the last 15 years, however, this situation haschanged.Thisisdueinparttotheriseofvariousimagingtech-niques and accompanying statistical tools that are less sensi-tive to these problems, but also to the increasing interest inthe cognitive processes associated with any movement.Indeed, rather than focusing on the movement directly, thisresearch is focusing on the processes leading up to the move-ment and the evaluation of the consequences of the move-ment following its execution. All these processes thatprecede and directly follow observable movements are nowa-days collectively referred to as actions. These developments,in turn, have led to a large rise in popularity of action-relatedresearch. Thegoal of this specialissueof Cortexis to illustratethe diverse experimental approaches currently employed instudying the neural control of actions and the consequencesthis is having on other domains of cognitive neuroscience.Thecontributions inthisissueconstituteamixtureof reviewsandnovelexperimentaldata.Thepaperspartlyoriginatefroma symposium on this topic held at the Radboud UniversityNijmegen, November 9–10, 2006.The techniques most often used to probe neural activityduring selection, preparation, and monitoring of actions in-clude various signals derived from EEG and MEG, such as theevent-related potential (ERP), motor-evoked potentials in theelectromyogramelicitedusingtranscranial magneticstimula-tion(TMS),andimagingtechniques suchaspositronemissiontomography (PET) and functional magnetic resonance imag-ing (fMRI). By necessity, the overt movements that can be per-formed by participants in these experiments are limited. EEGsignals are highly sensitive to movement artifacts, as are im-ages obtained with fMRI. Although recently solutions havebeenfoundtostudymorecomplexmovementsinthefMRIen-vironment (Diedrichsen et al., 2005; Majdandzic´ et al., 2007;Re´my et al., 2008, this issue), the movement studied is oftennothing more than a simple button press. However, a numberof paradigms have been proposed that allow imaging of therepresentations underlying actions uncontaminated by theexecution of complex movements (Jeannerod, 2006). Forinstance, a window on the neural processesunderlying actionspeciﬁcation can be provided by studying imagery of theseactions (De Lange et al., 2008, this issue) or the preparationof actions. These paradigms allow the researcher a windowon processes far more complex than the simple execution of


title: Brief Report: Emotional Processing in High-Functioning Autism—Physiological Reactivity and Affective Report

abstract: This study examined physiological response and affective report in 10 adult individuals with autism and 10 typically developing controls. An emotion induction paradigm using stimuli from the International Affective Picture System was applied. Blood pressure, heart and self-ratings of experienced valence (pleasure), arousal and dominance (control) were assessed during the experiment. Physiological response profiles correlated low to significantly negative between groups. Individuals with autism experienced less arousal when viewing sad pictures but higher arousal while processing neutral stimuli. In addition, they reported more control than the normative group when viewing fearful and sad stimuli. Findings indicate altered physiological reactivity and affective report in autism, which may be related to more general impairments in socio-emotional functioning.


title: Research Review: A new perspective on attention-deficit/hyperactivity disorder: emotion dysregulation and trait models

abstract: Attention-deficit/hyperactivity disorder (ADHD) is a common example of developmental psychopathology that might be able to be better understood by taking an emotion regulation perspective. As discussed herein, emotion regulation is understood to consist of two component processes, emotion (e.g., positive and negative emotionality) and regulation (e.g., effortful and reactive forms of control), which interact with one another at the behavioral level. Review of work to date suggests that the heterogeneous behavioral category of ADHD may encompass two distinct kinds of inputs: inattentive ADHD symptoms may be primarily associated with breakdowns in the regulation side, whereas hyperactivity-impulsive ADHD symptoms may be associated with breakdowns in the emotionality side. It is argued that breakdowns in control may be a signature for ADHD specifically, while increased negative emotionality may serve as non-specific risk factors for disruptive behavior disorders, explaining their comorbidity. Increased understanding of the interrelations and interactions of component emotion regulation processes may elucidate developmental, sex, and neural mechanisms of ADHD and associated comorbid disruptive disorders.


title: Segmental Spatio-Temporal CNNs for Fine-grained Action Segmentation and Classification

abstract: Joint segmentation and classification of fine-grained actions is important for applications in human-robot interaction, video surveillance, and human skill evaluation. However, despite substantial recent progress in large scale action classification, the performance of state-of-the-art fine-grained action recognition approaches remains low. In this paper, we propose a new spatio-temporal CNN model for fine-grained action classification and segmentation, which combines (1) a spatial CNN to represent objects in the scene and their spatial relationships; (2) a temporal CNN that captures how object relationships within an action change over time; and (3) a semi-Markov model that captures transitions from one action to another. In addition, we introduce an efficient segmental inference algorithm for joint segmentation and classification of actions that is orders of magnitude faster than state-of-the-art approaches. We highlight the effectiveness of our approach on cooking and surgical action datasets for which we observe substantially improved performance relative to recent baseline methods.


Answering English questions by computer: a survey


title: High-speed VLSI architectures for the AES algorithm

abstract: This paper presents novel high-speed architectures for the hardware implementation of the Advanced Encryption Standard (AES) algorithm. Unlike previous works which rely on look-up tables to implement the SubBytes and InvSubBytes transformations of the AES algorithm, the proposed design employs combinational logic only. As a direct consequence, the unbreakable delay incurred by look-up tables in the conventional approaches is eliminated, and the advantage of subpipelining can be further explored. Furthermore, composite field arithmetic is employed to reduce the area requirements, and different implementations for the inversion in subfield GF(2/sup 4/) are compared. In addition, an efficient key expansion architecture suitable for the subpipelined round units is also presented. Using the proposed architecture, a fully subpipelined encryptor with 7 substages in each round unit can achieve a throughput of 21.56 Gbps on a Xilinx XCV1000 e-8 bg560 device in non-feedback modes, which is faster and is 79% more efficient in terms of equivalent throughput/slice than the fastest previous FPGA implementation known to date.


title: Web-Based Shopping: Consumers' Attitudes Towards Online Shopping in New Zealand

abstract: The growing use of Internet in New Zealand provides a developing prospect for E-marketers. If E-marketers know the factors affecting online New Zealand buyers’ behaviour, and the relationships between these factors and the type of online buyers, then they can further develop their marketing strategies to convert potential customers into active ones, while retaining existent online customers. This paper is part of larger study, and focuses on factors which online New Zealand buyers keep in mind while shopping online. It also investigates how different types of online buyers perceive websites differently. This research found that website design, website reliability/fulfilment, website customer service and website security/privacy are the four dominant factors which influence consumer perceptions of online purchasing. The four types of online New Zealand buyers; i.e., trial, occasional, frequent and regular online buyers; perceived the four website factors differently. These buyers have different evaluations of website design and website reliability/fulfilment but similar evaluations of website security/privacy issues, which implies that security/privacy issues are important to most online buyers. The significant discrepancy in how online purchasers perceived website design and website reliability accounts for the difference in online purchase frequencies.


title: Towards Metamerism via Foveated Style Transfer

abstract: Given the recent successes of deep learning applied to style transfer and texture synthesis, we propose a new theoretical framework to construct visual metamers: \textit{a family of perceptually identical, yet physically different images}. We review work both in neuroscience related to metameric stimuli, as well as computer vision research in style transfer. We propose our NeuroFovea metamer model that is based on a mixture of peripheral representations and style transfer forward-pass algorithms for \emph{any} image from the recent work of Adaptive Instance Normalization (Huang~\&~Belongie). Our model is parametrized by a VGG-Net versus a set of joint statistics of complex wavelet coefficients which allows us to encode images in high dimensional space and interpolate between the content and texture information. We empirically show that human observers discriminate our metamers at a similar rate as the metamers of Freeman~\&~Simoncelli (FS) In addition, our NeuroFovea metamer model gives us the benefit of near real-time generation which presents a $\times1000$ speed-up compared to previous work. Critically, psychophysical studies show that both the FS and NeuroFovea metamers are discriminable from the original images highlighting an important limitation of current metamer generation methods.


title: Modified Floyd-Warshall algorithm for equal cost multipath in software-defined data center

abstract: Load balancing in data centers have been a common practice in the last couple of decades. This has been done statically in traditional networks with little or no feedback information from the underlying network state. With the current large cloud data centers and continuous changing traffic patterns, the drive for more interactive and dynamic solution to reduce the latency and improve the network resources utilization has brought about the Software Defined Networking Paradigm. However, some load balancing solutions have been proposed, utilizing OpenFlow without a well-defined algorithm to reduce the path computation complexity as requests arrive on the network. This paper proposes a path load-balancing algorithm which utilizes a modified Floyd-Warshall All-Pairs Shortest Paths algorithm to compute and store equal cost paths information, utilizes the stored information for path selection, and maintain a real-time updates of those paths. Our evaluation demonstrates that the proposed algorithm performs better than the Global First Fit algorithm as we have significantly reduced the time to service request as a result of eliminating the recursive path computation for every client request.


title: Wildfire monitoring using a mixed air-ground mobile network

abstract: Forest fires are a challenging problem for many countries. They often cause economical lost and ecological damage, and they can sometimes even cost human lives. Finding hot spots immediately after a fire is an important part of fighting forest fires. The main objective is to obtain a temperature map of the burned area, to locate the most critical embers. This information can help firefighter managers make the correct decisions about ground crew movements. The pervasive application described in this article lets firefighters obtain images of hot spots directly from an unmanned aircraft and receive commands from their manager through a communication network. Every firefighter holds a personal electronic device (PED), which includes a touch screen, Wi-Fi connectivity, a GPS receiver, and temperature sensors. Because terrain conditions such as abrupt ravines, rocks, and dense vegetation can introduce obstacles to connectivity, a balloon with a Wi-Fi device is tethered to every firefighter's truck to improve communication. In addition, a fixed-wing unmanned aircraft augments the number of communication layers to three. This article studies the quality of this three-layered network in maintaining the application's bandwidth requirements.


title: Determining the Self-organized Criticality State of Power Systems by the Cascading Failures Searching Method

abstract: According to complex system theory, power systems in self-organized criticality (SOC) state have high probability of occurring of cascading failures. For the purpose of determining if a power system is in SOC state, a cascading failure searching method is established. The method is based on revised Newton method and considers hidden failures in protection systems. And then it is used to analyze the Huabei power system and the system is testified in SOC state. Finally, the impact of loading on the probability of occurring of cascading failures is studied, and a phenomenon that power systems with more uniform loading have lower probability of occurring of cascading failures is found.


title: Smart smartphone development: iOS versus android

abstract: In a remarkably short timeframe, developing apps for smartphones has gone from an arcane curiosity to an essential skill set. Employers are scrambling to find developers capable of transforming their ideas into apps. Educators interested in filling that void are likewise trying to keep up, and face difficult decisions in designing a meaningful course. There are a plethora of development platforms, but two stand out because of their popularity and divergent approaches - Apple's iOS, and Google's Android. In this paper, we will compare the two, and address the question: which should faculty teach?


title: Evaluating Performance of Containerized IoT Services for Clustered Devices at the Network Edge

abstract: The constant and fast increase in the number of heterogeneous Internet of Things (IoT) devices that populate everyday life environments brings new challenges to the full exploitation of the computation, memory, sensing, and actuation resources associated to them. In this context, device virtualization solutions and platforms may definitely play a key role in enabling the desired tradeoff between flexibility and performance. This paper focuses on lightweight virtualization technologies for IoT devices, suitably thought to effectively deploy new integrated applications and to create a novel distributed and virtualized ecosystem. Two different frameworks for container-based IoT service provisioning are compared, the one based on a direct interaction between two cooperating devices and the other based on the presence of a manager supervising the operations between cooperating devices forming a cluster. In the latter case, accounting for the growing impetus to move intelligence toward the edge of the network, management features are implemented at the network access point to provide short latency responses. We also introduce the outcomes of a thorough performance evaluation campaign conducted via a real IoT testbed. The measurements, performed by accounting for the constraints of typical IoT nodes, shed light on the actual feasibility of container-based IoT frameworks.


title: Adversarial learning for modeling human motion

abstract: We investigate how adversarial learning may be used for various animation tasks related to human motion synthesis. We propose a learning framework that we decline for building various models corresponding to various needs: a random synthesis generator that randomly produces realistic motion capture trajectories; conditional variants that allow controlling the synthesis by providing high-level features that the animation should match; a style transfer model that allows transforming an existing animation in the style of another one. Our work is built on the adversarial learning strategy that has been proposed in the machine learning field very recently (2014) for learning accurate generative models on complex data, and that has been shown to provide impressive results, mainly on image data. We report both objective and subjective evaluation results on motion capture data performed under emotion, the Emilya Dataset. Our results show the potential of our proposals for building models for a variety of motion synthesis tasks.


title: Large scale recurrent neural network on GPU

abstract: Large scale artificial neural networks (ANNs) have been widely used in data processing applications. The recurrent neural network (RNN) is a special type of neural network equipped with additional recurrent connections. Such a unique architecture enables the recurrent neural network to remember the past processed information and makes it an expressive model for nonlinear sequence processing tasks. However, the large computation complexity makes it difficult to effectively train a recurrent neural network and therefore significantly limits the research on the recurrent neural network in the last 20 years. In recent years, the use of graphics processing units (GPUs) becomes a significant advance to speed up the training process of large scale neural networks by taking advantage of the massive parallelism capabilities of GPUs. In this paper, we propose an efficient GPU implementation of the large scale recurrent neural network and demonstrate the power of scaling up the recurrent neural network with GPUs. We first explore the potential parallelism of the recurrent neural network and propose a fine-grained two-stage pipeline implementation. Experiment results show that the proposed GPU implementation can achieve 2 ~ 11 x speed-up compared with the basic CPU implementation with the Intel Math Kernel Library. We then use the proposed GPU implementation to scale up the recurrent neural network and improve its performance. The experiment results of the Microsoft Research Sentence Completion Challenge demonstrate that the large scale recurrent network without class layer is able to beat the traditional class-based modest-size recurrent network and achieve an accuracy of 47%, the best result achieved by a single recurrent neural network on the same dataset.


title: TanPro-kit: a tangible programming tool for children

abstract: This paper describes a new tangible programming tool--- TanPro-Kit which was designed for children aged 5 to 9. It consists of programming blocks and a LED pad. The LED pad presents visual animations and audible feedback according to the arrangement of blocks with which children make program to play a maze game. Aiming at lowering the cost of TanPro-Kit, we adopted LED, RFID, wireless and infrared technology to develop the whole system. The system acquires the programming blocks' physical information which is then translated into programming semantic. TanPro-Kit is low-cost, which is more acceptable in developing countries. We ran a user study with 16 children involved, which showed TanPro-Kit to be attractive to children and easy to learn and use.


title: On the compression of recurrent neural networks with an application to LVCSR acoustic modeling for embedded speech recognition

abstract: We study the problem of compressing recurrent neural networks (RNNs). In particular, we focus on the compression of RNN acoustic models, which are motivated by the goal of building compact and accurate speech recognition systems which can be run efficiently on mobile devices. In this work, we present a technique for general recurrent model compression that jointly compresses both recurrent and non-recurrent inter-layer weight matrices. We find that the proposed technique allows us to reduce the size of our Long Short-Term Memory (LSTM) acoustic model to a third of its original size with negligible loss in accuracy.


title: Vsion: Vehicle Occlusion Handling for Traffic Monitoring

abstract: Recently, the pervasiveness of street cameras for security and traffic monitoring opens new challenges to the computer vision technology to provide reliable monitoring schemes. These monitoring schemes require the basic processes of detecting and tracking objects, such as vehicles. However, object detection performance often suffers under occlusion. This work proposes a vehicle occlusion handling improvement of an existing traffic video monitoring system, which was later integrated. Two scenarios were considered in occlusion: indistinct and distinct - wherein the occluded vehicles have similar and dissimilar colors, respectively. K-means clustering using the HSV color space was used for distinct occlusion while sliding window algorithm was used for indistinct occlusion. The proposed method also applies deep convolutional neural networks to further improve vehicle recognition and classification. The CNN model obtained a 97.21% training accuracy and a 98.27% testing accuracy. Moreover, it minimizes the effect of occlusion to vehicle detection and classification. It also identifies common vehicle types (bus, truck, van, sedan, SUV, jeepney, and motorcycle) rather than classifying these as small, medium and large vehicles, which were the previous categories. Despite the implementation and results, it is recommended to improve the occlusion handling issue. The disadvantage of the sliding window algorithm is that it requires a lot of memory and is time-consuming. In case of deploying this research for more substantial purposes and intentions, it is ideal to enhance the CNN model by training it with more varied images of vehicles and to implement the system real-time. The results of this work can serve as a contribution for future works that are significant to traffic monitoring and air quality surveillance.


title: Individual differences in executive control relate to metaphor processing: an eye movement study of sentence reading

abstract: Metaphors are common elements of language that allow us to creatively stretch the limits of word meaning. However, metaphors vary in their degree of novelty, which determines whether people must create new meanings on-line or retrieve previously known metaphorical meanings from memory. Such variations affect the degree to which general cognitive capacities such as executive control are required for successful comprehension. We investigated whether individual differences in executive control relate to metaphor processing using eye movement measures of reading. Thirty-nine participants read sentences including metaphors or idioms, another form of figurative language that is more likely to rely on meaning retrieval. They also completed the AX-CPT, a domain-general executive control task. In Experiment 1, we examined sentences containing metaphorical or literal uses of verbs, presented with or without prior context. In Experiment 2, we examined sentences containing idioms or literal phrases for the same participants to determine whether the link to executive control was qualitatively similar or different to Experiment 1. When metaphors were low familiar, all people read verbs used as metaphors more slowly than verbs used literally (this difference was smaller for high familiar metaphors). Executive control capacity modulated this pattern in that high executive control readers spent more time reading verbs when a prior context forced a particular interpretation (metaphorical or literal), and they had faster total metaphor reading times when there was a prior context. Interestingly, executive control did not relate to idiom processing for the same readers. Here, all readers had faster total reading times for high familiar idioms than literal phrases. Thus, executive control relates to metaphor but not idiom processing for these readers, and for the particular metaphor and idiom reading manipulations presented.


title: Common oral lesions: Part II. Masses and neoplasia.

abstract: Certain common oral lesions appear as masses, prompting concern about oral carcinoma. Many are benign, although some (e.g., leukoplakia) may represent neoplasia or cancer. Palatal and mandibular tori are bony protuberances and are benign anomalies. Oral pyogenic granulomas may appear in response to local irritation, trauma, or hormonal changes of pregnancy. Mucoceles represent mucin spillage into the oral soft tissues resulting from rupture of a salivary gland duct. Oral fibromas form as a result of irritation or masticatory trauma, especially along the buccal occlusal line. Oral cancer may appear clinically as a subtle mucosal change or as an obvious mass. Oral leukoplakia is the most common premalignant oral lesion. For persistent white or erythematous oral lesions, biopsy should be performed to rule out neoplastic change or cancer. Most oral cancers are squamous cell carcinomas. Tobacco and heavy alcohol use are the principal risk factors for oral cancer. Family physicians should be able to recognize these lesions and make appropriate referrals for biopsy and treatment.


title: Metamaterial-Inspired, Electrically Small Huygens Sources

abstract: Electrically small electric and magnetic dipole antennas are introduced that are based on electric and magnetic near-field resonant parasitic (NFRP) elements that are electrically coupled to the driven element. By properly combining these two NFRP antenna types, electrically small Huygens sources are realized. The proposed Huygens sources have one feed point, are well matched to 50 Ω, and have high radiation efficiencies. Their predicted directivity patterns approach the known Huygens source theoretical limit.


title: Betweenness Centrality as a Driver of Preferential Attachment in the Evolution of Research Collaboration Networks

abstract: We analyze whether preferential attachment in scientific coauthorship networks is different for authors with different forms of centrality. Using a complete database for the scientific specialty of research about “steel structures,” we show that betweenness centrality of an existing node is a significantly better predictor of preferential attachment by new entrants than degree or closeness centrality. During the growth of a network, preferential attachment shifts from (local) degree centrality to betweenness centrality as a global measure. An interpretation is that supervisors of PhD projects and postdocs broker between new entrants and the already existing network, and thus become focal to preferential attachment. Because of this mediation, scholarly networks can be expected to develop differently from networks which are predicated on preferential attachment to nodes with high degree centrality.


Deeper Attention to Abusive User Content Moderation


title: Multi-Layer Packet Classification with Graphics Processing Units

abstract: The rapid growth of server virtualization has ignited a wide adoption of software-based virtual switches, with significant interest in speeding up their performance. In a similar trend, software-defined networking (SDN), with its strong reliance on rule-based flow classification, has also created renewed interest in multi-dimensional packet classification. However, despite these recent advances, the performance of current software-based packet classifiers is still limited, mostly by the low parallelism of general-purpose CPUs. In this paper, we explore how to accelerate packet classification using the high parallelism and latency-hiding capabilities of graphic processing units (GPUs). We implement GPU-accelerated versions for both linear and tuple search, currently deployed in virtual switches, and also introduce a novel algorithm called Bloom search. These algorithms are integrated with high-speed packet I/O to build GSwitch, a GPU-accelerated software switch. Our experimental evaluation shows that GSwitch is at least 7x faster than an equally-priced CPU classifier and is able to reach 10 Gbps with minimum-sized packets and a rule set containing 128K OpenFlow entries with 512 different wildcard patterns.


title: Imbalanced Protein Data Classification Using Ensemble FTM-SVM

abstract: Classification of protein sequences into functional and structural families based on machine learning methods is a hot research topic in machine learning and Bioinformatics. In fact, the underlying protein classification problem is a huge multiclass problem. Generally, the multiclass problem can be reduced to a set of binary classification problems. The protein in one class are seen as positive examples while those outside the class are seen as negative examples. However, the class imbalance problem will arise in this case because the number of protein in one class is usually much smaller than that of the protein outside the class. To handle the challenge, we propose a novel framework to classify the protein. We firstly use free scores (FS) to perform feature extraction for protein; then, the inverse random under sampling (IRUS) is used to create a large number of distinct training sets; next, we use a new ensemble approach to combine these distinct training sets with a new fuzzy total margin support vector machine (FTM-SVM) that we have constructed. we call the novel ensemble classifier as ensemble fuzzy total margin support vector machine (EnFTM-SVM). We then give a full description of our method, including the details of its derivation. Finally, experimental results on fourteen benchmark protein data sets indicate that the proposed method outperforms many state-of-the-art protein classifying methods.


title: Learning similarity matching in multimedia content-based retrieval

abstract: Many multimedia content-based retrieval systems allow query formulation with the user setting the relative importance of features (e.g., color, texture, shape, etc.) to mimic the user's perception of similarity. However, the systems do not modify their similarity matching functions, which are defined during the system development. We present a neural network-based learning algorithm for adapting the similarity matching function toward the user's query preference based on his/her relevance feedback. The relevance feedback is given as ranking errors (misranks) between the retrieved and desired lists of multimedia objects. The algorithm is demonstrated for facial image retrieval using the NIST Mugshot Identification Database with encouraging results.


title: Toward a Neurobiology of Psychotherapy: Basic Science and Clinical Applications

abstract: Psychotherapy is used commonly to treat a variety of mental illnesses, yet surprisingly little is known about its biological mechanisms especially in comparison with pharmacotherapy. In this review we survey the current knowledge about changes in brain function following psychotherapeutic intervention that are detectable with current neuroimaging techniques. We also consider the possible role for neuroimaging in refining clinical diagnoses and predicting treatment outcome, which would benefit both clinical decision-making and the cognitive neuroscience of psychotherapy.


title: Cursor control by Kalman filter with a non-invasive body–machine interface

abstract: Objective. We describe a novel human–machine interface for the control of a two-dimensional (2D) computer cursor using four inertial measurement units (IMUs) placed on the user's upper-body. Approach. A calibration paradigm where human subjects follow a cursor with their body as if they were controlling it with their shoulders generates a map between shoulder motions and cursor kinematics. This map is used in a Kalman filter to estimate the desired cursor coordinates from upper-body motions. We compared cursor control performance in a centre-out reaching task performed by subjects using different amounts of information from the IMUs to control the 2D cursor. Main results. Our results indicate that taking advantage of the redundancy of the signals from the IMUs improved overall performance. Our work also demonstrates the potential of non-invasive IMU-based body–machine interface systems as an alternative or complement to brain–machine interfaces for accomplishing cursor control in 2D space. Significance. The present study may serve as a platform for people with high-tetraplegia to control assistive devices such as powered wheelchairs using a joystick.


title: Multi-Document Summarization By Sentence Extraction

abstract: This paper discusses a text extraction approach to multi-document summarization that builds on single-document summarization methods by using additional, available information about the document set as a whole and the relationships between the documents. Multi-document summarization differs from single in that the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Our approach addresses these issues by using domain-independent techniques based mainly on fast, statistical processing, a metric for reducing redundancy and maximizing diversity in the selected passages, and a modular framework to allow easy parameterization for different genres, corpora characteristics and user requirements.


title: Sidelobe Level Suppres for Proxim

abstract: A four-way unequal power divider is developed to feed a linear array proximity coupled microstrip antenna. The unequal power divider excitation coefficients follow the chebyshev polynomial. The simulation result shows that the four element proximity coupled microstrip antenna array with unequal power divider has achieved a sidelobe level suppression of 18.7 dB with backlobe level suppression of 16.5 dB. Furthermore the antenna gain of 8.81 dB was also achieved. Measurement results of the antenna with unequal power divider show similar result with the simulation. Measurement results show sidelobe level suppression of 18.1 dB with backlobe level suppression of 19.4 dB.


title: Review of the Nomenclature of the Retaining Ligaments of the Cheek: Frequently Confused Terminology

abstract: Since the time of its inception within facial anatomy, wide variability in the terminology as well as the location and extent of retaining ligaments has resulted in confusion over nomenclature. Confusion over nomenclature also arises with regard to the subcutaneous ligamentous attachments, and in the anatomic location and extent described, particularly for zygomatic and masseteric ligaments. Certain historical terms-McGregor's patch, the platysma auricular ligament, parotid cutaneous ligament, platysma auricular fascia, temporoparotid fasica (Lore's fascia), anterior platysma-cutaneous ligament, and platysma cutaneous ligament-delineate retaining ligaments of related anatomic structures that have been conceptualized in various ways. Confusion around the masseteric cutaneous ligaments arises from inconsistencies in their reported locations in the literature because the size and location of the parotid gland varies so much, and this affects the relationship between the parotid gland and the fascia of the masseter muscle. For the zygomatic ligaments, there is disagreement over how far they extend, with descriptions varying over whether they extend medially beyond the zygomaticus minor muscle. Even the 'main' zygomatic ligament's denotation may vary depending on which subcutaneous plane is used as a reference for naming it. Recent popularity in procedures using threads or injectables has required not only an accurate understanding of the nomenclature of retaining ligaments, but also of their location and extent. The authors have here summarized each retaining ligament with a survey of the different nomenclature that has been introduced by different authors within the most commonly cited published papers.


title: Managerial Remuneration in India: Of Changing Guidelines, Fatter Pay Packets, and Incentives to Performance

abstract: In this study, we look into the changes made in Indian corporate laws regarding managerial remuneration. We also evaluate the effect of these changes on corporate performance. Based on the comparison of corporate performance data of 16 large Indian manufacturing firms vis-a-vis their managerial remuneration data, it is concluded that managerial remuneration has significantly increased over the last two decades in sharp contrast to corporate financial performance (both from efficiency and growth point of view). Questions have been raised whether such increase in managerial remuneration was at all justified.


title: Aggregate Computation over Data Streams

abstract: Nowadays, we have witnessed the widely recognized phenomenon of high speed data streams. Various statistics computation over data streams is often required by many applications, including processing of relational type queries, data mining and high speed network management. In this paper, we provide survey for three important kinds of aggregate computations over data streams: frequency moment, frequency count and order statistic.


title: Action and object words are differentially anchored in the sensory motor system - A perspective on cognitive embodiment

abstract: Embodied and grounded cognition theories have assumed that the sensorimotor system is causally involved in processing motor-related language content. Although a causal proof on a single-cell basis is ethically not possible today, the present fMRI study provides confirmation of this longstanding speculation, as far as it is possible with recent methods, employing a new computational approach. More specifically, we were looking for common activation of nouns and objects, and actions and verbs, representing the canonical and mirror neuron system, respectively. Using multivariate pattern analysis, a resulting linear classifier indeed successfully generalized from distinguishing actions from objects in pictures to distinguishing the respective verbs from nouns in written words. Further, these action-related pattern responses were detailed by recently introduced predictive pattern decomposition into the constituent activity atoms and their relative contributions. The findings support the concept of canonical neurons and mirror neurons implementing embodied processes with separate roles in distinguishing objects from actions, and nouns from verbs, respectively. This example of neuronal recycling processing algorithms is consistent with a multimodal brain signature of human action and object concepts. Embodied language theory is thus merged with actual neurobiological implementation.


title: Single channel audio source separation using convolutional denoising autoencoders

abstract: Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. We use as many CDAEs as the number of sources to be separated from the mixed signal. Each CDAE is trained to separate one source and treats the other sources as background noise. The main idea is to allow each CDAE to learn suitable spectral-temporal filters and features to its corresponding source. Our experimental results show that CDAEs perform source separation slightly better than the deep feedforward neural networks (FNNs) even with fewer parameters than FNNs.


UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes


title: Digital Identity: The Effect of Trust and Reputation Information on User Judgement in the Sharing Economy

abstract: The Sharing Economy (SE) is a growing ecosystem focusing on peer-to-peer enterprise. In the SE the information available to assist individuals (users) in making decisions focuses predominantly on community generated trust and reputation information. However, how such information impacts user judgement is still being understood. To explore such effects, we constructed an artificial SE accommodation platform where we varied the elements related to hosts' digital identity, measuring users' perceptions and decisions to interact. Across three studies, we find that trust and reputation information increases not only the users' perceived trustworthiness, credibility, and sociability of hosts, but also the propensity to rent a private room in their home. This effect is seen when providing users both with complete profiles and profiles with partial user-selected information. Closer investigations reveal that three elements relating to the host's digital identity are sufficient to produce such positive perceptions and increased rental decisions, regardless of which three elements are presented. Our findings have relevant implications for human judgment and privacy in the SE, and question its current culture of ever increasing information-sharing.


title: Patent citation analysis: Calculating science linkage based on citing motivation

abstract: Science linkage is a widely used patent bibliometric indicator to measure patent linkage to scientific research based on the frequency of citations to scientific papers within the patent. Science linkage is also regarded as noisy because the subject of patent citation behavior varies from inventors/applicants to examiners. In order to identify and ultimately reduce this noise, we analyzed the different citing motivations of examiners and inventors/applicants. We built 4 hypotheses based upon our study of patent law, the unique economic nature of a patent, and a patent citation's market effect. To test our hypotheses, we conducted an expert survey based on our science linkage calculation in the domain of catalyst from U.S. patent data 2006-2009 over 3 types of citations: self-citation by inventor/applicant, non-self-citation by inventor/applicant, and citation by examiner. According to our results, evaluated by domain experts, we conclude that the non-self-citation by inventor/applicant is quite noisy and cannot indicate science linkage and that self-citation by inventor/applicant, although limited, is more appropriate for understanding science linkage.


title: A probabilistic approach to nearest-neighbor classification: naive hubness bayesian kNN

abstract: Most machine-learning tasks, including classification, involve dealing with high-dimensional data. It was recently shown that the phenomenon of hubness, inherent to high-dimensional data, can be exploited to improve methods based on nearest neighbors (NNs). Hubness refers to the emergence of points (hubs) that appear among the k NNs of many other points in the data, and constitute influential points for kNN classification. In this paper, we present a new probabilistic approach to kNN classification, naive hubness Bayesian k-nearest neighbor (NHBNN), which employs hubness for computing class likelihood estimates. Experiments show that NHBNN compares favorably to different variants of the kNN classifier, including probabilistic kNN (PNN) which is often used as an underlying probabilistic framework for NN classification, signifying that NHBNN is a promising alternative framework for developing probabilistic NN algorithms.


title: Strokelets: A Learned Multi-scale Representation for Scene Text Recognition

abstract: Driven by the wide range of applications, scene text detection and recognition have become active research topics in computer vision. Though extensively studied, localizing and reading text in uncontrolled environments remain extremely challenging, due to various interference factors. In this paper, we propose a novel multi-scale representation for scene text recognition. This representation consists of a set of detectable primitives, termed as strokelets, which capture the essential substructures of characters at different granularities. Strokelets possess four distinctive advantages: (1) Usability: automatically learned from bounding box labels, (2) Robustness: insensitive to interference factors, (3) Generality: applicable to variant languages, and (4) Expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of strokelets and demonstrate the effectiveness of the proposed algorithm for text recognition.


title: Bridging the Archipelago between Row-Stores and Column-Stores for Hybrid Workloads

abstract: Data-intensive applications seek to obtain trill insights in real-time by analyzing a combination of historical data sets alongside recently collected data. This means that to support such hybrid workloads, database management systems (DBMSs) need to handle both fast ACID transactions and complex analytical queries on the same database. But the current trend is to use specialized systems that are optimized for only one of these workloads, and thus require an organization to maintain separate copies of the database. This adds additional cost to deploying a database application in terms of both storage and administration overhead. To overcome this barrier, we present a hybrid DBMS architecture that efficiently supports varied workloads on the same database. Our approach differs from previous methods in that we use a single execution engine that is oblivious to the storage layout of data without sacrificing the performance benefits of the specialized systems. This obviates the need to maintain separate copies of the database in multiple independent systems. We also present a technique to continuously evolve the database's physical storage layout by analyzing the queries' access patterns and choosing the optimal layout for different segments of data within the same table. To evaluate this work, we implemented our architecture in an in-memory DBMS. Our results show that our approach delivers up to 3x higher throughput compared to static storage layouts across different workloads. We also demonstrate that our continuous adaptation mechanism allows the DBMS to achieve a near-optimal layout for an arbitrary workload without requiring any manual tuning.


title: Semantic Matching by Non-Linear Word Transportation for Information Retrieval

abstract: A common limitation of many information retrieval (IR) models is that relevance scores are solely based on exact (i.e., syntactic) matching of words in queries and documents under the simple Bag-of-Words (BoW) representation. This not only leads to the well-known vocabulary mismatch problem, but also does not allow semantically related words to contribute to the relevance score. Recent advances in word embedding have shown that semantic representations for words can be efficiently learned by distributional models. A natural generalization is then to represent both queries and documents as Bag-of-Word-Embeddings (BoWE), which provides a better foundation for semantic matching than BoW. Based on this representation, we introduce a novel retrieval model by viewing the matching between queries and documents as a non-linear word transportation (NWT) problem. With this formulation, we define the capacity and profit of a transportation model designed for the IR task. We show that this transportation problem can be efficiently solved via pruning and indexing strategies. Experimental results on several representative benchmark datasets show that our model can outperform many state-of-the-art retrieval models as well as recently introduced word embedding-based models. We also conducted extensive experiments to analyze the effect of different settings on our semantic matching model.


title: Fine-grained Video Classification and Captioning.

abstract: We describe a DNN for fine-grained action classification and video captioning. It gives state-of-the-art performance on the challenging Something-Something dataset, with over 220, 000 videos and 174 fine-grained actions. Classification and captioning on this dataset are challenging because of the subtle differences between actions, the use of thousands of different objects, and the diversity of captions penned by crowd actors. The model architecture shares features for classification and captioning, and is trained end-to-end. It performs much better than the existing classification benchmark for Something-Something, with impressive fine-grained results, and it yields a strong baseline on the new Something-Something captioning task. Our results reveal that there is a strong correlation between the degree of detail in the task and the ability of the learned features to transfer to other tasks.


title: Deep Learning Based Damage Detection on Post-Hurricane Satellite Imagery.

abstract: After a hurricane, damage assessment is critical to emergency managers and first responders. To improve the efficiency and accuracy of damage assessment, instead of using windshield survey, we propose to automatically detect damaged buildings using image classification algorithms. The method is applied to the case study of 2017 Hurricane Harvey.


title: Adaptive-repetitive control of a hard disk drive

abstract: This paper presents a control scheme intended to target both the non-repeatable and repeatable runout disturbances affecting the dynamics of a hard disk drive (HDD). The design can be broken up into three stages. The first stage consists of a LTI feedback controller designed using classical techniques. The second stage consists of a RLS-based adaptively tuned controller. Finally, the last stage incorporates a new controller which integrates repetitive and adaptive elements. The repetitive part of this controller allows us to target specific periodic disturbances visible in the disturbance spectrum. The adaptive part is intended to cancel the inter-notch stochastic disturbances. The key element in this design is the formulation of an appropriate optimization problem, solvable recursively by the use of some of the available adaptive-filtering algorithms. Experimental results obtained from the implementation of this method in a commercial HDD demonstrates the effectiveness of this approach.


title: Understanding people lifestyles: construction of urban movement knowledge graph from GPS trajectory

abstract: Technologies are increasingly taking advantage of the explosion in the amount of data generated by social multimedia (e.g., web searches, ad targeting, and urban computing). In this paper, we propose a multi-view learning framework for presenting the construction of a new urban movement knowledge graph, which could greatly facilitate the research domains mentioned above. In particular, by viewing GPS trajectory data from temporal, spatial, and spatiotemporal points of view, we construct a knowledge graph of which nodes and edges are their locations and relations, respectively. On the knowledge graph, both nodes and edges are represented in latent semantic space. We verify its utility by subsequently applying the knowledge graph to predict the extent of user attention (high or low) paid to different locations in a city. Experimental evaluations and analysis of a real-world dataset show significant improvements in comparison to state-of-the-art methods.


title: Extreme Programming and Agile Software Development Methodologies

abstract: Several agile (i.e., lightweight) development methodologies, especially extreme programming (XP), have been argued to be a solution to many of the problems that continue to plague software development projects. The authors provide a useful evaluation of such approaches, including a discussion of the values that underlie the XP methodology.


title: An Optimization Approach to Airline Integrated Recovery

abstract: Although the airline industry has benefited from advancements made in computational and operational research methods, most implementations arise from the frictionless environment of the planning stage. Because 22% of all flights have been delayed and 3% have been cancelled in the United States since 2001, schedule perturbations are inevitable. The complexity of the operational environment is exacerbated by the need for obtaining a solution in as close to real-time as possible. Given some time horizon, the recovery process seeks to repair the flight schedule, aircraft rotations, crew schedule, and passenger itineraries in a tractable manner. Each component individually can be difficult to solve, so early research on irregular operations has studied these problems in isolation, leading to a sequential process by which the recovery process is conducted. Recent work has integrated a subset of these four components, usually abstracting from crew recovery. We present an optimization-based approach to solve the fully integrated airline recovery problem. After our solution methodology is presented, it is tested using data from an actual U.S. carrier with a dense hub-and-spoke network using a single-day horizon. It is shown that in several instances an integrated solution is delivered in a reasonable runtime. Moreover, we show the integrated approach can substantially improve the solution quality over the incumbent sequential approach. To the best of our knowledge, we are the first to present computational results on the fully integrated problem.


title: A logical design methodology for relational databases using the extended entity-relationship model

abstract: A database design methodology is defined for the design of large relational databases. First, the data requirements are conceptualized using an extended entity-relationship model, with the extensions being additional semantics such as ternary relationships, optional relationships, and the generalization abstraction. The extended entity-relationship model is then decomposed according to a set of basic entity-relationship constructs, and these are transformed into candidate relations. A set of basic transformations has been developed for the three types of relations: entity relations, extended entity relations, and relationship relations. Candidate relations are further analyzed and modified to attain the highest degree of normalization desired. The methodology produces database designs that are not only accurate representations of reality, but flexible enough to accommodate future processing requirements. It also reduces the number of data dependencies that must be analyzed, using the extended ER model conceptualization, and maintains data integrity through normalization. This approach can be implemented manually or in a simple software package as long as a "good" solution is acceptable and absolute optimality is not required.


title: Extending electronic sourcing theory: An exploratory study of electronic reverse auction outcomes

abstract: Existing electronic sourcing theory has predominantly focused on the impact of electronic reverse auctions on price savings and has proposed various factors influencing this outcome. Such price savings have been widely claimed to come at the expense of the buyer-supplier relationship. Only a few research studies, however, have investigated the impact of electronic reverse auctions on the buyer-supplier relationship. Based on an extensive literature review and multiple case study research, we extend electronic sourcing theory by developing a conceptual model that considers a broader set of outcomes, posits organizational and project antecedents that a buying firm can manipulate to impact those outcomes, and introduces supplier-related factors that moderate the antecedent-outcome relationships. In contrast to prior research, our model shows that buying organizations consider a broad set of financial, operational, as well as strategic e-sourcing outcomes, and that, under certain conditions, the traditional trade-off in electronic reverse auctions between price savings and the buyer-supplier relationship does not hold.


title: Motion Estimation Based on EMG and EEG Signals to Control Wearable Robots

abstract: An EMG signal shows almost one-to-one relationship with the corresponding muscle. Therefore, each joint motion can be estimated relatively easily based on the EMG signals to control wearable robots. However, necessary EMG signals are not always able to be measured with every user. On the other hand, an EEG signal is one of the strongest candidates for the additional input signals to control wearable robots. Since the EEG signals are available with almost all people, an EEG based method can be applicable to many users. However, it is more difficult to estimate the user's motion intention based on the EEG signals compared with the EMG signals. In this paper, a user's motion estimation method is proposed to control the wearable robots based on the user's motion intention. In the proposed method, the motion intention of the user is estimated based on the user's EMG and EEG signals. The EMG signals are used as main input signals because the EMG signals have higher correlation with the motion. Furthermore, the EEG signals are used to estimate the part of the motion which is not able to be estimated based on EMG signals because of the muscle unavailability.


title: Improving MapReduce Performance in Heterogeneous Environments

abstract: MapReduce is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation. Hadoop is an open-source implementation of MapReduce enjoying wide adoption and is often used for short jobs where low response time is critical. Hadoop's performance is closely tied to its task scheduler, which implicitly assumes that cluster nodes are homogeneous and tasks make progress linearly, and uses these assumptions to decide when to speculatively re-execute tasks that appear to be stragglers. In practice, the homogeneity assumptions do not always hold. An especially compelling setting where this occurs is a virtualized data center, such as Amazon's Elastic Compute Cloud (EC2). We show that Hadoop's scheduler can cause severe performance degradation in heterogeneous environments. We design a new scheduling algorithm, Longest Approximate Time to End (LATE), that is highly robust to heterogeneity. LATE can improve Hadoop response times by a factor of 2 in clusters of 200 virtual machines on EC2.


title: Robust face detection from still images

abstract: Facial recognition is one of the most studied topics in the field of biometrics because of its varied applications. Detection of dark colored faces and poorly illuminated faces are not well studied in the literature due to several challenges. The most critical challenge is that there is inadequate contrast among facial features. To overcome this challenge, a new face detection methodology, which consists of histogram analysis, Haar wavelet transformation and Adaboost learning techniques, is proposed. The extended Yale Face Database B is used to examine the performance of the proposed method and compared against commonly used OpenCV's Haar detection algorithm. The experimental results with 9,883 positive images and 10,349 negative images showed a considerable improvement in face hit rates without a significant change in false acceptance rates.


title: Educational Gamification Based on Personality

abstract: Gamification in education is gaining an increasing attention, since it enhances the learner's engagement and motivation. While carrying a big promise, prior research offers little guidance regarding how learners with different personalities may react to game elements in a gamified learning environment. Therefore, this study investigates the relationship between the learner's personality to be distinguished on an introvert/extrovert dimension and the attitude of preferring to use game elements. In this context, a gamified online Moodle Learning Management System was developed where fifty seven learners used it to learn "Object Oriented Design Methodology" course. These learners then answered two questionnaires about preferences on game elements and personality. Data gathered from the instruments was analyzed using the statistical ChiSquare test. The obtained results highlighted that the learner's personality can affect the attitude of preferring to use a particular game element. These findings can be exploited by researchers and practitioners to provide personalized and gamified learning experiences based on personality. They also can be used by designers by providing design guidelines for targeting specific learners based on personality.


title: Dissociated functional significance of decision-related activity in the primate dorsal stream

abstract: During decision making, neurons in multiple brain regions exhibit responses that are correlated with decisions. However, it remains uncertain whether or not various forms of decision-related activity are causally related to decision making. Here we address this question by recording and reversibly inactivating the lateral intraparietal (LIP) and middle temporal (MT) areas of rhesus macaques performing a motion direction discrimination task. Neurons in area LIP exhibited firing rate patterns that directly resembled the evidence accumulation process posited to govern decision making, with strong correlations between their response fluctuations and the animal's choices. Neurons in area MT, in contrast, exhibited weak correlations between their response fluctuations and choices, and had firing rate patterns consistent with their sensory role in motion encoding. The behavioural impact of pharmacological inactivation of each area was inversely related to their degree of decision-related activity: while inactivation of neurons in MT profoundly impaired psychophysical performance, inactivation in LIP had no measurable impact on decision-making performance, despite having silenced the very clusters that exhibited strong decision-related activity. Although LIP inactivation did not impair psychophysical behaviour, it did influence spatial selection and oculomotor metrics in a free-choice control task. The absence of an effect on perceptual decision making was stable over trials and sessions and was robust to changes in stimulus type and task geometry, arguing against several forms of compensation. Thus, decision-related signals in LIP do not appear to be critical for computing perceptual decisions, and may instead reflect secondary processes. Our findings highlight a dissociation between decision correlation and causation, showing that strong neuron-decision correlations do not necessarily offer direct access to the neural computations underlying decisions.


title: Gross motor skill performance in children with and without visual impairments—Research to practice

abstract: Abstract The aim of this study was to provide an empirical basis for teaching gross motor skills in children with visual impairments. For this purpose, gross motor skill performance of 23, 6–12 year old, boys and girls who are blind (ICD-10 H54.0) and 28 sighted controls with comparable age and gender characteristics was compared on six locomotor and six object control tasks using the Test of Gross Motor Development-Second Edition. Results indicate that children who are blind perform significantly (p


title: Bridging the gap between theory and implementation in cognitive networks: developing reasoning in today's networks

abstract: Communication networks are becoming increasingly complex and dynamic. The networking paradigm commonly employed, on the other hand, has not changed over the years, and, as a result, performs poorly in today's environments. ::: Only very recently, a new paradigm named cognitive networking has been devised with the objective to make networks more intelligent, thereby overcoming traditional limitations and potentially achieving better performance. According to such vision, networks should be able to monitor themselves, reason upon the environment, act towards the achievement of specific goals and learn from experience. ::: Thus far, several cognitive network architectures have been conceived and proposed in the literature, but, despite researchers seem to agree on the need for a holistic approach, their architectures pursue such a global vision only in part, as they do not consider networks nor network nodes in their entirety. ::: ::: In the present work, we analyze the aspects to be tackled in order to enable this holistic view and propose to base reasoning on both intra- and inter-node interactions, with the ultimate aim to devise a complete cognitive network architecture. ::: After a thorough analysis of advantages and drawbacks of generic reasoning framework, we select the most apt to form the basis on which to build the cognitive network we envision. ::: We first formalize its application in network environments, by determining the steps to follow in the process to equip traditional network with cognitive capabilities. ::: Then, we shift the focus from the design side to the implementation side, by identifying the problems that could be faced when realizing such a network, and by proposing a set of optional refinements that could be taken into account to further improve the performance in some specific situations. ::: Finally, we tackle the problem of reducing the time needed for the cognitive process to reason. ::: ::: Validation through simulations shows that explicitly considering cross-layer intra- and inter-node interactions when reasoning has a twofold effect. First, it leads to better performance levels than those that can be achieved by today's non-intelligent networks, and second, it helps to better understand existent causal relationships between variables in a network.


title: Counting in the Wild

abstract: In this paper we explore the scenario of learning to count multiple instances of objects from images that have been dot-annotated through crowdsourcing. Specifically, we work with a large and challenging image dataset of penguins in the wild, for which tens of thousands of volunteer annotators have placed dots on instances of penguins in tens of thousands of images. The dataset, introduced and released with this paper, shows such a high-degree of object occlusion and scale variation that individual object detection or simple counting-density estimation is not able to estimate the bird counts reliably.


title: Squiggle: a Semantic Search Engine for Indexing and Retrieval of Multimedia Content

abstract: Search engines are becoming such an easy way to find textual resources that we wish to use them also for multimedia content; however, syntactic techniques, even if promising, are not up to the task: future search engines must consider new approaches. Experimental prototypes of this search engine of the future are appearing. Most of them employs "smart machines" able to directly elaborate multimedia resources, but we believe that the solution should embrace also "smart data", able to capture lexical and conceptual characteristics of a domain in an ontology. In order to prove that Semantic Web technologies provide real benefits to end users in terms of an easier and more effective access to information, we developed Squigle, a Semantic Web framework that eases the deployment of semantic search engines. Following a model-driven approach to application development, Squiglemakes ontologies (both the SKOS model and the domain knowledge) part of the running code. We evaluate the advantages of Squigleagainst traditional approaches in two real world deployments: one to search images of skiers for Torino 2006 Winter Olympic Games and one to search music files.


title: OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems

abstract: The OpenCL standard offers a common API for program execution on systems composed of different types of computational devices such as multicore CPUs, GPUs, or other accelerators.


title: Young Children's Moral Judgments, Justifications, and Emotion Attributions in Peer Relationship Contexts

abstract: Children (n = 160, 4- to 9-year-olds; Mage = 6.23 years, SD = 1.46) judged, justified, attributed emotions, and rated intent for hypothetical physical harm, psychological harm, and resource distribution transgressions against close friends, acquaintances, disliked peers, or bullies. Transgressions against bullies were judged more acceptable than against friends and disliked peers and less deserving of punishment than against acquaintances and disliked peers. Transgressions against friends were judged least intended and resulting in more negative emotions for transgressors; actors transgressing against disliked peers, as compared to bullies or acquaintances, were happy victimizers. Across relationships, children viewed moral transgressions as wrong independent of rules and authority, based primarily on welfare and fairness justifications. Peer context colors but does not fundamentally change moral evaluations.


title: Canny edge detection on NVIDIA CUDA

abstract: The Canny edge detector is a very popular and effective edge feature detector that is used as a pre-processing step in many computer vision algorithms. It is a multi-step detector which performs smoothing and filtering, non-maxima suppression, followed by a connected-component analysis stage to detect ldquotruerdquo edges, while suppressing ldquofalserdquo non edge filter responses. While there have been previous (partial) implementations of the Canny and other edge detectors on GPUs, they have been focussed on the old style GPGPU computing with programming using graphical application layers. Using the more programmer friendly CUDA framework, we are able to implement the entire Canny algorithm. Details are presented along with a comparison with CPU implementations. We also integrate our detector in to MATLAB, a popular interactive simulation package often used by researchers. The source code will be made available as open source.


title: Evaluating Passage Retrieval Approaches for Question Answering

abstract: Automatic open domain question answering (QA) has been the focus of much recent research, stimulated by the introduction of a QA track in TREC in 1999. Many QA systems have been developed and most follow the same broad pattern of operation: first an information retrieval (IR) system, often passage-based, is used to find passages from a large document collection which are likely to contain answers, and then these passages are analysed in detail to extract answers from them. Most research to date has focused on this second stage, with relatively little detailed investigation into aspects of IR component performance which impact on overall QA system performance. In this paper, we (a) introduce two new measures, coverage and answer redundancy, which we believe capture aspects of IR performance specifically relevant to QA more appropriately than do the traditional recall and precision measures, and (b) demonstrate their use in evaluating a variety of passage retrieval approaches using questions from TREC-9 and TREC 2001.


title: Design of a 10 GHz low phase noise oscillator using split-ring resonator array

abstract: A series feedback split ring resonator oscillator design emphasizing on low phase noise has been proposed. The split ring resonator oscillator is consisted of an active device of ATF-36077, a GaAs PHEMT from Agilent, an array of split ring resonator for frequency determining element and microstrip-based output matching network. Mainly, this work focused on modeling and designing an optimum performance of SRRs for the best oscillator performance. The simulation of SRRs oscillator has been modeled using Agilent Advanced Design System and CST Microwave Studio software. The optimum design is fabricated on RO4003C printed circuit board which has a dielectric constant of 3.55 and height of 0.813 mm. The SRR oscillator is analyzed for functionality and performance by using PXA N9030A Signal Analyzer. Subsequently, based on this design, the oscillator exhibited a low phase noise of -90.51 dBc/Hz at offset frequency of 100 kHz and an output power of -0.65 dBm at the oscillating frequency of 9.15 GHz.


The microbiome: A key regulator of stress and neuroinflammation


title: A Survey on the Roles of Communication Technologies in IoT-Based Personalized Healthcare Applications

abstract: The vision of the Internet of Things (IoT) is to enable systems across the globe to share data using advanced communication technologies. With the recent technological advancements, IoT-based solutions are no longer a challenging vision. IoT will offer numerous and potentially revolutionary benefits to today’s digital world. Future personalized and connected healthcare is one of the promising areas to see the benefits of IoT. This paper surveys emerging healthcare applications, including detailed technical aspects required for the realization of a complete end-to-end solution for each application. The survey explores the key application-specific requirements from the perspective of communication technologies. Furthermore, a detailed exploration from the existing to the emerging technologies and standards that would enable such applications is presented, highlighting the critical consideration of short-range and long-range communications. Finally, the survey highlights important open research challenges and issues specifically related to IoT-based future healthcare systems.


title: Development of underground mine monitoring and communication system integrated ZigBee and GIS

abstract: Abstract An automated underground mine monitoring and communication system based on the integration of new technologies is introduced to promote safety and health, operational management and cost-effectiveness. The proposed system integration considering Wireless Sensor Network (WSN) assisted Geographic Information System (GIS) enables to monitor and control underground mining applications from surface office. Based on the capabilities of WSNs, ZigBee network is adapted for near real-time monitoring, ventilation system control and emergency communication in underground mine. ZigBee nodes were developed to sense environmental attributes such as temperature, humidity and gases concentration; switching ON and OFF ventilation fans; and texting emergency messages. A trigger action plan for monitored attributes above normal and threshold value limits is programmed in the surface GIS management server. It is designed to turn the auxiliary fans on remotely or automatically in orange condition and sending evacuation messages for underground miners in unsafe (red) condition. Multi-users operation and 3D visualisations are other successful achievements of the proposed system for the underground monitoring and communication.


title: Phase Retrieval via Matrix Completion

abstract: This paper develops a novel framework for phase retrieval, a problem which arises in X-ray crystallography, diffraction imaging, astronomical imaging and many other applications. Our approach combines multiple structured illuminations together with ideas from convex programming to recover the phase from intensity measurements, typically from the modulus of the diffracted wave. We demonstrate empirically that any complex-valued object can be recovered from the knowledge of the magnitude of just a few diffracted patterns by solving a simple convex optimization problem inspired by the recent literature on matrix completion. More importantly, we also demonstrate that our noise-aware algorithms are stable in the sense that the reconstruction degrades gracefully as the signal-to-noise ratio decreases. Finally, we introduce some theory showing that one can design very simple structured illumination patterns such that three diffracted figures uniquely determine the phase of the object we wish to recover.


title: CSLIM: contextual SLIM recommendation algorithms

abstract: Context-aware recommender systems (CARS) take contextual conditions into account when providing item recommendations. In recent years, context-aware matrix factorization (CAMF) has emerged as an extension of the matrix factorization technique that also incorporates contextual conditions. In this paper, we introduce another matrix factorization approach for contextual recommendations, the contextual SLIM (CSLIM) recommendation approach. It is derived from the sparse linear method (SLIM) which was designed for Top-N recommendations in traditional recommender systems. Based on the experimental evaluations over several context-aware data sets, we demonstrate that CLSIM can be an effective approach for context-aware recommendations, in many cases outperforming state-of-the-art CARS algorithms in the Top-N recommendation task.


title: DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection

abstract: Android malware has continued to grow in volume and complexity posing significant threats to the security of mobile devices and the services they enable. This has prompted increasing interest in employing machine learning to improve Android malware detection. In this paper, we present a novel classifier fusion approach based on a multilevel architecture that enables effective combination of machine learning algorithms for improved accuracy. The framework (called DroidFusion), generates a model by training base classifiers at a lower level and then applies a set of ranking-based algorithms on their predictive accuracies at the higher level in order to derive a final classifier. The induced multilevel DroidFusion model can then be utilized as an improved accuracy predictor for Android malware detection. We present experimental results on four separate datasets to demonstrate the effectiveness of our proposed approach. Furthermore, we demonstrate that the DroidFusion method can also effectively enable the fusion of ensemble learning algorithms for improved accuracy. Finally, we show that the prediction accuracy of DroidFusion, despite only utilizing a computational approach in the higher level, can outperform stacked generalization, a well-known classifier fusion method that employs a meta-classifier approach in its higher level.


title: Learning a kernel matrix for nonlinear dimensionality reduction

abstract: We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into a nonlinear feature space, we show how to discover a mapping that "unfolds" the underlying manifold from which the data was sampled. The kernel matrix is constructed by maximizing the variance in feature space subject to local constraints that preserve the angles and distances between nearest neighbors. The main optimization involves an instance of semidefinite programming---a fundamentally different computation than previous algorithms for manifold learning, such as Isomap and locally linear embedding. The optimized kernels perform better than polynomial and Gaussian kernels for problems in manifold learning, but worse for problems in large margin classification. We explain these results in terms of the geometric properties of different kernels and comment on various interpretations of other manifold learning algorithms as kernel methods.


title: Corpus-Driven Knowledge Acquisition for Discourse Analysis

abstract: The availability of large on-line text corpora provides a natural and promising bridge between the worlds of natural language processing (NLP) and machine learning (ML). In recent years, the NLP community has been aggressively investigating statistical techniques to drive part-of-speech taggers, but application-specific text corpora can be used to drive knowledge acquisition at much higher levels as well. In this paper we will show how ML techniques can be used to support knowledge acquisition for information extraction systems. It is often very difficult to specify an explicit domain model for many information extraction applications, and it is always labor intensive to implement hand-coded heuristics for each new domain. We have discovered that it is nevertheless possible to use ML algorithms in order to capture knowledge that is only implicitly present in a representative text corpus. Our work addresses issues traditionally associated with discourse analysis and intersentential inference generation, and demonstrates the utility of ML algorithms at this higher level of language analysis. ::: ::: The benefits of our work address the portability and scalability of information extraction (IE) technologies. When hand-coded heuristics are used to manage discourse analysis in an information extraction system, months of programming effort are easily needed to port a successful IE system to a new domain. We will show how ML algorithms can reduce this development time to a few days of automated corpus analysis without any resulting degradation of overall system performance.


title: Nessie: A Decoupled, Client-Driven Key-Value Store Using RDMA

abstract: Key-value storage systems are an integral part of many data centre applications, but as demand increases so does the need for high performance. This has motivated new designs that use Remote Direct Memory Access (RDMA) to reduce communication overhead. Current RDMA-enabled key-value stores (RKVSes) target workloads involving small values, running on dedicated servers on which no other applications are running. Outside of these domains, however, there may be other RKVS designs that provide better performance. In this paper, we introduce Nessie, an RKVS that is fully client-driven, meaning no server process is involved in servicing requests. Nessie also decouples its index and storage data structures, allowing indices and data to be placed on different servers. This flexibility can decrease the number of network operations required to service a request. These design elements make Nessie well-suited for a different set of workloads than existing RKVSes. Compared to a server-driven RKVS, Nessie more than doubles system throughput when there is CPU contention on the server, improves throughput by 70 percent for PUT -oriented workloads when data value sizes are 128 KB or larger, and reduces power consumption by 18 percent at 80 percent system utilization and 41 percent at 20 percent system utilization compared with idle power consumption.


title: Querying k-truss community in large and dynamic graphs

abstract: Community detection which discovers densely connected structures in a network has been studied a lot. In this paper, we study online community search which is practically useful but less studied in the literature. Given a query vertex in a graph, the problem is to find meaningful communities that the vertex belongs to in an online manner. We propose a novel community model based on the k-truss concept, which brings nice structural and computational properties. We design a compact and elegant index structure which supports the efficient search of k-truss communities with a linear cost with respect to the community size. In addition, we investigate the k-truss community search problem in a dynamic graph setting with frequent insertions and deletions of graph vertices and edges. Extensive experiments on large real-world networks demonstrate the effectiveness and efficiency of our community model and search algorithms.


title: Quantifying herding effects in crowd wisdom

abstract: In many diverse settings, aggregated opinions of others play an increasingly dominant role in shaping individual decision making. One key prerequisite of harnessing the "crowd wisdom" is the independency of individuals' opinions, yet in real settings collective opinions are rarely simple aggregations of independent minds. Recent experimental studies document that disclosing prior collective opinions distorts individuals' decision making as well as their perceptions of quality and value, highlighting a fundamental disconnect from current modeling efforts: How to model social influence and its impact on systems that are constantly evolving? In this paper, we develop a mechanistic framework to model social influence of prior collective opinions (e.g., online product ratings) on subsequent individual decision making. We find our method successfully captures the dynamics of rating growth, helping us separate social influence bias from inherent values. Using large-scale longitudinal customer rating datasets, we demonstrate that our model not only effectively assesses social influence bias, but also accurately predicts long-term cumulative growth of ratings solely based on early rating trajectories. We believe our framework will play an increasingly important role as our understanding of social processes deepens. It promotes strategies to untangle manipulations and social biases and provides insights towards a more reliable and effective design of social platforms.


title: Support-Vector Networks

abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. ::: ::: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.


title: Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles

abstract: Several tasks approached by using text mining techniques, like text categorization, document clustering, or information retrieval, operate on the document level, making use of the so-called bag-of-words model. Other tasks, like document summarization, information extraction, or question answering, have to operate on the sentence level, in order to fulfill their specific requirements. While both groups of text mining tasks are typically affected by the problem of data sparsity, this is more accentuated for the latter group of tasks. Thus, while the tasks of the first group can be tackled by statistical and machine learning methods based on a bag-of-words approach alone, the tasks of the second group need natural language processing (NLP) at the sentence or paragraph level in order to produce more informative features.


title: Automatic emotion recognition from speech a PhD research proposal

abstract: This paper contains a PhD research proposal related to the domain of automatic emotion recognition from speech signal. We started by identifying our research problem, namely the acute confusion problem between emotion classes and we have cited different sources of this ambiguity. In the methodology section, we presented a method based on simililarity concept between a class and an instance patterns. We dubbed this method as Weighted Ordered classes - Nearest Neighbors. The first result obtained exceeds in performance the best result of the state-of-the art. Finally, as future work, we have made a proposition to improve the performance of the proposed system.


title: Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization.

abstract: We propose a new unsupervised sentence salience framework for Multi-Document Summarization (MDS), which can be divided into two components: latent semantic modeling and salience estimation. For latent semantic modeling, a neural generative model called Variational Auto-Encoders (VAEs) is employed to describe the observed sentences and the corresponding latent semantic representations. Neural variational inference is used for the posterior inference of the latent variables. For salience estimation, we propose an unsupervised data reconstruction framework, which jointly considers the reconstruction for latent semantic space and observed term vector space. Therefore, we can capture the salience of sentences from these two different and complementary vector spaces. Thereafter, the VAEs-based latent semantic model is integrated into the sentence salience estimation component in a unified fashion, and the whole framework can be trained jointly by back-propagation via multi-task learning. Experimental results on the benchmark datasets DUC and TAC show that our framework achieves better performance than the state-of-the-art models.


title: Swallow swarm optimization algorithm: a new method to optimization

abstract: This paper presents an exposition of a new method of swarm intelligence–based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO.


title: Composite Scale of Morningness: Psychometric properties, validity with Munich ChronoType Questionnaire and age/sex differences in Poland

abstract: Abstract The present study aimed at testing psychometric properties of the Composite Scale of Morningness (CSM) and validating it with mid sleep on free days (MSF) derived from the Munich ChronoType Questionnaire (MCTQ) in Poland, along with analyzing age and sex differences in the CSM and MSF. A sample of 952 Polish residents (62.6% females) aged between 13 and 46 was tested. Additionally, a sample of 33 university students were given MCTQ and filled in a sleep diary for 8 days. MSF derived from MCTQ was related to the one from sleep diary ( r =.44). The study revealed good reliability of the CSM ( α =.84) and its validity: greater morningness preference was associated with earlier MSF from MCTQ ( r =–.52). CSM scores were distributed over its full range, with a mean of 34, and did not differ between sexes, although females were earlier than males by 23minutes in MSF. Regarding age, eveningness estimated with both CSM and MSF was greatest in subjects aged 16–18years, and a shift toward eveningness during puberty and a shift back toward morningness in older age was observed. The Polish version of the CSM consisted of two components of morningness. Cutoff scores were: for evening types (lower 10%) 24 or less, for morning types (upper 10%) 43 or more. The Polish CSM presents good psychometric properties, which are similar to those reported in other language versions, and also presents sex/age patterns similar to those found previously.


title: How executive functions are related to intelligence in Williams syndrome

abstract: Williams syndrome is characterized by impairments in executive functions (EFs). However, it remains unknown how distinct types of EFs relate to intelligence in this syndrome. The present study analyzed performance on working memory, inhibiting and shifting, and its links to IQ in a sample of 17 individuals with WS, and compared them with a group of 17 typically developing individuals matched on chronological age and gender. In conclusion, our results suggest that working memory, inhibiting, and shifting relate differently to intelligence in WS as well as in typical development, with working memory being the EF most closely related to intelligence in both groups. Notably, the magnitude of the associations between the three EFs and IQ was substantially higher in the WS group than in the TD group, bringing further confirmation to the notion that frontal lobe impairments may produce a general compromise of several EFs.


The Role of Prosody and Speech Register in Word Segmentation: A Computational Modelling Perspective


title: New insights into the structure and function of phyllosphere microbiota through high-throughput molecular approaches

abstract: The phyllosphere is an ecologically and economically important ecosystem that hosts a large and diverse microbial community. Phyllosphere microbiota play a critical role in protecting plants from diseases as well as promoting their growth by various mechanisms. There are serious gaps in our understanding of how and why microbiota composition varies across spatial and temporal scales, the ecology of leaf surface colonizers and their interactions with their host, and the genetic adaptations that enable phyllosphere survival of microorganisms. These gaps are due in large part to past technical limitations, as earlier studies were restricted to the study of culturable bacteria only and used low-throughput molecular techniques to describe community structure and function. The availability of high-throughput and cost-effective molecular technologies is changing the field of phyllosphere microbiology, enabling researchers to begin to address the dynamics and composition of the phyllosphere microbiota across a large number of samples with high, in-depth coverage. Here, we discuss and connect the most recent studies that have used next-generation molecular techniques such as metagenomics, proteogenomics, genome sequencing, and transcriptomics to gain new insights into the structure and function of phyllosphere microbiota and highlight important challenges for future research.


title: Personalized Travel Package With Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints

abstract: Location-based social networks (LBSNs) provide people with an interface to share their locations and write reviews about interesting places of attraction. The shared locations form the crowdsourced digital footprints, in which each user has many connections to many locations, indicating user preference to locations. In this paper, we propose an approach for personalized travel package recommendation to help users make travel plans. The approach utilizes data collected from LBSNs to model users and locations, and it determines users’ preferred destinations using collaborative filtering approaches. Recommendations are generated by jointly considering user preference and spatiotemporal constraints. A heuristic search-based travel route planning algorithm was designed to generate travel packages. We developed a prototype system, which obtains users’ travel demands from mobile client and generates travel packages containing multiple points of interest and their visiting sequence. Experimental results suggest that the proposed approach shows promise with respect to improving recommendation accuracy and diversity.