title: Mental health implications of detaining asylum seekers: systematic review

abstract: BACKGROUND: The number of asylum seekers, refugees and internally displaced people worldwide is rising. Western countries are using increasingly restrictive policies, including the detention of asylum seekers, and there is concern that this is harmful. AIMS: To investigate mental health outcomes among adult, child and adolescent immigration detainees. METHOD: A systematic review was conducted of studies investigating the impact of immigration detention on the mental health of children, adolescents and adults, identified by a systematic search of databases and a supplementary manual search of references. RESULTS: Ten studies were identified. All reported high levels of mental health problems in detainees. Anxiety, depression and post-traumatic stress disorder were commonly reported, as were self-harm and suicidal ideation. Time in detention was positively associated with severity of distress. There is evidence for an initial improvement in mental health occurring subsequent to release, although longitudinal results have shown that the negative impact of detention persists. CONCLUSIONS: This area of research is in its infancy and studies are limited by methodological constraints. Findings consistently report high levels of mental health problems among detainees. There is some evidence to suggest an independent adverse effect of detention on mental health. Language: en


title: Assured Data Deletion With Fine-Grained Access Control for Fog-Based Industrial Applications

abstract: The advances of cloud computing, fog computing and Internet of Things (IoT) make the industries more prosperous than ever. A wide range of industrial systems such as transportation systems and manufacturing systems have been developed by integrating cloud computing, fog computing and IoT successfully. Security and privacy issues are a major concern that hinders the wide adoptions of these novel techniques. In this paper, we focus on assured data deletion, an issue which is important but received less attention in academia and industry. We firstly propose a framework to integrate the cloud, the fog and the things together to manage the stored data from industries or individuals. We then focus on secure data deletion in this framework by proposing an assured data deletion scheme which fulfills fine-grained access control over sensitive data and verifiable data deletion. Only the data owners and the fog devices are involved when deleting a data key and validating the data deletion, which makes the protocol practical due to the features of low latency and real-time interaction of fog computing. The proposed protocol takes advantage of attribute-based encryption and is provably secure under the standard model. The theoretical analysis shows the good performance and functionality requirements while the implementation results demonstrate the feasibility of our proposal.


Chapter 2 Reliability of 3 D NAND Flash Memories


A new device for internal fixation of thoracolumbar and lumbar spine fractures: the ‘fixateur interne’


title: CtrlCloud: Performance-Aware Adaptive Control for Shared Resources in Clouds

abstract: Consolidating applications of conflicting service level objectives (SLOs) to share virtualized resources in cloud datacenters requires efficient resource management to ensure overall high Quality-of-Service (QoS). Applications of different performance targets often exhibit different resource demands. Thus, it is not trivial to translate individual application SLOs to corresponding resource shares in a shared virtualized environment to meet performance targets. In this paper, we present CtrlCloud, a performance-aware resource controlling system, that adaptively allocates resources, with a resource-share controller and an allocation optimization model. The controller automatically adapts resource demands based on performance deviations, while the optimization model resolves conflicts in resource demands from multiple co-located applications based on their ongoing performance achieved. We implement a proof-of-concept prototype of CtrlCloud in Python on top of Xen hypervisor. Our experimental results indicate that CtrlCloud can optimize allocations of CPU resources across multiple applications to maintain the 95th percentile latency within predefined SLO targets. CtrlCloud also provides QoS differentiation and yet fulfilling of CPU share demands from applications is maximized given resource availability. We further compare CtrlCloud against two other resource allocation methods commonly used in current clouds. CtrlCloud improves resource utilization by allocating resource shares optimal to 'actual needs' as it employs share-performance online modeling.


title: Low-cost fog-assisted health-care IoT system with energy-efficient sensor nodes

abstract: A better lifestyle starts with a healthy heart. Unfortunately, millions of people around the world are either directly affected by heart diseases such as coronary artery disease and heart muscle disease (Cardiomyopathy), or are indirectly having heart-related problems like heart attack and/or heart rate irregularity. Monitoring and analyzing these heart conditions in some cases could save a life if proper actions are taken accordingly. A widely used method to monitor these heart conditions is to use ECG or electrocardiography. However, devices used for ECG are costly, energy inefficient, bulky, and mostly limited to the ambulatory environment. With the advancement and higher affordability of Internet of Things (IoT), it is possible to establish better health-care by providing real-time monitoring and analysis of ECG. In this paper, we present a low-cost health monitoring system that provides continuous remote monitoring of ECG together with automatic analysis and notification. The system consists of energy-efficient sensor nodes and a fog layer altogether taking advantage of IoT. The sensor nodes collect and wirelessly transmit ECG, respiration rate, and body temperature to a smart gateway which can be accessed by appropriate care-givers. In addition, the system can represent the collected data in useful ways, perform automatic decision making and provide many advanced services such as real-time notifications for immediate attention.


title: The neural correlates of moral sensitivity: A functional magnetic resonance imaging investigation of basic and moral emotions

abstract: Humans are endowed with a natural sense of fairness that permeates social perceptions and interactions. This moral stance is so ubiquitous that we may not notice it as a fundamental component of daily decision making and in the workings of many legal, political, and social systems. Emotion plays a pivotal role in moral experience by assigning human values to events, objects, and actions. Although the brain correlates of basic emotions have been explored, the neural organization of “moral emotions” in the human brain remains poorly understood. Using functional magnetic resonance imaging and a passive visual task, we show that both basic and moral emotions activate the amygdala, thalamus, and upper midbrain. The orbital and medial prefrontal cortex and the superior temporal sulcus are also recruited by viewing scenes evocative of moral emotions. Our results indicate that the orbital and medial sectors of the prefrontal cortex and the superior temporal sulcus region, which are critical regions for social behavior and perception, play a central role in moral appraisals. We suggest that the automatic tagging of ordinary social events with moral values may be an important mechanism for implicit social behaviors in humans.


title: Coreference resolution survey

abstract: This survey is an extended summarization of state of the art of coreference resolution. The key concepts related to coreference and anaphora are presented, the most relevant approaches to coreference resolution are discussed, and existing systems are classified and compared. Finally, the evaluation methods shared by researchers in the area and the commonly used data sets corpora are presented and compared.


title: Frequency of gamma oscillations routes flow of information in the hippocampus

abstract: Gamma oscillations are thought to transiently link distributed cell assemblies that are processing related information, a function that is probably important for network processes such as perception, attentional selection and memory. This 'binding' mechanism requires that spatially distributed cells fire together with millisecond range precision; however, it is not clear how such coordinated timing is achieved given that the frequency of gamma oscillations varies substantially across space and time, from approximately 25 to almost 150 Hz. Here we show that gamma oscillations in the CA1 area of the hippocampus split into distinct fast and slow frequency components that differentially couple CA1 to inputs from the medial entorhinal cortex, an area that provides information about the animal's current position, and CA3, a hippocampal subfield essential for storage of such information. Fast gamma oscillations in CA1 were synchronized with fast gamma in medial entorhinal cortex, and slow gamma oscillations in CA1 were coherent with slow gamma in CA3. Significant proportions of cells in medial entorhinal cortex and CA3 were phase-locked to fast and slow CA1 gamma waves, respectively. The two types of gamma occurred at different phases of the CA1 theta rhythm and mostly on different theta cycles. These results point to routeing of information as a possible function of gamma frequency variations in the brain and provide a mechanism for temporal segregation of potentially interfering information from different sources.


title: Review and retrospective analysis of degenerative lumbosacral stenosis in 156 dogs treated by dorsal laminectomy.

abstract: The medical records of 156 dogs with degenerative lumbosacral stenosis (DLS) that underwent decompressive surgery were reviewed for signalment, history, clinical signs, imaging and surgical findings. The German Shepherd Dog (GSD) was most commonly affected (40/156, 25.6%). Pelvic limb lameness, caudal lumbar pain and pain evoked by lumbosacral pressure were the most frequent clinical findings. Radiography showed lumbosacral step formation in 78.8% (93/118) of the dogs which was associated with elongation of the sacral lamina in 18.6% (22/118). Compression of the cauda equina was diagnosed by imaging (epidurography, CT, or MRI) in 94.2% (147/156) of the dogs. Loss of the bright nucleus pulposus signal of the L7-S1 disc was found on T2-weighted MR images in 73.5% (25/34) of the dogs. The facet joint angle at L7-S1 was significantly smaller, and the tropism greater in GSD than in the other dog breeds. The smaller facet joint angle and higher incidence of tropism seen in the GSD may predispose this breed to DLS. Epidurography, CT, and MRI allow adequate visualization of cauda equina compression. During surgery, disc protrusion was found in 70.5% (110/156) of the dogs. Overall improvement after surgery was recorded in the medical records in 79.0% (83/105) of the dogs. Of the 38 owners that responded to questionnaires up to five years after surgery, 29 (76%) perceived an improvement.


title: Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning

abstract: Word-sense recognition and disambiguation (WERD) is the task of identifying word phrases and their senses in natural language text. Though it is well understood how to disambiguate noun phrases, this task is much less studied for verbs and verbal phrases. We present Werdy, a framework for WERD with particular focus on verbs and verbal phrases. Our framework first identifies multi-word expressions based on the syntactic structure of the sentence; this allows us to recognize both contiguous and non-contiguous phrases. We then generate a list of candidate senses for each word or phrase, using novel syntactic and semantic pruning techniques. We also construct and leverage a new resource of pairs of senses for verbs and their object arguments. Finally, we feed the so-obtained candidate senses into standard word-sense disambiguation (WSD) methods, and boost their precision and recall. Our experiments indicate that Werdy significantly increases the performance of existing WSD methods.


title: Content-based retrieval of 3D models

abstract: In the past few years, there has been an increasing availability of technologies for the acquisition of digital 3D models of real objects and the consequent use of these models in a variety of applications, in medicine, engineering, and cultural heritage. In this framework, content-based retrieval of 3D objects is becoming an important subject of research, and finding adequate descriptors to capture global or local characteristics of the shape has become one of the main investigation goals. In this article, we present a comparative analysis of a few different solutions for description and retrieval by similarity of 3D models that are representative of the principal classes of approaches proposed. We have developed an experimental analysis by comparing these methods according to their robustness to deformations, the ability to capture an object's structural complexity, and the resolution at which models are considered.


title: Goal-Directed Evaluation of Binarization Methods

abstract: This paper presents a methodology for evaluation of low-level image analysis methods, using binarization (two-level thresholding) as an example. Binarization of scanned gray scale images is the first step in most document image analysis systems. Selection of an appropriate binarization method for an input image domain is a difficult problem. Typically, a human expert evaluates the binarized images according to his/her visual criteria. However, to conduct an objective evaluation, one needs to investigate how well the subsequent image analysis steps will perform on the binarized image. We call this approach goal-directed evaluation, and it can be used to evaluate other low-level image processing methods as well. Our evaluation of binarization methods is in the context of digit recognition, so we define the performance of the character recognition module as the objective measure. Eleven different locally adaptive binarization methods were evaluated, and Niblack's method gave the best performance.


title: Transfer in Reinforcement Learning via Shared Features

abstract: We present a framework for transfer in reinforcement learning based on the idea that related tasks share some common features, and that transfer can be achieved via those shared features. The framework attempts to capture the notion of tasks that are related but distinct, and provides some insight into when transfer can be usefully applied to a problem sequence and when it cannot. We apply the framework to the knowledge transfer problem, and show that an agent can learn a portable shaping function from experience in a sequence of tasks to significantly improve performance in a later related task, even given a very brief training period. We also apply the framework to skill transfer, to show that agents can learn portable skills across a sequence of tasks that significantly improve performance on later related tasks, approaching the performance of agents given perfectly learned problem-specific skills.


title: A 260-mW Ku-Band FMCW Transceiver for Synthetic Aperture Radar Sensor With 1.48-GHz Bandwidth in 65-nm CMOS Technology

abstract: Monolithic integration of synthetic aperture radar (SAR) transceiver with small size, lightweight, and low power consumption is suitable for uploading to a compact unmanned aerial vehicle (UAV) for SAR imaging. This paper presents a monolithic frequency-modulated continuous-wave (FMCW) SAR transceiver that works at Ku-band and centers at 15 GHz. Techniques to address the special requirements of UAV SAR are proposed. A digital-tunable mixed-signal-mode FMCW chirp synthesizer is designed to be power efficient, to provide a tunable chirp rate, and to enable digital minimization of the root-mean-square (RMS) frequency errors. A saturated driver-amplifier-power-amplifier chain and an input-load peak-staggering low-noise amplifier are implemented to nullify the ripple effects so that the degradations of imaging performances can be prevented. Moreover, a 12th order active-RC bandpass filter is used to suppress the intermediate frequency interferences of both ground reflections and antenna leakage. A binary-weighted programmable gain amplifier and a successive approximation analog-to-digital converter are also integrated into the chip. Fabricated using a 65-nm complementary metal-oxide-semiconductor technology, the prototype demonstrates 1.48-GHz chirp bandwidth and <186-kHz rms frequency errors in a programmable modulation period from 1.18 to 10 ms. The transmitter and receiver RF front end attains 1.1- and 0.51-dB ripples, respectively. The function of the FMCW SAR transceiver is validated through the delay line and near-field ranging tests. The SAR imaging experiment with a distance of around 110 m is successfully carried out using the chip prototype and a range migration algorithm. At 1.2-V supply, the transceiver chip consumes 259.4 mW.


title: Ultrasonic cardiac pacing in the porcine model

abstract: Periodic pulses of intense ultrasound energy at 70 kHz with 5-ms duration at 3 MPa SPL applied to the exposed pig myocardium at physiologic rhythms were observed to produce cardiac pacing. Ten animals were successfully paced at pulse width and energy conditions above a certain threshold. Sigmoid strength-duration curves characterizing ultrasound's effectiveness in pacing were observed to resemble those of electrical stimulation. We suggest that bioelectrical stimulatory effects of ultrasound on the heart are a radiation pressure effect and a manifestation of the known myocyte sensitivity to stretch.


title: TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning

abstract: Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear function approximators. These results suggest that RL methods that use temporal differencing (TD) are superior to direct Monte Carlo estimation (MC). How do these results hold up in deep RL, which deals with perceptually complex environments and deep nonlinear models? In this paper, we re-examine the role of TD in modern deep RL, using specially designed environments that control for specific factors that affect performance, such as reward sparsity, reward delay, and the perceptual complexity of the task. When comparing TD with infinite-horizon MC, we are able to reproduce classic results in modern settings. Yet we also find that finite-horizon MC is not inferior to TD, even when rewards are sparse or delayed. This makes MC a viable alternative to TD in deep RL.


title: What's in a Like?: Attitudes and behaviors around receiving Likes on Facebook

abstract: What social value do Likes on Facebook hold? This research examines peopleâ s attitudes and behaviors related to receiving one-click feedback in social media. Likes and other kinds of lightweight affirmation serve as social cues of acceptance and maintain interpersonal relationships, but may mean different things to different people. Through surveys and de-identified, aggregated behavioral Facebook data, we find that in general, people care more about who Likes their posts than how many Likes they receive, desiring feedback most from close friends, romantic partners, and family members other than their parents. While most people do not feel strongly that receiving “enough” Likes is important, roughly two-thirds of posters regularly receive more than “enough.” We also note a “Like paradox,” a phenomenon in which peopleâ s friends receive more Likes because their friends have more friends to provide those Likes. Individuals with lower levels of self-esteem and higher levels of self-monitoring are more likely to think that Likes are important and to feel bad if they do not receive “enough” Likes. The results inform product design and our understanding of how lightweight interactions shape our experiences online.


title: Influence of the human body on a new coplanar-fed Ultra-Wideband antenna

abstract: A new coplanar-fed planar monopole antenna is presented for off-body Ultra-Wideband wearable communication systems. The antenna structure comprises a dome-topped, bowl-shaped radiating element and a 50 Ω coplanar feed line for excitation. Its impedance bandwidth is greater than 118%, from 3.05 to beyond 12 GHz, based on a reflection coefficient smaller than -10 dB criterion. The influence of the human body on the operation of this antenna was studied. In particular, a simplified geometry of a human limb was found to be accurate enough for use in input reflection coefficient simulations. The limb tissues were modelled as frequency-dependent materials. Measurement results show the suitability of the simplified geometry to simulate the antenna in the proximity of a human limb.


title: Reading the videos: temporal labeling for crowdsourced time-sync videos based on semantic embedding

abstract: Recent years have witnessed the boom of online sharing media contents, which raise significant challenges in effective management and retrieval. Though a large amount of efforts have been made, precise retrieval on video shots with certain topics has been largely ignored. At the same time, due to the popularity of novel time-sync comments, or so-called "bullet-screen comments", video semantics could be now combined with timestamps to support further research on temporal video labeling. In this paper, we propose a novel video understanding framework to assign temporal labels on highlighted video shots. To be specific, due to the informal expression of bullet-screen comments, we first propose a temporal deep structured semantic model (T-DSSM) to represent comments into semantic vectors by taking advantage of their temporal correlation. Then, video highlights are recognized and labeled via semantic vectors in a supervised way. Extensive experiments on a real-world dataset prove that our framework could effectively label video highlights with a significant margin compared with baselines, which clearly validates the potential of our framework on video understanding, as well as bullet-screen comments interpretation.


title: The bipolarity of light and dark: A review on Bipolar Disorder and circadian cycles

abstract: Abstract Background Bipolar Disorder is characterized by episodes running the full mood spectrum, from mania to depression. Between mood episodes, residual symptoms remain, as sleep alterations, circadian cycle disturbances, emotional deregulation, cognitive impairment and increased risk for comorbidities. The present review intends to reflect about the most recent and relevant information concerning the biunivocal relation between bipolar disorder and circadian cycles. Methods It was conducted a literature search on PubMed database using the search terms “bipolar”, “circadian”, “melatonin”, “cortisol”, “body temperature”, “Clock gene”, “Bmal1 gene”, “Per gene”, “Cry gene”, “GSK3β”, “chronotype”, “light therapy”, “dark therapy”, “sleep deprivation”, “lithum” and “agomelatine”. Search results were manually reviewed, and pertinent studies were selected for inclusion as appropriate. Results Several studies support the relationship between bipolar disorder and circadian cycles, discussing alterations in melatonin, body temperature and cortisol rhythms; disruption of sleep/wake cycle; variations of clock genes; and chronotype. Some therapeutics for bipolar disorder directed to the circadian cycles disturbances are also discussed, including lithium carbonate, agomelatine, light therapy, dark therapy, sleep deprivation and interpersonal and social rhythm therapy. Limitations This review provides a summary of an extensive research for the relevant literature on this theme, not a patient-wise meta-analysis. Conclusions In the future, it is essential to achieve a better understanding of the relation between bipolar disorder and the circadian system. It is required to establish new treatment protocols, combining psychotherapy, therapies targeting the circadian rhythms and the latest drugs, in order to reduce the risk of relapse and improve affective behaviour.


title: A Case of Wegener's Granulomatosis with Central Nervous System Involvement Mimicking Lung Cancer with Brain Metastasis

abstract: Wegener's granulomatosis (WG) classically consists of necrotizing granulomatous inflammation of the upper and/or lower respiratory tract, necrotizing glomerulonephritis, and an autoimmune necrotizing systemic vasculitis affecting predominantly small vessels. We report a case of WG with central nervous system (CNS) involvement. WG is being diagnosed through pulmonary nodule biopsy. A small nodular lesion in the left posterior basal ganglia of brain being highly suspicious for granulomatosis was detected by MRI. After IV pulse cyclophosphamide and oral corticosteroid treatment for over 4 months, clinical manifestations and CNS lesions in brain MRI is improved. WG might have multiple granulomatous lesions which could be misdiagnosed due to malignancy. CNS involvement in WG is rare but careful evaluation is necessary when there are suspicious symptoms or lesions in CNS.


title: Otedama: Fast Rule-Based Pre-Ordering for Machine Translation

abstract: Abstract We present Otedama, a fast, open-source tool for rule-based syntactic pre-ordering, a well established technique in statistical machine translation. Otedama implements both a learner for pre-ordering rules, as well as a component for applying these rules to parsed sentences. Our system is compatible with several external parsers and capable of accommodating many source and all target languages in any machine translation paradigm which uses parallel training data. We demonstrate improvements on a patent translation task over a state-of-the-art English-Japanese hierarchical phrase-based machine translation system. We compare Otedama with an existing syntax-based pre-ordering system, showing comparable translation performance at a runtime speedup of a factor of 4.5-10.


title: Predicting and Characterising User Impact on Twitter

abstract: The open structure of online social networks and their uncurated nature give rise to problems of user credibility and influence. In this paper, we address the task of predicting the impact of Twitter users based only on features under their direct control, such as usage statistics and the text posted in their tweets. We approach the problem as regression and apply linear as well as nonlinear learning methods to predict a user impact score, estimated by combining the numbers of the user’s followers, followees and listings. The experimental results point out that a strong prediction performance is achieved, especially for models based on the Gaussian Processes framework. Hence, we can interpret various modelling components, transforming them into indirect ‘suggestions’ for impact boosting.


title: The microbiota–gut–brain axis and its potential therapeutic role in autism spectrum disorder

abstract: Abstract Autism spectrum disorder (ASD) is a series of neurodevelopmental disorders that are characterized by deficits in both social and cognitive functions. Although the exact etiology and pathology of ASD remain unclear, a disorder of the microbiota–gut–brain axis is emerging as a prominent factor in the generation of autistic behaviors. Clinical studies have shown that gastrointestinal symptoms and compositional changes in the gut microbiota frequently accompany cerebral disorders in patients with ASD. A disturbance in the gut microbiota, which is usually induced by a bacterial infection or chronic antibiotic exposure, has been implicated as a potential contributor to ASD. The bidirectional microbiota–gut–brain axis acts mainly through neuroendocrine, neuroimmune, and autonomic nervous mechanisms. Application of modulators of the microbiota–gut–brain axis, such as probiotics, helminthes and certain special diets, may be a promising strategy for the treatment of ASD. This review mainly discusses the salient observations of the disruptions of the microbiota–gut–brain axis in the pathogenesis of ASD and reveals its potential therapeutic role in autistic deficits.


title: An Assessment of Name Matching Algorithms

abstract: In many computer applications involving the recording and processing of personal data there is a need to allow for variations in surname spelling, caused for example by transcription errors. A number of algorithms have been developed for name matching, i.e. which attempt to identify name spelling variations, one of the best known of which is the Soundex algorithm. This paper describes a comparative analysis of a number of these algorithms and, based on an analysis of their comparative strengths and weaknesses, proposes a new and improved name matching algorithm, which we call the Phonex algorithm. The analysis takes advantage of the recent creation of a large list of “equivalent surnames”, published in the book Family History Knowledge UK [Park1992]. This list is based on data supplied by some thousands of individual genealogists, and can be presumed to be representative of British surnames and their variations over the last two or three centuries. It thus made it possible to perform what we would argue were objective tests of name matching, the results of which provide a solid basis for the analysis that we have performed, and for our claims for the merits of the new algorithm, though these are unlikely to hold fully for surnames emanating largely from other countries.


title: Green IS: Are We Still Thinking in Mere Economic Imperatives or Are We Striving for Eco-Effectiveness?

abstract: Organizations are under increasing pressure to account for their environmental footprint and to adopt environmental sustainable practices, and information systems can support organizations in becoming more environmentally sustainable. Through the analysis of expert interviews with eleven representatives from organizations concerned with environmental sustainability, and drawing on a conceptual model of information systems and ecological sustainability, we provide evidence that these organizations see key capabilities of information systems in automating, informing, and transforming to support goals related to eco-efficiency, eco-equity, and eco-effectiveness. However, our analysis also suggests that there is still little awareness of the potential of information systems in fundamentally transforming business models and business processes towards eco-effectiveness as the ultimate goal of environmental protection.


Learning Efficient Object Detection Models with Knowledge Distillation


title: Classification of interstitial lung disease patterns using local DCT features and random forest

abstract: Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.


title: A speech parameter generation algorithm considering global variance for HMM-based speech synthesis

abstract: This paper describes a novel parameter generation algorithm for an HMM-based speech synthesis technique. The conventional algorithm generates a parameter trajectory of static features that maximizes the likelihood of a given HMM for the parameter sequence consisting of the static and dynamic features under an explicit constraint between those two features. The generated trajectory is often excessively smoothed due to the statistical processing. Using the over-smoothed speech parameters usually causes muffled sounds. In order to alleviate the over-smoothing effect, we propose a generation algorithm considering not only the HMM likelihood maximized in the conventional algorithm but also a likelihood for a global variance (GV) of the generated trajectory. The latter likelihood works as a penalty for the over-smoothing, i.e., a reduction of the GV of the generated trajectory. The result of a perceptual evaluation demonstrates that the proposed algorithm causes considerably large improvements in the naturalness of synthetic speech.


title: Path diversification for future internet end-to-end resilience and survivability

abstract: Path Diversification is a new mechanism that can be used to select multiple paths between a given ingress and egress node pair using a quantified diversity measure to achieve maximum flow reliability. The path diversification mechanism is targeted at the end-to-end layer, but can be applied at any level for which a path discovery service is available. Path diversification also takes into account service requirements for low-latency or maximal reliability in selecting appropriate paths. Using this mechanism will allow future internetworking architectures to exploit naturally rich physical topologies to a far greater extent than is possible with shortest-path routing or equal-cost load balancing. We describe the path diversity metric and its application at various aggregation levels, and apply the path diversification process to 13 real-world network graphs as well as 4 synthetic topologies to asses the gain in flow reliability. Based on the analysis of flow reliability across a range of networks, we then extend our path diversity metric to create a composite compensated total graph diversity metric that is representative of a particular topology's survivability with respect to distributed simultaneous link and node failures. We tune the accuracy of this metric having simulated the performance of each topology under a range of failure severities, and present the results. The topologies used are from national-scale backbone networks with a variety of characteristics, which we characterize using standard graph-theoretic metrics. The end result is a compensated total graph diversity metric that accurately predicts the survivability of a given network topology.


title: Slacker: fast distribution with lazy docker containers

abstract: Containerized applications are becoming increasingly popular, but unfortunately, current container-deployment methods are very slow. We develop a new container benchmark, HelloBench, to evaluate the startup times of 57 different containerized applications. We use HelloBench to analyze workloads in detail, studying the block I/O patterns exhibited during startup and compressibility of container images. Our analysis shows that pulling packages accounts for 76% of container start time, but only 6.4% of that data is read. We use this and other findings to guide the design of Slacker, a new Docker storage driver optimized for fast container startup. Slacker is based on centralized storage that is shared between all Docker workers and registries. Workers quickly provision container storage using backend clones and minimize startup latency by lazily fetching container data. Slacker speeds up the median container development cycle by 20× and deployment cycle by 5×.


title: Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization

abstract: By reducing optimization to a sequence of small subproblems, working set methods achieve fast convergence times for many challenging problems. Despite excellent performance, theoretical understanding of working sets is limited, and implementations often resort to heuristics to determine subproblem size, makeup, and stopping criteria. We propose BLITZ, a fast working set algorithm accompanied by useful guarantees. Making no assumptions on data, our theory relates subproblem size to progress toward convergence. This result motivates methods for optimizing algorithmic parameters and discarding irrelevant variables as iterations progress. Applied to l1-regularized learning, BLITZ convincingly outperforms existing solvers in sequential, limited-memory, and distributed settings. BLITZ is not specific to l1-regularized learning, making the algorithm relevant to many applications involving sparsity or constraints.


Empirical-Based Analysis of a Cooperative Location-Sensing System


title: Emotion and Cognition: Insights from Studies of the Human Amygdala

abstract: Traditional approaches to the study of cognition emphasize an information-processing view that has generally excluded emotion. In contrast, the recent emergence of cognitive neuroscience as an inspiration for understanding human cognition has highlighted its interaction with emotion. This review explores insights into the relations between emotion and cognition that have resulted from studies of the human amygdala. Five topics are explored: emotional learning, emotion and memory, emotion's influence on attention and perception, processing emotion in social stimuli, and changing emotional responses. Investigations into the neural systems underlying human behavior demonstrate that the mechanisms of emotion and cognition are intertwined from early perception to reasoning. These findings suggest that the classic division between the study of emotion and cognition may be unrealistic and that an understanding of human cognition requires the consideration of emotion.


title: Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]

abstract: Decision making in uncertain and risky environments is a prominent area of research. Standard economic theories fail to fully explain human behaviour, while a potentially promising alternative may lie in the direction of Reinforcement Learning (RL) theory. We analyse data for 46 players extracted from a financial market online game and test whether Reinforcement Learning (Q-Learning) could capture these players behaviour using a risk measure based on financial modeling. Moreover we test an earlier hypothesis that players are "na\"ive" (short-sighted). Our results indicate that a simple Reinforcement Learning model which considers only the selling component of the task captures the decision-making process for a subset of players but this is not sufficient to draw any conclusion on the population. We also find that there is not a significant improvement of fitting of the players when using a full RL model against a myopic version, where only immediate reward is valued by the players. This indicates that players, if using a Reinforcement Learning approach, do so na\"ively


title: The FLDA model for aspect-based opinion mining: addressing the cold start problem

abstract: Aspect-based opinion mining from online reviews has attracted a lot of attention recently. The main goal of all of the proposed methods is extracting aspects and/or estimating aspect ratings. Recent works, which are often based on Latent Dirichlet Allocation (LDA), consider both tasks simultaneously. These models are normally trained at the item level, i.e., a model is learned for each item separately. Learning a model per item is fine when the item has been reviewed extensively and has enough training data. However, in real-life data sets such as those from Epinions.com and Amazon.com more than 90% of items have less than 10 reviews, so-called cold start items. State-of-the-art LDA models for aspect-based opinion mining are trained at the item level and therefore perform poorly for cold start items due to the lack of sufficient training data. In this paper, we propose a probabilistic graphical model based on LDA, called Factorized LDA (FLDA), to address the cold start problem. The underlying assumption of FLDA is that aspects and ratings of a review are influenced not only by the item but also by the reviewer. It further assumes that both items and reviewers can be modeled by a set of latent factors which represent their aspect and rating distributions. Different from state-of-the-art LDA models, FLDA is trained at the category level and learns the latent factors using the reviews of all the items of a category, in particular the non cold start items, and uses them as prior for cold start items. Our experiments on three real-life data sets demonstrate the improved effectiveness of the FLDA model in terms of likelihood of the held-out test set. We also evaluate the accuracy of FLDA based on two application-oriented measures.


title: Preventing traffic accidents with in-vehicle decision support systems - The impact of accident hotspot warnings on driver behaviour

abstract: Despite continuous investment in road and vehicle safety, as well as improvements in technology standards, the total amount of road traffic accidents has been increasing over the last decades. Consequently, identifying ways of effectively reducing the frequency and severity of traffic accidents is of utmost importance. In light of the depicted challenge, latest studies provide promising evidence that in-vehicle decision support systems (DSSs) can have significant positive effects on driving behaviour and collision avoidance. Going beyond existing research, we developed a comprehensive in-vehicle DSS, which provides accident hotspot warnings to drivers based on location analytics applied to a national historical accident dataset, composed of over 266,000 accidents. As such, we depict the design and field evaluation of an in-vehicle DSS, bridging the gap between real world location analytics and in-vehicle warnings. The system was tested in a country-wide field test of 57 professional drivers, with over 170,000km driven during a four-week period, where vehicle data were gathered via a connected car prototype system. Ultimately, we demonstrate that in-vehicle warnings of accident hotspots have a significant improvement on driver behaviour over time. In addition, we provide first evidence that an individual's personality plays a key role in the effectiveness of in-vehicle DSSs. However, in contrast to existing lab experiments with very promising results, we were unable to find an immediate effect on driver behaviour. Hence, we see a strong need for further field experiments with high resolution car data to confirm that in-vehicle DSSs can deliver in diverse field situations.


title: Fabrication of a Novel Nanofluidic Device Featuring ZnO Nanochannels

abstract: We developed a novel fabrication method for nanochannels that are easily scaled up to mass production by selectively growing zinc oxide (ZnO) nanostructures and covering using a flat PDMS surface to make hollow nanochannels. Nanochannels are used in the biotechnological and environmental fields, being employed for DNA analysis and water purification, due to their unique features of capillary-induced negative pressure and an electrical double-layer overlap. However, existing nanochannel fabrication methods are complicated, costly, and not amenable to mass production. Here, we developed a novel nanochannel fabrication method. The pillar-like dense ZnO nanostructures were grown in a solution process, which is easily applicable to mass production. The size of the fabricated ZnO nanostructures has a thickness of 30–300 nm and a diameter on the order of 102 nm, which are easily adjusted by synthesis times. The ZnO nanostructures were covered by the flat polydimethylsiloxane (PDMS) surface, and then the cracks b...


Programming in Prolog


title: Repeating Words in Spontaneous Speech

abstract: Abstract Speakers often repeat the first word of major constituents, as in, “I uh I wouldn't be surprised at that.” Repeats like this divide into four stages: an initial commitment to the constituent (with “I”); the suspension of speech; a hiatus in speaking (filled with “uh”); and a restart of the constituent (“I wouldn't . . .”). An analysis of all repeated articles and pronouns in two large corpora of spontaneous speech shows that the four stages reflect different principles. Speakers are more likely to make a premature commitment, immediately suspending their speech, as both the local constituent and the constituent containing it become more complex. They plan some of these suspensions from the start as preliminary commitments to what they are about to say. And they are more likely to restart a constituent the more their stopping has disrupted its delivery. We argue that the principles governing these stages are general and not specific to repeats.


title: Elephant Herding Optimization for Energy-Based Localization

abstract: This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations, we approach it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics are applied to this type of problem. More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as a performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.


title: Automatic Music Genre Classification using Convolution Neural Network

abstract: Music Genre classification is very important in today's world due to rapid growth in music tracks, both online and offline. In order to have better access to these we need to index them accordingly. Automatic music genre classification is important to obtain music from a large collection. Most of the current music genre classification techniques uses machine learning techniques. In this paper, we present a music dataset which includes ten different genres. A Deep Learning approach is used in order to train and classify the system. Here convolution neural network is used for training and classification. Feature Extraction is the most crucial task for audio analysis. Mel Frequency Cepstral Coefficient (MFCC) is used as a feature vector for sound sample. The proposed system classifies music into various genres by extracting the feature vector. Our results show that the accuracy level of our system is around 76% and it will greatly improve and facilitate automatic classification of music genres.


title: Fit Gap Analysis Methods for ERP Systems Literature Review

abstract: Enterprise Resource Planning (ERP) systems are used for enterprise business processes execution and management. Selection of an appropriate ERP system is one of the important tasks in the overall implementation process, and a fit-gap analysis is used for these purposes. The fit-gap analysis allows evaluating gaps in business requirements and functionality of ERP systems considered for evaluation. Despite the importance of the fit-gap analysis in the implementation processes, it heavily relies on a judgmental analysis and requires a significant effort. The objective of the proposed research is to simplify the fit-gap analysis process. The present paper aims at identifying the existing fit-gap analysis in the related literature. A systematic literature review is performed using the defined scientific databases. It focuses on the fit-gap method design for specific ERP systems, as well as methods suitable for different types of ERP systems. The research results are the fit-gap analysis methods and their advantages and limitations. The selected literature also reviews typically used methods for reducing gaps.


title: A review on deep learning for recommender systems: challenges and remedies

abstract: Recommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer users. Although existing recommender systems are successful in producing decent recommendations, they still suffer from challenges such as accuracy, scalability, and cold-start. In the last few years, deep learning, the state-of-the-art machine learning technique utilized in many complex tasks, has been employed in recommender systems to improve the quality of recommendations. In this study, we provide a comprehensive review of deep learning-based recommendation approaches to enlighten and guide newbie researchers interested in the subject. We analyze compiled studies within four dimensions which are deep learning models utilized in recommender systems, remedies for the challenges of recommender systems, awareness and prevalence over recommendation domains, and the purposive properties. We also provide a comprehensive quantitative assessment of publications in the field and conclude by discussing gained insights and possible future work on the subject.


title: Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming

abstract: This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool for processing vision sensor signals. A patch-based DNN is trained with supervised data to classify fixed-size image patches into “trail” and “non-trail” categories, and reshaped to a fully convolutional architecture to produce trail segmentation map for arbitrary-sized input images. As trail and non-trail patches do not exhibit clearly defined shapes or forms, the patch-based classifier is prone to misclassification, and produces sub-optimal trail segmentation maps. Dynamic programming is introduced to find an optimal trail on the sub-optimal DNN output map. Experimental results showing accurate trail detection for real-world trail datasets captured with a head mounted vision system are presented.


title: A visual bag of words method for interactive qualitative localization and mapping

abstract: Localization for low cost humanoid or animal-like personal robots has to rely on cheap sensors and has to be robust to user manipulations of the robot. We present a visual localization and map-learning system that relies on vision only and that is able to incrementally learn to recognize the different rooms of an apartment from any robot position. This system is inspired by visual categorization algorithms called bag of words methods that we modified to make fully incremental and to allow a user-interactive training. Our system is able to reliably recognize the room in which the robot is after a short training time and is stable for long term use. Empirical validation on a real robot and on an image database acquired in real environments are presented.


title: Histamine and deep brain stimulation: the pharmacology of regularizing a brain.

abstract: Parkinson's disease (PD) patients have increased histamine in their basal ganglia, but the role of this neurotransmitter in PD is poorly understood. In this issue of the JCI, Zhuang et al. demonstrate that histamine levels rise in the subthalamic nucleus (STN) to compensate for abnormal firing patterns. Injection of histamine into the STN restores normal firing patterns and motor activity, whereas merely changing firing rates has no behavioral effect. Moreover, STN deep brain stimulation, a widespread therapy for PD, regularizes firing through endogenous histamine release. This suggests that abnormal firing patterns, rather than rates, cause PD symptoms, and this histaminergic pathway may lead to new treatments for the disease.


title: Deep context of citations using machine-learning models in scholarly full-text articles

abstract: Information retrieval systems for scholarly literature rely heavily not only on text matching but on semantic- and context-based features. Readers nowadays are deeply interested in how important an article is, its purpose and how influential it is in follow-up research work. Numerous techniques to tap the power of machine learning and artificial intelligence have been developed to enhance retrieval of the most influential scientific literature. In this paper, we compare and improve on four existing state-of-the-art techniques designed to identify influential citations. We consider 450 citations from the Association for Computational Linguistics corpus, classified by experts as either important or unimportant, and further extract 64 features based on the methodology of four state-of-the-art techniques. We apply the Extra-Trees classifier to select 29 best features and apply the Random Forest and Support Vector Machine classifiers to all selected techniques. Using the Random Forest classifier, our supervised model improves on the state-of-the-art method by 11.25%, with 89% Precision-Recall area under the curve. Finally, we present our deep-learning model, the Long Short-Term Memory network, that uses all 64 features to distinguish important and unimportant citations with 92.57% accuracy.


title: DASX: Hardware Accelerator for Software Data Structures

abstract: Recent research [3,37,38] has proposed compute accelerators to address the energy efficiency challenge. While these compute accelerators specialize and improve the compute efficiency, they have tended to rely on address-based load/store memory interfaces that closely resemble a traditional processor core. The address-based load/store interface is particularly challenging in data-centric applications that tend to access different software data structures. While accelerators optimize the compute section, the address-based interface leads to wasteful instructions and low memory level parallelism (MLP). We study the benefits of raising the abstraction of the memory interface to data structures. We propose DASX (Data Structure Accelerator), a specialized state machine for data fetch that enables compute accelerators to efficiently access data structure elements in iterative program regions. DASX enables the compute accelerators to employ data structure based memory operations and relieves the compute unit from having to generate addresses for each individual object. DASX exploits knowledge of the program's iteration to i) run ahead of the compute units and gather data objects for the compute unit (i.e., compute unit memory operations do not encounter cache misses) and ii) throttle the fetch rate, adaptively tile the dataset based on the locality characteristics and guarantee cache residency. We demonstrate accelerators for three types of data structures, Vector, Key-Value (Hash) maps, and BTrees. We demonstrate the benefits of DASX on data-centric applications which have varied compute kernels but access few regular data structures. DASX achieves higher energy efficiency by eliminating data structure instructions and enabling energy efficient compute accelerators to efficiently access the data elements. We demonstrate that DASX can achieve 4.4x the performance of a multicore system by discovering more parallelism from the data structure.


title: Expressive Visual Text-to-Speech Using Active Appearance Models

abstract: This paper presents a complete system for expressive visual text-to-speech (VTTS), which is capable of producing expressive output, in the form of a 'talking head', given an input text and a set of continuous expression weights. The face is modeled using an active appearance model (AAM), and several extensions are proposed which make it more applicable to the task of VTTS. The model allows for normalization with respect to both pose and blink state which significantly reduces artifacts in the resulting synthesized sequences. We demonstrate quantitative improvements in terms of reconstruction error over a million frames, as well as in large-scale user studies, comparing the output of different systems.


title: Facial expression recognition with discriminatory graphical models

abstract: Discriminating probabilistic graphical models are reliable tools for a sequence labeling task. Conditional Random Fields (CRFs) are discriminative models which will enable us to label a sequence of input data. Other variations of CRFs have been proposed. Hidden Conditional Random Fields (HCRFs) incorporate hidden states to the CRF model and assign a label for the whole input sequence as the model's output. Latent-Dynamic Conditional Random Fields (LDCRFs) also incorporate hidden variable states to the CRFs, in addition, these models are able to label each output variables separately. These models can capture subtle changes among different classes which will help us to achieve better recognition results. In this work we experiment various models and settings in order to achieve better results in facial expression recognition from sequence of videos. We use CRF and LDCRF models and train them with Limited-memory BFGS and Conjugate Gradient parameter learning methods. For each model we use various feature vectors in order to achieve better recognition results. We use Active Appearance Model (AAM) landmark points, Histogram of Oriented Gradients (HOG) and Uniform Local Binary Pattern (U-LBP) as our feature vectors in our models. We show which combination of learning methods and feature vectors are suitable for CRF and LDCRF discriminative models.


title: Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions

abstract: We present an end-to-end text mining methodology for relation extraction of adverse drug reactions (ADRs) from medical forums on the Web. Our methodology is novel in that it combines three major characteristics: (i) an underlying concept of using a head-driven phrase structure grammar (HPSG) based parser; (ii) domain-specific relation patterns, the acquisition of which is done primarily using unsupervised methods applied to a large, unlabeled text corpus; and (iii) automated post-processing algorithms for enhancing the set of extracted relations. We empirically demonstrate the ability of our proposed approach to predict ADRs prior to their reporting by the Food and Drug Administration (FDA). Put differently, we put our approach to a predictive test by demonstrating that our methodology can credibly point to ADRs that were not uncovered in clinical trials for evaluating new drugs that come to market but were only reported later on by the FDA as a label change.


title: $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

abstract: In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activity in this field has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. Both of these techniques have been considered, but this topic is largely still open. In this paper we propose a novel algorithm for adapting dictionaries in order to achieve sparse signal representations. Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints. We present a new method-the K-SVD algorithm-generalizing the K-means clustering process. K-SVD is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. The update of the dictionary columns is combined with an update of the sparse representations, thereby accelerating convergence. The K-SVD algorithm is flexible and can work with any pursuit method (e.g., basis pursuit, FOCUSS, or matching pursuit). We analyze this algorithm and demonstrate its results both on synthetic tests and in applications on real image data


title: A Hyperband Antenna to Launch and Focus Fast High-Voltage Pulses Onto Biological Targets

abstract: This paper briefly reviews the design of a prolate-spheroidal impulse-radiating hyperband antenna system to launch and focus fast (100 ps) high-voltage ( >; 100 kV) pulses onto biological targets. Design and experiments on a graded five-layer dielectric lens, designed to match the pulses into the target medium, are outlined. The design and numerical simulations of a switch system to launch high-voltage spherical TEM pulses are also presented.


title: The Influence of Maturational State on the Acquisition of English as a Second Language

abstract: Abstract Lenneberg (1967) hypothesized that language could be acquired only within a critical period, extending from early infancy until puberty. In its basic form, the critical period hypothesis need only have consequences for first language acquisition. Nevertheless, it is essential to our understanding of the nature of the hypothesized critical period to determine whether or not it extends as well to second language acquisition. If so, it should be the case that young children are better second language learners than adults and should consequently reach higher levels of final proficiency in the second language. This prediction was tested by comparing the English proficiency attained by 46 native Korean or Chinese speakers who had arrived in the United States between the ages of 3 and 39, and who had lived in the United States between 3 and 26 years by the time of testing. These subjects were tested on a wide variety of structures of English grammar, using a grammatically judgment task. Both correlational and t -test analyses demonstrated a clear and strong advantage for earlier arrivals over the later arrivals. Test performance was linearly related to age of arrival up to puberty; after puberty, performance was low but highly variable and unrelated to age of arrival. This age effect was shown not to be an inadvertent result of differences in amount of experience with English, motivation, self-consciousness, or American identification. The effect also appeared on every grammatical structure tested, although the structures varied markedly in the degree to which they were well mastered by later learners. The results support the conclusion that a critical period for language acquisition extends its effects to second language acquisition.


Income Inequality , Poverty and Development Policy in Malaysia


title: Undetectable image tampering through JPEG compression anti-forensics

abstract: Recently, a number of digital image forensic techniques have been developed which are capable of identifying an image's origin, tracing its processing history, and detecting image forgeries. Though these techniques are capable of identifying standard image manipulations, they do not address the possibility that anti-forensic operations may be designed and used to hide evidence of image tampering. In this paper, we propose an anti-forensic operation capable of removing blocking artifacts from a previously JPEG compressed image. Furthermore, we show that by using this operation along with another anti-forensic operation which we recently proposed, we are able to fool forensic methods designed to detect evidence of JPEG compression in decoded images, determine an image's origin, detect double JPEG compression, and identify cut-and-paste image forgeries.


title: Three Spaces of Spatial Cognition

abstract: As we move about and interact in the world, we keep track of different spaces, among them the space of navigation, the space immediately around the body, and the space of the body. We review research showing that these spaces are conceptualized differently. Knowledge of the space of navigation is systematically distorted. For example, people mentally rotate roads and land masses to greater correspondence with global reference frames, they mentally align roads and land masses, they overestimate distances near the viewpoint relative to those far from it. These and other distortions indicate that the space of navigation is schematized to elements and spatial relations relative to reference frames and perspective. The space around the body is organized into a mental framework consisting of extensions of the major axes of the body. Times to report objects around the body suggest that the relative accessibility of the axes depends on their perceptual and functional properties and the relation of the body to the...


title: The WILDTRACK Multi-Camera Person Dataset

abstract: People detection methods are highly sensitive to the perpetual occlusions among the targets. As multi-camera set-ups become more frequently encountered, joint exploitation of the across views information would allow for improved detection performances. We provide a large-scale HD dataset named WILDTRACK which finally makes advanced deep learning methods applicable to this problem. The seven-static-camera set-up captures realistic and challenging scenarios of walking people. ::: Notably, its camera calibration with jointly high-precision projection widens the range of algorithms which may make use of this dataset. In aim to help accelerate the research on automatic camera calibration, such annotations also accompany this dataset. ::: Furthermore, the rich-in-appearance visual context of the pedestrian class makes this dataset attractive for monocular pedestrian detection as well, since: the HD cameras are placed relatively close to the people, and the size of the dataset further increases seven-fold. ::: In summary, we overview existing multi-camera datasets and detection methods, enumerate details of our dataset, and we benchmark multi-camera state of the art detectors on this new dataset.


title: All I know: a study in autoepistemic logic

abstract: Abstract Current approaches to formalizing nonmonotonic reasoning using logics of belief require the definition of new metalogical properties over sets of sentences. This research attempts to show how some of these patterns of reasoning can be captured using only the classical notions of logic (satisfiability, validity, implication). This is done by extending a logic of belief so that it is possible to say that only a certain proposition (or finite set of them) is believed. This research also extends previous approaches to handle quantifiers and equality, provides a semantic account of certain types of nonmonotonicity and, through a simple proof theory, allows formal derivations to be generated.


title: E-commerce in Your Inbox: Product Recommendations at Scale

abstract: In recent years online advertising has become increasingly ubiquitous and effective. Advertisements shown to visitors fund sites and apps that publish digital content, manage social networks, and operate e-mail services. Given such large variety of internet resources, determining an appropriate type of advertising for a given platform has become critical to financial success. Native advertisements, namely ads that are similar in look and feel to content, have had great success in news and social feeds. However, to date there has not been a winning formula for ads in e-mail clients. In this paper we describe a system that leverages user purchase history determined from e-mail receipts to deliver highly personalized product ads to Yahoo Mail users. We propose to use a novel neural language-based algorithm specifically tailored for delivering effective product recommendations, which was evaluated against baselines that included showing popular products and products predicted based on co-occurrence. We conducted rigorous offline testing using a large-scale product purchase data set, covering purchases of more than 29 million users from 172 e-commerce websites. Ads in the form of product recommendations were successfully tested on online traffic, where we observed a steady 9% lift in click-through rates over other ad formats in mail, as well as comparable lift in conversion rates. Following successful tests, the system was launched into production during the holiday season of 2014.


Predicting Price Changes in Ethereum


title: Proper Use of Allele-Specific Expression Improves Statistical Power for cis-eQTL Mapping with RNA-Seq Data.

abstract: Studies of expression quantitative trait loci (eQTLs) offer insight into the molecular mechanisms of loci that were found to be associated with complex diseases and the mechanisms can be classified into cis- and trans-acting regulation. At present, high-throughput RNA sequencing (RNA-seq) is rapidly replacing expression microarrays to assess gene expression abundance. Unlike microarrays that only measure the total expression of each gene, RNA-seq also provides information on allele-specific expression (ASE), which can be used to distinguish cis-eQTLs from trans-eQTLs and, more importantly, enhance cis-eQTL mapping. However, assessing the cis-effect of a candidate eQTL on a gene requires knowledge of the haplotypes connecting the candidate eQTL and the gene, which cannot be inferred with certainty. The existing two-stage approach that first phases the candidate eQTL against the gene and then treats the inferred phase as observed in the association analysis tends to attenuate the estimated cis-effect and reduce the power for detecting a cis-eQTL. In this article, we provide a maximum-likelihood framework for cis-eQTL mapping with RNA-seq data. Our approach integrates the inference of haplotypes and the association analysis into a single stage, and is thus unbiased and statistically powerful. We also develop a pipeline for performing a comprehensive scan of all local eQTLs for all genes in the genome by controlling for false discovery rate, and implement the methods in a computationally efficient software program. The advantages of the proposed methods over the existing ones are demonstrated through realistic simulation studies and an application to empirical breast cancer data from The Cancer Genome Atlas project.


title: Nonparametric Detection of Anomalous Data Streams

abstract: A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally $n$ observed sequences out of which $s$ anomalous sequences are to be detected. Each typical sequence consists of $m$ independent and identically distributed (i.i.d.) samples drawn from a distribution $p$ , whereas each anomalous sequence consists of $m$ i.i.d. samples drawn from a distribution $q$ that is distinct from $p$ . The distributions $p$ and $q$ are assumed to be unknown in advance. Distribution-free tests are constructed by using the maximum mean discrepancy as the metric, which is based on mean embeddings of distributions into a reproducing kernel Hilbert space. The probability of error is bounded as a function of the sample size $m$ , the number $s$ of anomalous sequences, and the number $n$ of sequences. It is shown that with $s$ known, the constructed test is exponentially consistent if $m$ is greater than a constant factor of $\log n$ , for any $p$ and $q$ , whereas with $s$ unknown, $m$ should have an order strictly greater than $\log n$ . Furthermore, it is shown that no test can be consistent for arbitrary $p$ and $q$ if $m$ is less than a constant factor of $\log n$ . Thus, the order-level optimality of the proposed test is established. Numerical results are provided to demonstrate that the proposed tests outperform (or perform as well as) tests based on other competitive approaches under various cases.


title: Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment

abstract: Abstract Sustainable supply chain management (SSCM) provides economic, social end environmental requirements in material and service flows occurring between suppliers, manufacturers and customers. SSCM structure is considered as a prerequisite for a sustainable success. Thus designing an effective SCM structure provides competitive advantages for the companies. In order to achieve an effective design of this structure, it is possible to apply quality function deployment (QFD) approach which is successfully applied as an effective product and system development tool. This study presents a decision framework where analytic network process (ANP) integrated QFD and zero-one goal programming (ZOGP) models are used in order to determine the design requirements which are more effective in achieving a sustainable supply chain (SSC). The first phase of the QFD is the house of quality (HOQ) which transforms customer requirements into product design requirements. In this study, after determining the sustainability requirements named customer requirements (CRs) and design requirements (DRs) of a SSC, ANP is employed to determine the importance levels in the HOQ considering the interrelationships among the DRs and CRs. Furthermore ZOGP approach is used to take into account different objectives of the problem. The proposed method is applied through a case study and obtained results are discussed.


title: Invariant feature extraction for image classification via multi-channel convolutional neural network

abstract: The invariance for feature extraction, such as invariance for specificity of homogeneous sample and rotation invariance, is crucial for object detection and classification applications. Current researches mainly focus on a specific invariance of features, such as rotation invariance. In this paper, a novel multi-channel convolutional neural network (mCNN) is proposed to extract invariant features for object classification. Multi-channel convolutions sharing identical weights are used to alleviate the feature variance of sample pairs with different rotations in the same category. As a result, the invariance for specificity of homogeneous object and rotation invariance are simultaneously encountered to improve the invariance of features. More importantly, the proposed mCNN is especially effective for small training samples. Experimental results on two benchmark datasets for handwriting recognition demonstrate that the proposed mCNN is very effective to extract invariant feature with small amount of training samples.


title: TIE: Principled Reverse Engineering of Types in Binary Programs

abstract: A recurring problem in security is reverse engineering binary code to recover high-level language data abstractions and types. High-level programming languages have data abstractions such as buffers, structures, and local variables that all help programmers and program analyses reason about programs in a scalable manner. During compilation, these abstractions are removed as code is translated down to operations on registers and one globally addressed memory region. Reverse engineering consists of “undoing” the compilation to recover high-level information so that programmers, security professionals, and analyses can all more easily reason about the binary code. In this paper we develop novel techniques for reverse engineering data type abstractions from binary programs. At the heart of our approach is a novel type reconstruction system based upon binary code analysis. Our techniques and system can be applied as part of both static or dynamic analysis, thus are extensible to a large number of security settings. Our results on 87 programs show that TIE is both more accurate and more precise at recovering high-level types than existing mechanisms.


title: A Hardware-Assisted Realtime Attack on A5/2 without

abstract: A5/2 is a synchronous stream cipher that is used for protecting GSM communication. Recently, some powerful attacks [2,5] on A5/2 have been proposed. In this contribution we enhance the ciphertext-only attack [2] by Barkan, Biham, and Keller by designing special-purpose hardware for generating and solvingthe required systems of linear equations. For realizing the LSE solver component, we use an approach recently introduced in [5,6] describing a parallelized hardware implementation of the Gauss-Jordan algorithm. Our hardware-only attacker immediately recovers the initial secret state of A5/2 - which is sufficient for decrypting all frames of a session - using a few ciphertext frames without any precomputations and memory. More precisely, in contrast to [2] our hardware architecture directly attacks the GSM speech channel (TCH/FS and TCH/EFS). It requires 16 ciphertext frames and completes the attack in about 1 second. With minor changes also input from other GSM channels (e.g., SDCCH/8) can be used to mount the attack.


Design of a Compact Tapered Slot Vivaldi Antenna Array for See Through Concrete Wall UWB Applications


title: Analysis of DNA polymerase ν function in meiotic recombination, immunoglobulin class-switching, and DNA damage tolerance

abstract: DNA polymerase ν (pol ν), encoded by the POLN gene, is an A-family DNA polymerase in vertebrates and some other animal lineages. Here we report an in-depth analysis of pol ν–defective mice and human cells. POLN is very weakly expressed in most tissues, with the highest relative expression in testis. We constructed multiple mouse models for Poln disruption and detected no anatomic abnormalities, alterations in lifespan, or changed causes of mortality. Mice with inactive Poln are fertile and have normal testis morphology. However, pol ν–disrupted mice have a modestly reduced crossover frequency at a meiotic recombination hot spot harboring insertion/deletion polymorphisms. These polymorphisms are suggested to generate a looped-out primer and a hairpin structure during recombination, substrates on which pol ν can operate. Pol ν-defective mice had no alteration in DNA end-joining during immunoglobulin class-switching, in contrast to animals defective in the related DNA polymerase θ (pol θ). We examined the response to DNA crosslinking agents, as purified pol ν has some ability to bypass major groove peptide adducts and residues of DNA crosslink repair. Inactivation of Poln in mouse embryonic fibroblasts did not alter cellular sensitivity to mitomycin C, cisplatin, or aldehydes. Depletion of POLN from human cells with shRNA or siRNA did not change cellular sensitivity to mitomycin C or alter the frequency of mitomycin C-induced radial chromosomes. Our results suggest a function of pol ν in meiotic homologous recombination in processing specific substrates. The restricted and more recent evolutionary appearance of pol ν (in comparison to pol θ) supports such a specialized role.


title: SuperPoint: Self-Supervised Interest Point Detection and Description

abstract: This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.


title: EffectiveSan: type and memory error detection using dynamically typed C/C++

abstract: Low-level programming languages such as C and C++ are vulnerable to errors related to the misuse of memory. Such errors include bounds overflows, sub-object bounds overflows, use-after-free, "reuse"-after-free and type confusion. These errors account for many of the exploits in programs implemented in such unsafe languages. Most bug detection tools (sanitizers) tend to focus only on detecting specific classes of errors, e.g. bounds overflow or type confusion only. In this paper, we present a new type confusion and memory error sanitizer based on dynamically checking the "effective type" (a.k.a. the dynamic type) of C/C++ objects at runtime. We show that this "effective type sanitizer" (EffectiveSan) can detect the memory misuse errors mentioned above, all while using the same underlying methodology (dynamic typing). Our approach uses a combination of low-fat pointers, type meta data and type check instrumentation. We also have a novel approach to preventing sub-object bound overflow errors leveraging on the C/C++ types. We show EffectiveSan finds type confusion, (sub-)object bounds overflow, and use-after-free bugs in the SPEC2006 benchmark suite.


title: Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection

abstract: This paper presents a non-intrusive approach for monitoring driver drowsiness using the fusion of several optimized indicators based on driver physical and driving performance measures, obtained from ADAS (Advanced Driver Assistant Systems) in simulated conditions. The paper is focused on real-time drowsiness detection technology rather than on long-term sleep/awake regulation prediction technology. We have developed our own vision system in order to obtain robust and optimized driver indicators able to be used in simulators and future real environments. These indicators are principally based on driver physical and driving performance skills. The fusion of several indicators, proposed in the literature, is evaluated using a neural network and a stochastic optimization method to obtain the best combination. We propose a new method for ground-truth generation based on a supervised Karolinska Sleepiness Scale (KSS). An extensive evaluation of indicators, derived from trials over a third generation simulator with several test subjects during different driving sessions, was performed. The main conclusions about the performance of single indicators and the best combinations of them are included, as well as the future works derived from this study.


title: Improvement of Generalization Ability of Deep CNN via Implicit Regularization in Two-Stage Training Process

abstract: Optimization of deep learning is no longer an imminent problem, due to various gradient descent methods and the improvements of network structure, including activation functions, the connectivity style, and so on. Then the actual application depends on the generalization ability, which determines whether a network is effective. Regularization is an efficient way to improve the generalization ability of deep CNN, because it makes it possible to train more complex models while maintaining a lower overfitting. In this paper, we propose to optimize the feature boundary of deep CNN through a two-stage training method (pre-training process and implicit regularization training process) to reduce the overfitting problem. In the pre-training stage, we train a network model to extract the image representation for anomaly detection. In the implicit regularization training stage, we re-train the network based on the anomaly detection results to regularize the feature boundary and make it converge in the proper position. Experimental results on five image classification benchmarks show that the two-stage training method achieves a state-of-the-art performance and that it, in conjunction with more complicated anomaly detection algorithm, obtains better results. Finally, we use a variety of strategies to explore and analyze how implicit regularization plays a role in the two-stage training process. Furthermore, we explain how implicit regularization can be interpreted as data augmentation and model ensemble.


Hugo Karl Liepmann (1863–1925)


title: Physician Presence in an Ambulance Car Is Associated with Increased Survival in Out-of-Hospital Cardiac Arrest: A Prospective Cohort Analysis

abstract: The presence of a physician seems to be beneficial for pre-hospital cardiopulmonary resuscitation (CPR) of patients with out-of-hospital cardiac arrest. However, the effectiveness of a physician's presence during CPR before hospital arrival has not been established. We conducted a prospective, non-randomized, observational study using national data from out-of-hospital cardiac arrests between 2005 and 2010 in Japan. We performed a propensity analysis and examined the association between a physician's presence during an ambulance car ride and short- and long-term survival from out-of-hospital cardiac arrest. Specifically, a full non-parsimonious logistic regression model was fitted with the physician presence in the ambulance as the dependent variable; the independent variables included all study variables except for endpoint variables plus dummy variables for the 47 prefectures in Japan (i.e., 46 variables). In total, 619,928 out-of-hospital cardiac arrest cases that met the inclusion criteria were analyzed. Among propensity-matched patients, a positive association was observed between a physician's presence during an ambulance car ride and return of spontaneous circulation (ROSC) before hospital arrival, 1-month survival, and 1-month survival with minimal neurological or physical impairment (ROSC: OR = 1.84, 95% CI 1.63–2.07, p = 0.00 in adjusted for propensity and all covariates); 1-month survival: OR = 1.29, 95% CI 1.04–1.61, p = 0.02 in adjusted for propensity and all covariates); cerebral performance category (1 or 2): OR = 1.54, 95% CI 1.03–2.29, p = 0.04 in adjusted for propensity and all covariates); and overall performance category (1 or 2): OR = 1.50, 95% CI 1.01–2.24, p = 0.05 in adjusted for propensity and all covariates). A prospective observational study using national data from out-of-hospital cardiac arrests shows that a physician's presence during an ambulance car ride was independently associated with increased short- and long-term survival.


title: Object-object interaction affordance learning

abstract: This paper presents a novel object-object affordance learning approach that enables intelligent robots to learn the interactive functionalities of objects from human demonstrations in everyday environments. Instead of considering a single object, we model the interactive motions between paired objects in a human-object-object way. The innate interaction-affordance knowledge of the paired objects are learned from a labeled training dataset that contains a set of relative motions of the paired objects, human actions, and object labels. The learned knowledge is represented with a Bayesian Network, and the network can be used to improve the recognition reliability of both objects and human actions and to generate proper manipulation motion for a robot if a pair of objects is recognized. This paper also presents an image-based visual servoing approach that uses the learned motion features of the affordance in interaction as the control goals to control a robot to perform manipulation tasks.


title: Utility data annotation with Amazon Mechanical Turk

abstract: We show how to outsource data annotation to Amazon Mechanical Turk. Doing so has produced annotations in quite large numbers relatively cheaply. The quality is good, and can be checked and controlled. Annotations are produced quickly. We describe results for several different annotation problems. We describe some strategies for determining when the task is well specified and properly priced.


REM: Resource-Efficient Mining for Blockchains


title: SpotFi: Decimeter Level Localization Using WiFi

abstract: This paper presents the design and implementation of SpotFi, an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems. SpotFi makes two key technical contributions. First, SpotFi incorporates super-resolution algorithms that can accurately compute the angle of arrival (AoA) of multipath components even when the access point (AP) has only three antennas. Second, it incorporates novel filtering and estimation techniques to identify AoA of direct path between the localization target and AP by assigning values for each path depending on how likely the particular path is the direct path. Our experiments in a multipath rich indoor environment show that SpotFi achieves a median accuracy of 40 cm and is robust to indoor hindrances such as obstacles and multipath.


Ontolingua: A Mechanism to Support Portable Ontologies


title: Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks

abstract: Most learning algorithms require the practitioner to manually set the values of many hyperparameters before the learning process can begin. However, with modern algorithms, the evaluation of a given hyperparameter setting can take a considerable amount of time and the search space is often very high-dimensional. We suggest using a lower-dimensional representation of the original data to quickly identify promising areas in the hyperparameter space. This information can then be used to initialize the optimization algorithm for the original, higher-dimensional data. We compare this approach with the standard procedure of optimizing the hyperparameters only on the original input. We perform experiments with various state-of-the-art hyperparameter optimization algorithms such as random search, the tree of parzen estimators (TPEs), sequential model-based algorithm configuration (SMAC), and a genetic algorithm (GA). Our experiments indicate that it is possible to speed up the optimization process by using lower-dimensional data representations at the beginning, while increasing the dimensionality of the input later in the optimization process. This is independent of the underlying optimization procedure, making the approach promising for many existing hyperparameter optimization algorithms.


title: Visualizing Bibliometric Networks

abstract: This chapter provides an introduction to the topic of visualizing bibliometric networks. First, the most commonly studied types of bibliometric networks (i.e., citation, co-citation, bibliographic coupling, keyword co-occurrence, and coauthorship networks) are discussed, and three popular visualization approaches (i.e., distance-based, graph-based, and timeline-based approaches) are distinguished. Next, an overview is given of a number of software tools that can be used for visualizing bibliometric networks. In the second part of the chapter, the focus is specifically on two software tools: VOSviewer and CitNetExplorer. The techniques used by these tools to construct, analyze, and visualize bibliometric networks are discussed. In addition, tutorials are offered that demonstrate in a step-by-step manner how both tools can be used. Finally, the chapter concludes with a discussion of the limitations and the proper use of bibliometric network visualizations and with a summary of some ongoing and future developments.


title: Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces

abstract: Objective: This paper tackles the problem of transfer learning in the context of electroencephalogram (EEG)-based brain–computer interface (BCI) classification. In particular, the problems of cross-session and cross-subject classification are considered. These problems concern the ability to use data from previous sessions or from a database of past users to calibrate and initialize the classifier, allowing a calibration-less BCI mode of operation. Methods: Data are represented using spatial covariance matrices of the EEG signals, exploiting the recent successful techniques based on the Riemannian geometry of the manifold of symmetric positive definite (SPD) matrices. Cross-session and cross-subject classification can be difficult, due to the many changes intervening between sessions and between subjects, including physiological, environmental, as well as instrumental changes. Here, we propose to affine transform the covariance matrices of every session/subject in order to center them with respect to a reference covariance matrix, making data from different sessions/subjects comparable. Then, classification is performed both using a standard minimum distance to mean classifier, and through a probabilistic classifier recently developed in the literature, based on a density function (mixture of Riemannian Gaussian distributions) defined on the SPD manifold. Results: The improvements in terms of classification performances achieved by introducing the affine transformation are documented with the analysis of two BCI datasets. Conclusion and significance: Hence, we make, through the affine transformation proposed, data from different sessions and subject comparable, providing a significant improvement in the BCI transfer learning problem.


title: Automated Assembly Using 3D and 2D Cameras

abstract: 2D and 3D computer vision systems are frequently being used in automated production to detect and determine the position of objects. Accuracy is important in the production industry, and computer vision systems require structured environments to function optimally. For 2D vision systems, a change in surfaces, lighting and viewpoint angles can reduce the accuracy of a method, maybe even to a degree that it will be erroneous, while for 3D vision systems, the accuracy mainly depends on the 3D laser sensors. Commercially available 3D cameras lack the precision found in high-grade 3D laser scanners, and are therefore not suited for accurate measurements in industrial use. In this paper, we show that it is possible to identify and locate objects using a combination of 2D and 3D cameras. A rough estimate of the object pose is first found using a commercially available 3D camera. Then, a robotic arm with an eye-in-hand 2D camera is used to determine the pose accurately. We show that this increases the accuracy to < 1 and < 1 . This was demonstrated in a real industrial assembly task where high accuracy is required.


title: Shielding Applications from an Untrusted Cloud with Haven

abstract: Today’s cloud computing infrastructure requires substantial trust. Cloud users rely on both the provider’s staff and its globally distributed software/hardware platform not to expose any of their private data. We introduce the notion of shielded execution, which protects the confidentiality and integrity of a program and its data from the platform on which it runs (i.e., the cloud operator’s OS, VM, and firmware). Our prototype, Haven, is the first system to achieve shielded execution of unmodified legacy applications, including SQL Server and Apache, on a commodity OS (Windows) and commodity hardware. Haven leverages the hardware protection of Intel SGX to defend against privileged code and physical attacks such as memory probes, and also addresses the dual challenges of executing unmodified legacy binaries and protecting them from a malicious host. This work motivated recent changes in the SGX specification.


title: A Novel Term Weighting Scheme Model

abstract: The use of textual data has increased exponentially in recent years due to the networking infrastructure such as Facebook, Twitter, Wikipedia, Blogs, and so one. Analysis of this massive textual data can help to automatically categorize and label new content. Before classification process, term weighting scheme is the crucial step for representing the documents in a way suitable for classification algorithms. In this paper, we are conducting a survey on the term weighting schemes and we propose an efficient term weighting scheme that provide a better classification accuracy than those obtained with the famous TF-IDF, the recent IF-IGM and the others term weighting schemes in the literature.


abstract: This book constitutes the refereed proceedings of the 19th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2018, held in Poitiers, France, in September 2018. ::: The 52 full papers presented in this volume were carefully reviewed and selected from 91 submissions. They were organized in topical sections named: video analysis; segmentation and classification; remote sending; biometrics; deep learning; coding and compression; and image restauration and reconstruction.


title: A framework for evaluating multimodal music mood classification

abstract: This research proposes a framework for music mood classification that uses multiple and complementary information sources, namely, music audio, lyric text, and social tags associated with music pieces. This article presents the framework and a thorough evaluation of each of its components. Experimental results on a large data set of 18 mood categories show that combining lyrics and audio significantly outperformed systems using audio-only features. Automatic feature selection techniques were further proved to have reduced feature space. In addition, the examination of learning curves shows that the hybrid systems using lyrics and audio needed fewer training samples and shorter audio clips to achieve the same or better classification accuracies than systems using lyrics or audio singularly. Last but not least, performance comparisons reveal the relative importance of audio and lyric features across mood categories.