title: The Child Sexual Abuse Accommodation Syndrome

abstract: Child victims of sexual abuse face secondary trauma in the crisis of discovery. Their attempts to reconcile their private experiences with the realities of the outer world are assaulted by the disbelief, blame and rejection they experience from adults. The normal coping behavior of the child contradicts the entrenched beliefs and expectations typically held by adults, stigmatizing the child with charges of lying, manipulating or imagining from parents, courts and clinicians. Such abandonment by the very adults most crucial to the child's protection and recovery drives the child deeper into self-blame, self-hate, alienation and revictimization. In contrast, the advocacy of an empathic clinician within a supportive treatment network can provide vital credibility and endorsement for the child. Evaluation of the responses of normal children to sexual assault provides clear evidence that societal definitions of "normal" victim behavior are inappropriate and procrustean, serving adults as mythic insulators against the child's pain. Within this climate of prejudice, the sequential survival options available to the victim further alienate the child from any hope of outside credibility or acceptance. Ironically, the child's inevitable choice of the "wrong" options reinforces and perpetuates the prejudicial myths. The most typical reactions of children are classified in this paper as the child sexual abuse accommodation syndrome. The syndrome is composed of five categories, of which two define basic childhood vulnerability and three are sequentially contingent on sexual assault: (1) secrecy, (2) helplessness, (3) entrapment and accommodation, (4) delayed, unconvincing disclosure, and (5) retraction. The accommodation syndrome is proposed as a simple and logical model for use by clinicians to improve understanding and acceptance of the child's position in the complex and controversial dynamics of sexual victimization. Application of the syndrome tends to challenge entrenched myths and prejudice, providing credibility and advocacy for the child within the home, the courts, and throughout the treatment process. The paper also provides discussion of the child's coping strategies as analogs for subsequent behavioral and psychological problems, including implications for specific modalities of treatment.


title: A Manufacturing Big Data Solution for Active Preventive Maintenance

abstract: Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.


title: Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

abstract: This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu


title: Adversarial Attribute-Image Person Re-identification

abstract: While attributes have been widely used for person re-identification (Re-ID) which aims at matching the same person images across disjoint camera views, they are used either as extra features or for performing multi-task learning to assist the image-image matching task. However, how to find a set of person images according to a given attribute description, which is very practical in many surveillance applications, remains a rarely investigated cross-modality matching problem in person Re-ID. In this work, we present this challenge and formulate this task as a joint space learning problem. By imposing an attribute-guided attention mechanism for images and a semantic consistent adversary strategy for attributes, each modality, i.e., images and attributes, successfully learns semantically correlated concepts under the guidance of the other. We conducted extensive experiments on three attribute datasets and demonstrated that the proposed joint space learning method is so far the most effective method for the attribute-image cross-modality person Re-ID problem.


title: Design, Analysis and Application of A Volumetric Convolutional Neural Network

abstract: The design, analysis and application of a volumetric convolutional neural network (VCNN) are studied in this work. Although many CNNs have been proposed in the literature, their design is empirical. In the design of the VCNN, we propose a feed-forward K-means clustering algorithm to determine the filter number and size at each convolutional layer systematically. For the analysis of the VCNN, the cause of confusing classes in the output of the VCNN is explained by analyzing the relationship between the filter weights (also known as anchor vectors) from the last fully-connected layer to the output. Furthermore, a hierarchical clustering method followed by a random forest classification method is proposed to boost the classification performance among confusing classes. For the application of the VCNN, we examine the 3D shape classification problem and conduct experiments on a popular ModelNet40 dataset. The proposed VCNN offers the state-of-the-art performance among all volume-based CNN methods.


title: Fasting-mimicking diet and markers/risk factors for aging, diabetes, cancer, and cardiovascular disease

abstract: Calorie restriction or changes in dietary composition can enhance healthy aging, but the inability of most subjects to adhere to chronic and extreme diets, as well as potentially adverse effects, limits their application. We randomized 100 generally healthy participants from the United States into two study arms and tested the effects of a fasting-mimicking diet (FMD)-low in calories, sugars, and protein but high in unsaturated fats-on markers/risk factors associated with aging and age-related diseases. We compared subjects who followed 3 months of an unrestricted diet to subjects who consumed the FMD for 5 consecutive days per month for 3 months. Three FMD cycles reduced body weight, trunk, and total body fat; lowered blood pressure; and decreased insulin-like growth factor 1 (IGF-1). No serious adverse effects were reported. After 3 months, control diet subjects were crossed over to the FMD program, resulting in a total of 71 subjects completing three FMD cycles. A post hoc analysis of subjects from both FMD arms showed that body mass index, blood pressure, fasting glucose, IGF-1, triglycerides, total and low-density lipoprotein cholesterol, and C-reactive protein were more beneficially affected in participants at risk for disease than in subjects who were not at risk. Thus, cycles of a 5-day FMD are safe, feasible, and effective in reducing markers/risk factors for aging and age-related diseases. Larger studies in patients with diagnosed diseases or selected on the basis of risk factors are warranted to confirm the effect of the FMD on disease prevention and treatment.


title: MnasNet: Platform-Aware Neural Architecture Search for Mobile

abstract: Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8× faster than MobileNetV2 with 0.5% higher accuracy and 2.3× faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection. Code is at https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet.


title: Spontaneous point mutations in the capsule synthesis locus leading to structural and functional changes of the capsule in serogroup A meningococcal populations

abstract: Whole genome sequencing analysis of 100 Neisseria meningitidis serogroup A isolates has revealed that the csaABCD-ctrABCD-ctrEF capsule polysaccharide synthesis locus represents a spontaneous point mutation hotspot. Structural and functional properties of the capsule of 11 carriage and two disease isolates with non-synonymous point mutations or stop codons in capsule synthesis genes were analyzed for their capsular polysaccharide expression, recognition by antibodies and sensitivity to bactericidal killing. Eight of eleven carriage isolates presenting capsule locus mutations expressed no or reduced amounts of capsule. One isolate with a stop codon in the O-acetyltransferase gene expressed non-O-acetylated polysaccharide, and was not recognized by anti-capsule antibodies. Capsule and O-acetylation deficient mutants were resistant to complement deposition and killing mediated by anti-capsular antibodies, but not by anti-lipopolysaccharide antibodies. Two capsule polymerase mutants, one carriage and one case isolate, showed capsule over-expression and increased resistance against bactericidal activity of both capsule- and lipopolysaccharide-specific antibodies. Meningococci have developed multiple strategies for changing capsule expression and structure, which is relevant both for colonization and virulence. Here we show that point mutations in the capsule synthesis genes substantially contribute to the repertoire of genetic mechanisms in natural populations leading to variability in capsule expression.


title: Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm

abstract: In this work, we study the 1-bit convolutional neural networks (CNNs), of which both the weights and activations are binary. While being efficient, the classification accuracy of the current 1-bit CNNs is much worse compared to their counterpart real-valued CNN models on the large-scale dataset, like ImageNet. To minimize the performance gap between the 1-bit and real-valued CNN models, we propose a novel model, dubbed Bi-Real net, which connects the real activations (after the 1-bit convolution and/or BatchNorm layer, before the sign function) to activations of the consecutive block, through an identity shortcut. Consequently, compared to the standard 1-bit CNN, the representational capability of the Bi-Real net is significantly enhanced and the additional cost on computation is negligible. Moreover, we develop a specific training algorithm including three technical novelties for 1-bit CNNs. Firstly, we derive a tight approximation to the derivative of the non-differentiable sign function with respect to activation. Secondly, we propose a magnitude-aware gradient with respect to the weight for updating the weight parameters. Thirdly, we pre-train the real-valued CNN model with a clip function, rather than the ReLU function, to better initialize the Bi-Real net. Experiments on ImageNet show that the Bi-Real net with the proposed training algorithm achieves 56.4% and 62.2% top-1 accuracy with 18 layers and 34 layers, respectively. Compared to the state-of-the-arts (e.g., XNOR Net), Bi-Real net achieves up to 10% higher top-1 accuracy with more memory saving and lower computational cost.


title: Cutting Down Training Memory by Re-fowarding

abstract: Deep Neutral Networks(DNN) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory is always finite, which limits the image resolution, batch size, and learning rate that could be tuned for better performances. In this paper, we propose a novel approach, called Re-forwarding, that substantially reduces memory usage in training. Our approach only saves the tensors at a subset of layers during the first forward, and conduct extra local forwards (the Re-forwarding process) to compute the missing tensors needed during backward. The total memory cost becomes the sum of (1) the cost at the subset of layers and (2) the maximum cost of the re-forwarding processes. We propose theories and algorithms that achieve the optimal memory solutions for DNNs with either linear or arbitrary optimization graphs. Experiments show that Re-forwarding cut down huge amount of training memory on all popular DNNs such as Alexnet, VGG net, ResNet, Densenet and Inception net.


title: Design of a MIMO Dielectric Resonator Antenna for LTE Femtocell Base Stations

abstract: A novel multiple-input multiple-output (MIMO) dielectric resonator antenna (DRA) for long term evolution (LTE) femtocell base stations is described. The proposed antenna is able to transmit and receive information independently using TE and HE modes in the LTE bands 12 (698-716 MHz, 728-746 MHz) and 17 (704-716 MHz, 734-746 MHz). A systematic design method based on perturbation theory is proposed to induce mode degeneration for MIMO operation. Through perturbing the boundary of the DRA, the amount of energy stored by a specific mode is changed as well as the resonant frequency of that mode. Hence, by introducing an adequate boundary perturbation, the TE and HE modes of the DRA will resonate at the same frequency and share a common impedance bandwidth. The simulated mutual coupling between the modes was as low as - 40 dB . It was estimated that in a rich scattering environment with an Signal-to-Noise Ratio (SNR) of 20 dB per receiver branch, the proposed MIMO DRA was able to achieve a channel capacity of 11.1 b/s/Hz (as compared to theoretical maximum 2 × 2 capacity of 13.4 b/s/Hz). Our experimental measurements successfully demonstrated the design methodology proposed in this work.


title: Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning

abstract: Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.


title: Vaginal lacerations from consensual intercourse in adolescents.

abstract: OBJECTIVE ::: (1) To describe lacerations of the vaginal fornices, an injury known to be associated with consensual sexual intercourse, including known complications and treatment course, (2) to contrast these injuries with injuries sustained during sexual assault, and (3) to discuss the assessment of adolescent patients for sexual injuries. ::: ::: ::: METHODS ::: We present a case series of 4 female adolescent patients seen at a children's hospital over a period of 6 months. Each patient developed significant vaginal bleeding after sexual intercourse, and 3 of the patients presented to the emergency department with vital signs consistent with compensated shock. ::: ::: ::: RESULTS ::: Each patient was evaluated by pediatric surgery, and found to have a laceration of the vagina. Three of the patients described consensual intercourse prior to the onset of bleeding, and had lacerations of the vaginal fornices; these patients were determined to have injuries resulting from consensual sexual intercourse. The fourth patient reported sexual assault as the cause of her injuries, and was treated for longitudinal lacerations of the vaginal wall. ::: ::: ::: CONCLUSIONS ::: Lacerations of the upper vagina are not frequently reported in forced vaginal intercourse, but are occasionally reported as injuries sustained during consensual coitus. In the absence of reported sexual assault, a severe vaginal fornix laceration is consistent with the diagnosis of coital injury from consensual intercourse. Diagnosis and treatment of this injury can be delayed due to the sensitive nature of these injuries. Bleeding can be profuse, leading to hemorrhagic shock, and these injuries may require transfusion of blood products and surgical repair in some cases. Complications may include hemoperitoneum, pneumoperitoneum, or retroperitoneal hematoma, even in the absence of complete vaginal perforation. ::: ::: ::: PRACTICE IMPLICATIONS ::: Knowledge of the consensual sexual injuries that may occur in adolescent patients can guide diagnosis, treatment, and counseling for the patient and her family, preventing long-term medical complications and legal consequences.


title: A Machine Learning Framework for Predicting Purchase by Online Customers based on Dynamic Pricing

abstract: Abstract Pricing in the online world is highly transparent & can be a primary driver for online purchase. While dynamic pricing is not new & used by many to increase sales and margins, its benefit to online retailers is immense. The proposed study is a result of ongoing project that aims to develop a generic framework and applicable techniques by applying sound machine learning algorithms to enhance right price purchase (not cheapest price) by customers on e-commerce platform. This study focuses more on inventory led e-commerce companies, however the model can be extended to online marketplaces without inventories. Facilitated by statistical and machine learning models the study seeks to predict the purchase decisions based on adaptive or dynamic pricing of a product. Different data sources which capture visit attributes, visitor attributes, purchase history, web data, and context understanding, lays a strong foundation to this framework. The study focuses on customer segments for predicting purchase rather than on individual buyers. Personalization of adaptive pricing and purchase prediction will be the next logical extension of the study once the results for this are presented. Web mining and use of big data technologies along with machine learning algorithms make up the solution landscape for the study.


title: Respiratory parameter measurement and analysis using differential pressure sensor

abstract: Force Vital Capacity (FVC) of lungs and respiration rate is measured using single differential pressure sensor. MPXV7025DP pressure sensor of range −3.6 psi to +3.6 psi(−25Kpa to +25Kpa) is selected. Sensor gives output proportional to differential pressure in terms of voltage. Sensor is connected to MLT1000L flow head using plastic tubing. MLT1000L flow head functions as a pneumotachometer and gives output signal proportional to airflow during breathing. Signal conditioning is done using dual supply AD623 instrumentation amplifier. Air is blown into the MLT1000L flow head through mouthpiece. HEPA filter is attached to disposable mouthpiece. It will reduce the infection risk while breathing into the system. In this system, sensor is calibrated for different flow rates. AM4201 digital anemometer is used in calibration process to measure the velocity of the air flow in terms of m/sec. Simultaneously, sensor output is measured in terms of voltage. In order to show the effectiveness of the proposed system a real life experimentation is carried out on the five male and five female subjects to demonstrate the Force Vital Capacity (FVC) and Respiration rate (RR). Forced vital capacity (FVC) for female subject is in between 3 litres to 5 litres and for male subject it is in between 4 litres to 6 litres. From measurement it is observed that male subjects are having more force vital capacity than female subjects. Respiration rate is measured for both male and female subjects and it is in between 12 to 14 breaths per minute.


title: Rail-to-rail two stage CMOS operational amplifier: A comparative analysis

abstract: This paper presents different low voltage CMOS operational amplifiers. Threshold reduction techniques are foreseen with the utilization of Bulk driven approach to reduce the supply voltage. Disadvantages identified with bulk driven approach are talked about and strategies to enhance them are proposed. To increase transconductance of the Op-Amp, cascode input stage and cross coupled output stage are utilized with no expansion in area as well as bulk driven MOSFETS can be utilized alongside partial positive feedback. These methods help in increment of dc gain. Supply voltage scaling has been the central factor because of which gain increments, efficient power utilization is there and on-chip are reduce.


title: Graph Algorithms for Topology Identification Using Power Grid Probing

abstract: To perform any meaningful optimization task, power distribution operators need to know the topology and line impedances of their electric networks. Nevertheless, distribution grids currently lack a comprehensive metering infrastructure. Although smart inverters are widely used for control purposes, they have been recently advocated as the means for an active data acquisition paradigm: Reading the voltage deviations induced by intentionally perturbing inverter injections, the system operator can potentially recover the electric grid topology. Adopting inverter probing for feeder processing, a suite of graph-based topology identification algorithms is developed here. If the grid is probed at all leaf nodes but voltage data are metered at all nodes, the entire feeder topology can be successfully recovered. When voltage data are collected only at probing buses, the operator can find a reduced feeder featuring key properties and similarities to the actual feeder. To handle modeling inaccuracies and load non- stationarity, noisy probing data need to be preprocessed. If the suggested guidelines on the magnitude and duration of probing are followed, the recoverability guarantees carry over from the noiseless to the noisy setup with high probability.


title: Are Bitcoin Users Less Sociable? An Analysis of Users’ Language and Social Connections on Twitter

abstract: Bitcoin, a peer-to-peer payment system and digital currency, has seen much growth and controversy in the four years since its introduction. Yet, despite Bitcoin’s growing importance, little is known about its users. Our research explores what type of people use this domain and what concepts they tend to emphasize in their language. We analyzed over 50,000 messages from over 6,000 users of the social networking community, Twitter. Our analyses show a consistent pattern that people interested in Bitcoin are far less likely to emphasize social relations than typical users of the site. Specifically, Bitcoin followers (1) are less likely to mention family, friends, religion, sex, and emotion related words in their tweets and (2) have significantly less social connection to other users on the site. These findings offer the first empirical look at what exactly makes Bitcoin users distinct from others and can have implications for the future of the currency.


title: Building call graphs for embedded client-side code in dynamic web applications

abstract: When developing and maintaining a software system, programmers often rely on IDEs to provide editor services such as syntax highlighting, auto-completion, and "jump to declaration". In dynamic web applications, such tool support is currently limited to either the server-side code or to hand-written or generated client-side code. Our goal is to build a call graph for providing editor services on client-side code while it is still embedded as string literals within server-side code. First, we symbolically execute the server-side code to identify all possible client-side code variations. Subsequently, we parse the generated client-side code with all its variations into a VarDOM that compactly represents all DOM variations for further analysis. Based on the VarDOM, we build conditional call graphs for embedded HTML, CSS, and JS. Our empirical evaluation on real-world web applications show that our analysis achieves 100% precision in identifying call-graph edges. 62% of the edges cross PHP strings, and 17% of them cross files - in both situations, navigation without tool support is tedious and error prone.


title: Direct and realistic handover of a virtual object

abstract: We propose a method to induce a realistic two-hand manipulation of virtual objects by representing their reactions to multiple contacts of hands and fingers in a unified way. One of the key problems in achieving physics-based virtual interaction is that there is no accurate haptic feedback to the user's hands and fingers, which must introduce a mismatch between the real hands and their virtual counterparts. This problem becomes more serious when multiple hands or fingers are involved to a single virtual object, for example, a hand-over situation from one hand to the other. The key contribution of this paper is to represent and describe the physical status of a virtual object in the local object space and to change the status in the object point of view in order to avoid its drastic changes by the physical-virtual mismatch. The experiments compare the performance of the proposed method with objects in different shapes when using 2D and 3D visualization devices.


title: Analyzing growing plants from 4D point cloud data

abstract: Studying growth and development of plants is of central importance in botany. Current quantitative are either limited to tedious and sparse manual measurements, or coarse image-based 2D measurements. Availability of cheap and portable 3D acquisition devices has the potential to automate this process and easily provide scientists with volumes of accurate data, at a scale much beyond the realms of existing methods. However, during their development, plants grow new parts (e.g., vegetative buds) and bifurcate to different components --- violating the central incompressibility assumption made by existing acquisition algorithms, which makes these algorithms unsuited for analyzing growth. We introduce a framework to study plant growth, particularly focusing on accurate localization and tracking topological events like budding and bifurcation. This is achieved by a novel forward-backward analysis, wherein we track robustly detected plant components back in time to ensure correct spatio-temporal event detection using a locally adapting threshold. We evaluate our approach on several groups of time lapse scans, often ranging from days to weeks, on a diverse set of plant species and use the results to animate static virtual plants or directly attach them to physical simulators.


title: Cost analysis of Turku University Central Hospital PACS in 1998

abstract: Introduction of a picture archiving and communication system (PACS) into an old hospital poses several problems since the system cannot be implemented instantaneously. We analysed the costs of a halfway implemented PACS in Turku University Central Hospital (TUCH) during 1998. Manual and digital archiving in 1998 continued parallel so that only 10% reduction of film usage was achieved. A 21% increase in image manipulation and storage expenses occurred. A calculation based on potential 90% reduction of film usage and changes in the amount of personnel suggest some direct savings. Also, indirect savings due to more efficient patient treatment and the reduction of time spent in the hospital, and overhead costs of information handling are expected to lead to savings. However, these savings were not included in our calculations. When full-scale PACS is installed, equipment investments, network and digital ware maintenance costs will drop from 121 to 116% compared with traditional film archiving activity costs.


title: EULAR recommendations for the treatment of systemic sclerosis: a report from the EULAR Scleroderma Trials and Research group (EUSTAR)

abstract: PURPOSE ::: The optimal treatment of systemic sclerosis (SSc) is a challenge because the pathogenesis of SSc is unclear and it is an uncommon and clinically heterogeneous disease affecting multiple organ systems. The aim of the European League Against Rheumatism (EULAR) Scleroderma Trials and Research group (EUSTAR) was to develop evidence-based, consensus-derived recommendations for the treatment of SSc. ::: ::: ::: METHODS ::: To obtain and maintain a high level of intrinsic quality and comparability of this approach, EULAR standard operating procedures were followed. The task force comprised 18 SSc experts from Europe, the USA and Japan, two SSc patients and three fellows for literature research. The preliminary set of research questions concerning SSc treatment was provided by 74 EUSTAR centres. ::: ::: ::: RESULTS ::: Based on discussion of the clinical research evidence from published literature, and combining this with current expert opinion and clinical experience, 14 recommendations for the treatment of SSc were formulated. The final set includes the following recommendations: three on SSc-related digital vasculopathy (Raynaud's phenomenon and ulcers); four on SSc-related pulmonary arterial hypertension; three on SSc-related gastrointestinal involvement; two on scleroderma renal crisis; one on SSc-related interstitial lung disease and one on skin involvement. Experts also formulated several questions for a future research agenda. ::: ::: ::: CONCLUSIONS ::: Evidence-based, consensus-derived recommendations are useful for rheumatologists to help guide treatment for patients with SSc. These recommendations may also help to define directions for future clinical research in SSc.


title: Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings

abstract: We study a symmetric collaborative dialogue setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.


MATERNAL LIPOPOLYSACCHARIDE ADMINISTRATION LEADS TO DEVELOPMENTAL ALTERATIONS IN HIPPOCAMPAL NEURONAL DENSITY AND EXPRESSION OF CORTICAL DOPAMINERGIC RECEPTORS IN RAT OFFSPRING


title: Analysis and Validation of Control-Flow Complexity Measures with BPMN Process Models

abstract: Evaluating the complexity of business processes during the early stages of their development, primarily during the process modelling phase, provides organizations and stakeholders with process models which are easier to understand and easier to maintain. This presents advantages when carrying out evolution tasks in process models – key activities, given the current competitive market. In this work, we present the use and validation of the CFC metric to evaluate the complexity of business processes modelled with BPMN. The complexity of processes is evaluated from a control-flow perspective. An empirical evaluation has been carried out in order to demonstrate that the CFC metric can be useful when applied to BPMN models, providing information about their ease of maintenance.


title: Cascoded flipped voltage follower based output-capacitorless low-dropout regulator for SoCs

abstract: A novel cascoded flipped voltage follower (CAFVF) based output-capacitorless low-dropout (LDO) regulator is proposed and implemented in 0.18-μm CMOS technology. With a cascode current source (CCS) embedded into the CAFVF structure, the proposed LDO regulator achieves 58.6-dB DC gain in heavy loading condition (100 mA), which is 44-dB for the conventional CAFVF counterpart under identical conditions. The cascode compensation technique is introduced to widen the loop bandwidth and reduce the minimal loading requirement. With a 5-pF compensation capacitor, the minimum load current to keep the proposed LDO regulator stable is reduced to 50 μA. In addition, the unity-gain frequency (UGF) is extended from 1.51 MHz to 2.36 MHz in 100-mA loading condition. Moreover, an accurate stability analysis without ignoring any channel resistance has been presented in this work. Simulation results show that the LDO regulator consumes an ultra-low quiescent current (Iq) of 14 μA for input voltage ranging from 1.2 V to 1.8 V, with a dropout voltage (Vdrop) of 200 mV.


title: Fast dictionary attacks on passwords using time-space tradeoff

abstract: Human-memorable passwords are a mainstay of computer security. To decrease vulnerability of passwords to brute-force dictionary attacks, many organizations enforce complicated password-creation rules and require that passwords include numerals and special characters. We demonstrate that as long as passwords remain human-memorable, they are vulnerable to "smart-dictionary" attacks even when the space of potential passwords is large.Our first insight is that the distribution of letters in easy-to-remember passwords is likely to be similar to the distribution of letters in the users' native language. Using standard Markov modeling techniques from natural language processing, this can be used to dramatically reduce the size of the password space to be searched. Our second contribution is an algorithm for efficient enumeration of the remaining password space. This allows application of time-space tradeoff techniques, limiting memory accesses to a relatively small table of "partial dictionary" sizes and enabling a very fast dictionary attack.We evaluated our method on a database of real-world user password hashes. Our algorithm successfully recovered 67.6% of the passwords using a 2 x 109 search space. This is a much higher percentage than Oechslin's "rainbow" attack, which is the fastest currently known technique for searching large keyspaces. These results call into question viability of human-memorable character-sequence passwords as an authentication mechanism.


title: Wideband Omnidirectional Circularly Polarized Patch Antenna Based on Vortex Slots and Shorting Vias

abstract: A wideband omnidirectional circularly polarized (CP) microstrip antenna is proposed. The proposed antenna is composed of a disk-loaded coaxial probe, six shorting vias and a circular patch with six vortex slots. Three resonant modes, which include the TM ::: 01 ::: , top-loaded monopole and TM ::: 02 ::: modes, with conical radiation patterns can be simultaneously excited in the antenna structure by a disk-loaded coaxial probe through capacity coupling, and they can be coupled together to realize a wideband operation. Six shorting vias, located symmetrically about the center of the patch, are introduced for generating the θ-polarization and six vortex slots are employed for obtaining the φ-polarization. Omnidirectional CP fields can be obtained at all these three resonant modes when the two orthogonal polarizations are equal in amplitude but different in phase by 90°. Both the wideband and CP principles are analyzed and verified by the parametric study. The obtained results show that the wideband CP antenna has a 10-dB impedance bandwidth of 51.7% and a 3-dB axial ratio (AR) bandwidth of 57.8%. In addition, a comparison between the proposed wideband antenna and the related wideband CP antennas in the literature is made.


title: Automatic Trimap Generation and Consistent Matting for Light-Field Images

abstract: In this paper, we introduce an automatic approach to generate trimaps and consistent alpha mattes of foreground objects in a light-field image. Our method first performs binary segmentation to roughly segment a light-field image into foreground and background based on depth and color. Next, we estimate accurate trimaps through analyzing color distribution along the boundary of the segmentation using guided image filter and KL-divergence. In order to estimate consistent alpha mattes across sub-images, we utilize the epipolar plane image (EPI) where colors and alphas along the same epipolar line must be consistent. Since EPI of foreground and background are mixed in the matting area, we propagate the EPI from definite foreground/background regions to unknown regions by assuming depth variations within unknown regions are spatially smooth. Using the EPI constraint, we derive two solutions to estimate alpha when color samples along epipolar line are known, and unknown. To further enhance consistency, we refine the estimated alpha mattes by using the multi-image matting Laplacian with an additional EPI smoothness constraint. In experimental evaluations, we have created a dataset where the ground truth alpha mattes of light-field images were obtained by using the blue screen technique. A variety of experiments show that our proposed algorithm produces both visually and quantitatively high-quality alpha mattes for light-field images.


title: Idolizing sport celebrities: a gateway to psychopathology?

abstract: Purpose – Sport celebrities often endorse their team, their sport, and non-sports-related products. Increased idolizing of sport celebrities by adolescents is one artifact of this promotional practice. Although seemingly innocuous, adolescents who idolize sport celebrities may, as adults, come to worship such celebrities; this unhealthy obsession may afflict 10 percent or more of adults. If adolescent hero worship of sport celebrities is a gateway to this adult psychopathology, then alerting parents, as well as encouraging social responsibility among advertisers and sport teams/leagues, is critical. This paper aims to address the issues. Design/methodology/approach – After a brief review of the literature on adolescent hero worship, the literature on the determinants and effects of celebrity worship are explored. Findings – Once parents, advertisers, sport team/leagues are sensitized to the problem, adolescent hero worship of sport celebrities can be mitigated as a likely gateway to many adults’ unhealthy obsession with celebrities. Research limitations/implications – Directions for future sport celebrity worship research are suggested. Practical implications – The incidence of a potentially psychologically damaging affliction can be reduced without harm to advertisers, sport teams/leagues, and athletes. Social implications – Ways to reduce promotion-induced sport celebrity worship – without eliminating sport promotion per se – are suggested. Recommendations are targeted for sport-related and non-sport-related products as well as teams and leagues/conferences.


title: Civitas: A Secure Remote Voting System

abstract: Civitas is the first implementation of a coercion-resistant, universally verifiable, remote voting scheme. This paper describes the design of Civitas, details the cryptographic protocols used in its construction, and illustrates how language-enforced information-flow security policies yield assurance in the implementation. The performance of Civitas scales well in the number of voters and offers reasonable tradeoffs between time, cost, and security. These results suggest that secure electronic voting is achievable.


title: Biped walking stabilization based on linear inverted pendulum tracking

abstract: A novel framework of biped walking stabilization control is introduced. The target robot is a 42 DOF humanoid robot HRP-4C which has a body dimensions close to the average Japanese female. We develop a body posture controller and foot force controllers on the joint position servo of the robot. By applying this posture/force control, we can regard the robot system as a simple linear inverted pendulum with ZMP delay. After a preliminary experiment to confirm the linear dynamics, we design a tracking controller for walking stabilization. It is evaluated in the experiments of HRP-4C walking and turning on a lab floor. The robot can also perform an outdoor walk on an uneven pavement.


title: Detecting ironic intent in creative comparisons

abstract: Irony is an effective but challenging mode of communication that allows a speaker to express sentiment-rich viewpoints with concision, sharpness and humour. Irony is especially common in online documents that express subjective and deeply-felt opinions, and thus represents a significant obstacle to the accurate analysis of sentiment in web texts. In this paper we look at one commonly used framing device for linguistic irony --the simile --to show how irony is often marked in ways that make it computationally feasible to detect. We conduct a very large corpus analysis of web-harvested similes to identify the most interesting characteristics of ironic comparisons, and provide an empirical evaluation of a new algorithm for separating ironic from non-ironic similes.


title: NILMTK: an open source toolkit for non-intrusive load monitoring

abstract: Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.


title: Course signals at Purdue: using learning analytics to increase student success

abstract: In this paper, an early intervention solution for collegiate faculty called Course Signals is discussed. Course Signals was developed to allow instructors the opportunity to employ the power of learner analytics to provide real-time feedback to a student. Course Signals relies not only on grades to predict students' performance, but also demographic characteristics, past academic history, and students' effort as measured by interaction with Blackboard Vista, Purdue's learning management system. The outcome is delivered to the students via a personalized email from the faculty member to each student, as well as a specific color on a stoplight -- traffic signal -- to indicate how each student is doing. The system itself is explained in detail, along with retention and performance outcomes realized since its implementation. In addition, faculty and student perceptions will be shared.


title: Open IE as an Intermediate Structure for Semantic Tasks

abstract: Semantic applications typically extract information from intermediate structures derived from sentences, such as dependency parse or semantic role labeling. In this paper, we study Open Information Extraction’s (Open IE) output as an additional intermediate structure and find that for tasks such as text comprehension, word similarity and word analogy it can be very effective. Specifically, for word analogy, Open IE-based embeddings surpass the state of the art. We suggest that semantic applications will likely benefit from adding Open IE format to their set of potential sentencelevel structures.


title: A Scale to Assist the Diagnosis of Autism and Asperger’s Disorder in Adults (RAADS): A Pilot Study

abstract: An empirically based 78 question self-rating scale based on DSM-IV-TR and ICD-10 criteria was developed to assist clinicians’ diagnosis of adults with autism and Asperger’s Disorder-the Ritvo Autism and Asperger’s Diagnostic Scale (RAADS). It was standardized on 17 autistic and 20 Asperger’s Disorder and 57 comparison subjects. Both autistic and Asperger’s groups scored significantly higher than comparison groups with no overlap; sensitivity, specificity, and content validity equaled one. Cronbach’s alpha coefficients of internal consistency of three subscales were satisfactory. Gender, age, and diagnostic categories were not significantly associated factors. The RAADS can be administered and scored in less than an hour and may be useful as a clinical scale to assist identification of autism and Asperger’s Disorder in adults. The RAADS does not distinguish between autism and Asperger’s Disorder.


title: Issues in Mixing Qualitative and Quantitative Approaches to Research

abstract: That mixed methods studies have become “trendy” again after a period of disrepute does not mean the issues such methods raise have gone away. Definitional, paradigmatic and methodological issues continue to be raised when researchers write about mixed methods, while design issues, issues in sampling, analysis and reporting and wide - ranging demands on researcher skills, finances and time are faced daily by those involved in a mixed methods study. Mixed methods researchers, in bringing together the benefits of both qualitative and quantitative approaches to research, often claim greater validity of results as a reason for their methodological choices, but without adequate consideration of the issues involved such validity may be more imagined than real.


title: Security of Trusted Repeater Quantum Key Distribution Networks

abstract: A Quantum Key Distribution (QKD) network is an infrastructure capable of performing long-distance and high-rate secret key agreement with information-theoretic security. In this paper we study security properties of QKD networks based on trusted repeater nodes. Such networks can already be deployed, based on current technology. We present an example of a trusted repeater QKD network, developed within the SECOQC project. The main focus is put on the study of secure key agreement over a trusted repeater QKD network, when some nodes are corrupted. We propose an original method, able to ensure the authenticity and privacy of the generated secret keys.


title: Advances in natural language processing

abstract: Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition, and speech synthesis. Today’s researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We describe successes and challenges in this rapidly advancing area.


title: Improving the Diversity of Top-N Recommendation via Determinantal Point Process

abstract: Recommender systems take the key responsibility to help users discover items that they might be interested in. Many recommendation algorithms are built upon similarity measures, which usually result in low intra-list diversity. The deficiency in capturing the whole range of user interest often leads to poor satisfaction. To solve this problem, increasing attention has been paid on improving the diversity of recommendation results in recent years. ::: In this paper, we propose a novel method to improve the diversity of top-$N$ recommendation results based on the determinantal point process (DPP), which is an elegant model for characterizing the repulsion phenomenon. We propose an acceleration algorithm to greatly speed up the process of the result inference, making our algorithm practical for large-scale scenarios. We also incorporate a tunable parameter into the DPP model which allows the users to smoothly control the level of diversity. More diversity metrics are introduced to better evaluate diversification algorithms. We have evaluated our algorithm on several public datasets, and compared it thoroughly with other reference algorithms. Results show that our proposed algorithm provides a much better accuracy-diversity trade-off with comparable efficiency.


title: Extracting Semantic Information for e-Commerce

abstract: Rakuten Ichiba uses a taxonomy to organize the items it sells. Currently, the taxonomy classes that are relevant in terms of profit generation and difficulty of exploration are being manually extended with data properties deemed helpful to create pages that improve the user search experience and ultimately the conversion rate. In this paper we present a scalable approach that aims to automate this process, automatically selecting the relevant and semantically homogenous subtrees in the taxonomy, extracting from semi-structured text in items descriptions a core set of properties and a popular subset of their ranges, then extending the covered range using relational similarities in free text. Additionally, our process automatically tags the items with the new semantic information and exposes them as RDF triples. We present a set of experiments showing the effectiveness of our approach in this business context.


title: Analysis and application of Wireshark in TCP/IP protocol teaching

abstract: TCP/IP is widely employed to interconnect computing facilities in today's network environments, and it is the most important content of networking elements study. This paper introduces the functions and characteristics of Wireshark, and expounds its analysis and application in TCP/IP protocol teaching by some specific examples. The practice shows that good teaching result will be received with the aid of Wireshark and it is worth popularization and application.


title: Predicting box-office success of motion pictures with neural networks

abstract: Predicting box-office receipts of a particular motion picture has intrigued many scholars and industry leaders as a difficult and challenging problem. In this study, the use of neural networks in predicting the financial performance of a movie at the box-office before its theatrical release is explored. In our model, the forecasting problem is converted into a classification problem-rather than forecasting the point estimate of box-office receipts, a movie based on its box-office receipts in one of nine categories is classified, ranging from a 'flop' to a 'blockbuster.' Because our model is designed to predict the expected revenue range of a movie before its theatrical release, it can be used as a powerful decision aid by studios, distributors, and exhibitors. Our prediction results is presented using two performance measures: average percent success rate of classifying a movie's success exactly, or within one class of its actual performance. Comparison of our neural network to models proposed in the recent literature as well as other statistical techniques using a 10-fold cross validation methodology shows that the neural networks do a much better job of predicting in this setting.


title: Compact Substrate Integrated Waveguide (SIW) Bandpass Filter With Complementary Split-Ring Resonators (CSRRs)

abstract: A novel cross-coupled bandpass filter using new substrate integrated waveguide (SIW) resonators is presented. The resonator is built up by a SIW cavity loaded with a complementary split-ring resonator (CSRR) on its top metal plane. The resonant modes of the proposed resonator are analyzed, with the Q-factor of the dominant mode also extracted. It is shown that our proposed compact filter operates below the cutoff frequency of conventional SIWs. No spurious responses, corresponding to the higher-order resonant modes of the resonator, appear within an octave band of the central frequency. Its attractive performance is validated numerically and experimentally.


title: Personalized clothing recommendation combining user social circle and fashion style consistency

abstract: With the rapid expansion of social networks and fashion websites, clothing recommendation has attracted more attention of researchers, since various web data bring opportunities for recommender systems to solve the problems of cold start and sparsity. For clothing recommender system, user social circle and fashion style consistency of clothing items are two important factors, which have critical impacts on user decision making. In this paper, two practical problems are considered: how to visually analyze fashion style consistency between clothing items and how to implement personalized clothing recommendation by combining user social circle and fashion style consistency. To address the first problem, a Siamese Convolutional Neural Network (SCNN) with a novel sampling strategy is employed to measure the fashion style consistency of clothing items. It can learn a feature transformation from clothing images to a latent feature space, where the representations of clothing items with similar styles locate closer than those with different styles. For the second problem, three social factors (i.e., personal interest, interpersonal interest similarity and interpersonal influence) and fashion style consistency are fused into a unified personalized recommendation model based on probabilistic matrix factorization (PMF). To comprehensively evaluate our model, extensive experiments have been conducted on two real world datasets collected from a popular social fashion website, which demonstrate the effectiveness of the proposed method for personalized clothing recommendation.


Voice Assisted Text Reading System for Visually Impaired Persons Using TTS Method


title: Understanding Data Augmentation for Classification: When to Warp?

abstract: In this paper we investigate the benefit of augmenting data with synthetically created samples when training a machine learning classifier. Two approaches for creating additional training samples are data warping, which generates additional samples through transformations applied in the data-space, and synthetic over-sampling, which creates additional samples in feature-space. We experimentally evaluate the benefits of data augmentation for a convolutional backpropagation-trained neural network, a convolutional support vector machine and a convolutional extreme learning machine classifier, using the standard MNIST handwritten digit dataset. We found that while it is possible to perform generic augmentation in feature-space, if plausible transforms for the data are known then augmentation in data-space provides a greater benefit for improving performance and reducing overfitting.


title: On the Kinematic Motion Primitives (kMPs) – Theory and Application

abstract: Human neuromotor capabilities guarantee a wide variety of motions. A full understanding of human motion can be beneficial for rehabilitation or performance enhancement purposes, or for its reproduction on artificial systems like robots. This work aims at describing the complexity of human motion in a reduced dimensionality, by means of kinematic Motion Primitives (kMPs). A set of five invariant kMPs are identified for periodic motions, and a set of two kMPs for discrete motions. It is shown how these two sets of kMPs can be combined to synthesize more complex motion as the simultaneous execution of the periodic and the discrete motions. The results reported are an evidence of the theory of Central Pattern Generators (CPG), showing its effects on the kinematics, and are related to what presented in the literature on the Motor Primitives extracted from EMG signals. Experimental tests with the COmpliant huMANoid (COMAN) were performed to show that the kMPs extracted from human subjects can be used to transfer the features of human locomotion to the gait of a robot.


title: Adolescent Thriving: The Role of Sparks, Relationships, and Empowerment

abstract: Although most social science research on adolescence emphasizes risks and challenges, an emergent field of study focuses on adolescent thriving. The current study extends this line of inquiry by examining the additive power of identifying and nurturing young people’s “sparks,” giving them “voice,” and providing the relationships and opportunities that reinforce and nourish thriving. A national sample of 1,817 adolescents, all age 15 (49% female), and including 56% white, 17% Hispanic/Latino, and 17% African-American adolescents, completed an online survey that investigated their deep passions or interests (their “sparks”), the opportunities and relationships they have to support pursuing those sparks, and how empowered they feel to make civic contributions (their “voice”). Results consistently supported the hypothesis that linking one’s spark with a sense of voice and supportive opportunities and relationships strengthens concurrent outcomes, particularly those reflecting prosociality, during a key developmental transition period. The three developmental strengths also predicted most outcomes to a greater degree than did demographics. However, less than 10 percent of 15-year-olds reported experiencing high levels of all three strengths. The results demonstrate the value of focusing on thriving in adolescence, both to reframe our understanding of this age group and to highlight the urgency of providing adolescents the opportunities and relationships they need to thrive.


title: Pediatric Toxicology: Specialized Approach to the Poisoned Child

abstract: The poisoned child presents unique considerations in circumstances of exposure, clinical effects, diagnostic approach, and therapeutic interventions. The emergency provider must be aware of the pathophysiologic vulnerabilities of infants and children and substances that are especially toxic. Awareness is essential for situations in which the risk of morbidity and mortality is increased, such as child abuse by poisoning. Considerations in treatment include the need for attentive supportive care, pediatric implications for antidotal therapy, and extracorporeal removal methods such as hemodialysis in children. In this article, each of these issues and emerging poison hazards are discussed. Language: en


A Study on Evaluation of E-Government Service Quality


title: Albumin-binding domain from Streptococcus zooepidemicus protein Zag as a novel strategy to improve the half-life of therapeutic proteins.

abstract: Recombinant antibody fragments belong to the promising class of biopharmaceuticals with high potential for future therapeutic applications. However, due to their small size they are rapidly cleared from circulation. Binding to serum proteins can be an effective approach to improve pharmacokinetic properties of short half-life molecules. Herein, we have investigated the Zag albumin-binding domain (ABD) derived from Streptococcus zooepidemicus as a novel strategy to improve the pharmacokinetic properties of therapeutic molecules. To validate our approach, the Zag ABD was fused with an anti-TNFα single-domain antibody (sdAb). Our results demonstrated that the sdAb-Zag fusion protein was highly expressed and specifically recognizes human, rat and mouse serum albumins with affinities in the nanomolar range. Moreover, data also demonstrated that the sdAb activity against the therapeutic target (TNFα) was not affected when fused with Zag ABD. Importantly, the Zag ABD increased the sdAb half-life ∼39-fold (47min for sdAb versus 31h for sdAb-Zag). These findings demonstrate that the Zag ABD fusion is a promising approach to increase the half-life of small recombinant antibodies molecules without affecting their therapeutic efficacy. Moreover, the present study strongly suggests that the Zag ABD fusion strategy can be potentially used as a universal method to improve the pharmokinetics properties of many others therapeutics proteins and peptides in order to improve their dosing schedule and clinical effects.


title: Binary-block embedding for reversible data hiding in encrypted images

abstract: This paper first introduces a binary-block embedding (BBE) method to embed secret data in a binary image. Using BBE, we propose an algorithm for reversible data hiding in encrypted images (BBE-RDHEI). It uses BBE to embed binary bits in lower bit-planes of the original image into its higher bit-planes such that the lower bit-planes can be reserved for hiding secret data in subsequent processes. BBE-RDHEI employs a bit-level scrambling process after secret data embedding to spread embedded secret data to the entire marked encrypted image so that it can prevent secret data from loss. A security key design mechanism is proposed to enhance the security level of BBE-RDHEI. The processes of BBE-RDHEI are fully reversible. The secret data and original image can be reconstructed independently and separately. Experiments and comparisons show that BBE-RDHEI has an embedding rate nearly twice larger than the state-of-the-art algorithms, generates the marked decrypted images with high quality, and is able to withstand the brute-force, differential, noise and data loss attacks. Improved embedding capacity.Separable method and can fully recover the original image and secret data.High visual quality in the marked decrypted images.It is able to withstand the brute-force, differential, noise and data loss attacks.


Small-Scale Systems and Computational Creativity


title: Deploying PAWS: field optimization of the protection assistant for wildlife security

abstract: Poaching is a serious threat to the conservation of key species and whole ecosystems. While conducting foot patrols is the most commonly used approach in many countries to prevent poaching, such patrols often do not make the best use of limited patrolling resources. To remedy this situation, prior work introduced a novel emerging application called PAWS (Protection Assistant for Wildlife Security); PAWS was proposed as a game-theoretic ("security games") decision aid to optimize the use of patrolling resources. ::: ::: This paper reports on PAWS's significant evolution from a proposed decision aid to a regularly deployed application, reporting on the lessons from the first tests in Africa in Spring 2014, through its continued evolution since then, to current regular use in Southeast Asia and plans for future worldwide deployment. In this process, we have worked closely with two NGOs (Panthera and Rimba) and incorporated extensive feedback from professional patrolling teams. We outline key technical advances that lead to PAWS's regular deployment: (i) incorporating complex topographic features, e.g., ridge-lines, in generating patrol routes; (ii) handling uncertainties in species distribution (game theoretic payoffs); (iii) ensuring scalability for patrolling large-scale conservation areas with fine-grained guidance; and (iv) handling complex patrol scheduling constraints.


title: MySQL and NoSQL database comparison for IoT application

abstract: Internet of Things (IoT) concept has been around in tech world for few years now. IoT focuses on connection of number of smart devices. In near future, IoT will have applications in various domains and these applications are going to produce tremendous amount of data. With the continuous generation of heterogeneous data, problem arises to store, transfer & manage the data efficiently. Traditional database systems used Structured Query Language (SQL) database which has supported all the user requirements along with simplicity, robustness, flexibility, scalability, performance. But the main limitation they are facing is their static schema which is making RDBMS not suitable for IoT applications. On the other hand, NoSQL databases emerging in market have claimed to perform better than SQL database. The NoSQL databases are non-relational, schema free, no joins, easy replication support, horizontally scalable, etc. Does NoSQL perform better than SQL in all application scenarios? An effort to answer the same has been made in this paper. This paper compares SQL and NoSQL databases for a small scale IoT application of water sprinkler system and investigates whether NoSQL performs better than SQL in different scenarios.


title: A Markov-Chain Monte-Carlo Approach to Musical Audio Segmentation

abstract: This paper describes a method for automatically segmenting and labelling sections in recordings of musical audio. We incorporate the user’s expectations for segment duration as an explicit prior probability distribution in a Bayesian framework, and demonstrate experimentally that this method can produce accurate labelled segmentations for popular music.


title: Cancer cell metabolism, epigenetics and the potenti al influence of dietary components - A perspective.

abstract: Cancer cells are characterized by alterations in ce ll metabolism including increased glucose consumption and aerobic glycolysis, commonly known as the “Warburg effect”. Underlying mechanisms for this metabolic switch have been associated with genetic defects in pathways regulating metabolic processes. Recent research indicates that epigenetic alterations, e.g. in DNA methylation of promoter regions of glycolytic enzymes, might contribute to the deregulated expression of enzymes involved i n cell metabolism. Alternatively, changes in cell metabolism could alter the availabi lity of co-factors for epigenetic processes. This perspective will summarize current knowledge on interactions between cancer cell metabolism and epigenetic modulation of gene regulation, and how both processes can be affected by dietary components. Natural compounds with reported mechanisms targeting cancer cell metabolism include folate, flavonoi ds, green tea polyphenols, genistein from soy, silymarin from milk thistle, and the anthraqui none derivative shikonin. Reversible acetylation of histones and non-histone proteins ha s been shown to affect cell metabolism and other cellular processes, and can be targeted b y inhibitors of histone deacetylases (HDACs) and histone acetyl transferases (HATs). Natural compounds with HDAC or HAT inhibitory activity include butyrate, sulforaphane from broccoli, diallyldisulfide (DADS) from garlic, cambinol and dihydrocoumarin, diindolylmethane, epigallocatechin gallate (EGCG), curcumin, anacardic acid and garcinol. Currently we can only speculate whether their influence on epigenetic mechanisms is of impo rtance for normalization of the deregulated cancer cell metabolism. It can be anticip ated that the emergence of sensitive metabonomics technologies and genome-wide detection of epigenetic alterations in DNA methylation, histone modifications, and miRNA expression, will improve our understanding of the relationship between epigenetics and cancer cell metabolism.


title: A visual attention based convolutional neural network for image classification

abstract: This paper presents a visual attention based convolutional neural network (CNN) to solve the image classification problem in the real complex world scene. The presented method can simulate the process of recognizing objects and find the area of interest which is related with the task. Compared with the CNN method in image classification, the model is proficient in fine-grained classification problem and has a better robustness due to its mechanism of multi-glance and visual attention. We evaluate the model on vehicle dataset, where its performance exceeds CNN baseline on image classification.


title: Data mining: Prediction for performance improvement of graduate students using classification

abstract: Student performance in university courses is of great concern to the higher education where several factors may affect the performance. This paper is an attempt to apply the data mining processes, particularly classification, to help in enhancing the quality of the higher educational system by evaluating student data to study the main attributes that may affect the student performance in courses. For this purpose, we have used data obtained from Vikram University, Ujjain of course B.A. first year student. The classification rule generation process is based on the decision tree as a classification method where the generated rules are studied and evaluated. A system that facilitates the use of the generated rules is built which allows students to predict the final grade in a course under study.


title: Keepin' it real: pushing the desktop metaphor with physics, piles and the pen

abstract: We explore making virtual desktops behave in a more physically realistic manner by adding physics simulation and using piling instead of filing as the fundamental organizational structure. Objects can be casually dragged and tossed around, influenced by physical characteristics such as friction and mass, much like we would manipulate lightweight objects in the real world. We present a prototype, called BumpTop, that coherently integrates a variety of interaction and visualization techniques optimized for pen input we have developed to support this new style of desktop organization.


title: Bibliometric Analysis of Articles Published from 1980 to 2009 in Physical Therapy, Journal of the American Physical

abstract: Background Recent evidence demonstrates growth in both the quality and quantity of evidence in physical therapy. Much of this work has focused on randomized controlled trials and systematic reviews. ::: ::: Objective The purpose of this study was to conduct a comprehensive bibliometric assessment of Physical Therapy (PTJ) over the past 30 years to examine trends for all types of studies. ::: ::: Design This was a bibliometric analysis. ::: ::: Methods All manuscripts published in PTJ from 1980 to 2009 were reviewed. Research reports, topical reviews (including perspectives and nonsystematic reviews), and case reports were included. Articles were coded based on type, participant characteristics, physical therapy focus, research design, purpose of article, clinical condition, and intervention. Coding was performed by 2 independent reviewers, and author, institution, and citation information was obtained using bibliometric software. ::: ::: Results Of the 4,385 publications identified, 2,519 were included in this analysis. Of these, 67.1% were research reports, 23.0% were topical reviews, and 9.9% were case reports. Percentage increases over the past 30 years were observed for research reports, inclusion of “symptomatic” participants (defined as humans with a current symptomatic condition), systematic reviews, qualitative studies, prospective studies, and articles focused on prognosis, diagnosis, or metric topics. Percentage decreases were observed for topical reviews, inclusion of only “asymptomatic” participants (defined as humans without a current symptomatic condition), education articles, nonsystematic reviews, and articles focused on anatomy/physiology. ::: ::: Limitations Quality assessment of articles was not performed. ::: ::: Conclusions These trends provide an indirect indication of the evolution of the physical therapy profession through the publication record in PTJ. Collectively, the data indicated an increased emphasis on publishing articles consistent with evidence-based practice and clinically based research. Bibliometric analyses indicated the most frequent citations were metric studies and references in PTJ were from journals from a variety of disciplines.


Neural Conversational Model with Mutual Information Ranking


title: Proposing a Capability Perspective on Digital Business Models

abstract: Business models comprehensively describe the functioning of businesses in contemporary economic, technological, and societal environments. This paper focuses on the characteristics of digital business models from the perspective of capability research and develops a capability model for digital businesses. Following the design science research (DSR) methodology, multiple evaluation and design iterations were performed. Contributions to the design process came from IS/IT practice and the research base on business models and capabilities.


title: Robust and Efficient Stereo Feature Tracking for Visual Odometry

abstract: Visual odometry can augment or replace wheel odometry when navigating in high slip terrain which is quite important for autonomous navigation on Mars. We present a computationally efficient and robust visual odometry algorithm developed for the Mars Science Laboratory mission. This algorithm is a significant improvement over the algorithm developed for the Mars Exploration Rover Mission because it is at least four time more computationally efficient and it tracks significantly more features. The core of the algorithm is an integrated motion estimation and stereo feature tracking loop that allows for feature recovery while guiding feature correlation search to minimize computation. Results on thousands of terrestrial and Martian stereo pairs show that the algorithm can operate with no initial motion estimate while still obtaining subpixel attitude estimation performance.


title: An octopus anatomy-inspired robotic arm

abstract: This paper focuses on the design of a robotic arm inspired to the anatomy and morphology of an octopus arm. The octopus is a boneless animal and its amazing dexterity is due to its muscular structure where longitudinal (axial), transverse (radial) and oblique muscles seamlessly interact while preserving hydrostaticity i.e. volume conservation (“muscular hydrostat” [19]). Mimicking some features of the octopus is instrumental to design a dexterous and compliant system. After analysing the relevant anatomical and morphological characteristics of the octopus arm, the key biomechanical features of interest to the design of a robotic arm have been identified. A design methodology has been developed based on the analysis of the muscular hydrostat properties. A prototype arm has been built using bespoke contracting pneumatic muscles and expanding elements. In the current stage of development the system has 15 actuated degrees of motion (DOM) and 8 degrees of freedom (DOF), all independently controllable through valves and a dedicated electronics and software interface. Pros and cons of the current design as well as practical prototyping trade-offs are thoroughly described.


title: Design of a novel gate driver circuit for a Marx generator based 40kV electric fence energizer

abstract: Marx generator is a High Voltage Pulsed Power Supply (HVPPS) technique that consists of switches and capacitors. This paper considers design of a Marx generator consisting of 100 stages for a 40kV non-lethal electric fence. The gate drivers for the employed semiconductor switches have to meet the requirements of synchronized switching and high voltage isolation up to 40kV. Gate driver schemes generally used for Marx generators employ optocouplers and pulse transformers. To meet the requirements, these schemes result in costly, bulky and complex circuits when implemented for a large number of stages of a Marx generator making it an unattractive choice for low power applications (such as an electric fence) despite its many superior features over other HVPPS schemes. Electric fence application has an additional requirement of controlled release of energy to the output to ensure non-lethal behavior. Therefore, a novel gate driver design is presented here which is compact, economical and also ensures control over output energy, hence making Marx generator an attractive design option for electric fence applications. The proposed novel gate drivers were simulated and implemented in hardware for 100 stages to generate 40kV pulses of 15μs at repetition rate of 100Hz with each pulse delivering up to 18mJ energy. The reliability of the systems was tested for a successful continuous run of 10 hours.


title: Flexible lithium-ion planer thin-film battery

abstract: Commercialization of wearable electronics requires miniaturized, flexible power sources. Lithium ion battery is a strong candidate as the next generation high performance flexible battery. The development of flexible materials for battery electrodes suffers from the limited material choices. In this work, we present a flexible inorganic lithium-ion battery with no restrictions on the materials used. The battery showed an enhanced normalized capacity of 146 µAh/cm2.


title: An extemal-capacitor-less low-dropout regulator with less than −36dB PSRR at all frequencies from 10kHz to 1GHz using an adaptive supply-ripple cancellation technique to the body-gate

abstract: An external capacitor-less low-dropout regulator (LDO) that provides high power-supply rejection ratio (PSRR) at all low-to-high frequencies was presented. The LDO was designed to have the dominant pole, ωD, at the gate of the pass-transistor, V G , to secure stability without an external capacitor, even when the load current was large. Using the proposed adaptive supply-ripple cancellation (ASRC) technique, where the ripples copied from the supply are injected adaptively to the body-gate, the PSRR-hump of conventional LDOs with wd at Vg can be suppressed significantly. Since the ASRC continues to adjust the magnitude of the injecting ripples, the LDO of this work is able to maintain high PSRRs, irrespective of the amount of the load current, I L , or the dropout voltage, V Do . The proposed LDO was fabricated in a 65-nm CMOS process, and it had an input voltage of 1.2V. When having a 240-pF load capacitor, the measured PSRRs were less than −36dB at all frequencies from 10kHz to 1GHz, despite changes in Il and V Do . The active area was 0.087mm2 including the 240-pF load capacitor, and the total power consumption was 360μW.


title: Collaboration in Psychodynamic Psychotherapy

abstract: The concept of the collaborative relationship between patient and therapist has its roots in the psychodynamic literature. We trace the concept of collaboration in psychodynamic psychotherapy from classical psychoanalysis to contemporary psychoanalysis and psychodynamic therapies. The active collaboration between the participants central to Bordin's pan-theoretical perspective on the alliance is highlighted. Developments in alliance-fostering techniques and in relational therapy offer the clinician innovative ways to enhance the collaboration and to repair strained or ruptured collaboration. A case study illustrates how the collaborative work in psychodynamic therapy serves as both a means of productive work and as an arena for exploring the evolving here-and-now matrix of the relationship.


title: A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

abstract: Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans. However, the segmentation accuracy of some small organs (e.g., the pancreas) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupies a large fraction of the input volume. In this paper, we formulate this problem into a fixed-point model which uses a predicted segmentation mask to shrink the input region. This is motivated by the fact that a smaller input region often leads to more accurate segmentation. In the training process, we use the ground-truth annotation to generate accurate input regions and optimize network weights. On the testing stage, we fix the network parameters and update the segmentation results in an iterative manner. We evaluate our approach on the NIH pancreas segmentation dataset, and outperform the state-of-the-art by more than \(4\%\), measured by the average Dice-Sorensen Coefficient (DSC). In addition, we report \(62.43\%\) DSC in the worst case, which guarantees the reliability of our approach in clinical applications.


title: Effectiveness and safety of artemether–lumefantrine versus artesunate–amodiaquine for unsupervised treatment of uncomplicated falciparum malaria in patients of all age groups in Nanoro, Burkina Faso: a randomized open label trial

abstract: BackgroundSeveral studies have reported high efficacy and safety of artemisinin-based combination therapy (ACT) mostly under strict supervision of drug intake and limited to children less than 5 years of age. Patients over 5 years of age are usually not involved in such studies. Thus, the findings do not fully reflect the reality in the field. This study aimed to assess the effectiveness and safety of ACT in routine treatment of uncomplicated malaria among patients of all age groups in Nanoro, Burkina Faso.MethodsA randomized open label trial comparing artesunate–amodiaquine (ASAQ) and artemether–lumefantrine (AL) was carried out from September 2010 to October 2012 at two primary health centres (Nanoro and Nazoanga) of Nanoro health district. A total of 680 patients were randomized to receive either ASAQ or AL without any distinction by age. Drug intake was not supervised as pertains in routine practice in the field. Patients or their parents/guardians were advised on the time and mode of administration for the 3 days treatment unobserved at home. Follow-up visits were performed on days 3, 7, 14, 21, and 28 to evaluate clinical and parasitological resolution of their malaria episode as well as adverse events. PCR genotyping of merozoite surface proteins 1 and 2 (msp-1, msp-2) was used to differentiate recrudescence and new infection.ResultsBy day 28, the PCR corrected adequate clinical and parasitological response was 84.1 and 77.8 % respectively for ASAQ and AL. The cure rate was higher in older patients than in children under 5 years old. The risk of re-infection by day 28 was higher in AL treated patients compared with those receiving ASAQ (p < 0.00001). Both AL and ASAQ treatments were well tolerated.ConclusionThis study shows a lowering of the efficacy when drug intake is not directly supervised. This is worrying as both rates are lower than the critical threshold of 90 % required by the WHO to recommend the use of an anti-malarial drug in a treatment policy.Trial registration: NCT01232530


title: What do people do with porn? Qualitative research into the comsumption, use, and experience of pornography and other sexually explicit media

abstract: This article reviews qualitative research into the consumption of pornography and other sexually explicit media emerging from a range of subject areas. Taking a critique of quantitative methods and a focus on measuring sexual effects and attitudes as a starting point, it considers the proposition that qualitative work is more suited to an examination of the complex social, cultural, and political constructions of sexuality. Examining studies into the way men, women, and young people see, experience, and use explicit media texts, the article identifies the key findings that have emerged. Qualitative work shows that sexually explicit media texts are experienced and understood in a variety of ways and evoke strong and often contradictory reactions, not all of which are represented in public debates about pornography. These texts function in a range of different ways, depending on context; as a source of knowledge, a resource for intimate practices, a site for identity construction, and an occasion for performing gender and sexuality. The article reviews these studies and their findings, identifying what they suggest about directions for future research, both in terms of developing methodology and refining approaches to sexuality and media consumption.


title: Learning Convolutional Networks for Content-Weighted Image Compression

abstract: Lossy image compression is generally formulated as a joint rate-distortion optimization problem to learn encoder, quantizer, and decoder. Due to the non-differentiable quantizer and discrete entropy estimation, it is very challenging to develop a convolutional network (CNN)-based image compression system. In this paper, motivated by that the local information content is spatially variant in an image, we suggest that: (i) the bit rate of the different parts of the image is adapted to local content, and (ii) the content-aware bit rate is allocated under the guidance of a content-weighted importance map. The sum of the importance map can thus serve as a continuous alternative of discrete entropy estimation to control compression rate. The binarizer is adopted to quantize the output of encoder and a proxy function is introduced for approximating binary operation in backward propagation to make it differentiable. The encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner. And a convolutional entropy encoder is further presented for lossless compression of importance map and binary codes. In low bit rate image compression, experiments show that our system significantly outperforms JPEG and JPEG 2000 by structural similarity (SSIM) index, and can produce the much better visual result with sharp edges, rich textures, and fewer artifacts.


title: A Simple Bayesian Modelling Approach to Event Extraction from Twitter

abstract: With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media. LEM is fully unsupervised and does not require annotated data for training. We evaluate LEM on a Twitter corpus. Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%.


title: Full Integrity and Freshness for Outsourced Storage

abstract: Data outsourcing relieves cloud users of the heavy burden of infrastructure management and maintenance. However, the handover of data control to untrusted cloud servers significantly complicates the security issues. Conventional signature verification widely adopted in cryptographic storage system only guarantees the integrity of retrieved data, for those rarely or never accessed data, it does not work. This paper integrates proof of storage technique with data dynamics support into cryptographic storage design to provide full integrity for outsourced data. Besides, we provide instantaneously freshness check for retrieved data to defend against potential replay attacks. We achieve these goals by designing flexible block structures and combining broadcast encryption, key regression, Merkle hash tree, proof of storage and fine-grained access control policies together to provide a secure storage service for outsourced data. Experimental evaluation of our prototype shows that the cryptographic cost and throughput is reasonable and acceptable.


title: Toward dynamic ensembles: the BAGA approach

abstract: Summary form only given. Existing studies on ensemble classifiers typically take a static approach in assembling individual classifiers, in which all the important features are specified in advance. In this paper, we propose a new concept, dynamic ensemble, as an advanced classifier that could have dynamic component classifiers and have dynamic configurations. Toward this goal, we have substantially expanded the existing "overproduce and choose" paradigm for ensemble construction. A new algorithm called BAGA is proposed to explore this approach. Taking a set of decision tree component classifiers as input, BAGA generates a set of candidate ensembles using combined bagging and genetic algorithm techniques so that component classifiers are determined at execution time. Empirical studies have been carried out on variations of the BAGA algorithm, where the sizes of chosen classifiers, effects of bag size, voting function and evaluation functions on the dynamic ensemble construction, are investigated.


Perceived e-service quality ( PeSQ ) Measurement validation and effects on consumer satisfaction and web site loyalty


title: Telecommunication Fraud Detection Using Data Mining techniques

abstract: This document presents the final report of the thesis “Telecommunication Fraud Detection Using Data Mining Techniques”, were a study is made over the effect of the unbalanced data, generated by the Telecommunications Industry, in the construction and performance of classifiers that allows the detection and prevention of frauds. In this subject, an unbalanced data set is characterized by an uneven class distribution where the amount of fraudulent instances (positive) is substantially smaller than the amount normal instances (negative). This will result in a classifier which is most likely to classify data has belonging to the normal class then to the fraud class. At first, an overall inspection is made over the data characteristics and the Naive Bayes model, which is the classifier selected to do the anomaly detection on these experiments. After the characteristics are presented, a feature engineering stage is done with the intent to extend the information contained in the data creating a depper relation with the data itself and model characteristics. A previously proposed solution that consists on undersampling the most abundant class (normal) before building the model, is presented and tested. In the end, the new proposals are presented. The first proposal is to study the effects of changing the intrinsic class distribution parameter in the Naive Bayes model and evaluate its performance. The second proposal consists in estimating margin values that when applied to the model output, attempt to bring more positive instances from previous negative classification. All of these suggested models are validated over a monte-carlo experiment, using data with and without the engineered features.


title: Improved Multimodal Representation Learning with Skip Connections

abstract: Multimodal Deep Boltzmann Machines (DBMs) have demonstrated huge successes in multimodal representation learning tasks. During inference, DBMs function as Recurrent Neural Nets (RNNs) because of the intractable distributions. To learn the parameters, optimizations can alternatively be operated on these surrogate RNNs with "truncated message passing". As a consequence, the gradient will propagate through a long chain without any local guidance which can potentially affects the optimization procedure. In this paper, we address this problem by adding skip connections during back-propagation while keeping the forward propagation (inference) untouched. With skip connections, we implicitly assign local "targets" for the states of intermediate inference loops to approach. Applied to different training criteria on different data sets, we demonstrate the proposed algorithms can consistently help to train better models while at a lower cost of training time. Experimental results show that our algorithms can achieve state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.


title: Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation

abstract: Monaural source separation is important for many real world applications. It is challenging because, with only a single channel of information available, without any constraints, an infinite number of solutions are possible. In this paper, we explore joint optimization of masking functions and deep recurrent neural networks for monaural source separation tasks, including speech separation, singing voice separation, and speech denoising. The joint optimization of the deep recurrent neural networks with an extra masking layer enforces a reconstruction constraint. Moreover, we explore a discriminative criterion for training neural networks to further enhance the separation performance. We evaluate the proposed system on the TSP, MIR-1K, and TIMIT datasets for speech separation, singing voice separation, and speech denoising tasks, respectively. Our approaches achieve 2.30-4.98 dB SDR gain compared to NMF models in the speech separation task, 2.30-2.48 dB GNSDR gain and 4.32-5.42 dB GSIR gain compared to existing models in the singing voice separation task, and outperform NMF and DNN baselines in the speech denoising task.


title: Case Study: Explaining Diabetic Retinopathy Detection Deep CNNs via Integrated Gradients

abstract: In this report, we applied integrated gradients to explaining a neural network for diabetic retinopathy detection. The integrated gradient is an attribution method which measures the contributions of input to the quantity of interest. We explored some new ways for applying this method such as explaining intermediate layers, filtering out unimportant units by their attribution value and generating contrary samples. Moreover, the visualization results extend the use of diabetic retinopathy detection model from merely predicting to assisting finding potential lesions.


title: Feasibility Study of a Hybrid Mechanical-Static DC Circuit Breaker for Superconducting Magnet Protection

abstract: This paper describes the feasibility studies of a hybrid DC circuit breaker (CB) for quench protection of superconducting magnets rated for current up to some tens of kiloamperes (kA) and voltage of several kilovolts (kV). The proposed scheme is based on a mechanical switch paralleled to a fully controlled static CB; integrated gate commutated thyristors (IGCTs) were selected. In normal operation, the mechanical switch handles the continuous flow of DC current minimizing the on-state losses. When protection against quench is requested, the current is diverted into the static breaker that quickly interrupts the current, withstanding the reapplied voltage of several kV. The reliable turn-ON of several IGCTs in parallel with low voltage applied between anode and cathode is an issue to be assessed to confirm the feasibility of this design solution. The paper highlights that the components commonly used as snubber or protection circuit can guarantee the turn-ON of all the devices in parallel and that the IGCTs can be turned-ON with few volts applied; this latter aspect was proved by means of specific tests. Finally, a comparison between a static only and a hybrid CB is performed, highlighting the benefits introduced by the hybrid solution.


title: The Impact of Content, Context, and Creator on User Engagement in Social Media Marketing

abstract: Social media has become an important tool in establishing relationships between companies and customers. However, creating effective content for social media marketing campaigns is a challenge, as companies have difficulty understanding what drives user engagement. One approach to addressing this challenge is to use analytics on user-generated social media content to understand the relationship between content features and user engagement. In this paper we report on a quantitative study that applies machine learning algorithms to extract textual and visual content features from Instagram posts, along with creator- and context-related variables, and to statistically model their influence on user engagement. Our findings can guide marketing and social media professionals in creating engaging content that communicates more effectively with their audiences.