title: A PFC based isolated Sheppard-Taylor converter feeding brushless DC motor drive

abstract: This work presents a new configuration of power factor correction (PFC) based isolated Sheppard-Taylor converter for feeding brushless DC (BLDC) motor drive. In this work, fundamental frequency switching (FFS) of voltage source inverter (VSI) is used for reducing the switching losses in six solid-state switches of the VSL This is achieved by electronic commutation of BLDC motor and the DC bus voltage of the VSI feeding BLDC motor is controlled for adjusting the speed of BLDC motor. An operation of triple-discontinuous conduction mode (DCM) is used for proposed isolated-Sheppard Taylor converter for voltage control and PFC is achieved inherently at the AC mains using a single DC link voltage sensing. Performance evaluation of proposed drive is carried out at varying speeds and supply voltages with PFC operation at AC mains. The power quality indices thus achieved are within the limits of IEC 61000-3-2.


title: Game Theory for Network Security

abstract: As networks become ubiquitous in people's lives, users depend on networks a lot for sufficient communication and convenient information access. However, networks suffer from security issues. Network security becomes a challenging topic since numerous new network attacks have appeared increasingly sophisticated and caused vast loss to network resources. Game theoretic approaches have been introduced as a useful tool to handle those tricky network attacks. In this paper, we review the existing game-theory based solutions for network security problems, classifying their application scenarios under two categories, attack-defense analysis and security measurement. Moreover, we present a brief view of the game models in those solutions and summarize them into two categories, cooperative game models and non-cooperative game models with the latter category consisting of subcategories. In addition to the introduction to the state of the art, we discuss the limitations of those game theoretic approaches and propose future research directions.


title: RaptorQ code based concurrent transmissions in dual connectivity LTE network

abstract: Dual Connectivity(DC) is one of the key technologies standardized in Release 12 of the 3GPP specifications for the Long Term Evolution (LTE) network. It attempts to increase the per-user throughput by allowing the user equipment (UE) to maintain connections with the MeNB (master eNB) and SeNB (secondary eNB) simultaneously, which are inter-connected via non-ideal backhaul. In this paper, we focus on one of the use cases of DC whereby the downlink U-plane data is split at the MeNB and transmitted to the UE via the associated MeNB and SeNB concurrently. In this case, out-of-order packet delivery problem may occur at the UE due to the delay over the non-ideal backhaul link, as well as the dynamics of channel conditions over the MeNB-UE and SeNB-UE links, which will introduce extra delay for re-ordering the packets. As a solution, we propose to adopt the RaptorQ FEC code to encode the source data at the MeNB, and then the encoded symbols are separately transmitted through the MeNB and SeNB. The out-of-order problem can be effectively eliminated since the UE can decode the original data as long as it receives enough encoded symbols from either the MeNB or SeNB. We present detailed protocol design for the RaptorQ code based concurrent transmission scheme, and simulation results are provided to illustrate the performance of the proposed scheme.


title: Comparison of two concept-mapping techniques: Implications for scoring, interpretation, and use

abstract: We examine the equivalence of two construct-a-concept-map techniques: construct-a-map with created linking phrases (C), and construct-a-map with selected linking phrases (S). The former places few constraints on the respondent and has been considered the gold standard; the latter is cost and time efficient. Both their products and processes are compared quantitatively and qualitatively as to total accuracy score, individual proposition scores, proposition choice, map structure complexity, proposition generation rate, and proposition generation procedures. We conclude that the two mapping techniques are not equivalent: The C technique is better than the S technique in capturing students' partial knowledge, even though the S can be scored more efficiently than C. Based on their characteristics, if used as an assessment tool, the C technique is more suitable for formative assessment while the S technique is a better fit for large- scale assessments. 2005 Wiley Periodicals, Inc. J Res Sci Teach 42: 166-184, 2005


title: Development of a Metamodel for Medical Database Management on a Grid Network: Application to Health Watch and Epidemiology for Cancer and Perinatal Health

abstract: Centralized management of patient data is no more a viable solution. In many countries, patient identification restrictions due to privacy laws implies developing thorough mechanism to avoid duplicates and information loss. In this paper we present a work in progress dealing with a grid distributed medical data base. GPU based identification algorithms for disease surveillance, medical data exchange and epidemiological analyses.


Intelligent video surveillance systems for public spaces – a survey


title: A meta-model of business interaction for assisting intelligent workflow systems

abstract: Business interaction is a significant aspect of intelligent workflow systems, which are envisaged to deal with dynamic and interorganisational environments. However, automating business interaction in an intelligent way is not an easy task. To address this issue, this paper presents an ontological approach for modelling business interaction. The developed meta-model can support the process of automating business interaction, as well as assist its automated analysis. The ontology's development is described and its application is demonstrated through an example use case.


title: A Glucose Fuel Cell for Implantable Brain–Machine Interfaces

abstract: We have developed an implantable fuel cell that generates power through glucose oxidation, producing 3.4 μW cm(-2) steady-state power and up to 180 μW cm(-2) peak power. The fuel cell is manufactured using a novel approach, employing semiconductor fabrication techniques, and is therefore well suited for manufacture together with integrated circuits on a single silicon wafer. Thus, it can help enable implantable microelectronic systems with long-lifetime power sources that harvest energy from their surrounds. The fuel reactions are mediated by robust, solid state catalysts. Glucose is oxidized at the nanostructured surface of an activated platinum anode. Oxygen is reduced to water at the surface of a self-assembled network of single-walled carbon nanotubes, embedded in a Nafion film that forms the cathode and is exposed to the biological environment. The catalytic electrodes are separated by a Nafion membrane. The availability of fuel cell reactants, oxygen and glucose, only as a mixture in the physiologic environment, has traditionally posed a design challenge: Net current production requires oxidation and reduction to occur separately and selectively at the anode and cathode, respectively, to prevent electrochemical short circuits. Our fuel cell is configured in a half-open geometry that shields the anode while exposing the cathode, resulting in an oxygen gradient that strongly favors oxygen reduction at the cathode. Glucose reaches the shielded anode by diffusing through the nanotube mesh, which does not catalyze glucose oxidation, and the Nafion layers, which are permeable to small neutral and cationic species. We demonstrate computationally that the natural recirculation of cerebrospinal fluid around the human brain theoretically permits glucose energy harvesting at a rate on the order of at least 1 mW with no adverse physiologic effects. Low-power brain-machine interfaces can thus potentially benefit from having their implanted units powered or recharged by glucose fuel cells.


title: Topical word embeddings

abstract: Most word embedding models typically represent each word using a single vector, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance discriminativeness, we employ latent topic models to assign topics for each word in the text corpus, and learn topical word embeddings (TWE) based on both words and their topics. In this way, contextual word embeddings can be flexibly obtained to measure contextual word similarity. We can also build document representations, which are more expressive than some widely-used document models such as latent topic models. In the experiments, we evaluate the TWE models on two tasks, contextual word similarity and text classification. The experimental results show that our models outperform typical word embedding models including the multi-prototype version on contextual word similarity, and also exceed latent topic models and other representative document models on text classification. The source code of this paper can be obtained from https://github.com/largelymfs/topical_word_embeddings.


title: Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context

abstract: Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontology Alignment Evaluation Initiative (OAEI) largebio SNOMED-NCI subtask, comparable with the entity-level matchers in a SOTA system.


title: Efficient Condition-based Representations for Long-Term Visual Localization.

abstract: We propose an approach to localization from images that is designed to explicitly handle the strong variations in appearance happening when capturing conditions change throughout the day or across seasons. As revealed by recent long-term localization benchmarks, both traditional feature-based and retrieval-based approaches still struggle to handle such changes. Our novel retrieval-based method introduces condition-specific sub-networks allowing the computation of global image descriptors that are explicitly dependent of the capturing conditions. We compare our approach to previous localization methods on very recent challenging benchmarks, and observe that our method outperforms them by a large margin in case of day-night variation, where repeatable feature points cannot be identified or matched.


title: Collaborative Filtering Bandits

abstract: Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.


title: Ischemic Gangrene of the Glans following Penile Prosthesis Implantation

abstract: The development of ischemic gangrene of the penis following implantation of prosthesis is unusual, and very few cases are available in the literature. As a result, no established treatment protocol is available. We report our experience within a case of gangrene of the glans following implantation of a three-component prosthesis. We present a 53-year-old male, smoker with diabetes and hypercholesterolemia, who underwent surgery for the insertion of a penile prosthesis with 3 components to correct his erectile dysfunction and severe Peyronie's disease. The procedure was carried out without incidents. During the postoperative period, the patient began to complain from penile and perineal pain. He developed avascular necrosis of the glans. The necrosed area was excised. Four weeks later, he developed fever and perineal pain arriving to the emergency room with the prosthesis extruding through the glans. He had emergency surgery to remove the prosthesis plus surgical lavage and was prescribed broad-spectrum antibiotic therapy. Four weeks later, the penis was completely revascularized and reepithelialized. Ischemic gangrene following penile prosthesis implantation takes place in patients with poor peripheral vascularisation. Diabetes mellitus has been the common denominator to all of the reported cases.


title: Slab Air-Filled Substrate Integrated Waveguide

abstract: The slab air-filled substrate integrated waveguide (SAFSIW) is presented as a compromise between the high performance air-filled substrate integrated waveguide (AFSIW) and low profile dielectric-filled substrate integrated waveguide (DFSIW). Both theoretical and experimental studies are presented in order to investigate SAFSIW structures. To the authors' best knowledge, this is the first time that an SAFSIW-to-DFSIW transition is reported. A 90° phase shifter based on SAFSIW and a patent pending mounting process is also introduced. Measured results show a very high figure of merit (FoM) over the Ka-band. A phase difference of $\mathbf{86}\pm \mathbf{5}.\mathbf{4}^{\pmb{\circ}}$ , an amplitude imbalance of $\mathbf{0}.\mathbf{05}\pm \mathbf{0}.\mathbf{15}\ \mathbf{dB}$ and a FoM of $\mathbf{457}^{\pmb{\circ}}/\mathbf{dB}$ are obtained.


Blog: Relational modeling with unknown objects


abstract: Web-based learning enables more students to have access to the distance-learning environment, and provides students and teachers with unprecedented flexibility and convenience. However, the early experience of using this new learning means in China exposes a few problems. Among others, teachers accustomed to traditional teaching methods often find it difficult to put their courses online, and some students, especially the adult students, find themselves overloaded with too much information. In this paper, we present an open framework to solve these two problems. This framework allows students to interact with an automated question answering system to get their answers. It enables teachers to analyze students’ learning patterns and organize the web-based contents efficiently. The framework is intelligent due to the data mining and case-based reasoning features, and user-friendly because of its personalized services to both teachers and students. Data Mining and Case-Based Reasoning for Distance Learning


Affective and Behavioral Assessment for Adaptive Intelligent Tutoring Systems


title: Self-taught hashing for fast similarity search

abstract: The ability of fast similarity search at large scale is of great importance to many Information Retrieval (IR) applications. A promising way to accelerate similarity search is semantic hashing which designs compact binary codes for a large number of documents so that semantically similar documents are mapped to similar codes (within a short Hamming distance). Although some recently proposed techniques are able to generate high-quality codes for documents known in advance, obtaining the codes for previously unseen documents remains to be a very challenging problem. In this paper, we emphasise this issue and propose a novel Self-Taught Hashing (STH) approach to semantic hashing: we first find the optimal l-bit binary codes for all documents in the given corpus via unsupervised learning, and then train l classifiers via supervised learning to predict the l-bit code for any query document unseen before. Our experiments on three real-world text datasets show that the proposed approach using binarised Laplacian Eigenmap (LapEig) and linear Support Vector Machine (SVM) outperforms state-of-the-art techniques significantly.


title: The immediate effects of sigmoid colon manipulation on pressure pain thresholds in the lumbar spine.

abstract: Visceral manual therapy is increasingly used by UK osteopaths and manual therapists, but there is a paucity of research investigating its underlying mechanisms, and in particular in relation to hypoalgesia. The aim of this study was to investigate the immediate effects of osteopathic visceral mobilisation on pressure pain thresholds. A single-blinded, randomised, within subjects, repeated measures design was conducted on 15 asymptomatic subjects. Pressure pain thresholds were measured at the L1 paraspinal musculature and 1st dorsal interossei before and after osteopathic visceral mobilisation of the sigmoid colon. The results demonstrated a statistically significant improvement in pressure pain thresholds immediately after the intervention (P<0.001). This effect was not observed to be systemic, affecting only the L1 paraspinal musculature. This novel study provides new experimental evidence that visceral manual therapy can produce immediate hypoalgesia in somatic structures segmentally related to the organ being mobilised, in asymptomatic subjects.


RPW Method for Simple Assembly Line Balancing


title: The static analysis of WannaCry ransomware

abstract: Hacking weapons come in handy for cyber criminals anytime. Ransomware has increased in popularity. Its creators are playing our fears. The rapid proliferation of ransomware attack indicates the growing tendency of ransomware-as-a-service (RaaS) and the integration of hacking weapons. This paper presents the static analysis of the infamous WannaCry ransomware, which is one of the most impacted and propagated malware in 2017. This international wave of cyber threats is reported to have struck over 150 countries worldwide. Through the static analysis, the details of WannaCry processes and functions are revealed. The anatomy of ransomware attack is discussed to dissect the multi-staged execution of Wannacry, including deployment, installation, destruction, and command-and-control. The WannaCry ransomware not only implements the strong encrypting algorithm and key structure, but also integrates the hacking weapons leaked by the Shadow Brokers. In this paper, the reverse engineering analysis is conducted to explore each chain of malware execution.


title: SGAD: Soft-Guided Adaptively-Dropped Neural Network

abstract: Deep neural networks (DNNs) have been proven to have many redundancies. Hence, many efforts have been made to compress DNNs. However, the existing model compression methods treat all the input samples equally while ignoring the fact that the difficulties of various input samples being correctly classified are different. To address this problem, DNNs with adaptive dropping mechanism are well explored in this work. To inform the DNNs how difficult the input samples can be classified, a guideline that contains the information of input samples is introduced to improve the performance. Based on the developed guideline and adaptive dropping mechanism, an innovative soft-guided adaptively-dropped (SGAD) neural network is proposed in this paper. Compared with the 32 layers residual neural networks, the presented SGAD can reduce the FLOPs by 77% with less than 1% drop in accuracy on CIFAR-10.


title: A 5.8 GHz Circularly Polarized Rectenna With Harmonic Suppression and Rectenna Array for Wireless Power Transfer

abstract: A compact circularly polarized (CP) rectenna based on wide-slot antenna is designed at 5.8 GHz. The feedback wide-slot antenna is used for its broad band, easy-to-integrate circuitry, simple structure, and low sidelobes. The designed receiving antenna has a bandwidth of 31.8%. In order to minimize the profile of the rectenna, the rectifier is integrated on the back side of the substrate with a matching circuit and a dc-pass filter. It features stable conversion efficiency on three azimuthal planes. The maximum efficiency of 66.2% and an output dc voltage of 6.33 V have been measured with a 900 Ω load at the power density of 11.4 mW/cm2. A 5 × 5 rectenna array is built for large physical aperture and increased output power. The maximum conversion efficiency of 62% is observed with the array. Taking the advantage of immunity to misalignment of the CP antenna, the proposed rectenna and array are suitable to be used in mobile applications for wireless power transfer.


title: CyberFactory#1 — Securing the industry 4.0 with cyber-ranges and digital twins

abstract: The 4th Industrial revolution is driven by increased connectivity, digitization, automation and autonomy of manufacturing assets, which trigger strong optimization perspectives, new businesses as well as a handful of new threats which need to be properly addressed. The CyberFactory#1 project (ITEA nr 17032) aims at solving this dilemma between productivity and security through the design, development and demonstration of a System of Systems that embraces the technical, economical, human and the societal dimensions of future factories. It relies on innovative simulation, optimization and resilience capabilities. Pilots from transportation, automotive, electronics and machine manufacturing will host demonstrations of these achievements in a real operational environment at the end of a 3 years collaborative project. An approach based on digital twins' integration with cyber-range will be implemented, to analyze the impact of attacks in the quality monitoring of avionics electronic, to manufacturing cobotic systems in a virtual environment to improve their ability to withstand manipulation attempts, and as a tool for iterative design of safety/security functions for cobots.


title: Supervised Attentions for Neural Machine Translation

abstract: In this paper, we improve the attention or alignment accuracy of neural machine translation by utilizing the alignments of training sentence pairs. We simply compute the distance between the machine attentions and the "true" alignments, and minimize this cost in the training procedure. Our experiments on large-scale Chinese-to-English task show that our model improves both translation and alignment qualities significantly over the large-vocabulary neural machine translation system, and even beats a state-of-the-art traditional syntax-based system.


title: Structured Data Fusion

abstract: We present structured data fusion (SDF) as a framework for the rapid prototyping of knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set—stored as a dense, sparse, or incomplete tensor—is factorized with a matrix or tensor decomposition. Factorizations can be coupled, or fused, with each other by indicating which factors should be shared between data sets. At the same time, factors may be imposed to have any type of structure that can be constructed as an explicit function of some underlying variables. With the right choice of decomposition type and factor structure, even well-known matrix factorizations such as the eigenvalue decomposition, singular value decomposition and QR factorization can be computed with SDF. A domain specific language (DSL) for SDF is implemented as part of the software package Tensorlab, with which we offer a library of tensor decompositions and factor structures to choose from. The versatility of the SDF framework is demonstrated by means of four diverse applications, which are all solved entirely within Tensorlab’s DSL.


title: Geometric discrepancy revisited

abstract: Discrepancy theory addresses the general issue of approximating one measure by another one. Originally an offshoot of diophantine approximation theory, the area has expanded into applied mathematics, and now, computer science. Besides providing the theoretical foundation for sampling, it holds some of the keys to understanding the computational power of randomization. A few applications of discrepancy theory are listed. We give elementary algorithms for estimating the discrepancy between various measures arising in practice. We also present a general technique for proving discrepancy lower bounds. >


title: Seven pernicious kingdoms: A taxonomy of software security errors

abstract: Taxonomies can help software developers and security practitioners understand the common coding mistakes that affect security. The goal is to help developers avoid making these mistakes and more readily identify security problems whenever possible. Because developers today are by and large unaware of the security problems they can (unknowingly) introduce into code, a taxonomy of coding errors should provide a real tangible benefit to the software security community. Although the taxonomy proposed here is incomplete and imperfect, it provides an important first step. It focuses on collecting common errors and explaining them in a way that makes sense to programmers. This new taxonomy is made up of two distinct kinds of sets, which we're stealing from biology: a phylum (a type of coding error, such as illegal pointer value) and a kingdom (a collection of phyla that shares a common theme, such as input validation and representation). Both kingdoms and phyla naturally emerge from a soup of coding rules relevant to enterprise software, and it's for this reason that this taxonomy is likely to be incomplete and might lack certain coding errors. In some cases, it's easier and more effective to talk about a category of errors than to talk about any particular attack. Although categories are certainly related to attacks, they aren't the same as attack patterns.


title: A Computability Argument Against Superintelligence

abstract: Using the contemporary view of computing exemplified by recent models and results from non-uniform complexity theory, we investigate the computational power of cognitive systems. We show that in accordance with the so-called extended Turing machine paradigm such systems can be modelled as non-uniform evolving interactive systems whose computational power surpasses that of the classical Turing machines. Our results show that there is an infinite hierarchy of cognitive systems. Within this hierarchy, there are systems achieving and surpassing the human intelligence level. Any intelligence level surpassing the human intelligence is called the superintelligence level. We will argue that, formally, from a computation viewpoint the human-level intelligence is upper-bounded by the \(\Upsigma_2\) class of the Arithmetical Hierarchy. In this class, there are problems whose complexity grows faster than any computable function and, therefore, not even exponential growth of computational power can help in solving such problems, or reach the level of superintelligence.


title: Synthetic Medical Images from Dual Generative Adversarial Networks

abstract: Currently there is strong interest in data-driven approaches to medical image classification. However, medical imaging data is scarce, expensive, and fraught with legal concerns regarding patient privacy. Typical consent forms only allow for patient data to be used in medical journals or education, meaning the majority of medical data is inaccessible for general public research. We propose a novel, two-stage pipeline for generating synthetic medical images from a pair of generative adversarial networks, tested in practice on retinal fundi images. We develop a hierarchical generation process to divide the complex image generation task into two parts: geometry and photorealism. We hope researchers will use our pipeline to bring private medical data into the public domain, sparking growth in imaging tasks that have previously relied on the hand-tuning of models. We have begun this initiative through the development of SynthMed, an online repository for synthetic medical images.


title: Development of an unit type robot "KOHGA2" with stuck avoidance ability

abstract: To search victims in the narrow space at the disaster site, we have developed the snake-like rescue robot called "KOHGA". The robot is constructed by connecting multiple crawler vehicles serially by active joints. KOHGA has a problem that obstacles are caught to the joints and then the robot is stuck. To solve this problem, we developed an unit assembled robot "KOHGA2". It can be rearranged. The robot can swing crawler-arms and avoid the stuck. In this paper, we report the construction of the hardware and the control system of KOHGA2, the basic mobility performance, and the stuck avoidance strategy.


title: Rumor analysis framework in social media

abstract: When a disaster occurred, people try to acquire information through social media such as electronic bulletin boards, SNSs, and video services. Their decision-making tend to be affected by social media information. Sometimes, social media information is useful, so people can get valuable knowledge. On the other hand, social media information is occasionally unreliable. Harmful rumors spread and cause people to panic. In today's information oriented society, a mechanism to detect rumor information in social media has become very important. This paper proposes a framework to detect the rumor information in social media. Our proposed framework clarifies topics in social media, visualizes topic structures in time series variation. Then it extracts rumor candidates and seeks related information from other media such as TV program, newspapers and so on in order to confirm the reliability of rumor candidates. By our framework, potential rumors will be shown.


Productivity and Schematicity in Metaphors


Intermittent Query Processing


title: Translating Birdsong: Songbirds as a Model for Basic and Applied Medical Research

abstract: Songbirds, long of interest to basic neuroscience, have great potential as a model system for translational neuroscience. Songbirds learn their complex vocal behavior in a manner that exemplifies general processes of perceptual and motor skill learning and, more specifically, resembles human speech learning. Song is subserved by circuitry that is specialized for vocal learning and production but that has strong similarities to mammalian brain pathways. The combination of highly quantifiable behavior and discrete neural substrates facilitates understanding links between brain and behavior, both in normal states and in disease. Here we highlight (a) behavioral and mechanistic parallels between birdsong and aspects of speech and social communication, including insights into mirror neurons, the function of auditory feedback, and genes underlying social communication disorders, and (b) contributions of songbirds to understanding cortical-basal ganglia circuit function and dysfunction, including the possibility...


title: MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations

abstract: Author co-citation analysis (ACA) is a well-known and frequently-used method to exhibit the academic researchers and the professional field sketch according to co-citation relationships between authors in an article set. However, visualizing subtle examination is limited because only author co-citation information is required in ACA. The proposed method, called modified author co-citation analysis (MACA), exploits author co-citation relationship, citations published time, citations published carriers, and citations keywords, to construct MACA-based co-citation matrices. According to the results of our experiments: (1) MACA shows a good clustering result with more delicacy and more clearness; (2) more information involved in co-citation analysis performs good visual acuity; (3) in visualization of co-citation network produced by MACA, the points in different categories have far more distance, and the points indicating authors in the same category are closer together. As a result, the proposed MACA is found that more detailed and subtle information of a knowledge domain analyzed can be obtained, compared to ACA.


title: E-commerce purchase prediction approach by user behavior data

abstract: while e-commerce has grown quickly in recent years, more and more people are used to utilize this popular channel to purchase products and services on the Internet. Therefore, it becomes very important for shopping sites to predict precisely which items their customers would buy so as to increase sales or improve customer satisfaction. Traditional algorithms such as Collaborative Filtering, has been very popular in predicting users' preferences in movie, book, or music recommendation areas, but they face the problem that rating data is very sparse or even not available in shopping domain. Compared to the small amount of ratings in e-commerce shopping sites, the quantity of user clicking data is abundant and also contains sufficient information about users' purchase preferences. Therefore, in this paper we propose a prediction method based on probability statistics making use of user clicking behavior data. To evaluate the proposed approach, we use the data set provided by Ali Mobile Recommendation Competition held in 2015 which consisted of the huge amount of the clicking behavior log from 10,000 mobile users in one month. The experimental results show that our proposed method significantly alleviates the problem of data sparseness which traditional algorithms fail to deal with.


title: CANDiS: Coupled&Attention-Driven Neural Distant Supervision

abstract: Distant Supervision for Relation Extraction uses heuristically aligned text data with an existing knowledge base as training data. The unsupervised nature of this technique allows it to scale to web-scale relation extraction tasks, at the expense of noise in the training data. Previous work has explored relationships among instances of the same entity-pair to reduce this noise, but relationships among instances across entity-pairs have not been fully exploited. We explore the use of inter-instance couplings based on verb-phrase and entity type similarities. We propose a novel technique, CANDiS, which casts distant supervision using inter-instance coupling into an end-to-end neural network model. CANDiS incorporates an attention module at the instance-level to model the multi-instance nature of this problem. CANDiS outperforms existing state-of-the-art techniques on a standard benchmark dataset.


title: A multi-modulation retrodirective feed network for backscatter communications

abstract: Short ranges and limited field-of-views in semi-passive radio frequency identification (RFID) tags are the most prominent obstacles that limit the number of RFID applications relying on backscatter modulation to exchange data between a reader and a tag. We propose a retrodirective array structure that, if equipped on a tag, can increase the field-of-view and the coverage area of RFID systems by making the tag insensitive to its orientation with respect to one or more RFID readers. In this article, we derive and experimentally validate the conditions under which a rat-race coupler is retrodirective. The performance of the fabricated passive retrodirective structure is evaluated through the retrodirective ideality factor (RIF) amounting to a value of 1.003, which is close to the ideal RIF of one. The article ends with a discussion on how the proposed design can improve current RFID systems from a communication perspective.


title: Statistical Parsing by Machine Learning from a Classical Arabic Treebank

abstract: Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. ::: Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i’rāb (إعغاة ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis, dropped pronouns and hybrid representations. ::: A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic. ::: The Quran was chosen for annotation as a large body of work exists providing detailed syntactic analysis. Volunteer crowdsourcing is used for annotation in combination with expert supervision. A practical result of the annotation effort is the corpus website: http://corpus.quran.com, an educational resource with over two million users per year.


title: A Dual-Loop Antenna Design for Hepta-Band WWAN/LTE Metal-Rimmed Smartphone Applications

abstract: A simple direct-fed dual-loop antenna capable of providing hepta-band WWAN/LTE operation under surroundings of an unbroken metal rim in smartphone applications is proposed. The greatest highlight of this proposed antenna is that it provides a simple and effective multiband antenna solution for an unbroken metal-rimmed smartphone. The unbroken metal rim with 5 mm in height embraces the system circuit board of 130 $\, \times \,$ 70 mm $^{2}$ . Two no-ground portions of 10 $\, \times \,$ 70 mm $^{2}$ and 5 $\, \times \,$ 70 mm $^{2}$ are set on the top and bottom edge of the system circuit board, respectively. In-between the two separate no-ground portions, there is a system ground of 115 $\, \times \,$ 70 mm $^{2}$ connected with the unbroken metal rim via a small grounded patch which divides the unbroken metal rim into two strips. Finally, a dual-loop antenna is formed by combining the inner system ground and two strips. This proposed dual-loop antenna is capable of covering GSM850/900/DCS/PCS/UMTS2100/LTE 2300/2500 operating bands. Detailed design considerations of the proposed antenna are described and both experimental and simulation results are also presented and discussed.


title: TRICHOSCOPY: A NEW METHOD FOR DIAGNOSING HAIR LOSS

abstract: Videodermoscopy of hair and scalp (trichoscopy) is gaining popularity as a valuable tool in differential diagnosis of hair loss. This method allows viewing of the hair and scalp at X20 to X160 magnifications. Structures which may be visualized by trichoscopy include hair shafts of different types: vellus, terminal, micro-exclamation mark type, monilethrix, Netherton type, and pili annulati hairs. The number of hairs in one pilosebaceous unit may be assessed. It may be distinguished whether hair follicles are normal, empty, fibrotic (“white dots”), filled with hyperkeratotic plugs (“yellow dots”) or containing cadaverized hair (“black dots”). Abnormalities of scalp skin color or structure which may be visualized by trichoscopy include honeycomb-type hyperpigmentation, perifollicular discoloration (hyperpigmentation), and scaling.


title: Bit-Wise Iterative Decoding of Polar Codes using Stochastic Computing

abstract: Polar codes have received recent attention due to their potential to be applied in advanced wireless communication protocols such as the fifth generation mobile communication system (5G). Among the existing decoding algorithms, Belief Propagation (BP) exhibits high-throughput, low-latency and soft output with a high hardware cost. A form of approximate computing called stochastic computing provides a low-cost implementation solution for the BP algorithm. However, existing stochastic BP decoders suffer from a relatively long decoding latency resulting in low hardware efficiency. In this paper, a novel bit-wise iterative stochastic decoding architecture for the BP algorithm is proposed to improve the throughput and hardware efficiency. Multiple methods at the algorithm and architecture levels are presented to further speed up convergence and hardware efficiency.


title: A Study on Coexistence Capability Evaluations of the Enhanced Channel Hopping Mechanism in WBANs

abstract: As an important coexistence technology, channel hopping can reduce the interference among Wireless Body Area Networks (WBANs). However, it simultaneously brings some issues, such as energy waste, long latency and communication interruptions, etc. In this paper, we propose an enhanced channel hopping mechanism that allows multiple WBANs coexisted in the same channel. In order to evaluate the coexistence performance, some critical metrics are designed to reflect the possibility of channel conflict. Furthermore, by taking the queuing and non-queuing behaviors into consideration, we present a set of analysis approaches to evaluate the coexistence capability. On the one hand, we present both service-dependent and service-independent analysis models to estimate the number of coexisting WBANs. On the other hand, based on the uniform distribution assumption and the additive property of Possion-stream, we put forward two approximate methods to compute the number of occupied channels. Extensive simulation results demonstrate that our estimation approaches can provide an effective solution for coexistence capability estimation. Moreover, the enhanced channel hopping mechanism can significantly improve the coexistence capability and support a larger arrival rate of WBANs.


title: Fundamentals of Sentiment Analysis: Concepts and Methodology

abstract: Internet has opened the new doors for information exchange and the growth of social media has created unprecedented opportunities for citizens to publicly raise their opinions, but it has serious bottlenecks when it comes to do analysis of these opinions. Even urgency to gain a real time understanding of citizens concerns has grown very rapidly. Since, the viral nature of social media which is fast and distributed one, some issues get rapidly distributed and unpredictably become important through this word of mouth opinions expressed online which in turn has known as sentiments of the users. The decision makers and people do not yet realized to make sense of this mass communication and interact sensibly with thousands of others with the help of sentiment analysis. To understand thoroughly use of sentiment analysis in today’s business world, this chapter covers the brief about sentiment analysis including introduction of sentiment analysis, early history of sentiment analysis, problems of sentiment analysis, basic concepts of sentiment analysis with mathematical treatment, sentiment and subjectivity classification comprises of opinion mining and summarization, past scenarios of opinion or sentiment collection and their analysis. Methodologies like Sentiment Analysis as Text Classification Problem, Sentiment analysis as Feature Classification with mathematical treatment are explored. Also, Economic consequences of sentiment analysis on individual, society and organization with the help of social media sentiment analysis are provided as supporting component.


title: The applications and future of synthetic impulse and aperture radar

abstract: The Synthetic Impulse and Aperture Radar (SIAR), which is a typical Multiple Input Multiple Output (MIMO) radar with orthogonal frequency waveforms, has the advantages of more degrees of freedom, higher angular resolution and target detection probability than traditional phased array radar. In this paper, a brief introduction of the basic features and the advantages of SIAR is given first. Then two applications used in high frequency surface wave and microwave band are presented with some real data results. Finally, we discuss the possible applications and its future.


title: permission. Factorized Latent Spaces with Structured Sparsity

abstract: Recent approaches to multi-view learning have shown that factorizing the information into parts that are shared across all views and parts that are private to each view could effectively account for the dependencies and independencies between the different input modalities. Unfortunately, these approaches involve minimizing non-convex objective functions. In this paper, we propose an approach to learning such factorized representations inspired by sparse coding techniques. In particular, we show that structured sparsity allows us to address the multi-view learning problem by alternately solving two convex optimization problems. Furthermore, the resulting factorized latent spaces generalize over existing approaches in that they allow having latent dimensions shared between any subset of the views instead of between all the views only. We show that our approach outperforms state-of-the-art methods on the task of human pose estimation.


title: Origami Robot: A Self-Folding Paper Robot With an Electrothermal Actuator Created by Printing

abstract: A piece of paper has many useful characteristics; it is affordable, lightweight, thin, strong, and highly absorbent. These features allow inexpensive and flexible devices to be fabricated easily and rapidly. We have proposed a new field, “paper mechatronics,” which merges printed robotics and paper electronics, and to realize electronic and mechanical systems by printing. Herein, we develop a method to print an actuator and a structure on a sheet of paper. A trilayer electrothermal actuator is printed to activate a printed robot. The paper self-folds along the printed pattern to form the three-dimensional (3-D) structure of the robot body. We also investigate important factors necessary to develop a printed robot. Experiments, including finite element analysis (FEA), confirm our bimetal modeling assumption for the printed actuator and improve the locomotive ability. The key factors in self-folding are paper thickness and humidity. Our findings can improve the reliability of printed robot designs. A self-folding A7-sized paper robot demonstrates locomotion at 10 mm per step.


title: Sigmoid profiled septum: Evaluation of the parabolic reflector with the septum feed horn

abstract: A sigmoid function is used to define a smooth profile for possible use in septum designs. In addition, Particle Swarm Optimization (PSO) is used to optimize the sigmoid-profiled septum polarizer. PSO is applied to a sigmoid septum as well as a stepped septum, and the performance of both designs is further compared. Both septum designs are selected to be optimized for a 25% bandwidth, and the impedance matching and axial ratio is analyzed. Furthermore, the reflector antenna system is investigated utilizing the optimized septum horns as a feed for a 15λ parabolic reflector antenna. A directivity of approximately 32 dB is attained for both parabolic reflector antenna systems.


title: Intelligent driver drowsiness detection through fusion of yawning and eye closure

abstract: Driver drowsiness is a major factor in most driving accidents. In this paper we present a robust and intelligent scheme for driver drowsiness detection employing the fusion of eye closure and yawning detection methods. In this approach, the driver's facial appearance is captured via a camera installed in the car. In the first step, the face region is detected and tracked in the captured video sequence utilizing computer vision techniques. Next, the eye and mouth areas are extracted from the face; and they are studied to find signs of driver fatigue. Finally, in a fusion phase the driver state is determined and a warning message is sent to the driver if the drowsiness is detected. Our experiments prove the high efficiency of the proposed idea.


title: Image-based ghostings for single layer occlusions in augmented reality

abstract: In augmented reality displays, X-Ray visualization techniques make hidden objects visible through combining the physical view with an artificial rendering of the hidden information. An important step in X-Ray visualization is to decide which parts of the physical scene should be kept and which should be replaced by overlays. The combination should provide users with essential perceptual cues to understand the relationship of depth between hidden information and the physical scene. In this paper we present an approach that addresses this decision in unknown environments by analyzing camera images of the physical scene and using the extracted information for occlusion management. Pixels are grouped into perceptually coherent image regions and a set of parameters is determined for each region. The parameters change the X-Ray visualization for either preserving existing structures or generating synthetic structures. Finally, users can customize the overall opacity of foreground regions to adapt the visualization.


title: Adaptive fuzzy inference neural network

abstract: An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.


Genome-wide Profiling of Urinary Extracellular Vesicle microRNAs Associated With Diabetic Nephropathy in Type 1 Diabetes


title: Sequential Learning of Movement Prediction in Dynamic Environments using LSTM Autoencoder

abstract: Predicting movement of objects while the action of learning agent interacts with the dynamics of the scene still remains a key challenge in robotics. We propose a multi-layer Long Short Term Memory (LSTM) autoendocer network that predicts future frames for a robot navigating in a dynamic environment with moving obstacles. The autoencoder network is composed of a state and action conditioned decoder network that reconstructs the future frames of video, conditioned on the action taken by the agent. The input image frames are first transformed into low dimensional feature vectors with a pre-trained encoder network and then reconstructed with the LSTM autoencoder network to generate the future frames. A virtual environment, based on the OpenAi-Gym framework for robotics, is used to gather training data and test the proposed network. The initial experiments show promising results indicating that these predicted frames can be used by an appropriate reinforcement learning framework in future to navigate around dynamic obstacles.


title: Fraud detection on event logs using fuzzy association rule learning

abstract: Enterprise Resource Planning (ERP) is an enterprise information system designed to coordinate resources, information, and all activities required for a complete business process within a company. The most important requirement of an ERP system is integration. Integration in ERP system is used to combine the needs of a single software in a logical database, so that all departments in a company can share information and communicate with each other well. However, in its application, business process always changes dynamically. The changes will cause many variations to the original business process. This research discusses some frauds occured due to variations in business processes. Some frauds can be detected by using a method of process mining with Association Rule Learning approach. Process mining technique mine the fraud in the business process by checking the mismatch between event logs of business process with business process model of a company, which is better known as Standard Operating Procedure (SOP).


title: User-controllable personalization

abstract: In this research we investigated the role of user controllability on personalized systems by implementing and studying a novel interactive recommender interface, SetFusion. We examined whether allowing the user to control the process of fusing or integrating different algorithms (i.e., different sources of relevance) resulted in increased engagement and a better user experience. The essential contribution of this research stems from the results of a user study (N=40) of controllability in a scenario where users could fuse different recommendation approaches, with the possibility of inspecting and filtering the items recommended. First, we introduce an interactive Venn diagram visualization, which combined with sliders, can provide an efficient visual paradigm for information filtering. Second, we provide a three-fold evaluation of the user experience: objective metrics, subjective user perception, and behavioral measures. Through the analysis of these metrics, we confirmed results from recent studies, such as the effect of trusting propensity on accepting the recommendations and also unveiled the importance of features such as being a native speaker. Our results present several implications for the design and implementation of user-controllable personalized systems. We explored user-controllable interfaces as extension of traditional-ranked lists.We introduced SetFusion, a controllable interface with sliders and a Venn diagram.We conducted a controlled user study on online conference article recommendation.Our evaluation had three dimensions: users' perception, behavioral and IR metrics.Controllable interface had a positive effect influenced by users' characteristics.


title: The behavioural impact of a visually represented virtual assistant in a self-service checkout context

abstract: Our research investigated whether the presence of an interface agent -- or virtual assistant (VA) -- in a self-service checkout context has behavioural effects on the transaction process during particular tasks. While many participants claimed to have not noticed a VA within the self-service interface, behaviour was still affected, i.e. fewer people made errors with the VA present than in the voice-only and control conditions. The results are explained as reflective of an unconscious observation of non-verbal cues exhibited by the VA. The results are discussed in relation to possible behavioural outcomes of VA presence.


title: Scaling CNNs for High Resolution Volumetric Reconstruction from a Single Image

abstract: One of the long-standing tasks in computer vision is to use a single 2-D view of an object in order to produce its 3-D shape. Recovering the lost dimension in this process has been the goal of classic shape-from-X methods, but often the assumptions made in those works are quite limiting to be useful for general 3-D objects. This problem has been recently addressed with deep learning methods containing a 2-D (convolution) encoder followed by a 3-D (deconvolution) decoder. These methods have been reasonably successful, but memory and run time constraints impose a strong limitation in terms of the resolution of the reconstructed 3-D shapes. In particular, state-of-the-art methods are able to reconstruct 3-D shapes represented by volumes of at most 323 voxels using state-of-the-art desktop computers. In this work, we present a scalable 2-D single view to 3-D volume reconstruction deep learning method, where the 3-D (deconvolution) decoder is replaced by a simple inverse discrete cosine transform (IDCT) decoder. Our simpler architecture has an order of magnitude faster inference when reconstructing 3-D volumes compared to the convolution-deconvolutional model, an exponentially smaller memory complexity while training and testing, and a sub-linear run-time training complexity with respect to the output volume size. We show on benchmark datasets that our method can produce high-resolution reconstructions with state of the art accuracy.


title: Experimental Microwave Imaging With a Novel Corrugated Vivaldi Antenna

abstract: In this communication, a novel corrugated Vivaldi antenna (CVA) is developed for microwave imaging (MWI) applications. In particular, different from the previous works, the lengths of the corrugations are independently optimized to reach the final design. It is shown that by letting more parameters to be optimized in the design process, we can obtain a more effective CVA, which has more suitable characteristics for MWI applications (i.e., higher gain, broader bandwidth) compared with the previous designs. Apart from that, the imaging performance of the proposed design is compared with a generic VA having the same size of the proposed antenna. As imaging algorithm, a recently introduced qualitative MWI technique, the scattering parameter-based linear sampling method (S-LSM), is employed. The similarity between exact shapes of the targets and the obtained qualitative results are compared with the help of the well-known Jaccard index. Experimental results show that the proposed CVA performs better than a generic VA in such real-world MWI problems.


title: Analysis, Design and Experimental Results of Wide Range ZVS Active-Clamped L-L Type Current-Fed DC/DC Converter for Fuel Cells to Utility Interface

abstract: A wide range zero-voltage switching (ZVS) active-clamped L-L type current-fed isolated dc-dc converter is proposed for fuel cells to utility interface application. The proposed converter maintains ZVS of all switches from full load down to very light load condition for wide input voltage variation. Detailed operation, analysis, design, simulation and experimental results for the proposed converter are presented. The auxiliary active clamping circuit absorbs the turn-off voltage spike and also assists in achieving ZVS of main switches. The ZVS of auxiliary switches and main switches is achieved by the energy stored in the boost inductors and series inductor (aided by parallel inductor), respectively. Rectifier diodes operate with zero-current switching. An experimental converter rated at 200 W has been designed, built and tested in the laboratory to verify the analysis, design and performance of the proposed converter for wide variations in input voltage and load.


title: Efficient simulation of knitted cloth using persistent contacts

abstract: Knitted cloth is made of yarns that are stitched in regular patterns, and its macroscopic behavior is dictated by the contact interactions between such yarns. We propose an efficient representation of knitted cloth at the yarn level that treats yarn-yarn contacts as persistent, thereby avoiding expensive contact handling altogether. We introduce a compact representation of yarn geometry and kinematics, capturing the essential deformation modes of yarn loops and stitches with a minimum cost. Based on this representation, we design force models that reproduce the characteristic macroscopic behavior of knitted fabrics. We demonstrate the efficiency of our method on simulations with millions of degrees of freedom (hundreds of thousands of yarn loops), almost one order of magnitude faster than previous techniques.


title: How Do Social Networking Sites Users Become Loyal? A Social Exchange Perspective.

abstract: Since its emergence as a new electronic communication media supported by Web 2.0, social media has become a popular means of communication among the public. Among different types of social media that depend on user-generated content (UGC), social networking sites (SNS) are becoming the best-known communication mode. While many researchers have examined significant factors affecting SNS users’ behaviors and their impact on SNS usage, the current research extends the traditional concept of IS loyalty and examines factors impacting SNS users’ creation of SNS loyalty through a lens of social exchange theory (SET) and satisfaction. A total of 291 college students participated in an empirical test. The findings indicate that SET has an effect on creation of loyalty in the context of Facebook, which is currently the most popular SNS. Implications of these findings and limitations of the research are discussed.


title: Why Johnny Still, Still Can't Encrypt: Evaluating the Usability of a Modern PGP Client

abstract: This paper presents the results of a laboratory study involving Mailvelope, a modern PGP client that integrates tightly with existing webmail providers. In our study, we brought in pairs of participants and had them attempt to use Mailvelope to communicate with each other. Our results shown that more than a decade and a half after \textit{Why Johnny Can't Encrypt}, modern PGP tools are still unusable for the masses. We finish with a discussion of pain points encountered using Mailvelope, and discuss what might be done to address them in future PGP systems.


Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension


title: A Bearingless Slice Motor with a Solid Iron Rotor for Disposable Centrifugal Blood Pump

abstract: In the field of extracorporeal circulation, the use of high cost NdFeB magnets (PMs) in the disposable pump head of centrifugal blood pumps (CBP) need be avoided. In this paper, a bearingless slice motor consisting of a solid iron rotor and a reusable part having ring PMs which can be applied to a CBP is introduced. It realizes a cost- effective disposable pump head and a compact reusable part. To eliminate PMs from the disposable rotor, two PM rings and two iron rings are axial-symmetrically arranged in respect of the stator. The bias magnetic flux generated by the PM flows past the iron rings, rotor tooth and stator tooth. The radial motion and rotational speed of the rotor are controlled using displacement and Hall sensor signals and electromagnetic actuators, and the axial and tilt motions are passively supported and stabilized by the magnetic coupling between the stator and the rotor. A prototype bearingless slice motor is fabricated and experimentally evaluated.


UTILIZATION OF ANTENATAL SERVICES AMONG ADOLESCENTS IN WESTERN UGANDA


SYMMETRY IN ETHNOMATHEMATICS CONSTRUCTING SYMMETRIC CHOKWE SAND DRAWINGS


title: On the Trusted Use of Large-Scale Personal Data

abstract: Large-scale personal data has become the new oil of the Internet. However, personal data tend to be monopolized and siloed by online services which not only impedes beneficial uses but also prevents users from managing the risks associated with their data. While there is substantial legal and social policy scholarship concerning ownership and fair use of personal data, a pragmatic technical solution that allows governments and companies easy access to such data and yet protects individual rights has yet to be realized and tested. We introduce openPDS, an implementation of a Personal Data Store, which follows the recommendations of the WEF, the US NSTIC and the US Consumer Privacy Bill of Rights. openPDS allows users to collect, store, and give fine-grained access to their data in the cloud. openPDS also protects users’ privacy by only sharing anonymous answers, not raw data. Indeed, a mechanism to install third-parties applications on a user’s PDS allows for the sensitive part of the data processing to take place within the PDS. openPDS can also engage in privacy-preserving group computations to aggregate data across users without the need to share sensitive data with an intermediate entity.


title: Applying VLC in 5G Networks: Architectures and Key Technologies

abstract: Visible light communication has gained tremendous attention recently and has become a favorable complementary technology to millimeter- wave communication in short-range communication scenarios for future 5G networks. VLC possesses a number of prominent features to address the highly demanding 5G system requirements for high capacity, high data rate, high spectral efficiency, high energy efficiency, low battery consumption, and low latency. These prominent features include but are not limited to abundant license-free spectrum, the ability to provide multiple gigabit-per-second data rates, low energy consumption, and low implementation costs. In this article a heterogeneous multi-layer 5G cellular architecture with a control plane and user plane separation scheme is presented. Three layers — the macrocell layer operating below 3 GHz, the picocell layer operating in the mmWave spectrum, and the optical attocell layer operating at the visible spectrum — are considered in this architecture, with each layer targeting a different coverage and communication need. We further address potential solutions for uplink communications, backhaul connections, and downlink communications in an optical attocell. Several key technologies employed in VLC downlink communications, including modulation, multiple access, and error control schemes, are also discussed.


title: Iot based patient monitoring and diagnostic prediction tool using ensemble classifier

abstract: The ubiquitous growth of Internet of Things (IoT) and its medical applications has improved the effectiveness in remote health monitoring systems of elderly people or patients who need long-term personal care. Nowadays, chronic illnesses, such as, stroke, heart disease, diabetes, cancer, chronic respiratory diseases are major causes of death, in many parts of the world. In this paper, we propose a patient monitoring system for stroke-affected people to minimize future recurrence of the same by alarming the doctor and caretaker on variation in risk factors of stroke disease. Data analytics and decision-making, based on the real-time health parameters of the patient, helps the doctor in systematic diagnosis followed by tailored restorative treatment of the disease. The proposed model uses classification algorithms for the diagnosis and prediction. The ensemble method of tree-based classification-Random Forest give an accuracy of 93%.


title: Robust Mask-Layout and Process Synthesis

abstract: A method for automated mask-layout and process synthesis for MEMS is presented. The synthesis problem is approached by use of a genetic algorithm. For a given desired device shape, and several fabrication process choices, this synthesis method will produce one or more mask-layouts and associated fabrication process sequences (which when used can generate shapes close to the desired one). Given complicated device shapes and wide range of fabrication process possibilities, the designer may encounter difficulty producing the right mask-layout and fabrication procedure by experience and trial and error. An automated synthesis tool like this will be helpful to the designer by increasing the efficiency and accuracy of the design of MEMS devices.


title: Kinematic control of redundant robot manipulators: A tutorial

abstract: In this paper, we present a tentatively comprehensive tutorial report of the most recent literature on kinematic control of redundant robot manipulators. Our goal is to lend some perspective to the most widely adopted on-line instantaneous control solutions, namely those based on the simple manipulator's Jacobian, those based on the local optimization of objective functions in the null space of the Jacobian, those based on the task space augmentation by additional constraint tasks (with task priority), and those based on the construction of inverse kinematic functions.


title: Database Security Threats and Challenges in Database Forensic: A Survey

abstract: Relational Database Management Systems (RDBMS) is collection of applications that manage the storage, retrieval, and manipulation of database data. At the industry level SQL Server, Oracle, Sybase, DB2, MySQL, and other popular database applications are widely accepted as RDBMSs. As in the current scenario large data security breaches are occurring at a very high rate so we aim here to excavate the database systems which makes several redundant copies of sensitive data that can be found in the table storage, auditlogs, materialized views, data dictionary, SQL server artifacts etc. for forensic analysis. Also plenty of forensic data is lying around a database infrastructure to do a proper investigation and the most information necessary to piece together an incident after the fact. So in this paper we present a survey which explores the various beliefs upon database forensics through different methodologies using forensic algorithms and tools for investigations. Finally we point out challenges and opportunities by stimulating the area of database forensic which is said to be still in dark ages.


A Finite-Volume Method for Compressible Viscous Multiphase Flows


Deep Learning at Alibaba


title: Exercise and chronic low back pain: what works?

abstract: The aim of this review was to investigate current evidence for the type and quality of exercise being offered to chronic low back pain (CLBP) patients, within randomised controlled trials (RCTs), and to assess how treatment outcomes are being measured. A two-fold methodological approach was adopted: a methodological assessment identified RCTs of 'medium' or 'high' methodological quality. Exercise quality was subsequently assessed according to the predominant exercise used. Outcome measures were analysed based on current recommendations. Fifty-four relevant RCTs were identified, of which 51 were scored for methodological quality. Sixteen RCTs involving 1730 patients qualified for inclusion in this review based upon their methodological quality, and chronicity of symptoms; exercise had a positive effect in all 16 trials. Twelve out of 16 programmes incorporated strengthening exercise, of which 10 maintained their positive results at follow-up. Supervision and adequate compliance were common aspects of trials. A wide variety of outcome measures were used. Outcome measures did not adequately represent the guidelines for impairment, activity and participation, and impairment measures were over-represented at the expense of others. Despite the variety offered, exercise has a positive effect on CLBP patients, and results are largely maintained at follow-up. Strengthening is a common component of exercise programmes, however, the role of exercise co-interventions must not be overlooked. More high quality trials are needed to accurately assess the role of supervision and follow-up, together with the use of more appropriate outcome measures.


AN APPROACH FOR OVERLAPPING CELL SEGMENTATION IN MULTI-LAYER CERVICAL CELL VOLUMES


title: Discovering Graph Temporal Association Rules

abstract: Detecting regularities between complex events in temporal graphs is critical for emerging applications. This paper proposes graph temporal association rules (GTAR). A GTAR extends traditional association rules to discover temporal associations for complex events captured by a class of temporal pattern queries. We introduce notions of support and confidence for GTARS and formalize the discovery problem for GTARS. We show that despite the enhanced expressive power, GTARS discovery is feasible over large temporal graphs. We develop an effective rule discovery algorithm, which integrates event mining and rule discovery as a single process, and reduces the redundant computation by leveraging their interaction. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms. Our case study also verifies that GTARS demonstrate highly interpretable associations in real-world networks.


title: An End-to-End Measurement of Certificate Revocation in the Web's PKI

abstract: Critical to the security of any public key infrastructure (PKI) is the ability to revoke previously issued certificates. While the overall SSL ecosystem is well-studied, the frequency with which certificates are revoked and the circumstances under which clients (e.g., browsers) check whether certificates are revoked are still not well-understood. In this paper, we take a close look at certificate revocations in the Web's PKI. Using 74 full IPv4 HTTPS scans, we find that a surprisingly large fraction (8%) of the certificates served have been revoked, and that obtaining certificate revocation information can often be expensive in terms of latency and bandwidth for clients. We then study the revocation checking behavior of 30 different combinations of web browsers and operating systems; we find that browsers often do not bother to check whether certificates are revoked (including mobile browsers, which uniformly never check). We also examine the CRLSet infrastructure built into Google Chrome for disseminating revocations; we find that CRLSet only covers 0.35% of all revocations. Overall, our results paint a bleak picture of the ability to effectively revoke certificates today.


FAST CALCULATION OF HARALICK TEXTURE FEATURES


title: 3D metal printing technology: the need to re-invent design practice

abstract: 3D printing or additive manufacturing is a novel method of manufacturing parts directly from digital model using layer-by-layer material build-up approach. This tool-less manufacturing method can produce fully dense metallic parts in short time, with high precision. Features of additive manufacturing like freedom of part design, part complexity, light weighting, part consolidation, and design for function are garnering particular interests in metal additive manufacturing for aerospace, oil and gas, marine, and automobile applications. Powder bed fusion, in which each powder bed layer is selectively fused using energy source like laser, is the most promising additive manufacturing technology that can be used for manufacturing small, low-volume, complex metallic parts. This review presents overview of 3D Printing technologies, materials, applications, advantages, disadvantages, challenges, economics, and applications of 3D metal printing technology, the DMLS process in detail, and also 3D metal printing perspectives in developing countries.


title: How Does Corporate Ethics Contribute to Firm Financial Performance? The Mediating Role of Collective Organizational Commitment and Organizational Citizenship Behavior

abstract: Despite the increasing significance of corporate ethics, few studies have explored the intermediate mechanisms that explain the relationship between corporate ethics and firm financial performance. Drawing on institutional theory and strategic human resource management literature, the authors hypothesize that the internal collective processes based on employees’ collective organizational commitment and organizational citizenship behavior (OCB) mediate the ethics–performance relationship at the organizational level. The authors’ hypotheses are tested using data collected from 3,821 employees from 130 Korean companies and the respective companies’ financial performance data. The results indicate that collective organizational commitment and interpersonal OCB are meaningful intervening processes that connect corporate ethics to firm financial performance. To complement prior studies that identify a firm’s reputation and external relations as mediators between corporate ethics and performance, the present stu...


title: An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection

abstract: In the past years, several support vector machines (SVM) novelty detection approaches have been applied on the network intrusion detection field. The main advantage of these approaches is that they can characterize normal traffic even when trained with datasets containing not only normal traffic but also a number of attacks. Unfortunately, these algorithms seem to be accurate only when the normal traffic vastly outnumbers the number of attacks present in the dataset. A situation which can not be always hold. This work presents an approach for autonomous labeling of normal traffic as a way of dealing with situations where class distribution does not present the imbalance required for SVM algorithms. In this case, the autonomous labeling process is made by SNORT, a misuse-based intrusion detection system. Experiments conducted on the 1998 DARPA dataset show that the use of the proposed autonomous labeling approach not only outperforms existing SVM alternatives but also, under some attack distributions, obtains improvements over SNORT itself.


title: From Superpixel to Human Shape Modelling for Carried Object Detection

abstract: Abstract Detecting carried objects is one of the requirements for developing systems to reason about activities involving people and objects. We present an approach to detect carried objects from a single video frame with a novel method that incorporates features from multiple scales. Initially, a foreground mask in a video frame is segmented into multi-scale superpixels. Then the human-like regions in the segmented area are identified by matching a set of extracted features from superpixels against learned features in a codebook. A carried object probability map is generated using the complement of the matching probabilities of superpixels to human-like regions and background informa- tion. A group of superpixels with a high carried object probability and a strong edge support is then merged to obtain the shape of the carried object. We applied our method to two challenging datasets, and results show that our method is competitive with or better than the state-of-the-art.


title: QuoteRec: Toward Quote Recommendation for Writing

abstract: Quote is a language phenomenon of transcribing the statement of someone else, such as a proverb and a famous saying. An appropriate usage of quote usually equips the expression with more elegance and credibility. However, there are times when we are eager to stress our idea by citing a quote, while nothing relevant comes to mind. Therefore, it is exciting to have a recommender system which provides quote recommendations while we are writing. This article extends previous study of quote recommendation, the task that recommends the appropriate quote according to the context (i.e., the content occurring before and after the quote). In this article, a quote recommender system called QuoteRec is presented to tackle the task. We investigate two models to learn the vector representations of quotes and contexts, and then rank the candidate quotes based on the representations. The first model learns the quote representation according to the contexts of a quote. The second model is an extension of the neural network model in previous study, which learns the representation of a quote by concerning both its content and contexts. Experimental results demonstrate the effectiveness of the two models in learning the semantic representations of quotes, and the neural network model achieves state-of-the-art results on the quote recommendation task.


title: Opportunistic message routing using multi-layer social networks

abstract: In opportunistic networks, the nodes usually exploit a contact opportunity to perform hop-by-hop routing, since an end-to-end path between the source node and destination node may not exist. Most social-based routing protocols use social information extracted from real-world encounter networks to select an appropriate message relay. A protocol based on encounter history, however, takes time to build up a knowledge database from which to take routing decisions. An opportunistic routing protocol which extracts social information from multiple social networks, can be an alternative approach to avoid suboptimal paths due to partial information on encounters. While contact information changes constantly and it takes time to identify strong social ties, online social network ties remain rather stable and can be used to augment available partial contact information. In this paper, we propose a novel opportunistic routing approach, called ML-SOR (Multi-layer Social Network based Routing), which extracts social network information from multiple social contexts. To select an effective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. These metrics are computed by ML-SOR on different social network layers. Trace driven simulations show that ML-SOR, when compared to other schemes, is able to deliver messages with high probability while keeping overhead ratio very small.


Linear Time Euclidean Distance Transform Algorithms


title: GRID codes: Strip-based erasure codes with high fault tolerance for storage systems

abstract: As storage systems grow in size and complexity, they are increasingly confronted with concurrent disk failures together with multiple unrecoverable sector errors. To ensure high data reliability and availability, erasure codes with high fault tolerance are required. In this article, we present a new family of erasure codes with high fault tolerance, named GRID codes. They are called such because they are a family of strip-based codes whose strips are arranged into multi-dimensional grids. In the construction of GRID codes, we first introduce a concept of matched codes and then discuss how to use matched codes to construct GRID codes. In addition, we propose an iterative reconstruction algorithm for GRID codes. We also discuss some important features of GRID codes. Finally, we compare GRID codes with several categories of existing codes. Our comparisons show that for large-scale storage systems, our GRID codes have attractive advantages over many existing erasure codes: (a) They are completely XOR-based and have very regular structures, ensuring easy implementation; (b) they can provide up to 15 and even higher fault tolerance; and (c) their storage efficiency can reach up to 80p and even higher. All the advantages make GRID codes more suitable for large-scale storage systems.


title: INTERPERSONAL RELATIONS AND GROUP PROCESSES Maintaining Sexual Desire in Intimate Relationships: The Importance of Approach Goals

abstract: Three studies tested whether adopting strong (relative to weak) approach goals in relationships (i.e., goals focused on the pursuit of positive experiences in one’s relationship such as fun, growth, and development) predict greater sexual desire. Study 1 was a 6-month longitudinal study with biweekly assessments of sexual desire. Studies 2 and 3 were 2-week daily experience studies with daily assessments of sexual desire. Results showed that approach relationship goals buffered against declines in sexual desire over time and predicted elevated sexual desire during daily sexual interactions. Approach sexual goals mediated the association between approach relationship goals and daily sexual desire. Individuals with strong approach goals experienced even greater desire on days with positive relationship events and experienced less of a decrease in desire on days with negative relationships events than individuals who were low in approach goals. In two of the three studies, the association between approach relationship goals and sexual desire was stronger for women than for men. Implications of these findings for maintaining sexual desire in long-term relationships are discussed.


title: An Analysis of Internal Environment of a Commercial-oriented Research Organization: Using Mckinsey 7S Framework in a Ghanaian Context

abstract: The largest research organization in Ghana has of part of its mandate to raise internally generated funds through the commercialization of research products and services in order to supplement government’s budgetary allocation. How could its internal structures, vision and mission be strategically aligned for effective commercialization performa nce? The study utilizes the McKinsey 7 S Framework which is an analytical tool used to help in a variety of situations, including how to improve the performance of a company, and determine how best to implement a proposed strategy. Finally, the study makes some strategic recommendations for consideration by policy makers within the public sector.


Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data


title: An Overview of Graph-Based Keyword Extraction Methods and Approaches

abstract: The paper surveys methods and approaches for the task of keyword extraction. The systematic review of methods was gathered which resulted in a comprehensive review of existing approaches. Work related to keyword extraction is elaborated for supervised and unsupervised methods, with a special emphasis on graph-based methods. Various graph-based methods are analyzed and compared. The paper provides guidelines for future research plans and encourages the development of new graph-based approaches for keyword extraction.


title: Basic Human Activity Recognition based on sensor fusion in smartphones

abstract: Smartphones are ubiquitous devices that offer endless possibilities for health-related applications such as Ambient Assisted Living (AAL). They are rich in sensors that can be used for Human Activity Recognition (HAR) and monitoring. The emerging problem now is the selection of optimal combinations of these sensors and existing methods to accurately and efficiently perform activity recognition in a resource and computationally constrained environment. To accomplish efficient activity recognition on mobile devices, the most discriminative features and classification algorithms must be chosen carefully. In this study, sensor fusion is employed to improve the classification results of a lightweight classifier. Furthermore, the recognition performance of accelerometer, gyroscope and magnetometer when used separately and simultaneously on a feature-level sensor fusion is examined to gain valuable knowledge that can be used in dynamic sensing and data collection. Six ambulatory activities, namely, walking, running, sitting, standing, walking upstairs and walking downstairs, are inferred from low-sensor data collected from the right trousers pocket of the subjects and feature selection is performed to further optimize resource use.


title: Compact Ring-LWE Cryptoprocessor

abstract: In this paper we propose an efficient and compact processor for a ring-LWE based encryption scheme. We present three optimizations for the Number Theoretic Transform NTT used for polynomial multiplication: we avoid pre-processing in the negative wrapped convolution by merging it with the main algorithm, we reduce the fixed computation cost of the twiddle factors and propose an advanced memory access scheme. These optimization techniques reduce both the cycle and memory requirements. Finally, we also propose an optimization of the ring-LWE encryption system that reduces the number of NTT operations from five to four resulting in a 20% speed-up. We use these computational optimizations along with several architectural optimizations to design an instruction-set ring-LWE cryptoprocessor. For dimension 256, our processor performs encryption/decryption operations in 20/9 μs on a Virtex 6 FPGA and only requires 1349 LUTs, 860 FFs, 1 DSP-MULT and 2 BRAMs. Similarly for dimension 512, the processor takes 48/21 μs for performing encryption/decryption operations and only requires 1536 LUTs, 953 FFs, 1 DSP-MULT and 3 BRAMs. Our processors are therefore more than three times smaller than the current state of the art hardware implementations, whilst running somewhat faster.


title: Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks

abstract: In this paper, we focus on tackling the problem of automatic accurate localization of detected objects in high-resolution remote sensing images. The two major problems for object localization in remote sensing images caused by the complex context information such images contain are achieving generalizability of the features used to describe objects and achieving accurate object locations. To address these challenges, we propose a new object localization framework, which can be divided into three processes: region proposal, classification, and accurate object localization process. First, a region proposal method is used to generate candidate regions with the aim of detecting all objects of interest within these images. Then, generic image features from a local image corresponding to each region proposal are extracted by a combination model of 2-D reduction convolutional neural networks (CNNs). Finally, to improve the location accuracy, we propose an unsupervised score-based bounding box regression (USB-BBR) algorithm, combined with a nonmaximum suppression algorithm to optimize the bounding boxes of regions that detected as objects. Experiments show that the dimension-reduction model performs better than the retrained and fine-tuned models and the detection precision of the combined CNN model is much higher than that of any single model. Also our proposed USB-BBR algorithm can more accurately locate objects within an image. Compared with traditional features extraction methods, such as elliptic Fourier transform-based histogram of oriented gradients and local binary pattern histogram Fourier, our proposed localization framework shows robustness when dealing with different complex backgrounds.


title: BAR fault tolerance for cooperative services

abstract: This paper describes a general approach to constructing cooperative services that span multiple administrative domains. In such environments, protocols must tolerate both Byzantine behaviors when broken, misconfigured, or malicious nodes arbitrarily deviate from their specification and rational behaviors when selfish nodes deviate from their specification to increase their local benefit. The paper makes three contributions: (1) It introduces the BAR (Byzantine, Altruistic, Rational) model as a foundation for reasoning about cooperative services; (2) It proposes a general three-level architecture to reduce the complexity of building services under the BAR model; and (3) It describes an implementation of BAR-B the first cooperative backup service to tolerate both Byzantine users and an unbounded number of rational users. At the core of BAR-B is an asynchronous replicated state machine that provides the customary safety and liveness guarantees despite nodes exhibiting both Byzantine and rational behaviors. Our prototype provides acceptable performance for our application: our BAR-tolerant state machine executes 15 requests per second, and our BAR-B backup service can back up 100MB of data in under 4 minutes.


title: Ontology-Based Integration of Information - A Survey of Existing Approaches

abstract: We review the use on ontologies for the integration of heterogeneous information sources. Based on an in-depth evaluation of existing approaches to this problem we discuss how ontologies are used to support the integration task. We evaluate and compare the languages used to represent the ontologies and the use of mappings between ontologies as well as to connect ontologies with information sources. We also ask for ontology engineering methods and tools used to develop ontologies for information integration. Based on the results of our analysis we summarize the state of the art in ontology-based information integration and name areas of further research activities.


title: Sequential fault diagnosis in system maintenance

abstract: The results of the study of the impact of symptomatic information on sequential fault diagnosis are presented. Typical test sequencing algorithms based on the concepts from information theory and heuristic AND/OR graph search methods are considered. The estimation of optimal weights of statistical data of faulty elements associated with given symptoms is discussed. Empirical results are presented for the case in which the symptomatic information is processed separately and in advance of the global failure rate data. >


title: Bacterial Vaginosis: An Overview for 2009

abstract: Bacterial vaginosis has been appreciated as a unique clinical entity for well over 50 years. Its essential manifestations are well established: a loss of the normal bacterial population of the vagina and their replacement by other species. Investigations into this condition have led to a better understanding of its prevalence and epidemiology. Microbiologic and biochemical studies have exposed the remarkably complex pathophysiologic events that occur with bacterial vaginosis. Several major morbidities accompany this condition. Advances have been made in treatment, including the recent availability of a new therapeutic agent, tinidazole. However, the root cause of the condition is elusive, and as a result managing bacterial vaginosis and its complications is unsatisfactory; moreover, data suggest that therapy now is less successful than in the past. This article brings together the current fund of knowledge about bacterial vaginosis in a way that offers clinicians a realistic view of our capabilities and concerns.


title: Text Classification by Augmenting Bag of Words (BOW) Representation with Co-occurrence Feature

abstract: Text classification is the task of assigning predefined categories to free-text documents based on their content. Traditional approaches used unigram based models for text classification. Unigram based models such as Bag Of Words(BOW) models are not considering co-occurrence of set of words in a document level. This paper proposes a way to find co-occurrence feature from anchor text of wikipedia pages, proposes a way to incorporate co-occurrence feature to BOW model. Finally the method is analyzed to know how it performs in task of text classification.


title: A 4-bit CMOS Phase Shifter Using Distributed Active Switches

abstract: This paper presents a novel 4-bit phase shifter using distributed active switches in 0.18-mum RF CMOS technology. The relative phase shift, which varies from 0deg to 360deg in steps of 22.5deg, is achieved with a 3-bit distributed phase shifter and a 180deg high-pass/low-pass phase shifter. The distributed phase shifter is implemented using distributed active switches that consist of a periodic placement of series inductors and cascode transistors, thereby obtaining linear phase shift versus frequency with a digital control. The design guideline of the distributed phase shifter is presented. The 4-bit phase shifter achieves 3.5 plusmn 0.5 dB of gain, with an rms phase error of 2.6deg at a center frequency of 12.1 GHz. The input and output return losses are less than -15 dB at all conditions. The chip size is 1880 mum times 915 mum including the probing pads.


title: SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing

abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.


title: Feature selection for support vector machines by means of genetic algorithm

abstract: The problem of feature selection is a difficult combinatorial task in machine learning and of high practical relevance, e.g. in bioinformatics. genetic algorithms (GAs) offer a natural way to solve this problem. In this paper, we present a special genetic algorithm, which especially takes into account the existing bounds on the generalization error for support vector machines (SVMs). This new approach is compared to the traditional method of performing cross-validation and to other existing algorithms for feature selection.


title: On experimenting 5G: Testbed set-up for SDN orchestration across network cloud and IoT domains

abstract: In this paper, we present an experimental set-up reproducing a convergent 5G service scenario spanning over SDN-based Edge network, Cloud and IoT domains. To address reliability and robustness requirements of future 5G networks, the set-up also includes an SDN orchestrator able to adaptively provision data delivery paths connecting service components running in those different domains. In particular, we demonstrate SDN orchestration capabilities in adapting data paths across IoT, Cloud and network domains based on the real-time load state of switches thereby recovering from congestions and assuring reliable data delivery services.