Reviewerly/exHarmony huggingface dataset:
MemAlign: A Memory Structure to Accelerate Gene Sequencing

Since 2003, when the human reference genome was discovered, several applications found gene sequencing a promising mechanism to help improve their results. These include studying hereditary diseases, prenatal monitoring and others. Gene sequencing in its typical configuration consists of several elaborate processing stages, each performed by a separate software package. The intermediate results are transferred via large files between gene sequencing different steps, hence making gene sequencing a processing and I/O demanding task. Taking advantage of advances memory speed and capacity and with the ultimate goal of pipelining the gene sequencing steps and avoiding utilizing file storage to communicate intermediate results, in this paper we present MemAlign, a novel pre-sorted memory structure to pipeline the first and second processing steps of gene sequencing; Alignment and Sort. The number of memory locations of MemAlign corresponds the positions on the human reference genome. Combined with techniques to compress the alignment results, MemAlign essentially eliminates the Sort step by storing alignment results in the memory location that corresponds to the alignment position. MemAlign not only speeds up the combined processing time of Alignment and Sort, but also saves the considerable amount of storage required to store the intermediate results between the two steps.
A CNN based approach to Phrase-Labelling through classification of N-Grams

Modern approaches address the task of Phrase-labelling within any input sentence (eg: NER, Chunking etc.) as a variant of word-tagging problem. These approaches extract the desired phrases as word-sequences which are mapped to specific tag-sequences (with Begin, Intermediate and End tag-types).
Construction and Evaluation of Attribute-Based Challenge-and-Response Authentication on Asymmetric Bilinear Map

We propose a construction of an attribute-based authentication scheme (ABAuth). Our ABAuth is a challenge-and-response protocol which uses an attribute-based key-encapsulation mechanisum (ABKEM). The ABKEM is basically the one proposed by Ostrovsky-Sahai-Waters (ACM-CCS 2007), but in contrast to the original ABKEM our ABKEM is based on an asymmetric bilinear map for better computational efficiency. We also give a proof of one-way-CCA security of ABKEM in the asymmetric case, which leads to concurrent man-in-the-middle security of ABAuth. We note that the selective security is often enough for the case of authentication in contrast to the case of encryption. Then we evaluate our ABAuth by implementation as well as by discussion. We use the TEPLA library TEPLA for the asymmetric bilinear map that is Type-3 pairing on the BN curve.
Blur Kernel Estimation by Structure Sparse Prior

Blind image deblurring tries to recover a sharp version from a blurred image, where blur kernel is usually unknown. Recently, sparse representation has been successfully applied to estimate the blur kernel. However, the sparse representation has not considered the structure relationships among original pixels. In this paper, a blur kernel estimation method is proposed by introducing the locality constraint into sparse representation framework. Both the sparsity regularization and the locality constraint are incorporated to exploit the structure relationships among pixels. The proposed method was evaluated on a real-world benchmark dataset. Experimental results demonstrate that the proposed method achieve comparable performance to the state-of-the-art methods.
Attention alignment by linear space projection for video features extraction

Video feature extraction is the basis of research on video data. At present, many models based on convolution neural networks for features extracting need to stack deeper network layers to improve the robustness, which means that the amount of parameters and computation is greatly increased while the practicability of such models is constrained. In this paper, a global projection self-attention mechanism is proposed to empower shallow models generate robust video representations. Compared to Non-local [17] and Squeeze Excitation [18] networks, we adopt more succinct but sufficient context modeling strategy to establish complete global interaction space, introduce a lighter transform block for attention coefficients predicting, which improves the running efficiency of attention models greatly. We further embed our attention module to several modern networks including ResNet[3], R(2+1)D[9] and I3D[7], in that ResNet-18 and R(2+1)D-18 meet considerably promotion upon its original version by more than 18.9 percent on UCF101 dataset, this result implies huge potential of proposed attention blocks.
Constrained Motion Planning for Flipping Stick-like Objects

This paper presents a constrained motion planning to flip objects grasped considering soft-finger contact while avoiding rotational slip (or namely drooping). The drooping motion in inclined planes is studied under the effect of the object weight and the friction torque exerted by soft fingertips. The analytical model of the drooping motion is derived and used in the proposed constrained planner to restrict the robot poses that can cause the object held by the gripper to slip. The constrained planner is analyzed and verified through both simulation and real-world experiments. The results show that the generated robot motion sequences enable correct execution of the flipping task while avoiding slip.
Rule-Based Sequence Diagram Generation with Application of the Visitor Pattern

A design pattern provides a tested and proven solution to a problem that commonly occurs in software design. This paper presents a knowledge-based framework for representing design patterns. The structural schema of a design pattern is represented using the Ontology Web Language and the general reusable design solution suggested by the pattern is represented as rules in Jena. Through rule instantiation, a suggested design solution is applied in an application-specific context for use case realization. Using the Visitor pattern as an example, application of the framework to the generation of object interaction designs, represented as sequence diagrams in the Unified Modelling Language, from requirements specified in the form of operation contracts is demonstrated.
Around View Monitoring System with Motion Estimation in ADAS Application

Around View Monitoring (AVM) system uses multiple cameras as the sensor that is mounted on several positions on the vehicle to produce a display of top view image from the surrounding environment of the vehicle that is not readily visible to the driver because of the limited field of view of the driver. The risk of parking accident could be reduced by developing the system that can monitor the surrounding area, detecting parking slot lane and obstacle. A few seconds of early warning would significantly decrease the chances of accidents. This system can assist in the parking area and navigating through a narrow space area. Current AVM available usually needed another sensor to ensure a good performance output. But this is cost consuming besides increasing the computational time and resource. Here, proposed an AVM system that will integrate with the motion estimation algorithm to produce a good result. The AVM image sequence is from a camera input mounted on the vehicle. The algorithm to be tested is Gunnar Farneback. Movement in sequential frames is detected and converted to the real-world position change. This paper will compare the algorithms in various condition. The accuracy of the result was measured.
PSYHODIDACTIC BASES OF PREPARING FUTURE PHYSICS TEACHERS IN THE UNIVERSITY

The article deals with the psychological and didactic foundations of the preparation of teachers of physics. It is necessary to consider the training of teachers of physics systemically, and not sporadically. Specifies the types of training aimed at the formation of common professional competencies of future teachers and communication disciplines, the form of their training.
RETRACTED: A hybrid security strategy (HS2) for reliable video streaming in fog computing

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. The article is a duplicate of a paper that has already been published in Wireless Netw 26, 1389–1416 (2020), https://doi.org/10.1007/s11276-019-02203-3. One of the conditions of submission of a paper for publication is that authors declare explicitly that the paper has not been previously published and is not under consideration for publication elsewhere. As such this article represents a misuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.
SMAK-Net: Self-Supervised Multi-level Spatial Attention Network for Knowledge Representation towards Imitation Learning

In this paper, we propose an end-to-end self-supervised feature representation network for imitation learning. The proposed network incorporates a novel multi-level spatial attention module to amplify the relevant and suppress the irrelevant information while learning task-specific feature embeddings. The multi-level attention module takes multiple intermediate feature maps of the input image at different stages of the CNN pipeline and results a 2D matrix of compatibility scores for each feature map with respect to the given task. The weighted combination of the feature vectors with the scores estimated from attention modules leads to a more task specific feature representation of the input images. We thus name the proposed network as SMAK-Net, abbreviated from Self-supervised Multi-level spatial Attention Knowledge representation Network. We have trained this network using a metric learning loss which aims to decrease the distance between the feature representations of simultaneous frames from multiple view points and increases the distance between the neighboring frames of the same view point. The experiments are performed on the publicly available Multi-View pouring dataset [1]. The outputs of the attention module are demonstrated to highlight the task specific objects while suppressing the rest of the background in the input image. The proposed method is validated by qualitative and quantitative comparisons with the state-of-the art technique TCN [1] along with intensive ablation studies. This method is shown to significantly outperform TCN by 6.5% in the temporal alignment error metric while reducing the total number of training steps by 155K.
A Generic Framework for Task Selection Driven by Synthetic Emotions

Given a certain complexity level, humanized agents may select from a wide range of possible tasks, with each activity corresponding to a transient goal. In general there will be no overarching credit assignment scheme allowing to compare available options with respect to expected utilities. For this situation we propose a task selection framework that is based on time allocation via emotional stationarity (TAES). Emotions are argued to correspond to abstract criteria, such as satisfaction, challenge and boredom, along which activities that have been carried out can be evaluated. The resulting timeline of experienced emotions is then compared with the 'character' of the agent, which is defined in terms of a preferred distribution of emotional states. The long-term goal of the agent, to align experience with character, is achieved by optimizing the frequency for selecting the individual tasks. Upon optimization, the statistics of emotion experience becomes stationary.
Research on Comments of Online Merchandise Based on Sentiment Mining in Attribute Level

With big data has become a current and future research frontier, sentiment analysis has attracted significant research attention, too. This paper proposed an attribute words mining algorithm based on Naive Bayesian classification method combined with the association rules to research the commented consumers' emotion of entry-level SLR cameras in Jingdong Mall. The aims of this study are summarized into two aspects: 1) to verify if the new method can effectively analyze the consumer's emotion; 2) to establish a set of factor analysis system based on the product attributes indexes to help consumers and businesses identify the properties of a single product is good or bad through the comments information.
Analysis of Students Ability Assessment Based on Bloom’s Taxonomy Using Fuzzy Signatures

One process in the student learning stage is assessment. Formation of cognitive aspects through Bloom Taxonomy will make it easier for assessors to measure students' ability levels. This assessment becomes more complex by describing the problem according to the level of cognitive dimensions in Bloom's Taxonomy. Fuzzy signatures are introduced as methods that can work in multidimensional regions. This study uses Fuzzy Signatures to assess student's abilities from the three lowest levels of cognitive dimensions based on Bloom Taxonomy, namely Remember, Understand and Application. Fuzzy Signatures make it possible to do calculations using different weights at each level and choose the right aggregation method. Assessment of students' abilities in this study used 664 students' responses in answering questions. Students come from 6 different schools with 4-grade levels consisting of 184 third grade students, 225 fourth grade students, 148 fifth grade students, and 107 sixth grade students. The value of students' abilities can be improved by up to 21% approaching the grade level in their school. With question mapping by the cognitive dimension, students with better grades have the opportunity to answer the questions correctly at a higher level than the class level.
Eyes and Neck Kinematic Model Analysis with Limited Human Gestures

Human emotions such as happiness, sadness, despair, amazement among others are produced by the limbic system which causes the movement of the facial muscles as a result of those emotions. In the realization of Rudolf, the animatronic, was considered, the simulation, interpretation and imitation of human movements of eyes, neck and mouth, in order to respond immediately to the emotions of the human being. Each of these movements were analysed kinematically and dynamically by the Denavit-Hartenberg matrices. In order to imitate the facial gestures of a human, the dynamics of the eyes, their degrees of freedom (DOF), as well as the neck and lower jaw, were physically analysed in order to perfect the imitation of a human being. On the other hand, the neural system of real-time image storage, activation of actuators and facial recognition was made through programming in Python and decoding with the Raspberry Pi Hardware.
Context– sensitive rules in Urhobo Language, Delta State -Nigeria

Linguistic description exists at varying levels, namely: phonology, morphology, syntax, and semantics, among others. Within the framework of Generative Grammar one observes that there is an interaction among these different levels of linguistic description within their lexical structure. Our aim in this paper is to demonstrate how context- sensitive rules showcase this network of interaction between syntaxes and semantics, using Urhobo people and their language as a case study. The research observed after all that a subset of context-sensitive rules in the language structure of any given people brings about an occurrence of polysemy. From this discovery and other findings, the study has proceeded to conclude that for an effective choice and proficient use of words in Urhobo language in particular and effective communication of any language as a whole, these context-sensitive rules must be observed. Urhobo is currently being taught at all levels of learning in Delta State, Nigeria. It is hoped that the significance of the current study is that it would help to give focus to the teaching and learning of Urhobo grammar within Delta State and wherever Urhobo language is also studied outside of Delta State and beyond Nigeria.Keywords: Generative Grammar, context-sensitive rules, Urhobo language, polysemy
A Comprehensive Survey on Web Recommendations Systems with Special Focus on Filtering Techniques and Usage of Machine Learning

In present scenario, to improve the consumers buying/purchasing experience, use of technological innovations like Recommender system is very prominent. This small yet powerful utility analyzes the buying pattern of a consumer and suggests which items to buy or use. Recommender systems can be applied to varied fields of consumer’s interest like online shopping, ticket booking and other online contents. Most of the e-commerce sites are attracting huge number of potential customers by providing useful suggestions regarding buying a product or service. This technique is mainly based on the use of Machine Learning, which enables the system to make decisions efficiently. Earlier, these recommendations were mainly relying on filtering process such as collaborative filtering, content based, knowledge based, demographic and hybrid filtering. These filtering techniques, which constitute the recommender system, are discussed in detail in this study. The survey is conducted to describe the various means and methods of recommendation to consumer in real time. This survey presents a comparative study among different types of recommender system based on various parameters and filtering schemes. Moreover, it shows a significant improvement in recommender system by using machine learning based approaches.
Multilingual Machine Translation Generic Framework with Sanskrit Language as Interlingua

Machine Translation is considered as integral part of natural language processing. The translation from one language to another has always been a challenging issue in terms of percentage of the sentences that can be translated and the rate of accuracy of translated sentences. Rule-based machine translation is a definitive approach toward machine translation. It helps to translate statements from one language (source) to other (target) language on the basis of the defined rules. The software tool ‘EtranS’ provides translation of English to Sanskrit language. It has so far reported more than ninety percent of accuracy. The accuracy obtained in translation is large because of mathematical approach of Sanskrit grammar. Since it is a formulized language; therefore, it has great potential to act as Interlingua. This explores the potential of Sanskrit language to act as Interlingua. A generic framework for multiple language translation is presented here. Sanskrit can act as Interlingua for many Indian as well as foreign languages. Sanskrit language is mother of eighty-five percent of Indian languages and its grammar has resemblance with foreign languages like German, Russian, etc. The ‘EtranS’ can be further extended in order to obtain multilingual translation.
Research on Manipulator Stability Control

Abstract In order to have good accuracy, robustness and stability, it is necessary to control the manipulator accurately. But at present, for the precise control of manipulator, for the control mode and control system of manipulator, the research is not in-depth and perfect. This paper mainly summarizes the manipulator control system and control mode, and analyzes and summarizes the dynamics modeling, control accuracy, PLC control of the manipulator. At the same time, the control direction of the manipulator is prospected, such as cluster control, planning coordination control, autonomous motion control, etc.
An improved human-in-the-loop model for fine-grained object recognition with batch-based question answering

Fine-grained object recognition refers to a subordinate level of object recognition such as recognition of bird species and car models. It has become crucial for recognition of previously unknown classes. While fine-grained object recognition has seen unprecedented progress with the advent of neural networks, many of the existing works are cost-sensitive as they are acutely picture-dependent and fail without the adequate number of quality pictures. Efforts have been made in the literature for a picture-independent recognition with hybrid human-computer recognition methods via single question answering with a human-in-the-loop. To this end, we propose an improved batch-based local question answering method for making the recognition efficient and picture-independent. When pictures are unavailable, at each time-step, the proposed method mines a batch of binary cluster-centric local questions to pose to a human-in-the-loop and incorporates the responses received to the questions into the model. After a preset number of time-steps, the most probable class of the target object is returned as the final prediction. When pictures are available, our model facilitates the plug-in of computer vision algorithms into the framework for better performance. Experiments on three challenging datasets show significant performance improvement with respect to accuracy and computation time as compared to the existing schemes.
Evaluation of extremist cohesion in a darknet forum using ERGM and LDA

ISIS and similar extremist communities are increasingly using forums in the darknet to connect with each other and spread news and propaganda. In this paper, we attempt to understand their network in an online forum by using descriptive statistics, an exponential random graph model (ERGM) and Topic Modeling. Our analysis shows how the cohesion between active members forms and grows over time and under certain thread topics. We find that the top attendants of the forum have high centrality measures and other attributes of influencers.
A New Annotation Method With Five Labels For Constructing Semantic Textual Similarity Corpus

This paper mainly introduces the new annotation method with 5 labels for Semantic Textual Similarity (STS) task. In order to analyze the method, we construct a new STS corpus on the top of the new method, and the universality and necessity of the new method, as well as the challenges posed to the existing STS model, are analyzed from multiple perspectives.
Experiments with Random Projections Ensembles: Linear Versus Quadratic Discriminants

Gaussian classifiers make the modelling assumption that each class follows a multivariate Gaussian distribution, and include the Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). In high dimensional low sample size settings, a general drawback of these methods is that the sample covariance matrix may be singular, so their inverse is not available. Among many regularisation methods that attempt to get around this problem, the random projection ensemble approach for LDA has been both practically successful and theoretically well justified. The aim of this work is to investigate random projection ensembles for QDA, and to experimentally determine the relative strengths of LDA vs QDA in this context. We identify two different ways to construct averaging ensembles for QDA. The first is to aggregate low dimensional matrices to construct a smoothed estimate of the inverse covariance, which is then plugged into QDA. The second approach is to aggregate the predictions of low dimensional QDAs instead. By implementing experiments in eight different kinds of data sets, we conduct a thorough evaluation. From it we conclude that both QDA ensembles are able to improve on vanilla QDA, however the LDA ensemble tends to be more stable with respect to the choice of target dimension of the random projections.