Reviewerly/exHarmony huggingface dataset:
Study and Understanding the Significance of Multilayer-ELM Feature Space

Multi-layer Extreme Learning Machine (Multi-layer ELM) is one of the most popular deep learning classifiers among other traditional classifiers because of its good characteristics such as being able to manage a huge volume of data, no backpropagation, faster learning speed, maximum level of data abstraction etc. Another distinct feature of Multi-layer ELM is that it can be able to make the input features linearly separable by mapping them non-linearly to an extended feature space. This architecture shows acceptable performance as compared to other deep networks. The paper studies the high dimensional feature space of Multi-layer ELM named as MLELM-HDFS in detail by performing different conventional unsupervised and semi-supervised clustering techniques on it using text data and comparing it with the traditional TF-IDF vector space named as TFIDF-VS in order to show its importance. Results on both unsupervised and semi-supervised clustering techniques show that MLELM-HDFS is more promising than the TFIDF-VS.
Unsupervised Attention Based Instance Discriminative Learning for Person Re-Identification

Recent advances in person re-identification have demonstrated enhanced discriminability, especially with supervised learning or transfer learning. However, since the data requirements-including the degree of data curations-are becoming increasingly complex and laborious, there is a critical need for unsupervised methods that are robust to large intra-class variations, such as changes in perspective, illumination, articulated motion, resolution, etc. Therefore, we propose an unsupervised framework for person re-identification which is trained in an end-to-end manner without any pre-training. Our proposed framework leverages a new attention mechanism that combines group convolutions to (1) enhance spatial attention at multiple scales and (2) reduce the number of trainable parameters by 59.6%. Additionally, our framework jointly optimizes the network with agglomerative clustering and instance learning to tackle hard samples. We perform extensive analysis using the Market1501 and DukeMTMC-reID datasets to demonstrate that our method consistently outperforms the state-of-the-art methods (with and without pre-trained weights).
Data Selection for Unsupervised Translation of German–Upper Sorbian

This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2020 Unsupervised Machine Translation task for German–Upper Sorbian. We investigate the usefulness of data selection in the unsupervised setting. We find that we can perform data selection using a pretrained model and show that the quality of a set of sentences or documents can have a great impact on the performance of the UNMT system trained on it. Furthermore, we show that document-level data selection should be preferred for training the XLM model when possible. Finally, we show that there is a trade-off between quality and quantity of the data used to train UNMT systems.
Capsule Networks for Malaria Parasite Classification: An Application Oriented Model

The epidemic of malaria is a death-dealing infectious disease caused by mosquito spreading across the world. In this technological era, automated diagnosis is worthwhile for accurate and faster solutions. In this work, a novel application-oriented diagnostic model was deployed to detect and classify thin-blood smear images infected with malaria. The Features from thin-blood smears are extracted using a series of Convolution-Neural-Networks and classified with novel Capsule-Networks by understanding the spatial relationship of thin-blood films. The experimentation is compared to VGG-16, ResNet-50, DenseNet-121 architectures with immense depth varying from 16 to 121 layers. The proposed Capsule-Network is 8 layer deep and tends to outperform attaining classification accuracy of 96.9% and specificity, sensitivity scores were 94.95%, 98.18%. This novel model was deployed as a web-application to act as an aid for such a havoc problem and to transfer applicability to every user in need by classifying images in less than 3 seconds.
Answer Counting under Guarded TGDs

We study the complexity of answer counting for ontology-mediated queries and for querying under constraints, considering conjunctive queries and unions thereof (UCQs) as the query language and guarded TGDs as the ontology and constraint language, respectively. Our main result is a classification according to whether answer counting is fixed-parameter tractable (FPT), W[1]-equivalent, #W[1]-equivalent, #W[2]-hard, or #A[2]-equivalent, lifting a recent classification for UCQs without ontologies and constraints due to Dell et al. The classification pertains to various structural measures, namely treewidth, contract treewidth, starsize, and linked matching number. Our results rest on the assumption that the arity of relation symbols is bounded by a constant and, in the case of ontology-mediated querying, that all symbols from the ontology and query can occur in the data (so-called full data schema). We also study the meta-problems for the mentioned structural measures, that is, to decide whether a given ontology-mediated query or constraint-query specification is equivalent to one for which the structural measure is bounded.
Predicting the Winners of Borda, Kemeny and Dodgson Elections with Supervised Machine Learning

Voting methods are widely used in collective decision making, not only among people but also for the purposes of artificial agents. Computing the winners of voting for some voting methods like Borda count is computationally easy, while for others, like Kemeny and Dodgson, this is a computationally hard problem. The question we explore here is can winners of Kemeny and Dodgson elections be predicted using supervised machine learning methods? We explore this question empirically using common machine learning methods like XGBoost, Linear Support Vector Machines, Multilayer Perceptron and regularized linear classifiers with stochastic gradient descent. We analyze elections of 20 alternatives and 25 voters and build models that predict the winners of the Borda, Kemeny and Dodgson methods. We find that, as expected, Borda winners are predictable with high accuracy (99%), while for Kemeny and Dodgson the best accuracy we could obtain is 85% for Kemeny and 89% for Dodgson.
Conflict-Free Route Planning For Autonomous Transport Vehicle

This study deals with the problem of conflict-free route planning for multiple autonomous transport vehicles. In this study, an algorithm based on A* algorithm to solve conflict-free routing problems for multiple autonomous transport vehicles, is proposed. While classical A* algorithm plans routes for a single start and end node at the same time, the proposed algorithm plans routes for multiple start and end nodes without conflicts. The proposed algorithm apply waiting or change the route strategies to resolve conflict types. The proposed algorithm has been tested on multiple autonomous transport vehicles having different velocities (heterogeneous). Tests conducted within the scope of the study, show that the proposed algorithm finds effective conflictfree routes.
Effective Architectures for Low Resource Multilingual Named Entity Transliteration

In this paper, we evaluate LSTM, biLSTM, GRU, and Transformer architectures for the task of name transliteration in a many-to-one multilingual paradigm, transliterating from 590 languages to English. We experiment with different encoder-decoder combinations and evaluate them using accuracy, character error rate, and an F-measure based on longest continuous subsequences. We find that using a Transformer for the encoder and decoder performs best, improving accuracy by over 4 points compared to previous work. We explore whether manipulating the source text by adding macrolanguage flag tokens or pre-romanizing source strings can improve performance and find that neither manipulation has a positive effect. Finally, we analyze performance differences between the LSTM and Transformer encoders when using a Transformer decoder and find that the Transformer encoder is better able to handle insertions and substitutions when transliterating.
Multimodal features for shots boundary detection

Shot Boundary Detection (SBD) also known as a temporal video segmentation is a preprocessing task for multiple videos applications, such as indexing and retrieval. The SBD output provides coherent temporal units which are easy to manipulate. The Most previous works implement theirs frameworks based on visual features to measure similarity for transition detection task. However, the video is very enriched by data which could be beneficial. In this paper, referring to recent multimodal works, we propose to introduce the audio components to increase the SBD task. Firstly, we worked on candidate segments obtained by measuring similarity between low features (SURF, HSF) from original video. Then we used deep features obtained from trained model (Resnet-50) for visual similarity and we introduced the audio segmentation based on Power Spectrum Density (PSD) to contribute for transition detection. The proposed method is evaluated on the clip shots dataset. Experiments on this data show that the proposed multimodal approach can achieve a better performance compared with the state-of-the-art of methods that used visual approach.
DGRL: Text Classification with Deep Graph Residual Learning

Text classification is one of the most important problems in natural language processing. There are many useful features cannot be captured by traditional methods of text classification. Deep learning models have been proven that is able to extract features from data effectively. In this paper, we propose a deep graph convolutional network model that construct graph base on words and documents. We construct a new text graph based on the relevance of words and the relationship between words and documents in order to capture information from words and documents effectively. To obtain the sufficient representation information, we propose a deep graph residual learning (DGRL) method, which can slow down the risk of gradient disappearance. Experimental results demonstrate the effectiveness of the proposed model on various text datasets.
Optimization of Shell and Tube Heat Exchanger Using Alpha Tuning Elephant Herding Optimization (EHO) Technique

Swarm Intelligence algorithms like PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), Glow-worm swarm Optimization, etc. have been utilized by researchers for solving optimization problems. This work presents the application of a novel modified EHO (Elephant Herding Optimization) for cost optimization of shell and tube heat exchanger. A comparison of the results obtained by EHO in two benchmark problems shows that it is superior to those obtained with genetic algorithm and particle swarm optimization. The overall cost reduction is 13.3 % and 9.68% for both the benchmark problem compared to PSO. Results indicate that EHO can be effectively utilized for solving real-life optimization problems.
How to Make a BLT Sandwich? Learning VQA towards Understanding Web Instructional Videos

Understanding web instructional videos is an essential branch of video understanding in two aspects. First, most existing video methods focus on short-term actions for a-few-second-long video clips; these methods are not directly applicable to long videos. Second, unlike unconstrained long videos, e.g., movies, instructional videos are more structured in that they have step-by-step procedures constraining the understanding task. In this work, we study problem-solving on instructional videos via Visual Question Answering (VQA). Surprisingly, it has not been an emphasis for the video community despite its rich applications. We thereby introduce YouCookQA, an annotated QA dataset for instructional videos based on YouCook2 [27]. The questions in YouCookQA are not limited to cues on a single frame but relations among multiple frames in the temporal dimension. Observing the lack of effective representations for modeling long videos, we propose a set of carefully designed models including a Recurrent Graph Convolutional Network (RGCN) that captures both temporal order and relational information. Furthermore, we study multiple modalities including descriptions and transcripts for the purpose of boosting video understanding. Extensive experiments on YouCookQA suggest that RGCN performs the best in terms of QA accuracy and better performance is gained by introducing human-annotated descriptions. YouCookQA dataset is available at https://github.com/Jossome/YoucookQA.
IT Service Management Framework to Improve Business Information Structures

The aim of the project is to provide an overview of the improvements from the implementation of IT Service Management Framework in business and government IT infrastructures. IT Service Management or ITSM is a framework combining all organizational operations with their IT services. In this exponentially growing digital era these processes are more important than ever. ITSM and the set of libraries describing best practices for deploying IT service, which ITIL represents, are not new concepts. However, they have grown and evolved in so many ways. We advise businesses to adopt ITSM and ITIL best practices to work with their specific environments and meet their needs. Here we will compare challenges and advantages of organizations with different degrees of implementing IT service management frameworks and additionally take a look at the quantity of executed procedures and processes at different levels. The implementation of ITSM leads to a reduction of issues and ease of business processes in the future.
Basic quantum circuits for classification and approximation tasks

Abstract We discuss a quantum circuit construction designed for classification. The circuit is built of regularly placed elementary quantum gates, which implies the simplicity of the presented solu...
A hybrid encryption scheme based on optical scanning cryptography and Fibonacci–Lucas transformation

This paper describes a new opto-hybrid technique of two-stage encryption for the secure transmission and reception of sensitive information contained in the form of images and data. An advanced encryption–decryption technique based on optical scanning holography (optical scanning cryptography) and Fibonacci–Lucas transformation is proposed. The first stage of this hybrid system includes a point spread function engineered optical scanning cryptographic system. A new key based on fused biometric array is used in this stage. A digital encryption strategy follows this stage. This hybrid encryption scheme can be used for the secure transfer and storage of medical images in the Internet, especially in cloud-based services. The quantitative analysis is performed using different figure of merits. The quantitative investigations have been conducted through computer simulations. The parameters analyzed for establishing the performance characteristics of this system are Structural Similarity Index, Correlation Coefficient, Maximum Absolute Deviation, etc. This system shows a remarkable performance improvement during encryption. The method can be explicitly used in the radiograph image transfer over the Internet for telemedicine applications.
GoBrunch – An Option for Open-Access Distance Training Platform

The article presents GoBrunch Distance Learning Platform and the options for creating and administering distance learning courses. The description includes the directions for joining an already created by the teacher course, as well as how to communicate synchronously between teacher and students.
From the gut? Questions on Artificial Intelligence and music

AI applications are manifold in the music industry, both as tools assisting composers in creating and as music generating machines. AI applications assisting composers are widely used, for example in providing drum sequences or mastering services. AI-generated music is mainly used as production music, for example in synchronizing YouTube videos. Copyright implications relate initially to the use of existing works to train the computer, and secondly to the copyright protection for AI-generated musical works or sound recordings. This article firstly looks at the copyright acts involved in the training process in the EU, UK and US as well as potentially applicable exceptions. Secondly, it addresses the copyright position of AI-generated music and in particular the legal requirement of human creativity as the basis of copyright protection for musical works. The situation for sound recordings might be different.
A Study of Effectiveness of Activity-Based Teaching Method in the English Subject of Standard IV

Quality of education basically depends on the method of teaching adopted by the teachers in the classroom. Generally, child cantered and participatory approach should be followed in the classroom transactions. Teaching through activities thus yields good results among the students in teaching-learning process. A variety of interactive activities designed based on social constructionist principles had stimulated interest and involvement in the learning process with the positive outcome of seeing students actually participating in the construction of their own knowledge rather than being passive learners. The research was an experimental study aimed to evaluate the effectiveness of activity-based method vs. traditional method. The objectives were to compare the learning effects of group with traditional method and Activity based method. The study was delimited to 30 samples from PCMC area. The research was true experimental in nature. The research design followed by researcher is the pre-test post-test group design. Initially pre-test was administered in control group and latterly Activity based teaching strategy was applied on experimental group. Post-test was taken after completed the experiment. The reaction scale was taken from the student of the experimental group. This implies that the experimental group taught English concepts using Activity-Based teaching strategy achieved significantly higher than the control group taught the same concepts using the Lecture teaching strategy
Practical decoy-state BB84 quantum key distribution with quantum memory*

We generalize BB84 quantum key distribution (QKD) to the scenario where the receiver adopts a heralded quantum memory (QM). With the heralded QM, the valid dark count rate of the receiver’s single photon detectors can be mitigated obviously, which will lower the quantum bit error rate, and thus improve the performance of decoy-state BB84 QKD systems in long distance range. Simulation results show that, with practical experimental system parameters, decoy-state BB84 QKD with QM can exhibit performance comparable to that of without QM in short distance range, and exhibit performance better than that without QM in long distance range.
Automated Text Generation

Feedforward neural nets are generally great for classification and regression problems. CNNs are great for complex image classification. But activations for feedforward nets and CNNs flow only in one direction, from the input layers to the output layer. Since signals flow in only one direction, feedforward and convolutional nets are not ideal if patterns in data change over time. So we need a different network architecture to work with data impacted by time.
Hand Gesture Recognition to Implement Virtual Mouse Using Open Source Computer Vision Library: Python

Hand Movement Identification plays a vital part in a human–machine interconnection and to interact with a computer in a most effortless way. As many modern improvements occurring in today’s world, such as Natural language processing, Bio-metric Authentication, Face detection, etc., which can be frequently seen in our Tablets, iPads, Computers and smart phones. In the same way, Hand Movement Identification was a contemporary method of Human–Machine Interconnection, that is, the mouse cursor of the system can be controlled just on appearing our figure’s before the computer’s web camera. These finger gestures are captured and controlled through a Colour Detection technique of webcam. This system allows us to direct the system pointer by using our finger bearing colour caps or tapes and the operations like dragging of files and the left click would be performed by using distinct finger gestures. It also performs the transfer of files among two PC's in a single similar network. This developed system makes use of only a less resolution webcam which acts as a sensor for tracking the user’s hands in two dimensions. This system would be developed by using a Library named Open Source Computer Vision (OpenCV) and Python Server Programming. Based upon the idea of virtual mouse implementation, the paper was introduced. In the paper, we will provide a complete description of all the methodologies along with the libraries and packages that are used for the implementation of a Virtual Mouse.
Communications and Context: Strategies for Onboarding New E-Resources Librarians

The Electronic Resources Life Cycle created by Oliver Pesch is presented as a standard reference for e-resources librarians within the NASIG Core Competencies for Electronic Resources Librarians. The life cycle chart, while good for understanding e-resources as managed objects, may better be understood within the context of an expanded graphic inclusive of the people who manage these resources. This contextualization better serves early career librarians in realizing the extensive scope of communication with stakeholders plays in the management of the e-resources life cycle. This presentation discusses expanding the scope of the e-resources life cycle within the context of stakeholder communications, as well as onboarding strategies institutions may utilize to help new e-resources librarians acclimate to the relationships intersecting with their position and the resources they manage.
Policy Optimization in Zero-Sum Markov Games: Fictitious Self-Play Provably Attains Nash Equilibria

Fictitious Self-Play (FSP) has achieved significant empirical success in solving extensive-form games. However, from a theoretical perspective, it remains unknown whether FSP is guaranteed to converge to Nash equilibria in Markov games. As an initial attempt, we propose an FSP algorithm for two-player zero-sum Markov games, dubbed as smooth FSP, where both agents adopt an entropy-regularized policy optimization method against each other. Smooth FSP builds upon a connection between smooth fictitious play and the policy optimization framework. Specifically, in each iteration, each player infers the policy of the opponent implicitly via policy evaluation and improves its current policy by taking the smoothed best-response via a proximal policy optimization (PPO) step. Moreover, to tame the non-stationarity caused by the opponent, we propose to incorporate entropy regularization in PPO for algorithmic stability. When both players adopt smooth FSP simultaneously, i.e., with self-play, we prove that the sequence of joint policies converges to a neighborhood of a Nash equilibrium at a sublinear O~(1/T) rate, where T is the number of iterations. To our best knowledge, we establish the first finite-time convergence guarantee for FSP-type algorithms in zero-sum Markov games.
Case Study: Dark Web Markets

Hansa and The Wall Street were two of the biggest Dark Web markets in Europe and across the world. They consisted of thousands of merchants, tens of thousands of illegal listings and hundreds of thousands of users. Both were taken over after months of planning, investigation and close cooperation between law enforcement agencies in Europe and the United States. The combined technical and operational strengths of law enforcement agencies led to such significant success and provided an evidence that cooperation is the ideal mean to defeat cross-border and virtual illegal activities.