Reviewerly/exHarmony huggingface dataset:
RSINet: Rotation-Scale Invariant Network for Online Visual Tracking

Most Siamese network-based trackers perform the tracking process without model update, and cannot learn targetspecific variation adaptively. Moreover, Siamese-based trackers infer the new state of tracked objects by generating axis-aligned bounding boxes, which contain extra background noise, and are unable to accurately estimate the rotation and scale transformation of moving objects, thus potentially reducing tracking performance. In this paper, we propose a novel Rotation-Scale Invariant Network (RSINet) to address the above problem. Our RSINet tracker consists of a target-distractor discrimination branch and a rotation-scale estimation branch, the rotation and scale knowledge can be explicitly learned by a multi-task learning method in an end-to-end manner. In addtion, the tracking model is adaptively optimized and updated under spatio-temporal energy control, which ensures model stability and reliability, as well as high tracking efficiency. Comprehensive experiments on OTB-100, VOT2018, and LaSOT benchmarks demonstrate that our proposed RSINet tracker yields new state-of-the-art performance compared with recent trackers, while running at real-time speed about 45 FPS.
A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings

This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP.Unlike the prior work using a SGDbased method and quantization of real-valued vectors, the proposed method directly optimizes binary embedding vectors by a series of bit flipping operations.On the standard knowledge graph completion tasks, the B-CP model trained with the proposed method achieved comparable performance with that trained with SGD as well as state-of-the-art real-valued models with similar embedding dimensions.
Light dual multinets of order six in the projective plane

Embedded multinets are line arrangements of the projective plane with a rich combinatorial structure. In this paper, we first classify all abstract light dual multinets of order 6 which have a unique line of length at least two. Then we classify the weak projective embeddings of these objects in projective planes over fields of characteristic zero. For the latter we present a computational algebraic method for the study of weak projective embeddings of finite point-line incidence structures.
Transferred Fusion Learning using Skipped Networks

Identification of an entity that is of interest is prominent in any intelligent system. The visual intelligence of the model is enhanced when the capability of recognition is added. Several methods such as transfer learning and zero shot learning help to reuse the existing models or augment the existing model to achieve improved performance at the task of object recognition. Transferred fusion learning is one such mechanism that intends to use the best of both worlds and build a model that is capable of outperforming the models involved in the system. We propose a novel mechanism to amplify the process of transfer learning by introducing a student architecture where the networks learn from each other.
Utilization of Blockchain Technology in Greek Public Administration

The use of the blockchain technology in Greek public sector appears to be beneficial for both the citizens and organisations. However, since this innovative technology is still in its infancy, a series of challenges exist in e-goverment. This paper presents the existing e-goverment infrastructure in Greece, the benefits and obstacles of the adoption of this promising trend for the recruitment process in public sector. More precisely ASEP which is an independent authority that acts as the institutional guardian for meritocracy regarding recruitment in Greek public administrator aims through the QualiChain research project to take advantage of the blockchain technology so as to improve and simplify the recruitment and competency management procedures.Â Keywords: Blockchain, e-Government, Public administration, Public sector recruitment.
On Estimating the Training Cost of Conversational Recommendation Systems

Conversational recommendation systems have recently gain a lot of attention, as users can continuously interact with the system over multiple conversational turns. However, conversational recommendation systems are based on complex neural architectures, thus the training cost of such models is high. To shed light on the high computational training time of state-of-the art conversational models, we examine five representative strategies and demonstrate this issue. Furthermore, we discuss possible ways to cope with the high training cost following knowledge distillation strategies, where we detail the key challenges to reduce the online inference time of the high number of model parameters in conversational recommendation systems
Look Before You Leap: Trusted User Interfaces for the Immersive Web

Part of what makes the web successful is that anyone can publish content and browsers maintain certain safety guarantees. For example, it's safe to travel between links and make other trust decisions on the web because users can always identify the location they are at. If we want virtual and augmented reality to be successful, we need that same safety. On the traditional, two-dimensional (2D) web, this user interface (UI) is provided by the browser bars and borders (also known as the chrome). However, the immersive, three-dimensional (3D) web has no concept of a browser chrome, preventing routine user inspection of URLs. In this paper, we discuss the unique challenges that fully immersive head-worn computing devices provide to this model, evaluate three different strategies for trusted immersive UI, and make specific recommendations to increase user safety and reduce the risks of spoofing.
Contextual Graph Reasoning Networks

Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks. However, the graph representation learned by existing methods is not effective enough as the relation between feature and graph is under-explored. In this work, we propose a novel method named Contextual Graph Reasoning (CGR) that learns a context-aware relation between feature and graph. This is achieved by constructing the projection matrix based on a global set of descriptors during graph projection, and calibrating the evolved graph based on the self-attention of all nodes during graph reprojection. Therefore, contextual information is well explored in both graph projection and reprojection with our method. To verify the effectiveness of our method, we conduct extensive experiments on semantic segmentation, instance segmentation, and 2D human pose estimation. Our method consistently achieves remarkable improvements over state-of-the-art methods, demonstrating the effectiveness and generalization ability of our method.
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains

Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory.Aiming to assist domain experts in their first steps into a new task over a new corpus, we present an unsupervised approach to reveal complex rules which cluster the unexplored corpus by its prominent categories (or facets).These rules are human-readable, thus providing an important ingredient which has become in short supply lately -explainability.Each rule provides an explanation for the commonality of all the texts it clusters together.We present an extensive evaluation of the usefulness of these rules in identifying target categories, as well as a user study which assesses their interpretability.
СОЦИЈАЛНА ЕКСКЛУЗИЈА РОМСКИХ УЧЕНИКА У ОСНОВНИМ ШКОЛАМА У НИШУ

Indicating that social exclusion is one of the key reasons for low academic achievement, the authors analise social exclusion of Roma students in primary schools in Niš. The paper constists of three parts: the data of social and educational status of Roma population are presented first, then the most important laws of Roma inclusion, and finally, the results of research of Roma student’s social exclusion in elementary schools in Niš are depicted. The conclusion is that Roma students are not integrated into their peer groups and that there is a noticeable social distance towards them. Keywords: Roma students, education, social exclusion, inclusion
AN ANALYSIS OF TEACHER PEDAGOGICAL MISCONDUCT IN TEACHING ENGLISH

This study was aimed to find out the types of teacher pedagogical misconduct and the reasons of teacher pedagogical misconduct. The research was conducted by using descriptive qualitative. The data were the result of questionnaire and interviewing of English teacher at SMP Swasta Josua Medan. The data were analyzed based on types of teacher pedagogical misconduct by Kearney and reason of teacher pedagogical misconduct by Riley and Lewis. The findings showed that there were three types of teacher pedagogical misconduct. They were indolence, incompetence, and offensiveness. Moreover, the dominant category of indolence is changing assignments every time, while failure to treat student properly is in incompetence and involving sarcasm in offensiveness. The teacher conducted pedagogical misconduct due to treat students differently, teaching in sharp sarcasm technique, restricted, releasing anger and hardly braid good relationship.Keywords: teacher pedagogical misconduct, teaching English
Dimerized decomposition of quantum evolution on an arbitrary graph

The study of quantum evolution on graphs for diversified topologies is beneficial to modeling various realistic systems. A systematic method, the dimerized decomposition, is proposed to analyze the dynamics on an arbitrary network. By introducing global "flows" among interlinked dimerized subsystems, each of which locally consists of an input and a output port, the method provides an intuitive picture that the local properties of the subsystem are separated from the global structure of the network. The pictorial interpretation of quantum evolution as multiple flows through the graph allows for the analysis of the complex network dynamics supplementary to the conventional spectral method.
Securing Through Pseudorandom Number Generator and Hashing in Cryptography: Review

Authentication and confidentiality are two major aspect of the cryptography. In no way you can say that your message is completely secure i.e. any third party can attack you message at any time/stage of processing. For sending confidential messages the security feature is essential to avoid any unauthorized access. We have to encode a message before encryption in ECC as describe by Sir Koblitz. In this paper we employed a new mechanism to secure the value of k by using PRN generator (because of confusion and diffusion) and the value of m by using SHA (for the use of one-way property). Based on the simulated results we achieve strong mechanism of authentication and confidentiality that ultimately enhance the data security.
Open Area Path Finding to Improve Wheelchair Navigation

Navigation is one of the most widely used applications of the Location Based Services (LBS) which have become part of our digitally informed daily lives. Navigation services, however, have generally been designed for drivers rather than other users such as pedestrians or wheelchair users. For these users the directed networks of streets and roads do not limit their movements, but their movements may have other limitations, including lower speed of movement, and being more dependent on weather and the pavement surface conditions. This paper proposes and implements a novel path finding algorithm for open areas, i.e. areas with no network of pathways such as grasslands and parks where the conventional graph-based algorithms fail to calculate a practically traversable path. The new method provides multimodality, a higher level of performance, efficiency, and user satisfaction in comparison with currently available solutions. The proposed algorithm creates a new graph in the open area, which can consider the obstacles and barriers and calculate the path based on the factors that are important for wheelchair users. Factors, including slope, width, and surface condition of the routes, are recognised by mining the actual trajectories of wheelchairs users using trajectory mining and machine learning techniques. Unlike raster-based techniques, a graph-based open area path finding algorithm allows the routing to be fully compatible with current transportation routing services, and enables a full multimodal routing service. The implementations and tests show at least a 76.4% similarity between the proposed algorithm outputs and actual wheelchair users trajectories.
Going from stable creep to aseismic slow slip events in the ductile realm

&amp;lt;p&amp;gt;The accommodation of motion on faults spans a large spectrum of slip modes, ranging from stable creep to earthquakes. While seismic slip modes certainly have the largest impact on the surface due to the induced ground shaking, it has been recognized that slow aseismic slip modes relax most of the accumulated stresses on a fault. It has also been suggested that aseismic slip controls seismic events, thus making this kind of slip mode key for earthquake prediction.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Despite the importance of aseismic slow slip, its underlying physical mechanisms are still unclear. Commonly, slow slip events are modeled in terms of frictional failure, employing a rate-and-state&amp;amp;#160;model of fault friction, often also invoking fluids that alter frictional properties on the fault. However, at larger depths, frictional processes become increasingly difficult to activate due to the increase in ambient pressure and ductile processes are more likely to dominate deformation.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here we therefore investigate deep aseismic slip processes governed by ductile deformation mechanisms using 2D numerical models, where we employ a composite viscoelastic rheology combined with grain size reduction and shear heating as weakening processes. We show that the collaborative action of these two weakening mechanisms is sufficient to create the entire spectrum of aseismic slip, ranging from stable creep to long-term slow slip events. The results show that ductile deformation does not necessarily result in stable slip and induces slip modes with considerably larger velocities than the far-field plate velocities. Moreover, the propagation of ductile ruptures induces large stresses in front of the rupture tip which may also trigger short-term seismic events.&amp;lt;/p&amp;gt;
Electronic Presence of the Arabic National Libraries: Webometrics study

Study of the National Libraries Websites available on the Web from theWebometrics Perspective. the Study deals with Webometric monitoring andanalyzing of these Sites, by Identifying the age of them and their Size accordingto the number of their Pages and analyzing the Super-links of them with threeTypes: In Links, Out links and Self links, the study also examines The impact ofthese Sites Depending on one of the measuring tools which is Known as WebImpact Factor (WIF).The most important results of the study is: that the Egyptian NationalLibrary (Dar Alkutub Almisria) site is the most present on the web according tothe Over All Web Impact Factor with an (WIF) of 4,8, followed by the King FahdNational Library site with an WIF of 4,1, then the Lebanese National Librarywebsite with an WIF of 1.1, while the King Fahd National Library was the firstaccording to the Revised WIF with an WIF of 3.2, then the Egyptian NationalLibrary ranked second with an WIF of 3.2, and thirdly came each of TheDepartment of the Jordan National Library, and the Site of the Yemeni NationalLibrary, with an WIF of 2,1 each.The study recommended that: One of the Arab specialized institutions inthe field of libraries and information, as the Arab Federation for Libraries andInformation, should be responsible for measuring the WIF for all kinds of Arablibraries sites, and Issuance a periodic report that includes ranking of sitesaccording to WIF
When a Credible Source Turns ‘Fake’

This chapter examines the recent German anti-fake-news law in a very practical context. The whole idea surrounding the law, which takes a novel and unique approach, was built on the premise that the greatest risk for the flow of information comes from the spread of fake news on social media. These platforms have largely replaced traditional media to a large extent as the primary source of news. The chapter focuses on the issues such as the limits of truth and fact checking, responsibility of the State vis-a-vis the individual in the field of free speech and the limitations, which exist as to what can be achieved by legislating. It explains the Relotius affair, its significance, and its consequences. The chapter briefly analyses the new legal system aimed at combating hate speech and fake news in place in Germany as a starting point for any contextual analysis of the Relotius affair.
Iterative classical superadiabatic algorithm for combinatorial optimization

We consider a classical and superadiabatic version of an iterative quantum adiabatic algorithm to solve combinatorial optimization problems. This algorithm is deterministic because it is based on purely classical dynamics, that is, it does not rely on any stochastic approach to mimic quantum dynamics. Moreover, use of shortcuts to adiabaticity makes the algorithm independent of the annealing time. We apply this algorithm to a certain class of hard instances of the 3-SAT problem. We find that more than 90\% of such 64-bits hard instances can be resolved by a few iteration. Our approach can also be used to analyze properties of instances themselves apart from stochastic uncertainty and shortage of adiabaticity.
Persuasive Dialogue Understanding: the Baselines and Negative Results

Persuasion aims at forming one's opinion and action via a series of persuasive messages containing persuader's strategies. Due to its potential application in persuasive dialogue systems, the task of persuasive strategy recognition has gained much attention lately. Previous methods on user intent recognition in dialogue systems adopt recurrent neural network (RNN) or convolutional neural network (CNN) to model context in conversational history, neglecting the tactic history and intra-speaker relation. In this paper, we demonstrate the limitations of a Transformer-based approach coupled with Conditional Random Field (CRF) for the task of persuasive strategy recognition. In this model, we leverage inter- and intra-speaker contextual semantic features, as well as label dependencies to improve the recognition. Despite extensive hyper-parameter optimizations, this architecture fails to outperform the baseline methods. We observe two negative results. Firstly, CRF cannot capture persuasive label dependencies, possibly as strategies in persuasive dialogues do not follow any strict grammar or rules as the cases in Named Entity Recognition (NER) or part-of-speech (POS) tagging. Secondly, the Transformer encoder trained from scratch is less capable of capturing sequential information in persuasive dialogues than Long Short-Term Memory (LSTM). We attribute this to the reason that the vanilla Transformer encoder does not efficiently consider relative position information of sequence elements.
The Longest Run Subsequence Problem: Further Complexity Results

Longest Run Subsequence is a problem introduced recently in the context of the scaffolding phase of genome assembly (Schrinner et al.,WABI 2020). The problem asks for a maximum length subsequence of a given string that contains at most one run for each symbol (a run is a maximum substring of consecutive identical symbols). The problem has been shown to be NP-hard and to be fixed-parameter tractable when the parameter is the size of the alphabet on which the input string is defined. In this paper we further investigate the complexity of the problem and we show that it is fixed-parameter tractable when it is parameterized by the number of runs in a solution, a smaller parameter. Moreover, we investigate the kernelization complexity of Longest Run Subsequence and we prove that it does not admit a polynomial kernel when parameterized by the size of the alphabet or by the number of runs. Finally, we consider the restriction of Longest Run Subsequence when each symbol has at most two occurrences in the input string and we show that it is APX-hard.
Explainable Knowledge Tracing Models for Big Data: Is Ensembling an Answer?

In this paper, we describe our Knowledge Tracing model for the 2020 NeurIPS Education Challenge. We used a combination of 22 models to predict whether the students will answer a given question correctly or not. Our combination of different approaches allowed us to get an accuracy higher than any of the individual models, and the variation of our model types gave our solution better explainability, more alignment with learning science theories, and high predictive power.
Sistem Scheduling Pelaporan Data Akademik di UIN Sunan Kalijaga ke Pangkalan Data Pendidikan Tinggi (PDDikti) dengan Menggunakan Fitur Cron Job di Linux

Data reporting from higher education to the Higher Education Data Centre (PDDikti) is a form of reporting that must be sent to The Indonesian Ministry of Research, Technology and Higher Education (Ristekdikti). This reporting includes aspects of students, academic transactions, and some other data, which usually has a time limit for each academic year. UIN Sunan Kalijaga like other higher eductions, reports a lot of data to PDDikti with a limited time, which sometimes makes reporting officers need to do overtime to report existing data. This study aims to create an application that can schedule data reporting times to the PDDikti feeder application, to reduce overtime that needs to be done by the operator. The application is created using the cron job feature owned by Linux to run reporting commands.
Research Guides: Agriculture and Bioresources 492/494: Tutorials for Finding and Evaluating Information Sources: 2.2 Search Strategies and Techniques

This guide contains two modules designed to assist 492/494 AgBio students in contributing to the scholarly conversation in their fields, and finding and retrieving relevant and credible information.
REEF: A Framework for Information Extraction and Automated Knowledge Graph Construction

Summary We present Reef (Recursive Evidence Extraction Framework), a Python framework for automated information extraction from Petroleum Geoscience databases. Reef enables an end to end pipeline from raw documents to a Knowledge Graph. Reef makes possible two essential operations: 1/ discover entities in documents, characterize them and connect them to abstract concepts present in a knowledge graph and 2/ discover new knowledge with distant supervision. Knowledge graphs are key to build better search engines, Question Answering systems, recommendation engines, feed algorithms for the cross analysis of multiple datasets. Reef unique approach leverages a comprehensive stack of open source and state-of-the-art libraries for documents digitalization and parsing, Natural Language Processing, Language Modeling, Logic Reasoning and Graph Analysis. These foundational components are seconded by custom applications for specific tasks. Documents processed in Reef are digitized and sent through a pipeline where their content is filtered according to a flexible, easily extensible, Petroleum Geoscience specific object model. Information can be extracted from text, tables, figures, diagrams. Reef contains functions to infer information nature, digitize it, disambiguate and reconcile it into a graph database. Reef can be deployed in any cloud and delivers production ready knowledge graphs which can be served to third party applications.