Reviewerly/exHarmony huggingface dataset:
Energy Conserving Path when Chasing an Intruder by Autonomous Guards

The rapid deployment of autonomous mobile guards require the guards to smartly use their energy in order to patrol and chase intruders where applicable. This paper provides solutions and strategies for conserving energy when a guard robot is chasing a mobile robot intruder while having limited information about the intruder's location. In this paper we look at energy conservation of the Moving Target Search with Snapshots problem where a mobile guard A <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</inf> is chasing a mobile intruder At in R2. Here, A <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</inf> can obtain A <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> 's location either by requesting a snapshot or by sensing At if A <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</inf> is close enough to At. The objective is to reduce the amount of energy that the guard robot is spending, where the total energy consists of the energy spent to roam around and the energy for taking/receiving a snapshot. In this paper we present 1) Solution for computing the minimum amount of energy required to catch the intruder in the worst case. 2) Algorithmic solutions for conserving energy used by A <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</inf> when using a limited number of snapshots computing the path, the locations and the time that A <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</inf> should take the snapshots.
Implementation and Evaluation of a Visual Query Language in the Context of Blockchain

Visual programming has become a term that stands for intuitive software development. It dispenses largely with textual notation and instead uses graphical program modules. The use of visual techniques is connected with the hope, among other things, to be able to create and understand programs more easily and better than before, which would open up access to software development for non-programmers. This could lead to a reduction of the enormous application backlog in many places. In this research, we design and build a web-based Visual Query builder tool to query the Waves blockchain database, using a flow-based programming language as well as an empirical evaluation of the tool that, on the one hand, measures the semantic understandability of a visual representation of a query, and compare it to its textual representation in SQL. On the other hand, we evaluate the user acceptance of the tool.
DAFV: A Unified and Real-Time Framework of Joint Detection and Attributes Recognition for Fast Vehicles

In the past decade, with the development of computing equipment and CNN, target detection has made great progress, which has promoted the development of specific target detection. The purpose of vehicle detection is not only to extract the vehicle from a large number of traffic surveillance cameras, but also for some follow-up research, such as the structured storage of vehicle information, which needs to quickly identify the attributes of the vehicle. Based on those demands, we propose a method of joint Detection and Attributes recognition for Fast Vehicles (DAFV). Firstly, we present Feature Rapidly Extract Module (FREM), which is to quickly shrink the feature map size and enhance the run-time efficiency. Secondly, we present Feature Refinement Module (FRM) to increase feature utilization rate and improve the performance. Lastly, we present the Cross-Stage and Multi-Scale (CS-MS) Module to optimize scale-invariant design. Related experiments based on UA-DETRAC dataset proves that DAFV is a feasible and effective method. The DAFV is fast and the speed does not change with the number of vehicles. For 416 \(\times \) 416 pictures, DAFV can reach 53 FPS with only 775 Mib GPU memory, which can meet the needs of real-time applications.
NV-QALSH: An NVM-Optimized Implementation of Query-Aware Locality-Sensitive Hashing

Locality-Sensitive Hashing (LSH) is a popular method for answering c-approximate nearest neighbor queries in high-dimensional spaces. Existing LSH methods are either DRAM-based ones which consume a vast amount of expensive DRAM and are time-consuming to rebuild after programs reboot, or disk-based ones such as the state-of-the-art QALSH (Query-Aware LSH), which suffers from high latency of disk I/O. In this paper, we find that the emerging non-volatile memory (NVM) can be leveraged to solve the above problems. Its economic characteristics and data durability urge us to persist most of the LSH index in NVM to reduce DRAM occupancy; and its byte-addressability and low latency contribute to fast query processing. Since QALSH uses B+-Trees as index data structures and LB-Tree is the state-of-the-art NVM-optimized B+-Tree, we first directly combine QALSH with LB-Tree to get LB-QALSH. However, LB-QALSH shows poor query performance under NVM. To fully utilize the advantages of NVM, we propose an NVM-optimized implementation of QALSH, named NV-QALSH, which is the first NVM-optimized LSH. NV-QALSH adopts three optimization designs to achieve a high query performance. Experiments show that NV-QALSH outperforms LB-QALSH with a 1.5-4.7x speedup. Furthermore, compared with the state-of-the-art DRAM-based LSH, NV-QALSH greatly reduces the DRAM occupancy and the index rebuilt time.
Rx-PETaL: A Reactive Data Publishing Framework

Organisations rely on the publishing of internal datasets to achieve crucial objectives, such as the implementation of an open data policy. Rx-PETaL is a framework that leverages concepts of reactive streaming and the Actor model to design efficient ETL processes for data publishers. We describe Rx-PETaL and illustrate its applicability to a number of real-world problems, as well as the results of the empirical evaluation of one application of Rx-PETaL, improving runtime duration of a legacy sequentially-processing application by more than 95%.
Learning Pseudo-Backdoors for Mixed Integer Programs

We propose a machine learning approach for quickly solving Mixed Integer Programs (MIPs) by learning to prioritize sets of branching variables at the root node which result in faster solution times, which we call pseudo-backdoors. Learning-based approaches have seen success in combinatorial optimization by flexibly leveraging common structures in a given distribution of problems. Our approach takes inspiration from the concept of strong backdoors, which are small sets of variables such that only branching on these variables yields an optimal integral solution and a proof of optimality. Our notion of pseudo-backdoors corresponds to a small set of variables such that prioritizing branching on them when possible leads to faster solve time. A key advantage of pseudo-backdoors over strong backdoors is that they retain the solver’s optimality guarantees and are amenable to data-driven identification. Our proposed method learns to estimate the relative solver speed of a candidate pseudo-backdoor and determine whether or not to use it. This pipeline can be used to identify high-quality pseudo-backdoors on unseen MIP instances for a given MIP distribution. We evaluate our method on five problem distributions and find that our approach can efficiently identify high-quality pseudo-backdoors. In addition, we compare our learned approach against Gurobi, a state-of-the-art MIP solver, demonstrating that our method can be used to improve solver performance.
Information-Based Georeferencing of Multi-Sensor-Systems by Particle Filter with Implicit Measurement Equations

Multi-Sensor-System (MSS) georeferencing is a challenging task in engineering that should be dealt with in the most reliable way possible. The most straight forward way for localizing a MSS is to rely on the Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, these data might not be always reliable enough or even available. Therefore, suitable filtering techniques are required to deal with such problems and to increase the reliability of the estimated states. In global localization and when it comes to real scenarios, particle filters are proven to deliver more realistic results than Kalman filter realizations. However, in MSS georeferencing where multiple sensors are used, different observation models are needed some of which could be of implicit type. In such a case, the likelihood estimation is challenging due to impossibility of estimating the observations by means of the generated samples. Therefore, the current paper offers a new particle filter methodology that can handle both implicit and explicit observation models. Final results of this methodology, which is applied on a simulated environment for georeferencing a MSS, are shown to be satisfactory.
Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.
OPTIMIZATION OF THE PROCESS OF ADAPTATION OF A STUDENT TO A HIGHER EDUCATIONAL INSTITUTION

Formulation of the problem. The problem of adaptation of students to institutions of higher education in our time is of great scientific and practical importance. The breakdown of dynamic stereotypes during the transition from the school system to higher education, the change in living conditions for nonresident students, a new social environment − all this accompanies the freshman and requires the formation of psychological mechanisms of adaptation of the personality of yesterday's schoolchild to new conditions of life and activity. The purpose of the research: determination of the structural components of the adaptation process, determination of the main directions of optimization of the adaptation process, creation of a multifunctional model of the student's adaptation process to the university. Conclusions. The main goal of optimizing the process of adaptation of a student to the university is to create conditions for the development, self-improvement and disclosure of the creative potential of the student's personality. So, we see the same ways of solving the indicated problems: psychological and pedagogical training of the supervisors of student groups; introduction of innovative technologies in the pedagogical process; improving the psychological and pedagogical qualifications of the teaching staff of a higher educational institution; work with gifted youth, development of students' creativity in the learning process; development of student initiative and self-government; psychological support of methodological and educational work in a higher educational institution; psychological support for the development of the student's personality in the learning process: mastering the skills of independent work, communication skills, disclosing the psychological characteristics of the development of one's creative potential, solving personal problems.
A Distributed Integrated Feature Selection Scheme for Column Subset Selection

Most of the existing distributed feature selection schemes neglect how good the subsets are that are mapped to the computational nodes, which causes a waste of time and hardware resources. A distributed integrated feature selection scheme (DIFS) with Subset Quality Evaluation (SQE) is proposed. SQE studies the relevance between the quality of a subset and the number of selected features from this subset, which helps shorten the feature selection time efficiently. We have given the implementation of our scheme for the Column Subset Selection (CSS) problem. We integrate a CSS algorithm in DIFS and information entropy as the SQE metric. We prove that the speedup of DIFS can reach m^3 compared to the centralized algorithm in ideal situations where m is the number of computational nodes, and give a well bounded approximation guarantee of the solution for CSS problem. Extensive experiments on eight data sets are used to verify the performance of scheme. Experiments results demonstrate the effectiveness of SQE and the impressive speedup DIFS can achieve. Although there is a slight increase of the reconstruction error value in some situations. Additional experiments of classification tasks reveal that the performance of DIFS is better than existing state-of-the-art distributed algorithms.
Symbiosis: A Novel Framework for Integrating Hierarchies from Knowledge Graph into Recommendation System

Integrating knowledge graphs into recommendation systems is promising as knowledge graphs can be side information to address cold start and data sparsity issues in recommendation systems. However, existing methods largely assume that knowledge graphs are under a closed-world assumption, this may lead to suboptimal performances. Furthermore, many existing approaches fail to model semantic hierarchies, which are common in real-world applications. Therefore, it is crucial to consider the incomplete nature of knowledge graphs as well as to represent hierarchical structure when incorporating it into recommendation system. In this paper, we propose Symbiosis, which is an end-to-end model that utilizes link prediction task in knowledge graphs to assist recommendation task. A general motivation for Symbiosis is that the two tasks automatically share the latent features between items and entities. We also incorporated a hierarchical structure method that maps entities into the polar coordinate system into the Symbiosis. Under this framework, not only users can get better recommendations but also knowledge graphs can be completed as these two tasks have a mutual effect. To evaluate the performance of each component, we conduct extensive experiments with two real-world datasets from different scenarios. The extensive results show that Symbiosis can be trained substantially improving F1-score by 59.7% on movie dataset and MR by 59.3% on music dataset compared to state-of-the-art methods.
Model and Data Driven Pseudo-HDR Imaging

It is an ill-posed problem to restore a high dynamic range (HDR) image from one low dynamic range (LDR) image. Instead of using conventional reverse tone mapping to restore the HDR image, two differently exposed LDR images are generated from the underlying LDR image by fusing model-driven and data-driven approaches. One is brighter than the input image and the other is darker than the input image. The three images are fused together via a multi-scale exposure fusion algorithm to produce a desired HDR image which can be displayed directly by existing LDR digital devices. The proposed algorithm can be embedded in smart phones or digital cameras to produce a pseudo-HDR image.
A Hybrid Approach for Counting Templates in Images

In the research, hybrid algorithm for counting repeated objects in the image is proposed. Proposed algorithm consists of two parts. Template matching sub-algorithm is based on normalized cross correlation function which is widely used in image processing application. Template matching can be used to recognize and/or locate specific objects in an image. Neural network sub-algorithm is needed to filter out false positives that may occur during cross correlation function evaluation.
An AI-Based Detection System for Mudrabharati: A Novel Unified Fingerspelling System for Indic Scripts

Sign Language (SL) is a potential tool for communication in the hearing and speech-impaired community. As individual words cannot be communicated accurately using the SL gestures, fingerspelling is adopted to spell out names of people and places. Due to rich vocabulary and diversity in Indic scripts, and the abugida nature of Indic scripts that distinguish them from a prominent world script like the Roman script, it is cumbersome to use American Sign Language (ASL) convention for fingerspelling in Indian languages. Moreover, due to the existence of 10 major scripts in India, it is a futile task to develop a separate fingerspelling convention for each individual Indic script based on the geometry of the characters. In this paper, we propose a novel and unified fingerspelling system known as Mudrabharati for Indic scripts. The gestures of Mudrabharati are constructed based on the phonetics of Indian scripts and not the geometry of the glyphs that compose the individual characters. Unlike ASL that utilizes just one hand, Mudrabharati uses both the hands - one for consonants and the other for vowels; swarayukta aksharas (Consonant-Vowel combinations) are gestured by using both the hands. An Artificial Intelligence (AI) based recognition system for Mudrabharati that returns the character in Devanagari and Tamil scripts is developed.
SEAT: statistically sound infra-side deployment and integration testing

Most A/B testing frameworks in the industry are based on randomized allocation of users to control and test groups. Such systems are not suitable to test infrastructure changes, and engineers in charge of such releases have developed practical solutions to manage releases. At Facebook, a common approach is to roll out the new “feature” to a subset of traffic on multiple staging environments, and make a decision based on observed metrics. This approach is subjective and manual. To improve the efficiency of the process for Facebook Video Infrastructure team (“Video Infra”), we developed Shadow Experiment Analysis Tool (SEAT), a framework containing hypothesis testing library and visualization tool. SEAT was adopted and proven to be successful in a variety of scenarios, ranging from deployment A/B testing analysis to service healthcheck at scale. Most recently, it was also integrated into end-to-end Media Validation platform where SEAT helps verify system, network, playback and computer vision metrics. With SEAT, teams are able to catch issues earlier in the process and can prevent them from impacting end users.
Hypersuccinct Trees - New Universal Tree Source Codes for Optimal Compressed Tree Data Structures and Range Minima

We present a new universal source code for distributions of unlabeled binary and ordinal trees that achieves optimal compression to within lower order terms for all tree sources covered by existing universal codes. At the same time, it supports answering many navigational queries on the compressed representation in constant time on the word-RAM; this is not known to be possible for any existing tree compression method. The resulting data structures, hence combine the compression achieved by the best known universal codes with the operation support of the best succinct tree data structures.We apply hypersuccinct trees to obtain a universal compressed data structure for range-minimum queries. It has constant query time and the optimal worst-case space usage of 2n+o(n) bits, but the space drops to 1.736n + o(n) bits on average for random permutations of n elements, and 2lg binom{n}{r} + o(n) for arrays with r increasing runs, respectively. Both results are optimal; the former answers an open problem of Davoodi et al. (2014) and Golin et al. (2016).Compared to prior work on succinct data structures, we do not have to tailor our data structure to specific applications; hypersuccinct trees automatically adapt to the trees at hand. We show that they simultaneously achieve the optimal space usage to within lower order terms for a wide range of distributions over tree shapes, including: binary search trees (BSTs) generated by insertions in random order / Cartesian trees of random arrays, random fringe-balanced BSTs, binary trees with a given number of binary/unary/leaf nodes, random binary tries generated from memoryless sources, full binary trees, unary paths, as well as uniformly chosen weight-balanced BSTs, AVL trees, and left-leaning red-black trees.
Inducing Bilingual Word Representations for Non-isomorphic Spaces by an Unsupervised Way

Bilingual word representations (BWRs) play a very key role in many natural language processing (NLP) tasks, especially cross-lingual applications such as machine translation and cross-lingual information retrieval et al. Most existing methods are based on offline unsupervised methods to learn BWRs. Those offline methods mainly rely on the isomorphic assumption that word representations have a similar distribution for different languages. Several authors also question this assumption and argue that word representation spaces are non-isomorphic for many language pairs. In this paper, we adopt a novel unsupervised method to implement joint training BWRs. We first use a dynamic programming algorithm to detect continuous bilingual segments. Then, we use the extracted bilingual data and monolingual corpora to train BWRs jointly. Experiments show that our approach improves the performance of BWRs compared with several baselines in the real-world dataset.(By unsupervised, we mean that no cross-lingual resources like parallel text or bilingual lexicons are directly used.)
Implementation and Analysis of Reusability Framework Design for Event User Interface Component in Phaser 3

The growth of game development is now widely supported by software such as game engines and game frameworks. Phaser 3 is a popular HTML5 based game framework on the browser platform. However, Phaser 3 does not facilitate managing code of User Interface (UI) event component. Sometimes a game developer discovers that UI event component codes, such as buttons or joysticks that have been created, must be regenerated on subsequent projects with the same function. In this research, we proposed a reusability framework for Phaser 3 by combining the factory method and the singleton pattern in managing UI event components. The aim is to make developer productivity increased so that there is no need to repeat the algorithm or code that has been created previously. Our results show that the Phaser 3 UI standard has less a reusability value of 64,241%, while the proposed framework that was built has relatively higher reusability, that is equal to 84,576%. Game developers can add the combination of design patterns, as good software development practices, into an existing game framework to achieve code reusability without major changing on the existing game framework. This approach can also be used for the creators of game framework to use design patterns from the outset of development of game framework.