Reviewerly/exHarmony huggingface dataset:
Safe Path Planning Algorithms for Mobile Robots Based on Probabilistic Foam

The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.
Morph Call: Probing Morphosyntactic Content of Multilingual Transformers

The outstanding performance of transformerbased language models on a great variety of NLP and NLU tasks has stimulated interest in exploring their inner workings.Recent research has focused primarily on higherlevel and complex linguistic phenomena such as syntax, semantics, world knowledge, and common sense.The majority of the studies are anglocentric, and little remains known regarding other languages, precisely their morphosyntactic properties.To this end, our work presents Morph Call, a suite of 46 probing tasks for four Indo-European languages of different morphology: English, French, German and Russian.We propose a new type of probing task based on the detection of guided sentence perturbations.We use a combination of neuron-, layer-and representation-level introspection techniques to analyze the morphosyntactic content of four multilingual transformers, including their less explored distilled versions.Besides, we examine how fine-tuning for POS-tagging affects the model knowledge.The results show that fine-tuning can improve and decrease the probing performance and change how morphosyntactic knowledge is distributed across the model.The code and data are publicly available, and we hope to fill the gaps in the less studied aspect of transformers.
ViziTex: Interactive Visual Sense-Making of Text Corpora

Information visualization is critical to analytical reasoning and knowledge discovery.We present an interactive studio that integrates perceptive visualization techniques with powerful text analytics algorithms to assist humans in sense-making of large complex text corpora.The novel visual representations introduced here encode the features delivered by modern text mining models using advanced metaphors such as hypergraphs, nested topologies and tessellated planes.They enhance humancomputer interaction experience for various tasks such as summarization, exploration, organization and labeling of documents.We demonstrate the ability of the visuals to surface the structure, relations and concepts from documents across different domains.
Big data processing technology in a distributed information system

Digital technologies exist in almost all areas of human life. The volume of data recorded in global warehouses increases every second, which means that the conditions for storing information change at the same rate, and new opportunities must appear to increase its volume. The introduction of big data technology is a new step in scientific and technical development in various sectors of society. Despite the fact that big data technology is at the initial stage of development, it has achieved high results in the field of Information Technology. There are no universal methods for processing big data, but they allow you to use different methods to partially solve the problem. Successful use of Big Data Increases work efficiency several times. Currently, Big Data Processing is focused on the effectiveness of using new technologies. The article discusses methods and approaches to working with big data, emerging complex problems and the importance of using big data in a distributed Information System, Functional Requirements for a distributed information system that supports scientific and educational activities, and the big data architecture that supports scientific and educational activities of a distributed information system.
IIITN NLP at SMM4H 2021 Tasks: Transformer Models for Classification on Health-Related Imbalanced Twitter Datasets

Non-availability of well annotated and balanced datasets is considered as one of the major hurdles in analysing and extracting meaningful information from health-related tweets.Herein, we present transformer based deep learning binary classifiers for distinguishing the health related tweets for the three shared tasks 1a, 4 and 8 of the 6 th edition of SMM4H Workshop.We evaluate the different transformer based models viz.RoBERTa (for Task 1a & 4) and BioBERT (for Task 8), along with various dataset balancing techniques.We implement augmentation and sampling techniques so as to improve performance on the imbalanced datasets.
ROP Defense Using Trie Graph for System Security

Most Intrusion Detection Systems (IDS) / Intrusion Prevention Systems (IPS) cannot defend the attacks from a Return Oriented Program (ROP) which applies code reusing and exploiting techniques without the need for code injection. Malicious attackers chain a short sequence as a gadget and execute this gadget as an arbitrary (Turing-complete) behavior in the target program. Lots of ROP defense tools have been developed with satisfactory performance and low costs overhead, but malicious attackers can evade ROP tools. Therefore, it needs security researchers to continually improve existing ROP defense tools, because the defense ability of target devices, such as smartphones is weak, and such devices are being increasingly targeted. Our contribution in this paper is to propose an ROP defense method that has provided a better performance of defense against ROP attacks than existing ROP defense tools.
Understanding In The Context Of Remote Training

In a pedagogical reality the understanding is determined as one of leading principles of the organization of process of education which allows to systematize and comprehend earlier turned out pedagogical experience; promotes development of valuable semantic sphere of subjects of educational process; enables to carry out pedagogical activity on the basis of the valuable semantic communications basing consciousness of dialogue, defines intelligent behaviour of the person in modern society, promotes becoming of the conceiving, working and estimating person.In article problems of remote training through a prism of the understanding approach accenting attention on values and senses of professional pedagogical activity are examined.It is paid attention to technical and semantic specificity of remote training.Positions and reasons as apologets of online training, and critics of remote education are examined.Authors of article consider that the given discussions should lead to a birth actual practices online of teaching at preservation of alive dialogue, personal focused training.An empirical material in article became approbation of the practical tasks developed by
MoCo‐Flow: Neural Motion Consensus Flow for Dynamic Humans in Stationary Monocular Cameras

Abstract Synthesizing novel views of dynamic humans from stationary monocular cameras is a specialized but desirable setup. This is particularly attractive as it does not require static scenes, controlled environments, or specialized capture hardware. In contrast to techniques that exploit multi‐view observations, the problem of modeling a dynamic scene from a single view is significantly more under‐constrained and ill‐posed. In this paper, we introduce Neural Motion Consensus Flow ( MoCo‐Flow ), a representation that models dynamic humans in stationary monocular cameras using a 4D continuous time‐variant function. We learn the proposed representation by optimizing for a dynamic scene that minimizes the total rendering error, over all the observed images. At the heart of our work lies a carefully designed optimization scheme, which includes a dedicated initialization step and is constrained by a motion consensus regularization on the estimated motion flow. We extensively evaluate MoCo‐Flow on several datasets that contain human motions of varying complexity, and compare, both qualitatively and quantitatively, to several baselines and ablated variations of our methods, showing the efficacy and merits of the proposed approach. Pretrained model, code, and data will be released for research purposes upon paper acceptance.
An Analysis of the Students’ Difficulties in TOEFL Prediction Test of Listening Section

This study is to know the students’ difficulty in doing the TOEFL prediction test, especially the listening section in ITB AAS Indonesia. This study uses descriptive qualitative research by using a questionnaire. The result shows that the students’ difficulty in doing TOEFL listening both external and internal factors. In external factors, include: 1) the speaker's accent (75.5 %), 2) the speakers’ speed (75.9 %), 3) the speakers’ intonation/emphasis (73 %), 4) the speakers’ pause in pronouncing a sentence (70.3 %), 5) the choice of words and foreign terms conveyed by the speaker (71 %), 6) the sentence structure conveyed by the speaker is too complex (54.8 %), 7) audio interruption causes the audio sounds less/unclear (54.8 %). In internal factors, include: 1) 64.3 % of students do not have previous experience doing TOEFL test, 2) 58.5 % of students have lack of practice in TOEFL listening, 3) 78.8 % of students have limited time in doing TOEFL listening test, 4) 62.2 % of students feel a lot of listening questions which consist of 50 questions, 5) 76.2 % of students do not have hearing impairment in listening, 6) 51 % of students have memory limitations when listening to TOEFL, 7) 48.1 % of students lack of motivation and enthusiasm, 8) 52.7 % of students lack of concentration or focus, 9) 53.5 % of students have limited mastery of foreign/unfamiliar vocabularies, 10) 47.3 % of students feel boredom when listening, 11) 56 % of students feel easily distracted by sounds or other things, 12) 51 % of students tend to translate any foreign vocabularies when listening, 13) 52.7 % of students have trouble catching or finding keywords, 14) 44.4 % of students are busy along with other activities when listening, such as playing writing instruments, taking notes or doing other things. Keywords: Listening, TOEFL, TOEFL Prediction Test
A new approach to Web Crawling — DHEKTS Crawler in comparison with various Crawlers

Objectives: To propose a crawler to visit websites for collecting information and create a search engine index for reference; To compare various crawler License, language used for creation, effectiveness with proposed DHEKTS crawler; To compare various characteristics, tasks and functions with proposed DHEKTS crawler; To identify the merits of the DHEKTS Crawler.Methods: A new Crawler called DHEKTS is developed to filter and synchronize documents like Images, Link, and HTML code from a given website.This Crawler is unique in nature since it returns all the details of a particular website having Images, Links, html code and contents.It can crawl through links in a specified website and crawl further to other links on the website.The DHEKTS Crawler is designed for Depth and Relevance crawling.The entire DHEKTS crawler has a few crawling mechanism supporting variety of information.The requirements are Operating System: Win 7 and higher, Front End: PHP, BackEnd: MySQL, RAM: Minimum 4GB and SERVER: High Speed Server with good storage Capacity.Findings: The DHEKTS Crawler has brought web related Links, Images, HTML Code, Information about to fifth level of crawling and Relevance Search giving relevant information.Multiple crawlers fulfill the major functions of crawling but DHEKTS CRAWLER is built to execute all functions in one crawler.Applications: This is applied in Crawling of various Websites and to retrieve valuable data.
Stencil Solvers for PDEs on GPUs: An Example From Cosmology

The increasingly diverse ecosystem of high-performance architectures and programming models presents a mounting challenge for programmers seeking to accelerate scientific computing applications. Code generation offers a promising solution, transforming a simple and general representation of computations into lower level, hardware-specialized and -optimized code. We describe the philosophy set forth by the Python package Pystella, a high-performance framework built upon such tools to solve partial differential equations on structured grids. A hierarchy of abstractions provides increasingly expressive interfaces for specifying physical systems and algorithms. We present an example application from cosmology, using finite-difference methods to solve coupled hyperbolic partial differential equations on (multiple) GPUs. Such an approach may enable a broad range of domain scientists to make efficient use of increasingly heterogeneous computational resources while mitigating the drastic effort and expertise nominally required to do so.
High-Energy Standard Model from the Gauge Group Contraction

The evolution of properties and interactions of elementary particles is described, beginning with the Planck scale of 1019 GeV. The description is based on the hypothesis that the high-temperature (high-energy) limit of the Standard Model is generated by the gauge group contraction. In the infinite-temperature limit, properties of particles fundamentally change: all particles lose their masses, and only massless neutral $$Z$$ bosons and $$u$$ quarks together with neutrinos and photons survive. Weak interactions become long-range ones and are generated by neutral currents. Quarks have only one color degree of freedom.
First Step toward Gestural Recognition in Harsh Environments

We are witnessing a rise in the use of ground and aerial robots in first response missions. These robots provide novel opportunities to support first responders and lower the risk to people’s lives. As these robots become increasingly autonomous, researchers are seeking ways to enable natural communication strategies between robots and first responders, such as using gestural interaction. First response work often takes place in harsh environments, which hold unique challenges for gesture sensing and recognition, including in low-visibility environments, making the gestural interaction non-trivial. As such, an adequate choice of sensors and algorithms needs to be made to support gestural recognition in harsh environments. In this work, we compare the performances of three common types of remote sensors, namely RGB, depth, and thermal cameras, using various algorithms, in simulated harsh environments. Our results show 90 to 96% recognition accuracy (respectively with or without smoke) with the use of protective equipment. This work provides future researchers with clear data points to support them in their choice of sensors and algorithms for gestural interaction with robots in harsh environments.
Research on College English Online Teaching Mode Based on MOOCs

During the COVID-19, China's Ministry of Education opened 22 online teaching platforms, providing over 24 thousand courses in total, so that all the students in the country can attend classes at their homes.This paper introduces how the author uses MOOCs network platforms and other teaching tools, as well as a variety of Instant Messengers, such as QQ and DingTalk, to complete College English teaching through voice or video link.Through practice, it is found that by online teaching, the teaching objectives can be successfully completed, and online teaching has received satisfying results according to students' feedback.In view of this new teaching form, this paper intends to evaluate and summarize from the following three aspects: teaching preparation, online teaching and teaching effect.Moreover, this paper also tries to judge the advantages and disadvantages of online teaching, hoping to enlighten the reform of teaching mode in the future.
Generating An Optimal Interview Question Plan Using A Knowledge Graph And Integer Linear Programming

Given the diversity of the candidates and complexity of job requirements, and since interviewing is an inherently subjective process, it is an important task to ensure consistent, uniform, efficient and objective interviews that result in high quality recruitment.We propose an interview assistant system to automatically, and in an objective manner, select an optimal set of technical questions (from question banks) personalized for a candidate.This set can help a human interviewer to plan for an upcoming interview of that candidate.We formalize the problem of selecting a set of questions as an integer linear programming problem and use standard solvers to get a solution.We use knowledge graph as background knowledge in this formulation, and derive our objective functions and constraints from it.We use candidate's resume to personalize the selection of questions.We propose an intrinsic evaluation to compare a set of suggested questions with actually asked questions.We also use expert interviewers to comparatively evaluate our approach with a set of reasonable baselines.
THE CORRELATION BETWEEN INDONESIAN UNIVERSITY STUDENTS’ RECEPTIVE VOCABULARY KNOWLEDGE AND THEIR READING COMPREHENSION LEVEL

While the literature has shown that vocabulary mastery affects reading comprehension, only a few Indonesian researchers investigated this. To narrow this gap, this study aims to: 1) measure Indonesian university students' knowledge of receptive vocabulary, 2) measure their reading comprehension level, and 3) investigate the association between the two variables. The participants in this study were 168 first-year university students from a private university implementing English-medium instruction (EMI). We utilized the updated vocabulary level test to measure their receptive vocabulary size and the IELTS academic reading test to measure their reading comprehension level. The participants mean scores on the updated VLT were 28.73, 26.63, 22.27, 22.42 and 23.12 at the 1,000, 2,000, 3,000, 4,000, and 5,000-word level respectively. Only 34 out of 168 participants could be considered to have a sufficient vocabulary size. On average they scored 16.08 (SD = 7.96) on the IELTS academic reading test, which was roughly equivalent to 5 in IELTS band score. We also found strong and significant correlations between the participants’ scores on the IELTS academic reading test and the updated VLT at all levels, with the largest effect size on the 4,000-word level (r = .71, p .001, BCa 95% CI [0.64, 0.78], r2 = .50). These findings indicated that the participants did not have sufficient vocabulary knowledge and had a low reading proficiency, potentially inhibiting progress in their academic pursuits. We discussed the findings in relation to the teaching of English in EFL, ESL, and EMI contexts. Practical implications of the findings are also discussed.
Data Mining Technics and Cyber Hygiene Behaviors in Social Media

The authors present in this work information about social media and data mining usage for that. It is represented information about social networking sites, where Facebook dominates the industry by boasting an account of 85% of the internet user’s worldwide. Applying data mining techniques to large social media data sets has the potential to continue to improve search results for everyday search engines, realize specialized target marketing for businesses, help psychologist study behavior, provide new insights into social structure for sociologists, personalize web services for consumers, and even help detect and prevent spam for all of us. The most common data mining applications related to social networking sites is represented. Authors have also gave information about different data mining techniques and list of these techniques. It is important to protect personal privacy when working with social network data. Recent publications highlight the need to protect privacy as it has been shown that even anonymizing this type of data can still reveal personal information when advanced data analysis techniques are used. A whole range of different threat of social networks is represented. Authors explain cyber hygiene behaviors in social networks, such as backing up data, identity theft and online behavior.
Natural Language Processing Applications: A New Taxonomy using Textual Entailment

Textual entailment recognition is one of the recent challenges of the Natural Language Processing (NLP) domain.Deep learning strategies are used in the work of text entailment instead of traditional Machine learning or raw coding to achieve new enhanced results.Textual entailment is also used in the substantial applications of NLP such as summarization, machine translation, sentiment analysis, and information verification.Text entailment is more precise than traditional Natural Language Processing techniques in extracting emotions from text because the sentiment of any text can be clarified by textual entailment.For this purpose, when combining a textual entailment with deep learning, they can hugely showed an improvement in performance accuracy and aid in new applications such as depression detection.This paper lists and describes applications of natural language processing regarding textual entailment.Various applications and approaches are discussed.Moreover, datasets, algorithms, resources, and performance evaluation for each model is included.Also, it compares textual entailment application models according to the method used, the result for each model, and the pros and cons of each model.
Infrared imaging enhancement through local window‐based saliency extraction with spatial weight

Infrared image enhancement is an effective way to solve contrast reduction or details degradation in infrared imagery. An infrared enhancement approach based on local saliency extraction is proposed here. First, saliency maps are extracted within a local window by combining spatial weight. Second, with the change of the window size, potential targets and details in different sizes can be extracted. Considering window sizes as the scales, the saliency maps are obtained and infrared images are enhanced at different scales, and finally, multi-scale fusion is used to achieve the enhancement. Eight popular infrared enhancement approaches are introduced for comparison. Subjective qualitative observation experiments show that our strategy based on local saliency analysis and multi-scale fusion can well extract potential targets and areas of varying size from the source images with different sizes, to obtain a good enhancement effect. Meanwhile, we introduce six objective evaluation methods to measure the results, and the evaluation data to prove the effectiveness of the proposed algorithm. The experiments also indicate the real-time processing capability of the proposed method. The proposed method is finally well applied in the hardware system and shows its good performance.
Multitasking Inhibits Semantic Drift

When intelligent agents communicate to accomplish shared goals, how do these goals shape the agents' language?We study the dynamics of learning in latent language policies (LLPs), in which instructor agents generate natural-language subgoal descriptions and executor agents map these descriptions to lowlevel actions.LLPs can solve challenging long-horizon reinforcement learning problems and provide a rich model for studying taskoriented language use.But previous work has found that LLP training is prone to semantic drift (use of messages in ways inconsistent with their original natural language meanings).Here, we demonstrate theoretically and empirically that multitask training is an effective counter to this problem: we prove that multitask training eliminates semantic drift in a well-studied family of signaling games, and show that multitask training of neural LLPs in a complex strategy game reduces drift and while improving sample efficiency.