Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF IMMERSIVE TECHNOLOGIES IN FOREIGN LANGUAGE LEARNING: ANALYSIS OF FOREIGN AND DOMESTIC (UKRAINIAN) EXPERIENCE

The use of modern immersive technologies in the educational process is due to changes in the development of information and communication technologies and the challenges of the pandemic, which forced learning and working remotely. The article summarizes the Ukrainian and foreign experience of using immersive technologies in education in general and in the process of learning foreign languages in particular. A general description of the concepts “virtual reality”, “immersion” and “immersive technologies” in the context of their use in the educational process has been presented. The advantages of using immersive technologies over traditional ones have been defined and proved. They are visibility, concentration, involvement and effectiveness It has been determined the expediency of using immersive technologies in the process of learning foreign languages as a method and technology of long-term immersion of students in a foreign language space based on a foreign language or bilingual education, which involves the use of video, audio, and text information by students to form relevant professional competencies.&#x0D; The research of scientists and their main results related to the selection and application of immersive technologies in teaching foreign languages have been analyzed. Virtual applications that can be used for learning a foreign language have been analyzed. The conclusion has been formulated that immersive technologies provide the formation and development of a new information method of presenting and learning educational material, which positively affects the formation of basic and professional competencies of students studying a foreign language. The opinion about the possibility of combining immersive learning methods with other interactive methods has been substantiated.
Гадаад хэлний бичих чадварыг сайжруулах арга замын талаарх зарим асуудалд

This report has purposes to study chances and opportunities to develop independent writing skills of learners in foreign languages and methods to use them in classes and foreign language training. One of the goals of English program is to help students develop their writing skills We aimed to determine whether intensive reading could improve students’ reading skills, vocabulary acquisition and attitudes. The participants were the first year cadets of UIA. To control the study, they were divided into the control group (12 students) and the experimental group (12 students). Pre-test and post-test were also selected to check their reading and vocabulary knowledge. Students in experimental group were asked questions as to their attitudes of vocabulary learning and reading skills at the end of Semester. The control group and the experimental group had the same reading proficiency and vocabulary knowledge before theexperiment. After the experiment, both groups made the progress, but the experimental group made more progress than the control group. The result also showed that students had positive attitude towards the intensive reading.
Good Neighbors Are All You Need for Chinese Grapheme-to-Phoneme Conversion

Most Chinese Grapheme-to-Phoneme (G2P) systems employ a three-stage framework that first transforms input sequences into character embeddings, obtains linguistic information using language models, and then predicts the phonemes based on global context about the entire input sequence. However, linguistic knowledge alone is often inadequate. Language models frequently encode overly general structures of a sentence and fail to cover specific cases needed to use phonetic knowledge. Also, a handcrafted post-processing system is needed to address the problems relevant to the tone of the characters. However, the system exhibits inconsistency in the segmentation of word boundaries which consequently degrades the performance of the G2P system. To address these issues, we propose the Reinforcer that provides strong inductive bias for language models by emphasizing the phonological information between neighboring characters to help disambiguate pronunciations. Experimental results show that the Reinforcer boosts the cutting-edge architectures by a large margin. We also combine the Reinforcer with a large-scale pre-trained model and demonstrate the validity of using neighboring context in knowledge transfer scenarios.
Structure and Rank of Cyclic codes over a class of non-chain rings

The rings $Z_{4}+\nu Z_{4}$ have been classified into chain rings and non-chain rings on the basis of the values of $\nu^{2} \in Z_{4}+\nu Z_{4}.$ In this paper, the structure of cyclic codes of arbitrary length over the rings $Z_{4}+\nu Z_{4}$ for those values of $\nu^{2}$ for which these are non-chain rings has been established. A unique form of generators of these codes has also been obtained. Further, rank and cardinality of these codes have been established by finding minimal spanning sets for these codes.
Digital Image Processing.

Digital image processing is the use of a digital computer to process a digital image using an algorithm. As a subcategory or area of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as noise accumulation and distortion during processing. This review article provides a comprehensive literature review of various image processing techniques along with a brief introduction to digital image processing that defines its scope and importance, thereby highlighting the importance of its use in the field of electronic engineering applications that are used in today's commercial and industrial scenarios. It takes into consideration the research work done by various leading scholars in the field of electronic engineering and also the description of various software tools like MATLAB which is used for the practical implementation of the given problem lying in its domain.
Self-attention for Enhanced OAMP Detection in MIMO Systems

Multiple-Input Multiple-Output (MIMO) systems are essential for wireless communications. Sinceclassical algorithms for symbol detection in MIMO setups require large computational resourcesor provide poor results, data-driven algorithms are becoming more popular. Most of the proposedalgorithms, however, introduce approximations leading to degraded performance for realistic MIMOsystems. In this paper, we introduce a neural-enhanced hybrid model, augmenting the analyticbackbone algorithm with state-of-the-art neural network components. In particular, we introduce aself-attention model for the enhancement of the iterative Orthogonal Approximate Message Passing(OAMP)-based decoding algorithm. In our experiments, we show that the proposed model canoutperform existing data-driven approaches for OAMP while having improved generalization to otherSNR values at limited computational overhead.
Analisis Desain Aplikasi Mobile dalam Membantu Aktivitas Olahraga Berkelompok

During the COVID-19 pandemic, it is quite difficult to carry out activities outside the home such as studying, working, worshiping, and even sports are recommended at home so that there are no gathering activities and direct contact with other people, but people are starting to realize the importance of health so that sports trends have formed since the beginning of the pandemic, and continues to this day, such as cycling, futsal, and badminton. Sports venue rental applications can make it easier for people who want to exercise without having to bother coming to the rental place. The user interface is a liaison for interaction between users and the system, therefore an attractive user interface must be communicative and easily understood by users in order to provide a good experience for users. Visual design that is easy to understand is very important in designing the user interface of the application, because users get convenience and comfort when interacting with the application and the design can help to meet user needs
Robust Fusion for Bayesian Semantic Mapping

The integration of semantic information in a map allows robots to understand better their environment and make high-level decisions. In the last few years, neural networks have shown enormous progress in their perception capabilities. However, when fusing multiple observations from a neural network in a semantic map, its inherent overconfidence with unknown data gives too much weight to the outliers and decreases the robustness. To mitigate this issue we propose a novel robust fusion method to combine multiple Bayesian semantic predictions. Our method uses the uncertainty estimation provided by a Bayesian neural network to calibrate the way in which the measurements are fused. This is done by regularizing the observations to mitigate the problem of overconfident outlier predictions and using the epistemic uncertainty to weigh their influence in the fusion, resulting in a different formulation of the probability distributions. We validate our robust fusion strategy by performing experiments on photo-realistic simulated environments and real scenes. In both cases, we use a network trained on different data to expose the model to varying data distributions. The results show that considering the model's uncertainty and regularizing the probability distribution of the observations distribution results in a better semantic segmentation performance and more robustness to outliers, compared with other methods. Video - https://youtu.be/5xVGm7z9c-0
"Distributed collection development: How to grow your library without knowing everything!"

This article is designed to help librarians, especially solo librarians, recognize how the professionals around them can assist with the development of their library collection and to provide practical instruction and tools to encourage their collaborative process.--------But I was an English major as an undergrad.What do I know about biblical theology resources?If this type of thought has crossed your mind, you are not alone!No one can know everything about every academic discipline.There are people who know, however, who can help us develop a more complete library collection.But guess what?They do not know everything either!It takes the collective effort of many to truly provide a well-rounded collection that supports your degree programs.Collaboration is key, especially for solo librarians with limited time and resources, but this applies to all librarians.The information in this article is the collaborative result of five of the Association of Christian Librarian's (ACL's) solo librarians and was presented to the Solo Librarian Interest Group at the 2021 ACL Conference.Some of the comments from other solos during the presentation discussion are also included.By using resources already at your disposal and by building a rapport with experts in their field, you can share the collection development responsibility and maintain a robust and relevant library. LibGuidesSpringshare describes a LibGuide as "an easy-to-use content management system deployed at thousands of libraries worldwide.Librarians use it to curate knowledge and share information, organize class and subject specific resources, and to create and manage websites" (Springshare, 2020).A dedicated librarian has already put in the
Half-Day Vulnerabilities: A study of the First Days of CVE Entries

The National Vulnerability Disclosure Database is an invaluable source of information for security professionals and researchers. However, in some cases, a vulnerability report is initially published with incomplete information, a situation that complicates incident response and mitigation. In this paper, we perform an empirical study of vulnerabilities that are initially submitted with an incomplete report, and present key findings related to their frequency, nature, and the time needed to update them. We further present a novel ticketing process that is tailored to addressing the problems related to such vulnerabilities and demonstrate the use of this system with a real-life use case.
Canonical Sphere Bases for Simplicial and Cubical Complexes

Sphere-bases are constructed for the $\mathbb{Z}_2$ vector space formed by the $k$-dimensional subcomplexes, of $n$-simplex (or $n$-cube), for which every $(k{-}1)$-face is contained in a positive even number of $k$-cells; addition is symmetric difference of the corresponding sets of $k$-cells. The bases consist of the boundaries of an algorithmically-specified family of $k{+}1$-simplexes or $k{+}1$-cubes. Geometric properties of these bases are investigated.
Practically Solving LPN in High Noise Regimes Faster Using Neural Networks

We conduct a systematic study of solving the learning parity with noise problem (LPN) using neural networks. Our main contribution is designing families of two-layer neural networks that practically outperform classical algorithms in high-noise, low-dimension regimes. We consider three settings where the numbers of LPN samples are abundant, very limited, and in between. In each setting we provide neural network models that solve LPN as fast as possible. For some settings we are also able to provide theories that explain the rationale of the design of our models. Comparing with the previous experiments of Esser, Kubler, and May (CRYPTO 2017), for dimension $n = 26$, noise rate $\tau = 0.498$, the ''Guess-then-Gaussian-elimination'' algorithm takes 3.12 days on 64 CPU cores, whereas our neural network algorithm takes 66 minutes on 8 GPUs. Our algorithm can also be plugged into the hybrid algorithms for solving middle or large dimension LPN instances.
Examining the Production of Co-active Channels on YouTube and BitChute

A concern among content moderation researchers is that hard moderation measures, such as banning content producers, will push users to more extreme information environments. Research in this area is still new, but predominately focuses on one-way migration (from mainstream to alt-tech) due to this concern. However, content producers on alt-tech social media platforms are not always banned users from mainstream platforms, instead they may be co-active across platforms. We explore co-activity on two such platforms: YouTube and BitChute. Specifically, we describe differences in video production across 27 co-active channels. We find that the majority of channels use significantly more moral and political words in their video titles on BitChute than in their video titles on YouTube. However, the reasoning for this shift seems to be different across channels. In some cases, we find that channels produce videos on different sets of topics across the platforms, often producing content on BitChute that would likely be moderated on YouTube. In rare cases, we find video titles of the same video change across the platforms. Overall, there is not a consistent trend across co-active channels in our sample, suggesting that the production on alt-tech social media platforms does not fit a single narrative.
Edit-A-Video: Single Video Editing with Object-Aware Consistency

Despite the fact that text-to-video (TTV) model has recently achieved remarkable success, there have been few approaches on TTV for its extension to video editing. Motivated by approaches on TTV models adapting from diffusion-based text-to-image (TTI) models, we suggest the video editing framework given only a pretrained TTI model and a single <text, video> pair, which we term Edit-A-Video. The framework consists of two stages: (1) inflating the 2D model into the 3D model by appending temporal modules and tuning on the source video (2) inverting the source video into the noise and editing with target text prompt and attention map injection. Each stage enables the temporal modeling and preservation of semantic attributes of the source video. One of the key challenges for video editing include a background inconsistency problem, where the regions not included for the edit suffer from undesirable and inconsistent temporal alterations. To mitigate this issue, we also introduce a novel mask blending method, termed as sparse-causal blending (SC Blending). We improve previous mask blending methods to reflect the temporal consistency so that the area where the editing is applied exhibits smooth transition while also achieving spatio-temporal consistency of the unedited regions. We present extensive experimental results over various types of text and videos, and demonstrate the superiority of the proposed method compared to baselines in terms of background consistency, text alignment, and video editing quality.
Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code

We analyzed effectiveness of three generative pre-trained transformer (GPT) models in answering multiple-choice question (MCQ) assessments, often involving short snippets of code, from introductory and intermediate programming courses at the postsecondary level. This emerging technology stirs countless discussions of its potential uses (e.g., exercise generation, code explanation) as well as misuses in programming education (e.g., cheating). However, the capabilities of GPT models and their limitations to reason about and/or analyze code in educational settings have been under-explored. We evaluated several OpenAI's GPT models on formative and summative MCQ assessments from three Python courses (530 questions). We found that MCQs containing code snippets are not answered as successfully as those that only contain natural language. While questions requiring to fill-in a blank in the code or completing a natural language statement about the snippet are handled rather successfully, MCQs that require analysis and/or reasoning about the code (e.g., what is true/false about the snippet, or what is its output) appear to be the most challenging. These findings can be leveraged by educators to adapt their instructional practices and assessments in programming courses, so that GPT becomes a valuable assistant for a learner as opposed to a source of confusion and/or potential hindrance in the learning process.
Blind Video Deflickering by Neural Filtering with a Flawed Atlas

Many videos contain flickering artifacts. Common causes of flicker include video processing algorithms, video generation algorithms, and capturing videos under specific situations. Prior work usually requires specific guidance such as the flickering frequency, manual annotations, or extra consistent videos to remove the flicker. In this work, we propose a general flicker removal framework that only receives a single flickering video as input without additional guidance. Since it is blind to a specific flickering type or guidance, we name this "blind deflickering." The core of our approach is utilizing the neural atlas in cooperation with a neural filtering strategy. The neural atlas is a unified representation for all frames in a video that provides temporal consistency guidance but is flawed in many cases. To this end, a neural network is trained to mimic a filter to learn the consistent features (e.g., color, brightness) and avoid introducing the artifacts in the atlas. To validate our method, we construct a dataset that contains diverse real-world flickering videos. Extensive experiments show that our method achieves satisfying deflickering performance and even outperforms baselines that use extra guidance on a public benchmark.
Handwritten signature identification based on MobileNets model and support vector machine classifier

Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.
Bengali Slang detection using state-of-the-art supervised models from a given text

Almost all Bengalis who own smartphones also have social media accounts. People from different regions occasionally employ regional Slang that is unfamiliar to outsiders and confuses the meaning of the sentence. Nearly all languages can now be translated thanks to modern technology, but only in very basic ways, which is a concern. Bengali Slang terms are difficult to translate due to a dearth of rich corpora and frequently occurring new Slang terms developed by people, making it impossible for speakers of other languages to understand the context of a sentence in which Slang is used. We developed a solution to this issue. To create models that can detect Bengali Slang terms from social media, we gather various Slang phrases from various regions and develop a modest corpus. Our suggested method nearly always succeeds in extracting Bengali Slang terms from fresh material. We create a total of 7 supervised models and assess which is the most effective for our study. One of them has a 70% accuracy and 86% recall rate for successful identification. Our models may be linked to the social media platform's backend to restrict the use of Bengali Slang in posts, blogs, comments, and other areas.
Federated learning security mechanisms for protecting sensitive data

One of the new trends in the field of artificial intelligence is federated learning (FL), which will have promising roles in many real-world applications due to the work characteristics of its architecture. The learning mechanism for this technique is based on making training in a distributed manner on the local data for each client using decentralized data, then collecting parameters for each local training and uploading it to the server, which in turn will send model updates to all clients to give the final learning result. To provide a broad study on FL from security and privacy aspects, this research paper introduces a general view of FL and its categories, most attacks that can befall it, the safety mechanisms used by existing works in attacks defense, enhancing the safety and privacy of FL whether in the transmission or collecting of data. Then, the usage of FL in network security by many research papers has been presented, and how good results were achieved, and finally a comparison has been made between these papers.