Reviewerly/exHarmony huggingface dataset:
Beyond Arabic: Software for Perso-Arabic Script Manipulation

This paper presents an open-source software library that provides a set of finite-state transducer (FST) components and corresponding utilities for manipulating the writing systems of languages that use the Perso-Arabic script.The operations include various levels of script normalization, including visual invariance-preserving operations that subsume and go beyond the standard Unicode normalization forms, as well as transformations that modify the visual appearance of characters in accordance with the regional orthographies for eleven contemporary languages from diverse language families.The library also provides simple FST-based romanization and transliteration.We additionally attempt to formalize the typology of Perso-Arabic characters by providing one-to-many mappings from Unicode code points to the languages that use them.While our work focuses on the Arabic script diaspora rather than Arabic itself, this approach could be adopted for any language that uses the Arabic script, thus providing a unified framework for treating a script family used by close to a billion people.
Learning to Detect Noisy Labels Using Model-Based Features

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation.Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions.Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize.In light of these pros and cons, we propose SENT (Selection-Enhanced Noisy label Training) that does not rely on meta learning while having the flexibility of being data-driven.SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features.Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption.
Collaborative three-stream transformers for video captioning

As the most critical components in a sentence, subject, predicate and object require special attention in the video captioning task. To implement this idea, we design a novel framework, named COllaborative three-Stream Transformers (COST), to model the three parts separately and complement each other for better representation. Specifically, COST is formed by three branches of transformers to exploit the visual-linguistic interactions of different granularities in spatial–temporal domain between videos and text, detected objects and text, and actions and text. Meanwhile, we propose a cross-granularity attention module to align the interactions modeled by the three branches of transformers, then the three branches of transformers can support each other to exploit the most discriminative semantic information of different granularities for accurate predictions of captions. The whole model is trained in an end-to-end fashion. Extensive experiments conducted on three large-scale challenging datasets, i.e., YouCookII, ActivityNet Captions and MSVD, demonstrate that the proposed method performs favorably against the state-of-the-art methods.
Coherent-cluster-state generation in networks of degenerate optical parametric oscillators

Cluster states are versatile quantum resources and an essential building block for measurement-based quantum computing. The possibility to generate cluster states in specific systems may thus serve as an indicator regarding if and to what extent these systems can be harnessed for quantum technologies and quantum information processing in particular. Here, we apply this analysis to networks of degenerate optical parametric oscillators (DOPOs), also called coherent Ising machines (CIMs). CIMs are distinguished by their highly flexible coupling capabilities, which makes it possible to use them, e.g., to emulate large spin systems. As CIMs typically operate with coherent states (and superpositions thereof), it is natural to consider cluster states formed by superpositions of coherent states, i.e., coherent cluster states. As we show, such coherent cluster states can, under ideal conditions, be generated in DOPO networks with the help of beam splitters and classical pumps. Our subsequent numerical analysis provides the minimum requirements for the generation of coherent cluster states under realistic conditions. Moreover, we discuss how nonequilibrium pumps can improve the generation of coherent cluster states. In order to assess the quality of the cluster-state generation, we map the generated states to an effective spin space using modular variables, which allows us to apply entanglement criteria tailored for spin-based cluster states.
Adaptation: Blessing or Curse for Higher-way Meta-learning

The prevailing literature typically assesses the ef-fectiveness of Meta-learning (ML) approaches on tasks that involve no more than 20 classes. However, we challenge this convention by conducting a more complex and natural task setup to test the fundamental initialization, metric, and optimization approaches. In particular, we increase the number of classes in the Omniglot and tieredImagenet datasets to 200 and 90, respectively. Interestingly, we observe that as the number of classes increases, ML approaches perform in reverse order of their degree of adaptation, with ProtoNet outperforming ANIL and MAML. ProtoNet, which does not require adaptation, is marginally affected by the increase in task complexity, while ANIL and MAML are highly affected. Despite performing full feature backbone and classifier adaptation, MetaLSTM++ exhibits an intriguing behavior of performing well. To this end, we analyze the backbone learned by different algorithms and the influence of adaptation from different perspectives. We uncover that ProtoNet learns better, and MetaLSTM++ learns the worst backbone, but the generalizability to unseen data for MetaLSTM++ is compensated by powerful adaptation to the meta-test support sets, due to its learned data-driven optimizer.
SUMMIT: Scaffolding Open Source Software Issue Discussion Through Summarization

For Open Source Software (OSS) projects, discussions in Issue Tracking Systems (ITS) serve as a crucial collaboration mechanism for diverse stakeholders. However, these discussions can become lengthy and entangled, making it hard to find relevant information and make further contributions. In this work, we study the use of summarization to aid users in collaboratively making sense of OSS issue discussion threads. We reveal a complex picture of how summarization is used by issue users in practice as a strategy to help develop and manage their discussions. Grounded on the different objectives served by the summaries and the outcome of our formative study with OSS stakeholders, we identified a set of guidelines to inform the design of collaborative summarization tools for OSS issue discussions. We then developed SUMMIT, a tool that allows issue users to collectively construct summaries of different types of information discussed, as well as a set of comments representing continuous conversations within the thread. To alleviate the manual effort involved, SUMMIT uses techniques that automatically detect information types and summarize texts to facilitate the generation of these summaries. A lab user study indicates that, as the users of SUMMIT, OSS stakeholders adopted different strategies to acquire information on issue threads. Furthermore, different features of SUMMIT effectively lowered the perceived difficulty of locating information from issue threads and enabled the users to prioritize their effort. Overall, our findings demonstrated the potential of SUMMIT, and the corresponding design guidelines, in supporting users to acquire information from lengthy discussions in ITSs. Our work sheds light on key design considerations and features when exploring crowd-based and machine-learning-enabled instruments for asynchronous collaboration on complex tasks such as OSS development.
Quantum-resistant End-to-End Secure Messaging and Email Communication

With recent advances on the capabilities of quantum computers and the seminal work by Shor on the prime factorization of integers with quantum algorithms, the threat of quantum computers is looming over public-key based cryptographic systems. Two distinct areas of research – post-quantum and quantum cryptography – provide the technical means to secure cryptographic systems against potentially powerful enough quantum computers. As both technologies are now becoming readily available, integration in a wide range of applications to understand their characteristics and trade-offs and the associated evaluation is of importance.
Research on Reform Measures for Internet of Things and Cloud Computing Teaching

The concept of the Internet of Things was proposed in 1999, the goal is to use a variety of sensing devices and wireless sensor networks to connect items to the Internet, in order to unified monitoring and management, known as the third wave of information industry after the Internet with computers as terminals.In the context of countries constantly putting forward the Internet of Things development strategy, China is also actively promoting related technical research and application promotion, resulting in a sharp increase in the demand for Internet of Things talents.The State Education Commission proposed the Internet of Things major in the declaration and approval of strategic emerging industries related professions launched in 2010.So far, many universities have applied for the establishment of this major and have been approved.The rapid and vigorous development of the Internet of Things and cloud computing also puts forward higher requirements for colleges and universities to train students majoring in computer science and technology.Colleges and universities should deeply analyze the characteristics of the Internet of Things and cloud computing technology, and carry out reforms in the aspects of talent training objectives, teaching content and teaching methods, so that students can better adapt to the development of the Internet of Things and cloud computing technology.Starting from the characteristics of the Internet of Things and cloud computing technology, this paper discusses how to strengthen the teaching reform of the Internet of Things and cloud computing in universities.
AI in Human-Computer Interaction

The emergence of human-computer interaction as a concept can be attributed to the progress made in computer technology. Educated and technically proficient young adults often take part in research experiments related to human-computer interaction. The mental model in human-computer interaction is the main topic of this essay. One of the tactics used in this review study is to call attention to current theories, findings, and advancements in HCI. A second strategy is to look for study areas that haven't been explored yet but are falling behind. In order to produce better user-friendly products, this study emphasizes the importance of fidelity prototyping, which highlights the emotional intelligence of the user. This process is still being developed and designed as an automated method. The area of human-computer interaction explores the interactions between people and computers, focusing on the extent to which computers are designed to effectively accommodate human needs and preferences. A user- friendly method of computer access is speech and pattern recognition. The suggested system will launch the speech driver before capturing user input using the microphone. We'll translate the speech input into text. The text input is transformed into tokens by the lexical analyzer and saved in the symbol table. Tokens are fed into the parser, which creates a parse tree. This vocal input can be used to execute various Linux OS commands. The speech will be analyzed for keywords that apply to other activities, and the corresponding command will be carried out. The suggested system will function in both terminal and word processor modes. It will carry out both internal and external commands in terminal mode. When in word processor mode, it will carry out actions like open, type, save, and exit. Operations will be carried out by the system in response to oral commands. Keywords— Human Computer Interaction, Natural Language Processing, Application Program Interface, Speech to text, Emotional intelligence, Interactivity, Younger participants, Fidelity Prototyping.
Full Issue

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 60, Issue 6 p. I-I Full Issue Full Issue First published: 10 August 2023 https://doi.org/10.1111/j.1467-6346.2023.11157.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume60, Issue6August 2023Pages I-I RelatedInformation
General Provisioning Strategy for Local Specialized Cloud Computing Environments

The well-known management strategies in cloud computing based on SLA requirements are considered. A deterministic parallel provisioning algorithm has been prepared and used to show its behavior for three different requirements: load balancing, consolidation, and fault tolerance. The impact of these strategies on the total execution time of different sets of services is analyzed for randomly chosen sets of data. This makes it possible to improve the project and to implement the proper strategies for the local TASKcloud environment used in our institution.
Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values

Many NLP classification tasks, such as sexism/racism detection or toxicity detection, are based on human values.Yet, human values can vary under diverse cultural conditions.Therefore, we introduce a framework for valuealigned classification that performs prediction based on explicitly written human values in the command.Along with the task, we propose a practical approach that distills value-aligned knowledge from large-scale language models (LLMs) to construct value-aligned classifiers in two steps.First, we generate value-aligned training data from LLMs by prompt-based fewshot learning.Next, we fine-tune smaller classification models with the generated data for the task.Empirical results show that our VA-MODELs surpass multiple baselines by at least 15.56% on the F1-score, including few-shot learning with OPT-175B and existing text augmentation methods.We suggest that using classifiers with explicit human value input improves both inclusivity & explainability in AI.
IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks

Backdoor attacks are an insidious security threat against machine learning models.Adversaries can manipulate the predictions of compromised models by inserting triggers into the training phase.Various backdoor attacks have been devised which can achieve nearly perfect attack success without affecting model predictions for clean inputs.Means of mitigating such vulnerabilities are underdeveloped, especially in natural language processing.To fill this gap, we introduce IMBERT, which uses either gradients or self-attention scores derived from victim models to self-defend against backdoor attacks at inference time.Our empirical studies demonstrate that IMBERT can effectively identify up to 98.5% of inserted triggers.Thus, it significantly reduces the attack success rate while attaining competitive accuracy on the clean dataset across widespread insertion-based attacks compared to two baselines.Finally, we show that our approach is model-agnostic, and can be easily ported to several pre-trained transformer models.* Now at Google DeepMind. 1 According to statistics from Hugging Face, BERT receives 15M downloads per month.
SENTIMENT ANALYSIS OF COMMENTS ON SEXUAL HARASSMENT IN COLLEGES ON FOUR POPULAR SOCIAL MEDIA

Sexual harassment is a sexual act in the form of verbal and nonverbal that is carried out intentionally, and there are indications of coercion on the victim. Often the victim refuses to report or tell stories because the victim is afraid that something untoward will happen in the future. To find helpful information for survivors and research in this study, we will get the sentiment on comments related to matters related to UMN by using the CRISPDM framework method, FastText, and the SVM algorithm to identify a statement on sexual relations with UMN.Results From this study used a model with an accuracy of 55.14% and sexual harassment data collected on March 16, 2022, with 287 data obtained from Twitter, Instagram, Medium, and Line Today sites. Positive sentiment is the least found sentiment from the overall data, only 8.7%, while negative sentiment is 36.6%. Of the four platforms, the best is the Twitter platform because it gets a pretty good response in terms of positive and neutral sentiments compared to others.
Peningkatan Kemampuan Mahasiswa Dalam Memanajemen Referensi Ilmiah Melalui Pelatihan Mendeley

Mendeley is a reference management software that helps organize scientific articles by creating references and bibliographies, and software users can also collaborate on social networks. This training aims to train students in citing articles, citations, and making a bibliography correctly. The methods in this activity are the registration stage, the implementation stage, and the evaluation stage. The Mendeley training activities went well, the participants enthusiastically participated in the training activities. Participants can create accounts, install, search references, make citations and create bibliography automatically. In addition, based on the pre-test and post-test data, the knowledge and skills of the participants increased, it was proven that the participants could use the Mendeley application well and could utilize the application to manage their scientific work references.
An Internet of Things based Multi-Tiered Wireless Security System

In today’s contemporary culture, the need of taking precautions to protect one’s own belongings and privacy cannot be stressed. Because of this, an adaptive multilayer wireless security system, also known as ML-WSS, has been developed in order to monitor also safeguard a location with the assistance of the internet of things (IoT). In the hardware and software design of the ML- WSS, the sensors, the Wi-Fi module, also the process and monitoring mobile application all play key roles. The proposed method enables remote monitoring and management to take place finished the Internet also the ThingSpeak cloud, which is where the OMM application keeps the data that it has collected. The idea suggests slicing the crucial area into three distinct sectors, with sector 1 being the least dangerous, sector 2 being intermediate, and sector 3 being the most dangerous of the three. The criteria for the level of security should serve as a reference for both the placement of sensors and the classification of sensors according to the level of protection they give. Both of these processes should be carried out in accordance with the criteria. In the event that there is a breach in security, actions are carried out in accordance with these principles. A exact model also pseudocode were used in order to demonstrate the functionality of the planned system. It is reasonable to assert that the strategy used up to this point has been successful, given that level-3 locations now suffer from around half as many security breaches as level-1 areas.
PERSONALIZATION OF BULGARIAN LANGUAGE AND LITERATURE TRAINING FOR STUDENTS AT THE INITIAL STAGE OF PRIMARY EDUCATION

The modern person-oriented educational paradigm is associated with the need to master key competencies, education in values, development of abilities and talents, effective inclusion and educational integration. Achieving these goals requires expanding the possibilities for applying an individual approach to the needs of each student and for personalizing education for the development of personal potential. The conceptual basis of the analysis in the article is the model of personalized learning, which covers both ends of the personalized learning spectrum – personalization for the learner and personalization by the learner. The socio-psychological and normative prerequisites for personalization of education in Bulgaria are discussed, the essential features of personalized education and the difficulties of its implementation in the general educational environment are revealed. The focus of the article is on individualized (personalized for the student) training, for the implementation of which there is a normative regulation in Bulgaria and the practices for its provision are already sustainable. Some specifics in the personalization of Bulgarian language and literature training for students in grades I-IV have been brought out.
CREATIVE TECHNIQUES FOR DEVELOPING CHILDREN’S IMAGINATION

The paper presents art pedagogical techniques, designed to be used as an inspirational provocation for children’s imagination. In the modern, rapidly changing reality, new abilities have to be obtained. What children need perhaps more than ever is the possibility of being assertive towards a challenging environment. Managing to keep their imagination alive and fruitful is a way to make it possible being flexible and versatile towards the often overflowing stimuli from the society. The techniques presented in the paper aim to provoke and inspire children’s lateral thinking and creativity by the means of fine art. The objective is the complexity and completeness of the creative process. The art pedagogical techniques are supposed to be used as a pedagogical tool in the learning process.
Mixing paper and digital: 2020 online summer English drama kamishibai workshop

This paper discusses the results of an intervarsity picture kamishibai1-storytelling workshop that integrated communication skills and art via the online real-time conference system, Zoom©. As in previous years, the goal of the annual summer drama workshop was to support the participants’ communication and leadership skills. In the light of the COVID-19 pandemic however, the chief purpose of the 2020 Online Summer English Drama Kamishibai Workshop was to support the mental well-being of students. Thirteen low-to-intermediate level English language students, both English language majors and non-majors from three different universities in Japan worked together for the workshop project in three 90-minute online slots over two days in August 2020. After learning the basic theory and techniques of the storytelling art, students were divided into groups to create their original English kamishibai story to perform online. In the preparation stage, each student created their own work using simple A4 paper sheets and pens, and these were amalgamated with other members of their group to form a coherent narrative. At the end of the third session, each group presented their work on Zoom©, and these performances demonstrated the students’ creativity through unique plots and characters. Their feedback indicated that they developed stress-tolerance and resilience, both features of negative capability.