Reviewerly/exHarmony huggingface dataset:
Development of Edge Enabled Smart Water Tank System for Rural Household Deployment

The work investigates the application of edge-enabled Internet of Things (IoT) technology to control water tanks in rural residences, and establishments. Integrating sensors, microcontrollers, and cloud-based platforms allows the system to monitor water levels and control the water pump via an Android app at the network's edge. The Blynk platform is utilized to help us integrate IoT devices into an Android interface. This project eliminates the routine work of every household, and the project runs errands for the user to manage the water tank. Furthermore, this work showcases the efficiency and convenience of IoT in everyday tasks, highlighting the increasing demand for skills in this area. Overall, the project showcases the potential of IoT to enhance efficiency and convenience in daily tasks.
BGCFormer: A Text Event Feature Fusion Learning Model based on Transformer

Event extraction is an essential task in natural language processing, as it involves extracting meaningful events from text documents, which is important for a variety of applications, such as information retrieval, question answering, and text summarization, yet challenges remain when extracting events from documents, which is that a document usually contain multiple sentences together form a complete event, and entities in the same event may span multiple sentences. To address these challenges, this paper proposes a transformer with features fusion learning model (BGCFormer), which is based on a transformer architecture with GCN and encoder attention mechanism, and it can build a feature fusion learning network to capture global interaction features between different sentences and entity mentions. Experiments conducted on a large-scale dataset have demonstrated the proposed model outperforms existing methods in terms of accuracy and efficiency.
A Green Supply Chain Evaluation System Based on Blockchain

For the material supply chain network entities, the horizontal integration between the entities in the traditional supply chain information management mode will inevitably lead to closed information, low data transparency, and insufficient node control ability, which greatly inhibits the efficiency of the supply chain and is not conducive to collaboration. Therefore, combined with the characteristics of decentralization, openness and transparency of blockchain and smart contract rules, a green supply chain evaluation system is designed and implemented based on blockchain. The evaluation index management module is responsible for quantifying the weight matrix of the index system corresponding to the evaluation model. The distributed database module is responsible for the collection of basic historical data and model data maintenance. The smart contract module is responsible for automatically exporting the online contract to the record contract for chain tracking. Supplier performance analysis and integrity evaluation module rely on contract performance data to monitor and supervise the whole process of suppliers. Analysis results show that the system has the characteristics of non-tampering, decentralization, multi-agent transparent participation in the whole process evaluation, and effectively improves the efficiency and level of green supply chain management.
Consumption Level Classification System Based on Clustering Algorithm and Data Mining

As one of the most important drivers of economic growth, consumption is the ultimate driver of sustainable economic growth. The purpose of this article is to design and develop a consumption level classification system. Firstly, it analyzes and summarizes the relevant literature on consumption level and clustering algorithm, analyzes the factors that affect Chinese residents’ consumption, and designs and realizes a visual electronic consumption level according to the consumption level classification model combined with multidimensional analysis technology, PMML and Web visualization technology classification system. To improve the test coverage and simulate a more realistic high-load environment, the system uses the Load Runner tool to complete the system scan. Allow more than 2000 users to log in at the same time and submit relevant data. In this high-intensity work environment, the system can respond quickly and successfully, receiving and generating relevant data. The system response time test results show that after more than 6,000 high load conditions, the system operation mode response test, through the data, so the system response time does not exceed 2.5 seconds.
TRANSFORMING THE STATUS AND ROLE OF THE TEACHER AT THE INSTITUTE OF HIGHER EDUCATION IN THE DIGITAL EDUCATIONAL ENVIRONMENT

The purpose of this study was to investigate the role of teachers in the modern educational process. To achieve this goal, a large-scale survey of teachers (N=419) and students (N=1372) was conducted in 2022 at Kazan (Volga Region) Federal University (Russia), one of the leading universities in Russia. The research provides insights into the transformation of teaching status and role. The function of transferring theoretical knowledge is gradually giving way to a more student-centered approach, characterized by an increased emphasis on mentoring. The study identifies both traditional and emerging roles performed by higher education teachers in the context of digitalization within the modern information society.
Evaluating the Effectiveness of Knowledge Management in Enhancing Customer Satisfaction (2020)

Knowledge administration (KM) has emerged as a pivotal strategy for organizations to augment their competitive edge and fulfil customer expectations in the present knowledgeintensive economy.This investigation aims to assess the efficacy of knowledge administration in enhancing customer contentment.The study employs a blended methodology, amalgamating quantitative scrutiny of customer contentment surveys and qualitative interviews with key stakeholders.This analysis encompasses the assemblage and examination of customer contentment data, focusing on variables such as product/service quality, responsiveness, and comprehensive customer experience.The qualitative interviews endeavour to procure insights into how knowledge administration practices, encompassing knowledge sharing, collaboration, and organizational learning, influence customer contentment.Preliminary findings allude to a positive correlation between efficient knowledge administration and customer contentment.Organizations that adeptly administer their knowledge assets, foster knowledge sharing, and cultivate a culture of learning demonstrate elevated levels of customer contentment.Furthermore, knowledge administration practices contribute to ameliorated product/service quality, swifter issue resolution, and enhanced customer support.
The Effectiveness of Different Cyber Security Measures in Protecting Organization for Cyber Threats (2021)

Data theft is becoming more and more of a danger as data value rises.In today's digital world, the necessity of cyber security has grown because of several cyberattacks on numerous organizations.Cyber assaults are nothing new in the realm of technology.The relevance of cyber security in every area has increased because of the cyberattacks.Due to unexpected cyberattacks, as technology advances daily, the necessity for cyber security measures also grows.Today, every single firm working on a small scale is just as concerned about the significant concerns associated with cyber assaults as the biggest organizations in the world.Numerous organizations deal with sensitive and huge data that must be kept private.Cyber and internet assaults are getting riskier for every firm due to the rising value of data.To shield the data from danger, comprehensive and sophisticated technology is required.Different security measures are required to shield the firm against deadly cyber assaults.The protection of data is the primary goal of cyber security.The numerous cyber security procedures guard against data theft and corruption.Data breaching, or unlawful access to and dissemination of sensitive data, is one of these cyber assault threats.There is a need for good threat management and cyber security technologies to limit cyber assaults since data breaches might negatively affect the person to whom the data is linked.
Drilling Methods

Water is obtained by hand pumps constructed above shallow (less than 60 m deep) boreholes in many developing countries. However, if typical machine drilling rigs are employed, drilling the borehole might be costly. This technical briefly explains how to employ simple, low-cost drilling methods in a variety of scenario. Each one is simple to use and maintain. Making holes in rock is referred to as drilling. Drilling is used to create deep, high-capacity wells. Brown drilling is for a production well, whereas green drilling is for an exploratory well. When going for drilling, there are a few things to keep in mind For thousands of years, hand dug wells and other laborious techniques of digging a well have existed. Despite the fact that mechanical methods are more efficient and accurate, people and communities in need of water frequently have no other options.
Transformer-Based Approaches to Sentiment Detection

The use of transfer learning methods is largely responsible for the present breakthrough in Natural Learning Processing (NLP) tasks across multiple domains. In order to solve the problem of sentiment detection, we examined the performance of four different types of well-known state-of-the-art transformer models for text classification. Models such as Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pre-training Approach (RoBERTa), a distilled version of BERT (DistilBERT), and a large bidirectional neural network architecture (XLNet) were proposed. The performance of the four models that were used to detect disaster in the text was compared. All the models performed well enough, indicating that transformer-based models are suitable for the detection of disaster in text. The RoBERTa transformer model performs best on the test dataset with a score of 82.6% and is highly recommended for quality predictions. Furthermore, we discovered that the learning algorithms’ performance was influenced by pre-processing techniques, the nature of words in the vocabulary, unbalanced labeling, and the model parameters.
Multi-Speaker Identification with IoT Badges for Collaborative Learning Analysis

Collaborative learning fosters the ability to creatively solve problems in collaboration with other learners. Researchers in learning science have transcribed learners' speech to qualitatively analyze collaborative learning to reveal various patterns that increase learning performance. Although prior studies have identified speakers to support the process of transcription, those studies were limited in simultaneously identifying multiple speakers. We propose a novel speaker-identification algorithm that can simultaneously recognize multiple speakers using business-card-type sensors. The algorithm can remove ambient noise with low-cost sensors and still identify multiple simultaneous speakers. The experimental evaluations show that the algorithm accurately identifies simultaneous multiple speakers in a multi-person activity under conditions with varying numbers of users, environmental noise, and users' short utterances.
Adaptive-Modeling Multi-Agent Learning System for Video Behavioral Clustering Recognition

Multi-agent systems are suitable for handling complex problems due to their high parallelism and autonomous evolution ability. In this paper, we propose an adaptive clustering multi-agent learning system for intelligent applications with continuously changing requirements. Each agent model changes temporal sequences using the longest common subsequence (LCS) algorithm. Multiple agents collaborate in a multilayer decentralized approach to enhance learning adaptability and achieve self-supervised behavioral clustering. The system is constructed using a “memory-like” method and operates primarily on memory access and comparison, avoiding extensive matrix operations of artificial neural networks while achieving learning and prediction functions. We chose an unsupervised vehicle behavioral clustering scenario for feasibility validation in which the system’s cognitive objective is to cluster and recognize vehicle behaviors. In a low computational environment, the system can complete clustering functions and exhibit continuous learning capabilities when new behavioral changes occur. The proposed approach achieves an accuracy of 97.4% while processing at a speed 1–5 times faster than similar clustering algorithms. The verification results indicate that this system has excellent potential to enhance intelligent sensing front ends.
Research on the Practical Path of Collaborative Innovation Talent Training in Supply-Side Applied Undergraduate Colleges

The cultivation of supply-side collaborative innovation talents mainly focuses on four elements: labor, capital, technology, and system. The improvement of these four elements is essentially a long-term process, which determines the potential growth rate in the medium and long term. In this paper, we analyzed the current situation, put forward two major problems, the low degree of specialization and insufficient participation, and focus on the research on the practical path of collaborative innovation talent training in applied undergraduate colleges from the perspective of the system.
A Secret Image Sharing Based on Logistic-Chebyshev Chaotic Map and Chinese Remainder Theorem

Visual Cryptography, Logistic-Chebyshev map, Chinese Remainder Theorem, share.lves breaking up a secret image into $n$ secured components known as shares. The secret image is recovered with utmost secrecy when all of these shares are lined up and piled together. A (3, 3)-secret image sharing scheme (SIS) is provided in this paper by fusing the Chinese Remainder Theorem (CRT) and the Logistic-Chebyshev map (LC). Sharing a confidential image created with CRT has various benefits, including lossless recovery, the lack of further encryption, and minimal recovery calculation overhead. Firstly, we build a chaotic sequence using an LC map. The secret value pixel for the secret image is permuted in order to fend off differential attackers. To encrypt the scrambled image, we apply our CRT technique to create three shares. Finally, the security analysis of our (3, 3)-SIS scheme is demonstrated and confirmed by some simulation results. <!-- se va indica textul rezumatului-->
Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection

In a number of applications where anonymity is critical, users act under pseudonyms to preserve their privacy. For instance, in scientific peer review using forums like OpenReview.net, reviewers make comments on papers that are publicly viewable. Reviewers who have been assigned multiple papers operate under different pseudonyms across their papers to remain anonymous. Other examples of publicly visible tasks where users operate under pseudonyms include Wikipedia editing and cryptocurrency transactions. In these settings, it is common for users to engage in batching - the completion of several similar tasks at the same time. Batching occurs both due to natural bursts in activity (e.g., a person visits a website and makes many comments at once) or as a productivity strategy used to streamline work. In peer-review forums such as computer science conferences, reviewers and meta-reviewers are often assigned multiple papers. We find empirically that reviewers are highly likely to batch their comments and/or reviews across papers. In analysis of data from a top Computer Science conference with thousands of papers, reviewers, and discussion comments we find that when reviewers and meta-reviewers comment on multiple papers, they have a 30.10% chance of batching their comments within 5 minutes of one other. In comparison, any randomly chosen pair of reviewers and meta- reviewers had only a 0.66% chance of making comments on different papers within 5 minutes of each other.
Action Classification in Human Robot Interaction Cells in Manufacturing

Action recognition has become a prerequisite approach to fluent Human-Robot Interaction (HRI) due to a high degree of movement flexibility. With the improvements in machine learning algorithms, robots are gradually transitioning into more human-populated areas. However, HRI systems demand the need for robots to possess enough cognition. The action recognition algorithms require massive training datasets, structural information of objects in the environment, and less expensive models in terms of computational complexity. In addition, many such algorithms are trained on datasets derived from daily activities. The algorithms trained on non-industrial datasets may have an unfavorable impact on implementing models and validating actions in an industrial context. This study proposed a lightweight deep learning model for classifying low-level actions in an assembly setting. The model is based on optical flow feature elicitation and mobilenetV2-SSD action classification and is trained and assessed on an actual industrial activities' dataset. The experimental outcomes show that the presented method is futuristic and does not require extensive preprocessing; therefore, it can be promising in terms of the feasibility of action recognition for mutual performance monitoring in real-world HRI applications. The test result shows 80% accuracy for low-level RGB action classes. The study's primary objective is to generate experimental results that may be used as a reference for future HRI algorithms based on the InHard dataset.
Preventing RNN from Using Sequence Length as a Feature

Recurrent neural networks are deep learning topologies that can be trained to classify long documents. However, in our recent work, we found a critical problem with these cells: they can use the length differences between texts of different classes as a prominent classification feature. This has the effect of producing models that are brittle and fragile to concept drift, can provide misleading performances and are trivially explainable regardless of text content. This paper illustrates the problem using synthetic and real-world data and provides a simple solution using weight decay regularization.
SSPAttack: A Simple and Sweet Paradigm for Black-Box Hard-Label Textual Adversarial Attack

Hard-label textual adversarial attack is a challenging task, as only the predicted label information is available, and the text space is discrete and non-differentiable. Relevant research work is still in fancy and just a handful of methods are proposed. However, existing methods suffer from either the high complexity of genetic algorithms or inaccurate gradient estimation, thus are arduous to obtain adversarial examples with high semantic similarity and low perturbation rate under the tight-budget scenario. In this paper, we propose a simple and sweet paradigm for hard-label textual adversarial attack, named SSPAttack. Specifically, SSPAttack first utilizes initialization to generate an adversarial example, and removes unnecessary replacement words to reduce the number of changed words. Then it determines the replacement order and searches for an anchor synonym, thus avoiding going through all the synonyms. Finally, it pushes substitution words towards original words until an appropriate adversarial example is obtained. The core idea of SSPAttack is just swapping words whose mechanism is simple. Experimental results on eight benchmark datasets and two real-world APIs have shown that the performance of SSPAttack is sweet in terms of similarity, perturbation rate and query efficiency.
Towards Diverse, Relevant and Coherent Open-Domain Dialogue Generation via Hybrid Latent Variables

Conditional variational models, using either continuous or discrete latent variables, are powerful for open-domain dialogue response generation. However, previous works show that continuous latent variables tend to reduce the coherence of generated responses. In this paper, we also found that discrete latent variables have difficulty capturing more diverse expressions. To tackle these problems, we combine the merits of both continuous and discrete latent variables and propose a Hybrid Latent Variable (HLV) method. Specifically, HLV constrains the global semantics of responses through discrete latent variables and enriches responses with continuous latent variables. Thus, we diversify the generated responses while maintaining relevance and coherence. In addition, we propose Conditional Hybrid Variational Transformer (CHVT) to construct and to utilize HLV with transformers for dialogue generation. Through fine-grained symbolic-level semantic information and additive Gaussian mixing, we construct the distribution of continuous variables, prompting the generation of diverse expressions. Meanwhile, to maintain the relevance and coherence, the discrete latent variable is optimized by self-separation training. Experimental results on two dialogue generation datasets (DailyDialog and Opensubtitles) show that CHVT is superior to traditional transformer-based variational mechanism w.r.t. diversity, relevance and coherence metrics. Moreover, we also demonstrate the benefit of applying HLV to fine-tuning two pre-trained dialogue models (PLATO and BART-base).
Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning

Neural machine translation (NMT) has achieved remarkable success in producing high-quality translations. However, current NMT systems suffer from a lack of reliability, as their outputs that are often affected by lexical or syntactic changes in inputs, resulting in large variations in quality. This limitation hinders the practicality and trustworthiness of NMT. A contributing factor to this problem is that NMT models trained with the one-to-one paradigm struggle to handle the source diversity phenomenon, where inputs with the same meaning can be expressed differently. In this work, we treat this problem as a bilevel optimization problem and present a consistency-aware meta-learning (CAML) framework derived from the model-agnostic meta-learning (MAML) algorithm to address it. Specifically, the NMT model with CAML (named CoNMT) first learns a consistent meta representation of semantically equivalent sentences in the outer loop. Subsequently, a mapping from the meta representation to the output sentence is learned in the inner loop, allowing the NMT model to translate semantically equivalent sentences to the same target sentence. We conduct experiments on the NIST Chinese to English task, three WMT translation tasks, and the TED M2O task. The results demonstrate that CoNMT effectively improves overall translation quality and reliably handles diverse inputs.
Improving Simultaneous Machine Translation with Monolingual Data

Simultaneous machine translation (SiMT) is usually done via sequence-level knowledge distillation (Seq-KD) from a full-sentence neural machine translation (NMT) model. However, there is still a significant performance gap between NMT and SiMT. In this work, we propose to leverage monolingual data to improve SiMT, which trains a SiMT student on the combination of bilingual data and external monolingual data distilled by Seq-KD. Preliminary experiments on En-Zh and En-Ja news domain corpora demonstrate that monolingual data can significantly improve translation quality (e.g., +3.15 BLEU on En-Zh). Inspired by the behavior of human simultaneous interpreters, we propose a novel monolingual sampling strategy for SiMT, considering both chunk length and monotonicity. Experimental results show that our sampling strategy consistently outperforms the random sampling strategy (and other conventional typical NMT monolingual sampling strategies) by avoiding the key problem of SiMT -- hallucination, and has better scalability. We achieve +0.72 BLEU improvements on average against random sampling on En-Zh and En-Ja. Data and codes can be found at https://github.com/hexuandeng/Mono4SiMT.