Reviewerly/exHarmony huggingface dataset:
A Proposed Model for Applying Total Quality Management in Vocational and Technical Training Institutes

The study proposes a model for management development in the vocational and technical training institutes of the Vocational Training Corporation in Jordan from the perspective of total quality management. The descriptive survey approach and the qualitative approach were used to develop the proposed model of total quality management. The study population consisted of all administrators, teachers, and trainers of the vocational and technical training institutes. The study sample was chosen randomly. To achieve the objectives of the study, the researchers developed a questionnaire to collect data after reviewing the theoretical literature and previous studies. The findings revealed that the degree of applying total quality management standards in vocational and technical training institutes was medium. Also, a proposed model for total quality management standards in vocational and technical training institutes was developed. In addition, there were no statistically significant differences at (0.05) in applying total quality management standards in the vocational and technical training institutes due to the variables of gender, job title, academic qualification, years of experience, and region. In light of these findings, the researchers recommended activating the standards of total quality management within the proposed model and starting to apply them actually in vocational and technical training institutes. This could be achieved by developing the values and trends within these institutes to assist in building a solid organizational culture for the standards of total quality management.
Sentiment-aware Review Summarization with Personalized Multi-task Fine-tuning

Personalized review summarization is a challenging task in recommender systems, which aims to generate condensed and readable summaries for product reviews. Recently, some methods propose to adopt the sentiment signals of reviews to enhance the review summarization. However, most previous works only share the semantic features of reviews via preliminary multi-task learning, while ignoring the rich personalized information of users and products, which is crucial to both sentiment identification and comprehensive review summarization. In this paper, we propose a sentiment-aware review summarization method with an elaborately designed multi-task fine-tuning framework to make full use of personalized information of users and products effectively based on Pretrained Language Models (PLMs). We first denote two types of personalized information including IDs and historical summaries to indicate their identification and semantics information respectively. Subsequently, we propose to incorporate the IDs of the user/product into the PLMs-based encoder to learn the personalized representations of input reviews and their historical summaries in a fine-tuning way. Based on this, an auxiliary context-aware review sentiment classification task and a further sentiment-guided personalized review summarization task are jointly learned. Specifically, the sentiment representation of input review is used to identify relevant historical summaries, which are then treated as additional semantic context features to enhance the summary generation process. Extensive experimental results show our approach could generate sentiment-consistent summaries and outperforms many competitive baselines on both review summarization and sentiment classification tasks.
Application of Deep Clustering Algorithms

Deep clustering algorithms have gained popularity for clustering complex, large-scale data sets, but getting started is difficult because of numerous decisions regarding architecture, optimizer, and other hyperparameters. Theoretical foundations must be known to obtain meaningful results. At the same time, ease of use is necessary to get used by a broader audience. Therefore, we require a unified framework that allows for easy execution in diverse settings. While this applies to established clustering methods like k-Means and DBSCAN, deep clustering algorithms lack a standard structure, resulting in significant programming overhead. This complicates empirical evaluations, which are essential in both scientific and practical applications. We present a solution to this problem by providing a theoretical background on deep clustering as well as practical implementation techniques and a unified structure with predefined neural networks. For the latter, we use the Python package ClustPy. The aim is to share best practices and facilitate community participation in deep clustering research.
Faster Approximation Algorithms for Parameterized Graph Clustering and Edge Labeling

Graph clustering is a fundamental task in network analysis where the goal is to detect sets of nodes that are well-connected to each other but sparsely connected to the rest of the graph. We present faster approximation algorithms for an NP-hard parameterized clustering framework called LambdaCC, which is governed by a tunable resolution parameter and generalizes many other clustering objectives such as modularity, sparsest cut, and cluster deletion. Previous LambdaCC algorithms are either heuristics with no approximation guarantees, or computationally expensive approximation algorithms. We provide fast new approximation algorithms that can be made purely combinatorial. These rely on a new parameterized edge labeling problem we introduce that generalizes previous edge labeling problems that are based on the principle of strong triadic closure and are of independent interest in social network analysis. Our methods are orders of magnitude more scalable than previous approximation algorithms and our lower bounds allow us to obtain a posteriori approximation guarantees for previous heuristics that have no approximation guarantees of their own.
Role of Judiciary in Protecting Witnesses in the Criminal Justice System: A Critical Analysis

Witnesses are the eyes and ears of the Criminal Justice System. Witnesses being the only spectator of an offence are the indispensable aid in the Criminal Justice Delivery System but are yet at a vulnerable position that they turn victims at several instances. Starting from the investigation stage to the trial proceedings of the court witness assist the Court in every possible way to punish the accused person by deposing every relevant fact which can lead to a fair justice delivery and conviction of the accused person without getting any gain. Witnesses are often subjected to threatening, inducement, harassment, and different kinds of intimidation to them and also their family members. Due to this, in many cases due to lack of Witness Protection Laws the witnesses not getting adequate protection they become victims. Unlike the accused, the witnesses or victims have no rights for their protection. And when the state agencies fail to do their duty, as has often happened in many cases in the recent past, the witnesses turn out to be a victim and subsequently left to suffer injustice silently. Protection of witness is also one of the attributes of fair trial which is guaranteed under Article 21 of the Constitution of India wherein proper safety and security should be ensured. But due to lack of effective Witness Protection Regime and statutory framework for giving protection in our country the witness has lost confidence when it’s about their safety and security. This has also given rise to high acquittal rate and decrease in convictions in the justice administration system. The Judiciary has actively given importance for having witness protections Laws in India and at the same time has enumerated the duties of Witnesses towards the state when the state is providing protection.
Some Useful Things to Know When Combining IR and NLP: the Easy, the Hard and the Ugly

Deep nets such as GPT are at the core of the current advances in many systems and applications. Things are moving very fast, and it appears that techniques are out of date within weeks. How can we take advantage of new discoveries and incorporate them into our existing work? Are these radical new developments, repetitions of older concepts, or both?
Multitask Ranking System for Immersive Feed and No More Clicks: A Case Study of Short-Form Video Recommendation

In recent years, social media users spend significant amount of time on Short-Form Video (SFV) platforms. Its success in creating an immersive viewership experience is not only from the content, but also due to its unique UI innovation: instead of providing choices for users to click, SFV platforms actively recommend content to users to watch one at a time. In this paper, we highlight unique challenges rooted from such UI changes for SFV recommendation system design. Firstly, there is yet much unexplored for sources of system biases under the new UI, as there are no clicks nor the common click-based position biases. Additionally, when training multiple types of user activities, positive labels for activities like sharing and commenting can be much sparser and more skewed than traditional click-based recommendation systems, as the latter can filter non-click impressions when generating "post-click" activities.
Graph Inference via the Energy-efficient Dynamic Precision Matrix Estimation with One-bit Data

Graph knowledge discovery from graph-structured data is a fascinating data mining topic in various domains, especially in the Internet of Things, where inferring the graph structure from such informative data can benefit many downstream tasks. Deep neural networks are typically used to perform such predictions, but they produce unreliable results without sufficient high-quality data. Therefore, researchers introduce lightweight statistical precision matrix learning to infer the graph structure in many IoT scenarios with limited communication and resolution of sensors. However, these methods still suffer from low-resolution data or the omission of hidden information in time-series data. To address the challenges, we propose a novel approach for Energy-efficient Dynamic Sparse Graph Structure Estimation with one-bit data, EDGE. Our method proposes a novel estimator to estimate the covariance matrix from one-bit data, and then utilize the covariance matrices to capture the dynamic structure. We theoretically demonstrate the effectiveness of the estimators by deriving two non-asymptotic estimation error bounds for the estimated covariance matrix and precision matrix, respectively. The theoretical results show that our method can achieve a consistent result of the precision matrix at the rate O(log p/n). On multiple synthetic and real-world datasets, the experimental results demonstrate that our proposed estimator is able to obtain a relatively high detection rate using one-bit data, which exceeds the baseline by 35%, and identify potentially perturbed nodes in real-time dynamic network inference.
Causal Discovery in Temporal Domain from Interventional Data

Causal learning from observational data has garnered attention as controlled experiments can be costly. To enhance identifiability, incorporating intervention data has become a mainstream approach. However, these methods have yet to be explored in the context of time series data, despite their success in static data. To address this research gap, this paper presents a novel contribution. Firstly, a temporal interventional dataset with causal labels is introduced, derived from a data center IT room of a cloud service company. Secondly, this paper introduces TECDI, a novel approach for temporal causal discovery. TECDI leverages the smooth, algebraic characterization of acyclicity in causal graphs to efficiently uncover causal relationships. Experimental results on simulated and proposed real-world datasets validate the effectiveness of TECDI in accurately uncovering temporal causal relationships. The introduction of the temporal interventional dataset and the superior performance of TECDI contribute to advancing research in temporal causal discovery. Our datasets and codes have released at~\hrefhttps://github.com/lpwpower/TECDI https://github.com/lpwpower/TECDI.
Analysing Breast Cancer using Convolution Neural Network

Technological development of Soft Computing and Artificial Intelligence contributed a lot towards disease diagnosis in medical science. For providing solutions to the biological inspired problems in medical domain like Breast Cancer (BC) soft computing methods can give the flexible information. As per 2020 about 2 million women were detected with Breast Cancer (BC) creating most common malignancy among women worldwide. This rises both incidence and mortality has occurred during the past three decades due to evolving risk factors, improved cancer registries, and earlier diagnosis. There is a large number of risk factors for BC, some of which can be changed and others cannot. Eighty percent of people diagnosed with BC nowadays are over the age of fifty. Molecular subtype and developmental stage are both important in determining the likelihood of survival. When it comes to clinical presentation, behaviour and shape, invasive BC span a broad spectrum of tumours. In this paper, Convolution Neural Network (CNN) used to recognize the BC tumor because it is another sort of neural network that can discover key information in both image and time series data. By applying CNN on the 2023 RSNA (Radiological Society for North America) Screening Mammography Breast Cancer data we analysed how best CNN algorithm is for identifying breast cancer with accuracy and also tried to analyse at what age Breast Cancer is mostly occurred in women.
Tutorial: Data Denoising Metrics in Recommender Systems

Recommender systems play a pivotal role in navigating users through vast reservoirs of information. However, data sparseness can compromise recommendation accuracy, making it challenging to improve recommendation performance. To address this issue, researchers have explored incorporating multiple data types. Yet, this approach can introduce noise that impairs the recommendations' accuracy. Therefore, it is crucial to denoise the data to enhance recommendation quality. This tutorial highlights the importance of data denoising metrics for improving the accuracy and quality of recommendations. Four groups of data denoising metrics are introduced: feature, item, pattern, and modality level. For each group, various denoising methods are presented. The tutorial emphasizes the significance of selecting the right data denoising methods to enhance recommendation quality. It provides valuable guidance for practitioners and researchers implementing reliable data denoising metrics in recommender systems. Finally, the tutorial proposes open research questions for future studies, making it a valuable resource for the research community.
Effects of Task Difficulty and Presentation Order in Subjective Usability Measurement

The System Usability Scale (SUS) is a tool widely used in industry for measuring the usability of products and systems. Users are often asked to complete two or more tasks of varying complexity before evaluating a product using the SUS. However, task order effects may influence the overall usability rating of a product, but previous literature has not examined this issue. To test the effect of task order on the SUS, participants were asked to complete two tasks involving locating specific information on a college website. Participants completed an easy task and a hard task, and presentation of the tasks was randomized. Results showed that when participants completed the easy task first, they rated the overall usability of the website lower than when the hard task was presented first. This suggests that practitioners should be cautious when designing usability studies with tasks of varying difficulties.
Research On the Current Situation of Teaching English in Lower Secondary Schools In an Giang Province, Vietnam

The quality and effectiveness of teaching English in secondary schools depend on several factors, including the objectives, curriculum content, teaching methods, instructional organization, and the teaching capacity of the teachers. It also relies on the student's motivation, awareness, and study attitude during English learning. Additionally, it is influenced by the organizational management mechanisms of the secondary schools. The author conducted a study to examine the current state of English teaching in secondary schools in An Giang province, aiming to shed light on the current status of English teaching activities in these schools.
An In-Depth Analysis of Pythonic Paradigms for Rapid Hardware Prototyping and Instrumentation

This in-depth examination review about pythonic paradigms for quick hardware prototyping and instrumentation to solve the widening gap between the fluidity of software development and the more conventional methodology used in hardware design. The proliferation of specialized hardware and accelerators has resulted in an increased necessity for development strategies that are flexible. Reconfigurable hardware, such as FPGAs and coarse-grain reconfigurable arrays, enables incremental changes in early design stages, which is a key component of agile software development. This article goes into modern agile development methods, emphasizing the integration of services, repositories, and specialized tools to meet particular users' requirements. Continuous improvement in areas such as software and hardware design, testing, analysis, and evaluation, as well as collaboration with current development tools, are all supported by an ecosystem that has been successfully integrated.
Neighborhood Homophily-based Graph Convolutional Network

Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks. However, many real-world graphs are heterophilous, challenging the homophily assumption of classical GNNs. To solve the universality problem, many studies deepen networks or concatenate intermediate representations, which does not inherently change neighbor aggregation and introduces noise. Recent studies propose new metrics to characterize the homophily, but rarely consider the correlation of the proposed metrics and models. In this paper, we first design a new metric, Neighborhood Homophily (NH), to measure the label complexity or purity in node neighborhoods. Furthermore, we incorporate the metric into the classical graph convolutional network (GCN) architecture and propose Neighborhood Homophily-based Graph Convolutional Network (NHGCN). In this framework, neighbors are grouped by estimated NH values and aggregated from different channels, and the resulting node predictions are then used in turn to estimate and update NH values. The two processes of metric estimation and model inference are alternately optimized to achieve better node classification. NHGCN achieves top overall performance on both homophilous and heterophilous benchmarks, with an improvement of up to 7.4% compared to the current SOTA methods.
Suitability of Virtual Reality for Supplemental Scenario-based Training to Facilitate Crew Resource Management Outcomes

Researchers and practitioners are exploring the efficacy of virtual reality (VR) in education and training, including in aviation (Hight et al., 2022). In this poster presentation, we briefly discuss existing literature that has examined the effectiveness of VR for training within aviation. We then specifically address the potential adoption of VR as a simulation training tool to enhance crew resource management (CRM) training in what we refer to as supplemental Scenario-based Training (SBT).
Non-Axiomatic Logic Modeling of English Texts for Knowledge Discovery and Commonsense Reasoning

Non-axiomatic logic (NAL) is a term-based, non-monotonic, multi-valued logic with evidence-based formal semantics. All those characteristics position NAL as an excellent candidate for modeling natural language expressions and supporting artificial agents while performing knowledge discovery and commonsense reasoning tasks. In this article, we propose a set of rules for the automatic translation of natural language (NL) text into the formal language of non-axiomatic logic (NAL). Several free available tools are used to support a previous linguistic analysis, and a common sense ontology is used to populate a background knowledge base that helps to delimit the scope and the semantics of logical formulas translated. Experimentation shows our set to be the most comprehensive NL-to-NAL translation rule set known so far. Furthermore, we included an extensive set of examples to show how our proposed set of rules can be used for translating a wide range of English statements with varying grammatical structures.
A Self-Learning Resource-Efficient Re-Ranking Method for Clinical Trials Search

Complex search scenarios, such as those in biomedical settings, can be challenging. One such scenario is matching a patient's profile to relevant clinical trials. There are multiple criteria that should match for a document (clinical trial) to be considered relevant to a query (patient's profile represented with an admission note). While different neural ranking methods have been proposed for searching clinical trials, resource-efficient approaches to ranker training are less studied. A resource-efficient method uses training data in moderation. We propose a self-learning reranking method that achieves results comparable to those of more complicated, fully supervised, systems. Our experiments demonstrate our method's robustness and competitiveness compared to the state-of-the-art approaches in clinical trial search.
Adaptation Speed Analysis for Fairness-aware Causal Models

For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one can adapt most quickly to a domain shift is of significant importance in many fields. Specifically, consider an original distribution p that changes due to an unknown intervention, resulting in a modified distribution p*. In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable. To explore this scenario, we examine a simple structural causal model (SCM) with a cause-bias-effect structure, where variable A acts as a sensitive variable between cause (X) and effect (Y). The two models respectively exhibit consistent and contrary cause-effect directions in the cause-bias-effect SCM. After conducting unknown interventions on variables within the SCM, we can simulate some kinds of domain shifts for analysis. We then compare the adaptation speeds of two models across four shift scenarios. Additionally, we prove the connection between the adaptation speeds of the two models across all interventions.