Reviewerly/exHarmony huggingface dataset:
A multi-function wearable radio transceiver device based on radio communication technology

This study proposes a multi-function wearable radio transceiver device based on radio communication technology that utilizes the 2.4 GHz channel, which is open to the public, for communication; the suggested approach aims to support climbers. Each climber is equipped with a wearable device that bears the following functions: (1) radio communication function; users can engage in communication via the radio transceiver; (2) physiological sensor function; the device consistently detects the user's body temperature and heart rate and triggers an alert if any said index shows an anomaly; (3) a one-touch panic button function; when the user presses down the emergency button, the device triggers a distress signal; (4) power-saving and positioning functions. Utilize drones and radio communication technology to conduct search and rescue, the system positions by analyzing the strength of the distress signal, which incorporates a heart rate index that helps to assess the climber's current physiological condition.
Gamification Trends in Educational Communications in a Digital Society

The role and degree of influence of the concept of gamification on the transformation of the content and forms of educational communications within the digital society is shown. approach to solving modern educational goals and objectives, building educational communication strategies in the process of continuous development of new ideas and ways of their implementation. Examples of gamification in digital and non-digital forms of education are given. The potential of game mechanics and gamification tools in hybrid forms of education is considered. An example of the mobile application «Kahoot!» - a service for creating and organizing interactive texts, quizzes and polls, created in the framework of gamification.
A Quotient Space Formulation for Generative Statistical Analysis of Graphical Data

Complex analyses involving multiple, dependent random quantities often lead to graphical models—a set of nodes denoting variables of interest, and corresponding edges denoting statistical interactions between nodes. To develop statistical analyses for graphical data, especially towards generative modeling, one needs mathematical representations and metrics for matching and comparing graphs, and subsequent tools, such as geodesics, means, and covariances. This paper utilizes a quotient structure to develop efficient algorithms for computing these quantities, leading to useful statistical tools, including principal component analysis, statistical testing, and modeling. We demonstrate the efficacy of this framework using datasets taken from several problem areas, including letters, biochemical structures, and social networks.
An ontology knowledge inspection methodology for quality assessment and continuous improvement

Ontology-learning methods were introduced in the knowledge engineering area to automatically build ontologies from natural language texts related to a domain. Despite the initial appeal of these methods, automatically generated ontologies may have errors, inconsistencies, and a poor design quality, all of which must be manually fixed, in order to maintain the validity and usefulness of automated output. In this work, we propose a methodology to assess ontologies quality (quantitatively and graphically) and to fix ontology inconsistencies minimizing design defects. The proposed methodology is based on the Deming cycle and is grounded on quality standards that proved effective in the software engineering domain and present high potential to be extended to knowledge engineering quality management. This paper demonstrates that software engineering quality assessment approaches and techniques can be successfully extended and applied to the ontology-fixing and quality improvement problem. The proposed methodology was validated in a testing ontology, by ontology design quality comparison between a manually created and automatically generated ontology.
Semi-Supervised Classification on Credit Card Fraud Detection using AutoEncoders

The use of credit cards for online purchases has increased dramatically and led to an explosion in credit card fraud. Credit card companies need to be able to identify fraudulent credit card transactions so that customers are not charged for items they do not buy. In this study, we will use semi-supervised learning and combine it with AutoEncoders to identify fraudulent credit card transactions. In this paper, we will implement the use of T-SNE to visualize fraud and non-fraud transactions, then improve the visualization using autoencoders. Classification report proved that it is possible to achieve very acceptable precision using semi-supervised classification to detect credit card fraud.
Detection Face Parts in Image Using Neural Network Based on MATLAB

ConvolutionalNeural Networks, face detection
Continuous Learning in Neural Machine Translation using Bilingual Dictionaries

While recent advances in deep learning led to significant improvements in machine translation, neural machine translation is often still not able to continuously adapt to the environment.For humans, as well as for machine translation, bilingual dictionaries are a promising knowledge source to continuously integrate new knowledge.However, their exploitation poses several challenges: The system needs to be able to perform one-shot learning as well as model the morphology of source and target language.In this work, we proposed an evaluation framework to assess the ability of neural machine translation to continuously learn new phrases.We integrate one-shot learning methods for neural machine translation with different word representations and show that it is important to address both in order to successfully make use of bilingual dictionaries.By addressing both challenges we are able to improve the ability to translate new, rare words and phrases from 30% to up to 70%.The correct lemma is even generated by more than 90%.
A Two-Block RNN-Based Trajectory Prediction From Incomplete Trajectory

Trajectory prediction has gained great attention and significant progress has been made in recent years. However, most works rely on a key assumption that each video is successfully preprocessed by detection and tracking algorithms and the complete observed trajectory is always available. However, in complex real-world environments, we often encounter miss-detection of target agents (e.g., pedestrian, vehicles) caused by the bad image conditions, such as the occlusion by other agents. In this paper, we address the problem of trajectory prediction from incomplete observed trajectory due to miss-detection, where the observed trajectory includes several missing data points. We introduce a two-block RNN model that approximates the inference steps of the Bayesian filtering framework and seeks the optimal estimation of the hidden state when miss-detection occurs. The model uses two RNNs depending on the detection result. One RNN approximates the inference step of the Bayesian filter with the new measurement when the detection succeeds, while the other does the approximation when the detection fails. Our experiments show that the proposed model improves the prediction accuracy compared to the three baseline imputation methods on publicly available datasets: ETH and UCY (9% and 7% improvement on the ADE and FDE metrics). We also show that our proposed method can achieve better prediction compared to the baselines when there is no miss-detection.
Evaluating the Evaluation of Diversity in Natural Language Generation

Despite growing interest in natural language generation (NLG) models that produce diverse outputs, there is currently no principled method for evaluating the diversity of an NLG system.In this work, we propose a framework for evaluating diversity metrics.The framework measures the correlation between a proposed diversity metric and a diversity parameter, a single parameter that controls some aspect of diversity in generated text.For example, a diversity parameter might be a binary variable used to instruct crowdsourcing workers to generate text with either low or high content diversity.We demonstrate the utility of our framework by: (a) establishing best practices for eliciting diversity judgments from humans, (b) showing that humans substantially outperform automatic metrics in estimating content diversity, and (c) demonstrating that existing methods for controlling diversity by tuning a "decoding parameter" mostly affect form but not meaning.Our framework can advance the understanding of different diversity metrics, an essential step on the road towards better NLG systems. ContextFire next door.John woke up smelling like something was burning.He went outside.He saw the fire next door.
Pseudotext Injection and Advance Filtering of Low-Resource Corpus for Neural Machine Translation

Scaling natural language processing (NLP) to low-resourced languages to improve machine translation (MT) performance remains enigmatic. This research contributes to the domain on a low-resource English-Twi translation based on filtered synthetic-parallel corpora. It is often perplexing to learn and understand what a good-quality corpus looks like in low-resource conditions, mainly where the target corpus is the only sample text of the parallel language. To improve the MT performance in such low-resource language pairs, we propose to expand the training data by injecting synthetic-parallel corpus obtained by translating a monolingual corpus from the target language based on bootstrapping with different parameter settings. Furthermore, we performed unsupervised measurements on each sentence pair engaging squared Mahalanobis distances, a filtering technique that predicts sentence parallelism. Additionally, we extensively use three different sentence-level similarity metrics after round-trip translation. Experimental results on a diverse amount of available parallel corpus demonstrate that injecting pseudoparallel corpus and extensive filtering with sentence-level similarity metrics significantly improves the original out-of-the-box MT systems for low-resource language pairs. Compared with existing improvements on the same original framework under the same structure, our approach exhibits tremendous developments in BLEU and TER scores.
Cascade CenterNet: Robust Object Detection for Power Line Surveillance

The use of object detection methods is becoming increasingly important in the internet of smart energy. Accurate detection of power line region and suspicious objects can reduce the probability of accidents and save manpower. However, the noisy outdoor background and time-varying lighting conditions bring difficulties to those tasks. To address those challenges, this paper investigates mainstream methods of power line region surveillance and proposes a new method for improving the accuracy of power line region and risk object detection by modeling the heat map branch of CenterNet, which is the most representative of anchor-free detectors, with a cascade guiding structure and an improved loss function. Finally, comprehensive experiments are conducted to compare some state-of-the-art detectors and the proposed model on power line surveillance tasks. The test results demonstrate both the effectiveness and superiority of the proposed method.
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates

Annotating training data for sequence tagging of texts is usually very time-consuming.Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget.We are the first to thoroughly investigate this powerful combination for the sequence tagging task.We conduct an extensive empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pretrained models in the active learning framework and find the best combinations for different types of models.Besides, we also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance and reduces obstacles for applying deep active learning in practice.
Design and implementation of a semantic gateway based on SSN ontology

The massive data in the industrial big data field mainly comes from a large number of sensors. Sensor data is discrete numerical data with heterogeneity. In scenarios such as edge computing, it is often necessary to mine the relationships between the data in the data stream so as to realize the full use of heterogeneous sensor data. Because the sensor data does not contain contextual semantic relations, the relationship between the data is difficult to be discovered. Aiming at the above shortcomings, we design and implement a semantic gateway with semantic annotation function. The original sensor data will become RDF data with semantic information after being processed by the semantic gateway. Our work is mainly divided into two aspects: according to the source and actual characteristics of the data, we choose to carry out the semantic work based on the SSN ontology; next, we propose an OB2R algorithm to realize the semantic annotation function of the data, and the correctness of the algorithm is verified on the PHM2010 dataset.
Information Culture And Ethical Education In The Globalization Century

Today we live in a rapidly changing, extremely turbulently and at the same time extremely complex world that is radically different from the times that humanity has ever experienced. Experts call this turbulent period the "media era" or "globalization." The article discusses the concept of globalization. It also explores the social aspects of information culture and moral education in the era of globalization.
Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model

Security has become a central and unavoidable aspect of today's software development. Practitioners and researchers have proposed many code analysis tools and techniques to mitigate security risks. These tools apply static and dynamic analysis or, more recently, machine learning. Machine learning models can achieve impressive results in finding and forecasting possible security issues in programs. However, most of the current approaches fall short of developer demands in two areas at least: explainability and granularity of predictions. In this paper, we propose a novel and simple yet, promising approach to identify potentially vulnerable source code in JavaScript programs. The model improves the state-of-the-art in terms of explainability and prediction granularity as it gives results at the level of individual source code lines, which is fine-grained enough for developers to take immediate actions. Additionally, the model explains each predicted line (i.e., provides the most similar vulnerable line from the training set) using a prototype-based approach. In a study of 186 real-world and confirmed JavaScript vulnerability fixes of 91 projects, the approach could flag 60% of the known vulnerable lines on average by marking only 10% of the code-base, but in particular cases, the model identified 100% of the vulnerable code lines while flagging only 8.72% of the code-base.
Homo Digitus: Its Dependable and Resilient Smart Ecosystem

This paper evaluates the status quo of modern society and identifies the neglect of humanism as the root cause of many of today’s global challenges. Note that “smart cities” are not excluded from this indictment. The “Ptolemaic Universe” offers a means to restore our symbiosis with the environment. The ReSeT model is proposed as a tool to analyze the Ptolemaic Universe. Using ReSeT: Homo Sapiens becomes dependent on AI resulting in Homo Digitalis, with further evolution in concert with AI resulting in Homo Digitus. All of these stages are then analyzed in the context of global trusted dependability (GTD). The wellness domain, provides the design specification framework for Homo Digitus’ human-centered and resilient “smart city” ecosystem. This ultimately leads to a better world of increased wellness for Homo Hominus, with better smart cities emphasizing education and science, promoting wisdom and common sense, and rejecting violence. In summary, humanity has generated diverse social structures with erratic outcomes. On the other hand, technology provides a successful foundation for modern society especially in the Pandemic Era. However, technology’s contributions are generally not publicly acknowledged. The paper concludes with several initiatives designed to establish a trusted and resilient society.
Improving Business Performance by Employing Virtualization Technology: A Case Study in the Financial Sector

The financial crisis of the last decade has left many financial institutions with limited personnel and equipment resources. Thus, the IT departments of these institutions are being asked to explore novel approaches to resolve these constraints in a cost-effective and efficient manner. The goal of this paper is to measure the impact of modern enabling technologies, such as virtualization, in the process of replacing legacy infrastructures. This paper proposes an IT services upgrade plan approach for an organization by using modern technologies. For this purpose, research took place in an operating financial institution, which required a significant upgrade of both its service-level and its hardware infrastructure. A virtualization implementation and deployment assessment for the entire infrastructure was conducted and the resulting consolidated data are presented and analysed. The paper concludes with a five-year financial-based evaluation of the proposed approach with respect to the projection of expenditures, the return of investment and profitability.
Graph-based Multilingual Product Retrieval in E-Commerce Search

Hanqing Lu, Youna Hu, Tong Zhao, Tony Wu, Yiwei Song, Bing Yin. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers. 2021.
A Proposed Architecture for Pharmaceutical Supply Chain Based Semantic Blockchain

This paper introduces an architecture to improve the pharmaceutical supply chain's security by using the Internet of Things, semantic web, and blockchain.This architecture increases transparency and visibility of drug flows and improves the representation of the increasing amounts of data that have been generated from pharmaceutical supply chain transactions.The pharmaceutical companies have problems dealing with the complexity of their supply chains, which represent the full life cycle of drugs from extracting raw materials, production, distribution, tracking of drugs, and quality assurance to use by patients.They need to create an efficient, effective, transparent, immutable, and secured supply chains to achieve a competitive advantage in a fast-changing market.A proposed architecture which applies the semantic web technology is implemented to enhance the representation capability of the IoT-blockchain based pharmaceutical supply chain data by annotating them with semantically rich languages to conduct formal reasoning, and aggregating data from heterogeneous sources in easy way and an interoperable manner.The proposed architecture integrates IoT, blockchain, and semantic web to help pharmaceutical companies improving their supply chains in transit and storage, improving patient satisfaction, trust through transparency, preventing drug counterfeit and sharing and reusing knowledge related to the pharmaceutical supply chain with other systems.
Spam Detection Based on Feature Evolution to Deal with Concept Drift

Electronic messages are still considered the most significant tools in business and personal applications due to their low cost and easy access. However, e-mails have become a major problem owing to the high amount of junk mail, named spam, which fill the e-mail boxes of users. Several approaches have been proposed to detect spam, such as filters implemented in e-mail servers and user-based spam message classification mechanisms. A major problem with these approaches is spam detection in the presence of concept drift, especially as a result of changes in features over time. To overcome this problem, this work proposes a new spam detection system based on analyzing the evolution of features. The proposed method is divided into three steps: 1) spam classification model training; 2) concept drift detection; and 3) knowledge transfer learning. The first step generates classification models, as commonly conducted in machine learning. The second step introduces a new strategy to avoid concept drift: SFS (Similarity-based Features Se- lection) that analyzes the evolution of the features taking into account similarity obtained between the feature vectors extracted from training data and test data. Finally, the third step focuses on the following questions: what, how, and when to transfer acquired knowledge? The proposed method is evaluated using two public datasets. The results of the experiments show that it is possible to infer a threshold to detect changes (drift) in order to ensure that the spam classification model is updated through knowledge transfer. Moreover, our anomaly detection system is able to perform spam classification and concept drift detection as two parallel and independent tasks.
Readiness towards the implementation of open science initiatives in the Malaysian Comprehensive Public Universities

This research explored the readiness towards the implementation of open science initiatives in the Malaysian Comprehensive Public Universities; both quantitative and qualitative research methods were implemented. The survey questionnaire was distributed to the academic researchers and library professionals from the selected universities and a semi-structured interview was conducted with the heads of the libraries together with their deputies to complement the first method. The findings indicate that the public universities in the country were partially involved with open science, even though the term is still new to most of the participants. The Institutional Repository is the commonly known means through which these universities promote and practice open science, although only 10% to 30% of the contents were made available to the general public. However, these institutions are currently working on implementing a policy on how to operate and fully engage other aspects of the open science practices, such as open data, open collaboration, open innovation, and the likes.
Human factor aspects in information security management in the traditional IT and cloud computing models

This paper attempts to classify the main areas of threats occurring in enterprises in the information management processes. Particular attention was attracted to the effect of the human factor which is present in virtually every area of information security management. The author specifies the threats due to the IT techniques and technologies used and the models of information systems present in business entities. The empirical part of the paper presents and describes the research conducted by the author on information security in business organisations using the traditional IT model and the cloud computing model. The results obtained for both IT models are compared.