Reviewerly/exHarmony huggingface dataset:
Factors that determine open data readiness among scholars: experience from selected universities in Tanzania

Purpose This study aims to investigate the factors determining the readiness for uptake of open data (OD) in Tanzania. Specifically, this study intended to answer the question that sought to find out the factors that influence the implementation of OD in universities under study in a view to aligning with recommended strategies for optimizing the use of data in the open science era. Design/methodology/approach This study used a cross-sectional survey design whereby data were collected using quantitative and qualitative research approaches. A sample size of 212 respondents was drawn from the sampling frame of a population of 1,846 researchers from the participating universities using both probability and nonprobability sampling techniques. A self-administered questionnaire was used to collect data from researchers while interviews were administered to decision-makers. These two groups were believed to have the necessary knowledge for this study. Findings The findings indicate low or lack of skills and awareness on issues related to OD among researchers and decision-makers. This study also reveals inadequate infrastructure to support open science initiatives including OD. Moreover, this study shows a lack of supportive institutional strategies and policies that trigger the implementation of OD initiatives in Tanzania. This study recommends that universities should uplift the level of confidence of researchers by ensuring all necessary factors determining the uptake of OD are in place before and during the implementation of OD initiatives. Research limitations/implications This study was conducted during the time when researchers in universities are in an infant stage of adopting the concept from the developed world. In this regard, it is important to carry out a tracer study on establishing the OD phenomena after a number of universities have implemented OD initiatives in the country. Practical implications The researchers recommend the establishment of institutional policy and strategies to guide the implementation of OD among universities in Tanzania. Including awareness creation awareness through providing training among researchers and academics in universities. The results shed light to decision-makers on the understanding of the role of sharing research data in enhancing openness and validation of findings to increase the authenticity of results among researchers. Social implications The authors have revealed the factors affecting the implementation of OD among scholars in universities. This study reveals the level of acceptance of OD initiatives and the factors that could trigger adoption of OD. Originality/value This paper presents factors that determine the readiness for the uptake of OD in universities in Tanzania from the researcher’s perspective. This study was conducted to fill the knowledge gap that sought to establish understanding of researchers about OD. The gap was established through literature and it was found that none of the presented studies researched on this phenomenon in Tanzania.
IoT Applications for Recommended Methods of Physical Education Online Course Resources Based on Collaborative Filtering Technology

Educational resources are available in different repositories and on the web. For example, these resources are in the form of courses, tutorials, simulations, tests, etc., and are available on the web. And these resources are constantly increasing. In this electronic age, it is necessary to develop systems to help people to find what they want and particularly what is more suitable to their personal subject interest. Recommender systems can help professors, researchers, and students to find the best educational resources suitable for his/her profile. The “e”-course characteristics and user profiles have to be considered, while providing the recommendations. A system can be developed where users can express a subject query to satisfy their information needs. During this stage, user characters and preferences are not considered and all users get the same results for that query. When the characteristics like language and preferences like practical and demonstrations are considered, then the information retrieval process should be improved by personalization. The set of characteristics and preferences of each user has to be stored and to be matched with e-course characteristics. There might be some characteristics that may be expressed using fuzzy values. It would be discussed how to find electronic educational resources that are suitable to the needs and characteristics of a user based on the user’s preferences and educational resources available. So, the present article recommended a method of physical education online course resources based on collaborative filtering technology.
RSD-GAN: Regularized Sobolev Defense GAN Against Speech-to-Text Adversarial Attacks

This letter introduces a new synthesis-based defense algorithm for counteracting with a varieties of adversarial attacks developed for challenging the performance of the cutting-edge speech-to-text transcription systems. Our algorithm implements a Sobolev-based GAN and proposes a novel regularizer for effectively controlling over the functionality of the entire generative model, particularly the discriminator network during training. Our achieved results upon carrying out numerous experiments on the victim DeepSpeech, Kaldi, and Lingvo speech transcription systems corroborate the remarkable performance of our defense approach against a comprehensive range of targeted and non-targeted adversarial attacks.
Analyzing How 9th Grade Students Participate in the Online Mathematics Lessons

This study aims to analyze how 9th grade students participate in their online mathematics lessons implemented in school mathematics. We especially focused on the differentiated features that appear when students participate in online mathematics lessons. We identified various features provided by online lessons through theoretical review and designed lessons based on these features as follows. Students could participate in lessons through written interactions based on anonymity, visit and use various internet sites and software such as statistical analysis program, and participate in multi chat rooms. We had designed lessons that require these interactions and the utilization of online resources. The results were as follows: First, various online resources such as Tong-Gramy and interactions with peers supported students to collect and analyze the authentic data. Second, written interaction and multi-logue allowed students to get out of time and space constraints and provided students with the opportunities to participate in mathematical discussions performed in other groups. Written interaction also provided an opportunity to expand mathematical thinking through expanding teacher’s support. Third, the anonymity promoted students to participate in interactions more actively and strengthened the discussions focused on the mathematical ideas.
A Deep Spiking Neural Network Anomaly Detection Method

Cyber-attacks on specialized industrial control systems are increasing in frequency and sophistication, which means stronger countermeasures need to be implemented, requiring the designers of the equipment in question to re-evaluate and redefine their methods for actively protecting against advanced mass cyber-attacks. The attacks in question have huge motivations, ranging from corporate espionage to political targets, but in any case, they have a substantial financial impact and severe real-world implications. It should also be said that it is challenging to defend against cyber threats because a single point of entry can be enough to destroy an entire organization or put it out of business. This paper examines threats to the digital security of vibration monitoring systems used in petroleum infrastructure protection services, such as pipelines, pumps, and tank farms, where malicious interventions can cause explosions, fires, or toxic releases, with incalculable economic and environmental consequences. Specifically, a deep spiking neural network anomaly detection method is presented, which models the spike sequences and the internal presentation mechanisms of the information to discover with very high accuracy anomalies in vibration analysis systems used in oil infrastructure protection services. This is achieved by simulating the complex structures of the human brain and the way neural information is processed and transmitted. This work uses a particularly innovative form of the Galves-Löcherbach Spiking Model (GLSM) [1], which is a spiking neural network model with intrinsic stochasticity, ideal for modeling complex spatiotemporal situations, which is enhanced with possibilities of exploiting confidence intervals by modeling optimally stochastic variable-length memory chains that have a finite state space.
DiscoPG

Property graphs are becoming pervasive in a variety of graph processing applications using interconnected data. They allow to encode multi-labeled nodes and edges, as well as their properties, represented as key/value pairs. Although property graphs are widely used in several open-source and commercial graph databases, they lack a schema definition, unlike their relational counterparts. The property graph schema discovery problem consists of extracting the underlying schema concepts and types from such graph datasets. We showcase DiscoPG, a system for efficiently and accurately discovering and exploring property graph schemas. To this end, it leverages hierarchical clustering using a Gaussian Mixture Model, which accounts for both node labels and properties. DiscoPG allows users to perform schema discovery for both static and dynamic graph datasets. Suitable visualization layouts and dedicated dashboards enable the user perception of the static and dynamic inferred schema on the node clusters, as well as the differences in runtimes and clustering quality. To the best of our knowledge, DiscoPG is the first system to tackle the property graph schema discovery problem. As such, it supports the insightful exploration of the graph schema components and their evolving behavior, while revealing the underpinnings of the clustering-based discovery process.
Construction of College Students’ Employment Quality Evaluation Model System under the Background of Digitalization

In the era of knowledge economy, human resources are being valued by various countries and regions. The report of the 19th National Congress of the Communist Party of China pointed out that “talent is a strategic resource for realizing national rejuvenation and winning the initiative of international competition. We must adhere to the principle of the party’s management of talents, gather talents from all over the world and use them, and accelerate the construction of a strong country with talents.” College students are an important part of talents, and their employment intentions directly affect employment behavior. With the development of education in our country, the enrollment quota of most colleges and universities in the country has gradually increased, and the number of graduates has also increased. Social and economic development has different needs for different professional and technical personnel, and the employment situation in different regions is uneven. Under the increasingly complex employment environment, college students have to face greater employment pressure and compete with each other in a narrower employment field. Therefore, it is necessary to conduct better employment guidance and employment quality evaluation for college students. Based on the improved algorithm of BP network, an artificial intelligence-based employment quality evaluation model is constructed. The design model is optimized by introducing a momentum variable factor, adjusting the learning rate and quasi-Newton method, and training and recommending each optimization model through the training data. The experimental results show that the iterations of the gradient descent algorithm and the additional momentum optimization algorithm are far more than 1000 times. Second, the optimal validation errors of the two algorithms are large and the model performance is poor. The quasi-Newton ring algorithm also has faster coordination speed, stronger stability, and better overall performance. The adaptive learning rate optimization algorithm is performed in these 4 algorithms. In terms of accuracy, the accuracy of the adaptive learning rate BP optimization algorithm is 76.4%, followed by the Newton algorithm and the additional momentum algorithm, and the gradient descent algorithm is the worst.
#34;You have been in Close Contact with a Person Infected with COVID-19 and you may have been Infected#34;

Through the past two and a half years, COVID-19 has swept through the world and new technologies for mitigating spread, such as exposure notification applications and contact tracing, have been implemented in many countries. However, the uptake has differed from country to country and it has not been clear if culture, death rates or information dissemination have been a factor in their adoption rate. However, these apps introduce issues of trust and privacy protection, which can create challenges in terms of adoptions and daily use. In this paper we present the results from a cross-country survey study of potential barriers to adoption of in particular COVID-19 contact tracing apps. We found that people's existing privacy concerns are an have a reverse correlation with adoption behavior but that the geographical location, as well as other demographics, such as age and gender, do not have significant effect on either adoption of the app or privacy concerns. Instead, a better understanding of what data is collected through the apps lead to a higher level of adoption. We provide suggestions for how to approach the development and deployment of contact tracing apps and more broadly health tracking apps.
Construction of Intelligent Nursing System Based on Visual Action Recognition Algorithm

Due to advancements in scientific research and technological innovation, as well as the proliferation of the Internet of things, the Internet, and big data, the general public has gradually become aware of a new type of intelligent nursing system model known as the smart nursing system. The smart nursing system is a sensing system and information platform for the elderly in their homes, communities, and institutions for elderly care. Based on this, it provides timely, efficient, and cost-effective elderly care services in real time. These services utilize the Internet of things and the Internet as well. Through the monitoring of video data, we are able to differentiate the visual motions of these elderly individuals and determine whether they are in a normal life state or a fall state. This has the potential to better meet the diverse and multifaceted needs of senior citizens, enhance the quality of life of senior citizens in their final years, and provide senior citizens with greater humanistic care and spiritual solace. Our team has developed an intelligent nursing system based on the visual action recognition algorithm, also known as the deep learning (DL) algorithm. As a result of our simulation tests, we discovered that the algorithm can accurately identify the living situations of elderly individuals at home.
Learning Emotion-Aware Contextual Representations for Emotion-Cause Pair Extraction

Emotion-Cause Pair Extraction (ECPE) focuses on analyzing emotions and its corresponding causes in a document. Two reasons have made ECPE a more challenging, but more applicable task than the previous Emotion-Cause Extraction (ECE) task: 1) an ECPE model needs to identify both emotions and their corresponding causes without the annotation of emotions. 2) the ECPE task involves finding causes for multiple emotions in a document, while ECE is for one emotion. However, existing ECPE methods fail to meet the second challenge, since they are evaluated on a dataset which exhibits a bias that nearly 90% of documents have only one emotion-cause pair. Thus, we reconstruct the dataset to better meet ECPE settings. We observe that previous SOTA approaches suffer from performance degradation in extracting multiple emotion-cause pairs due to the use of shared context encoder in the joint learning process. In this work, we propose a new pipelined approach that builds on two independent models with unshared context encoders, in which the emotion extraction model only provides input features for the cause extraction model. Experimental results demonstrate that our model can learn distinct contextual representations specific to each emotion, reaching state-of-the-art performance on both datasets and showing robustness in the analysis of more complex document context.
A Side-Channel Based Disassembler for the ARM-Cortex M0

The most common application for side-channel attacks is the extraction of secret information, such as key material, from the implementation of a cryptographic algorithm. However, using side-channel information, we can extract other types of information related to the internal state of a computing device, such as the instructions executed and the content of registers. We used machine learning to build a side channel disassembler for the ARM-Cortex M0 architecture, which can extract the executed instructions from the power traces of the device. Our disassembler achieves a success rate of 99% under ideal conditions and 88.2% under realistic conditions when distinguishing between groups of instructions. We also provide an overview of the lessons learned in relation to data preparation and noise minimization techniques.
A Distributed Architecture for Privacy-Preserving Optimization Using Genetic Algorithms and Multi-party Computation

In many industries, competitors are required to cooperate in order to conduct optimizations, e.g., to solve an assignment problem. For example, in air traffic flow management (ATFM), flight prioritization in case of temporarily reduced capacity of the air traffic network is an instance of the assignment problem. Participants, however, are typically reluctant to share sensitive information regarding their preferences for the optimization, which renders conventional approaches to optimization inadequate. This paper proposes a method for combining genetic algorithms with multi-party computation (MPC) as the basis for building a platform for optimizing the assignment of resources to different agents under the assumption of an honest-but-curious platform provider; the method is illustrated on the ATFM use case. In the proposed method a genetic algorithm iteratively generates a population of candidate solutions to the assignment problem while a Privacy Engine component evaluates the population in each iteration step. The participants’ private inputs are kept from competitors and not even the platform provider knows those inputs, receiving only encrypted input which is processed by MPC nodes in a way that preserves the secrecy of the inputs.
Interactive Teaching Based on Artificial Intelligence and Its Application in Improving National Music Appreciation Ability

The limitations of the traditional interactive teaching model are gradually becoming apparent in the current music teaching process. Based on the interactive teaching theory of artificial intelligence, this paper constructs an evaluation model of ethnic music appreciation ability and conducts research by using a novel method of fusion of long- and short-term audio features. The model proposes the classical features describing the sound quality and the beat histogram as the feature of the long-term rhythm of the music to form a mixed feature; secondly, the representation method of the song style vector is proposed, and the quantitative problem of music teaching is solved. During the simulation process, the model adopts the popular Model-View-Controller (MVC) design pattern and Unified Modeling Language (UML) and is developed with the Java 2 Platform Enterprise Edition (J2EE) architecture. It realized the user login function of three identities of students, teachers, and system administrators, and the subsystem can complete the management of students’ personal information, browse related courseware information and other resource information, and download courseware and tutorials. The experimental results show that the average value of the students’ evaluation is 0.606; the average value of case is 0.5852; it also reduces the workload of later maintenance of the system.
An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data. Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method. Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis. Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.