Reviewerly/exHarmony huggingface dataset:
FlexHM: A Practical System for Heterogeneous Memory with Flexible and Efficient Performance Optimizations

With the rapid development of cloud computing, numerous cloud services, containers, and virtual machines have been bringing tremendous demands on high-performance memory resources to modern data centers. Heterogeneous memory, especially the newly released Optane memory, offer appropriate alternatives against DRAM in clouds with the advantages of larger capacity, lower purchase cost, and promising performance. However, cloud services suffer serious implementation inconvenience and performance degradation when using hybrid DRAM and Optane memory. This article proposes FlexHM, a practical system to manage transparent heterogeneous memory resources and flexibly optimize memory access performance for all VMs, containers, and native applications. We present an open-source prototype of FlexHM in Linux with several main contributions. First, FlexHM raises a novel two-level NUMA design to manage DRAM and Optane memory as transparent main memory resources. Second, FlexHM provides flexible and efficient memory management, helping optimize memory access performance or save purchase costs of memory resources for differential cloud services with customized management strategies. Finally, the evaluations show that cloud workloads using 50% Optane slow memory on FlexHM can achieve up to 93% of the performance when using all-DRAM, and FlexHM provides up to 5.8× improvement over the previous heterogeneous memory system solution when workloads use the same ratio of DRAM and Optane memory.
Numerical Simulation of the Performance of Single Qubit Gates for Trapped Ions

Finite gate errors limit performance of modern quantum computers. In this work, we study single qubit gate fidelities for trapped ions. For this we have numerically solved Schrödinger equation using full Hamiltonian of the system for one, two, three and four ions. This approach allows us to analyze gate errors beyond the LambDicke approximation and to take into account not only a finite occupation of the phonon modes, but also the effects related to the ion–phonon entanglement. As a result, we show how infidelity of the global single qubit gates depend on the initial phonon mode occupations, the Lamb–Dicke parameter, Rabi frequency and the number of ions.
A multi-level weighted concept drift detection method

The concept drift detection method is an online learner. Its main task is to determine the position of drifts in the data stream, so as to reset the classifier after detecting the drift to improve the learning performance, which is very important in practical applications such as user interest prediction or financial transaction fraud detection. Aiming at the inability of existing drift detection methods to balance the detection delay, false positives, false negatives, and space–time efficiency, a new level transition threshold parameter is proposed, and a multi-level weighted mechanism including "Stable Level-Warning Level-Drift Level" is innovatively introduced in the concept drift detection. The instances in the window are weighted in levels, and the double sliding window is also applied. Based on this, a multi-level weighted drift detection method (MWDDM) is proposed. In particular, two variants which are MWDDM_H and MWDDM_M are proposed based on Hoeffding inequality and Mcdiarmid inequality, respectively. Experiments on artificial datasets show that MWDDM_H and MWDDM_M can detect abrupt and gradual concept drift faster than other comparison algorithms while maintaining a low false positive ratio and false negative ratio. Experiments on real-world datasets show that MWDDM has the highest classification accuracy in most cases while maintaining good space time efficiency.
Deep learning based conference program organization system from determining articles in session to scheduling

• An efficient conference program is automatically prepared for the participants and conference owners. • A different clustering approach is proposed with as equal elements as possible in each cluster. • An approach considering article content similarity in addition to word distributions is proposed for topic modeling. • A method is proposed for the scheduling problem by assigning similar-themed sessions to different non-parallel timeslots. • An improvement is achieved with the proposed method compared to the real conference programs. It is very important to create the conference programs correctly in terms of timing and content by preventing problems such as being of articles that do not have a common topic with each other in the same sessions, the parallel of the sessions containing articles on the same topic. It greatly affects the efficiency of conference for participants. Currently, conference programs are organized manually. Considering the conference scope and the number of articles in that conference, it is a difficult and time-consuming process. In this study, an automatic solution to this problem is presented. The use of the SBERT method is provided a more accurate calculation of article similarities compared to baseline methods and is increased the success of other stages. Unlike classical clustering methods, an approach that clusters in such a way that there are equal numbers of data points in the clusters is proposed. In order to find the topic of the clusters determined as sessions, a topic determination approach is proposed that takes into account both keyword and article content similarities. Furthermore, with the proposed approach for session scheduling, the conference program has been planned more effectively by considering the parallel sessions. The ICTAI conference has been chosen to test the proposed approach. The proposed program is compared with both the real program and the programs created using Word2vec and Glove methods. With the proposed program, 10% improvement is achieved in terms of session similarity. In addition, parallel sessions are better planned with no conflicts compared to the real program.
NRKM: News Recommendation Based on Knowledge Graph with Multi-View Learning

News recommendation is necessary to help users find interesting news, improve their experience, and alleviate information overload. Accurately learning news and user representations is a key task in news recommendation systems. News texts usually contain rich entities, however existing recommender systems ignore the importance of news entities. In order to effectively alleviate the above problems, we design a multi-view news recommendation system based on knowledge graph. First, with news headlines, summaries, categories, and knowledge graph features, we learn news representations using a graph interactive attention network and a multi-head attention mechanism. Second, we combine a recurrent neural network and an interactive attention network to learn user representations from user historical click news records. Finally, predict the probability that the user will click on the candidate news. This method effectively alleviates the problem that the current news recommendation model has a weak ability to capture news representations and user interest representations. Experiments on real datasets show that this method can effectively improve the performance of news recommendation.
Capsule Network Over Pre-Trained Language Model and User Writing Styles for Authorship Attribution on Short Texts

Authorship Attribution (AA) is a sub-field of Authorship Analysis and text classification, attributing a text to the correct author among a closed set of potential authors. Since short texts usually contain less information about the author, authorship attribution on short texts is often more challenging than authorship attribution on long texts. Recently, the widespread use of pre-trained language models has greatly improved the accuracy of text classification tasks. In this paper, we propose a model which uses the pre-trained language model BERTweet with capsule networks, to solve the authorship attribution on tweets. BERTweet is the first large-scale domain-specific pre-trained language model for English tweets, which can generate high-quality sentence representations of tweets. We combine BERTweet with capsule networks which are particularly powerful at capturing deep features of sentence representations. Thus, both BERTweet and capsule help us achieve remarkable improvements on AA tasks. We also incorporate user writing styles into our model. We design new architectures of capsule networks which combine multiple capsule layers, for generating representations from tweets and user writing styles, improving prediction accuracy and robustness. Our experimental results show that our BERTweet_Capsule_UWS combination shows the state-of-the-art result on the known tweet AA dataset.
Arabic Question Classification using Deep Learning

Question classification is one of the essential tasks in Natural Language Processing (NLP). Not many studies have addressed the question classification task where the language is Arabic. This study proposes a language-specific model structure for assessment question classification in Arabic. Approaches proposed in the literature that address Arabic assessment question classification typically have used traditional machine learning techniques. Therefore, the model performance is constrained by the inherent limitations of these traditional approaches. This study addresses this gap and proposes a language-specific feature representation combined with a deep learning model structure. The proposed model uses a modified term frequency-inverse document frequency (M-TF-IDF) term weighting combined with Arabic word embeddings using ArELMo for the question feature representations. The proposed model combines Bi-Directional Gated Recurrent Units (BiGRU) with an attention mechanism and a convolution neural network. The obtained experimental results show the efficacy of the proposed approach which outperforms previous studies on Arabic assessment question classification.
Analysis of the Influence of Innovative Teaching Management Mode in Universities on Students’ One-to-One Training and Psychotherapy

This study expects to investigate and verify the intervention effect of the university's innovative teaching management model on college students' resilience by implementing the university's innovative teaching management model for college students with low psychological resilience.The scientific scale is used to investigate the current level of college students' psychological resilience, and the development characteristics of college students' psychological resilience are obtained through statistical analysis. Based on the theoretical analysis of the application of psychological resilience intervention, combined with the theory of one-to-one tutoring and operational techniques, the university's innovative teaching management mode scheme is designed. The design adopts a quasi-experimental pre-experimental test to investigate and explore the intervention effect system of the university's innovative teaching management mode on college students' psychological resilience. In the intervention, each unit of activity is carried out in strict accordance with the established plan, and records and reflections are made at the end. One-to-one and face-to-face qualitative interviews were conducted with the subjects, and qualitative data were collected for qualitative analysis. Results/Discussion. Compared with the control group that did not receive the intervention, the psychological resilience of the subjects in the experimental group was significantly improved after receiving the intervention of the university's innovative teaching management model. The university's innovative teaching management model has a good intervention effect on the resilience of college students. The university's innovative teaching management model scheme compiled in this study integrates a variety of psychotherapy methods and combines one-to-one psychological counseling frameworks and techniques. It is an effective and easy-to-implement intervention scheme for college students' psychological resilience intervention.
Research on Camouflaged Human Target Detection Based on Deep Learning

In the task of camouflaged human target detection, the target is highly integrated with the complex environment background, which is difficult to identify and leads to false detection and missed detection. A detection algorithm MC-YOLOv5s is proposed for the characteristics of camouflaged targets. The algorithm takes YOLOv5s as the basic framework. First, a multispectral channel attention module is embedded in the backbone feature extraction network, which enhances the network's ability to extract camouflaged target features, weakens the attention to the surrounding background, and effectively improves the algorithm's antibackground interference. Second, the original upsampling operation is replaced by a lightweight general upsampling operator to achieve effective fusion of high-resolution low-level feature maps and low-resolution high-level feature maps. Finally, the K-means++ clustering method is used to optimize the anchor boxes of the dataset target, and the sizes of the generated priori boxes are allocated to each detection layer, which increases the matching degree between the priori boxes and the actual target boxes, and further improves the detection accuracy of the algorithm. The training and verification were carried out on the military camouflaged personnel dataset (MCPD), the precision (P), recall (R), and mean average precision (mAP) of the MC-YOLOv5s algorithm reached 97.4%, 86.1%, and 94%, respectively. Compared to the original YOLOv5s model, mean average precision (mAP) is increased by 3.7 percentage points. The improved algorithm has better detection effect, is more sensitive to camouflaged targets, and achieves accurate positioning and identification of camouflaged human targets. If the proposed MC-YOLOv5s is applied to personnel search and rescue in complex battlefields and natural disaster environments, it can greatly improve personnel search and rescue efficiency and life survival rate, and reduce the consumption of human and material resources in rescue.
Application of Optimized BP Neural Network Model for the Training Load Prediction in Physical Education Teaching

Load prediction is mandatory for analyzing the working condition of the teachers when new courses are to be introduced or change in the learning environment. During complex environment, the seasonal prediction about sports has to be analyzed and designs the physical education training program for the students to increase the fitness of the students. In this study, load prediction in physical education is performed with the implementation of a back propagation neural network model with the support of genetic algorithm and termed as BPNNGA. The proposed prediction algorithm is compared with the existing algorithms such as back propagation neural network, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -means neural network, and random forest algorithm. The results proved that the proposed algorithm outperforms the existing algorithms with the accuracy percentage of 99%.
Research on Data News Propagation Path Based on the Big Data Algorithm

News propagation originates from a person/location, dwelling with an event that grabs significance. News data propagation relies on telecommunication and big data for precise content distribution and mitigation of false news. Considering these factors, the event-dependent data propagation technique (EDPT) was introduced to improve the data precision. These data refer to the news information originating and propagating from digital media. The data analysis considers the external factors for fake information and precise projection medium for preventing multiviewed false circulations. In this technique, the liability of the information is analyzed using a linear pattern support vector classifier. The data modification and propagation changes are classified based on liability information across the circulation time. The SVM classifier identifies these two factors with close liability validation, preventing false data. The data accumulation and analysis rates for the abovementioned classifications are performed in the propagation process using the classifier hyperplane. This plane is updated from the previous propagation point from which the events are identified. The proposed technique’s performance is analyzed using propagation accuracy, precision, false rate, time, and rate.
The Role UX Design Attributes Play in the Perceived Persuasiveness of Contact Tracing Apps

Contact tracing apps (CTAs) were deployed worldwide in 2020 to combat COVID-19. Due to their low uptake, a growing amount of empirical research is being conducted to understand the factors that drive their adoption. For CTAs to be adopted, users must, first and foremost, find them persuasive. However, there is little research to understand the role user experience (UX) plays in their perceived persuasiveness. Consequently, we conducted an online study on Amazon Mechanical Turk among Canadian and American residents (n = 446) to investigate the most important UX design attributes associated with the perceived persuasiveness of CTAs. The study was based on two app designs (control and persuasive), each of which comprises three use cases: no exposure, exposure, and diagnosis report interfaces. One interface (screenshot) was randomly presented to a participant to view and provide their responses on the perceived UX design attributes and perceived persuasiveness of the interface. In the overall model, we found that perceived usefulness is the most important and consistent UX design attribute that influences perceived persuasiveness (β = 0.29, p &lt; 0.001), followed by perceived trustworthiness (β = 0.24, p &lt; 0.001) and perceived privacy protection (β = 0.16, p &lt; 0.05). Respectively, the three predictors were consistently significant in two-thirds, half, and one-third of the 12 submodels based on app design, adoption status, and country of residence. The relationships regarding the persuasive designs are more likely to be significant, with the variance of the target construct explained by the predictors ranging from 71% to 89% compared with 54% to 69% for the control designs. The three significant attributes will help designers know which UX design attributes to focus on when designing CTAs for future epidemics. More importantly, in predictive modeling, if their ratings are known, they hold potential in predicting new users’ responsiveness to multiple persuasive strategies/messages featured in behavior-change support systems.
THE LOGIC OF HYPERLOGIC. PART B: EXTENSIONS AND RESTRICTIONS

Abstract This is the second part of a two-part series on the logic of hyperlogic, a formal system for regimenting metalogical claims in the object language (even within embedded environments). Part A provided a minimal logic for hyperlogic that is sound and complete over the class of all models. In this part, we extend these completeness results to stronger logics that are sound and complete over restricted classes of models. We also investigate the logic of hyperlogic when the language is enriched with hyperintensional operators such as counterfactual conditionals and belief operators.
Privacy-Preserving Vertical Collaborative Logistic Regression without Trusted Third-Party Coordinator

Collaborative learning is an emerging distributed learning paradigm, which enables multiple parties to jointly train a shared machine learning (ML) model without causing the disclosure of the raw data of each party. As one of the fundamental collaborative learning algorithms, privacy-preserving collaborative logistic regression has recently gained attention from industry and academia, which utilizes cryptographic techniques to securely train joint logistic regression models across data from multiple parties. However, existing schemes have high communication and computational overhead, lose the ability to deal with high-dimensional sparse samples, cut down the accuracy of the model, or exist the risk of leaking private information. To overcome these issues, considering vertically distributed data, we propose a privacy-preserving vertical collaborative logistic regression ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </math> VCLR) based on approximate homomorphic encryption (HE), which enables two parties to jointly train a shared model without a trusted third-party coordinator. Our scheme utilizes batching method in approximate HE to encrypt multiple data into a single ciphertext and enable a parallel processing through single instruction multiple data (SIMD) manner. We evaluate our scheme by using three publicly available datasets, the experimental results indicate that our scheme outperforms existing schemes in terms of training time and model performance.
Time series data anomaly detection based on LSTM-GAN

With the improvement of modern technology, a large number of time series data have been produced. The anomaly detection of time series data can provide relevant information for key situations faced by various fields. This paper proposed an unsupervised temporal anomaly detection method based on generation countermeasure network. In this model, Wasserstein distance is used instead of the original measurement method, and LSTM is used as the basic network of GAN. The model uses the reconstruction loss of the generator and the loss of the discriminator to define the anomaly function to judge the anomaly. This paper uses real world time series data sets involving various fields to evaluate the model. Experiments show that the model is effective in anomaly detection of time series data.
Lightweight graph convolutional network with fusion data for skeleton based action recognition

Action recognition methods based on human skeletons can clearly express human actions. We present a lightweight graph convolutional network with various streams of data, given the network’s computational complexity and high computational cost of current mainstream human action recognition networks. First, four characteristic data streams are fused using a multi-stream data fusion algorithm, and the best result can be produced with only one training session, minimizing the network’s computational complexity. Second, a non-local graph convolution module based on the graph convolutional network is designed to collect the image’s global information and increase action recognition accuracy. Finally, the spatial Ghost graph convolution module and the temporal Ghost graph convolution module are intended to minimize the network’s computational complexity even more. On the action recognition datasets NTU60 RGB+D and NTU120 RGB+D dataset Our methods achieve highly competitive performance, with average precision of 96.4 and 87.5 percent respectively.
Unpacking the resource impacts of digitally-mediated domestic practices using resource trace interviewing

The home has been the subject of investigation in the social sciences and interaction design communities for decades. This has been driven not least by a wish to understand technology, energy demand, and how it might be understood in terms of social practices. In this paper, we reflect on several studies that have sought to capture this relationship. We introduce an evolving methodological approach we term 'Resource Trace Interviewing' that extends interview practice using visualizations of fine-grained quantitative data from sensors and software deployed in the home. By facilitating fuller accounts and joint sense-making between participants and researchers, this method better reveals the patterns of technology and energy use in the digitally connected home, and how this in turn relates to domestic practices. We reflect, for the first time, on the strengths and limitations of this approach as a guide to others studying similar socio-technical settings.