Reviewerly/exHarmony huggingface dataset:
Formal Methods for Characterization and Analysis of Quality Specifications in Component-based Systems

Component-based design paradigm is of paramount importance due to prolific growth in the complexity of modern-day systems. Since the components are developed primarily by multi-party vendors and often assembled to realize the overall system, it is an onus of the designer to certify both the functional and non-functional requirements of such systems. Several of the earlier works concentrated on formally analyzing the behavioral correctness, safety, security, reliability and robustness of such compositional systems. However, the assurance for quality measures of such systems is also considered as an important parameter for their acceptance. Formalization of quality measures is still at an immature state and often dictated by the user satisfaction. This paper presents a novel compositional framework for reliable quality analysis of component-based systems from the formal quality specifications of its constituent components. The proposed framework enables elegant and generic computation methods for quality attributes of various component-based system structures. In addition to this, we provide a formal query-driven quality assessment and design exploration framework which enables the designer to explore various component structures and operating setups and finally converge into better acceptable systems. A detailed case-study is presented over a component-based system structure to show the efficacy and practicality of our proposed framework.
Teaching through virtual tools and its effect on the perception of student satisfaction

In this context of virtualization, the educational sector has seen the need to make use of technological advances, for this reason it is important to know the perception of students, after having fully adapted distance learning through tools. virtual, which have allowed teachers and students to maintain the pedagogical link at a distance, either through the virtual classroom or through the use of simulation software. In this sense, the objective of this article is to identify the perception of teaching through virtual tools in university students and determine its level of effect or relationship in student satisfaction. This research is approached from a qualitative approach using the Likert measurement method and a content analysis methodology using virtual instruments. The results of the study indicate that 92.9% and 89.3% of the students are satisfied, these results focus on the indicators "absolves the questions asked regarding the use of virtual tools" and "knowledge shown by the teacher in the development of the sessions through virtual tools”. Likewise, the correlational analysis, through Spearman's Chi square test, establishes that there is a high relationship or significant effect of 0.850 between the perception of teaching through virtual tools with student satisfaction.
AN AUTOMATED SYSTEM TO SAVE YOUR LIFE

We are satisfied to introduce Save Your Life that will help the issues of patients assuming they can't get meetings with the specialists. This application is a stage to give assistance to the patients in having a superior discussion with the specialists. The patients would have to enlist themselves on the application. Everyday capacities like patient enrollment, checking blood donation center, overseeing affirmation, and by and large administration of different offices can be effectively performed with higher precision after the establishment of medical clinic applications. it is a coordinated mechanized framework planned and customized to manage everyday tasks and the executives of the medical clinic exercises. The program can take care of inpatients, short term patients, records, data set medicines, status sickness, billings in the drug store, and labs. In this project we will give the additional office to store the report in the information base and make it accessible from anyplace on the planet. The modules of the Save Your Life application are easy to use and simple to get to. It has a typical easy to understand interface having a few modules. The authorities can use these modules in their cycles with practically no problem and make the most ideal utilization of Save Your Life. Keywords: Doctor suggestion, Patient direct Communicate, Patient gave feedback, Hospitality, Administrator.
Analysis and experience of the Ministry of Education's supply and demand matching employment education project (first phase)

This paper describes the process of the first phase of the Ministry of Education's supply and demand docking employment and education project collection guide, enterprise apply, release guide, school apply, company review and Ministry of Education review and approval process, and make a simple data analysis of classification, participating universities and project types and listed the experience of Kailai Technology (Shenzhen) Co., Ltd. participating in the project. Thanks to the Ministry of Education, the National College Graduates Employment and Entrepreneurship Steering Committee, the companies, relevant colleges and it’teachers worked together, which lasted for more than 10 months, and successfully completed the first phase of the Ministry of Education's supply and demand docking employment and education project.
Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data

We consider the problem of a Parameter Server (PS) that wishes to learn a model that fits data distributed on the nodes of a graph. We focus on Federated Learning (FL) as a canonical application. One of the main challenges of FL is the communication bottleneck between the nodes and the parameter server. A popular solution in the literature is to allow each node to do several local updates on the model in each iteration before sending it back to the PS. While this mitigates the communication bottleneck, the statistical heterogeneity of the data owned by the different nodes has proven to delay convergence and bias the model. In this work, we study random walk (RW) learning algorithms for tackling the communication and data heterogeneity problems. The main idea is to leverage available direct connections among the nodes themselves, which are typically "cheaper" than the communication to the PS. In a random walk, the model is thought of as a "baton" that is passed from a node to one of its neighbors after being updated in each iteration. The challenge in designing the RW is the data heterogeneity and the uncertainty about the data distributions. Ideally, we would want to visit more often nodes that hold more informative data. We cast this problem as a sleeping multi-armed bandit (MAB) to design a near-optimal node sampling strategy that achieves variance-reduced gradient estimates and approaches sub-linearly the optimal sampling strategy. Based on this framework, we present an adaptive random walk learning algorithm. We provide theoretical guarantees on its convergence. Our numerical results validate our theoretical findings and show that our algorithm outperforms existing random walk algorithms.
P3S: A High Accuracy Probabilistic Prediction Processing System for CNN Acceleration

Convolutional Neural Networks (CNNs) achieve state-of-the-art performance for perception tasks at the cost of billions of computational operations. In this paper, we propose a probabilistic prediction processing system, dubbed P3S, to eliminate redundant compute-heavy convolution operations by predicting whether output activations are zero-valued. By exploiting the probability characteristic of Gaussian-like distributed activations and weights in CNNs, P3S calculates the partial convolution across values greater than a standard deviation-related threshold, to predict the ineffectual output activations. P3S skips remaining convolutions and sets outputs to zero in advance if output activations are predicted to be zero. P3S reduces 67% computations within 0.2% accuracy loss and does not even require retraining or fine-tuning CNNs. We further implement a P3S-based CNN accelerator that achieves 2.02x speedup and 2.23x energy efficiency on average over the traditional accelerator. Compared with the state-of-the-art prediction-based accelerator with 3% accuracy degradation, our P$^3$S yields up to 1.49x speedup and 1.69x energy efficiency.
Few-shot Subgoal Planning with Language Models

Pre-trained large language models have shown successful progress in many language understanding benchmarks. This work explores the capability of these models to predict actionable plans in real-world environments. Given a text instruction, we show that language priors encoded in pre-trained language models allow us to infer fine-grained subgoal sequences. In contrast to recent methods which make strong assumptions about subgoal supervision, our experiments show that language models can infer detailed subgoal sequences from few training sequences without any fine-tuning. We further propose a simple strategy to re-rank language model predictions based on interaction and feedback from the environment. Combined with pre-trained navigation and visual reasoning components, our approach demonstrates competitive performance on subgoal prediction and task completion in the ALFRED benchmark compared to prior methods that assume more subgoal supervision.
Initial Clustering Based on the Swarm Intelligence Algorithm for Computing a Data Density Parameter

To improve the accuracy and efficiency of cluster startup using data density parameters, the author proposes a large data cluster extraction algorithm based on a herd intelligence algorithm. Since clustering to initiate data density parameters is primarily data mining, the author explores data mining clustering based primarily on herd intelligence algorithms. First, the obscure c-key cluster algorithm in the clustering algorithm is analyzed, and then the hybrid jump algorithm in the sub-heuristic herd intelligence optimization technology is optimized in the case of a few parameters by combining the obscure c-means cluster algorithm. The simulation results show that the convergence speed of the fuzzy C-means clustering algorithm and hybrid leapfrog algorithm is slow; the convergence rate of the PSO-FCM algorithm has been improved. Since the fusion algorithm requires fewer adjustment parameters, the cluster centers can be obtained more accurately and quickly with strong robustness and fast convergence. Compared with other algorithms, the fusion algorithm proposed by the author has the best performance in clustering effect, accuracy, convergence rate, and robustness. It is proved that the swarm intelligence algorithm can effectively perform density parameter initialization clustering on computational data.
Assessment for the Quality of the Distance Education Delivered by the Emirati Public Schools during the Coronavirus Crisis

This study aimed to assess the quality of the distance education delivered by the Emirati public schools during the Coronavirus crisis.The researcher adopted the descriptive analytical approach.He designed and used a five point Likert questionnaire to obtain data from the sampled.He designed the questionnaire.The population consists from all the teachers who taught during the Coronavirus crisis in Emirati public schools.The sample was chosen purposively.It consists from 150 teachers chosen from twelve (12) Emirati public schools.Four schools of them are located in Abu Dhabi, four schools of them are located in Dubai and four schools of them are located in Sharjah.The questionnaire forms were passed by hand.All the forms were filled and retrieved.They are valid for the analysis.The SPSS program was used.The researcher found that the quality of the distance education delivered by the Emirati public schools during the Coronavirus crisis is high.That's because the extent of meeting the standards related to the design and development of curricula, student assessment, infrastructure, student services, teachers and teaching-learning process are high.He recommends encouraging teachers and administrators to meet the standards of quality in education.Financial and moral incentives must be set for encouraging them to meet such standards.
‘Lex Cryptographi(c)a,’ ‘Cloud Crypto Land’ or What? – Blockchain Technology on the Legal Hype Cycle

Based on the reception of DLT/blockchain, this article argues that legal discourse is subject to hype cycle dynamics. Thinking in hype cycle categories provides a structured way for analysing the legal implications of a particular innovation. This critical engagement with enthusiasts, sceptics and pragmatists through the different stages may help to present a more realistic picture of DLT/blockchain's potential from a legal perspective in the short and medium term. Consequently, this article discusses the potential for disruption to the legal system envisaged by enthusiasts at the height of inflated expectations, attempts to deconstruct the arguments levelled at the technology by its detractors during the trough of disillusionment, charts the emerging legal landscape that seeks to harness the potential of DLT/blockchain up the slope of enlightenment, and concludes by risking a glimpse towards the plateau of productivity.
On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model

In this paper, we study the generalization performance of overparameterized 3-layer NTK models. We show that, for a specific set of ground-truth functions (which we refer to as the "learnable set"), the test error of the overfitted 3-layer NTK is upper bounded by an expression that decreases with the number of neurons of the two hidden layers. Different from 2-layer NTK where there exists only one hidden-layer, the 3-layer NTK involves interactions between two hidden-layers. Our upper bound reveals that, between the two hidden-layers, the test error descends faster with respect to the number of neurons in the second hidden-layer (the one closer to the output) than with respect to that in the first hidden-layer (the one closer to the input). We also show that the learnable set of 3-layer NTK without bias is no smaller than that of 2-layer NTK models with various choices of bias in the neurons. However, in terms of the actual generalization performance, our results suggest that 3-layer NTK is much less sensitive to the choices of bias than 2-layer NTK, especially when the input dimension is large.
Diagnostic utility of cellblock technique in reporting body fluid cytology

Introduction: Body fluids are processed by routine cytology techniques like preparing a slide and staining it with PAP stain. By this method it is difficult to give definitive diagnosis particularly to the cases where some abnormal cells seen suspicious of malignancy. In our study we utilized cell lock technique for reporting body fluid cytology. Objectives: Our objective is to prepare cellblock from body fluid, to use it for diagnosis of benign and malignant lesions and also to check advantage and drawbacks of cell block technique over the conventional cytology examination. Materials and Method: This study was carried out in medical collage hospital in Department of Pathology. We selected total 52 body fluids with good cellularity for cell block preparation. Result: Out of 52 cases 45 were diagnosed as benign and 7 as malignant using cell block technique. Conclusion: We conclude with findings that cell block technique is better than routine cytology technique. By cell block technology tissue architecture can be well appreciated and definite diagnosis can be given. Also, we can utilize cell block for definitive diagnostic methods like Immunohistochemistry.
How Tempering Fixes Data Augmentation in Bayesian Neural Networks

While Bayesian neural networks (BNNs) provide a sound and principled alternative to standard neural networks, an artificial sharpening of the posterior usually needs to be applied to reach comparable performance. This is in stark contrast to theory, dictating that given an adequate prior and a well-specified model, the untempered Bayesian posterior should achieve optimal performance. Despite the community's extensive efforts, the observed gains in performance still remain disputed with several plausible causes pointing at its origin. While data augmentation has been empirically recognized as one of the main drivers of this effect, a theoretical account of its role, on the other hand, is largely missing. In this work we identify two interlaced factors concurrently influencing the strength of the cold posterior effect, namely the correlated nature of augmentations and the degree of invariance of the employed model to such transformations. By theoretically analyzing simplified settings, we prove that tempering implicitly reduces the misspecification arising from modeling augmentations as i.i.d. data. The temperature mimics the role of the effective sample size, reflecting the gain in information provided by the augmentations. We corroborate our theoretical findings with extensive empirical evaluations, scaling to realistic BNNs. By relying on the framework of group convolutions, we experiment with models of varying inherent degree of invariance, confirming its hypothesized relationship with the optimal temperature.
Bolstering user authentication

In most information systems today, static user authentication is accomplished when the user provides a credential (for example, user ID and the matching password). However, passwords appear to be the most insecure authentication method as they are vulnerable to attacks chiefly caused by poor password hygiene. We contend that an additional, non-intrusive level of security can be achieved by analyzing keystroke biometrics and coming up with a unique biometric template of a user's typing pattern. The paper proposes a new model for representing raw keystroke data collected when analyzing typing biometrics. The model is based on fuzzy sets and kernel functions. The corresponding algorithm is developed. In the static authentication problem, our model demonstrated relatively higher performance than some classic anomaly-detection algorithms, such as Mahalanobis, Manhattan, nearest neighbor, outlier counting, neural network, and the support-vector machine.
Resilient Recovery for In-vehicle Sensor Attack by LSTM

&lt;div&gt;Uncertain cyber-attack detection and effective recovery after attacks on in-vehicle sensors are a challenge in the domain of the Internet of Vehicles (IoV). Currently, modern automatic vehicles are equipped with a variety of low-cost sensors with relatively limited computation capabilities, which constrains state-of-the-art solutions. As a result of the embedded intelligence chip in the IoV devices, deep learning algorithms can be applied to provide a novel solution for sensor attack detection and recovery. This paper is concerned with the resilient recovery after attacking the in-vehicle reference-free sensor. Toward this aim, we firstly define a theoretical model to describe automatic ground vehicles with vehicular yaw rate and local state. Then, classical attack models are established to transfer the challenge to a convex optimization issue. Finally, we consider evaluating the performance of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and LSTM optimized by CNN (CNN-LSTM) for the general in-vehicle sensor units attack detection and recovery. The evaluation metrics for comparison experiments are accuracy, Receiver Operating Characteristic (ROC) curve, and the confusion matrix. The proposed CNN-LSTM model can obtain 95% testing accuracy, which is better than 56% for CNN and 79% for LSTM in detecting attacks. The predicting experimental results support that the LSTM is suitable for recovering signals of in-vehicle sensors.&lt;/div&gt;
Image blind deblurring network with back projection feature fusion

Abstract Aiming at the problem of image motion blur caused by hand-held camera jitter and object motion in the process of collecting photos, a generative adversarial network (GAN) based on feature fusion of back projection is proposed for blind image deblurring. Firstly, the generator network is established by using U-Net structure, and a feature fusion residual block based on back projection is designed according to the error feedback principle, which solves the problem of saving spatial information in U-Net structure. Secondly, the self-attention module is introduced into the generator network to extract the feature map that pays more attention to detail. Finally, the combination of perceptual loss, mean square error loss and relative generative adversarial loss effectively alleviates the mode collapse problem of traditional GAN and improves the stability of model training. The experimental results show that the peak signal to noise ratio (PSNR) and structural similarity (SSIM) of this method on GoPro data set are 30.183dB and 0.941 respectively, and 26.962 and 0.837 on the Kohler dataset, with the shortest running time, which are better than the existing mainstream methods. The restored image is clearer in subjective vision and richer in texture details, which can effectively improve the image deblurring effect.
Can Blockchain Improve Healthcare Management?

The next generation of healthcare will be decentralized, mobilized, and personalized.Instead of the blunt instruments of the past, we will be giving patients more precise medications and therapies.
Analisis Sentimen Gofood Berdasarkan Twitter Menggunakan Metode Naïve Bayes dan Support Vector Machine

The Covid-19 pandemic in Indonesia has an impact on every sector of life, including the economy. The government implements social activities that make people have to carry out activities at home. Because of this, humans choose to do everything digitally, including ordering food. With the application of public interest in ordering food online, the income of one of the food orders, namely Gojek (Gofood) has increased. However, Gofood has many pros and cons in the community. In this case, many people give their opinion about the use of social media, especially twitter. The purpose of this study was to analyze public opinion on the performance of Gojek (Gofood) in Indonesia. The grouping into three classes, namely positive, negative and neutral classes were tested using the Naïve Bayes and SVM methods and compared the two methods. The analysis of public sentiment regarding Gofood on Twitter resulted in 92.8% worthy neutral, 5.2% worthy positive and 2.0% worthy negative. Comparing the accuracy results, the Support Vector Machine method has greater accuracy than the Naïve Bayes method, with the Support Vector Machine accuracy values of 83% and 98.5%, while the Nave Bayes accuracy values are 74.6% and 91.5% respectively.
PENGEMBANGAN MEDIA PEMBELAJARAN APLIKASI YUK WUDU UNTUK MENINGKATKAN ANTUSIASME PEMBELAJARAN PAI BAB WUDU DI SD

This research is motivated by a condition where there are still many students who have misunderstanding of the ablution material, both from the intention of ablution, the procedure/sequence for ablution, and prayer after ablution. This is related to the learning conditions, namely the lack of enthusiasm from students. The purpose of this study was to increase the enthusiasm of elementary school students in PAI learning, especially the ablution chapter. The Yuk Wudu application learning media was developed as an effort to increase enthusiasm and overcome mistakes when learning ablution material. The research method used is descriptive qualitative with a D&amp;D model and the research procedure is ADDIE. The subjects of this study were students of class II SDN Panyingkiran II, totaling 28 people. The source of the data obtained in this study came from the results of interviews and direct observations of students. As for product validation, it is carried out by PAI teachers. The results of product validity by PAI teachers obtained results of 95% with a very valid / very feasible category. Based on the results of the trials that have been carried out, there was an increase in the enthusiasm of students when learning the wudu chapter PAI material using the Yuk Wudu application.