Reviewerly/exHarmony huggingface dataset:
The Effect of Microsoft PowerPoint Interactive Learning Media Application on Reading Comprehension Skills of 3rd Grade Elementary School Students for Indonesian Language Subjects

The purpose of this study was to analyze the effect of Microsoft PowerPoint interactive learning media application on the reading comprehension skills of 3rd grade elementary school students in Indonesian. This research method uses a quasi-experimental method with a Nonequivalent Control Group Design approach. The population of the study was the 3rd grade students of SDN Duren Sawit 07 Jakarta, totaling 60 students, divided into an experimental class and a control class. The research instrument used a pretest and posttest. While the data analysis technique used descriptive quantitative. The results of this study indicate that the average value of the experimental class at the pre-test = 56.67 has increased when the post-test is carried out = 82.00, while the results of the average value of the control class at the pre-test = 53, 00 experienced an increase when the post-test was carried out = 73.33. The results of the t-test in this study obtained tcount as much as 3.7582 at a significant level = 0.05 and degrees of freedom (dk) = 2.0032. Because the value of tcount&gt;ttable which is 3.7582 &gt; 2.003 so that it can be achieved H0 is rejected, meaning that there is a difference in the improvement of students' reading comprehension skills using the Microsoft PowerPoint interactive learning media application using only conventional media.
Adaptively Secure Single Secret Leader Election from DDH

Single Secret Leader Election protocols (SSLE, for short) allow a group of users to select a random leader so that the latter remains secret until she decides to reveal herself. Thanks to this feature, SSLE can be used to build an election mechanism for proof-of-stake based blockchains. In particular, a recent work by Azouvi and Cappelletti (ACM AFT 2021) shows that in comparison to probabilistic leader election methods, SSLE-based proof-of-stake blockchains have significant security gains, both with respect to grinding attacks and with respect to the private attack. Yet, as of today, very few concrete constructions of SSLE are known. In particular, all existing protocols are only secure in a model where the adversary is supposed to corrupt participants before the protocol starts -- an assumption that clashes with the highly dynamic nature of decentralized blockchain protocols. In this paper we make progress in the study of SSLE by proposing new efficient constructions that achieve stronger security guarantees than previous work. In particular, we propose the first SSLE protocol that achieves adaptive security. Our scheme is proven secure in the universal composability model and achieves efficiency comparable to previous, less secure, realizations in the state of the art.
Bibliographic review of nursing care of hospitalized patients with stroke

Introduction: Cerebrovascular accidents (CVA) are pathologies that generate functional limitation among people who do not die, which translates into frailty and worsening of the patient's condition and a significant demand for healthcare. For this reason, protocols and procedures for sanitary action have been developed. However, little has been said about the importance of care based on scientific evidence during the stabilization phase of patients who have suffered a stroke after exceeding critical hours. It is at this time, where the nursing care and educational role plays a leading role in early recovery and the prelude to the rehabilitation period. Objectives: To review the available scientific evidence of the meaninful care, from a nursing point of view, for a stabilized patient following a stroke. Methods: A bibliographic review was carried out in 5 databases of Health Sciences (PubMed, SciELO, IBECS, Cochrane, Cuiden) and also with other electronic resources, from which several articles were acceptable for the review. Results: A total of 2752 articles were found, of which, 18 were selected for reviewing after the exclusion criteria was applied. Conclusions: The importance for nursing approach to complications following a stroke is shown. Nursing interventions should be prompted for recovery as early as possible. It is also a key point the role as teacher for patients and their families.
Fusing Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity for POI Recommendation

&lt;p&gt;Nowadays, POI recommendation has been a hot research area, which are almost based on incomplete social relationships and geographical influence. However, few research simultaneously focuses on the refined social relationship and the user deep implicit topic similarity under a reachable region. Under this background, a novel Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity mining under a Reachable Region for POI Recommendation (DDR-PR) is proposed. DDR-PR first adopts kernel density estimation to compute the user checking-in reachable area. Under the reachable area, the combined relationship similarity based on the link relationship and common check-in social relationship is computed out. Then, the deep implicit interest topic similarity between users is mined out adopting the proposed topic model RTAU-TCP. We formulate the combined relationship similarity and implicit interest topic similarity as two regularization terms to incorporate into matrix factorization, which can recommend new POIs for a user under his or her reachable area. Extensive experiments prove the superiority of DDR-PR.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Solving Raven's Progressive Matrices Using RNN Reasoning Network

Although enormous progress has been made by Deep Neural Networks (DNN) in basic perception tasks, they are long criticized for lack of reasoning quality and interpretability. Raven's Progressive Matrices (RPMs) are standard tests for assessing human Intelligence Quotient, also acting as a tool to evaluate Artificial Intelligence. Existing methods of pure DNN combining perception and reasoning are difficult to confirm DNN's ability of logical reasoning. Hybrid DNN methods reasoning by algorithms, not DNN. Existing methods use end-to-end training, while perception and reasoning are detached modules functionally and neurologically. Here we propose a method of separating visual perception by a perfect convolutional neural network perception module abstracting RPMs to panel-level interpretable encoding and inferring it with our Recurrent Neural Network reasoning model. With a trained perfect perception module, our model attaches great performance on the RPM dataset Impartial-RAVEN of 96.72% and RAVEN of 90.77%. Our method tests the abstract reasoning quality of DNN s and also provides a platform for further research in neuroscience.
Visual Prompt Tuning

The current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning. This paper introduces Visual Prompt Tuning (VPT) as an efficient and effective alternative to full fine-tuning for large-scale Transformer models in vision. Taking inspiration from recent advances in efficiently tuning large language models, VPT introduces only a small amount (less than 1% of model parameters) of trainable parameters in the input space while keeping the model backbone frozen. Via extensive experiments on a wide variety of downstream recognition tasks, we show that VPT achieves significant performance gains compared to other parameter efficient tuning protocols. Most importantly, VPT even outperforms full fine-tuning in many cases across model capacities and training data scales, while reducing per-task storage cost.
Method Name Generation Based on Code Structure Guidance

The proper names of software engineering functions and methods can greatly assist developers in understanding and maintaining the code. Most researchers convert the method name generation task into the text summarization task. They take the token sequence and the abstract syntax tree (AST) of source code as input, and generate method names with a decoder. However, most proposed models learn semantic and structural features of the source code separately, resulting in poor performance in the method name generation task. Actually, each token in source code must have a corresponding node in its AST. Inspired by this observation, we propose SGMNG, a structure-guided method name generation model that learns the representation of two combined features. Additionally, we build a code graph called code relation graph (CRG) to describe the code structure clearly. CRG retains the structure of the AST of source code and contains data flows and control flows. SGMNG captures the semantic features of the code by encoding the token sequence and captures the structural features of the code by encoding the CRG. Then, SGMNG matches tokens in the sequence and nodes in the CRG to construct the combination of two features. We demonstrate the effectiveness of the proposed approach on the public dataset Java-Small with 700K samples, which indicates that our approach achieves significant improvement over the state-of-the-art baseline models in the ROUGE metric.
Sovereign Cloud Technologies for Scalable Data Spaces

Abstract The cloud has changed the way we consume technology either as individual users or in a business context. However, cloud computing can only transform organizations, create innovation, or provide the ability to scale digital business models if there is trust in the cloud and if the data that is being generated, processed, exchanged, and stored in the cloud has the appropriate safeguards. Therefore, sovereignty and control over data and its protection are paramount. Data spaces provide organizations with additional capabilities to govern strict data usage rules over the whole life cycle of information sharing with others and enable new use cases and new business models where data can be securely shared among a defined set of collaborators and with clear and enforceable usage rights attached to create new value. Open and sovereign cloud technologies will provide the necessary transparency, control, and the highest levels of privacy and security that are required to fully leverage the potential of such data spaces. Digital sovereignty, however, still means many things to many people. So to make it more concrete, in this article, we will look at digital sovereignty across three layers: data sovereignty, operational sovereignty, and software sovereignty. With these layers, we will create a spectrum of solutions that enable scalable data spaces that will be critical for the digital transformation of the European economy.
Deiksis dalam Novel Sumi karya Tiwiek SA serta Relevansinya sebagai Bahan Ajar Pembelajaran Bahasa Jawa di SMA

This study aims to describe the types and forms of deixis contained in the novel Sumi by Tiwiek SA and their relevance as teaching materials for Javanese language learning in high school. Based on the above objectives, this type of research is descriptive qualitative. The research data are all forms of deixis in the form of words, phrases, or sentences contained in the novel Sumi by Tiwiek SA. Data collection techniques using library techniques, listen, and take notes. The collected data were analyzed using content analysis. The data validation technique uses inter nal validity with persistence techniques. The results showed that there were several types of deixis found: (1) person deixis,(2) time deixis, (3) place deixis, (4) discourse deixis, and (5) social deixis. The five types of deixis found can be used as teaching materials for Javanese language learning in senior high school level XI odd semester in novel passages. The deixis found can be developed into word choice material (diction) to help interpret the meaning of words in a literary discourse. This is supported by the Basic Competence of understanding the meaning of the text and retelling the contents of the text contained in the 2013 curriculum syllabus. Word choice (diction) can also be used as an effort to form understanding in choosing words or phrases to improve writing, speaking, and reading skills.&#x0D; Keywords: deixis, Sumi novel by Tiwiek SA, relevance
Sensing Places: Making Room for More-Than-Human Encounters in the City

This paper reports on an on-going research project called 'The Augmented City' which aims to create augmented reality (AR) experiences in the city of Helsingør.The paper describes and discusses two 'sensitising workshops' intended to promote bodily, affective, and ecologically aware ways of relating to our urban surroundings.Augmented Reality.Design methodology.Affective encounters.Soma.More-than-human.
How is “solidarity” understood in discussions about contact tracing apps? An overview

Background In the context of the COVID-19 pandemic, there is much discussion about contact tracing apps, their use to contain the spread of the virus as well as the ethical, legal, and social aspects of their development, implementation, acceptance, and use. In these discussions, authors frequently mention “solidarity” when making key points in arguments. At the same time, authors rarely specify how they understand “solidarity”. This lack of specification about how they understand “solidarity” can lead to misunderstandings in discussions. Objective To prevent such misunderstandings, it is important to specify how one understands “solidarity” when mentioning it in the discussions on contact tracing apps. Therefore, the aim of this paper is to elaborate how “solidarity” is understood in the context of contact tracing apps, i.e., how different authors understand “solidarity” when using it in discussions about these apps. Methods In order to find out how different authors understand “solidarity” when discussing contact tracing apps, I conduct a literature review. I collect papers from several databases, inductively work out central differences and similarities between the different uses of “solidarity”, and use them to code and analyze relevant passages. Results In the final sample, five different understandings of “solidarity” in the context of contact tracing apps can be identified. These understandings differ in how different authors (1) imagine the basic concept of solidarity, i.e., what “solidarity” refers to, (2) how they temporally relate solidarity to contact tracing apps, and (3) how they perceive the causal interactions between solidarity and contact tracing apps, i.e., the different ways in which solidarity and contact tracing apps influence each other. Conclusions The five understandings of “solidarity” in the context of contact tracing apps presented here can serve as guidance for how “solidarity” can be understood in discussions—thus contributing to a better mutual understanding and preventing communicative misunderstandings.
Teaching mode of oral English in the age of artificial intelligence

With the deepening of cultural integration, people's demand for English learning is also increasing rapidly. However, traditional teaching methods have certain limitations, and teaching conditions are limited by the slow development of information technology, oral English courses have been shelved and stopped for a long time. With the rapid development of technology, the era of artificial intelligence has arrived. Learning assistance systems based on artificial intelligence have emerged in an endless stream, which has also innovatively solved the problem of oral language learning. Natural language processing is a computing mode of deep learning by artificial intelligence, which can carry out deep learning and training according to the current goal and finally get the desired result. But relying only on the auxiliary learning system cannot fundamentally solve the problem of oral language learning. Therefore, we aim to update the current spoken English learning methods using natural language processing technology, and propose a natural language processing-based oral English teaching model. In this mode, natural language processing can match different teaching methods according to the spoken language characteristics of different students, and give constructive suggestions. Moreover, the spoken English teaching mode based on natural language processing can be continuously upgraded and adjusted to adapt to the changing and developing era in time. Experiments show that the oral English teaching mode based on natural language processing can improve students' comprehensive ability of oral English. And it increased its comprehension by 19.7% year-on-year, and at the same time it also improved the enthusiasm for learning oral language by 33.3%.
Reduced complexity on micro-controller learning of ECG anomalies

An electrocardiograph (ECG) is used as a key diagnostic signal for cardiovascular diseases (CVDs) [1]. The acquired ECG time series (TS) signals are often manually evaluated by qualified medical doctors in order to discover any arrhythmia that the patient may have experienced. Much effort has been put into automatizing the process of evaluating ECG readings to learn the various forms of arrhythmia. In this paper, we propose a machine learning model based on reservoir computing (RC), followed by principal component analysis, and a readout module based on one-class support vector machine (OCSVM) for the anomaly detection task. This approach is used to perform on-device learning and for real-time anomaly detection of pathological conditions executed on a low-power microcontroller unit (MCU). The learning step requires a limited amount of input data eg. 600 Bytes to make it suitable for cheap deployment on a low-power device. We present practical modifications for optimizing the whole pipeline while keeping performances at the expected level and reducing the complexity of the proposed architecture. We also measured the complexity of the pipeline using X-CUBE-AI tool on STM32 MCU's. The detection performances has been evaluated on two different datasets; the publicly available MIT-BIH arrhythmia dataset and a proposed ST dataset collected from a low-cost sensors system. Proposed model achieved 83% in accuracy and 89% in f1 score on average, up to 97% as a maximum.
Edge Blockchain Construction Efficiency Maximization for COVID-19 Detection in a Body Area Network

Detecting traces of patients with novel coronavirus pneumonia (COVID-19) is a prerequisite for avoiding the virus’s rapid spread. However, too much patient privacy data uploaded to the cloud centre will overwhelm the network and cause user information security not to be guaranteed. In this paper, we propose a personal prediction method for COVID-19 infections by perceiving the information of worn biosensors and monitoring equipment in a body area network (BAN). Edge computing and blockchain technology are introduced to solve the problems of user privacy protection and perceptual data transmission and storage. We first construct an edge body area network (EBAN) and characterize the edge blockchain cost’s maximization function by considering the bandwidth, storage space, and energy consumption constraints. Then we build a blockchain without redundant perception information and select effective transmission paths by using the edge blockchain construction efficiency maximization (EBCEM) algorithm. Finally, we use the network simulator (NS-2) to simulate the performance of the EBCEM algorithm and compare it with the excellent assignment game algorithm (AGA) in terms of the effective requester ratio (ERR), effective provider ratio (EPR), edge blockchain construction success ratio (EBCSR), and average storage usage ratio (ASUR) in the EBAN.
Artificial Intelligence for Multimedia Signal Processing

At the ImageNet Large Scale Visual Re-Conversion Challenge (ILSVRC), a 2012 global image recognition contest, the University of Toronto Supervision team led by Prof [...]
Image Interpolation with Regional Gradient Estimation

This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity.
LightSolver -- A New Quantum-inspired Solver Cracks the 3-Regular 3-XORSAT Challenge

The increasing complexity of required computational tasks alongside the inherent limitations in conventional computing calls for disruptive innovation. LightSolver devised a new quantum-inspired computing paradigm, which utilizes an all-optical platform for solving hard optimization problems. In this work, LightSolver introduces its digital simulator and joins the 3-Regular 3-XORSAT (3R3X) challenge, which aims to map the best available state-of-the-art classical and quantum solvers. So far, the challenge has resulted in a clear exponential barrier in terms of time-to-solution (TTS), preventing the inspected platforms from solving problems larger than a few hundred variables. LightSolver's simulator is the first to break the exponential barrier, outperforming both classical and quantum platforms by several orders-of-magnitude and extending the maximal problem size to more than 16,000 variables.
Survival analysis for user disengagement prediction: question-and-answering communities’ case

Abstract We used survival analysis to model user disengagement in three distinct questions-and-answering communities in this work. We used the complete historical data from domains including Politics, Data Science, and Computer Science from Stack Exchange communities from their inception until May 2021, including information about all users who were members of one of these three communities. Furthermore, in formulating the user disengagement prediction as a survival analysis task, we employed two survival analysis techniques (Kaplan–Meier and random survival forests) to model and predicted the probabilities of members of each community becoming disengaged. Our main finding is that the likelihood of users with even a few contributions staying active is noticeably higher than those who were making no contributions; this distinction may widen as time passes. Moreover, the results of our experiments indicate that users with more favourable views toward the content shared on the platform may stay engaged longer. Finally, regardless of their themes, the observed pattern holds for all three communities.