Reviewerly/exHarmony huggingface dataset:
Quantum simulation of fermionic systems using hybrid digital–analog quantum computing approach

We consider a hybrid digital-analog quantum computing approach, which allows implementing any quantum algorithm without standard two-qubit gates. This approach is based on the always-on interaction between qubits, which can provide an alternative to such gates. We show how digital-analog approach can be applied to simulate the dynamics of fermionic systems, in particular, the Fermi-Hubbard model, using fermionic SWAP network and refocusing technique. We concentrate on the effects of connectivity topology, the spread of interaction constants as well as on errors of entangling operations. We find that an optimal connectivity topology of qubits for the digital-analog simulation of fermionic systems of arbitrary dimensionality is a chain for spinless fermions and a ladder for spin 1/2 particles. Such a simple connectivity topology makes digital-analog approach attractive for the simulation of quantum materials and molecules.
Application of Fuzzy <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math>-Means Clustering Algorithm in the Innovation of English Teaching Evaluation Method

As an important course in colleges, the teaching quality of English courses directly affects the efficiency of talent training. Carrying out English teaching evaluation is conducive to solve the problems existing in English teaching timely, improve the teaching level, and promote the demand of English curriculum teaching reform. However, the traditional English teaching evaluation methods adopted by colleges have some disadvantages, such as inaccurate evaluation results and long evaluation time, which urgently needs to innovate and reform the English teaching evaluation method. With the wide application of modern information technology in English teaching, the fuzzy <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>K</mi> </math> -means clustering algorithm can be used to construct a new English teaching evaluation model, which can effectively make up for the shortcomings of traditional teaching evaluation methods. This study proposes an English teaching evaluation method based on the fuzzy <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>K</mi> </math> -means clustering algorithm. This study uses the association rule analysis method under the data mining technology to preprocess the English teaching data, establishes the hierarchical structure model combined with the applying steps of the analytic hierarchy, and constructs the judgment matrix, carries out the hierarchical ranking and consistency test, and defines the specific weight of each index in the teaching evaluation index system, transforms the problem of English teaching evaluation into solving the K-means clustering objective function, realizes the scientific and accurate evaluation of English teaching, and provides a guarantee for improving the quality of English teaching. The results show that the English teaching evaluation method based on the fuzzy <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>K</mi> </math> -means clustering algorithm has a good evaluation accuracy, ensuring that the evaluation error is less than 6%, and the evaluation time is only 3.9 seconds, which significantly reduces the evaluation time.
Bilateral K-Means for Superpixel Computation (the SLIC Method)

As a substitute to a full segmentation of a digital image, or as preprocessing to a segmentation algorithm, superpixels provide an over-segmentation that offers several benefits: good adherence to edges, uniformity of color inside superpixels, a richer adjacency structure than the regular grid of pixels, and the fact that each node of the graph of superpixels has a shape, which can be used in subsequent processing.Moreover, their evaluation is less subjective than a full segmentation, which somehow always involves a semantic interpretation of the scene.The SLIC method (Simple Linear Iterative Clustering) has been a very popular algorithm to compute superpixels since its introduction.Its advantage is due to its simplicity and to its computing time performance.In essence, it consists in a K-means clustering in bilateral domain, involving both position and color.We study in detail this algorithm and propose a fast, simple postprocessing that ensures that superpixels are connected, a property not ensured by the original method.
An Examination of Practice-Based Virtual Simulations and Pre-Service Mathematics Teaching Efficacy and Outcome Expectancy

Authentic practice in pedagogical approaches is essential for preparing teachers to design effective learning experiences that foster student engagement during this digital era. There is an opportunity to explore novel and effective designs of virtual experiences that may augment or better prepare preservice teachers for field placements in physical classrooms. We proffer that virtual classroom simulations can and should be further explored and leveraged, now more than ever. In this paper, we examined a model of the impact of perceptual variables on instructional effectiveness that can enhance teaching efficacy and outcome expectancy when preservice teachers engage in practice teaching experiences in a virtual classroom simulation. The relationships between perceptual variables (presence, instructional time, and engagement) and teaching efficacy and outcome expectancy, as they relate to instructional effectiveness, were analyzed using the structural equation modeling approach. The results supported all of the hypothesized relationships. For example, presence and instructional time strongly and positively influenced engagement. Engagement was strongly related to instructional effectiveness. Instructional effectiveness was strongly and positively related to teaching self-efficacy, as well as outcome expectancy. The results support that virtual world classroom simulations can be an effective space for practice teaching for prospective mathematics teachers. Furthermore, this study provides insights for teacher educators, developers, and instructional designers interested in designing and utilizing practice-based simulations.
Hypertron: Explicit Social-Temporal Hypergraph Framework for Multi-Agent Forecasting

Forecasting the future trajectories of multiple agents is a core technology for human-robot interaction systems. To predict multi-agent trajectories more accurately, it is inevitable that models need to improve interpretability and reduce redundancy. However, many methods adopt implicit weight calculation or black-box networks to learn the semantic interaction of agents, which obviously lack enough interpretation. In addition, most of the existing works model the relation among all agents in a one-to-one manner, which might lead to irrational trajectory predictions due to its redundancy and noise. To address the above issues, we present Hypertron, a human-understandable and lightweight hypergraph-based multi-agent forecasting framework, to explicitly estimate the motions of multiple agents and generate reasonable trajectories. The framework explicitly interacts among multiple agents and learns their latent intentions by our coarse-to-fine hypergraph convolution interaction module. Our experiments on several challenging real-world trajectory forecasting datasets show that Hypertron outperforms a wide array of state-of-the-art methods while saving over 60% parameters and reducing 30% inference time.
Compositional Semantics and Inference System for Temporal Order based on Japanese CCG

Natural Language Inference (NLI) is the task of determining whether a premise entails a hypothesis.NLI with temporal order is a challenging task because tense and aspect are complex linguistic phenomena involving interactions with temporal adverbs and temporal connectives.To tackle this, temporal and aspectual inference has been analyzed in various ways in the field of formal semantics.However, a Japanese NLI system for temporal order based on the analysis of formal semantics has not been sufficiently developed.We present a logic-based NLI system that considers temporal order in Japanese based on compositional semantics via Combinatory Categorial Grammar (CCG) syntactic analysis.Our system performs inference involving temporal order by using axioms for temporal relations and automated theorem provers.We evaluate our system by experimenting with Japanese NLI datasets that involve temporal order.We show that our system outperforms previous logic-based systems as well as current deep learning-based models.
ACADEMIC READING DIFFICULTIES IN HIGHER EDUCATION

Academic reading is one of the challenges commonly faced by university students especially EFL learners. This study aims to identify academic reading difficulties in higher education focusing on Indonesian university students. It used mixed-method by collecting quantitative data from close-ended questionnaires and qualitative data from one open-ended question. In this study, 60 students were asked to rate the reading aspects listed in the questionnaires based on its level of difficulty and provide commentaries as well as possible additional aspect. The statistic results showed that overall students considered academic reading easy (M=2.445, SD=0.568). Reading carefully to understand the text was found as the aspect with the least difficulty (M=2.16, SD= 0.963) and working out the meaning of difficult words was identified as the most difficult one (M=2.75, SD=1.235). These statistics are supported by the qualitative finding whereas 43% students having problems translating and understanding difficult and formal words.
A Memory Hierarchy Protected against Side-Channel Attacks

In the vulnerability analysis of System on Chips, memory hierarchy is considered among the most valuable element to protect against information theft. Many first-order side-channel attacks have been reported on all its components from the main memory to the CPU registers. In this context, memory hierarchy encryption is widely used to ensure data confidentiality. Yet, this solution suffers from both memory and area overhead along with performance losses (timing delays), which is especially critical for cache memories that already occupy a large part of the spatial footprint of a processor. In this paper, we propose a secure and lightweight scheme to ensure the data confidentiality through the whole memory hierarchy. This is done by masking the data in cache memories with a lightweight mask generator that provides masks at each clock cycle without having to store them. Only 8-bit Initialization Vectors are stored for each mask value to enable further recomputation of the masks. The overall security of the masking scheme is assessed through a mutual information estimation that helped evaluate the minimum number of attack traces needed to succeed a profiling side-channel attack to 592 K traces in the attacking phase, which provides an acceptable security level in an analysis where an example of Signal to Noise Ratio of 0.02 is taken. The lightweight aspect of the generator has been confirmed by a hardware implementation that led to resource utilization of 400 LUTs.
COVID-19 and the Quest for Reconfiguration of Disciplines: Unpacking New Directions

This theoretical editorial piece sets the tone for a special issue that focuses on teasing new directions during and after the COVID-19 pandemic. The piece is underpinned by bricolage thinking, and we seek to show that it is essential to reimagine various educational disciplines in order to meet new challenges and opportunities presented by COVID-19. In doing so, we are convinced that the relationship between a serene scholarly quest and applied space has to be re-examined. Thus, to reimagine a better world during and post COVID-19, cross-disciplinarity is no longer an option for humanity, instead, it is essential, to ensure the collective efforts needed to address the pressing issues of the day. We end this editorial section by arguing that new strategies that are adopted need to be shared across disciplines and faculties, to reinvent multidisciplinary or interdisciplinary approaches to addressing human crises.
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation

Although Transformers have gained success in several speech processing tasks like spoken language understanding (SLU) and speech translation (ST), achieving online processing while keeping competitive performance is still essential for real-world interaction. In this paper, we take the first step on streaming SLU and simultaneous ST using a blockwise streaming Transformer, which is based on contextual block processing and blockwise synchronous beam search. Furthermore, we design an automatic speech recognition (ASR)-based intermediate loss regularization for the streaming SLU task to improve the classification performance further. As for the simultaneous ST task, we propose a cross-lingual encoding method, which employs a CTC branch optimized with target language translations. In addition, the CTC translation output is also used to refine the search space with CTC prefix score, achieving joint CTC/attention simultaneous translation for the first time. Experiments for SLU are conducted on FSC and SLURP corpora, while the ST task is evaluated on Fisher-CallHome Spanish and MuST-C En-De corpora. Experimental results show that the blockwise streaming Transformer achieves competitive results compared to offline models, especially with our proposed methods that further yield a 2.4% accuracy gain on the SLU task and a 4.3 BLEU gain on the ST task over streaming baselines.
How a Blockchain Approach Can Improve Data Reliability in the COVID-19 Pandemic

The rapid spread of COVID-19 made it necessary to quickly collect and share viral genomic sequences, sometimes making quantity prevail over the quality of information. Can research pay this price? Blockchain technology, based on the concept of a ledger that guarantees the authenticity and traceability of information, could be the best applicable solution.
Comparison of Multilayer Neural Network Models in Terms of Success of Classifications Based on EmguCV, ML.NET and Tensorflow.Net

In this paper, we compare three different models of multilayer neural networks in terms of their success in the classification phase. These models were designed for EmguCV, ML.NET and Tensorflow.Net libraries, which are currently among the most widely used libraries in the implementation of an automatic recognition system. Using the EmguCV library, we achieved a success rate in the classification of human faces of 81.95% and with ML.NET, which was based on the pre-trained ResNet50 model using convolution layers, up to 91.15% accuracy. The result of the success of the classification process was influenced by the time required for training and also the time required for the classification itself. The Tensorflow.Net model did not show sufficient classification ability when classifying using vector distances; the highest success rate of classification was only 13.31%. Neural networks were trained on a dataset with 1454 photographs of faces involving 43 people. At a time when neural networks are becoming more and more used for applications of different natures, it is necessary to choose the right model in the classification process that will be able to achieve the required accuracy with the minimum time required for training. The application created by us allows the insertion of images and the creation of their own datasets, on the basis of which the user can train a model with its own parameters. Models can then be saved and integrated into other applications.
Police-Community Partnerships and Community Relation Promotion in Rwanda National Police (RNP): The Case of Nyaruguru District

The purpose of this study was to determine the role of Police-community partnerships in promoting community relations with a case of three sectors, which are Nyabimata, Ruheru, and Muganza that were selected from the list of 14 sectors in Nyaruguru district. The study objectives were; to determine the role of information sharing between RNP and Community in promoting community relations, to examine how the fight against gender-based violence by RNP has promoted community relations, and to explore the role of a fight against organized crimes by RNP in promoting community relations in the selected three sectors of Nyaruguru district. The study applied mixed methods, which consisted of quantitative and qualitative methods to collect and analyze data. Data was collected from a sample size of 384 community members, 6 local leaders, and 15 police officers using questionnaires and interviews respectively. For data analysis, descriptive and inferential were used that consisted of frequencies, percentages, mean, standard deviation, correlation, and regression for quantitative data, and content analysis was used to analyze qualitative data from interviews. Findings revealed that there was a negative significant correlation between police-community information sharing and community relations (r= -200** P= 0.05) and a positive significant correlation between Police fight against GBV and community relations (r= 0.136**P= 0.05). There was no significant correlation between Police fight against organized crimes and community relations (r= -010**P= 0.05). The value of adjusted R-square is 0.0336 meaning that all the independent variables included in the model explained 33.6% of the dependent variable, which is community relations indicating that the model adequately fits the data. The strength of the model is further reconfirmed by ANOVA which indicates that P-value of 0.000 at a 95% level of significance. Therefore, it is concluded that police-community partnerships significantly influence community relations in the Nyaruguru district. The study recommended based on regression findings that the police should continue engaging in various community partnerships aimed at promoting community relations. Keywords: Police-Community Partnerships, Community Relation Promotion, Rwanda National Police, Rwanda
Content Validity in Android-Based Augmented Reality Media for High School Science Students on Covalent Bonds Topic: Rasch Model Analysis

The 21st-century chemistry learning that teaches abstract and complex concepts needs to develop learning media that can increase students' learning motivation and visualize abstract concepts in chemistry. One of the media in the 21st century developed in this study is Android-based augmented reality media. The purpose of this study was to test the validity of the Android-based Augmented Reality media and see the differences in perceptions between teachers and students in assessing media. The validation of android-based Augmented Reality learning media uses a questionnaire and is quantitative. The Rater for assessing the product consisted of 6 senior chemistry teachers at 5 different schools in Surakarta and 20 students who had obtained the material for covalent bonding at SMA N 1 Ngemplak Boyolali. The data obtained were analyzed by Rasch Model using Facets software. The results of the analysis show that exact agreements are 35.1% and exacted agreements are 35.2%. This happens because the aspects are considered easy by the teacher, but the students consider these aspects difficult. The aspect or item that is considered the most difficult to achieve is the suitability of the example with the material and improving understanding of the material. Based on this research, in developing media, it must be packaged properly so that it is easily understood and understood by students
A Lightweight System-On-Chip Based Cryptographic Core for Low-Cost Devices

The backbone of the Internet of things (IoT) platform consists of tiny low-cost devices that are continuously exchanging data. These devices are usually limited in terms of hardware footprint, memory capacity, and processing power. The devices are usually insecure because implementing standard cryptographic algorithms requires the use of a large hardware footprint which leads to an increase in the prices of devices. This study implements a System-on-Chip (SoC) based lightweight cryptographic core that consists of two encryption protocols, four authentication protocols, and a key generation/exchange protocol for ultra-low-cost devices. The hardware architectures use the concept of resource sharing to minimize the hardware area. The lightweight cryptographic SoC is tested by designing a desktop software application to serve as an interface to the hardware. The design is implemented using Verilog HDL and the 130 nm CMOS cell library is used for synthesis, which results in 33 k gate equivalents at a maximum clock frequency of 50 MHz.
New approach of KNN Algorithm in quantum computing based on new design of quantum circuits

Quantum computing has risen as the new type of computer that theoretically reduces the time complexity of classical methods exponentially because of the nature of superposition. It is promising to reduce runtimes on large data sets in machine learning (ML). Meanwhile, k-nearest neighbor (kNN) is a simple ML algorithm that can be translated to a quantum version (QkNN) to perform classification tasks efficiently. Here, we show a new version of QkNN which has a speed up in time complexity by using a new design of quantum circuits called integrated swap test. This quantum circuit can load two N-dimensional states and calculate the fidelity between them. Results show that QkNN allows us to take a different approach to the ML field.
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks

Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications. Recently, knowledge distillation (KD) for GNNs has enabled remarkable progress in graph model compression and knowledge transfer. However, most of the existing KD methods require a large volume of real data, which are not readily available in practice, and may preclude their applicability in scenarios where the teacher model is trained on rare or hard to acquire datasets. To address this problem, we propose the first end-to-end framework for data-free adversarial knowledge distillation on graph structured data (DFAD-GNN). To be specific, our DFAD-GNN employs a generative adversarial network, which mainly consists of three components: a pre-trained teacher model and a student model are regarded as two discriminators, and a generator is utilized for deriving training graphs to distill knowledge from the teacher model into the student model. Extensive experiments on various benchmark models and six representative datasets demonstrate that our DFAD-GNN significantly surpasses state-of-the-art data-free baselines in the graph classification task.
Enhancing Communication Reliability from the Semantic Level under Low SNR

In the low signal-to-noise ratio region, a large number of bit errors occur, and it may exceed the channel error correction capability of the receiver. Traditional communication system may use the technology of automatic repeat-request to deal with this problem, which is time consuming and a waste of resources. To enhance the reliability of the communication system, we investigate reasoning and decoding at the semantic level instead of the grammar level. In particular, we propose a semantic communication model for text transmission, assisting the communication system to be more robust in terrible channel environments. Based on the traditional communication system, the language model BERT, part of speech tagging, and prior information concerning bit-flipping are introduced to enhance the semantic reasoning ability of the transceiver. Furthermore, this paper analyzes the effects of the sub-strategies on the performances of the improved communication model, such as the existence of a candidate set and language model. The numerical results show the effectiveness of our model in terms of improving the semantic accuracy measured by BLUE, the METEOR score, and the similarity score based on BERT between transmitted messages and recovered messages.