Reviewerly/exHarmony huggingface dataset:
Optimal Identification of Unknown Parameters of Photovoltaic Models Using Dual-Population Gaining-Sharing Knowledge-Based Algorithm

Establishing an accurate equivalent model is a critical foundation to describe the energy conversion characteristics of a photovoltaic system, which can support the research of fault analysis, output power prediction, and performance analysis of the photovoltaic system. However, the widely used equivalent models are highly nonlinear and have many unknown parameters, making it difficult to identify these parameters accurately. Our previous work found that the gaining-sharing knowledge-based algorithm (GSK) shows promising performance in solving this problem. But its efficacy is not enough to achieve accurate parameters within a relatively limited computing resource. In this context, a dual-population GSK algorithm (DPGSK), which introduces a dual-population evolution strategy for more excellent searchability, is proposed to address this issue. In each iteration, the population splits equally and randomly into two subpopulations, one of which performs the junior gaining-sharing phase while the other performs the senior gaining-sharing phase. Then two updated subpopulations merge to form a new population. This allows for a grand reconciliation of convergence speed and population diversity, giving DPGSK powerful optimization performance. Afterward, DPGSK is applied to five photovoltaic models and validated for performance against other advanced metaheuristics. Besides, the impact of different components on DPGSK is also investigated. Results and comparisons show that either component is indispensable to DPGSK, and DPGSK strengthens the convergence and achieves accurate and reliable results, demonstrating its superiority over other algorithms in solving this studied problem.
First Steps Towards Predicting the Readability of Programming Error Messages

Reading a programming error message is the first step in understanding what it is trying to tell the programmer about how to fix an error in their code. However, these are often difficult to read, especially for novices which is not surprising given that error messages in many of the most popular languages in which novices learn to code were not written with readability in mind. As a result, novices frequently struggle to understand them. This is a long-standing problem, with researchers highlighting concerns about programming error message readability over the last six decades. Very recent work has put forward evidence of the need for measuring readability in error messages and a framework for doing so. This framework consists of four factors of readability for programming error messages: message length, vocabulary, jargon, and sentence construction. We use this framework to implement an approach to automatically assess the readability of programming error messages. Using established readability factors as predictors in a machine learning model, we train several models using a dataset of C and Java error messages. We examine the performance of these models, and apply the best performing model to a previously published set of messages evaluated for readability by experts, non-experts and students. Our results validate the previously proposed readability factors, and our model classifies messages similarly to human raters. Finally, we discuss future work needed to improve the accuracy of the model.
Line-Level Layout Recognition of Historical Documents with Background Knowledge

Digitization and transcription of historic documents offer new research opportunities for humanists and are the topics of many edition projects. However, manual work is still required for the main phases of layout recognition and the subsequent optical character recognition (OCR) of early printed documents. This paper describes and evaluates how deep learning approaches recognize text lines and can be extended to layout recognition using background knowledge. The evaluation was performed on five corpora of early prints from the 15th and 16th Centuries, representing a variety of layout features. While the main text with standard layouts could be recognized in the correct reading order with a precision and recall of up to 99.9%, also complex layouts were recognized at a rate as high as 90% by using background knowledge, the full potential of which was revealed if many pages of the same source were transcribed.
Unambiguous and High-Fidelity Backdoor Watermarking for Deep Neural Networks

The unprecedented success of deep learning could not be achieved without the synergy of big data, computing power, and human knowledge, among which none is free. This calls for the copyright protection of deep neural networks (DNNs), which has been tackled via DNN watermarking. Due to the special structure of DNNs, backdoor watermarks have been one of the popular solutions. In this article, we first present a big picture of DNN watermarking scenarios with rigorous definitions unifying the black-and white-box concepts across watermark embedding, attack, and verification phases. Then, from the perspective of data diversity, especially adversarial and open set examples overlooked in the existing works, we rigorously reveal the vulnerability of backdoor watermarks against black-box ambiguity attacks. To solve this problem, we propose an unambiguous backdoor watermarking scheme via the design of deterministically dependent trigger samples and labels, showing that the cost of ambiguity attacks will increase from the existing linear complexity to exponential complexity. Furthermore, noting that the existing definition of backdoor fidelity is solely concerned with classification accuracy, we propose to more rigorously evaluate fidelity via examining training data feature distributions and decision boundaries before and after backdoor embedding. Incorporating the proposed prototype guided regularizer (PGR) and fine-tune all layers (FTAL) strategy, we show that backdoor fidelity can be substantially improved. Experimental results using two versions of the basic ResNet18, advanced wide residual network (WRN28_10) and EfficientNet-B0, on MNIST, CIFAR-10, CIFAR-100, and FOOD-101 classification tasks, respectively, illustrate the advantages of the proposed method.
On arithmetic progressions in finite fields

In this paper, we explore the existence of m-term arithmetic progressions in $${\mathbb {F}}_{q^n}$$ with a given common difference whose terms are all primitive elements, and at least one of them is normal. We obtain asymptotic results for $$m \ge 4$$ and concrete results for $$m \in \{2,3\}$$ , where the complete list of exceptions when the common difference belongs to $${\mathbb {F}}_{q}$$ is obtained. The proofs combine character sums, sieve estimates and computational arguments using the software SageMath.
What’s (Not) Working in Programmer User Studies?

A key goal of software engineering research is to improve the environments, tools, languages, and techniques programmers use to efficiently create quality software. Successfully designing these tools and demonstrating their effectiveness involves engaging with tool users—software engineers. Researchers often want to conduct user studies of software engineers to collect direct evidence. However, running user studies can be difficult, and researchers may lack solution strategies to overcome the barriers, so they may avoid user studies. To understand the challenges researchers face when conducting programmer user studies, we interviewed 26 researchers. Based on the analysis of interview data, we contribute (i) a taxonomy of 18 barriers researchers encounter; (ii) 23 solution strategies some researchers use to address 8 of the 18 barriers in their own studies; and (iii) 4 design ideas, which we adapted from the behavioral science community, that may lower 8 additional barriers. To validate the design ideas, we held an in-person all-day focus group with 16 researchers.
Nonlocal generalized quantum measurement of product observables with mixed entanglement

Nonlocal observables of spacelike separated quantum systems in combination with their measurements contribute greatly to quantum theory and its applications. We present a nonlocal generalized quantum measurement protocol for measuring product observables, assisted by a meter in a mixed entangled state rather than maximally or partially entangled pure states. By tuning the entanglement of the meter, measurement strength of arbitrary values can be achieved for nonlocal product observables, since measurement strength equals the concurrence of the meter. Furthermore, we present a specific scheme to measure the polarization of two nonlocal photons using linear optics. We refer to the polarization and spatial-mode degrees of freedom of the same photon pair as the system and the meter, respectively, which significantly simplifies the interaction between the system and the meter. This protocol can be useful for applications involving nonlocal product observables and nonlocal weak values, and for tests of quantum foundations in nonlocal scenarios.
An evaluation of relational and NoSQL distributed databases on a low-power cluster

The constant growth of social media, unconventional web technologies, mobile applications, and Internet of Things (IoT) devices create challenges for cloud data systems in order to support huge datasets and very high request rates. NoSQL databases, such as Cassandra and HBase, and relational SQL databases with replication, such as Citus/PostgreSQL, have been used to increase horizontal scalability and high availability of data store systems. In this paper, we evaluated three distributed databases on a low-power low-cost cluster of commodity Single-Board Computers (SBC): relational Citus/PostgreSQL and NoSQL databases Cassandra and HBase. The cluster has 15 Raspberry Pi 3 nodes with Docker Swarm orchestration tool for service deployment and ingress load balancing over SBCs. We believe that a low-cost SBC cluster can support cloud serving goals such as scale-out, elasticity, and high availability. Experimental results clearly demonstrated that there is a trade-off between performance and replication, which provides availability and partition tolerance. Besides, both properties are essential in the context of distributed systems with low-power boards. Cassandra attained better results with its consistency levels specified by the client. Both Citus and HBase enable consistency but it penalizes performance as the number of replicas increases.
MOTIVATION DEVELOPMENT OF MENTAL HEALTH PRESERVATION OF SPECIALISTS IN THE FIELD OF SPECIAL AND INCLUSIVE EDUCATION: EUROPEAN PRACTICES

Aim: The aim is to study and to improve the motivation of mental health preservation of specialists in the field of special and inclusive education according to European experiences.Materials and methods: The experimental part of the research involved the use of the valid psychodiagnostic methods and tech¬niques: direct and indirect observation, standardized questionnaire survey, semi-standardized individual interviews, psychodiag¬nostic methods. The research was attended by 131 Master's degree students (aged 25-27), specialty 053 Psychology, Educational program - special, clinical psychology.Results: The program "European practices of motivation development of mental health preservation" expands perception of ca¬pabilities, which encourages the disclosure of the creative potential of the individual; anxiety symptoms disappear (or decrease); a system of value orientations is formed; the desire to strengthen spiritual and physical strength.Conclusions: Program contributes to the formation of an image of mentally healthy person, stimulation to adhere to a mentally healthy lifestyle and the motivation development of mental health preservation.
Evaluation Methods for the Development of New Power Systems Based on Cloud Model

With the proposal of the carbon peaking and carbon neutrality goals. The use of new energy sources such as wind and photovoltaic has become the main direction of energy development in China in the future. New energy sources will gradually replace traditional fossil energy sources and dominate the power generation in the electricity system. In this context, the construction and development of new power systems has become an important means of ensuring a clean, reliable, economic and efficient supply of power systems, it is also an effective measure to achieve the carbon peaking and carbon neutrality goals. How to achieve a reasonable assessment of the development status of new power systems has become a major challenge that constrains the development of new power systems. Therefore, the paper starts with four dimensions of the new power system, namely“reliability, cleanliness, economy and interactivity” By establishing the index evaluation system, use the analytic hierarchy process to assign weights to the indicators. Secondly, the cloud model is used to construct a cloud diagram of indicator criteria levels by means of expert scoring to provide criteria for determining the development status of new power systems. Finally, the validity of the indicator system and the cloud model evaluation method is verified through arithmetic examples.
Inculcating Context for Emoji Powered Bengali Hate Speech Detection using Extended Fuzzy SVM and Text Embedding Models

The massive growth of social webs offer opportunities to communicate with diverse languages, unstructured text, informal posts, misspelled contents and emojis. Social media users feel comfortable to express their emotions specially emotions with high intensity (hate speech) in their mother tongue. Hate speech in any form targets groups and individuals that may trigger antisocial activities, hate crimes, and terrorist acts. Bengali social media users use Bengali for posting implicit or indirect hate text. Existing Bengali hate speech detection research considers explicit hate speech detection but in actual hate is expressed more in implicit way. In order to detect both implicit and explicit hate speech from low resource content, social webs need highly efficient automated tools. Researchers applied discriminative learning approaches (i.e. SVM, MLP, CNN) to distinguish hate text with only clear-cut outcomes in detecting direct hate speech. The proposed novel Bengali hate speech detection model considers two parallel approaches: (i) It applies extended fuzzy SVM classifier for class imbalanced dataset (FSVMCIL) and multilingual BERT (mBERT) text embedding model to detect first hate label; (ii) Morphological analysis method to detect implicit and explicit hate content with the hate similarity (HS) scheme for second hate label. Linking both labeling methods, this research extracts contextual Bengali hate speech from informal text. This novel HS method considers Word2Vec word embedding model and Bengali hate lexicon. It also considers emoji to text conversion for efficient contextual analysis. This study also conducts extensive experiments for various categories with the Bengali hate speech dataset. It also evaluates the proposed model performance considering weighted F1 score, precision, recall and accuracy parameters. Results reveal significant improvement in Bengali hate speech detection with 2.35% increase in F1- score and 9.11 % increase in accuracy.
Wearable sensor-based human activity recognition with ensemble learning: a comparison study

&lt;p&gt;The spectacular growth of wearable sensors has provided a key contribution to the field of human activity recognition. Due to its effective and versatile usage and application in various fields such as smart homes and medical areas, human activity recognition has always been an appealing research topic in artificial intelligence. From this perspective, there are a lot of existing works that make use of accelerometer and gyroscope sensor data for recognizing human activities. This paper presents a comparative study of ensemble learning methods for human activity recognition. The methods include random forest, adaptive boosting, gradient boosting, extreme gradient boosting, and light gradient boosting machine (LightGBM). Among the ensemble learning methods in comparison, light gradient boosting machine and random forest demonstrate the best performance. The experimental results revealed that light gradient boosting machine yields the highest accuracy of 94.50% on UCI-HAR dataset and 100% on single accelerometer dataset while random forest records the highest accuracy of 93.41% on motion sense dataset.&lt;/p&gt;
Kernel Attention Transformer for Histopathology Whole Slide Image Analysis and Assistant Cancer Diagnosis

Transformer has been widely used in histopathology whole slide image analysis. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits its effectiveness and efficiency when applied to gigapixel histopathology images. In this paper, we propose a novel kernel attention Transformer (KAT) for histopathology WSI analysis and assistant cancer diagnosis. The information transmission in KAT is achieved by cross-attention between the patch features and a set of kernels related to the spatial relationship of the patches on the whole slide images. Compared to the common Transformer structure, KAT can extract the hierarchical context information of the local regions of the WSI and provide diversified diagnosis information. Meanwhile, the kernel-based cross-attention paradigm significantly reduces the computational amount. The proposed method was evaluated on three large-scale datasets and was compared with 8 state-of-the-art methods. The experimental results have demonstrated the proposed KAT is effective and efficient in the task of histopathology WSI analysis and is superior to the state-of-the-art methods.
Trends in the development of secondary vocational education and prospects for scientific research

The development of secondary vocational education in Russia under the influence of socio-economic factors requires full-fledged scientific and methodological support. Among scientific research, an important role is played by works related to forecasting, which is not limited to attempts to predict the future, but prepares for the adoption of promising management decisions, creates a meaningful ground for frontier developments of a scientific and scientific-methodological nature. Many aspects of the development of secondary vocational education are deservedly called “eternal”, since the issues of material and technical support, qualification and training of teaching staff, the ratio of fundamental and practical orientation acquire new meanings at any stage of socio-economic development of society. It seems that the development of vocational education takes place according to the laws of reciprocating motion or a pendulum. From the pragmatic mission of providing the economy with personnel, to the humanitarian mission of comprehensive and harmonious personal development, bypassing the state of equilibrium. At the same time, the previous configurations of the system have never been repeated in full yet. This suggests that it is advisable to apply to many research topics repeatedly, without fear of coming to previously obtained results. Modern pedagogy of vocational education needs reliable information about current topics, which makes it possible to make research really useful for the development of practice, to prepare researchers to solve problems of a proactive nature.
Multi-Granularity Relational Attention Network for Audio-Visual Question Answering

Recent methods for video question answering (VideoQA), aiming to generate answers based on given questions and video content, have made significant progress in cross-modal interaction. From the perspective of video understating, these existing frameworks concentrate on the various levels of visual content, partially assisted by subtitles. However, audio information is also instrumental in helping get correct answers, especially in videos with real-life scenarios. Indeed, in some cases, both audio and visual contents are required and complement each other to answer questions, which is defined as audio-visual question answering (AVQA). In this paper, we focus on importing raw audio for AVQA and contribute in three ways. Firstly, due to no dataset annotating QA pairs for raw audio, we introduce E-AVQA, a manually annotated and large-scale dataset involving multiple modalities. E-AVQA consists of 34,033 QA pairs on 33,340 clips of 18,786 videos from the e-commerce scenarios. Secondly, we propose a multi-granularity relational attention method with contrastive constraints between audio and visual features after the interaction, named MGN, which captures local sequential representation by leveraging the pairwise potential attention mechanism and obtains global multi-modal representation via designing the novel ternary potential attention mechanism. Thirdly, our proposed MGN outperforms the baseline on dataset E-AVQA, achieving 20.73% on WUPS@0.0 and 19.81% on BLEU@1, demonstrating its superiority with at least 1.02 improvement on WUPS@0.0 and about 10% on timing complexity over the baseline.
An Efficient Framework for Unsupervised Anomaly Detection over Edge-Assisted Internet of Things

The detection of anomaly status plays a pivotal role in the maintenance of public transportation and facilities in smart cities. Owing to the pervasively deployed sensing devices, one can collect and apply multi-dimensional sensing data to detect and analyze potential anomalies and react promptly. Current efforts concentrate on offline manners and fail to fit the situation in smart cities, where efficient and online solutions are expected. In this paper, a novel framework is designed for anomaly detection over edge-assisted Internet-of-Things (IoTs). The framework allows periodical data collection from sensors and continuous anomaly detection at the edge node. A novel efficient and unsupervised deep learning model is designed to balance the resource consumption and accuracy for anomaly detection, based on the combination of a convolutional autoencoder and adversarial training. Meanwhile, the proposed framework also adopts an adaptive strategy for continuous anomaly detection to reduce the overall resource consumption. According to theoretical analysis and evaluation on several real-world datasets, the proposed framework can discover the potential correlation features among multi-dimensional sensing data, and efficiently detect the abnormality of public transportation and facilities in smart cities.
Extension of the Trotterized Unitary Coupled Cluster to Triple Excitations

The Trotterized Unitary Coupled Cluster Single and Double (UCCSD) ansatz has recently attracted interest due to its use in Variation Quantum Eigensolver (VQE) molecular simulations on quantum computers. However, when the size of molecules increases, UCCSD becomes less interesting as it cannot achieve sufficient accuracy. Including higher-order excitations is therefore mandatory to recover the UCC's missing correlation effects. Here, we extend the Trotterized UCC approach via the addition of (true) Triple T excitations introducing UCCSDT. We also include both spin and orbital symmetries. Indeed, in practice, the latter help to reduce unnecessary circuit excitations and thus accelerate the optimization process enabling researchers to tackle larger molecules. Our initial numerical tests (12-14 qubits) show that UCCSDT improves the overall accuracy by at least two orders of magnitude with respect to standard UCCSD. Overall, the UCCSDT ansatz is shown to reach chemical accuracy and to be competitive with the CCSD(T) gold-standard classical method of quantum chemistry.