Reviewerly/exHarmony huggingface dataset:
RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

Human activity recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health and well being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.
Dual-channel convolutional neural network for power edge image recognition

Abstract In view of the low accuracy and poor processing capacity of traditional power equipment image recognition methods, this paper proposes a power equipment image recognition method based on a dual-channel convolutional neural network (DC-CNN) model and random forest (RF) classification. In the aspect of feature extraction, the DC-CNN model extracts the characteristics of power equipment through two independent CNN models. In the aspect of the recognition algorithm, by referring to the advantages of the traditional machine learning method and incorporating the advantages of the RF, an RF classification method incorporating deep learning is proposed. Finally, the proposed DC-CNN model and RF classification method are used to classify images of various types of power equipment. The results show that the proposed methods can be effectively applied to the image recognition of various types of power equipment, and they greatly improve the recognition rate of power equipment images.
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing

Data augmentation has attracted a lot of research attention in the deep learning era for its ability in alleviating data sparseness.The lack of labeled data for unseen evaluation databases is exactly the major challenge for cross-domain text-to-SQL parsing.Previous works either require human intervention to guarantee the quality of generated data, or fail to handle complex SQL queries.This paper presents a simple yet effective data augmentation framework.First, given a database, we automatically produce a large number of SQL queries based on an abstract syntax tree grammar.For better distribution matching, we require that at least 80% of SQL patterns in the training data are covered by generated queries.Second, we propose a hierarchical SQL-to-question generation model to obtain high-quality natural language questions, which is the major contribution of this work.Finally, we design a simple sampling strategy that can greatly improve training efficiency given large amounts of generated data.Experiments on three cross-domain datasets, i.e., WikiSQL and Spider in English, and DuSQL in Chinese, show that our proposed data augmentation framework can consistently improve performance over strong baselines, and the hierarchical generation component is the key for the improvement.
Training public speaking with virtual social interactions: effectiveness of real-time feedback and delayed feedback

Social signal processing and virtual social interaction technologies have allowed the creation of social skills training applications, and initial studies have shown that such solutions can lead to positive training outcomes and could complement traditional teaching methods by providing cheap, accessible, safe tools for training social skills. However, these studies evaluated social skills training systems as a whole and it is unclear to what extent which components contributed to positive outcomes. In this paper, we describe an experimental study where we compared the relative efficacy of real-time interactive feedback and after-action feedback in the context of a public speaking training application. We observed that both components provide benefits to the overall training: the real-time interactive feedback made the experience more immersive and improved participants’ motivation in using the system, while the after-action feedback led to positive training outcomes when it contained personalized feedback elements. Taken in combination, these results confirm that both social signal processing technologies and virtual social interactions are both contributing to social skills training systems’ efficiency. Additionally, we observed that several individual factors, here the subjects’ initial level of public speaking anxiety, personality and tendency to immersion significantly influenced the training experience. This finding suggests that social skills training systems could benefit from being tailored to participants’ particular individual circumstances.
The Decidability of Verification under PS 2.0

Abstract We consider the reachability problem for finite-state multi-threaded programs under the promising semantics () of Lee et al., which captures most common program transformations. Since reachability is already known to be undecidable in the fragment of with only release-acquire accesses (-), we consider the fragment with only relaxed accesses and promises (). We show that reachability under is undecidable in general and that it becomes decidable, albeit non-primitive recursive, if we bound the number of promises. Given these results, we consider a bounded version of the reachability problem. To this end, we bound both the number of promises and of “ view-switches ”, i.e., the number of times the processes may switch their local views of the global memory. We provide a code-to-code translation from an input program under (with relaxed and release-acquire memory accesses along with promises) to a program under SC, thereby reducing the bounded reachability problem under to the bounded context-switching problem under SC. We have implemented a tool and tested it on a set of benchmarks, demonstrating that typical bugs in programs can be found with a small bound.
Effective Missing Value Imputation Methods for Building Monitoring Data

To understand behaviors of natural and man-made events, such as energy consumption of buildings, which accounts for 40% of energy uses in the US, we deploy automated monitoring devices to record periodic observations.However, such experimental and observation data often contains problems and irregularities that have to be cleaned up before analyses.Due to various conditions affecting sensor operations, the communication channels, recording steps, or the recording media, the recorded data might have missing values, errors, or anomalous values.An effective way to clean up these problems is to replace these missing values, errors and anomalous values with expected values, a process generally known as imputation.In this work, we survey commonly used missing value imputation techniques and compare their performance on a set of building monitoring data.To compare the different types of sensor measurements with widely varying characteristics, we use normalized root mean squared error (NRMSE) as the key metric for the effectiveness of the imputation methods.We additionally consider periodicity and run time when considering comparing methods.Through extensive testing, we find that for small gap sizes, up to 8 consecutive missing values, linear interpolation performs the best; for larger gaps stretching up to 48 consecutive missing values, K-nearest neighbors provides the most accurate imputations; for even larger gaps, more computational intensive methods, such as matrix factorization, achieve the smallest NRMSE.Additionally, we observe that these computationally intensive algorithms not only provide accurate imputations for large gaps, but are also more robust across all types of sensors.
A Top-K QoS-Optimal Service Composition Approach Based on Service Dependency Graph

With the development of internet of things (IoT) technology, servitization of IoT device functions has become a trend. The cooperation between IoT devices can be equivalent to web service composition. However, current service composition approaches applied in the internet cannot work well in IoT environments due to weak adaptability, low accuracy, and poor time performance. This paper, based on service dependency graph, proposes a top-k QoS-optimal service composition approach suitable for IoT. It aims to construct the relationship between services by applying the service dependency model and to reduce the traversal space through effective filtering strategies. On the basis of a composition path traversal sequence, the generated service composition can be represented directly to avoid backtracking search. Meanwhile, the redundant services can be removed from the service composition with the help of dynamic programming. Experiments show that the approach can obtain the top-k QoS-optimal service composition and better time performance.
Investigating quality of institutional repository website design using usability testing framework

Quality of website design is one of the influential factors of website success. How the design helps the users using the website effectively and efficiently and satisfied at the end of the use. However, it is a common tendency that websites are designed based on the developer's perspectives and less consideration on the user's importance. Thus, the degree of website usability tends to be low, according to user perceptions. This study purposed to understand the user experiences using an institutional repository (IR) website in a public university in Indonesia. The research was performed based on usability testing framework as the usability testing method. About 12 participants were purposely involved regarding their key informant characteristics. Following three empirical data collection techniques (i.e., query technique, formal experiment, and thinking aloud), both descriptive analysis using usability scale matric and content analysis using qualitative data analysis (QDA) Miner Lite software were used in the data analysis stage. Lastly, several visual design recommendations were then proposed at the end of the study. In terms of a case study, besides the practical recommendations which may contextually useful for the next website development, the clarity of the research design may also help scholars how to combine more than one usability testing techniques within a multi-technique study design.
A virtual reality experiment system for an introductory computer hardware course

Abstract The University Computer Foundation (UCF) course, which is a compulsory theoretical course for freshmen, especially science and engineering department students, lays the foundation for more advanced courses. However, the UCF course covers a wide variety of concepts, including the computer hardware, which are difficult for freshmen to comprehend. In recent years, virtual reality (VR) has become prevalent in many educational settings. To improve the teaching of the UCF course, we developed a VR program about introductory computer hardware and invited freshmen from two universities in China to take part in the experiment as participants. Four aspects of computer contents, namely the evolutionary history of computers, the computer components, the computer assembly, and the workflow of computer hardware, are displayed in the program. During the experiment, students' behavioral data were recorded. Then, the data were analyzed and the reasons were speculated for the differences among groups of various categories. From the experiment, the relationships between the students' behavior and their groups were found, and we demonstrated that our VR program is effective at attracting the students' curiosity and increasing their understanding of computer hardware in the UCF course. The study concludes with suggestions for practitioners and researchers in the field of VR for university education.
RETRACTED: The effect of big-data on the management of higher education in China and its countermeasures

Nowadays, China has got into the era of big-data. In the background of big-data information era, the management of college students in China has undergone certain changes, and the trend of student management informationization is obvious. Universities are momentous places for cultivating talents. The student management work of universities directly affects the quality of talent training and affects the stability of colleges and universities (CAU). The internal structure and external environment faced by higher education are undergoing unprecedented profound changes, bringing many new situations, new problems and new challenges to the supervision of university students. Therefore, college education managers need to show solicitude for the impact of big-data technology on college education management (CEM) and propose corresponding countermeasures to raise the productivity of college education administration. This paper concentrates on the characteristics of big-data. On the basis of investigating the present situation and matters of the development of big-data education administration in CAU in China, this paper explores the countermeasures to raise the innovation of big-data education management (BDEM) in universities in China.
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set

Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools.In this paper, we introduce LaRoSeDa, a Large Romanian Sentiment Data Set 1 , which is composed of 15,000 positive and negative reviews collected from one of the largest Romanian e-commerce platforms.We employ two sentiment classification methods as baselines for our new data set, one based on low-level features (character n-grams) and one based on highlevel features (bag-of-word-embeddings generated by clustering word embeddings with kmeans).As an additional contribution, we replace the k-means clustering algorithm with self-organizing maps (SOMs), obtaining better results because the generated clusters of word embeddings are closer to the Zipf's law distribution, which is known to govern natural language.We also demonstrate the generalization capacity of using SOMs for the clustering of word embeddings on another recentlyintroduced Romanian data set, for text categorization by topic.
A Novel Modeling Framework Based on Customized Kernel-Based Fuzzy C-Means Clustering in Iron Ore Sintering Process

This article proposes a customized kernel-based fuzzy C-means (CKFCM) clustering, which provides an original framework for data interpretation and data analysis and delivers an effective solution for an accurate division of actual run data under multiple operating conditions in an iron ore sintering process (IOSP). The improvement of CKFCM clustering is achieved by including an adjustment factor introduced into the kernel-based fuzzy C-means clustering. The adjustment factor only needs to consider a small amount of labeled data that are determined based on expert experience. Subsequently, the CKFCM clustering is applied to the modeling of the IOSP, and the Spearman’s rank correlation coefficient method is utilized to determine input variables of the model under different operating conditions. Furthermore, the broad learning model is employed to build the prediction model for each operating condition. Finally, we conducted an in-depth analysis of the presented clustering method. Its performance has been experimentally demonstrated by many publicly available datasets. Meanwhile, simulation results involving actual run data of the IOSP demonstrate the superiority and effectiveness of the developed model in carbon efficiency prediction. We show that the developed model outperforms the state-of-the-art prediction models in achieving a good balance between simplicity and accuracy.
Розвиток цифрової компетентності педагогічних працівників у системі післядипломної освіти

The article reveals theoretical and applied issues of digital competence in pedagogical staff in the system of postgraduate education. The actualization of this issue is analyzed in the context of challenges caused by quarantine restrictions due to the spread of coronavirus infection COVID-19 and the use of distance learning technologies. The mechanisms of digital competence development of the modern teacher are offered based on domestic and foreign experience. The activity of M.V. Ostrogradsky Poltava Regional Institute of In-Service Teacher Training in the direction of the researched issue, in particular, the organization of meetups and methodical support of digital competence development, opening the Center of distance learning and digital literacy support, getting started of the regional repository of educational materials for a distance learning component of the educational process in general secondary education.
High-Performance FPGA Accelerator for SIKE

New primes were proposed for Supersingular Isogeny Key Encapsulation (SIKE) in NIST standardization process of Round 2 after further cryptanalysis research showed that the security levels of the initial primes chosen were over-estimated [#AdjCCMR18, #SIKESubmission2]. In this paper, we develop a highly optimized \mathbb{F}_{p} Montgomery multiplication algorithm and architecture that further utilizes the special form of SIKE prime compared to previous implementations available in the literature. We furthermore improve the scheduling for our program Rom. As SIKE Stays as an alternate candidate in Round 3 of standardization it has high potential to be standardized in Round 4. Therefore, we implement SIKE for all Round 2 NIST security levels (SIKEp434 for NIST security level 1, SIKEp503 for NIST security level 2, SIKEp610 for NIST security level 3, and SIKEp751 for NIST security level 5) on Xilinx Artix 7 and Xilinx Virtex 7 using the proposed multiplier. Our best implementation (NIST security level 1) runs 38% faster and occupies 30% less hardware resources in comparison to the leading counterpart available in the literature [#brian-2019] and implementations for other security levels achieved similar improvement.
Psychological Effects of Dominant Responses to Early Warning Alerts

Earthquake-related behaviors in Mexico and Japan have highlighted the need to better understand responses to demanding alerting scenarios. Both countries appear to have benefitted from an established early earthquake warning system for several years. However, recent alert responses documented in these settings have been unlikely to protect residents from death or severe injury. This represents a gap between alerting system investments and effectiveness which, among other implications, could result in very large numbers of avoidable injuries and even deaths. To help better understand and address this gap, the current paper presents a theoretical explanation of why alerted residents have responded in the ways that they did. Behavioral and cognitive theories are discussed towards an integrated but simple model of alert response behavior that can be used to guide further research. Challenges and opportunities for this further research are also outlined.
Two-photon controlled-phase gates enabled by photonic dimers

Photons are appealing as flying quantum bits due to their low-noise, long coherence times, light-speed transmission, and ease of manipulation at the single-qubit level using standard optical components such as beam splitters and waveguides. The challenge in optical quantum information processing has been the realization of two-qubit gates for photonic qubits due to the lack of highly efficient optical Kerr nonlinearities at the single-photon level. To date, only probabilistic two-qubit photonic controlled-phase gates based on linear optics and projective measurement using photon detectors have been demonstrated. Here we show that a high-fidelity frequency-encoded deterministic two-photon controlled-phase gate can be achieved by exploiting the strong photon-photon correlation enabled by photonic dimers, and the unique nonreciprocal photonic propagation in chiral quantum nanophotonic systems.
BSTC: A Large-Scale Chinese-English Speech Translation Dataset

This paper presents BSTC (Baidu Speech Translation Corpus), a large-scale Chinese-English speech translation dataset.This dataset is constructed based on a collection of licensed videos of talks or lectures, including about 68 hours of Mandarin data, their manual transcripts and translations into English, as well as automated transcripts by an automatic speech recognition (ASR) model.We have further asked three experienced interpreters to simultaneously interpret the testing talks in a mock conference setting.This corpus is expected to promote the research of automatic simultaneous translation as well as the development of practical systems.We have organized simultaneous translation tasks and used this corpus to evaluate automatic simultaneous translation systems.
On the benefits of representation regularization in invariance based domain generalization

Abstract A crucial aspect of reliable machine learning is to design a deployable system for generalizing new related but unobserved environments. Domain generalization aims to alleviate such a prediction gap between the observed and unseen environments. Previous approaches commonly incorporated learning the invariant representation for achieving good empirical performance. In this paper, we reveal that merely learning the invariant representation is vulnerable to the related unseen environment. To this end, we derive a novel theoretical analysis to control the unseen test environment error in the representation learning, which highlights the importance of controlling the smoothness of representation. In practice, our analysis further inspires an efficient regularization method to improve the robustness in domain generalization. The proposed regularization is orthogonal to and can be straightforwardly adopted in existing domain generalization algorithms that ensure invariant representation learning. Empirical results show that our algorithm outperforms the base versions in various datasets and invariance criteria.