Reviewerly/exHarmony huggingface dataset:
Construction of English-assisted Translation Learning System Based on Association Rules Mining

The educational goal of college English is to cultivate students' English application ability and improve cross-cultural communication awareness and communication ability. On the one hand, applying computer-assisted translation to translation teaching can stimulate students' learning motivation and make them form active discovery learning methods; on the other hand, it can extend students' classroom teaching, absorb more relevant knowledge and achieve the optimization of teaching. Under the background of information age, the main purpose of English-assisted translation learning system is to assist students to be more autonomous, personalized and flexible in learning English, so as to improve students' comprehensive English ability and cultural communication ability. Based on the association rule mining algorithm, this paper studies the effect of corpus-based English blended learning model. Combining massive open online courses and corpus-driven mixed teaching mode, this paper puts forward corresponding improved algorithms for the problems existing in current translation software, and constructs an English-assisted translation learning system based on association rule mining algorithm.
DLAFS CASCADE R-CNN: AN OBJECT DETECTOR BASED ON DYNAMIC LABEL ASSIGNMENT

Object detection methods based on Deep Learning are the revolution of the Computer Vision field in general and object detection problems in particular. In detail, they are methods that belonged to the R - CNN family: Faster R - CNN and Cascade R - CNN. The characteristic of them is the Region Proposal Network, which is utilized for generating proposal regions that may include objects or not, then the proposals will be classified by the IoU threshold. In this study, we apply dynamic training, which adjusts this IoU threshold depending on the statistic of proposal regions on the Faster R - CNN and Cascade R - CNN, training on the SeaShips and DODV dataset. Cascade R - CNN with dynamic training achieve higher results compared to normal on both two datasets (higher 0.2% and 5.7% on the SeaShips and DODV dataset, respectively). In the DODV dataset, Faster R - CNN with dynamic training also perform higher results compared to its normal version, 4.4% higher.
An Applied Research of Big Data in the Work of Higher Education Students

This article is an applied research that combines the technology and methods of big data with student work. This study used the canteen consumption data of freshman to junior students in a university in Guangdong from September 2018 to December 2020. And found the suspected lonely in the student group by constructing a co-occurrence network. The results showed that: (1) the frequency of co-occurrence among freshmen to junior students shows a decreasing trend, and the frequency of co-occurrence of freshmen friends was significantly higher than that of sophomores and juniors; (2) The number of college students who are suspected of being "lonely" increases with grades; (3) The average consumption level of students who are suspected of being "lonely" is significantly lower than that of students who are "non-lonely"; (4) Students who are suspected of being "lonely" are more inclined to use meal cards for consumption rather than WeChat/Alipay; (5) The degree of intimacy among freshman students is significantly higher than that of sophomores and juniors.
Procedural Knit: Exploring Underdetermined Fabrication via Knit, Procedural Generation and Posture Detection

Once a slow and reflective handcraft embodying a designer/maker’s skill, mood and imaginations, knitting has been automated through computer-aided design(CAD) and manufacturing technologies (CAM), reducing the role of the textile designer in the processes of textile production. In response, Procedural Knit presents a knit procedural generation machine, designed to enable collaboration between the designer/maker and a computational textile design and production technology. The design of the colorwork knitted pattern isn’t predetermined but created through generative design algorithms influenced in real-time by the designer’s posture during the knitting process. Using this system, we explore the design space of “underdetermined fabrication”, an approach where interactive making systems and procedural rules guide but do not determine the final outcome. Through this system, we raised the question of whether the laborious and repetitive nature of producing knit on semi-automated machines could instead be an integral part of the design-through-making process, similar to other handcrafts such as pottery wherein the potter makes active design choices during the pottery’s fabrication. Furthermore, we uncover how interactive underdetermined fabrication can create room for unpredictable emergent phenomena and aesthetics, and notions of imbuing a “maker’s signature” into the resultant knitted artefacts.
ANALYSIS OF LEARNING OBJECTS FOR OPTIMIZATION AND DIGITAL TRANSFER REPORT OF INTERMEDIATE RESULTS ON LEARNING PATH IDEAS - FIRST SURVEY

This paper is a progress report about a project that consists of five stages starting with an analysis of the current state of application of higher educational learning material with regards to structural format and media didactics.The aim of this analysis has been accomplished through an initial survey with the motivation to analyze didactic and digital aspects of final study competencies as well as individual learning styles.The next stages include a digitalization process and an upcoming evaluation section.The results of the initial survey show that key competence goals that are formulated by teachers are not always comprehended and accepted by students.Students sometimes do not understand the purpose of the teachers' learning path ideas and, thus, do not accept some subjects and intermediate goals.These lacks of acceptance provide a fundamental basis of refining teaching courses.This report summarizes an approach to optimize learning goals and digital transfer so that precise evaluation helps finding these misconceptions and therefore both course structure and learning materials can be improved.Furthermore, it advices teachers to focus on teaching relevant topics not only for exam purposes but also for future professional competencies.Finally, this paper serves an example for teachers, especially of higher education level, to find weaknesses in a teaching objective and how to prioritize optimization, thereby clarify the learning path to students.This increases motivation and improves digital transfer.
Intelligent Forecasting Method for Substation Operating Cost of Power Network with Nonstationary Characteristics

With the rapid development of the national economy, the power system has been greatly expanded. The operation cost of power grid substation also presents a trend of sharp increase, which brings great challenges to power system. In order to manage and control the operation cost of substation effectively, the intelligent prediction method of power system operation cost with non-stationary characteristics is proposed. Based on the MCSSD method, this paper extracts the non-stationary features of substation operation information, analyzes the economic load rate of substation, determines the substation capacity, constructs the intelligent forecast model of substation operation cost according to the operation law of substation equipment life cycle, and obtains the forecast result of operation cost, and based on this, formulates the intelligent forecast and control measures of substation operation cost. The experimental data shows that after the method should be proposed, the average time for forecasting the operation cost of substation is 11.6, which is lower than the maximum limit, and the highest prediction accuracy is 83%. The results of the two indicators are in line with the standard, which fully confirms that the proposed method has a better forecasting effect of substation operation cost.
Metric Identification Evaluating Security Information: A Systematic Literature Review

Metrics for the evaluation of information security basically contribute to risk reduction within organizations dealing with the manipulation of information. However, in information security metric approach, generally there is not a standard classified appreciation regarding essential metrics within the required scope to do this activity in risk reduction and asset protection. Therefore, this research is a Systematic Literature Review (SLR) regarding the evaluation of information security. In this study, 50 bibliographical data-base extracted articles such as ScienceDirect, Scopus, IEEE, ACM Digital Library, Hindawi, MDPI and Springer were gathered and analyzed. Scientific documents answered the proposed research question whereas results identified several information security metric classifications such as integrity, vulnerability, authorization and confidentiality.
A New Encryption Scheme Based on RSA Encryption Algorithm and Chen Chaotic System

Nowadays the security of images is obviously more important. Some scholars have applied Rivest-Shamir-Adleman (RSA) algorithm and Chen chaotic system to image encryption. However, the application of the two methods in image encryption can still be explored. In this paper, it proposes an encryption scheme which uses asymmetric cipher RSA encryption algorithm and Chen chaotic system to scramble and diffuse the image. In this encryption scheme, firstly, it applies the RSA algorithm to obtain the public key and uses the specified formula to calculate the initial value of the Chen chaotic system. Secondly, the four chaotic sequences generated by Chen chaotic system are applied to the scrambling stage and the diffusion stage of the image process respectively. In the scrambling phase, it uses Arnold scrambling. It performs forward XOR diffusion and then performs reverse XOR diffusion in the diffusion stage. Finally, the encryption process is all completed and the encrypted image is obtained. Moreover, after appropriate experiments, the encryption algorithm is proved to have a good guarantee for the security of the image.
Blockchain and Mobile Payment: Assessment on Privacy and Usability and a Scheme for Enhancement

Blockchain is a new paradigm to realize payment without a single Trusted-Third-Party. The technology exploits cryptography to secure all the transactions that are available to participating nodes for validation via distributed consensus algorithms. Blockchain-based architectures for mobile payment face challenges like transaction privacy and performance issues. We analyze these architectures and assess the privacy issues and the performance in the real world. Then, we propose a payment scheme to guarantee the privacy of the transactions with discussions on tricks to improve the performance. We study the feasibility of implementation of the proposed scheme, both on public and consortium Blockchains. The payment scheme ensures that a participating node has access to only a part of the meaningful data of a transaction. The expected performance with Hyperledger Blockchain (with less than 16 participating nodes) is more than 1000 TPS. We indicate updates to decrease the duration of a transaction from 15 S to less than 1 S with public Ethereum Blockchain.
Day-ahead Forecast of PV Systems and End-Users in the Contest of Renewable Energy Communities

Efficient forecasting algorithms represent a key issue for monitoring and control of energy communities (ECs). After recalling the basic steps to perform a forecasting analysis, this paper reports the preliminary results obtained within the framework of the Italian ComER project, that aims to develop methods and tools for management and control of renewable ECs. Day-ahead forecast is applied to photovoltaic (PV) systems, residential end-users, and a public building. For each user, firstly an accurate preliminary analysis of public and private datasets of the target variables is performed. Then, forecasting methods based on persistence, multiple linear regression (MLR) and autoregressive integrated moving average (ARIMA) models have been implemented. Finally, results are compared to identify the more accurate forecasting method for each user belonging to the EC.
Touch for Social Engagement and Therapy: A Narrative Review

This narrative review includes summaries of research on touch for social engagement and for therapy.Touch for social engagement includes greeting touch, affectionate touch and buffers for touch deprivation.Typical greeting behaviors including shaking hands and hugging were superseded by fist and elbow bumping following safety concerns during COVID-19 lockdowns.Affectionate touch as in handholding, cuddling, caressing and kissing have received less recent research attention likely because of the difficulty recruiting participants for affectionate touch during COVID.Instead, touch deprivation has been common including data suggesting that 60% reported being touch deprived during COVID lockdowns, only 21 % of children were being touched a lot and only 38% of partners were being touched a lot.This level of touch deprivation may have preceded COVID based on an airport gate study showing that people were only touching 4% of their waiting time as they were spending most of their time on cell phones scrolling and texting.Although natural activities have been noted to compensate for touch deprivation such as exercise, several studies have attempted to simulate social touch in robots.Touch as therapy has included several different types of touch including cuddling and handholding during painful procedures and massage therapy for innumerable medical and psychiatric conditions.The underlying mechanism for the positive effects of touch involves the stimulation of pressure receptors, as in moving the skin, resulting in a slowing of the nervous system including decreased heart rate, increased heart rate variability, less stress hormone (cortisol), and the increased production of healthy neurotransmitters and natural killer cells to ward off bacterial, viral and cancer cells.
A Model to Predict Daily Snow Albedo Change Over Time

As solar photovoltaic technology is deployed in snowier and more northern locations and bifacial module use continues to increase, ground albedo from snow plays a significant role in the energy output of these systems. Accurately knowing the ground albedo allows for more accurate production models. Here we use multiple years of measured snow and albedo data from 6 locations to develop a snow albedo change model that uses time and temperature as the major inputs.
An Improved Partition Filter Network for Entity-Relation Joint Extraction

The purpose of a joint entity-relation extraction task is to extract entity-relation triples from unstructured text to assist text analysis, knowledge graph construction, etc. The existing sequence-to-sequence or sequence-to-non-sequence models treat the joint extraction task as a triple generation task, sharing the feature space of entity and relation extraction in the same structure. However, fusing the information of both subtasks may cause the problem of feature conflicts and thus decrease model performance. In order to enable each extraction subtask has its own independent feature space to reduce feature conflicts, this paper proposes a dual-decoder to decode entity extraction subtask and relation extraction subtask separately based on an encoder-to-decoder structure. A Dual-Joint-Input-PFN model is proposed by improving the partition filter network as an interaction to capture connection information between two subtasks. The model consists of two Joint-Input-PFNs layers, and each layer accepts two inputs simultaneously and filters the other input according to one of them. The experiments are based on standard datasets WebNLG and NYT, and the effectiveness of the proposed model is verified by comparing with the state-of-the-art baseline methods.
Two case studies in approach to the renovation of volumetrically challenged music rehearsal spaces

A music rehearsal room renovation can be acoustically challenging in that there is often limited room volume, lack of loudness control, and budget constraints. Two case studies detail these challenges. (1) The University of North Texas’ choral rehearsal room was due for renovation and with a volume of only 44,000 cubic feet, the room had to accommodate choral ensembles varying from 20–85 people for daily use, over 100 voices for an annual festival, and an added recital use. At an appropriate budget, the renovation used room shaping to simultaneously break up parallel surfaces and support voices while also adding adjustable acoustics to allow for tuning of the room for each rehearsal or performance. (2) The University of Texas at San Antonio’s large band, orchestra, and jazz rehearsal room lacked loudness control even with an approximate volume of 73,500 cubic feet to serve 30 – 70 + musicians. At a much more limited budget, the solution was to flip the orientation of the musician layout in the room to take advantage of the existing room geometry and to introduce variable absorption stored exposed to the room which increased the overall absorption while also giving the ensembles an opportunity to tune the room.
Mapping the health quality in Sumenep using K-Medoids Algorithm

Health is an investment to support economic development and has an important role in poverty eradication. However, the quality of health in some subdistricts in Sumenep Regency, East Java, Indonesia is still below the standard based on three health indicators namely nutrition and health program, infectious disease control, and environmental health and behavior. Considering these indicators, the government encounters some difficulties in choosing which indicators should be prioritized in each subdistrict to improve the quality of health and allocate the budget. Here in this study, we employed cluster analysis to map 27 subdistricts in Sumenep based on three health indicators with the k-medoids method. The k-medoids method is a partitioning method of clustering that aims to find a set of k-clusters among the data that best characterize objects in a data set. The K-Medoids are robust to the existence of outliers and perform better for large data sets. The result reveals that the k-medoid method grouped 27 subdistricts into two clusters, cluster 1 consisting of 22 subdistricts and cluster 2 consisting of 5 subdistricts. Cluster 1 represents subdistricts with a low level of nutrition and health program implementation as well as infectious disease control, but a high level of environmental health and behavior indicators implementation. Moreover, Cluster 2 indicates subdistricts with low environmental health and behavior implementation, despite the high implementation of the other two indicators. Based on the findings we recommend the government should focus on improving nutrition and infectious disease programs for subdistricts in Cluster 1 and improving environmental conditions and behavior for subdistricts in Cluster 2.
Correction to: Chasing the Wrong Cloud: Mapping the 2019 Vaping Epidemic Using Data from Social Media

In an older version of this paper, there was error in figure 2. This has been corrected.
Enhancing Self-disclosure In Open-Domain Dialogue By Candidate Re-ranking

Neural language modelling has progressed the state-of-the-art in different downstream Natural Language Processing (NLP) tasks. One such area is of open-domain dialog modelling, neural dialog models based on GPT-2 such as DialoGPT have shown promising performance in single-turn conversation. However, such (neural) dialog models have been criticised for generating responses which although may have relevance to the previous human response, tend to quickly dissipate human interest and descend into trivial conversation. One reason for such performance is the lack of explicit conversation strategy being employed in human-machine conversation. Humans employ a range of conversation strategies while engaging in a conversation, one such key social strategies is Self-disclosure (SD). A phenomenon of revealing information about one-self to others. In this work, Self-disclosure enhancement architecture (SDEA) is introduced utilizing Self-disclosure Topic Model (SDTM) during inference stage of a neural dialog model to re-rank response candidates to enhance self-disclosure in single-turn responses from the model.
On Bidirectional Heuristic Search in Classical Planning: An Analysis of BAE*

Heuristic search is a successful approach to cost-optimal planning. Bidirectional heuristic search algorithms have been around for a long time, but only recent advances have led to algorithms like BAE* that have the potential to outperform unidirectional heuristic search algorithms like A* in practice. In this work, we analyze BAE* for classical planning and the challenges associated with the underlying assumption of an explicit state representation. We show that it is crucial to use mutexes and reachability analysis to reduce the potentially exponential number of goal states, which makes it possible to create an explicit representation of a reversed planning task that can be used for the backward search of BAE*. Our empirical evaluation shows that BAE* solves more instances than A* in multiple domains with significantly fewer node expansions, demonstrating the usefulness of BAE* in planning.
The effect of the forget gate on bifurcation boundaries and dynamics in Recurrent Neural Networks and its implications for gradient-based optimization

Recurrent Neural Networks (RNNs) are an internal dynamics approach to identify models from time series data. They have been successfully applied, e.g. in natural language, speech and video processing [1] and the identification of nonlinear state space models [2]. Conventional RNNs, such as the Elman-RNN, are notoriously hard to optimize, since they are highly initialization dependent, prone to slow convergence, and tend to converge to poor local minima. In recent years, the vanishing/exploding gradient phenomenon, which arises when employing gradient-based optimization techniques such as Backpropagation Through Time (BPTT), has been identified as the root cause of these difficulties. This led to the development of several new RNN-architectures, such as the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU), which were intended to prevent the vanishing-gradient problem and surpassed conventional RNNs in all areas of application. However, it has been shown that the gradient also vanishes in Gated Units [3] and there is no work showing, that the rate of decay is lower than in Elman-RNN. This suggests that the underlying mechanisms responsible for their success are at least in part not yet fully understood. The purpose of this paper is to provide an alternative explanation for the superior performance of Gated Units by viewing them as nonlinear dynamical systems and studying the stability of their fixed points. This work expands on the work of Doya et al. [4] and Pascanu et al. [5], who studied the effects of bifurcation boundaries in the parameter space of Elman-RNNs with one internal state on gradient-based learning.