Reviewerly/exHarmony huggingface dataset:
The Importance of the Correlation in Crossover Experiments

<i>Context:</i> In empirical software engineering, crossover designs are popular for experiments comparing software engineering techniques that must be undertaken by human participants. However, their value depends on the correlation ( <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> ) between the outcome measures on the same participants. Software engineering theory emphasizes the importance of individual skill differences, so we would expect the values of <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> to be relatively high. However, few researchers have reported the values of <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> . <i>Goal:</i> To investigate the values of <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> found in software engineering experiments. <i>Method:</i> We undertook simulation studies to investigate the theoretical and empirical properties of <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> . Then we investigated the values of <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> observed in 35 software engineering crossover experiments. <i>Results:</i> The level of <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> obtained by analysing our 35 crossover experiments was small. Estimates based on means, medians, and random effect analysis disagreed but were all between 0.2 and 0.3. As expected, our analyses found large variability among the individual <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> estimates for small sample sizes, but no indication that <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> estimates were larger for the experiments with larger sample sizes that exhibited smaller variability. <i>Conclusions:</i> Low observed <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> values cast doubts on the validity of crossover designs for software engineering experiments. However, if the cause of low <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> values relates to training limitations or toy tasks, this affects <i>all</i> Software Engineering (SE) experiments involving human participants. For all human-intensive SE experiments, we recommend more intensive training and then tracking the improvement of participants as they practice using specific techniques, before formally testing the effectiveness of the techniques.
A Short Text Classification Model Based on Cross-Layer Connected Gated Recurrent Unit Capsule Network

Text classification is an important task in natural language processing. In the past few years, some prominent methods have achieved great results in text classification, but there is still a lot of room for development in enhancing the expressive capabilities of text features. In addition, although deeper networks can better extract features, they are easy to produce gradient disappearance or gradient explosion problems. To solve these problems, this paper proposed a hybrid model based on cross-layer connected gated recurrent unit capsule network (CBiGRU_CapsNet). Firstly, the model fused word vectors trained by Word2Vec and GloVe to form the network input layer. In the stage of high-level semantic modeling of text, a cross-layer connected gated recurrent unit has been proposed, which can solve the problem of gradient disappearance or explosion, and strengthen the transfer between features of each layer. Furthermore, capsule network is applied to obtain the rich spatial position information in the deep high-level semantic representation, and the weight value of important features has been increasing through the core dynamic routing algorithm of the capsule network to improve the expression ability of the features, which is then inputted to the softmax layer to complete text classification task. The experimental results on several public datasets shown that CBiGRU_CapsNet outperforms the state-of-the-art models in terms of the accuracy for text classification tasks.
PAS: Enable Partial Consensus in the Blockchain

Permissioned Blockchain enables distributed collaboration among organizations that may not trust each other. However, existing systems cannot efficiently support the ordering and execution of transactions in different workflows parallelly, which seriously affects system scalability and performances in terms of throughput and latency. In this paper, we present a partial consensus mechanism named PAS to achieve fault tolerance and parallelism of transaction processing. In PAS, transactions in different workflows only need to be confirmed by the involved subset of nodes, which significantly enhances the system performance and scalability. Specifically, we introduce a novel data structure, called the hierarchical consensus tree (HCT). It is maintained in each node and used to coordinate the consensus process. HCT guarantees that the consistency reached in different sets of nodes is eventually agreed by all nodes without conflicts and rollbacks. Since there are many valid HCTs with different system improvements, we introduce an optimization problem, named OHCT, to obtain an HCT with respect to the optimal enhancement. We prove OHCT is NP-hard and propose a general framework with efficient algorithms to address it. Finally, we implement PAS on PBFT-based Hyperledger fabric and conduct extensive experiments to show the performance and scalability of PAS.
Unpacking and Overconfidence in a Production Management Game

People often make judgements irrationally. For example, people tend to underestimate the completion time of tasks and be overly confident in their estimations. Previous research shows that unpacking tasks into a series of required steps reduced the underestimation of completion times. The purpose of this research was to examine the phenomenon of overconfidence in simulation and gaming situations and study the relationship between unpacking and overconfidence. For this research, a production management game, the “OPT SCHEDULING GAME,” was used. Two different questionnaires were distributed before game run to examine whether some informative questions improved the quality of estimations by participants; the results for this were affirmative. A post-game questionnaire assessed the attitudes of participants regarding their understanding of the process, result prediction, etc. The results of this questionnaire showed that their attitudes remained the same whether they were provided with informative questions or not before game run. This was consistent with the results of previous research that showed unpacking did not improve the overall quality of performance on a task, even though unpacking does improve the quality of estimations on the time needed to complete that task. One reason would be those who have an incorrect understanding of the situation tend to be overconfident and convincing and those who have a correct understanding of the situation tend to be less confident and silent. If we could assist those with an incorrect understanding and a sense of overconfidence to become good listeners and support those with a correct understanding and a feeling of uncertainty to become more open and outspoken, then the effect of overconfidence would be reduced. If simulation and gaming can assist people to learn from each other, people can make better judgements as a team.
Selected Aspects of the New Recommendation on Subjective Methods of Assessing Video Quality in Recognition Tasks

It was once thought that high QoS (Quality of Service) performance solves recurrent problems of low-quality multimedia services. Since then, solutions have been proposed to ensure a high level of QoE (Quality of Experience). In this document, the authors attempt to outline his understanding of an accurate meaning of quality of multimedia services. Starting from QoS and passing through generalised QoE, the authors focus on aspects of subjective and objective quality modelling and optimisation of visual performance for TRV (Target Recognition Video) applications (such as video surveillance), outlining the path of ITU-T standardisation in this area. The authors revised the ITU-T Recommendation P.912 to reflect improved subjective test techniques developed since this Recommendation was approved. The authors also attempt to predict at least some existing errors of reasoning, which are likely to become evident for the industry in the next decade.
A preliminary phase on anatomizing multiple sensitive attribute by determining main sensitive attribute

Abstract This paper aims to determine the main sensitive attribute in anatomy with multiple sensitive attributes. Privacy in published data becomes important and interesting research recently. Many works have been conducted on keeping published data privately. K-anonymity was proposed as the first model in this study. Yet, this model still has some drawbacks. A better model called anatomy is proposed to fix k-anonymity. Many studies on anatomy are performed on a single sensitive attribute, while in the real world, a microdata table always contains multiple sensitive attributes. This work handles problems on how to publish data in private mode by rearranging the anatomy model. We adopted the method in determining main sensitive attribute. We conducted it in the preliminary phase and its result is a prepared model of the main sensitive attribute for anatomy. The result success in ensuring one sensitive attribute as a main sensitive attribute.
Contour: Penalty and Spotlight Mask for Abstractive Summarization

The act of transferring accurate information presented as proper nouns or exclusive phrases from the input document to the output summary is the requirement for Abstractive Summarization task. To address this problem, we propose Contour to emphasize the most suitable word in the original document that contains the crucial information in each predict step. Contour contains two independent parts: Penalty and Spotlight. Penalty helps to penalize inapplicable words in both training and inference time. Spotlight is to increase the potential of important related words. We examined Contour on multiple types of datasets and languages, which are large-scale (CNN/DailyMail) for English, medium-scale (VNTC-Abs) for Vietnamese, and small-scale (Livedoor News Corpus) for Japanese. Contour not only significantly outperforms baselines by all three Rouge points but also accommodates different datasets.
Capturing Miner and Mining Pool Decisions In A Bitcoin Blockchain Network: A Two-Layer Simulation Model

Motivated by the growing interests in Bitcoin blockchain technology, we build a Monte-Carlo simulation model to study the miners' and mining pool managers' decisions in the Bitcoin blockchain network. Our simulation model aims to capture the dynamics of participants of these two different parties and how their decisions collectively affect the system dynamics. Given the limited amount of monetary budget and mining power capacity, individual miners decide on which mining pools to join and determine how much hashing power to invest. Mining pool managers need to determine how to appropriately allocate the mining reward and how to adjust the membership fee. In addition to the aforementioned miner and pool behavior, we also characterize the system-level dynamics of the blockchain in terms of mining difficulty level and total hashing power.
Multimedia technology implementation to promote digital learning

Abstract The development of new technology has several impacts on people’s lives; one of which is the presence of multimedia technology. It is commonly known that one of the easily accessed multimedia technology are electronic books (e-books). This study identifies the implementation of multimedia technology implementation in forms of e-books to promote digital learning. The results showed despite the existence of several other options, e-books are still preferable due to its accessibility and usability. It has also been implied that e-books are relevant in both industrial and educational fields.
Detection and Fault Prediction in Electrolytic Capacitors Using Artificial Neural Networks

Capacitors are electronic components that present a considerable variation in their characteristics during their useful life. After being submitted to several charge/discharge cycles, capacitors present losses in capacitance values and operate differently from the nominal characteristics. PHM (Prognostics and Health Monitoring) techniques can be used to monitor the evolution of a capacitor health condition and to predict its RUL (Remaining Useful Life). This paper uses artificial neural networks to monitor the degradation index of capacitors and predict the corresponding RUL. Different neural network architectures are investigated: MLP (Multilayer Perceptron), RBF (Radial Basis Function), and ELM (Extreme Learning Machine). The performances of the different architectures are compared in terms of the coefficient of determination (R2) and the Mean Squared Error (MSE). The accuracy of RUL predictions are compared based on the Relative Accuracy (RA) indicator, which is a performance indicator proposed in the literature to evaluate PHM algorithms.
COVID-19 AND DISTANCE LEARNING

While microbes have evolved in human history in such a clever and clever way to survive, whenever people start to fight, after these wars, four horsemen of the apocalypse appear.The four horsemen of the apocalypse: war, famine,
TSN: Performance Creative Choreography Based on Twin Sensor Network

The purpose of this paper is to improve the efficiency of performance creative choreography (PCC). Our research work shows that we can realize the model integration and data optimization for PCC in complex environments based on the combined architecture of sensor network (SN) and machine-learning algorithm (MLA). In order to explain the process and content of this research better, this paper designs a specific problem description framework for PCC, which mainly includes the following content: (1) a twin sensor network (TSN) architecture based on digital twin information interaction is proposed, which defines and describes the acquisition method, classification (creative data, rehearsal data, and live data), and temporal and spatial features of performance data. (2) Proposed a mobile computing method based on director semantic annotation (DSA) as the core computing module of TSN. (3) A spatial dynamic line (SDL) model and a creative activation mechanism (CAM) based on DSA are proposed to realize fast and efficient PCC of dance with the TSN architecture. Experimental results show that the TSN architecture proposed in this article is reasonable and effective. The SDL model achieved significantly better performance with little time increase and improved the computability and aesthetics of PCC. New research ideas are proposed to solve the computational problem of PCC in complex environments.
Automatic parking of articulated vehicles using a soft computing approach

Vehicle driving for less-experienced individuals, even with daily use, is a difficult task, especially when the subject is parking or moving backward. The problem is even more complicated for articulated vehicles, most commonly in garages and custom parking spaces where the vehicles are parked for loading/unloading purposes. From a control-oriented point of view, the nonholonomic constraints and nonlinear and time-variant equations make the control and navigation of the vehicle more complicated. This chapter explains one of the methods of designing an automated parking system for articulated vehicles. Articulated vehicle for loading in special parking lots, according to the defined scenario, in two phases of movement, first moving forward, the vehicle is placed in a suitable place. Then move backwards to load the end of the trailer in the right place. Because of the nonlinear and sophisticated kinematic motion of the articulated vehicle and also the successful application of fuzzy logic and artificial neural networks in vehicle handling, in this research an adaptive-network-based fuzzy inference system (ANFIS) approach was proposed to solve this problem.
Communication as Social Phenomenon

The paper aims to reveal essence of communication as a social phenomenon which presents information exchange between individuals as social subjects. The research objective includes analysing such notions as “communication”, “dialogue”, “signal means of communication”, “technical means of communication”. The authors try to reveal correlation of these notions. Scientific originality of the study lies in the fact that the problem of communication is considered through the lens of social interaction. The paper argues that communication is a process of information transfer realized through socially acceptable forms of social interaction. Communication as a social phenomenon is characterized by rationality and technicalization. The findings indicate that communication is an external manifestation of dialogic process realized through socially acceptable, effective, rational forms of information transfer between social interaction subjects. Communication is considered as informational-instrumental influence manifesting itself in socially acceptable forms.
Survey on Reversible Watermarking Techniques of Echocardiography

In critical domains such as medical and military, reversible watermarking (RW) has been used. In the medical domain, different modalities are used to store patient information. The current study focuses on the application of RW in echocardiography data. Mostly, RW is applied to protect patient data without affecting the quality of the decoded image. The RW methods are benchmarked as per imperceptibility, robustness, and payload. The survey presents a comparison of state-of-the-art RW techniques. The imperceptibility and payload are balanced through a tradeoff. It has been observed in the literature that most of the RW methods lack robustness, and very small-scale robustness has been achieved in this domain of watermarking. Different types of RW, i.e., fragile, semifragile, and robust methods, are being compared and reviewed. Mostly, fragile methods are developed on the error-expansion techniques built on histogram shifting-based approach. In this study, several RW methods are compared and the results are presented.
LIBRARY OF THE CIRCLE OF DEFENDERS OF FORTRESS PORT-ARTHUR (1906-1913)

For the first time the history of creation and activity of the Circle of Defenders of the Fortress Port Arthur and the attached library are presented based on pre-revolutionary sources. Reports of the Circle of Defenders’ Council for the period from December 2, 1906 up to November 13, 1913, concerning the library activity and editions it possessed, devoted to the Russian-Japanese war of 1904-1905, are analyzed.
Build Website-Based Equipment Inventory Information System in Pesantren Al Ma’soem

Abstract Pesantren Siswa Al Ma’soem (PSAM) is an educational institution that has more than 500 equipment’s used to support operational activities. But in the management of equipment inventory is still done semi-computerized, namely using Microsoft excel. Thus, causing problems such as officers’ difficulty in performing physical checks of equipment with existing data, difficulty knowing the information of equipment in the PSAM, limited working hours to access equipment information and allow data loss and duplication of data. The purpose of this research is to overcome problems arising from the current system. The purpose of this research is to facilitate officers in performing physical checks of equipment with existing data, make it easier to know the information of equipment located in branch offices, equipment information can be accessed without limited working hours during the internet network and minimize the occurrence of data loss and duplication of data. The analysis carried out in this study includes analysis of the current system and analysis of the proposed system. The system design used is by approaching the OOAD (Object Oriented Analysis and Design) method with the Unified Modeling Language (UML) language. The design of this information system is expected to facilitate officers in conducting equipment inventory, presenting necessary equipment information anywhere and anytime, and minimizing the occurrence of data loss and duplication of data.
Model‐based diagnosis with uncertain observations

Classical model-based diagnosis uses a model of the system to infer diagnoses—explanations—of a given abnormal observation. In this study we address the case where there is uncertainty over a given observation and explore it. This can happen, for example, when the observation outputs are collected by sensors with some noise, that are known to return incorrect value with some probability. We formally define this problem for abductive and consistency-based forms domains. Furthermore, we propose two complete and sound algorithms for finding and ranking all diagnoses and analyze their complexity. We dive deeper and improve the first algorithm's efficiency even more by exploiting past results. Finally, we propose a third algorithm that returns the most likely diagnosis without finding all possible diagnoses, assuming the uncertainty over the observations and the components behavior modes are independents. Experimental evaluation shows that the last algorithm can be very efficient in cases where the diagnosis cardinality, as well as the uncertainty likelihood over the observation, are expected to be small. If, however, all possible diagnoses are desired, then the choice between the first two algorithms depends on whether the domain's diagnosis form is abductive or consistent.
Prediction of Grain Output in Anhui Province Based on Machine Learning

Food security has always been concerned by all countries. The grain production data of Anhui Province from 1990 to 2017 are selected, including: total grain production, sown area, etc. Low accuracy of food production forecasts has become a problem. We proposed a machine learning combination model GR-SVR based on Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and Support Vector Regression Machine (SVR) to predict grain yield, using RF, GBDT, and SVR at the same time compare with GR-SVR. The results show that the GRSVR model has an accuracy rate higher than 96% in the prediction of grain output in Anhui Province, which is significantly better than GBDT, RF and SVR.
Application of medical equipment procurement data mining using the Apriori method

The procurement of an item in an agency is an activity that is commonly carried out, including in a hospital, particularly the procurement of medical equipment. The procurement of medical equipment at Pengayoman Cipinang Hospital, Jakarta currently still uses the conventional method where the process is still using the manual method, namely by relying on disposable physical items as the main resource and seems to use many procedures that require a long working time. The influx of medical equipment in very large numbers can cause many risks, such as the amount of money it costs to store medical devices and also being vulnerable to the risk of theft. The conditions mentioned above can affect the quality of health services provided by the hospital to patients who should be well preserved. One of the data processing algorithms that is often used in the data mining process is the A algorithm. The benefit is to find trends between one item and another, where more and more medical equipment appears to be used as a reference in the hospital policy process in ensuring the availability of this medical equipment when needed so that the procurement process can always be fulfilled. Based on the results of the research conducted, it was found that the medical equipment that had the most demand was Surgical Ear Loop Masks 3 (58.33%), Non-Sterile Gloves Size M (58.33%), and Surgical Tie-On 3 PLY Masks (100%)