Reviewerly/exHarmony huggingface dataset:
Bringing Explainability to Model Deployment Pipeline in Deep Learning Workbench

Widespread use of highly accurate and trustworthy deep learning architectures is becoming a noticeable trend in the industry. Model accuracy and inferencing performance traditionally remain the two important factors to be considered before model deployment to any business application. Additionally to these two requirements, the problem of model explainability is becoming increasingly acute. In this paper, we propose a methodology for ensuring that pretrained deep learning models provide optimal performance, have an acceptable accuracy level, and make trustworthy decisions. The methodology is tested on a classification computer vision model, taking into account specified business requirements.
Towards Efficient Training for Neural Network Quantization

Quantization reduces computation costs of neural networks but suffers from performance degeneration. Is this accuracy drop due to the reduced capacity, or inefficient training during the quantization procedure? After looking into the gradient propagation process of neural networks by viewing the weights and intermediate activations as random variables, we discover two critical rules for efficient training. Recent quantization approaches violates the two rules and results in degenerated convergence. To deal with this problem, we propose a simple yet effective technique, named scale-adjusted training (SAT), to comply with the discovered rules and facilitates efficient training. We also analyze the quantization error introduced in calculating the gradient in the popular parameterized clipping activation (PACT) technique. Through SAT together with gradient-calibrated PACT, quantized models obtain comparable or even better performance than their full-precision counterparts, achieving state-of-the-art accuracy with consistent improvement over previous quantization methods on a wide spectrum of models including MobileNet-V1/V2 and PreResNet-50.
Learning and Evaluating Contextual Embedding of Source Code

Recent research has achieved impressive results on understanding and improving source code by building up on machine-learning techniques developed for natural languages. A significant advancement in natural-language understanding has come with the development of pre-trained contextual embeddings, such as BERT, which can be fine-tuned for downstream tasks with less labeled data and training budget, while achieving better accuracies. However, there is no attempt yet to obtain a high-quality contextual embedding of source code, and to evaluate it on multiple program-understanding tasks simultaneously; that is the gap that this paper aims to mitigate. Specifically, first, we curate a massive, deduplicated corpus of 7.4M Python files from GitHub, which we use to pre-train CuBERT, an open-sourced code-understanding BERT model; and, second, we create an open-sourced benchmark that comprises five classification tasks and one program-repair task, akin to code-understanding tasks proposed in the literature before. We fine-tune CuBERT on our benchmark tasks, and compare the resulting models to different variants of Word2Vec token embeddings, BiLSTM and Transformer models, as well as published state-of-the-art models, showing that CuBERT outperforms them all, even with shorter training, and with fewer labeled examples. Future work on source-code embedding can benefit from reusing our benchmark, and from comparing against CuBERT models as a strong baseline.
Quantum-inspired annealers as Boltzmann generators for machine learning  and statistical physics

Quantum simulators and processors are rapidly improving nowadays, but they are still not able to solve complex and multidimensional tasks of practical value. However, certain numerical algorithms inspired by the physics of real quantum devices prove to be efficient in application to specific problems, related, for example, to combinatorial optimization. Here we implement a numerical annealer based on simulating the coherent Ising machine as a tool to sample from a high-dimensional Boltzmann probability distribution with the energy functional defined by the classical Ising Hamiltonian. Samples provided by such a generator are then utilized for the partition function estimation of this distribution and for the training of a general Boltzmann machine. Our study opens up a door to practical application of numerical quantum-inspired annealers.
Privacy and Security Solution in Wireless Sensor Network for IoT in Healthcare System

Wireless sensor networks (WSNs) are a notable study matter right now, and they're proving to be a useful tool for a variety of reasons, including ecological, defense, medical, household, and workplace apps. WSN technologies are considered to be one of the key research areas in computer science and healthcare application industries. WSNs are classified as mobile wireless sensor networks (MWSNs) or static wireless sensor networks (SWSNs). The MWSN is a specialized wireless technology with a large range of portable sensors, but the topology's uncertainty causes some performance problems across packet transmission. In the healthcare system, the sensory nodes may be static or movable depending upon the application. Due to certain limits linked with the sensor nodes, SWSNs consisting of node density with static topology face a few of the same security problems as MWSNs. The sensor node carries and forwards the data and information related to patient health, environmental conditions, medical records etc. The healthcare information system, with the help of various sensor nodes connected over the WSN, can collect, store, manage, and transmit various information related to the health sector. WSNs face numerous challenges, including security, safety, computing and power limitations, and reliability problems, particularly while routing between the nodes. To address these issues, WSN routing protocols should protect the network's security, integrity, privacy, and trustworthiness in healthcare applications. To deter network interference, effective and energy-conscious safeguards must be planned. The chapter discusses various types of WSNs, their related problems and issues, resolutions, and a node-to-node multi-hop-based stable approach for efficient routing in WSNs in various healthcare applications.
THE CORRELATION BETWEEN STUDENTS’ MOTIVATION TOWARDS STUDENTS’ SPEAKING ABILITY

Speaking is a daily activity that must be done but sometimes people take it for granted. In university level, as the semester gets higher, the difficulty of learning these skills also increases. Speaking skills lectures have been taught periodically and scheduled, but the circumstances in the classroom show that the students appear to be less participatory in speaking. Based on observations and brief conversation with some students, it can be concluded that students actually like speaking lectures but students are often afraid to point out mistakes in speaking due to lack of confident, willpower, and motivation. While motivation is an important component that cannot be separated in the teaching and learning process, especially in foreign language learning. The researcher use quantitative method. The aim of this research is to find out whether there is a positive and significant correlation between students’ motivation towards students’ speaking ability. The correlation index between X variable and Y variable is 0.348. It means that it has weak correlation. From the calculation of both variable above and indicated to the table of interpretation of rxy above, itsinterpretation that in both of variables has correlation. It meansthat both of variables have positive correlation (one wayscorrelation).&#x0D; Keywords: Correlation, Motivation Speaking
Improving Use Case Points Techniques to Support Size Estimation of Software Projects

There are many different ways to size estimate of software projects. However, software developers always have problems in choosing the right method for their projects. The Use Case Points (UCP) is among these common methods, but there is still some complexity in using them in a way that greatly contributes to obtaining accurate estimates. This paper presents an improvement of the UCP method, whereby an activity diagram is used and adapted and a weighting mechanism is developed. The improved method “Merging use case points (M-UCP)” will contribute to reducing the complexity of the traditional UCP method in the estimation process. In addition, a case study is introduced to test the refined method, and the results showed that the method performed better in comparison with previous methods in size estimating software development.
Violations of Language in Learning Process at Toraja Christian University of Indonesia

Lecturers and students as academics are required to be polite not only in behaving, but also in language. But in reality, often we find lecturers and students who do not show such courtesy, especially in the language or often called violations of language courtesy. This research aims to reveal violations of language courtesy committed by the academic community of Toraja Christian University of Indonesia (UKI Toraja), specifically in the learning process. Through this research, it is expected that various forms of violations of language courtesy can be expressed. Research on the violation of language courtesy is a type of qualitative descriptive research. This research was conducted at UKI Toraja specifically in the Indonesian Language education and Satra study program. The data in this study is the use of language used by lecturers and students of UKI Toraja in the learning process, both orally and in writing. Data is collected with documentation, record, and record techniques. The data was further analyzed using sociopragmatic approaches, which are approaches used to express the meaning of language externally. The results showed violations of language courtesy in the learning process at UKI Toraja were expressed through violations of wisdom maxims, violations of generosity, violations of reward maxims, violations of match maxims, and violations of simplicity maxims.
Pose-Guided Multi-Granularity Feature Learning for Occluded Person Re-Identification

Persons are often occluded in real-world applications of person re-identification.To alleviate the occlusion problem, this paper proposes a pose-guided multi-granularity feature learning method for occluded person re-identification.At first, the residual atrous spatial pyramid pooling module is used to expand the receptive field to extract more hierarchical pedestrian features.Next, the visible head-and-shoulder region feature and the underneath region feature from the heatmap extracted by the pedestrian estimation algorithm are calculated.Finally, the multi-granularity strategy is adopted to learn the pedestrian features of different hierarchies of information in the visible pedestrian region.Experimental results on the Occluded-DukeMTMC, Occluded-REID, and Market1501 datasets demonstrate the effectiveness of our proposed method.
Video Projection on Transparent Materials

Abstract We propose a new coating for light projection on transparent materials that can open new possibilities for design. What we often struggle with is the lighting of clear glass. By definition, clear glass lets most of the light pass through. We have found a way to turn glass surfaces opaque or transparent by using ultraviolet (UV) fluorescence coating. In combination with a UVA light source, we can project the dynamic content onto a glass surface treated with a special coating that transforms the UVA light into visible light. The added benefit of such a coating is that it can be applied to any organically shaped surface using a spray gun, not just flat surfaces. Another advantage is that the light source is nearly invisible to the human eye, especially with a UV light pass-through filter. We have created a prototype with a modified overhead projector to measure the light characteristics and documented the steps to reproduce our results.
Patenting AI

As the use of AI grows ever more prevalent and sophisticated, the issuesof the patentability of AI will need be addressed by the US Congress, USPTO, and the courts. While the questions raised with respect to patenting AI have been debated and are now being considered more broadly, few have been definitively answered. Early address and resolution of these issues will allow patent law to keep pace with the new tide of AI-related technologies and inventions.
Solar Transposition Modeling via Deep Neural Networks with Sky Images.

arc from horizon to horizon. As input, the neural network uses the calculated solar position and clear-sky irradiance values, along with sky images. The target irradiance values are provided by the multiplanar irradiance sensor (MPIS). In order to account for varying irradiance conditions, the MPIS signal is normalized by the theoretical clear-sky global horizontal irradiance. Using this information, the neural network outputs an N -dimensional vector, where N is the number of points to approximate the MPIS curve via Fourier resampling. The output vector of the model is smoothed with a Gaussian kernel to account for error in the downsamping and subsequent upsampling steps, as well as to smooth the unconstrained output of the model. Furthermore, these profiles may be used to perform near-term prediction of angular irradiance, which can then inform the movement of a PV tracker.
Professional attitude, attitude towards senior staff, and self-perception of a law enforcement officer as determinants of their professional loyalty

Introduction. The author explores the loyalty of law enforcement officers, and its psychological organization. The importance of the study is justified by the lack of theoretical knowledge about the content, structure, and factors of loyalty formation in law enforcement officers. It is assumed that the professional loyalty of employees is conditioned by three types of relations, which are simultaneously the bases of its expression. The psychological structure of loyalty consists of a) attitude towards professional activity, b) the senior staff, and c) the employee’s selfperception as a specialist. The article lays grounds to the importance of studying these types of attitudes for assessing the state of loyalty, prognosis and correction, the author points out the necessity of developing tools for its diagnostics.&#x0D; The aim of study was to operationalize the construct of professional loyalty of law enforcement officers, based on assumption of role of the immediate supervisor in its formation.&#x0D; Research methodology, methods, and techniques. At the preliminary stage the author user statistical research methods: descriptive statistics measures, assessment of internal consistency y α-Cronbach’s calculation. At the construct stage confirmatory factor analysis method was used. Hypothesis solving involved creating and testing a set of statements assessing loyalty. Convergent indicator of loyalty was the scale «Continued loyalty» of the method «Scale of organizational commitment» by J. Meyer and N. Allen, adapted by the author of the research.&#x0D; The result of the study is a psychometric confirmation of the structural validity of the questionnaire based on the loyalty construct. There were empirically revealed relations of the indicators «Professional attitude» (p ≤ 0,001), «Attitude towards superior» (p ≤ 0,001), «Attitude towards self-esteem» (p ≤ 0,001) and «Attitude towards leaving the organization» (p ≤ 0,001).&#x0D; The research results allow us to operationalize the concept of loyalty and develop an instrument of its assessment. Theoretical significance of the results helps us to expand the scientific ideas about the subjective foundations of law enforcement officers’ loyalty formation, and to reveal the role of the superior in the formation of employee’s loyalty as a derivative of his self-concept and attitude towards the activity.
Effects of Giving Iodized Salt, Counseling of Iodine and Goitrogenic Sources of Food in Mothers Who Have Elementary School Children Against Urinary Iodine Excretion

As a result of IDD is the occurrence of impaired child growth makes researchers interested in conducting research on "Provision of iodized salt, food counseling about the source of iodine and goitrogenic substances with urinary iodine excretion status in elementary school children". Research on IDD is often carried out in primary school-age children, aged 6-12 years because of their vulnerability to iodine deficiency. The purpose of this study was to determine the effect of iodized salt interventions and counseling patterns of iodized and goitrogenic food consumption patterns on levels of urinary yodiun excretion in families with elementary school children. Research methods: The design of this study included quasi-experimental using a specific design that is "pre and post test control group design". The study population was elementary school children with a sample size of 30 children aged 9 -12 years in each group. Data collected included the consumption of nutrients by the 24-hour recall method, the results of urine iodine examination by the spectrophotometric method. The collected data is then analyzed with an independent sample T test. The results showed there were differences in urinary yodiun excretion levels in the two groups (treatment and control), while the mean in the treatment group before intervention was 106.97 ug / L and after the intervention was 43.19 ug / L. Whereas in the control group, the level of urinary yodiun excretion before intervention was 117.30 μg / L and after the intervention was 243.19 μg / L. The mean of respondents who consumed goitrogenic sources in the treatment group before the intervention (Yes = 63%, No = 37%), after the intervention (Yes = 23%, No = 77%). Whereas in the Control group before the intervention (Yes = 56%, No = 73%), after the intervention (Yes = 23%, No = 77%). The average amount of protein consumption before treatment was 47.91 µg/L ± 6.54 and 50.15 µg/L ± 12.52 after treatment. For consumption, an increase with a mean before treatment was 89.88 µg/L ± 38.45 and after treatment was 113 µg/L ± 26. The results of the independent sample t-test showed that in the treatment group there was no significant difference between after and before the intervention (p = 0.058). Whereas in the control group there were significant differences between before and after the intervention (p = 0.002). It can be concluded that there are many factors that need to be controlled in the provision of interventions, especially the use, type of salt and goitronic as well as the method of examination of iodine analysis in urine.&#x0D; &#x0D; Keywords: iodized salt; iodine food sources; goitrogenic; urinary iodine excretion
Identifying Significant Customer Opinion Information of Each Aspect from Hotel Reviews

Recognizing whether customers like or dislike a product or service from online reviews may not be sufficient for other customers to make decisions or for owners to improve their merchandising. This was taken up as a challenge in this study that focused on finding significant sentiment information from customer reviews on each hotel aspect. The proposed framework first separated customer reviews into sentences, and then assembled all customer review sentences relating to each aspect of customer reviews using the k-means clustering. Later, those customer sentences are classified them into positive and negative sentiment polarity classes. The classifier was developed by Support Vector Machines (SVM). This can help other customers or the owner to understand why customers like or dislike a particular hotel aspect. The experimental results were evaluated using recall, precision, F1 and accuracy. The clustering method returned satisfactory results of 0.81, 0.80, 0.80 and 0.80, respectively. Meanwhile, the classification method also gave satisfactory results at 0.81, 0.79, 0.80 and 0.79, respectively. Compared to the baseline using F1 and accuracy, our proposed method produces very similar experimental results to the baseline method but our proposed method requires less computational time than the baseline.
Nursing Students' Experience of Concurrent Non-face-to-face Learning and Note-taking Tasks during COVID-19: Focusing on the Human Body Structure and Function Class

Objectives This is a qualitative study to identify the experiences of nursing college students who participated in classes that applied remote learning and note-taking tasks during COVID-19.&#x0D; Methods The subjects of this study were eight first-year nursing students taking a class on human body structure and function, who voluntarily agreed to participate in the research. Individual interviews were conducted, which were then analyzed using the Content analysis method.&#x0D; Results Three central meanings (experiences convenience, feels difficulty concentrating on class, lack of online communication) and seven topic words (individual control, repeated learning, space-time transcendence, individual inclination, environment, interaction, and system unification) were derived from the experience of remote learning. Meanwhile, the effect of note-taking generated four central meanings (improvement of academic achievement, improvement of self-directed learning ability, need for interactive process, differences depending on individual learning inclination) and 12 topic words (interest in learning, accumulation of major knowledge, improvement of grades, stimulation of learning motivation, setting learning plan, the realization of how to learn, improving focus on learning, Instructor feedback, interaction with colleagues, time consumption, the burden on handwriting, and lack of motivation for learning).&#x0D; Conclusions Concurrent operation of remote learning and note-taking tasks can be a measure to supplement learning control ability according to the learner's individual inclination, which can be utilized in the design of classes for basic nursing subjects in the future.
A New Method for Estimating and Predicting Uncertain Acceleration Against Weaving Targets

In this paper, a new method of acceleration estimation and prediction is proposed using an extended state observer (ESO) and a long short-term memory (LSTM) network for weaving targets whose frequency gradually increases within the reasonable range. The unknown acceleration whose frequency is within the bandwidth of the ESO can be accurately estimated online, however, the estimation effect outside the bandwidth is not optimistic. Therefore, the proposed technique is to predict the acceleration at high frequency by the LSTM network based on the accurate estimated data when the bandwidth is constrained by the guidance system. Theoretically, the error range predicted by the network should be smaller than estimated only by a low bandwidth ESO. Moreover, unifying the ESO and the LSTM can simultaneously ensure the accurate acquisition of unknown acceleration at low and high frequency in the case of bandwidth constraints. The simulation results verify the superiority of the approach.
Survey on UAV Coverage Path Planning and Trajectory Optimization

The Unmanned Aerial Vehicles (UAVs) technology has developed rapidly in recent years, and it has been widely used in aerial mapping, disaster search and rescue, smart farms, geographic mapping, environmental monitoring, power line patrol, aerial photography and other fields. Even so, coverage path planning (CPP) and trajectory optimization remains a hot problem, that is, how to find a safe flyable path in line with UAV dynamics constraints in a given area under the premise of ensuring the completion of coverage tasks. The research status of UAV regional coverage and path planning from the aspects of regional decomposition, traversal mode, trajectory optimization were reviewed in this paper and the trend of path planning technology in future was prospected.