Reviewerly/exHarmony huggingface dataset:
Towards to Transfer the Directives of Communicability to Software Projects: Qualitative Studies

The software artifacts developed in the early stages of the development process describe the proposed solutions for the software. For this reason, these artifacts are commonly used to support communication among members of the development team. Miscommunication through software artifacts occurs because practitioners typically focus on their modeling, without reflecting on how other software development team members interpret them. In this context, we proposed the Directives of Communicability (DCs) to support practitioners analyzing characteristics that affect the artifact’s content on communication via artifact. We conducted preliminary studies in a controlled environment with our proposal. However, we noticed that new studies are necessary to evaluate the DCs concerning practitioners’ perceptions before transferring them to the industry. In this paper, we present two studies performed aiming to transfer the DCs to the software industry. In the first study, we evaluated the practitioners’ perception about the DCs. In the second study, we evaluated the feasibility of the DCs in a software development team. The studies’ results indicated that DCs have the potential to support improvements in artifacts’ content to reduce miscommunication via artifact. To facilitate the use of our proposal in the software industry, we created procedures that support the adoption of the DCs and checklists for the application of each directive in the software artifacts. We noticed positive perceptions of practitioners about the application of DCs in software artifacts. We hope that our contribution support software development teams that use artifacts in your projects.
Deep Learning on a Data Diet: Finding Important Examples Early in Training

The recent success of deep learning has partially been driven by training increasingly overparametrized networks on ever larger datasets. It is therefore natural to ask: how much of the data is superfluous, which examples are important for generalization, and how do we find them? In this work, we make the striking observation that, on standard vision benchmarks, the initial loss gradient norm of individual training examples, averaged over several weight initializations, can be used to identify a smaller set of training data that is important for generalization. Furthermore, after only a few epochs of training, the information in gradient norms is reflected in the normed error--L2 distance between the predicted probabilities and one hot labels--which can be used to prune a significant fraction of the dataset without sacrificing test accuracy. Based on this, we propose data pruning methods which use only local information early in training, and connect them to recent work that prunes data by discarding examples that are rarely forgotten over the course of training. Our methods also shed light on how the underlying data distribution shapes the training dynamics: they rank examples based on their importance for generalization, detect noisy examples and identify subspaces of the model's data representation that are relatively stable over training.
Facial Animation Using CycleGAN

This paper mainly shows the image translation from people's faces to anime characters by using Cycle-Consistent Adversarial Network (CycleGAN). Generative adversarial networks (GANs) have become a major field of deep learning in recent years. CycleGAN, as a variant of GAN, has made profound achievements in image translation problems. Experiments in this paper demonstrate that CycleGAN can generate realistic results of the transformation between real faces and anime faces under unsupervised learning.
Multi-Domain Multilingual Question Answering

Question answering (QA) is one of the most challenging and impactful tasks in natural language processing. Most research in QA, however, has focused on the open-domain or monolingual setting while most real-world applications deal with specific domains or languages. In this tutorial, we attempt to bridge this gap. Firstly, we introduce standard benchmarks in multi-domain and multilingual QA. In both scenarios, we discuss state-of-the-art approaches that achieve impressive performance, ranging from zero-shot transfer learning to out-of-the-box training with open-domain QA systems. Finally, we will present open research problems that this new research agenda poses such as multi-task learning, cross-lingual transfer learning, domain adaptation and training large scale pre-trained multilingual language models.
Measure of nonlinearity for underwater target tracking using hull-mounted sensor

Purpose Doppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and challenging problem in an underwater environment. Design/methodology/approach The system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken, the speeds of target and observer, environmental conditions, number of sensors considered for measurements and so on. Degrees of nonlinearity (DoNL) for these problems are analyzed using a proposed measure of nonlinearity (MoNL) for state estimation. Findings In this research, the authors analyzed MoNL for state estimation and computed the conditional MoNL (normalized) using different filtering algorithms where measurements are obtained from a single sensor array (i.e. HMS). MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is, that is, to measure nonlinearity of a problem. Originality/value Algorithms are evaluated for various scenarios with different angles on the target bow (ATB) in Monte-Carlo simulation. Computation of root mean squared (RMS) errors in position and velocity is carried out to assess the state estimation accuracy using MATLAB.
Improving Recycling Behaviour Through Waste Bank Implementation in Diponegoro University

One of the environmental problems in Diponegoro University is waste, because the number of people is directly proportional to the amount of waste produced. According to the Minister of Environment Regulation number 13 of 2012, a waste bank is a place for sorting and collecting waste that can be recycled and/or reused which has economic value. This study aims to implement the waste bank program realizing management, analyze information system in the implementation of the waste bank program and optimizing the program of waste bank at Diponegoro University. Waste bank operations consist of weighing, recording, selecting the type of waste, requirements for waste conditions, working hours and a profit-sharing system. The waste bank information system implemented on the website. Waste bank optimization is carried out in development periods with a duration of 3 months each period.
Thermal error analysis and compensation in structured light system by virtual-point-based method

Fringe Projection Profilometry (FPP) is considered to be one of the most reliable and accurate 3D measurement methods. However, in many applications, changes in ambient temperature or self-heating effects will cause different components expansion and contraction in optical systems. Owing to the the fact that parameters of structured light system are usually considered to be fixed after calibrated, the precision of high-accuracy measurement will be greatly reduced if the influence related to thermal error is ignored. In this study, we analyze the thermal error model of the structured light system, and verify our theory by simulation and experiment. Secondly, we propose an improved calibration method, based on traditional method with an additional virtual estimation points in the calibration process. The parameters are compensated at each temperature, and the experiments demonstrated that the thermal error can be greatly reduced after compensation, and the changes of parameters tend to be linear, it is in accordance with the physical phenomenon. Finally, our proposed method could effective compensate the error caused by temperature drift by over 85%.
Are the Multilingual Models Better? Improving Czech Sentiment with Transformers

In this paper, we aim at improving Czech sentiment with transformer-based models and their multilingual versions.More concretely, we study the task of polarity detection for the Czech language on three sentiment polarity datasets.We fine-tune and perform experiments with five multilingual and three monolingual models.We compare the monolingual and multilingual models' performance, including comparison with the older approach based on recurrent neural networks.Furthermore, we test the multilingual models and their ability to transfer knowledge from English to Czech (and vice versa) with zero-shot cross-lingual classification.Our experiments show that the huge multilingual models can overcome the performance of the monolingual models.They are also able to detect polarity in another language without any training data, with performance not worse than 4.4 % compared to stateof-the-art monolingual trained models.Moreover, we achieved new state-of-the-art results on all three datasets.
Application of Wearable Technology in Education

Wearable technology is very popular in recent years.Since the advent of Glass Google, lots of kinds of wearable devices have been developed.There is more and more wearable technology in education.Through the literature and field survey, the researchers conducted studies on wearable technology and its application in education.We find that the scope of application involves many fields, such as medical, entertainment, health, military, education, and so on.The effective use of wearable technology in education needs to improve the devices to meet the needs of education.
Ball-by-Ball Cricket Commentary Generation using Stateful Sequence-to-Sequence Model

Due to the availability of high performance computational devices and enormous video data, deep learning algorithms are assisting for human understandable description of videos. Automatic commentary generation of cricket videos take advantage of aforementioned intelligent techniques. VGG-16 network facilitates extraction of visual pattern from frames followed by encoder-decoder LSTM model. Proposed model can handle variable length input data to output variable number of sequential output. Moreover, the model has ability to encompass temporal information to predict the line and length bowled by bowler, the shot selection of batsman and outcome of the ball. Due to unavailability of cricket commentary dataset, a novel cricket commentary dataset containing video-commentary pairs is presented. Evaluation is also performed on benchmark video captioning datasets which are Microsoft Video Description Dataset (MSVD) and MSR - Video to Text dataset (MSRVTT). Captions generated by our model are evaluated on video captioning metrics which are METEOR, BLEU, ROGUE L and CIDEr and outperforms the baseline model.
A Privacy-Preserving Reinforcement Learning Approach for Dynamic Treatment Regimes on Health Data

Based on the clinical states of the patient, dynamic treatment regime technology can provide various therapeutic methods, which is helpful for medical treatment policymaking. Reinforcement learning is an important approach for developing this technology. In order to implement the reinforcement learning algorithm efficiently, the computation of health data is usually outsourced to the untrustworthy cloud server. However, it may leak, falsify, or delete private health data. Encryption is a common method for solving this problem. But the cloud server is difficult to calculate encrypted health data. In this paper, based on Cheon et al.’s approximate homomorphic encryption scheme, we first propose secure computation protocols for implementing comparison, maximum, exponentiation, and division. Next, we design a homomorphic reciprocal of square root protocol firstly, which only needs one approximate computation. Based on the proposed secure computation protocols, we design a secure asynchronous advantage actor-critic reinforcement learning algorithm for the first time. Then, it is used to implement a secure treatment decision-making algorithm. Simulation results show that our secure computation protocols and algorithms are feasible.
Hyperparameter Tuning over an Attention Model for Image Captioning

Considering the historical trajectory and evolution of image captioning as a research area, this paper focuses on visual attention as an approach to solve captioning tasks with computer vision. This article studies the efficiency of different hyperparameter configurations on a state-of-the-art visual attention architecture composed of a pre-trained residual neural network encoder, and a long short-term memory decoder. Results show that the selection of both the cost function and the gradient-based optimizer have a significant impact on the captioning results. Our system considers the cross-entropy, Kullback-Leibler divergence, mean squared error, and the negative log-likelihood loss functions, as well as the adaptive momentum, AdamW, RMSprop, stochastic gradient descent, and Adadelta optimizers. Based on the performance metrics, a combination of cross-entropy with Adam is identified as the best alternative returning a Top-5 accuracy value of 73.092, and a BLEU-4 value of 0.201. Setting the cross-entropy as an independent variable, the first two optimization alternatives prove the best performance with a BLEU-4 metric value of 0.201. In terms of the inference loss, Adam outperforms AdamW with 3.413 over 3.418 and a Top-5 accuracy of 73.092 over 72.989.
The use of online learning environments in higher education as a response to the confinement caused by COVID-19

In Colombia, a developing country, higher education has a gross coverage rate of about 40% (supply concerning the entire population). However, although this value is low, ten years ago this rate barely exceeded 20%. The increase in coverage is largely due to a policy that has promoted training by cycles. This model allows education by levels with the granting of professional degrees at each stage, which allows for rapid employment. Even so, places are limited, particularly for medium and low economic levels (which concentrate the majority of the population), and access to them in public universities (those with state-funded enrolment) is very restricted. Access to education is a major concern for institutions and the state, in particular for vulnerable social groups, and has been further depressed by the security and control measures implemented to slow down the spread of the COVID-19 virus. In a short time, and with limited resources, institutions have had to adapt their models to guarantee continuity and quality in academic processes. In this context, digital platforms have come to play a fundamental role by allowing access while reducing social interaction. However, the use of these platforms implies the development of specific learning environments adapted to academic, economic, and social conditions. This paper explores the design, development, and impact of some of these learning environments in the process of technological training of students from low economic strata in the most important public university in the Colombian capital. The initial results of this study show that the distance learning model adopted as a response to social isolation does affect students' academic performance. Besides, the results also show that there are effects on the students' interaction schemes and their motivational levels towards their training process. © Italian e-Learning Association.
Danger detection technology based on multimodal and multilog data for public safety services

Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.
Thermal Analisys of the Increase in Ambient Temperature Due to Motor Vehicle Activities

Global warming has become a problem of world wide, because it endangers living things. The consequences of global warming include the increase of the earth temperature and climate change. The increase of temperature (heating) in a city which is referred to urban heat island (UHI) is also the same problem in architecture. This research aimed to analyze the ambienttemperature of the UHI due to the motor vehicleactivities. Samples were taken from several locations: (1) Bahu Mall Parking area; (2) Manado Town Square 2 parking area; (3) along the Wolter Monginsidi street in front of Bahu Mall and (4) along the Piere Tendean street in front of Manado Town Square. This research used a quantitative method with a field survey. This research found that the ambient temperature increased when motor vehicles were stationary with engine on or moved. The temperature increased due to the heat from the engine and the reflection of the sun’s heat from the body of the vehicles. The heat level obtained from the survey was 34,8 0C to 39,4 0C. For this reason, a specific material for vehicle’s body is needed to prevent the increase of ambient temperature.
GCNear: A Hybrid Architecture for Efficient GCN Training with  Near-Memory Processing

Recently, Graph Convolutional Networks (GCNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. However, it is challenging to realize efficient GCN training, especially on large graphs. The reasons are many-folded: 1) GCN training incurs a substantial memory footprint. Full-batch training on large graphs even requires hundreds to thousands of gigabytes of memory to buffer the intermediate data for back-propagation. 2) GCN training involves both memory-intensive data reduction and computation-intensive features/gradients update operations. Such a heterogeneous nature challenges current CPU/GPU platforms. 3) The irregularity of graphs and the complex training dataflow jointly increase the difficulty of improving a GCN training system's efficiency. This paper presents GCNear, a hybrid architecture to tackle these challenges. Specifically, GCNear adopts a DIMM-based memory system to provide easy-to-scale memory capacity. To match the heterogeneous nature, we categorize GCN training operations as memory-intensive Reduce and computation-intensive Update operations. We then offload Reduce operations to on-DIMM NMEs, making full use of the high aggregated local bandwidth. We adopt a CAE with sufficient computation capacity to process Update operations. We further propose several optimization strategies to deal with the irregularity of GCN tasks and improve GCNear's performance. We also propose a Multi-GCNear system to evaluate the scalability of GCNear.
Hand Position Controlled Appliances

In daily life old and disable people face severe problems in operating appliances. In this paper we are presenting a very novel idea to make their life easy. We have developed electronic controller which can be operated just by hand gesture staying from the equipments. Here the electronic equipment senses the hand position of a person and actuates a particular appliance according to the hand gesture.
Value learning from trajectory optimization and Sobolev descent: A step toward reinforcement learning with superlinear convergence properties

The recent successes in deep reinforcement learning largely rely on the capabilities of generating masses of data, which in turn implies the use of a simulator. In particular, current progress in multi body dynamic simulators are under-pinning the implementation of reinforcement learning for end-to-end control of robotic systems. Yet simulators are mostly considered as black boxes while we have the knowledge to make them produce a richer information. In this paper, we are proposing to use the derivatives of the simulator to help with the convergence of the learning. For that, we combine model-based trajectory optimization to produce informative trials using 1st- and 2nd-order simulation derivatives. These locally-optimal runs give fair estimates of the value function and its derivatives, that we use to accelerate the convergence of the critics using Sobolev learning. We empirically demonstrate that the algorithm leads to a faster and more accurate estimation of the value function. The resulting value estimate is used in model-predictive controller as a proxy for shortening the preview horizon. We believe that it is also a first step toward superlinear reinforcement learning algorithm using simulation derivatives, that we need for end-to-end legged locomotion.
Elaboração de algoritmo para a escolha do banho do paciente coronariopata

The aim was to apply the validated criteria and clinical parameters used by nurses in an algorithm to support the choice of bathing for patients with coronary artery disease. This is a methodological study with a quantitative approach to develop an algorithm for decision making regarding the choice of bathing in patients with coronary artery disease. It resulted in 19 validated items and 19 items suggested and used by experts. Of these, after categorization, 21 items were selected, organized in an algorithm format, with dichotomous responses that suggest, at each end of the flow, the type of bath indicated for the patient, due to their clinical conditions at the time of evaluation. In conclusion, an elaborate algorithm guides nurses in choosing the type of bath that will be offered to patients with coronary artery disease, through validated clinical parameters based on findings in the literature. This study needs algorithm validation for its clinical use.
Design and research of a fine operation capability evaluation system based on Fitts' law

Aiming at the precision operation ability of the joystick in flight, an evaluation system for testing the precision operation ability of the 2-DOF joystick is developed, which includes the 2-DOF joystick and the control module. Based on FITTS theory, the effects of different difficulty index (ID) and action time (T) on the precision of fine movements were designed, and different load controls were introduced. Finally, validation tests were carried out under different load conditions of 10&#x0025; (INM), 30&#x0025; (3) and 60&#x0025; (6NM), and the validity of FITTS theory was preliminarily verified, providing an experimental basis for the next stage of tests.