Reviewerly/exHarmony huggingface dataset:
How to Improve AI Tools (by Adding in SE Knowledge): Experiments with the TimeLIME Defect Reduction Tool

AI algorithms are being used with increased frequency in SE research and practice. Such algorithms are usually commissioned and certified using data from outside the SE domain. Can we assume that such algorithms can be used ''off-the-shelf'' (i.e. with no modifications)? To say that another way, are there special features of SE problems that suggest a different and better way to use AI tools? To answer these questions, this paper reports experiments with TimeLIME, a variant of the LIME explanation algorithm from KDD'16. LIME can offer recommendations on how to change static code attributes in order to reduce the number of defects in the next software release. That version of LIME used an internal weighting tool to decide what attributes to include/exclude in those recommendations. TimeLIME improves on that weighting scheme using the following SE knowledge: software comes in releases; an implausible change to software is something that has never been changed in prior releases; so it is better to use plausible changes, i.e. changes with some precedent in the prior releases. By restricting recommendations to just the frequently changed attributes, TimeLIME can produce (a)~dramatically better explanations of what causes defects and (b)~much better recommendations on how to fix buggy code. Apart from these specific results about defect reduction and TimeLIME, the more general point of this paper is that our community should be more careful about using off-the-shelf AI tools, without first applying SE knowledge. As shown here, it may not be a complex matter to apply that knowledge. Further, once that SE knowledge is applied, this can result in dramatically better systems.
Aplicación de un modelo de ecuaciones estructurales para la medición de habilidades tecnológicas

A model is proposed to explain the influence exerted by anxiety/behavior and usefulness factors of computers at the level of technological skills as a dependent variable.The study was carried out with a sample of students from a business school of a public university located in northwestern Mexico for two educational programs: public accountant and administration.Based on the Theory of Technological Acceptance (TAM) and the application of a questionnaire that measured students' ICT skills, a Structural Equation Model (MES) is constructed to explore the variance in the basic levels of ICT skills that the students of each program have.The results of the model reveal that the level of technological skills has a significant and significant negative impact due to anxiety/behavior and a strong and significant perceived impact on the usefulness of computers.
The Meteorological Cloud Desktop System of CMA Meteorological Observation Center

The National-level Meteorological Observation Real-time Operation Platform was built by the CMA Meteorological Observation Center (MOC) In 2019, which integrated national real-time meteorological observation operations, research-oriented operations and external display. The real-time operation platform adopted the office form of the meteorological cloud desktop system, so that the operating system, applications and data were all centralized to the data center for unified management. The system was based on the core technology of Independent Computing Architecture (ICA) protocol and relied on the construction of servers, network devices and storage, and by means of distributed deployment and virtual architecture, the release and management functions were realized. NVIDIA vGPU was also applied in the system to release a VDI virtual design desktop based on GPU graphics, which satisfied the 3D modeling and CUDA computing needs of operating staff. At present, 80 cloud desktop systems had been built on the real-time operation platform, and customized operating systems and supporting software applications had met the requirements. The internal and external network cloud desktop physical isolation completely solves the problems of traditional office equipment decentralized management, low data security factor, high operation and maintenance cost, and inability to achieve mobile office. The technical advantages and architecture design of the meteorological cloud desktop system, the design and deployment process of the system and the key technologies involved in the operation application were described in detail in this paper.
Limitations for private randomness repeaters

Cryptographic protocols are often based on the two main resources: private randomness and private key. In this paper, we develop a relationship between these two resources. First, we show that any state containing perfect, directly accessible, private key (a private state) is a particular case of the state containing perfect, directly accessible, private randomness (an independent state). We then demonstrate a fundamental limitation on the possibility of transferring the privacy of random bits in quantum networks with an intermediate repeater station. More precisely, we provide an upper bound on the rate of repeated randomness in this scenario, similar to the one derived for private key repeaters. This bound holds for states with positive partial transposition. We further demonstrate the power of this upper bound by showing a gap between the localisable and the repeated private randomness for separable Werner states. In the case of restricted class of operations, we provide also a bound on repeated randomness which holds for arbitrary states.
Recent Advances in the Temple University Digital Pathology Corpus

The Neural Engineering Data Consortium (NEDC) is developing a large open source database of highr-esolution digital pathology images known as the Temple University Digital Pathology Corpus (TUDP) [1] . Our long-term goal is to release one million images. We expect to release the first 100,000 image corpus by December 2020. The data is being acquired at the Department of Pathology at Temple University Hospital (TUH) using a Leica Biosystems Aperio AT2 scanner [2] and consists entirely of clinical pathology images. More information about the data and the project can be found in Shawki et al. [3] . We currently have a National Science Foundation (NSF) planning grant [4] to explore how best the community can leverage this resource. One goal of this poster presentation is to stimulate community-wide discussions about this project and determine how this valuable resource can best meet the needs of the public.
A Web Application for Watermark Recognition

The study of watermarks is a key step for archivists and historians as it enables them to reveal the origin of paper. Although highly practical, automatic watermark recognition comes with many difficulties and is still considered an unsolved challenge. Nonetheless, Shen et al. [2019] recently introduced a new approach for this specific task which showed promising results. Building upon this approach, this work proposes a new public web application dedicated to automatic watermark recognition entitled Filigranes pour tous. The application not only hosts a detailed catalog of more than 17k watermarks manually collected from the French National Archives (Minutier central) or extracted from existing online resources (Briquet database), but it also enables non-specialists to identify a watermark from a simple photograph in a few seconds. Moreover, additional watermarks can easily be added by the users making the enrichment of the existing catalog possible through crowdsourcing. Our Web application is available at https://filigranes.inria.fr/.
Academic Writing in English in the Structure of Higher Education in Russia

Intensification of research work at engineering universities of Russian Federation leads to the increase of publication activity of graduate and PhD students in high-impact English language journals. However, the level of academic writing skills still remains rather low. On one hand, it results from the fact that the majority of applicants to engineering institutions have only basic knowledge of a foreign language. On the other hand, the curriculums of most Russian universities do not include developing academic writing skills as a stand-alone discipline or a part of the “Foreign Language” discipline. This paper aims at considering the main constituents of academic writing in a foreign language, such as the current state policy in higher education, the content of academic writing for engineering specialties, the publication process participants, the available resources for developing and improving the required skills in the realities of university language education; as well as identifying the external factors which create favorable conditions for developing academic writing skills of engineering students.
TanhExp: A Smooth Activation Function with High Convergence Speed for Lightweight Neural Networks

Lightweight or mobile neural networks used for real-time computer vision tasks contain fewer parameters than normal networks, which lead to a constrained performance. In this work, we proposed a novel activation function named Tanh Exponential Activation Function (TanhExp) which can improve the performance for these networks on image classification task significantly. The definition of TanhExp is f(x) = xtanh(e^x). We demonstrate the simplicity, efficiency, and robustness of TanhExp on various datasets and network models and TanhExp outperforms its counterparts in both convergence speed and accuracy. Its behaviour also remains stable even with noise added and dataset altered. We show that without increasing the size of the network, the capacity of lightweight neural networks can be enhanced by TanhExp with only a few training epochs and no extra parameters added.
Universal quantum reservoir computing

We show that quantum reservoir neural networks offer an alternative paradigm for universal quantum computing. In this framework, a dynamical random quantum network, called the quantum reservoir, is used as a quantum processor for performing operations on computational qubits. We show various quantum operations including a universal set of quantum gates, which can be obtained with a single quantum reservoir network with different tunnelling amplitudes between the reservoir and the qubits. The same platform can also implement non-unitary quantum gates, which are useful to simulate open quantum systems with tuneable parameters.
DoveNet: Deep Image Harmonization via Domain Verification

Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image. Image harmonization, aiming to make the foreground compatible with the background, is a promising yet challenging task. However, the lack of high-quality publicly available dataset for image harmonization greatly hinders the development of image harmonization techniques. In this work, we contribute an image harmonization dataset iHarmony4 by generating synthesized composite images based on COCO (resp., Adobe5k, Flickr, day2night) dataset, leading to our HCOCO (resp., HAdobe5k, HFlickr, Hday2night) sub-dataset. Moreover, we propose a new deep image harmonization method DoveNet using a novel domain verification discriminator, with the insight that the foreground needs to be translated to the same domain as background. Extensive experiments on our constructed dataset demonstrate the effectiveness of our proposed method. Our dataset and code are available at https://github.com/bcmi/Image_Harmonization_Datasets.
Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection

We propose a novel Enhanced Feature Aggregation and Selection network (EFASNet) for multi-person 2D human pose estimation. Due to enhanced feature representation, our method can well handle crowded, cluttered and occluded scenes. More specifically, a Feature Aggregation and Selection Module (FASM), which constructs hierarchical multi-scale feature aggregation and makes the aggregated features discriminative, is proposed to get more accurate fine-grained representation, leading to more precise joint locations. Then, we perform a simple Feature Fusion (FF) strategy which effectively fuses high-resolution spatial features and low-resolution semantic features to obtain more reliable context information for well-estimated joints. Finally, we build a Dense Upsampling Convolution (DUC) module to generate more precise prediction, which can recover missing joint details that are usually unavailable in common upsampling process. As a result, the predicted keypoint heatmaps are more accurate. Comprehensive experiments demonstrate that the proposed approach outperforms the state-of-the-art methods and achieves the superior performance over three benchmark datasets: the recent big dataset CrowdPose, the COCO keypoint detection dataset and the MPII Human Pose dataset. Our code will be released upon acceptance.
Robust Membership Encoding: Inference Attacks and Copyright Protection for Deep Learning

Machine learning as a service (MLaaS), and algorithm marketplaces are on a rise. Data holders can easily train complex models on their data using third party provided learning codes. Training accurate ML models requires massive labeled data and advanced learning algorithms. The resulting models are considered as intellectual property of the model owners and their copyright should be protected. Also, MLaaS needs to be trusted not to embed secret information about the training data into the model, such that it could be later retrieved when the model is deployed. In this paper, we present \emph{membership encoding} for training deep neural networks and encoding the membership information, i.e. whether a data point is used for training, for a subset of training data. Membership encoding has several applications in different scenarios, including robust watermarking for model copyright protection, and also the risk analysis of stealthy data embedding privacy attacks. Our encoding algorithm can determine the membership of significantly redacted data points, and is also robust to model compression and fine-tuning. It also enables encoding a significant fraction of the training set, with negligible drop in the model's prediction accuracy.
Efficient Clustering for Stretched Mixtures: Landscape and Optimality

This paper considers a canonical clustering problem where one receives unlabeled samples drawn from a balanced mixture of two elliptical distributions and aims for a classifier to estimate the labels. Many popular methods including PCA and k-means require individual components of the mixture to be somewhat spherical, and perform poorly when they are stretched. To overcome this issue, we propose a non-convex program seeking for an affine transform to turn the data into a one-dimensional point cloud concentrating around $-1$ and $1$, after which clustering becomes easy. Our theoretical contributions are two-fold: (1) we show that the non-convex loss function exhibits desirable geometric properties when the sample size exceeds some constant multiple of the dimension, and (2) we leverage this to prove that an efficient first-order algorithm achieves near-optimal statistical precision without good initialization. We also propose a general methodology for clustering with flexible choices of feature transforms and loss objectives.
USE OF INFORMATION TECHNOLOGIES FOR THE PREVENTION OF PROFESSIONAL BURNOUT OF TEACHERS

Professional burnout of the high school tutors and its negative impact on the educational process and the state of health of teachers, have been revealed in the article, signs, indicating the systemic nature of the problem, have been described. Strategies of overcoming such professional burnout have been considered, the preference of the problem-oriented strategy has been noted. The results of the analysis of the time spent by the teacher in the formation of assignments have been presented. The expediency of using information technologies as a tool to reduce the load on the teacher by reducing the time required to prepare for practical and laboratory classes has been substantiated. The stages of preparation for classes, that should be automated, have been highlighted.
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques

Reinforcement learning is one of the most popular approaches for automated game playing. This method allows an agent to estimate the expected utility of its state in order to make optimal actions in an unknown environment. We seek to apply reinforcement learning algorithms to the game Flappy Bird. We implement SARSA and Q-Learning with some modifications such as $\epsilon$-greedy policy, discretization and backward updates. We find that SARSA and Q-Learning outperform the baseline, regularly achieving scores of 1400+, with the highest in-game score of 2069.
A Novel Approach of Task Scheduling in Cloud Computing Environment

The recent era of technology is rapidly increasing with cloud environment, and it is just nothing but Internet computing via Internet technology. The demand of cloud computing is everywhere such as healthcare, physics, online marketing and so on. Scheduling problem is referred as NP-complete, and it is basically, allocation of the tasks in the available virtual machines. The primary objective of any task scheduling approach is to reduce the overall execution time. This paper presents an algorithm which is based on two steps process such as first step, to find the minimum value of all the tasks between entry task(s) and all nonentry tasks and to sort the tasks as per minimum value. This is basically used for priority of the tasks. Secondly, to allocate the tasks on available virtual machines. Proposed algorithm is tested on two different DAGs with ten tasks and fifteen tasks which gives better scheduling length as compared to heuristic algorithm such as HEFT algorithm.
A Scale Development Study to Determine the English Language Self Efficacy Beliefs of University Students

The aim of this study was to develop a self-efficacy belief for English scale for university students.For this purpose, a trial form of 73 items in Likert-type was prepared ant it was applied to a total of 365 university students in the fall semester of the 2016-2017 academic year.Explanatory factor analysis was conducted to prove the construct validity of the developed scale.For the factor analysis of the data, it was shown that the data set was suitable for factor analysis as the KMO value was 0,97 and the Barlett test significance value was 0,000.The analyses showed that the scale items were grouped under 5 factors, namely writing, speaking, reading, ability to learn English and listening, a total of 60 items.The value of Cronbach alpha calculated for reliability study was found as 0.98.In the confirmatory factor analysis study, it was found that the values of the "Self-Efficacy Belief for English Scale" were acceptable (χ2 / df =5.23; RMSEA =.10; CFI =.83; SRMR = .085).When the values of the other goodness of fit of the scale were examined, the values were obtained as GFI =.55, AGFI =.52; NNFI =.83 Findings related to the studies of validity and reliability show that the scale has a valid and reliable form.
The Impact of Tilt Angle on Photovoltaic Panel Output

Photovoltaic panels are getting embedded in our life because of their numerous advantages. In order to get the maximum output from the solar panels, it is very important to align the solar panels to the correct angle. These angles play an important role in the efficiency of the photovoltaic panels. For the correct alignment, one should know the proper path that the Sun appears to take during the day. Sun’s motion that is observed from the Earth is the result of two factors: Earth’s orbit around the Sun and its tilted axis of rotation contributes towards the Sun’s apparent motion in the sky as Earth’s orbit is elliptical. For a person living at a place which is inclined towards the Sun, then the Sun would rise much higher and might be present in the sky for longer duration of time, therefore, they will get sunlight for longer period. Therefore, all these factors have to be considered and the photovoltaic panels have to be aligned at the correct angles. In this paper, the effect of the tilt angle on the photovoltaic panel’s performance is determined. The photovoltaic module’s tilt angle changes from 0 degree to 45 degrees. The output voltage and current are calculated on all the different angles.
Multi-Task Learning Enhanced Single Image De-Raining

Rain removal in images is an important task in computer vision filed and attracting attentions of more and more people. In this paper, we address a non-trivial issue of removing visual effect of rain streak from a single image. Differing from existing work, our method combines various semantic constraint task in a proposed multi-task regression model for rain removal. These tasks reinforce the model's capabilities from the content, edge-aware, and local texture similarity respectively. To further improve the performance of multi-task learning, we also present two simple but powerful dynamic weighting algorithms. The proposed multi-task enhanced network (MENET) is a powerful convolutional neural network based on U-Net for rain removal research, with a specific focus on utilize multiple tasks constraints and exploit the synergy among them to facilitate the model's rain removal capacity. It is noteworthy that the adaptive weighting scheme has further resulted in improved network capability. We conduct several experiments on synthetic and real rain images, and achieve superior rain removal performance over several selected state-of-the-art (SOTA) approaches. The overall effect of our method is impressive, even in the decomposition of heavy rain and rain streak accumulation.The source code and some results can be found at:https://github.com/SumiHui/MENET.
WiEat: Fine-grained Device-free Eating Monitoring Leveraging Wi-Fi Signals

Eating is a fundamental activity in people's daily life. Studies have shown that many health-related problems such as obesity, diabetes and anemia are closely associated with people's unhealthy eating habits (e.g., skipping meals, eating irregularly and overeating). Traditional eating monitoring solutions relying on self-reports remain an onerous task, while the recent trend requiring users to wear expensive dedicated hardware is still invasive. To overcome these limitations, in this paper, we develop a device-free eating monitoring system using WiFi-enabled devices (e.g., smartphone or laptop). Our system aims to automatically monitor users' eating activities through identifying the fine-grained eating motions and detecting the chewing and swallowing. In particular, our system extracts the fine-grained Channel State Information (CSI) from WiFi signals to distinguish eating from non-eating activities and further recognizing users' detailed eating motions with different utensils (e.g., using a folk, knife, spoon or bare hands). Moreover, the system has the capability of identifying chewing and swallowing through detecting users' minute facial muscle movements based on the derived CSI spectrogram. Such fine-grained eating monitoring results are beneficial to the understanding of the user's eating behaviors and can be used to estimate food intake types and amounts. Extensive experiments with 20 users over 1600-minute eating show that the proposed system can recognize the user's eating motions with up to 95% accuracy and estimate the chewing and swallowing amount with 10% percentage error.