Reviewerly/exHarmony huggingface dataset:
Solution of a Problem in Concurrent Programming Control

chapter Share on Solution of a Problem in Concurrent Programming Control Author: E. W. Dijkstra View Profile Authors Info & Claims Edsger Wybe Dijkstra: His Life,Work, and LegacyJuly 2022 Pages 311–314https://doi.org/10.1145/3544585.3544603Published:13 July 2022Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Regional PV Energy Forecasting using Distributed Data and Deep Neural Networks

Despite the COVID-19 pandemic, the global Photovoltaic market once more grew significantly in 2020, mainly on Grid-Connected Systems. The exponential increase of these systems raises new challenges for Smart Grid operators trying to predict load demand. That occurs because of the panels’ output uncertainty and the leak of regional models for solar energy prediction. In this paper, we propose a distributed data approach to predict solar energy generated by Photovoltaic Systems. As input, we combine data from a community of solar panel owners and a historical weather website to build our dataset. This paper evaluates two scenarios: predicting regional next-day generation by using weather forecasting and the impact of a new system in that region. We sort the results seasonally and achieved 7% of MAE weighted percentage for summer predictions.
Adversarial Training Inspired Self-attention Flow for Universal Image Style Transfer

Flow-based model receives more and more attention and has been recently applied to image style transfer. While these methods can achieve splendid performance, there remains a problem that the stacked convolutions are inefficient and cannot focus on valuable features. Starting with training an adversarial robust model, we find that no matter in the perceptual loss network or the transfer model, robust features are beneficial for performing better universal style transfer (UST) results. Based on this initial conclusion, we improve the current Glow model by applying self-attention mechanism with three different blocks using ViT, non-local and involution, respectively. Designed feature extraction blocks can capture more valuable deep features with fewer parameters, making Glow more effective and efficient in UST. Our improved Glow can generate artistic images that look nicer and more stable. Both visual results and quantitative metrics are compared to prove that our improvement makes Glow more suitable for UST.
A Rapid Device Type Identification Method Based on Feature Reduction and Dynamical Feature Weights Assignment

The network device identification technology refers to using the network detection technology to obtain device data and transform the device data into device fingerprints to identify network devices. Currently, the mainstream network device identification method obtains network traffic data generated in the process of device communication, extract device features, and identify devices based on a variety of machine learning algorithms. However, these methods ignore the impact of redundant features and interference features when analyzing device traffic data, resulting in a high-false positive rate and heavy time cost. In order to identify network devices more efficiently and accurately, we propose a network device recognition method based on feature reduction and dynamical feature weights assignment. Firstly, feature redundancy analysis was carried out based on fast filtering algorithm and redundant features were deleted. Then, each feature is dynamically weighted according to its relevance to the device type. Finally, the target device type is identified by calculating the similarity between the target device and the known device type. Experimental results on existing public data sets show that the proposed method improves the recognition accuracy by 3.5%, 10.8% and reduces the time cost by 80%, 72% in random forest and LightGBM respectively. The proposed method is better than the existing method based on feature reduction for device type recognition.
Robust Approach for Detecting Face Mask Using Deep Learning and Its Comparative Analysis

The world is fighting against the novel coronavirus, and a lot of people have lost their lives with the scenario getting bad to worse and worst. This infection is communicated from one individual to another while wheezing or talking as drops. To prevent Covid-19, wearing masks is very beneficial. In this paper, an existing model, ‘DenseNet201’, is being modified to efficiently track the persons who are wearing a mask or not. This system uses a convolutional neural network (CNN) and computer vision to limit the risk of Covid-19 and make sure nobody violates the rule. The dataset used in the process contains two classes, namely 'with mask' and 'without a mask'. Data pre-processing and splitting take place before the model training; then comparative analysis has been made in between the modified versions of the three transfer learning models, viz. DenseNet201, InceptionResnetV2, and ResNet101V2 to validate the modified model's efficiency. Results suggest that the revised version of DenseNet201 is very effective and can detect the events where face masks are not used at all or in an improper manner, with an accuracy of 98.90%. Various other metrics for performance are also being evaluated and reported in the paper. This model can work with images and videos/CCTV cameras using the help of OpenCV, TensorFlow, and Keras.
Augmentation-Based Edge Differentially Private Path Publishing in Networks

Paths in a given network represent the occurrence sequences of nodes in many real world applications, such as disease transmission chains, object trajectories and data access sequences. In this paper, we address the problem of publishing edge-privacy preserved path information for a single path such that legitimate users with the full knowledge of the network can reconstruct the path with the published information, but not adversaries, even if they have the maximum background knowledge of all the vertices and all edges but one (on the path) of the network. Existing studies on edge privacy against inference attacks focus on publishing either differential privacy (DP) noise injected graph statistics or DP edge perturbed graph topology to achieve edge differential privacy preservation. However, none of them provides an assurance on both edge privacy and data utility. To effectively protect edge privacy and maintain data utility, we propose a novel scheme of DP augmentation instead of DP perturbation as did in existing work, that publishes a simple-topology graph containing an augmented path with fake edges and vertices applying differential privacy to protect the actual path, such that only the legitimate users are able to reconstruct the actual path with high probability. We theoretically analyse the performance of our algorithm in terms of output quality on differential privacy and utility, and execution efficiency. We also conduct extensive experimental evaluations on a high-performance cluster system to validate our analytical results.
Stroke-Based Handwritten Gujarati Font Synthesis in Personal Handwriting Style via Shape Simulation Approach

In this era, people want a handwriting style personal font for communication with the goal that a document or message can be displayed as they are written by their own hands. It can be achieved by the concept of handwriting synthesis. It has two approaches: Movement simulation, where neuromuscular hand movement is the key feature to synthesis the writing. Another is shape simulation, in which characters’ glyph can act as input elements and synthesized the writing based on the glyph. Our study aims to generate Gujarati handwritten font in personal handwriting style using stroke-based synthesis and style learning. Here, we have proposed the complete font generation model that has two main phases: A learning phase and a generation phase, in the learning phase we created rules set for generating Gujarati characters that include information like size, a position of strokes, width, endpoints, junction point, etc. In generating phase, the user needs to write a small subset of Gujarati characters and generate the others character glyph from the ruleset and prepare a font file for the same. Our algorithm extracts the strokes based on structural features of character and classified strokes with the help of extracted features. Then, character glyph is generated from extracted strokes, by concatenating them properly according to the extracted ruleset. Finally, the open type font is generated from the glyph, ready to use in any text editor that supports Gujarati script.
Video Object Segmentation Based on Guided Feature Transfer Learning

Video Object Segmentation (VOS) is a fundamental task with many real-world computer vision applications and challenging due to available distractors and background clutter. Many existing online learning approaches have limited practical significance because of high computational cost required to fine-tune network parameters. Moreover, matching based and propagation approaches are computationally efficient but may suffer from degraded performance in cluttered backgrounds and object drifts. In order to handle these issues, we propose an offline end-to-end model to learn guided feature transfer for VOS. We introduce guided feature modulation based on target mask to capture the video context information and a generative appearance model is used to provide cues for both the target and the background. Proposed guided feature modulation system learns the target semantic information based on modulation activations. Generative appearance model learns the probability of a pixel to be target or background. In addition, low-resolution features from deeper networks may not capture the global contextual information and may reduce the performance during feature refinement. Therefore, we also propose a guided pooled decoder to learn the global as well as local context information for better feature refinement. Evaluation over two VOS benchmark datasets including DAVIS2016 and DAVIS2017 have shown excellent performance of the proposed framework compared to more than 20 existing state-of-the-art methods.
A Machine Learning Perspective on Spectral Estimation Techniques for Time Signals From Electromagnetic Field Simulations

We propose a machine-learning-based workflow to solve the spectral estimation problem of time signals from electromagnetic simulations of highly resonant devices. An activation function for a three-layer radial basis function network is defined and used to fit the signal originated from a grid-based computation. Due to the proper choice of the activation function and a careful minimization of the loss expression, we are able to train this network using only a small part of the original time signal and finally extrapolate the signal up to a point it has sufficiently decayed. In the end, we obtain an estimation of the signal’s spectral content which is considerably more accurate than a Fourier transform of the training data alone. Therefore, using our approach one may abort a time domain simulation earlier, and still calculate important key quantities such as resonance frequencies and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -values with high precision. In order to test our method, we perform the spectral estimation of a highly resonant filter’s response.
Establishing a Student-Community Book Club for Civic Engagement

This paper articulates the reasons and the implementation steps for the forming of a student-community book club that aims to build small communities motivated by Raghuram Rajan’s 2019 book: The Third Pillar: How Markets and the States Leave the Community Behind. We believe humans and societies survive based on rational dialogues. A book club of this type can provide escape valves for individuals holding strong unbendable beliefs on how society should function, which has dichotomized America since 2016. Themes generated from books (fictions or non-fictions) contain scientific or humanistic views can encourage community network building of the type that will broaden people's view, rather than focus on specific disagreements. Disintegration of various factors, according to Rajan, is the crisis that communities all over the world are facing. Building communities have always been some historical endeavors, resulting often from wars and land grabbings. The urgent needs to do so now are due to technological changes. Technologies are disrupting the lifestyles in the world that can amplify as well as compromise disagreements. A web-ground co-development is necessarily for bringing out the goods while managing the bad of technologies.
How far can Deep Learning Improve Arabic Part of Speech Tagging?

The use of Deep Learning (DL) in Natural Language Processing (NLP) has seen a significant growth in the last few years. Part of Speech (POS) tagging is an important element for many NLP applications, including machine translation, sentiment analysis, and text summarization. It consists in identifying the very likely tag (noun, verb or particle, adverbs, adjectives etc.) for each word in a given text. The goal of this research is to conduct a systematic literature review of deep learning methods applied to Arabic POS tagging for the last two decades. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. More than 4000 papers were reviewed to extract all DL approaches used to develop POS taggers for the Arabic language. After multiple exclusion steps 12 articles were selected for a full review. Results show that Long Short-Term Memory (LSTM) and its extension Bidirectional LSTM (Bi-LSTM) models are the most used DL techniques for Arabic POS tagging, and they give better results according to the reviewed papers.
Shearlet and Patch Reordering Based Texture Preserving Denoising Method for Locust Slice Images

Locust slice image is a kind of cartoon-like images, in which the texture possesses the property of self-similarity. Both of the texture and the noises belong to the high-frequency signals, and so it is difficult to tell the difference between them for most denoising methods. Aim to the problem, we propose a novel denoising method by combining the patch reordering with the shearlet transform. In the reordering process, the patches are divided into smooth and texture components. The filters obtained from the training set are employed to process the patches in smooth regions and the shearlet transform are employed to process the texture regions. The experiments show that the values of PSNR and SSIM of the processed images obtained by the proposed method are better than the common methods.
Blockchain for Enhancing Security of IoT Devices

The Internet of Things (IoT) covers a vast area of automation, from cities to railways to cars and homes, and even wearables and implanted devices. The two common things in IoT are access networks through the Internet to transfer data from machine to machine (M2M) and very low-cost sensors. However, the data collected from IoT sensors contain a large amount of private information and must be preserved. Security and privacy are the two critical issues for IoT applications and still face some huge challenges. The fact that Blockchain emerges as the essential support mechanism for a cryptocurrency platform named Bitcoin. The origin story of Blockchain describes a distributed ledger that supports secure and immutable transactions without a central authority and only modifiable by agreement from all participants. This technological advancement has the potential to disrupt many industries, the IoT among them. Decentralizing information reduces the ability for data tampering and provides a trustless and secure environment for IoT. Considering the security advancements in centralized IoT ecosystems like cloud, fog and edge computing, the security implication is still insufficient to meet the challenges. In this study, we investigated fundamental working principles of Blockchain and how Blockchain-based systems achieve the characteristics of decentralization, security and trustless. We have analyzed some real-scenarios of the industries which indicates security enhanced using the integration of IoT and Blockchain concepts together to achieve the security mechanism in IoT devices some extend.
Bridging the Gap: Requirements of International Organizations and their Implications for the Training of Language Professionals

Abstract The International Annual Meeting on Language Arrangements, Documentation and Publications (IAMLADP), a mechanism that brings together international organizations (IOs), is led by the United Nations and addresses such functions as the organization and management of language services, publications and conferences. This study uses the R Project for Statistical Computing to collect and analyse the discourse patterns of job descriptions issued by IAMLADP members for their job vacancies for language professionals, including interpreters, translators, revisers, editors and verbatim reporters. We also make recommendations regarding the interaction between IOs and universities in training qualified language professionals. Our finding was that, among the six UN official languages, English and French language skills are still most frequently required by IAMLADP organizations. In addition, this study identifies the competencies, level of education and years of working experience most frequently required of job applicants. Therefore, it aims to provide some insights into job requirements of IAMLADP organizations, and to inspire universities to adapt their training programs accordingly.
The Distribution of Functions of Random Variables

Experiments do not always measure directly all quantities of interest to the analyst. Instead, it is sometimes necessary to infer properties of interesting variables based on the variables that have been measured directly. This chapter explains how to determine the probability distribution function of a variable that is the function of other variables of known distribution. The central limit theorem, one of the statistic’s key tools, also establishes that the sum of a large number of independent variables is asymptotically distributed like a Gaussian distribution.
Applied Research on Cultivating Advanced Technical Engineering Talents Based on the Internet of Things+ Platform with Dual-Professional Teachers as the Guidance

In order to realize the smooth implementation of the transformation of the old and new kinetic energy, cultivate and bring up the engineering and technical talents with craftsman spirit, talent cultivation should go ahead, especially the innovative engineering and technical talents. Craftsman spirit is the spirit of pursuing excellence in creation, the spirit of keeping improving in quality, and the spirit of customer first in service. The main connotation of craftsman spirit includes four aspects: ingenuity, craftsmanship, craftsman spirit, craftsman line. We should build a three-dimensional innovation and entrepreneurship system of innovation methods-information technology-entrepreneurial resources-creative cases-competition practice-lean entrepreneurship, and form an innovation and entrepreneurship framework with industry characteristics. With curriculum system reform, training platform, science and technology competition, innovation and entrepreneurship as the main line School enterprise alliance as the core, gradually establish a more reasonable, more perfect practice mode, based on this mode, through the rich theoretical knowledge, create excellent engineering and technical personnel, use electronic engineer training and participate in various types of electronic design competition, gradually cultivate the professional quality of engineering and technical personnel, and achieve the goal of engineering and technical personnel by writing scientific papers. Through school enterprise cooperation and practical training, the practical application ability of solving specific problems in the work practice can explore a feasible way for the cultivation of college students in engineering science and technology.
Single Image Dehazing Techniques for Different Types of Hazy Images

Single image dehazing is a big challenge in practical life due to the presence of a single input image for removing haze from hazy images. It has been seen that haze removal techniques are applied on homogenous light haze images, but dense haze also occurs in practical life. This paper has applied various image dehazing on different types of hazy images like homogenous light haze and dense haze for indoor and outdoor scenes. Results of different techniques are compared based on Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index Metric (SSIM).
Extracting Purposes from an Application to Enable Purpose Based Processing

In general, applications are built to serve certain business purposes. For example, a bank invests into an application development to offer its customers online services such as online shopping, FD services, utility bill payments, etc. Within the application, every screen has its own purpose. For example, the login page is used to authenticate a customer. Dashboard screen gives a high-level view of activities done by a customer. Similarly, every field appearing on the screen has purpose too. The username field of login screen enables customer to supply a username given to the customer by the bank. Every screen of a given enterprise application is not developed keeping the exact purposes in mind. Many a times, a screen may serve multiple purposes. While an application screen may serve different purposes and an application with less number of screens suits an enterprise in terms of reduced expenditure for the development and subsequent maintenance activities, it is at loggerhead with privacy laws which are demanding purpose-based processing. Therefore, it is necessary to first build a repository of purposes that a given application can serve. And, then subsequent refactoring of application can be done (if required) to comply with privacy laws. To find out purposes, we use keyword extraction method, K-means clustering on text data of application, to get keywords and respective purposes.