Miracle-dz/UniFAR-FTUs huggingface dataset:
ALGORITHM FOR ASSESSMENT OF TRUCK BRAKES TEMPERATURE EVOLUTION USING SVM

In this paper, an algorithm is proposed for a new system for trucks with heavy loading operating in steep grade conditions. The algorithm proposed in this paper is using SVM (Support Vector Machine) algorithm to predict brakes temperature evolution which is processing previously CFD (Computational Fluid Dynamics) simulation of the brake components’ cooling for the truck model. The brake system temperature is estimated and the driver is informed and warned about future dangerous situations. The proposed system is a new feature proposed in ADAS (Advanced Driver Assistance Systems) in this paper dedicated to a truck with heavy loading operating in steep grades.
Modeling of cloud/radiation processes for large-scale clouds and tropical anvils. Final report, 1 November 1991-31 March 1994

This final report includes five reprints and preprints of papers associated with the modeling of cloud/radiation processes for large-scale clouds and tropical anvils. These papers present (1) a three-dimensional large-scale cloud model for testing the role of radiative heating and ice phase processes, (2) parameterization of the radiative properties of cirrus clouds, (3) discussion of the scattering and absorption properties of ice crystals in relation to numerical modeling, (4) improvements of an ice-phase microphysics parameterization for use in cloud models, and (5) physical discussions on the interactions of radiation and convection in simulated tropical cloud clusters using a cumulus ensemble model.
Air entrapment of a neutral drop impacting onto a flat solid surface in electric fields

Abstract When a charge neutral drop impacts on a flat solid substrate, a small air bubble is always trapped underneath due to the lubrication pressure coming from the viscous stress in the squeezed air film. Herein we find experimentally and numerically that the process of the air entrapment and the initial contact state of the drop with the substrate can be profoundly altered via an external electric field. In an electric field, the induced electric stresses at the bottom of the drop increase drastically right before the drop contacts the substrate, which acts against the lubrication pressure, resulting in reduced initial contact radius and air bubble size. When the external electric field reaches a critical value, the electrical stress accelerates the flow near the bottom of the drop and generates a conical tip quickly instead of a dimple, resulting in a centre contact and eliminating the air bubble entrapment. Based on the dipole mirror charge model, we find the dimensionless strength of critical electric field scales with the square root of capillary number based on the air viscosity. This scaling law of the critical electric field for eliminating the air bubble entrapment is verified experimentally and numerically. This work may offer a new way to mitigate defects caused by air bubble entrapment for inkjet printing and droplet-based additive manufacturing.
Behaviour of the Landau–Lifschitz equation in a periodic thin layer

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Behaviour of the Landau-Lifschitz equation in a periodic thin layer David Sanchez
Contrastive Learning between Classical and Modern Chinese for Classical Chinese Machine Reading Comprehension

By leveraging self-supervised tasks, pre-trained language model (PLM) has made significant progress in the field of machine reading comprehension (MRC). However, in classical Chinese MRC (CCMRC), the passage is typically in classical style, but the question and options are given in modern style. Existing pre-trained methods seldom model the relationship between classical and modern styles, resulting in overall misunderstanding of the passage. In this paper, we propose a contrastive learning method between classical and modern Chinese in order to reach a deep understanding of the two different styles. In particular, a novel pre-training task and an enhanced co-matching network have been defined: (1) The synonym discrimination (SD) task is used to identify whether modern meaning corresponds to classical Chinese. (2) The enhanced dual co-matching (EDCM) network is employed for a more interactive understanding of the classical passage and the modern options. The experimental results show that our proposed method improves language understanding ability and outperforms existing PLMs on the Haihua, CCLUE, and ChID datasets.
A Sorting Fuzzy Min-Max Model in an Embedded System for Atrial Fibrillation Detection

Atrial fibrillation detection (AFD) has attracted much attention in the field of embedded systems. In this study, we propose a sorting fuzzy min-max (SFMM) model, and then develop an SFMM-based embedded system for AF detection. The proposed SFMM model is essentially enhanced the fuzzy min-max (FMM) model that have been successfully applied in many classification fields. In comparison with the typical FMM model, the proposed SFMM model can overcome the limitation of the input order problem encountered in the typical FMM model. The embedded system consists of a control chip and an analog-digital conversion (ADC) chip. The STM32F407 chip is used as the control chip and the ADS1292 chip, which has a high common-mode rejection ratio (CMRR), is used as the ADC chip. A series of machine learning benchmarks are included to evaluate the performance of the SFMM model. Experimental results on AF data further demonstrate the effectiveness of the SFMM-based embedded system.
The DARPA SEARCHLIGHT Dataset of Application Network Traffic

Researchers are in constant need of reliable data to develop and evaluate AI/ML methods for networks and cybersecurity. While Internet measurements can provide realistic data, such datasets lack ground truth about application flows. We present a ∼ 750GB dataset that includes ∼ 2000 systematically conducted experiments and the resulting packet captures with video streaming, video teleconferencing, and cloud-based document editing applications. This curated and labeled dataset has bidirectional and encrypted traffic with complete ground truth that can be widely used for assessments and evaluation of AI/ML algorithms.
Beyond Black-Boxing: Building Intuitions of Complex Machine Learning Ideas Through Interactives and Levels of Abstraction

Existing approaches to teaching artificial intelligence and machine learning often focus on the use of pre-trained models or fine-tuning an existing black-box architecture. We believe advanced ML topics, such as optimization and adversarial examples, can be learned by early high school age students given appropriate support. Our approach focuses on enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection.
Codespec: A Computer Programming Practice Environment

Computing education researchers (CERs) theorize that learning how to program requires the development of at least four skills: code reading and tracing, code writing, pattern comprehension, and pattern application [see 30]. Developing these skills requires deliberate practice which involves engaging in tasks specifically designed to improve ones’ skills in a particular domain [11]. Web-based interactive programming practice environments such as CloudCoder [23], CodeChef [7], CodeLab [1], CodeWars [8], CodingBat[25], Edabit [19], and LeetCode [17] can be considered informal and/or open-ended learning environments (OELEs) that support deliberate practice—the learner decides what to learn and when [3, 12, 24]. OE-LEs support self-directed learning [14, 16, 18] of computing topics by inspiring programmers to complete projects that are meaningful to them [21], by helping programmers build self-confidence, and by increasing programmers’ sense of control over their learning process [5]. But the absence of a predefined curriculum and/or scaffolding, which are tenets of OELEs [12, 16, 18]
Original Implementing ecosystem-based ﬁsheries management: from single-species to integrated ecosystem assessment and advice for Baltic Sea ﬁsh stocks

to Theorybehindecosystem-basedmanagement (EBM)andecosystem-basedﬁsheriesmanagement (EBFM)isnowwelldeveloped.However, the implementationof EBFM exempliﬁedby ﬁsheries managementin Europeis still largely based onsingle-species assessments and ignores the wider ecosystem context and impact. The reason for the lack or slow implementation of EBM and speciﬁcally EBFM is a lack of a co-herent strategy. Such a strategy is offered by recently developed integrated ecosystem assessments (IEAs), a formal synthesis tool to quantitatively analyse information on relevant natural and socio-economic factors, in relation to speciﬁed management objectives. Here, we focus on implementing the IEA approach for Baltic Sea ﬁsh stocks. We combine both tactical and strategic management aspects into a single strategy that supports the present Baltic Sea ﬁsh stock advice, conducted by the International Council for the Exploration of the Sea (ICES). We ﬁrst review the state of the art in the development of IEA within the current management framework. We then outline and discuss an approach that integrates ﬁsh stock advice and IEAs for the Baltic Sea. We intentionally focus on the central Baltic Sea and its three major ﬁsh stocks cod ( Gadus morhua ), herring ( Clupea harengus ), and sprat ( Sprattus sprattus ), but emphasize that our approach may be applied to other parts and stocks of the Baltic, as well as other ocean areas.
Spontaneous Utilization: A Classic Grounded Theory of Utilizing Ambient Displays in Professional, Large-Scale Agile Software Development Environments

ABSTRACT This research addresses an interdisciplinary problem area concerning the long-term deployment of socially embedded technology in authentic environments. It concentrates on the case of ambient displays, where long-term research in the real world is still scant and evidentially requires methodological development. This study seeks to advance on this situation at both findings and methodological levels. To this end, we introduce our Ambient Surfaces solution that was deployed in the agile software development department of a company for circa 5 years. Classic grounded theory was chosen to methodologically guide the evaluation, while the theoretical contribution of this work is a substantive theory exemplified by its core category of Spontaneous utilization. The theory reveals insights on how ambient displays are utilized by practitioners in professional and large-scale agile environments. We found, among others, that staff members used the Ambient Surfaces largely not on purpose, that our solution evolved toward having a strong emphasis on progress tracking information, and that inter-team awareness as well as intra-team communication were encouraged.
Interactive Ad Avoidance on Mobile Phones

Abstract Ad avoidance (e.g., “blinding out” digital ads) is a substantial problem for advertisers. Avoiding mobile banner ads differs from active ad avoidance in nonmobile (desktop) settings, because mobile phone users interact with ads to avoid them: (1) They classify new content at the bottom of their screens; if they see an ad, they (2) scroll so that it is out of the locus of attention and (3) position it at a peripheral location at the top of the screen while focusing their attention on the (non-ad) content in the screen center. Introducing viewport logging to marketing research, we capture granular ad-viewing patterns from users’ screens (i.e., viewports). While mobile users’ ad-viewing patterns are concave over the viewport (with more time at the periphery than in the screen center), viewing patterns on desktop computers are convex (most time in the screen center). Consequently, we show that the effect of viewing time on recall depends on the position of an ad in interaction with the device. An eye-tracking study and an experiment show that 43% to 46% of embedded mobile banner ads are likely to suffer from ad avoidance, and that ad recall is 6 to 7 percentage points lower on mobile phones (versus desktop).
Intelligent Use of Emotions: Impact of Manager’s Emotional Intelligence on Marketing Creativity - Study of Financial Service Providers in Sindh.

Marketing creativity is proved to be a competitive advantage in now days, and this is some-how known as healthy competition between entrepreneurs, owners of different business organizations, and ultimately it will be beneficial for consumers/ultimate users of product or services. Each and every owner of manufacturing or service oriented organization is busy in making their product/services more creative, in this regard study was carried out from financial service providers of Sindh by knowing the intelligent use of emotions. Data was collected from officers of targeted population (i. e. banks, insurance companies, mudarbas, musharikas, etc.) through a structured questionnaire, followed by pilot testing, reliability analysis of questionnaire, descriptive statistics, factor analysis, correlation, and regression analysis for checking the hypothesis. Results suggested that: marketing creativity can be enhanced by adding positive in Self Awareness, Motivation and Relationship Management from the results it is very much clear that Self Awareness, Motivation and Relationship Management are positively and significantly contributing to the marketing creativity. Value-based client administration predicts execution of chose pharmaceutical organizations in Kampala. The scientists utilized contextual analyses and cross-sectional exploration outlines, which utilized a specialist's made poll, for information gathering. Information was investigated utilizing means and relapses, which were figured utilizing the measurable bundle for social researcher (SPSS). Discoveries uncovered a high degree of relationship administration in downstream production network at a normal mean of 4.23, and, large amounts of pharmaceutical execution at a normal. The specialists along these lines prescribes supervisors, strategy producers and professionals to give impressive consideration in overseeing connections inside of the downstream inventory network, and specifically, guarantee fitting coordinated efforts with clients. Along these lines, associations will hold their clients, build deals levels and pieces of the overall industry, which will subsequently enhance authoritative execution. From the discoveries, the analysts closed on the theories as takes after: (1) relationship administration in downstream production network exceptionally predicts execution among chose pharmaceutical organizations in Kampala, (2) synergistic client administration exceptionally predicts execution among chose pharmaceutical organizations and in conclusion, (3) value-based client administration pitifully predicts execution among chose pharmaceutical organizations in Kampala. In such manner, directors, chiefs and specialists need to offer impressive consideration on overseeing downstream relationship, specifically, guarantee suitable joint efforts with their clients.
Detection of Hate or Offensive Phrase using Magnified Tf-Idf

The non-negotiable challenge that social media platforms are facing nowadays is the abundant presence of hate speeches in text messages. Thus, automatic hate speech detection becomes an important ethical concern and research should be carried out to overcome this challenge. In the present paper, we propose a tf-idf based binary classification framework that manipulates the scores obtained as the differences between hate and offensive (HOF) words and non-HOF (NOT) words. Employing this framework, we have achieved a Macro F1 score of 0.6813 and 0.6762 for the English and Hindi test datasets, respectively provided in subtask-1A of the HASOC 2021[13] shared task.
Categorizing Roles of Legal Texts via Sequence Tagging on Domain-Specific Language Models

Automatically understanding rhetorical roles of text snippets within a legal document provides an interesting problem, enabling several downstream tasks like summarization of legal judgments, similar legal text search, and case analysis. The task is challenging as legal case documents are domain-specific, usually not well-structured, and rhetorical roles may be subjective. To this end, we present how sentence embeddings from domain-specific pre-trained language model can be combined with a sequence tagging classifier , to better understand the implicit sections within legal documents via long-term relationships, for sentence classification. Our proposed methodology secured the 1st rank, with an F1 score of 0 . 557 on the shared task 1 in the “Artificial Intelligence for Legal Assistance” (AILA) track of the Forum of Information Retrieval Evaluation (FIRE), 2021.
Analyzing COVID-19 Vaccination

The outbreak of the coronavirus has resulted in unprecedented action, which has led authorities to decide to begin the blockade of the areas most hit by the infectious disease. Social media has been an important support for people during this difficult time. On November 9, 2020, when the first vaccine with an infection rate of 90% or higher was announced, social media responded with, and people around the world began to express the feelings of vaccination. It was no longer a hypothesis, but closer to,every day to become a reality Therefore, it becomes imperative to verify some of the information posted on social media during the pandemic situation, specially related to Covid vaccines. To this end, it is necessary to correctly identify fact-checkable posts, so that their information content can be verified.In this work, we have addressed the problem to identify 3 types of classification on the Twitter microblogging site. We organized a shared task in the FIRE 2021 conference to study the problem of identifyefficient classifier for prediction tweets posted during a particular pandemic scenario (the Covid 19). This paper describes the dataset used in the shared task, and compares the performance of different classification that are provax, antivax and last neutraal for identifying effective tweets related to Covid vaccines.We experimented with a classification-based approach. Our experiment shows that SVM classification performs well in order to effiective posts.Using this support vector machine in order to solve the antivax, provax,neutral classification of twets . We’re going to do this because vaccination is an important step for Covid19 so people can easily fix the news about the vaccine and grab their own slot. © 2021 Forum for Information Retrieval Evaluation, December 13-17, 2021, India.
Detection of Threat Records by Analyzing the Tweets in Urdu Language Exploring Deep Learning Transformer - Based Models

As humans, we express sadness, anger, happiness, frustration, bullying, etc., in both physical and virtual worlds. In the virtual world, i.e., social media, we use textual ways to express ourselves. Due to the lack of offensive and threatening language detection mechanisms aggressive behavior in social media is not always followed by an immediate consequence. But the impact of these posts on the victim can cause prolonged mental illness and instigate fear for social media platforms. This paper aims to identify threatening posts using deep learning transformer-based models such as Roberta. The Urdu tweet dataset used in this study has been provided by HASOC-2021 which aims to identify Hate speech and offensive remarks without human assistance. We submitted our model in its subtask B of the 4th subtrack(Abusive and Threatening language detection in Urdu), secured 2nd position on the public leaderboard, and obtained Weighted f1 of 0.5346 and ROC AUC of 0. 8199.
Comparative Analysis for Offensive Language Identification of Tamil Text using SVM and Logistic Classifier

Social media like Twitter, Facebook, YouTube provide an opportunity of the fastest communication be-tween people. The social media texts are largely filled with code-mixed comments/post and reactions and its content may be filled with offensive language or non-offensive language. It is necessary to classify the YouTube comments/post and reactions as offensive label and non-offensive label. As the offensive comments/post is very sensational to something or someone to react in the society, Government has responsibility to identify it in the social media, before it reaches a larger audience. In India, multi-lingual practices use code mixed comments/post in social media, which leads to difficulty in of-fensive text classification automatically. The Dravidian code mixed data set is used to train the machine learning model to classify the label as offensive language or non-offensive language. The text data set is transformed into numerical data based on relative occurrence in the available datasets of training and testing using TFIDF method. However, the imbalanced dataset may be biased to a particular class of label, and hence it is turned into balanced dataset using SMOTE method. It is trained on SVM classifier and Logistic Classifier. The F1 score is analsyed and it is observed that balanced dataset predictions are better than unbalanced dataset predictions.
Examination of User Pairing NOMA System Considering the DQN Scheme over Time-Varying Fading Channel Conditions

— In this paper, we employ the deep Q -network (DQN) algorithm to study user pairing downlink (D/L) non-orthogonal multiple access (NOMA) system considering the multiple user equipment (UEs). In this work, the independent and identically distributed ( i.i.d. ) fading links are considered. The channel is expected to become time-varying as a result of node mobility. User pairing and optimum power distribution algorithms based on reinforcement learning (RL) are investigated initially in NOMA systems. To make the analysis simpler and for reducing the computational complexity the Q -learning-based scheme is used jointly to investigate the user pairing and optimal power allocation problems. In real-time propagation conditions with numerous users, DQN was employed to conduct user pairing and power allocation at the same time. When the learning rate is 0.2000, the DQN method, on the other hand, converges quicker but does not reach the maximum throughput (or average sum rate). In our simulation, we have eight UEs, and it has been observed that employing near-user far-user (N-F) results in a better sum rate. The symbol error rate (SER) performance falls dramatically as the node velocity increases, according to simulation curves. This is because, when node mobility increases, the channel will change extremely quickly. The simulation results confirm the derived analytical expressions.