Miracle-dz/UniFAR-FTUs huggingface dataset:
Framework to Classify and Analyze Social Media Content

In the last decade, a large amount of data has been published in different fields and can be used as a data source for research and study. However, identifying a specific type of data requires processing, which involves machine learning classifying techniques. To facilitate this, we propose a general framework that can be applied to any social media content to develop an intelligent system. The framework consists of three main parts: an interface, classifier and ana-lyzer. The analyzer uses media recognition to identify specific features. Then, the classifier uses these features and involves them in the classification process. The interface organizes the interaction between the system compo-nents. We tested the framework and developed a system to be applied to im-age-based social media networks (Instagram). The system was implemented as a mobile application (My Interests) that works as a recommendation and filtering system for Instagram users and reduces the time they spend on irre-levant information. It analyzes the images, categorizes them, identifies the in-teresting ones, and finally, reports the results. We used the Cloud Vision API as a tool to analyze the images and extract their features. Furthermore, we adapted support vector machine (SVM), a machine learning method, to classify images and to predict the preferred ones.
Concurrency Analysis in Dynamic Dataflow Graphs

Dynamic dataflow scheduling enables effective exploitation of concurrency while making parallel programming easier. To this end, analyzing the inherent degree of concurrency available in dataflow graphs is an important task, since it may aid compilers or programmers to assess the potential performance a program can achieve via parallel execution. However, traditional concurrency analysis techniques only work for DAGs (directed acyclic graphs), hence the need for new techniques that contemplate graphs with cycles. In this paper we present techniques to perform concurrency analysis on generic dynamic dataflow graphs, even in the presence of cycles. In a dataflow graph, nodes represent instructions and edges describe dependencies. The novelty of our approach is that we allow concurrency between different iterations of the loops. Consequently, a set of concurrent nodes may contain instructions from different loops that can be proven independent. In this work, we provide a set of theoretical tools for obtaining bounds and illustrate implementation of parallel dataflow runtime on a set of representative graphs for important classes of benchmarks to compare measured performance against derived bounds.
A multiple-case study on students’ sourcing activities in a group task

Abstract Students need to apply evaluation strategies to find relevant and trustworthy information when completing open-inquiry tasks on the Internet. One such strategy, sourcing, implies attention to information about the sources of documents. This multiple-case study investigates how three small groups of secondary school students solved an open-inquiry task. The task took place after extensive instruction on how to benefit from the use of source information when selecting, reading, and integrating information. The multiple-case study aimed to ascertain how the taught sourcing skills were reflected in the different groups’ sourcing activities. Data consisted of observations, interviews, and documents. Results show that groups performed more sourcing activities than they displayed in their oral presentation. Though the integration of information from multiple documents was less prominent, teachers valued it highly. The findings suggest that instructions in school should not focus on the performance of sourcing activities but on discussions where students compare their selections and integration of information.
The Impact of Cues and User Interaction on the Memorability of System-assigned Random Passwords

The Impact of Cues and User Interaction on the Memorability of System-assigned Random Passwords Mahdi Nasrullah Al-Ameen, Ph.D. The University of Texas at Arlington, Supervising Professor: Matthew Wright Given the choice, users produce passwords reflecting common strategies and patterns that ease recall but offer uncertain and often weak security. Addressing this usability-security tension in user authentication remains the key research issue in password studies for decades. In this thesis, we aim to understand how humans’ cognitive abilities could be leveraged to design more secure and memorable authentication schemes. To achieve this goal, we draw upon multiple theories from cognitive psychology and implement them in the context of improving memorability for systemassigned random passwords. We argue that while the system assigns random passwords, it should also help users with memorization and recall. We investigated the feasibility of this approach with CuedR, a novel cued-recognition authentication scheme that provides users with multiple cues (visual, verbal, and spatial) and lets them choose the cues that best fit their learning process for later recognition of system-assigned keywords. The lab study on CuedR showed promise for providing multiple cues with a 100% memorability rate over the span of one week.
Optimization of die-temperature in pultrusion of thermosetting composites for improved cure

In this work, we will present a swram optimization based approach to optimize die-temperature and pull-speed in pultrusion of thermosetting composite. Pultrusion is a composite manufacturing technique for processing continuous composite profiles with a constant cross section. The materials which are used for pultrusion in the industry are continuous glass fibers with polyester or epoxy resins. During composite processing, the reinforcing fibers are impregnated with a liquid resin in an injection box or resin bath, fibers and resin are preheated in a mold in which the curing process takes place. High productivity and low operating costs are the main advantages of this processing method. During processing, the heat flux provided by the mold must be sufficient to promote the polymerization reaction of the thermosetting matrix (curing). Furthermore, curing of a composite should be uniform and sufficient in order to provide a good quality of the end product. The exothermic character of the curing reaction induces, inside the composite, exceed temperatures. This temperature rise can cause degradation of the final product. Also, in pultrusion process, transport phenomena are involved and mathematical models are necessary to predict the physico-chemical behavior of the process. For such studies, the region enclosed by the mold is usually considered the main part of the process in which the curing reaction occurs and heat is transfered. Thus, the optimization process is quite important for the prediction of die-heating temperature and pull-speed.
Improved lexical similarities for hybrid clustering through the use of noun phrases extraction

Clustering of hybrid document networks combining citation based links with lexical similarities suffered for a long time from the different properties of these underlying networks. In this paper we evaluate different processing options of noun phrases extracted from abstracts using natural language processing to improve the measurement of the lexical component. Term shingles of different length are created from each of the extracted noun phrases. We discuss twenty different extraction-shingling scenarios and compare their results. Some scenarios show no improvement compared with the previously used single term lexical approach used so far. But when all single term shingles are removed from the dataset the lexical network has properties which are comparable with those from a bibliographic coupling based network. Next, hybrid networks are built based on weighted combination of the two types of similarities with seven different weights. We demonstrate that removing all single term shingles provides the best results at the level of computational feasibility, comparability with bibliographic coupling and also in a community detection application.
Multichannel interference in high-order-harmonic generation from Ne+ driven by an ultrashort intense laser pulse

We apply the time-dependent R-matrix method to investigate harmonic generation from Ne(+) at a wavelength of 390 nm and intensities up to 10(15) Wcm(-2). The 1s(2)2s(2)2p(4) (3)P(e), (1)D(e), and (1)S(e) states of Ne(2+) are included as residual-ion states to assess the influence of interference between photoionization channels associated with these thresholds. The harmonic spectrum is well approximated by calculations in which only the (3)P(e) and (1)D(e) thresholds are taken into account, but no satisfactory spectrum is obtained when a single threshold is taken into account. Within the harmonic plateau, extending to about 100 eV, individual harmonics can be suppressed at particular intensities when all Ne(2+) thresholds are taken into account. The suppression is not observed when only a single threshold is accounted for. Since the suppression is dependent on intensity, it may be difficult to observe experimentally.
An Efficient Adaptive based Median Technique to De-noise Colour and Greyscale Images

The picture noise is an irregular variation of brightness and color information in pictures. It decreases picture quality and permeability of specific elements inside the picture. The most surely understood noise that corrupts the photo with impulse noise. In this work, an effective algorithm is intended to identify and remove noise from a picture. An improved de-noising calculation in view of the median filter is exhibited for greyscale and colored images. The algorithm incorporates two cases: I) if the chose window contains all pixel values "0" to "255" at that point center preparing pixel supplanted by the mean of qualities. II) If the chosen window does not contain all components "0" and "255" then eliminate "0" and "255" and central preparing pixel is replaced by the median of remaining pixels values. The performance is checked off the purposed algorithm by comparing it with corresponding filters. The experiment checked at various noise proportion 5% to 80% for greyscale and color pictures. Results are checked as far as MSE and PSNR and even at high noise proportion; it gives better outcomes over other existing techniques.
An Active De-anonymizing Attack Against Tor Web Traffic

Tor is pervasively used to conceal target websites that users are visiting. A de-anonymization technique against Tor, referred to as website fingerprinting attack, aims to infer the websites accessed by Tor clients by passively analyzing the patterns of encrypted traffic at the Tor client side. However, HTTP pipeline and Tor circuit multiplexing techniques can affect the accuracy of the attack by mixing the traffic that carries web objects in a single TCP connection. In this paper, we propose a novel active website fingerprinting attack by identifying and delaying the HTTP requests at the first hop Tor node. Then, we can separate the traffic that carries distinct web objects to derive a more distinguishable traffic pattern. To fulfill this goal, two algorithms based on statistical analysis and objective function optimization are proposed to construct a general packet delay scheme. We evaluate our active attack against Tor in empirical experiments and obtain the highest accuracy of 98.64%, compared with 85.95% of passive attack. We also perform experiments in the open-world scenario. When the parameter k of k-NN classifier is set to 5, then we can obtain a true positive rate of 90.96% with a false positive rate of 3.9%.
Design Considerations to Increase Block-based Language Accessibility for Blind Programmers Via Blockly

Block-based programming languages are a popular means to introduce novices, specifically children, to programming and computational thinking concepts. They are tools to broaden participation in computing. At the same time, block-based languages and environments are an obstacle in broadening participation for many users with disabilities. In particular, block-based programming environments are not accessible to users who are visually impaired. This lack of access impacts students who are participating in computing outreach, in the classroom, or in informal settings that foster interest in computing. This paper will discuss accessibility design issues in block-based programming environments, with a focus on the programming workflow. Using Google’s Blockly as a model, an accessible programming workflow is presented that works alongside the conventional mouse-driven workflow typical of block-based programming. The project presented is still in progress.
AN IMPROVED METHOD FOR IDENTIFICATION OF PATTERNS IN THE NON-OVERLAPPING TEMPLATE MATCHING TEST

We proposed a modiﬁed non-overlapping template matching test and a method for specifying a pattern that appears too many times, in Takeda et al. (2014). The method that we proposed was eﬀective for the most part, but had some diﬃculties. Our new contribution in this paper is to propose an improved method of identifying a pattern that appears too many times, in the non-overlapping template matching test for resolving these diﬃculties and to show how this identiﬁcation test works eﬀectively by some simulation studies.
An improved measurement method for large aviation part based on spatial constraint calibration and compression extraction

An accurate measurement of large aviation part plays a key role in the assembly of aircraft. However, due to the limitation of spatial size, a calibration with large field of view and an accurate surface measurement of large part is hard to achieve. In this paper, an improved measurement method with spatial constraint calibration method and feature compression extraction method is proposed. Firstly, based on the proposed spatial constraint calibration method, the vision system is conveniently and precisely calibrated by using the designed SBA and SLT. Images of scanning laser stripes are captured by the calibrated cameras, simultaneously. Then the proposed feature compression extraction method is adopted to accurately extract centers of laser stripes. Finally, based on the binocular vision principle, the surface of part is reconstructed. The accuracy of proposed calibration method is verified in the lab. The results of the measurement of a standard part show the validity and precision of the proposed method.
Application of deep neural networks for security analysis of digital infrastructure components

In this article the authors give a consideration to a problem of detecting errors and vulnerabilities in software components of different digital devices. The article shows an ever-increasing criticality of this problem in the course of time related to development of modern concepts the Industrial Internet and the Industry 4.0. It gives an overview of modern approaches to application of methods of computer-assisted learning and artificial intellect in the sphere of cyber security, problems and prospects of application thereof. A new approach is offered to searching software vulnerabilities on the basis of application of deep learning. The approach is based on building semantically significant vector representations of software code and multistage instructing the deep neural network on revealing hierarchical abstractions in computer code testifying to presence of vulnerabilities. The authors describe specific features of the goal of analyzing software code for presence of vulnerabilities and proceeding thereof it is offered to use a neural network with long short-term memory (LSTM). In order to solve a problem of the learning set, the authors offer to use learning with transfer in case of building vector representations of instructions. The article also provides results of experimental investigations on application of offered solutions.
Reducing complexity of processor front ends with static analysis and selective preloading

x Chapter 1: Introduction . 1 Chapter 2: Background and Related Work . 5 2.1 Early, Low Complexity Branch Predictors . 6 2.2 Predictors with Greater Complexity . 7 2.3 Improving Prediction Accuracy by Determining Most Useful History Segment 9 2.3.1 Dynamically (at Run Time) . 9 2.3.2 Using Profiling . 9 2.4 Other Profile Based Predictors . 10 Chapter 3: Profile Based Branch Prediction . 12 3.1 Benefits of Profile Based Predictors . 12 3.2 Static Branch Predictors . 13 3.3 The Agree Predictor . 14 3.4 The Elastic History Buffer Predictor . 15 3.5 The Spotlight Branch Predictor . 17 3.6 Summary . 19 Chapter 4: Instruction Profiling in the Real World, Profiling, Analysis and Annotation (PAA) . 20 4.1 Profiling and Sampling . 21 4.2 Analysis of Profiled Information . 22 4.3 Storing Configuration Information in Carry Instructions . 24 4.4 Build PAA and Dynamic PAA . 26 Chapter 5: Modifying Profile Based Predictors For Low Latency . 28 5.1 Reducing the Latency of Profile Based Predictors with a Hardware Table for Profiled Data . 28 5.2 Attaining Single Cycle Latency by Storing Data Upstream . 29 Chapter 6: Evaluating Performance of Spotlight Schemes . 32 6.1 Experimental Framework . 32 6.1. 1 Simulator Configuration . 32
Vehicle Classification and False Detection Filtering using a Single Magnetic Detector based Intelligent Sensor

Vehicle detection and classification is a very actual problem, because vehicle count and classification data are important inputs for traffic operation, pavement design, transportation planning and other applications. Magnetic detector based sensors provide many advantages compared to other technologies. In this work a new vehicle detection and classification method is presented using a single magnetic detector based system. Due to the relatively big number of false detections caused by vehicles with high metallic content passing in the neighboring lane, a technique for false detection filtering is also presented. Vehicle classes are determined using a feedforward neural network which is implemented in the microcontroller of the detector, together with the detection algorithm and the algorithm used for determining the neural network inputs. The gathering of training samples and testing of the trained neural network have been done in real environment. For the training of the neural network the back-propagation algorithm has been used with different training parameters. Keywords— vehicle detection, false detection filtering, vehicle classification, magnetic sensors, neural networks
69th Annual ISE Meeting 参加報告

この度,「平成 30年度前期(第 46回)若手研究者の国 際交流支援」により,渡航費の一部を援助していただき, 2018年 9月 2日から 7日にかけてイタリア・ボローニャで 開催された 69th Annual Meeting of the International Society of Electrochemistryに参加しました. 筆者の専門分野であ る蓄電池分野のみならず 21のシンポジウムで構成される 大きな学会で,電気化学に関する活発な議論が飛び交う 充実した学会参加となりました. とりわけ,蓄電池に関 するシンポジウムでは,聴衆が多く国際的にもエネル ギー貯蔵への関心が高いことを改めて認識することとな りました. 筆者は現在,信州大学において応用電気化学研究室に 所属しており,電気めっきによるエレクトロニクス用部 品の作製やカーボンナノチューブを用いた機能性材料 (熱伝導体・電界放出ディスプレイ素子・触媒など)の創 製などに取り組んでいます. Cuの電気めっきを扱う一連 の研究過程で,めっき浴へのポリアクリル酸の添加に よってシート形状を有したCuが三次元的に成長すること が分かりました. 大学院生(鳥取大学 坂口研究室)の時 に蓄電池研究に携わっていたことから,この特徴的な構 造体を集電体基板に使えないか? という考えから生まれ たのが,本学会で発表した研究内容です (Electrochemical Preparation of Roughened Current Collectors and Their Application to Sn Negative Electrode for Na-ion Batteries). めっき浴に添加するポリアクリル酸の濃度や電流密度に より集電体基板表面に成長するシートの形状や粗面化度 を制御し,合剤層-基板間の密着性を改善することで高 容量ナトリウムイオン電池負極として期待される Sn電極 の高性能化を達成したというものです (M. Shimizu, S. Arai et al., J. Phys. Chem. C, 121 (2017) 27285. ). 本発表は口頭 発表で申し込みましたが,残念ながらポスター発表に回 されてしまいました. しかしながら,直接海外の研究者 と活発な議論ができたことやアドバイスがもらえたこと もあり,後になって良かったものと感じています. 論文 でしか知らない研究者らや,第一線で活躍される著名な 先生方の研究を間近で拝見できたことは大きな経験とな りました. 今後に活かしたいと思います. 最後に,本研 究を進めるうえで献身的に支えて下さった同研究室 新井 進先生および実験をともに進めてくれた学生に感謝いた します. また,この度の学会参加にあたり,渡航費を支 援していただいた電気化学会の関係者各位に厚く御礼申 し上げます. (上)特徴的な Cu構造体が形成されるメカニズム. (下)会 場前での筆者. 電気化学, 87(Spring)
Öğretmen Adaylarının Özyeterlik Algıları ile Öğretmenlik Mesleğine Yönelik Motivasyonları Arasındaki İlişki

Basım Tarihi: 31.10. 2020 Bu çalışmanın amacı öğretmen adaylarının özyeterlik algıları ile öğretmenlik mesleğine yönelik motivasyonları arasındaki ilişkiyi incelemektir. Araştırma ilişkisel tarama modelinde betimsel bir çalışmadır. Araştırmanın çalışma grubunu 2013-2014 eğitim öğretmen yılında Kastamonu Üniversitesi’nde öğrenim gören 521 öğretmen adayı oluşturmaktadır. Veri toplama aracı olarak “Öğretmen Özyeterlik Ölçeği” ile “Öğretmenlik Mesleğine İlişkin Motivasyon Düzeyleri Ölçeği” kullanılmıştır. Verilerin analizi için Pearson Momentler Çarpımı Korelasyon Katsayısı hesaplanmış ve çoklu doğrusal regresyon analizi uygulanmıştır. Araştırma sonuçları değerlendirildiğinde öğretmen adaylarının özyeterlik algıları ile Öğretmenlik mesleğine yönelik motivasyonları arasında orta düzeyde pozitif yönde anlamlı bir ilişki bulunmuştur. öğretmenlik mesleğine ilişkin motivasyon öğretmen özyeterliğinin anlamlı bir yordayıcısıdır. Araştırma bulgularına göre, öğretmen özyeterliğine ilişkin toplam varyansın % 17’sinin öğretmen adaylarının öğretmenlik mesleğine ilişkin motivasyonları ile açıklandığı söylenebilir. Anahtar Sözcükler: Özyeterlik, öğretmenlik mesleğine yönelik motivasyon, öğretmen adayı
The Art of Game Design: A book of lenses

Anyone can master the fundamentals of game design - no technological expertise is necessary. "The Art of Game Design: A Book of Lenses" shows that the same basic principles of psychology that work for board games, card games and athletic games also are the keys to making top-quality videogames. Good game design happens when you view your game from many different perspectives, or lenses. While touring through the unusual territory that is game design, this book gives the reader one hundred of these lenses - one hundred sets of insightful questions to ask yourself that will help make your game better. These lenses are gathered from fields as diverse as psychology, architecture, music, visual design, film, software engineering, theme park design, mathematics, writing, puzzle design, and anthropology. Anyone who reads this book will be inspired to become a better game designer - and will understand how to do it.
A Large-Scale Analysis of How OpenSSL Is Used in Open-Source Software

A Large-Scale Analysis of How OpenSSL Is Used in Open Source Software Scott Jared Heidbrink Department of Computer Science, BYU Master of Science As vulnerabilities become more common the security of applications are coming under increased scrutiny. In regards to Internet security, recent work discovers that many vulnerabilities are caused by TLS library misuse. This misuse is attributed to large and confusing APIs and developer misunderstanding of security generally. Due to these problems there is a desire for simplified TLS libraries and security handling. However, as of yet there is no analysis of how the existing APIs are used, beyond how incorrect usage motivates the need to replace them. We provide an analysis of contemporary usage of OpenSSL across 410 popular secure applications. These insights will inform the security community as it addresses TLS library redesign.
Pengantar Statistika Pendidikan: Teori dan Aplikasi

Statistik adalah sekempulan fakta yang berbentuk angka yang disusun dalam tabel atau daftar yang menggambarkan suatu persoalan. Secara etimologis kata statistik berasal dari kata status (bahasa Latin), state (bahasa Inggris), staat (bahasa Belanda), yang dalam bahasa Indonesia diterjemahkan menjadi Negara. Pada mulanya, kata statistik diartikan sebagai kumpulan bahan keterangan yang mempunyai arti penting dan berguna bagi suatu Negara. Dimana mata kuliah statistik pendidikan ini bertujuan untuk memberikan pengetahuan dan pemahaman tentang filosofi, prinsip dan rumus-rumus statistik serta terampil mengaplikasikan baik dalam proses pengumpulan data, penyusunan dan mendeskripsikan data maupun untuk menganalisisnya guna kepentingan pengambilan keputusan khususnya berkaitan dengan penelitian pendidikan. Mengkrucut pada sebuah tujuan besar dari mata kuliah ini diharapkan mahasiswa dapat bersikap teliti, cermat dan terampil dalam menggunakan statistik sebagai alat analisis penelitian pendidikan bidang fisika.