Reviewerly/exHarmony huggingface dataset:
An Optically-encoded Loss-predictive Framework for Face Recognition Using Nonlinear Adaptive Margin

Recent face recognition strategies using deep neural networks (DNNs) mainly focus on the development of new loss functions and the evolution of network architecture. Due to the large capacity of face datasets, such DNN models usually suffer from a long training time. Motivated by freeform optics design, in this paper we propose a novel paradigm of an optical image encoder, DNN-decoder system for improved face recognition. To make the model learn better from unfamiliar samples, we introduce a covariance loss prediction module attached to the network backbone to dynamically adjust the loss objective. The model defines a nonlinear adaptive margin to measure the angular distance between high-dimensional features and utilizes a PID optimizer to update its parameters, resulting in a faster convergence. Empirical results have shown that the proposed model achieves higher training efficiency on public large training datasets such as WebFace42M, MSIMV2 and CASIA-WebFace, and enjoys state-of-the-art recognition performance on popular evaluation datasets including LFW, MegaFace and IJB-C.
Introduction to “Research Tools”: Tools for Publishing, and Improving Research Visibility

The use of information technology allow researchers to do their research process faster. There are several “Research Tools” available on the internet, that enable researchers to collect, organize, analyze, visualize and publicized research outputs. Dr. Nader has collected over 700 tools that assist researchers to follow the correct path in research and to ultimately produce high-quality research outputs with more accuracy and efficiency. In this workshop, he will introduce some tools from his Research Tools Mind Map.
A Web-Based Tool for analysing Formula 1 Races and Seasons

In this paper we describe an interactive web-based tool for visual analysis of Formula 1 data. A calendar-like representation provides an overview of all races on a yearly basis, either in absolute or normalized time. After selecting a dedicated race more details about this race can be explored. Furthermore it is possible to compare up to three different races. Beside visualizing details on dedicated races it is also possible to analyse driver and team performance over time. A user study was applied to get feedback about the usage of the application and decide between different visualization options.
Multi-Branch Supervised Learning on Semantic Segmentation

Autonomous driving is a research and development focus of various countries in recent years, and its popularity cannot be separated from the support of semantic segmentation. Aiming at the problem that the shallow layers of the semantic segmentation model cannot obtain effective and immediate supervision, this paper proposes a multiple branch supervised method, which can simultaneously supervise the shallow layers of model during the training process. In order to increase the information interaction among multiple network layers, a module named “Layer Attention Mechanism” is proposed, which can increase the network's attention to more effective information when the network layers are merged. Based on UNet, experiments in the autonomous driving datasets show that the structure proposed in this paper is better than the UNet3+ network, which improves the accuracy of semantic segmentation and optimizes the effect of semantic segmentation.
Depth Image Denoising Using Graph-Based Sparse Representation

In the field of image processing, image denoising is a very challenging task. The development of graph signal theory provides a new perspective for us to deal with this problem. By converting the image into several image patch groups, this paper turns to study the denoising of the image patch groups by using the graph signal approach, which modulates the image patch group as a set of signal values on the graph and defines the graph Fourier transform to transfer them to the graph frequency domain for denoising. Based on this idea, a graph-based algorithm is proposed for deep image denoising. Experiments show that our method outperforms Block-Matching and 3D (BM3D) filtering, especially in the case of low-level noise.
An improved evolutionary density peak clustering algorithm

Aiming at the problem that traditional density peak clustering algorithm can't adapt to discover knowledge in the database with dynamic growth of data volume, in this paper, we propose an improved evolutionary density peak clustering algorithm (EDPC). The EDPC algorithm does not need any prior knowledge, and can select the control parameters according to the comprehensive index <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\gamma$</tex> of the sample itself, which overcomes the defect that there is no reference in the selection of prior parameters in the current incremental algorithm. EDPC algorithm is based on incremental data, which effectively solves the problem that traditional DPC algorithm can not adapt to the dynamic change of data. At the same time, with the addition of new samples, EDPC algorithm can accurately find the cluster center, and adjust the control parameters according to the generated <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\gamma$</tex> graph, so as to decide whether to generate new clusters or merge old clusters. Finally, experiments on UCI and Synthetic datasets show that EDPC algorithm is able to outperform incremental K-means and incremental DBSCAN algorithm.
Sine-Cosine Algorithm for Software Fault Prediction

For developing an efficient and quality Software Fault Prediction (SFP) model, redundant and irrelevant features need to be removed. This task can be achieved, to a significant extent, with Feature Selection (FS) methods. Many empirical studies have been proposed on FS methods (Filter and Wrapper-based) and have shown effective results in reducing the problem of high dimensionality in metrics-based SFP models. This study evaluates the performance of novel wrapper-based Sine Cosine Algorithm (SCA) on five datasets of the AEEEM repository and compares the results with two metaheuristic techniques Genetic Algorithm (GA) and Cuckoo Search algorithm (CSA) on four different Machine Learning (ML) classifiers - Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (KNN). We found that the application of FS methods (SCA, GA & CSA) has improved the classifier performance. SCA has proved to be more efficient than GA and CSA in terms of lesser convergence time with the smallest subset of selected features and equivalent performance.
Analyzing the Texture of Nakagami Parametric Images for Classification of Breast Cancer

In this paper we analyze the capability of texture features extracted from Nakagami parametric images for the classification of breast cancer. Nakagami parametric maps were generated from ultrasound envelope images using a sliding window of length 0.75mm and 0.0385mm increment (95% overlap). Next, Gray Level Co-occurrence Matrix (GLCM) techniques were applied to the parametric maps in order to extract texture features. These texture features were utilized for the classification of breast lesions. An Area under the Receiver Operating Characteristics curve (AUC) of 0.90 and a sensitivity of 88.5% was obtained, suggesting that texture features derived from Nakagami parametric images have the potential to play an important role in the early diagnosis of breast cancer.
Analysis and suppression of stray light in micro mirror array waveguide

Micro mirror array waveguide technology is a kind of transmission display technology based on the principle of reflection of light, which has the ability of color display with large pupil, and has a wide application prospect in the field of AR display. In order to ensure the transmittance of the exterior light, the reflectivity of the micro mirror array cannot be very high, so the micro mirror array waveguide system is a low optical efficiency system. In this case, the stray light is very sensitive to the influence of the system, so the suppression of stray light inside and outside the system is an important part of the design of micro mirror array waveguide system. In this paper, a typical micro mirror array waveguide is taken as the analysis object, and the optical path of internal stray light and external stray light is analyzed respectively, and the simulation is carried out by using TracePro software. According to the simulation results of stray light optical path, the methods to suppress all kinds of stray light are proposed, which can eliminate most of the stray light, and provide a reference for the design optimization of micro mirror array waveguide.
Application of Mobile Micro-Classroom Based on Information Technology in College Medical English

Information technology has been widely used in all walks of life. As an emerging Internet teaching method, Micro-classroom is favored by college teachers in the current climate of rapid development in information technology and is widely used in college medical English due to its compact and convenient characteristics. Mobile Internet and mobile smart terminals have become popular on university campus. They not only pose challenges to traditional teaching, but also provide more opportunities for innovation. The reasonable and effective use of mobile Internet to carry out micro-class teaching in college medical English teaching is an effective means to improve the quality of medical English teaching. This article combines current teaching practice from teaching needs, and analyzes the application of mobile micro-classrooms in medical English based on Visual case teaching.
The Direction of Developing Courses and Assessment Tools for Enhancing Creativity Aligned with Critical Thinking

The intellectual activities of creativity and critical thinking are essential in the complex problem-solving process of modern society. This article starts from the position that it is necessary to develop courses and assessment tools that is needed for simultaneously facilitating creativity and critical thinking skills at the same time in universities. The purpose of this paper is to provide a starting point for discussion on the development of creative competency courses and assessment tools required to link creativity and critical thinking skills. In Korea, there are many studies dealing with creativity and critical thinking separately, while in foreign countries, there is an active discussion taking place about the development of courses and assessment tools. To this end, based on the creative competency area set as a core competency in the liberal arts education of a university, the discussion was conducted by citing relevant domestic and foreign materials.&lt;br/&gt;First, in the process of arranging the connection between creativity and critical thinking, the necessity of linking these two skills to solve creative problems was confirmed. Next, the direction of the development of courses that can foster creativity and critical thinking abilities together was explored, focusing on lesson goals, contents, and methods. In order to simultaneously enhance creativity and critical thinking abilities among our students, it is necessary to design an activity-centered curriculum that combines both skills based on the existing creativity education and critical thinking education. Finally, the method of developing an assessment tool that can authentically measure the students’ creativity and critical thinking ability is described mainly using existing tools and in terms of employing technologies. Research on how to integrate previously developed tools for measuring creativity and measuring critical thinking skills should be continued. In particular, it is necessary to prepare a plan to solve technical problems in order to use technology in the course management and development of assessment tools.&lt;br/&gt;Classes for cultivating creativity and critical thinking abilities in our students can be more effective in liberal arts education for the simple reason that lessons within their majors emphasize the acquisition of specialized knowledge. Thus, there is a limit to creative competency education. Since modern and future societies require us to possess the ability to generate creative ideas in abundance in order to solve complex problems, as well as the ability to critically reflect and evaluate ideas and apply them to problem solving, it is urgent to develop courses and assessment tools that can align creativity with critical thinking. Designing effective classes through in-depth discussions on the development of courses and assessment tools remains as a theme for further research.
Data Mining Techniques in the Agricultural Sector

Data mining denotes discovering the useful information from large volume of data. It has useful areas of implementation in many sectors. In this work, we are mainly concentrating on Data Mining Techniques in the Agricultural sector (DMTA). Agriculture is a fundamental human need. The economy is greatly affected by the Agricultural Sector in a Nation like India. Agricultural sector's success or failure depends on the weather conditions and soil parameters. Presently, farmers are growing crops based on their knowledge acquired from the past generation. Since the traditional technique of farming is practiced, plants are excessive or scarce without meeting the real necessity. No scheme is in place to educate the farmers, and there is a variety of new techniques available to solve such issues. This paper presents the results obtained by analyzing the trends followed in the past 10 years using DMTA Model to forecast optimal parameters required to get highest production for Ragi, Groundnut and Paddy. Techniques used for analysis are Bisecting K-Means, DBSCAN, OPTICS, Hierarchical Complete Linkage and STING. All the Districts of Karnataka and various parameters of individual crops are considered.
Software Development of Word Search Game on Smart Phones in English Vocabulary Learning

At present, mobile devices are a medium of learning in school that is enjoyable with educational content. They are becoming as a part of daily life with new software games, which are considered as entertainment. As we all know, a word search game has been implemented on mobile devices for decades. It facilitates English vocabulary acquisition of students. The word search game on mobile devices allows education to be delivered in a more flexible way, anytime and anywhere at the students’ own place. To this paper, a survey is conducted to target Thai students at Bangkok University, Thailand. Therefore the word search game on smart phones embedded with the English dictionary, which helps Thai students to memorize the words is developed. Additionally, the process in developing the word search game on smart phones has been presented. It is expected that our research findings in English learning on mobile devices for Thai students will be further shared in the future.
Anomaly detection in multi-class time series

Abstract For modern operation and maintenance systems, they are usually required to monitor multiple types and large quantities of machine’s key performance indicators (KPIs) at the same time with limited resources. In this paper, to tackle these problems, we propose a highly compatible time series anomaly detection model based on K-means clustering algorithm with a new Wavelet Feature Distance (WFD). Our work is inspired by some ideas from image processing and signal processing domain. Our model detects abnormalities in the time series datasets which are first clustered by K-means to boost the accuracy. Our experiments show significant accuracy improvements compared with traditional algorithms, and excellent compatibilities and operating efficiencies compared with algorithms based on deep learning.
MapReduce-Based Dynamic Partition Join with Shannon Entropy for Data Skewness

Join operations of data sets play a crucial role in obtaining the relations of massive data in real life. Joining two data sets with MapReduce requires a proper design of the Map and Reduce stages for different scenarios. The factors affecting MapReduce join efficiency include the density of the data sets and data transmission over clusters like Hadoop. This study aims to improve the efficiency of MapReduce join algorithms on Hadoop by leveraging Shannon entropy to measure the information changes of data sets being joined in different MapReduce stages. To reduce the uncertainty of data sets in joins through the network, a novel MapReduce join algorithm with dynamic partition strategies called dynamic partition join (DPJ) is proposed. Leveraging the changes of entropy in the partitions of data sets during the Map and Reduce stages revises the logical partitions by changing the original input of the reduce tasks in the MapReduce jobs. Experimental results indicate that the entropy-based measures can measure entropy changes of join operations. Moreover, the DPJ variant methods achieved lower entropy compared with the existing joins, thereby increasing the feasibility of MapReduce join operations for different scenarios on Hadoop.
A Software Debugger for E-textiles and Arduino Microcontrollers

Today's STEM classrooms have expanded the domain of computer science education from a basic two-toned terminal screen to now include helpful Integrated Development Environments(IDE) (BlueJ, Eclipse), block-based programming (MIT Scratch, Greenfoot), and even physical computing with embedded systems (Arduino, LEGO Mindstorms). But no matter which environment a student starts programming in, all students will eventually need help in finding and fixing bugs in their code. While the helpful IDE's have debugger tools built in (breakpoints for pausing your program, ways to view/modify variable values, and "stepping" through code execution), in many of the other programming environments, students are limited to using print statements to try and "see" what is happening inside their program.
An Optimal Curve Parameters for BLS12 Elliptic Curve Pairing and Its Efficiency Evaluation

Pairing-based cryptography is one of the popular cryptographic techniques for achieving safety and usability. Since a pairing computation has a complicated calculation, an efficient implementation is required for its practical usefulness. For this purpose, we investigate curve parameters to optimize Miller’s algorithm using a projective coordinate and so that the computational cost of the doubling step of Miller’s algorithm can be conducted efficiently. The proposal method is applied to the ELiPS pairing library and it is found that the execution time becomes slightly faster than the previous ones with the parameter that can ensure the 128-bit security level.
Measuring Concurrence in Qubit Werner States Without an Aligned Reference Frame

Alice lives on Venus, Bob lives on Mars\dots{} The biggest problem in their communication is to establish a common reference frame, so that they can use quantum cryptography for their secret letters. To help them, this study proposes a method for entanglement quantification that does $n\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}t$ rely on synchronized reference frames. Counterintuitively, measurements in random and unknown bases can be used to establish just how entangled a quantum state is. This strategy may prove useful in complex quantum communication networks, where establishing a common reference frame (measurement basis) is impractical or impossible.
Polymerized Tape

We present polymerized sports tape as a general purpose prototyping and sensor-design resource. We use it in a somewhat similar manner to how conductive copper tape is often used for fast production of lo-fi electrical prototypes. However, copper tape has a number of drawbacks if one is prototyping with soft materials, textiles, or even directly on the human body. Because it is not elastic, it does not adhere well to such materials. Sports tape, however, has the desired elastic qualities, and additionally is designed to adhere not only to arbitrary objects, but also to human skin. We polymerize sports tape to make it conductive. The resulting conductive tape has a series of electrical properties which make it an exciting sensor-material. It is piezo-resistive. This means its resistance changes with applied forces. It can also be used to create a voltage gradient, which enables simple and precise position sensing. This unique combination of mechanical and electrical properties makes it an exciting and unique prototyping resource.
Evaluation Performance of the Kaliurang Road Drainage System, Sumbersari District, Jember Regency

Land-use changes will be caused by surface runoff, which is possible to create floods in an area. One of the impacts is the flooding that occurred on Kaliurang road, Kecamtan Sumbersari, Jember Regency. Floods that occur on Jalan Kaliurang, Jember occur almost every year. This research aims to evaluate Kaliurang road drainage’s performance using the Storm Water Management Model (SWMM) because it is suitable for simulating hydrological and hydraulic processes in urban areas. The modelling stages include: calculated the regional average rainfall using the Thiessen polygon method, testing compatibility, calculated the planned flood discharge with a return period of 2 years, 5 year, and 10 years, calculated the hourly rainfall intensity using the mononobe method and modelling hydraulics using SWMM. Modelling results with SWMM in the 2-year return flood, there are 4 locations prone to flooding, on the 5-year return period there are 9 flood-prone locations, and on the 10 year return period, there are 13 flood-prone locations. The recommendation of this study is the normalization of drainage channels at flood-prone points.
Study of Bitcoin volatility dynamics: MSGARCH model approach

This paper investigated the existence of structural break and impact of Gold and Platinum value on Bitcoin value. We also examined changes in the regime using three MSGARCH models, namely single-regime, two-regime, and three-regime, and compared their performances. We found the presence of break and significant impact of the Platinum value. Previous studies found that Gold has an impact on Bitcoin value, but empirically we could not find any. The two-regime MSGARCH model performs well among three models, and there is evidence of low volatility and high volatility regime. The out-of-sample forecast performance is almost same in the three models. In the long run, the presence of low volatility regime is more prominent than the high volatility regime.
In-Storage Computation of Histograms with differential privacy

Network-attached Smart Storage is becoming increasingly common in data analytics applications. It relies on processing elements, such as FPGAs, close to the storage medium to offload compute-intensive operations, reducing data movement across distributed nodes in the system. As a result, it can offer outstanding performance and energy efficiency. Modern data analytics systems are not only becoming more distributed they are also increasingly focusing on privacy policy compliance. This means that, in the future, Smart Storage will have to offload more and more privacy-related processing. In this work, we explore how the computation of differentially private (DP) histograms, a basic building block of privacy-preserving analytics, can be offloaded to FPGAs. By performing DP aggregation on the storage side, untrusted clients can be allowed to query the data in aggregate form without risking the leakage of personally identifiable information. We prototype our idea by extending an FPGA-based distributed key-value store with three new components. First, a histogram module, that processes values at 100Gbps line-rate. Second, a random noise generator that adds noise to final histogram according to the rules dictated by DP. Third, a mechanism to limit the rate at which key-value pairs can be used in histograms, to stay within the DP privacy budget.