Reviewerly/exHarmony huggingface dataset:
Single Emotion Recognition from Multiple Faces with Fuzzy System

Emotional inference by recognizing facial expressions in an image or video has been actively conducted for several years, for example, visual emotion recognition and emotional state recognition. The limitations of illumination conditions, variations of head poses, and partial occlusion are the most challenging problems in image processing. Furthermore, recognizing and inferring insignificant objects in the background is complex. These factors degrade performance. We collected evidence from human sensing through a psychological approach rather than a numerical approach, and converted it into a language through fuzzy theory. This can not only be intuitively explained, but it is also possible to evaluate and analyze numerically. The predicted inference accuracy increased by approximately 9.7% compared with the simple average method.
Self-organizing maps and full GPU parallel approach to graph matching

Graph matching is an essential problem in computer science and communications. It can be applied to a variety of issues such as artificial intelligence, computer vision, and communication systems. In this paper, we propose a new Graphics Processing Unit framework written in CUDA C++ specifically dedicated to geometric graph matching but providing new parallel algorithms, with low computational complexity, as the self-organizing map in the plane, and a distributed local search method. Unlike state-of-the-art graph matching algorithms, available from Matlab platform, that most often need at least O(N2) memory size, with N the problem size, our proposals only require O(N) space and allows massively parallel execution. These parallel algorithms are evaluated and compared to the state-of-the-art methods available for graph matching and following the same experimental protocol.
Implementasi Model Problem Based Learning Secara Daring Pada Pembelajaran Matematika Untuk Meningkatkan Hasil Belajar Siswa

This study was prompted by students' difficulty in mathematics, particularly on the material to the power of three and the cube root. The use of learning methods in schools is still teacher-centered, therefore students are not overly engaged in the learning process. Online learning is the strategy employed by teachers in the classroom nowadays. The purposes of this study were to determine implementation of online Problem Based Learning Models in mathematics learning, the improvement of student learning outcomes after the application of online the Models in mathematics learning, and how much improvement of student learning. The research method employed was a quantitative approach combining experimental research and a pre-experimental design. The findings revealed that the online problem-based learning model has been implemented on the cube of the third material and the cube root on the teacher and student observation sheets in all phases and produced very good results, and a sig value of 0.00 indicates a substantial increase in the results of mathematics learning outcomes for class V on pretest and posttest data. Based on the findings of the N-Gain score test calculation, it was discovered that the average value of the N-Gain score using the online problem-based learning model was 0.6985, putting it in the moderate category for improving student learning outcomes.
Data complexity-based batch sanitization method against poison in distributed learning

The security of Federated Learning (FL)/Distributed Machine Learning (DML) is gravely threatened by data poisoning attacks, which destroy the usability of the model by contaminating training samples, so such attacks are called causative availability indiscriminate attacks. Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations, we propose a new supervised batch detection method for poison, which can fleetly sanitize the training dataset before the local model training. We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model, which will be used in an efficient batch hierarchical detection process. Our model stockpiles knowledge about poison, which can be expanded by retraining to adapt to new attacks. Being neither attack-specific nor scenario-specific, our method is applicable to FL/DML or other online or offline scenarios.
Analysis of Speech Cryptography Based on Segmentation and Chaotic Logistic Map Keys

Enhancing speech cryptography performance by increasing the cryptography process speed (throughput) is a vital study. In this research paper a detailed study and analysis will be performed to find a compromised solution of dividing the speech signal into segments with various lengths. For each segment a CLK will be generated to apply segment encryption-decryption, the encrypted-decrypted segments will be combined to form the encrypted-decrypted speech signal. The proposed method will use a complicated PK which contains information about the segments and the chaotic parameters, this information will be used for speech segmentation and segments CLKs to be generated. A simple and easy to implement method of speech cryptography will be introduced; this method will be based on speech signal segmentation. The proposed method will be implemented and the obtained results will be used to perform various data analysis method to prove the quality, security and efficiency provided by the proposed method.
Unsupervised person re-identification by patch-based nearest neighbor mining

Unsupervised person re-identification (re-ID) is still challenging due to the difficulty in learning discriminative features without labeling information. Some existing methods use the instance specificity learning to learn image features without direct supervision signals. However, this kind of instance specificity learning treats each sample as an individual instance and can not mine the inter-sample relationships, which limits the ability to explore the category boundaries. To address the unsupervised person re-ID, this paper proposes a patch-based nearest neighbor mining (PNNM) method. The proposed PNNM utilizes a patch-based feature extraction model to capture patch features from person images. Then the patch discrimination mining (PDM) is applied to supervise the model to learn a discriminative patch feature space. Meanwhile, the nearest neighbor mining (NNM) is introduced to guide the model to mine the image-level discrimination from unlabeled person images. The NNM aims to explore nearest neighbor sets with high category consistency on the whole training dataset. A two-stage training process is built with the PDM and NNM to enable the model to capture both patch-level and image-level visual clues. Extensive experiments are conducted to show the effectiveness of the proposed PNNM method in the unsupervised person re-ID task.
Software Engineering Course Restructured to Support Agile Software Development Projects

With the increasing adoption of agile methods in the industry, there is a need for restructuring the content and delivery of software engineering courses in universities and institutes. Such restructuring should also address the mismatch between the typical sequence of topics covered and the skills required to complete team project work. This paper presents the details of a restructured software engineering course and the findings from employing it in teaching undergraduate students in a computer science program. Student feedback has been positive on the restructured course topics to fit the knowledge and skills required to complete their project work. The paper summarizes the lessons learned with further improvements made to the approach in the subsequent course offerings.
A Framework for Designing Computing Curricula

Designing any curriculum is hard. Perhaps that may explain the scarcity of efforts to develop frameworks, especially comprehensive frameworks. We present a novel framework to design computing curricula. Our framework enables explicit intent statement of a degree program in Computing. We sketch the approach to developing a curricular division at two main levels. The first level corresponds to idea of semesters that sequence the learning paths. The second level corresponds to idea of courses that run in parallel to achieve distinct learning goals. This work is the initial step in an effort to develop a different approach to curriculum design. The next step that will follow is the development of a complete curriculum design within a specific context.
Seeding industry knowledge in computer science education

Industry knowledge forms the subject matter of the software being built in the IT industry. Academia faces challenges in seeding industry knowledge in its curriculum resulting in industry-academia gap. Knowledge Driven Development (KDD) is a knowledge management framework comprising of two complimenting components; Domain Knowledge Framework (DKF) and Atomic Knowledge Model (AKM). DKF addresses the industry knowledge gap via a domain agnostic common structure. The common structure is an experience-based empirical formulae bringing science to the art of learning industry knowledge. This paper explains the DKF proposition that claims to increase the employability of students significantly.
Application of the Double Smoothing and ARIMAX Methods for the Prediction of Polycristalline Photovoltaic Generation

Photovoltaic energy is very sensitive to the variation of the climatic parameters such as the solar radiation and the ambient temperature, which are characterized by their intermittent behavior. Thus, to optimize the use of the electrical power generated by the PV plant, it is necessary to have a good prediction of the PV generation. In this paper, 15-minute interval experimental measurements of the solar radiation, the ambient temperature and photovoltaic generation that correspond to the four seasons (winter, spring, summer and autumn) are used to develop models for the prediction of the PV power of a 10 kW polycrystalline photovoltaic plant installed in Mostoles (Madrid, Spain). Hence, a combination of prediction models which are the double exponential smoothing and autoregressive integrated moving average (ARIMAX) are applied to predict the PV generation. The results obtained illustrate the good performance of the proposed prediction models as a response for several weather measurements.
Robust Uncooperative Ground Target Surveillance using Vision-Based Sliding Mode Control of Quadrotor UAV

Search and tracking of non-cooperative mobile targets, maneuvering in an area monitored by a quadrotor UAV is investigated. In order to reach this aim, We propose a robust approach based on vision and sliding mode controller. The proposed strategy is an Image-Based Visual Servoing (IBVS) approach using targets visual data projected in a virtual camera. For an effective visual target searching, a circular search trajectory is followed, with a high altitude using the Camera Coverage Area (CCA). A Sliding Mode Controller (SMC) based on Reaching Law with a Power Rate (RLPR) is applied to ensure the QUAV control in the presence of external disturbances and measurement uncertainties. Simulation results are presented to assess the proposed strategy considering different scenarios.
Adaptive Update Tracking Algorithm for Fast Motion Object

Aiming at the problem of object loss and tracking precision decrease due to the fast movement of an object, this paper proposes an adaptive updating algorithm for a fast moving object in video based on the KCF algorithm. The target motion information is obtained by simple motion estimation based on the motion law of the object, and an object tracking strategy is proposed for the fast motion scene based on the adaptive search area and template updating combined with the relevant response. OTB100 dataset was selected for experiments. Experimental results show that the proposed algorithm has improved tracking precision and success rate compared with the baseline algorithm in the scene of fast object movement, which can effectively solve the problem of tracking failure caused by fast object movement and achieve stable object tracking.
Controllable Multiregister Transport on a Photonic Chip

Quantum transport is significant to understanding the evolution of states of particles in nature. Quantum transport, based on the fashion of adiabatic pumping and fully engineered graph, enables the quantum-enhanced capabilities of energy transport and quantum information processing. However, in large fully connected networks with multiple registers, the reliability and scalability of transport are limited by the unavoidable noises and crosstalk induced by the free interaction enabled in real space. Here, we propose and experimentally demonstrate a novel multiregister transport with a controllable interaction on a photonic chip. The remote locking registers, located at the boundaries sharing the common channel, can freely interact, supported by the bilocalized states under the norm of local magnetic fields. Excited photons are transferred effectively from one register to the locked one, which are both in the same subspace without crosstalk of different parties. Moreover, quantum correlation of photon states can be well preserved in the multiregister transport network. Our results of on-demand multiregister transport network with low-crosstalk integrated in the photonic chip, which may shed light on the avenue for building the quantum gate parallelism, is a promising route toward the scalable quantum computing and deep quantum circuits.
CipherFlow: A Playground for Developing Privacy-Preserving IoT in Node-RED

The emergence of privacy breaches in IoT surges demands for embedding privacy-by-design into IoT systems. Decades of work on privacy-enhancing technologies have resulted in myriad ways to secure outsourcing computation with homomorphic encryption (HE) that allows the third party (e.g., cloud) computes over encrypted data. All of them are still yet far away from the rapid development of practical privacy-preserving IoT systems. Abstraction and flexibility in development are keys to accelerating privacy-preserving IoT deployment. Nevertheless, HE tools and compilers are primarily written in low-level languages like C++, posing a high entry barrier to IoT developers. This paper proposes CipherFlow, the HE node extension for Node-RED, a visually and flexibly programmable dataflow tool for rapid IoT development. CipherFlow offers three pipeline abstractions: 1) a playground of drag-and-drop HE nodes in Node-RED, 2) arithmetic operators, and 3) optimal parameter setting guidelines. As a result, IoT developers can create and verify their secured computing functions on data flows. We demonstrate the applicability and effectiveness of CipherFlow through the performance evaluation. Furthermore, our evaluation shows the performance and security trade-off depending on the HE parameters.
Application and Data Security Patterns

Organizations typically focus on building applications and data-driven software to harvest the value from their data. Application security mainly attempts to prevent data or code from being stolen. Application security considerations include hardware, software, and procedures to minimize security vulnerabilities.
Modeling and implementation of a frequency sweeping signal generator based on FPGA and DAC

In this paper, a frequency sweeping signal generator based on FPGA and DAC was described. The signal generator used FPGA and Verilog to design direct digital synthesizer and then realize linear sweeping signal waveform. Several parameters can be adjusted in the software, such as frequency tuning word, delta frequency tuning word, delta frequency ramp rate word, the signal generator will generate linear sweeping waveform. Detailed design of FPGA is presented. Meanwhile, relevant experiments are given, and the experimental result shows that this signal generator can improve the frequency resolution of the direct digital frequency synthesizer effectively and has very good spectral characteristics.
Retzzles: Engaging Users towards Retention through Touchscreen Puzzles

Textual sources provide limited information to their readers which could be underwhelming and may reduce engagement. Augmentation approaches have been introduced to present information more engagingly and have shown the potential in supporting information retention. In this research, we inquire further into this opportunity through the use of interactive touchscreen visual elements such as puzzle pieces. We present Retzzles, where users get to solve puzzles applied in a tourist use-case. To evaluate this, we will do a within-subject study with participants n = 30 to determine whether such elements promote engagement which thereby supports information retention. Our preliminary findings shed light on some perspectives on the use of touchscreen displays for engagement but are subject to further investigation. We contribute to more discussions on the use of interactive screens in other similar learning scenarios.
Correcting delayed reporting of COVID‐19 using the generalized‐Dirichlet‐multinomial method

Abstract The COVID-19 pandemic has highlighted delayed reporting as a significant impediment to effective disease surveillance and decision-making. In the absence of timely data, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. We discuss the four key sources of systematic and random variability in available data for COVID-19 and other diseases, and critically evaluate current state-of-the-art methods with respect to appropriately separating and capturing this variability. We propose a general hierarchical approach to correcting delayed reporting of COVID-19 and apply this to daily English hospital deaths, resulting in a flexible prediction tool which could be used to better inform pandemic decision-making. We compare this approach to competing models with respect to theoretical flexibility and quantitative metrics from a 15-month rolling prediction experiment imitating a realistic operational scenario. Based on consistent leads in predictive accuracy, bias, and precision, we argue that this approach is an attractive option for correcting delayed reporting of COVID-19 and future epidemics.
Anomalous Sound Detection, Extraction, and Localization for Refrigerator Units Using a Microphone Array

Anomaly detection is one of the key applications of data utilization in smart factories, particularly in monitoring factory facilities. Early detection and resolution of anomalies, such as system failures, can lead to cost reduction and quality stabilization. One of the targets of abnormality detection applications in the industry section is a refrigerator unit used in food processing factories and warehouses. Anomalies in the early stages in refrigerator units appear in the operating sounds, which can enable their detection. In this study, we propose a method for detecting abnormal sound, extracting abnormal frequency components, and identifying the direction of the abnormal sound source. To identify the direction of the anomalous sound source, multi-channel sound recorded by a microphone array is used. To the best of our knowledge, no method has yet been proposed for anomaly sound detection using multi-channel acoustic data. In the proposed method, anomaly scores calculated in each channel of the microphone array are aggregated to determine whether the entire data is anomalous or not. Anomalous sounds were extracted from the anomaly data using a deep generative model. The extracted anomalous sounds were used to localize the sound source and the direction of the anomalous source was identified. The proposed method improved the precision of anomaly sound detection while maintaining the recall rate of a conservative comparison method. Using the proposed method, anomalous sounds were extracted from the anomaly data, and their arrival directions were identified.
Maintaining Control over Distributed Data Through a Data Sovereignty Model

In today’s global and collaborative virtual product development, massive amounts of data are shared and exchanged between a large number of participants. To store continuous increasing amounts of data, enterprises often outsource their storage to cloud providers. Due to economic factors, cloud providers often locate their servers (on which the data is stored) in other countries than they are located in themselves. However, the location where data is physically stored plays a critical role since it determines which jurisdiction it falls under and, consequently, which government can legally access the data without even needing permission. In contrast to that, most participants of the design process are lacking essential knowledge and transparency about where their data is located (after the distribution or storage in a cloud) and what consequences this has on their data privacy. Approaching this problem, we identified the necessity of transparent and secure data sharing and storing in virtual product development in order to ensure data owners sovereignty over their data. Therefore, we developed a concept which: 1) generates an overview about the actual location of distributed and stored design data and 2) provides data owners with the ability to maintain control over their data by regulating who can potentially access it. The concept proposes a novel data sovereignty model which consists of a data sovereignty graph and usage licenses (sovereignty criteria). To validate the concept, a prototypical implementation was conducted and evaluated. We came to the conclusion that the presented concept can be used effectively to create transparency, trust, and security of transferred data in the product development supply chain.
A Bibliometrics Analysis of Digital Forensics Research in Indonesia Based on Scopus Index: 2012-2021

This study aims to demonstrate step-by-step bibliometric data analysis through VOSViewer. Through mapping tools in the VOSViewer, this study provides insight into digital forensics over ten years (2012–2022). In order to describe the bibliometric analysis performance of the selected topic, we created a network visualization using qualitative descriptive methods. This study found 117 journal articles published in digital forensics between 2012 and 2021, and then the data was cleaned to produce 103 articles for analysis. The study concludes that Indonesian digital forensics research is still relatively incipient. This bibliometric analysis study will support discovering new things on similar topics. In addition, research in the field of study is essential because it contributes to understanding the validity of digital evidence, which can support decision-making in court.
Local Path Planning Based on Velocity Obstacle Considering Collision Probability and Kinematic Constraint for Mobile Robot

This paper proposes local path planning based on velocity obstacle (VO) considering the collision probability and kinematic constraint for a mobile robot. Several types of VO-based methods have been reported since VO is one of the practice path planning methods. In VO-based methods, the velocity pairs with collision probability are treated as VO. For collision avoidance, the velocity pair outside VO is selected as the velocity command. In other words, the velocities with even a little collision probability are treated as unselectable velocity pairs. Therefore, VO-based methods are conservative. In the proposed method, weight coefficients according to collision probability are set in VO. By using these weight coefficients, the velocity pairs inside VO are evaluated. In addition, only the feasible velocity pairs are selected by considering the kinematic constraint. If the collision probability is low, the mobile robot can select the velocity pair inside VO. The validity of the proposed method was confirmed by the simulation results.