Reviewerly/exHarmony huggingface dataset:
Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists

Fairness and interpretability are fundamental requirements for the development of responsible machine learning. However, learning optimal interpretable models under fairness constraints has been identified as a major challenge. In this paper, we investigate and improve on a state-of-the-art exact learning algorithm, called CORELS, which learns rule lists that are certifiably optimal in terms of accuracy and sparsity. Statistical fairness metrics have been integrated incrementally into CORELS in the literature. This paper demonstrates the limitations of such an approach for exploring the search space efficiently before proposing an Integer Linear Programming method, leveraging accuracy, sparsity and fairness jointly for better pruning. Our thorough experiments show clear benefits of our approach regarding the exploration of the search space.
Nearest Neighbor Classifier with Margin Penalty for Active Learning

As deep learning becomes the mainstream in the field of natural language processing, the need for suitable active learning method are becoming unprecedented urgent. Active Learning (AL) methods based on nearest neighbor classifier are proposed and demonstrated superior results. However, existing nearest neighbor classifiers are not suitable for classifying mutual exclusive classes because inter-class discrepancy cannot be assured. As a result, informative samples in the margin area can not be discovered and AL performance are damaged. To this end, we propose a novel Nearest neighbor Classifier with Margin penalty for Active Learning (NCMAL). Firstly, mandatory margin penalties are added between classes, therefore both inter-class discrepancy and intra-class compactness are both assured. Secondly, a novel sample selection strategy is proposed to discover informative samples within the margin area. To demonstrate the effectiveness of the methods, we conduct extensive experiments on three real-world datasets with other state-of-the-art methods. The experimental results demonstrate that our method achieves better results with fewer annotated samples than all baseline methods.
Masked Faces with Faced Masks

Modern face recognition systems (FRS) still fall short when the subjects are wearing facial masks. An intuitive partial remedy is to add a mask detector to flag any masked faces so that the FRS can act accordingly for those low-confidence masked faces. In this work, we set out to investigate the potential vulnerability of such FRS equipped with a mask detector, on large-scale masked faces, which might trigger a serious risk, e.g., letting a suspect evade the facial identity from FRS and not detected by mask detectors simultaneously. We formulate the new task as the generation of realistic & adversarial-faced mask and make three main contributions: First, we study the naive Delaunay-based masking method (DM) to simulate the process of wearing a faced mask, which reveals the main challenges of this new task. Second, we further equip the DM with the adversarial noise attack and propose the adversarial noise Delaunay-based masking method (AdvNoise-DM) that can fool the face recognition and mask detection effectively but make the face less natural. Third, we propose the adversarial filtering Delaunay-based masking method denoted as $$\text {MF}^2\text {M}$$ by employing the adversarial filtering for AdvNoise-DM and obtain more natural faces. With the above efforts, the final version not only leads to significant performance deterioration of the state-of-the-art (SOTA) deep learning-based FRS, but also remains undetected by the SOTA facial mask detector simultaneously.
Topic-Based Classification for Aggression Detection in a Social Network

These days, there are numerous advantages of utilizing the internet. However, there are plenty of dangers and risks too, which we should be aware of. One of these risks is cyberbullying. This is a kind of bullying that occurs online. It involves threatening, harassing, or humiliating a person through social network platforms like chat rooms, messaging apps, and even via email and websites. Cyberbullying can show either straightforwardly on the web or be a continuation of bullying that has occurred in the actual world (at school or in the youth club, for instance). Some of the most common kinds of cyberbullying include spreading rumors, posting humiliating photos or videos on social media, and creating fake profiles as well as websites that can cause problems to the victims. The idle way to tackle cyberbullying is to detect the signs and report it to an adult. With machine learning, we can detect and filter out aggressive comments and report those guilty. Certain methodologies with their proposed systems that are used by other authors are explained. This chapter outlines all the work involved in making a machine learning model learn about detecting and classifying cyberbullying text and describes the overall process.
Application of Disruptive Technology in Food Trackability

Blockchain technology has been around since 2008 and disrupts many traditional industries and business models, especially virtual ones. Digital and cryptocurrencies are the best examples, which have made a wave in the world. Blockchain vendors have created hype; however, there is no denial of the usage and importance of Blockchain in the food industry. This chapter aims to unravel the hype from the truth about Blockchain technology’s ability to provide track ability to the food supply chain. It further focuses on the strengths and weaknesses of the conventional tractability system’s assessment capability (an essential element in protecting public health and allowing health services to identify more). The final part of the chapter compares the conventional methods with Blockchain technology, considering the commercial analysis from the Blockchain implementation perspective. The chapter also tries to justify logically and by arguments about how Blockchain-based food trackability systems are more appropriate for velocity, disclosure, and implementation than traditional electronic trackability and existing systems. This chapter’s limitation perspective discusses how lack of interaction is presently the principal impediment to finding a food product from farm to fork in the entire system.
Enhance Domain-Invariant Transferability of Adversarial Examples via Distance Metric Attack

A general foundation of fooling a neural network without knowing the details (i.e., black-box attack) is the attack transferability of adversarial examples across different models. Many works have been devoted to enhancing the task-specific transferability of adversarial examples, whereas the cross-task transferability is nearly out of the research scope. In this paper, to enhance the above two types of transferability of adversarial examples, we are the first to regard the transferability issue as a heterogeneous domain generalisation problem, which can be addressed by a general pipeline based on the domain-invariant feature extractor pre-trained on ImageNet. Specifically, we propose a distance metric attack (DMA) method that aims to increase the latent layer distance between the adversarial example and the benign example along the opposite direction guided by the cross-entropy loss. With the help of a simple loss, DMA can effectively enhance the domain-invariant transferability (for both the task-specific case and the cross-task case) of the adversarial examples. Additionally, DMA can be used to measure the robustness of the latent layers in a deep model. We empirically find that the models with similar structures have consistent robustness at depth-similar layers, which reveals that model robustness is closely related to model structure. Extensive experiments on image classification, object detection, and semantic segmentation demonstrate that DMA can improve the success rate of black-box attack by more than 10% on the task-specific attack and by more than 5% on cross-task attack.
Measurement-based quantum computation using two-component BECs

We propose measurement-based quantum computation (MBQC) using two-component Bose-Einstein condensates (BECs). When constructing our theory, we need to introduce creation operator and annihilation operator for MBQC. This is realized by considering the Stokes operator (Schwinger boson). As a result, we find that the state after a measurement depends on the number of particles.
Governing Strategies in Re-Imaging Punjab Police: An Assessment

Good governance is the framework of activities and behaviour of the state institutions with the pursuance of the career of the individuals to achieve the objective of the public good at large.Transparency, accountability and rule of law are the foremost checks in an efficient governance system (Pacific, 2012).Like other public sector departments of the Punjab, governance issues of policing are not unique to the Punjab Police.Mainly, the problems of governance in Punjab Police are due to the inherited legacy of colonial rule and mindset.The paper argues that inadequate and extremely varied ways the Punjab Police exercise its authority in the prevention and detection of crime is the source of dissatisfaction for the general public.The given preferences and adopting the policies to deal the community without considering the ingrediencies of good governance mismatch the Police at par with the community needs.The orthodox police governance is critical in maintaining public trust, upholding human rights, ensuring accountability, transparency, rule of law, and legitimacy (Suddle, Police-Executive Relationship in Pakistan, 2007).Qualitative policing depends on considerable changes in the criminal justice system for re-imaging police perception and impression to achieve the ultimate goal of good governance with transparency, equality and rule of law for the satisfaction of the community.
Output-correlated adversarial attack for image translation network

Maliciously forged images generated by image translation networks can cause significant security threats to personal privacy and national security. An emerging solution for forged images is preventing image forgery models from tampering with user images through adversarial attacks. Currently, conventional adversarial generation algorithms use random noise as the starting point, which makes the final adversarial output similar to the original output but does not prevent image tampering. The output-correlated initialization is applied to improve the adversarial attack algorithm for the image translation network and improve the visual effect of adversarial attacks. Moreover, a comparative experiment is performed on multiple loss functions, and the loss function with the best performance is selected as the adversarial loss function to complete the adversarial attack on the image translation network. The selected initialization method makes the search process of adversarial examples more comprehensive and makes the generation results of adversarial examples more diverse. The analysis of the visual effects of the attack reveals how the proposed adversarial attack methods affect the forgery results of different image translation frameworks and generate more chaotic images. Comparison of multiple indicators demonstrates that the proposed method has a high attack success rate and expands the image distance between the adversarial output and the original output, thereby improving the attack efficiency, preventing malicious tampering with the image, and protecting the user image.
ASSISTANCE OF CODING EXTRACURRICULAR PROGRAM AT SD ANAK PANAH KENJERAN

Basic programming training and assistance for Elementary School at SD Kr. Anak Panah are an urgent need. This is caused by the demands of students to develop their potential. Elementary students Kr. Anak Panah requires an increase in skills in terms of making or coding basic games. This training aims to improve the basic abilities of elementary school (SD) in terms of information technology. Training for students to meet the need for basic and high-level coding mastery for elementary school age children. The training was conducted online for 5 (five) months involving 200 Elementary School (SD) students. The final result of the training showed an increase in students' competence in terms of game programming coding. Basic level. Meanwhile, the average ability of the trainees increased from level 4th (four) to level 8th (eight) from a scale of 1 – 10.
HOUSEHOLD VULNERABILITY TO ENVIRONMENTAL CHANGE: EXAMINING ADAPTIVE CAPACITY OF HOUSEHOLDS TO FLOOD RISKS IN PALEMBANG

Urban flood has plagued many metropolitan cities in Indonesia due to various reasons such as mismanagement of land use, overpopulation and poor drainage systems. Palembang on the other hand, is one of metropolitans in Indonesia, where most of its areas are dominated by wetlands, where many large and small rivers are located. The high demand to residential areas has caused many landuse changes from wetlands to dry lands by doing reclamation. As the result, the incidence of flood in Palembang has increased significantly in past years, which leave the society grappled with emerging challenges. This study aims to identify the adaptive capacity of the society affected by flood. The area selected as the case study is Macan Lindungan village which severely hit by flood. The survey was conducted on 210 families (KK) and the analytical method used is descriptive method of statistical analysis and correlation analysis to identify level of adaptive capacity and adaptive capacity relationship among the contributing factors. In addition, the analysis was also performed spatially to project the condition of the adaptive capacity based on the location in the study sites. This study demonstrates that the flood particularly exacerbates the economy of low-income society. Moreover, adaptation still largely focus on structural as elevating homes and storing other valuables, while the non- structural adaptation still focus on emergency funds. Finally, it concludes that preparedness of the society should be supported by both structural and non-structural policies from the government so that the risk of flood can be comprehensively reduced.
Multiview Regularized Discriminant Canonical Correlation Analysis: Sequential Extraction of Relevant Features From Multiblock Data

One of the important issues associated with real-life high-dimensional data analysis is how to extract significant and relevant features from multiview data. The multiset canonical correlation analysis (MCCA) is a well-known statistical method for multiview data integration. It finds a linear subspace that maximizes the correlations among different views. However, the existing methods to find the multiset canonical variables are computationally very expensive, which restricts the application of the MCCA in real-life big data analysis. The covariance matrix of each high-dimensional view may also suffer from the singularity problem due to the limited number of samples. Moreover, the MCCA-based existing feature extraction algorithms are, in general, unsupervised in nature. In this regard, a new supervised feature extraction algorithm is proposed, which integrates multimodal multidimensional data sets by solving maximal correlation problem of the MCCA. A new block matrix representation is introduced to reduce the computational complexity for computing the canonical variables of the MCCA. The analytical formulation enables efficient computation of the multiset canonical variables under supervised ridge regression optimization technique. It deals with the ``curse of dimensionality'' problem associated with high-dimensional data and facilitates the sequential generation of relevant features with significantly lower computational cost. The effectiveness of the proposed multiblock data integration algorithm, along with a comparison with other existing methods, is demonstrated on several benchmark and real-life cancer data.
English Reading Materials in Vocational High School: A Need Analysis

This research elucidates the students’ needs through need analysis in the context of vocational high school. Need analysis had been considered as the crucial factor and an integral part of establishing an ESP course as it is only through the process of need analysis researchers, course designers, and material developers are able to determine and refine the content for an ESP course that meets the learners’ need. This research was conducted online in Kapuas Regency. Therefore, the participants of the study are students of Agriculture program in some SMK in Kapuas Regency. They are SMKN 3 Kuala Kapuas, SMKN 4 Kuala Kapuas, SMKN 1 Bataguh, SMKN 1 Kapuas Murung, SMKN 1 Mantangai, SMKN 2 Mantangai, SMKS Bethel Kaladan, SMKN 1 Timpah. The research was descriptive quantitative research The writer used random sampling method. The data were gathered from research participants via questionnaire in order to analyze the students’ need in learning English especially for reading. The questionnaire was based on the Hutchinson &amp; Waters' model of Needs Analysis. The results of the research indicated that English language teaching should be taught in specific ways and materials. The literacy rate of students in vocational high schools in Kapuas regency was still quite low. The students also rarely to improve their receptive skills rather than productive skills. furthermore, the grammar was learnt inductively. Besides that, the average level of English proficiency of the students was at beginner level. In addition, the amount of English vocabulary about agriculture was about less than 100 words and the rest of students had 100-500 words. The students also had some problems in reading.
A Robust Illumination and Intensity invariant Face Recognition System

Face recognition has achieved more attention in computer vision with the focus on modelling the expression variations of human. However, in computer vision system, face recognition is a challenging task, due to variation in expressions, poses, and lighting conditions. This paper proposes a facial recognition technique based on 2D Hybrid Markov Model (2D HMM), Cat Swam Optimization (CSO), Local Directional Pattern (LDP), and Tetrolet Transform. Skin segmentation method is used for pre-processing followed by filtering to extract the region of interest. Resultant image is fed to proposed feature extraction method comprising of Tetrolet Transform and LDP. Extracted features are classified using proposed classifier “CSO trained 2D-HMM classification method”. To prove the superiority of method, four face datasets are used, and comparative results are presented. Quantitively results are measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Accuracy and the values are 0.0025, 0.0035 and 99.65% respectively
Science centres in post-pandemic reality – educational and market context

Science centres are an important element of the cultural and educational landscape of the modern world. Their role is changing. Due to the diverse activities of these centres, they are of interest to academics and experts from many disciplines. Their classification stands at the border of education and culture, non-profit and for-profit and this makes them especially interesting. The goal of the paper is to present the role of science centres in the current COVID-19 pandemic. Keywords: science centres, new customer, post-pandemic education, hybrid education
A Skin Pressure-type Grasping Device to Reproduce Impulse Force for Virtual Ball Games

Tactile feedback is crucial to improve the realism of virtual sports, which are popular applications of virtual reality (VR). However, few wearable tactile feedback devices can produce an impulse force in midair. We propose a graspable impulse force presentation de-vice by using a voice coil motor. In the evaluation experiment, the result suggests that the proposed device improves the sense of real-ism compared to a conventional VR controller with vibration.
Design, integration and implementation of crypto cores in an SoC environment

Purpose The paper aims to develop a systematic approach to design, integrate, and implement a set of crypto cores in a system-on-chip SoC) environment for data security applications. The advanced encryption standard (AES) and PRESENT block ciphers are deployed together, leading to a common crypto chip for performing encryption and decryption operations. Design/methodology/approach An integrated very large-scale integration (VLSI) architecture and its implementation for the AES and PRESENT ciphers is proposed. As per the choice, the architecture performs encryption or decryption operations for the selected cipher. Experimental results of the field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementations and related design analysis are provided. Findings FPGA implementation of the architecture on Xilinx xc5vfx70t-1-ff1136 device consumes 19% slices, whereas the ASIC design is implemented in 180 nm complementary metal-oxide semiconductor ASIC technology that takes 1.0746 mm 2 of standard cell area and consumes 14.26 mW of power at 50 MHz clock frequency. A secure audio application using the designed architecture on an open source SoC environment is also provided. A test methodology for validation of the designed chip using an FPGA-based platform and tools is discussed. Originality/value The proposed architecture is compared with a set of existing hardware architectures for analyzing various design metrics such as latency, area, maximum operating frequency, power, and throughput.
Estimation of multiple ordered ROC curves using placement values

In many diagnostic accuracy studies, a priori orders may be available on multiple receiver operating characteristic curves. For example, being closer to delivery, fetal ultrasound measures in the third trimester should be no less accurate than those in the second trimester in predicting small-for-gestational-age births. Such an a priori order should be incorporated in estimating receiver operating characteristic curves and associated summary accuracy statistics, as it can potentially improve statistical efficiency of these estimates. Early work in the literature has mainly taken an indirect approach to this task and has induced the desired a priori order through modeling test score distributions. We instead propose a new strategy that incorporates the order directly through the modeling of receiver operating characteristic curves. We achieve this by exploiting the link between placement value (the relative position of a diseased test score in the healthy score distribution), the cumulative distribution function of placement value, and receiver operating characteristic curve, and by building stochastically ordered random variables through mixture distributions. We take a Bayesian semiparametric approach in using Dirichlet process mixture models so that the placement values can be flexibly modeled. We conduct extensive simulation studies to examine the performance of the proposed methodology and apply the new framework to data from obstetrics and women’s health studies.
ANALISIS FAKTOR-FAKTOR REPEAT ORDER TERHADAP IMPLEMENTASI STRATEGI FLASH SALE PADA USER APLIKASI MARKETPLACE SHOPEE

The rapid development of e-commerce, coupled with advances in technology, makes the needs of the community increase. In addition, fulfilling needs is also easier with the use of e-commerce applications available on smartphones. Based on this, a fast-growing online shopping culture emerged in the community. Over time, many programs were intensified by e-commers=ce to attract more consumers and increase sales to the company, so that it became a lifestyle for consumers. This research uses a literature study approach by analyzing the topic of repeat orders on the marketplace application with a case study on Shopee. The results of this study found many factors that affect repeat orders on the marketplace application.