Reviewerly/exHarmony huggingface dataset:
Detection of Face mask using Machine Learning Algorithm to Prevent COVID 19 in Educational Institutions

Abstract: Novel Corona virus also known as COVID-19 has affected humans worldwide. Because of this pandemic the schools, universities and colleges were closed. One of the important guidelines to stop the spread of COVID-19 is wearing a facemask, but the knowledge of wearing facemask is gradually decreasing and the public are not aware of their safety towards corona virus disease. So, to alert the people who are not wearing the facemask, a facemask detection system is proposed in this paper. In this paper, the Tensorflow object detection algorithm is used to detect the face mask. The people who are in the institution are monitored through the camera, whether a person is wearing a face mask or not. If face mask is missing, then the person is alerted to follow the safety protocol. The proposed method achieves 95%accuracy.
THE ANALYSIS OF THE USES OF CODE SWITCHING AND CODE MIXING IN SOCIAL MEDIA AMONG FACEBOOKERS

The present study was aimed at identifying the types and function of code-mixing and switching used by Facebookers. This study was designed as a qualitative study. There were 50 Facebook accounts were observed in this study. The results showed that alternation code mixing was mostly used by Facebookers. A mix language between English and Indonesian was identified in their caption of Facebook wall. The functions of code-switching and code-mixing are 50 data, some of the data, namely 26 data, have code-switching with from 26 data code-switching there are 18 personalization and objectivization and 7 facilities of expression and 1 message qualification. And 50 data code-mixing with details of 31 data personalization objectivization and 12 data facility of expression and 5 data message qualification and 2 data repetition.
FEATURE AGGLOMERATION BASED FRAMEWORK FOR SOFTWARE BUG PREDICTION

Growing reliance on software products in the world has put high demand on quality software.Software Bug Prediction is an exercise in enhancing the quality of software by identifying potential bugs or faults in software constructs during the pre-deployment testing phase.Various Machine Learning models have been built to predict faults based on metrics derived from the software.Feature selection and feature reduction (extraction) are two strategies to weed-out redundant and non-useful features thereby reducing the dimensionality of features.The present research study aims to devise an ML model to effectively reduce the dimensionality of the feature-set of data by using a combination of Genetic Algorithm (feature selection) and Feature Agglomeration (feature reduction) without significantly affecting model performance.Datasets containing 62 features are employed to test the model using four classifiers and three cross-validated metrics.Results show that the proposed model reduces the feature-set by more than 80 %.The application of the Kruskal Wallis H test shows the statistical similarity between the results of the proposed model and results without a proposed model.In a majority of test cases, the proposed model has improved the performance of base classifiers by extracting the most informative features.
Hope for Resuming Our In-Person Events [President’s Message]

The hybrid format of IEEE Applied Power Electronics Conference and Exposition (APEC) 2022, held 20–24 March in Houston, Tx, USA, was a refreshing change from the virtual conferences of the last two years due to the COVID-19 pandemic. Thanks to the tremendous efforts of hundreds of organizers and volunteers, APEC 2022 signals the return to our in-person events. It was a joyful reunion of IEEE Power Electronics Society (PELS) members, volunteers and exhibitors. The breakfast event of “WiE, YP, and You: How to become involved with PELS and PSMA too” was a huge success, the fruit of the tireless work of the PELS Women in Engineering (WiE) and Young Professionals (YP) Committees. You can find detailed materials including FAQs of many programs of the PELS, prepared by dozens of PELS volunteers on the WiE events website: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.ieee-pels.org/membership/wie/wie-events</uri> . The “YP Reception at APEC 2022” cosponsored by PELS and Industry Applications Society (IAS) attracted over 100 young professionals and students to a social gathering at The Grotto, Houston. Our hybrid Spring Administrative Committee (AdCom) Meeting and several PELS standing committee meetings held during APEC 2022 presented a venue for our volunteers and friends to catch up.
IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes

Indoor scenes exhibit significant appearance variations due to myriad interactions between arbitrarily diverse object shapes, spatially-changing materials, and complex lighting. Shadows, highlights, and inter-reflections caused by visible and invisible light sources require reasoning about long-range interactions for inverse rendering, which seeks to recover the components of image formation, namely, shape, material, and lighting. In this work, our intuition is that the long-range attention learned by transformer architectures is ideally suited to solve longstanding challenges in single-image inverse rendering. We demonstrate with a specific instantiation of a dense vision transformer, IRISformer, that excels at both single-task and multi-task reasoning required for inverse rendering. Specifically, we propose a transformer architecture to simultaneously estimate depths, normals, spatially-varying albedo, roughness and lighting from a single image of an indoor scene. Our extensive evaluations on benchmark datasets demonstrate state-of-the-art results on each of the above tasks, enabling applications like object insertion and material editing in a single unconstrained real image, with greater photorealism than prior works. Code and data are publicly released at https://github.com/ViLab-UCSD/IRISformer.
The ITU Faroese Pairs Dataset

This article documents a dataset of sentence pairs between Faroese and Danish, produced at ITU Copenhagen. The data covers tranlsation from both source languages, and is intended for use as training data for machine translation systems in this language pair.
Quick Response Code for Inventory System Development (Case study : Accounting Unit At Ujung Pandang State Polytechnic)

QR stands for Quick Response, which is in accordance with its purpose is to convey information quickly and get a fast response as well. In contrast to barcodes which are only able to store information horizontally, QR codes are able to store information both horizontally and vertically. So that automatically QR codes have the ability to store more data than barcodes. QR codes can be used in the inventory system at the State Polytechnic of Ujung Padang to make it easier to read inventory data.In this study, the author will create an inventory system using a QR Code. The inventory system will be made online so that it can make it easier for admins and staff to process data and present information. This system is made in PHP and uses MySQL database for data storage, while the QR code is created using the Ciqrcode library. While in reading the QR code on the android application using the Zxing library. The inventory system built can make it easier to process inventory data and present information to the Accounting Unit at State Polytechnic of Ujung Padang.
AggNet: Learning to Aggregate Faces for Group Membership Verification

In some face recognition applications, we are interested to verify whether an individual is a member of a group, without revealing their identity. Some existing methods, propose a mechanism for quantizing precomputed face descriptors into discrete embeddings and aggregating them into one group representation. However, this mechanism is only optimized for a given closed set of individuals and needs to learn the group representations from scratch every time the groups are changed. In this paper, we propose a deep architecture that jointly learns face descriptors and the aggregation mechanism for better end-to-end performances. The system can be applied to new groups with individuals never seen before and the scheme easily manages new memberships or membership endings. We show through experiments on multiple large-scale wild-face datasets, that the proposed method leads to higher verification performance compared to other baselines.
Implementation and Analysis of Cryptocurrency

Abstract: A crypto currency is a digital currency mainly based on blockchain technology. The use of virtual currency has become widespread in many different systems in recent years. Virtual money is not fully controlled and regulated hence most of the countries have not admitted this currency in their economic activities. The project aims to understand the basic concepts of cryptocurrency, how they works , analyze the different type of crypto at market and the hardship they face . We will be also trying to use this knowledge to create a small prototype of a crypto system.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Single Anonymous • Conference submission management system : Morressier • Number of submissions received : 183 • Number of submissions sent for review : 144 • Number of submissions accepted : 143 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 78.1 • Average number of reviews per paper : 11 • Total number of reviewers involved : 13 • Contact person for queries : Name : Muhammad Rizqie Arbie Email : arbie@fi.itb.ac.id Affiliation : Institut Teknologi Bandung
Debugging using Orthogonal Gradient Descent

In this report we consider the following problem: Given a trained model that is partially faulty, can we correct its behaviour without having to train the model from scratch? In other words, can we ``debug" neural networks similar to how we address bugs in our mathematical models and standard computer code. We base our approach on the hypothesis that debugging can be treated as a two-task continual learning problem. In particular, we employ a modified version of a continual learning algorithm called Orthogonal Gradient Descent (OGD) to demonstrate, via two simple experiments on the MNIST dataset, that we can in-fact \textit{unlearn} the undesirable behaviour while retaining the general performance of the model, and we can additionally \textit{relearn} the appropriate behaviour, both without having to train the model from scratch.
Virtual education in universities

The Universidad Autónoma del Estado de México, with the commitment to incorporate information and communications technology, established the Distance Education Department in the 1990s. However, some academic institutions to date does not have a specific coordination of Distance Education, the School of Architecture and Design is no exception. This has been overwhelmed by the change in face-to-face education because of the pandemic COVID-19 to virtual education. Therefore, the need arises to set up strategies for the implementation of virtual and distance education in the School of Architecture and Design of the UAEM, which will be serves as a reference in other academic institutions of the University, based on the Law of the Universidad Autónoma del Estado de México and its Estatuto Universitario. To achieve this, a qualitative method with a descriptive design has been implemented, through a scientific literature review; including a historical framework, legal framework, contextual framework was determined; supported by the case study method; In addition, a procedure was defined to confirm the existence of the research problem. The results revealed the need for the FAD to develop a strategic planning adapted to the needs of virtual education. Finally, the most important elements of the proposal will be presented, based on a virtual education office that contributes to the current and future dynamics.
An E-haling Crime and Exploitation Classification Framework

Society 5.0 is defined as “a human-cantered society that balances economic advancement with the resolution of social problems through socio-technical platforms that integrate cyberspace with physical space.” Society 5.0 is still a relatively new field of study, investigating new forms of, and platforms for, human-computer engagement. With the evolution of Society 5.0, it has been recognized that there has been an increase in number of e-hailing services (making use of public transport services through electronic applications) within the transportation industry. Although the introduction of e-haling services are seen as positive, this has also resulted in e-haling related crimes and exploitation. This research aims to present a first attempt classification of e-haling crime and exploitation. The research was performed by conducting a literature review that considered a subset of 33 peer reviewed articles (through a selection process) on themes relevant to how e- hailing services are exploited and what the modus operandi are when parties engage in e- haling exploitation and crime. Additionally, 7 relevant news articles were also included to make up the full spectrum of literature. The paper concludes by proposing a classification of the different forms of e-hailing crime and exploitation. This classification can be extended (e.g. the transport of illegal substances and human trafficking) and used in future research as a starting point to limit or prevent e-haling exploitation and crime.
What makes Ethereum blockchain transactions be processed fast or slow? An empirical study

The Ethereum platform allows developers to implement and deploy applications called Dapps onto the blockchain for public use through the use of smart contracts. To execute code within a smart contract, a paid transaction must be issued towards one of the functions that are exposed in the interface of a contract. However, such a transaction is only processed once one of the miners in the peer-to-peer network selects it, adds it to a block, and appends that block to the blockchain This creates a delay between transaction submission and code execution. It is crucial for Dapp developers to be able to precisely estimate when transactions will be processed, since this allows them to define and provide a certain Quality of Service (QoS) level (e.g., 95% of the transactions processed within 1 minute). However, the impact that different factors have on these times have not yet been studied. Processing time estimation services are used by Dapp developers to achieve predefined QoS. Yet, these services offer minimal insights into what factors impact processing times. Considering the vast amount of data that surrounds the Ethereum blockchain, changes in processing times are hard for Dapp developers to predict, making it difficult to maintain said QoS. In our study, we build random forest models to understand the factors that are associated with transaction processing times. We engineer several features that capture blockchain internal factors, as well as gas pricing behaviors of transaction issuers. By interpreting our models, we conclude that features surrounding gas pricing behaviors are very strongly associated with transaction processing times. Based on our empirical results, we provide Dapp developers with concrete insights that can help them provide and maintain high levels of QoS.
Quantum Anomaly Detection for Collider Physics

Quantum Machine Learning (QML) is an exciting tool that has received significant recent attention due in part to advances in quantum computing hardware. While there is currently no formal guarantee that QML is superior to classical ML for relevant problems, there have been many claims of an empirical advantage with high energy physics datasets. These studies typically do not claim an exponential speedup in training, but instead usually focus on an improved performance with limited training data. We explore an analysis that is characterized by a low statistics dataset. In particular, we study an anomaly detection task in the four-lepton final state at the Large Hadron Collider that is limited by a small dataset. We explore the application of QML in a semi-supervised mode to look for new physics without specifying a particular signal model hypothesis. We find no evidence that QML provides any advantage over classical ML. It could be that a case where QML is superior to classical ML for collider physics will be established in the future, but for now, classical ML is a powerful tool that will continue to expand the science of the LHC and beyond.
Learning a Single Neuron with Adversarial Label Noise via Gradient Descent

We study the fundamental problem of learning a single neuron, i.e., a function of the form $\mathbf{x}\mapsto\sigma(\mathbf{w}\cdot\mathbf{x})$ for monotone activations $\sigma:\mathbb{R}\mapsto\mathbb{R}$, with respect to the $L_2^2$-loss in the presence of adversarial label noise. Specifically, we are given labeled examples from a distribution $D$ on $(\mathbf{x}, y)\in\mathbb{R}^d \times \mathbb{R}$ such that there exists $\mathbf{w}^\ast\in\mathbb{R}^d$ achieving $F(\mathbf{w}^\ast)=\epsilon$, where $F(\mathbf{w})=\mathbf{E}_{(\mathbf{x},y)\sim D}[(\sigma(\mathbf{w}\cdot \mathbf{x})-y)^2]$. The goal of the learner is to output a hypothesis vector $\mathbf{w}$ such that $F(\mathbb{w})=C\, \epsilon$ with high probability, where $C>1$ is a universal constant. As our main contribution, we give efficient constant-factor approximate learners for a broad class of distributions (including log-concave distributions) and activation functions. Concretely, for the class of isotropic log-concave distributions, we obtain the following important corollaries: For the logistic activation, we obtain the first polynomial-time constant factor approximation (even under the Gaussian distribution). Our algorithm has sample complexity $\widetilde{O}(d/\epsilon)$, which is tight within polylogarithmic factors. For the ReLU activation, we give an efficient algorithm with sample complexity $\tilde{O}(d\, \polylog(1/\epsilon))$. Prior to our work, the best known constant-factor approximate learner had sample complexity $\tilde{\Omega}(d/\epsilon)$. In both of these settings, our algorithms are simple, performing gradient-descent on the (regularized) $L_2^2$-loss. The correctness of our algorithms relies on novel structural results that we establish, showing that (essentially all) stationary points of the underlying non-convex loss are approximately optimal.
SISTEM TIKET HELPDESK PADA STMIK BANI SALEH

reporting problems related to personal computer (PC) damage, intranet or internet network troubles, requesting data on databases, and developing information systems. reporting of problems is still conventional by using the telephone for complaints or coming directly to the puskom/ukisi unit (main computer unit and information system) to provide information on problems or problems, if there is no urgent work, the carving team can immediately solve the problem, so there is no recording of damage or trouble reporting or data requests. The helpdesk ticket system for the stmik Bani Saleh uses method stages starting from requirements, design, implementation, and testing. It is hoped that the implementation of the helpdesk ticket system can make it easier to recap reports on repair work that has been carried out by the carving team, and can be analyzed for damage or problems that often occur
Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge Nodes

Quantization is widely employed in both cloud and edge systems to reduce the memory occupation, latency, and energy consumption of deep neural networks. In particular, mixed-precision quantization, i.e., the use of different bit-widths for different portions of the network, has been shown to provide excellent efficiency gains with limited accuracy drops, especially with optimized bit-width assignments determined by automated Neural Architecture Search (NAS) tools. State-of-the-art mixed-precision works layer-wise, i.e., it uses different bit-widths for the weights and activations tensors of each network layer. In this work, we widen the search space, proposing a novel NAS that selects the bit-width of each weight tensor channel independently. This gives the tool the additional flexibility of assigning a higher precision only to the weights associated with the most informative features. Testing on the MLPerf Tiny benchmark suite, we obtain a rich collection of Pareto-optimal models in the accuracy vs model size and accuracy vs energy spaces. When deployed on the MPIC RISC-V edge processor, our networks reduce the memory and energy for inference by up to 63% and 27% respectively compared to a layer-wise approach, for the same accuracy.
Validation of E-Comics as Learning Materials in Social Studies

This study aimed to know the validity of E-comics as learning materials in teaching Social Studies.The researcher used descriptive research to gather the necessary data in writing the learning material.It was conducted at Don Manuel Rivera Memorial Integrated Memorial National High School using one hundred seventy-eight (178) students in a modular learning modality during the Academic Year 2021-2022.
A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning

Inscrutable AI systems are difficult to trust, especially if they operate in safety-critical settings like autonomous driving. Therefore, there is a need to build transparent and queryable systems to increase trust levels. We propose a transparent, human-centric explanation generation method for autonomous vehicle motion planning and prediction based on an existing white-box system called IGP2. Our method integrates Bayesian networks with context-free generative rules and can give causal natural language explanations for the high-level driving behaviour of autonomous vehicles. Preliminary testing on simulated scenarios shows that our method captures the causes behind the actions of autonomous vehicles and generates intelligible explanations with varying complexity.
Knowledge, Exposure, Peer Pressure, and Awareness Among Teenagers on Perceived Factors Influencing Teenage Pregnancy

This study aimed to determine teenagers' knowledge, awareness, exposure, and peer pressure on perceived factors that influence teenage pregnancy which may serve as a basis for intervention program development.It employed a descriptive-survey method using a validated researcher-made questionnaire to gather data.The 106 respondents involved from the rural town of Tampilisan were obtained through a convenient sampling technique.Weighted mean and Kruskal Wallis H-Test were used for data analysis.The study revealed that teenagers were "moderately knowledgeable" in terms of sex education, they are "less exposed" too risky behaviors from the internet, and "less experienced" peer pressure.Furthermore, there was no significant difference existed among the respondents' exposure to risky behaviors, extent of peer pressure, and extent of awareness of teenage pregnancy when grouped according to educational level, however, respondents differ significantly in terms of knowledge of sex education.Moreover, when respondents were grouped according to family structure, there was no significant difference existed among them.The findings suggest that an intervention program enhancing information dissemination through various mediums can be recommended, to help and guide teenagers in achieving safe, fulfilling, and enjoyable relationships and prevent unplanned pregnancy in the future.
Using makeup to block surveillance

Altering one's facial features with a special type of makeup can keep them from being recognized by artificial intelligence.