Reviewerly/exHarmony huggingface dataset:
Gradient Aligned Attacks via a Few Queries

Black-box query attacks, which rely only on the output of the victim model, have proven to be effective in attacking deep learning models. However, existing black-box query attacks show low performance in a novel scenario where only a few queries are allowed. To address this issue, we propose gradient aligned attacks (GAA), which use the gradient aligned losses (GAL) we designed on the surrogate model to estimate the accurate gradient to improve the attack performance on the victim model. Specifically, we propose a gradient aligned mechanism to ensure that the derivatives of the loss function with respect to the logit vector have the same weight coefficients between the surrogate and victim models. Using this mechanism, we transform the cross-entropy (CE) loss and margin loss into gradient aligned forms, i.e. the gradient aligned CE or margin losses. These losses not only improve the attack performance of our gradient aligned attacks in the novel scenario but also increase the query efficiency of existing black-box query attacks. Through theoretical and empirical analysis on the ImageNet database, we demonstrate that our gradient aligned mechanism is effective, and that our gradient aligned attacks can improve the attack performance in the novel scenario by 16.1\% and 31.3\% on the $l_2$ and $l_{\infty}$ norms of the box constraint, respectively, compared to four latest transferable prior-based query attacks. Additionally, the gradient aligned losses also significantly reduce the number of queries required in these transferable prior-based query attacks by a maximum factor of 2.9 times. Overall, our proposed gradient aligned attacks and losses show significant improvements in the attack performance and query efficiency of black-box query attacks, particularly in scenarios where only a few queries are allowed.
Curras + Baladi: Towards a Levantine Corpus

The processing of the Arabic language is a complex field of research. This is due to many factors, including the complex and rich morphology of Arabic, its high degree of ambiguity, and the presence of several regional varieties that need to be processed while taking into account their unique characteristics. When its dialects are taken into account, this language pushes the limits of NLP to find solutions to problems posed by its inherent nature. It is a diglossic language; the standard language is used in formal settings and in education and is quite different from the vernacular languages spoken in the different regions and influenced by older languages that were historically spoken in those regions. This should encourage NLP specialists to create dialect-specific corpora such as the Palestinian morphologically annotated Curras corpus of Birzeit University. In this work, we present the Lebanese Corpus Baladi that consists of around 9.6K morphologically annotated tokens. Since Lebanese and Palestinian dialects are part of the same Levantine dialectal continuum, and thus highly mutually intelligible, our proposed corpus was constructed to be used to (1) enrich Curras and transform it into a more general Levantine corpus and (2) improve Curras by solving detected errors.
Physically-Based Editing of Indoor Scene Lighting from a Single Image

We present a method to edit complex indoor lighting from a single image with its predicted depth and light source segmentation masks. This is an extremely challenging problem that requires modeling complex light transport, and disentangling HDR lighting from material and geometry with only a partial LDR observation of the scene. We tackle this problem using two novel components: 1) a holistic scene reconstruction method that estimates scene reflectance and parametric 3D lighting, and 2) a neural rendering framework that re-renders the scene from our predictions. We use physically-based indoor light representations that allow for intuitive editing, and infer both visible and invisible light sources. Our neural rendering framework combines physically-based direct illumination and shadow rendering with deep networks to approximate global illumination. It can capture challenging lighting effects, such as soft shadows, directional lighting, specular materials, and interreflections. Previous single image inverse rendering methods usually entangle scene lighting and geometry and only support applications like object insertion. Instead, by combining parametric 3D lighting estimation with neural scene rendering, we demonstrate the first automatic method to achieve full scene relighting, including light source insertion, removal, and replacement, from a single image. All source code and data will be publicly released.
Automated Face Detection System for Presence Marking using OpenCV

The main objective of this project is to create a real-time face monitoring system for face recognition of educational institutions and offices in order to develop and improve the current attendance system to be more efficient and effective than ever before. The old system now has a lot of ambiguity that has led to inaccurate neutrality and inefficiency. The most difficult task for any organization is to mark attendees This background technology will be a real- time face recognition system. A person's face is one of the natural features that can identify a person differently. Therefore, it is used to track identification as the chances of the face turning or recurring are low. This program can be used to go to educational institutions and offices using real- time face recognition with the help of Python, OpenCV, Web Cam, and Excel file. Common methods such as eigen faces and fishermen's faces are sensitive to light, sound position, blockage, light etc. So, we used Python and AI to see real- time face recognition and overcome those difficulties. Attendance records will be automatically updated and stored on an excel sheet Key Words: Real-time attendance System, OpenCV, NumPy, Excel, Face-recognition.
El Proyecto C-ORAL-ROM y su aplicación a la enseñanza del español

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Predicate Logic

When propositional logic cannot adequately provide the appropriate meaning of a statement and yet the notion of quantification is involved, predicate logic is needed to reason. This chapter briefly presents basic aspects of predicate logic. Noting quantification is a way to express the extent to which a predicate is true over a range of elements, two widely known quantifications in predicate logic are introduced, namely, universal quantification and existential quantification. Then, the topic of negations of quantified statements using De Morgan’s laws for quantifiers is introduced. Last, major aspects of nested quantifiers, where one quantifier is within the scope of another, are discussed.
Macedonian Speech Synthesis for Assistive Technology Applications

Speech technology is becoming ever more ubiquitous with the advance of speech enabled devices and services. The use of speech synthesis in Augmentative and Alternative Communication tools, has facilitated inclusion of individuals with speech impediments allowing them to communicate with their surroundings using speech. Although there are numerous speech synthesis systems for the most spoken world languages, there is still a limited offer for smaller languages. We propose and compare three models built using parametric and deep learning techniques for Macedonian trained on a newly recorded corpus. We target low-resource edge deployment for Augmentative and Alternative Communication and assistive technologies, such as communication boards and screen readers. The listening test results show that parametric speech synthesis is as performant compared to the more advanced deep learning models. Since it also requires less resources, and offers full speech rate and pitch control, it is the preferred choice for building a Macedonian TTS system for this application scenario.
Primrose: Selecting Container Data Types by Their Properties

Context: Container data types are ubiquitous in computer programming, enabling developers to efficiently store and process collections of data with an easy-to-use programming interface. Many programming languages offer a variety of container implementations in their standard libraries based on data structures offering different capabilities and performance characteristics. Inquiry: Choosing the *best* container for an application is not always straightforward, as performance characteristics can change drastically in different scenarios, and as real-world performance is not always correlated to theoretical complexity. Approach: We present Primrose, a language-agnostic tool for selecting the best performing valid container implementation from a set of container data types that satisfy *properties* given by application developers. Primrose automatically selects the set of valid container implementations for which the *library specifications*, written by the developers of container libraries, satisfies the specified properties. Finally, Primrose ranks the valid library implementations based on their runtime performance. Knowledge: With Primrose, application developers can specify the expected behaviour of a container as a type refinement with *semantic properties*, e.g., if the container should only contain unique values (such as a `set`) or should satisfy the LIFO property of a `stack`. Semantic properties nicely complement *syntactic properties* (i.e., traits, interfaces, or type classes), together allowing developers to specify a container's programming interface *and* behaviour without committing to a concrete implementation. Grounding: We present our prototype implementation of Primrose that preprocesses annotated Rust code, selects valid container implementations and ranks them on their performance. The design of Primrose is, however, language-agnostic, and is easy to integrate into other programming languages that support container data types and traits, interfaces, or type classes. Our implementation encodes properties and library specifications into verification conditions in Rosette, an interface for SMT solvers, which determines the set of valid container implementations. We evaluate Primrose by specifying several container implementations, and measuring the time taken to select valid implementations for various combinations of properties with the solver. We automatically validate that container implementations conform to their library specifications via property-based testing. Importance: This work provides a novel approach to bring abstract modelling and specification of container types directly into the programmer's workflow. Instead of selecting concrete container implementations, application programmers can now work on the level of specification, merely stating the behaviours they require from their container types, and the best implementation can be selected automatically.
Visual Data Exploration as a Statistical Testing Procedure: Within-View and Between-View Multiple Comparisons

A fundamental problem in visual data exploration concerns whether observed patterns are true or merely random noise. This problem is especially pertinent in visual analytics, where the user is presented with a barrage of patterns, without any guarantees of their statistical validity. Recently this problem has been formulated in terms of statistical testing and the multiple comparisons problem. In this paper, we identify two levels of multiple comparisons problems in visualization: the within-view and the between-view problem. We develop a statistical testing procedure for interactive data exploration that controls the family-wise error rate on both levels. The procedure enables the user to determine the compatibility of their assumptions about the data with visually observed patterns. We present use-cases where we visualize and evaluate patterns in real-world data.
Penerapan Lesson Study pada Mata Pelajaran Bahasa Indonesia di SMA Kemala Bhayangkari Tahun Pelajaran 2021/2022

This article talks about The Implementation of Lesson Study on "Penerapan Leson study pada Mata Pelajaran Bahasa Indonesia" (Implementation of Language Error) at the subject. The purpose in this research is to describe the implementation of Indonesia lessons study. Based on the observation, there are some lesson learnt: a) the use of collaborative learning method could give space to student to make discussion, so it could increase student cooperative skill, b) there are intensive discussion on the partial group (consist of 2—4 members. It shows that implementation of lesson study could increase student cooperative
Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution

Due to cost and time-to-market constraints, many industries outsource the training process of machine learning models (ML) to third-party cloud service providers, popularly known as ML-asa-Service (MLaaS). MLaaS creates opportunity for an adversary to provide users with backdoored ML models to produce incorrect predictions only in extremely rare (attacker-chosen) scenarios. Bayesian neural networks (BNN) are inherently immune against backdoor attacks since the weights are designed to be marginal distributions to quantify the uncertainty. In this paper, we propose a novel backdoor attack based on effective learning and targeted utilization of reverse distribution. This paper makes three important contributions. (1) To the best of our knowledge, this is the first backdoor attack that can effectively break the robustness of BNNs. (2) We produce reverse distributions to cancel the original distributions when the trigger is activated. (3) We propose an efficient solution for merging probability distributions in BNNs. Experimental results on diverse benchmark datasets demonstrate that our proposed attack can achieve the attack success rate (ASR) of 100%, while the ASR of the state-of-the-art attacks is lower than 60%.
Reading Problems and Risk of Dyslexia Among Early Elementary Students in Thailand

Reading problems and dyslexia adversely affect a child’s learning, emotions, and behavior. This research aims to survey students with reading problems and at risk of dyslexia and find the relevant factors. This study considered students in the first grade of primary school. Eight schools in Pathum Thani province were selected by the purposive sampling method. Caregivers responded to questionnaires regarding information about their children and their family backgrounds. Research assistants tested the children's reading abilities with a reading ability assessment test. It was found that 161 of the 1,018 students are at-risk for dyslexia, with a calculated prevalence of 15.81% (95% CI, 13.63–18.20%). Following an analysis using the multivariable logistic regression model, it was found that the pertinent risk factors for reading problems in children were being male, a developmental or hereditary disease, a father with a low level of education, and type of school. Children with reading problems should receive the help they need early, while risk factors should be identified to improve the quality of education in different types of schools.
Management of literacy program implementation at elementary school post COVID-19 pandemic in Indonesia

This research is motivated by the low literacy management skills in elementary schools in the city of Padangsidimpuan, Indonesia. This makes it necessary to solve the problem by improving it through the management of literacy implementation programs. To achieve the objectives of this research, mix method with a sequential explanatory approach was adapted which begins with quantitative research and continues with qualitative research. The sampling technique used cluster random sampling method for quantitative research and snowball sampling for qualitative research. Quantitative research data were taken from five schools with a total of one hundred fifty students and eighteen teachers. Techniques Data collection were done through tests and distribution of questionnaires for quantitative research and in-depth interviews, documentation studies and observations for qualitative. The findings of this study indicate that was no conflict between the results of quantitative research and the results of qualitative research. The low literacy competence of students due to the management function has not followed the principles of planning, organizing, actuating, controlling, and evaluating.
Role of USG for evaluation of axillary lymph node status in carcinoma breast

Introduction: Breast cancer is the most frequent cancer detected globally and leading cause of cancer related death in women. One of the other methods which can assess the status of the axillary lymph node is USG. Methods and Materials: This is a Hospital based Observational Study, Patients who are admitted in the Department of General surgery, PBMH, Kalinga Institute of Medical Sciences, Bhubaneswar for Breast carcinoma during the period September 2018 to August 2020. A total of 70 patients will be selected after applying the following inclusion and exclusion criteria. Results It was observed that, the clinical presentation of patients with carcinoma breast in this series were breast lump, nipple discharge and nipple retraction. Most common presentation was breast lump. All cases presented with lump in the breast, 5(7.14%) cases presented with lump with nipple discharge and 6(8.57%) cases presented with lump with nipple retraction. Tumor size was less than or equal to 2 cm (T1) in 24 (34.29%) cases, tumor size greater than 2 cm but less than or equal to 5 cm were in 46 (65.71%) cases and tumor size more than 5cm were 0 (100%) cases.
A Study on the Impact of Affective Learning Experience on Attitude towards Technology, Self-regulated Learning and Online Learning Behaviour among MOOC Learners

Different thinking functions, such as sensation, cognition, memory, consciousness, rationality, and pattern recognition, are significantly influenced by the affective learning experience.These different thinking functions and cognitive control are intricately connected to the learning processes and cognitive processing.In this research study, the researcher analyses the influence of the affective learning experience of MOOC learners on their attitude towards technology, online learning behaviour and self-regulated learning behaviour.For the study, MOOC learners were selected.A quantitative approach was adopted by the researcher in this study.The present research follows the descriptive method of the causal-comparative type.It was found that the affective learning experience of the learners influences their attitude towards technology, online learning behaviour and self-regulated learning behaviour.
Integration of Industry and Education Services Jilin Province "One Master, Six Doubles" High-quality Development Strategy: Introduction to 2022 Jilin Province University-Industry Collaborative Education Program

This article introduces Jilin Province's University-Industry Collaborative Education Program from the aspects of project organization, first implementation, implemented years, work objectives, basic elements, basic conditions for enterprise participation, project types, key support directions, work flow, similar projects and enterprise cooperation education cases, etc. This article has certain significance for Jilin University-Industry Collaborative Education Program organization, apply, implementation and project closure.
Linear Regularizers Enforce the Strict Saddle Property

Satisfaction of the strict saddle property has become a standard assumption in non-convex optimization, and it ensures that many first-order optimization algorithms will almost always escape saddle points. However, functions exist in machine learning that do not satisfy this property, such as the loss function of a neural network with at least two hidden layers. First-order methods such as gradient descent may converge to non-strict saddle points of such functions, and there do not currently exist any first-order methods that reliably escape non-strict saddle points. To address this need, we demonstrate that regularizing a function with a linear term enforces the strict saddle property, and we provide justification for only regularizing locally, i.e., when the norm of the gradient falls below a certain threshold. We analyze bifurcations that may result from this form of regularization, and then we provide a selection rule for regularizers that depends only on the gradient of an objective function. This rule is shown to guarantee that gradient descent will escape the neighborhoods around a broad class of non-strict saddle points, and this behavior is demonstrated on numerical examples of non-strict saddle points common in the optimization literature.
ENERGY AND ECONOMIC ANALYSIS OF BIOGAS POWER PLANT FROM MUNICIPAL SOLID WASTE IN INDONESIA

Indonesia is the fourth most populous country with a population of more than 270 million people and is expected to continue to increase. The increasing population has an effect on increasing municipal solid waste (MSW) generation and energy demand. Waste-to-energy (WTE) can be a solution to these two problems. This article will discuss two types of waste-to-energy technology, namely anaerobic digestion and landfill gas recovery. The study began by conducting kinetic modeling on the two technologies to determine the potential of biogas energy produced from municipal waste in Indonesia, the case study applied was Yogyakarta. Then a feasibility study was carried out to find out whether these two technologies are possible to be applied in Indonesia in terms of various aspects such as initial capital costs, operating and maintenance costs, and waste processing service costs. At the end of the study, it was found that biogas power plant with both technology bases was economically feasible. Landfill gas recovery is more attractive because it has a smaller capital and a higher IRR of 22%. The scenario using anaerobic digestion requires a larger capital than the previous scenario and has a lower IRR value of 14%. Keywords: Biogas, Municipal Solid Waste (MSW), Kinetic modeling, Waste-to-energy (WTE), Feasibility study.
Penerapan Model Pembelajaran Teams Games Tournament dalam Meningkatkan Cara Berfikir Siswi

Teacher-Centered Learning that uses conventional methods often makes students bored and bored. This can be one of the causes of the inhibition of students' thinking abilities. The purpose of this study was to examine the application of the Teams Games Tournament (TGT) learning model so that the student's way of thinking became more honed and no longer bored during learning. This research uses the Classroom Action Research (CAR) method in its implementation. The research subjects were class VIII B Regular MTs N 1 Probolinggo, 24 students. Data collection techniques based on pre-test and post-test. The research model design is carried out through the following stages: planning action, implementing the action, observing/evaluating, and reflecting. The results show that the students have achieved the expected learning mastery, 75%. That is, the thinking level of students is getting higher so that the learning objectives that have been designed can be adequately achieved. This is evidenced by the average score of 80.25 students and 17 students who have completed their studies or 70.83%.
Differential Privacy: What is all the noise about?

Differential Privacy (DP) is a formal definition of privacy that provides rigorous guarantees against risks of privacy breaches during data processing. It makes no assumptions about the knowledge or computational power of adversaries, and provides an interpretable, quantifiable and composable formalism. DP has been actively researched during the last 15 years, but it is still hard to master for many Machine Learning (ML)) practitioners. This paper aims to provide an overview of the most important ideas, concepts and uses of DP in ML, with special focus on its intersection with Federated Learning (FL).