Reviewerly/exHarmony huggingface dataset:
Improving Autoencoder-based Outlier Detection with Adjustable Probabilistic Reconstruction Error and Mean-shift Outlier Scoring

AutoEncoders (AEs) are commonly used for machine learning tasks due to their intrinsic learning ability. This unique characteristic can be capitalized for Outlier Detection (OD). However conventional AE-based methods face the issue of overconfident decisions and unexpected reconstruction results of outliers, limiting their performance in OD. To mitigate these issues, the Mean Squared Error (MSE) and Negative Logarithmic Likelihood (NLL) were first analyzed, and the importance of incorporating aleatoric uncertainty to AE-based OD was elucidated. Then the Weighted Negative Logarithmic Likelihood (WNLL) was proposed to adjust for the effect of uncertainty for different OD scenarios. Moreover, the Mean-Shift Scoring (MSS) method was proposed to utilize the local relationship of data to reduce the issue of false inliers caused by AE. Experiments on 32 real-world OD datasets proved the effectiveness of the proposed methods. The combination of WNLL and MSS achieved 41% relative performance improvement compared to the best baseline. In addition, MSS improved the performance of multiple AE-based outlier detectors by an average of 20%. The proposed methods have the potential to advance AE's development in OD. The code is available on www.OutlierNet.com for reproducibility.
A principled approach to model validation in domain generalization

Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions. State-of-the-art domain generalization methods typically train a representation function followed by a classifier jointly to minimize both the classification risk and the domain discrepancy. However, when it comes to model selection, most of these methods rely on traditional validation routines that select models solely based on the lowest classification risk on the validation set. In this paper, we theoretically demonstrate a trade-off between minimizing classification risk and mitigating domain discrepancy, i.e., it is impossible to achieve the minimum of these two objectives simultaneously. Motivated by this theoretical result, we propose a novel model selection method suggesting that the validation process should account for both the classification risk and the domain discrepancy. We validate the effectiveness of the proposed method by numerical results on several domain generalization datasets.
Ensemble prosody prediction for expressive speech synthesis

Generating expressive speech with rich and varied prosody continues to be a challenge for Text-to-Speech. Most efforts have focused on sophisticated neural architectures intended to better model the data distribution. Yet, in evaluations it is generally found that no single model is preferred for all input texts. This suggests an approach that has rarely been used before for Text-to-Speech: an ensemble of models. We apply ensemble learning to prosody prediction. We construct simple ensembles of prosody predictors by varying either model architecture or model parameter values. To automatically select amongst the models in the ensemble when performing Text-to-Speech, we propose a novel, and computationally trivial, variance-based criterion. We demonstrate that even a small ensemble of prosody predictors yields useful diversity, which, combined with the proposed selection criterion, outperforms any individual model from the ensemble.
Android-Based Online Exam Information System In SMK Pertiwi Batam

Batam Pertiwi Vocational School is the first vocational school in Nongsa sub-district, this school has majoring in TKJ (Computer Network Engineering), Accounting and Nursing, the problems that exist in Batam Pertiwi Batam are implementation that is still manual and value processing takes a long time and costs Great for printing questions and answer sheets. Making an android-based online exam system in the form of an application to facilitate teachers and students in the implementation of the exam. The application is designed using the Mysql database, the interface design uses Android Studio and this application is installed using an android smartphone. After the online exam application, students and teachers are easier to carry out online examinations so it does not take a long time and incur huge costs and realizes SMK Pertiwi Batam as a modern and digital-based vocational school of industrial era 4.0.
Improving Quantum-to-Classical Data Decoding using Optimized Quantum Wavelet Transform

Abstract One of the challenges facing current Noisy-Intermediate-Scale-Quantum devices (NISQ) is achieving efficient quantum circuit measurement or readout. The process of extracting classical data from the quantum domain, termed in this work as quantum-to-classical (Q2C) data decoding, generally incurs significant overhead, since the quantum circuit needs to be sampled repeatedly to obtain useful data readout. In this paper, we propose and evaluate time-efficient and depth-optimized Q2C methods based on the multidimensional, multilevel-decomposable, quantum wavelet transform (QWT) whose packet and pyramidal forms are leveraged and optimized. We also propose a zero-depth technique that uses selective placement of measurement gates to perform the QWT operation. To demonstrate their efficiency, the proposed techniques are quantitatively evaluated in terms of execution time, circuit depth, and accuracy in comparison to existing Q2C techniques. Experimental evaluations of the proposed Q2C methods are performed using real high-resolution multispectral images on a 27-qubit state-of-the-art quantum computing device from IBM Quantum.
STRUCTURE OF FOREIGN LANGUAGE STRATEGIC COMPETENCE OF FUTURE PRIMARY TEACHERS

The article is devoted to highlighting the theoretical prerequisites for the formation of foreign language strategic competence of future primary school teachers. The definition of the term "strategic competence" is provided, its component composition is analyzed, and the purpose of training is determined. The purpose of the article is to highlight the structure of formation of foreign language strategic competence of future primary school teachers. In today's requirements for the foreign language training of students the role of the ability to obtain information from different sources, to absorb, supplement and evaluate it, to apply different ways of cognitive and creative activity is increasing. CEFR (2018) identifies strategic competence as a key element to form communicative competence. The methodological basis of the article is psychological, pedagogical and methodical theories and provisions that reveal conceptual approaches to the researched issues. The scientific novelty consists in determining the structural elements of strategic competence as the basis for the formation of foreign language strategic competence of future primary school teachers. The prospects for further scientific research should be the use of the identified elements of strategic competence as the basis for the formation of future primary school teachers' foreign language educational and strategic competence in the educational process to build a model of appropriate learning.
An Approach Based on Metadata to Implement Convention over Configuration Decoupled from Framework Logic

Frameworks are essential for software development, providing code design and reuse for its users. Well-known Java frameworks and APIs such as Spring, JUnit, and JPA rely on metadata usage, commonly defined by code annotations. Those frameworks use the Java Reflection API for consuming and processing these annotations. Code elements usually have some similarities and can also have the same annotation. This paper proposes a model for defining conventions over configuration for annotations usage decoupled from the metadata reading logic. With this model, if a convention is present, the framework will consider that the element is configured by a specific target annotation, even if the code element does not have the annotation. We implemented this model in the metadata reading framework Esfinge Metadata API. The model implementation was evaluated refactoring an existing framework to add support to conventions using our approach. As a result, it was possible to introduce the conventions only by adding configurations to the annotations. The model was further evaluated in an experiment in which participants implemented the Conventions over Configuration pattern using the Esfinge Metadata API and Java Reflection API. Based on the results, approach fulfilled its goal of supporting the definition of conventions decupled from the framework logic, making the code more readable and easier to maintain according to the participants perception.
Online Motion Planning for Safe Human-Robot Cooperation using B-Splines and Hidden Markov Models

&lt;p&gt;When humans and robots work together, ensuring safe cooperation must be a priority. This research aims to develop a novel real-time planning algorithm that can handle unpredictable human movements by both slowing down task execution and modifying the robot's path based on the proximity of the human operator. To achieve this, an efficient method for updating the robot's motion is developed using a two-fold control approach that combines B-Splines and Hidden Markov Models. This allows the algorithm to adapt to a changing environment and avoid collisions. The proposed framework is thus validated using the Franka Emika Panda robot in a simple start-goal task. Our algorithm successfully avoids collision with the moving hand of an operator monitored by a fixed camera.&lt;/p&gt;
Relationship between Parental Literacy Beliefs and Home Literacy Environment

Parental literacy beliefs and home environment play an important role in stimulating children's literacy development.Parents who believe that children's literacy development is highly dependent on them will be more likely to spend more time to create an atmosphere of literacy with their children.The main purpose of this study was to determine the relationship between parental literacy beliefs and home literacy environment.This study was conducted in Sungai Pelek, Selangor.Findings of this quantitative methods strategy study were based on data collected from 240 parents of KEMAS preschool children aged between five and six years.Data was collected with the aid of electronic questionnaire via Google Form.The Parental Reading Belief Inventory (PRBI) was used to measure parental literacy beliefs and the Home Observation for Measurement of the Environment (HOME) was used to measure the home literacy environment.The results showed that the parental 's literacy beliefs have a significant relationship with the home literacy environment.It showed weak correlation between parental literacy beliefs and home literacy environment.In conclusion, the literacy environment at home is not highly dependent on parents' belief in the important of literacy.It is recommended for future research can be expanded in various states in Malaysia with a larger sample size to increase generalization and produce more reliable data.Furthermore, it is recommended for future research to take into account of the predictive factors in children literacy development research.
Efficient Privacy-Preserving Framework for Federated and Blockchain Networks

&lt;p&gt;Machine learning (ML) models, at the core of Artificial intelligence (AI), are widely applied in our digital world. To build these models, huge amounts of data must be collected, and many assets must be protected under privacy law. Data privacy is a critical issue when training and testing ML models. For privacy concerns to be adequately addressed in today’s ML systems, there needs to be considered privacy gaps in ML, as trained ML models can be vulnerable to adversarial attacks. In this regard, federated learning and blockchain networks are the new paradigms that have emerged with the promise of privacy-preserving by design while utilizing ML models. The new paradigms have promising privacy-preserving potential; however, they neglect several fundamental privacy and security issues the fact that adversaries can exploit shared gradients and global parameters, and the parameter server may drop gradients that have been mistakenly or deliberately updated. Also, enough data is available to train models. The proposed research addresses privacy concerns in federated and distributed environments. This is a general overview to establish a privacy-preserving framework that can provide privacy in federated and blockchain-based networks while address to fundamental privacy and security issues.&lt;/p&gt;
Augmented Reality and Indoor Positioning in Context of Smart Industry: A Review

Presently, digitalization is causing continuous transformation of industrial processes.However, it does pose challenges like spatially contextualizing data from industrial processes.There are various methods for calculating and delivering real-time location data.Indoor positioning systems (IPS) are one such method, used to locate objects and people within buildings.They have the potential to improve digital industrial processes, but they are currently underutilized.In addition, augmented reality (AR) is a critical technology in today's digital industrial transformation.This article aims to investigate the use of IPS and AR in manufacturing, the methodologies and technologies employed, the issues and limitations encountered, and identify future research opportunities.This study concludes that, while there have been many studies on IPS and navigation AR, there has been a dearth of research efforts in combining the two.Furthermore, because controlled environments may not expose users to the practical issues they may face, more research in a real-world manufacturing environment is required to produce more reliable and sustainable results.
The Effectiveness of Sistem Informasi Pengelolaan Arsip Surat (SIPAS) for Retrieval of Incoming and Outgoing Letters at Vocational High Schools 8 Semarang

Vocational High School 8 Semarang is one of the educational institutions that manually still uses agenda books for managing their letters. Therefore, an innovative filing system was created in the form of a Sistem Informasi Pengelolan Arsip Surat (SIPAS). The purpose of this research is to find out how effective the application of SIPAS was for retrieval of incoming and outgoing letters at Vocational High School Negeri 8 Semarang. The research method used is descriptive quantitative. The results of the data analysis show that SIPAS obtains 100% accuracy in letters archive retrieval and the retrieval time is only 8 to 10 seconds.
CAPTIONBOT FOR ASSSISTIVE VISION

In view of the recent flow of work in intelligent robotics, to our knowledge, the results from this research field have scarcely been applied to mitigate sensorial, motor and cognitive damage for humans. We believe that such research, in particular technique of learning from demonstration in imitation learning, is well suited to addressing the problem during road crossing. We present our solution to road crossing challenge for blind individuals. We present the methodology followed in preparing the model and the quantitative assessment of the model. Here first the image is captured through cam which is fixed to forehead and the it sends a model and that transforms into sentence or caption. Here we use API (Google text to voice) its words into voice this voice goes to ear of blind person as an instruction by that blind person can identify easily the things when crossing road Keywords: Deep Learning, LSTM, Python, VGG16.
Factors Affecting Ranking of Colleges in India for an Individual

This research paper aims to investigate the various factors that affect college rankings, with a specific focus on the United States. The study examines the metrics used to calculate college rankings and the different factors that influence a college's ranking. The paper begins by discussing the increasing importance of college rankings in the current higher education landscape. It then delves into the methodologies used to rank colleges, including the weight given to factors such as faculty research output, student outcomes, campus resources, and institutional reputation. The study further explores how these factors can impact a college's ranking. The findings of the research indicate that there is a complex relationship between the various factors that impact college rankings. While some factors may have a greater influence on a college's ranking than others, it is often the combination of factors that determine a college's overall score. For the purpose of this research, we have used Interpretive Structure Modelling (ISM) technique. The research also highlights the need for colleges to consider these factors carefully when developing strategies to improve their rankings. Keywords: College Rankings; Choosing College; College Ranking ISM
IMPLEMENTATION OF STOCHASTIC MODEL FOR RISK ASSESSMENT ON INDONESIAN STOCK EXCHANGE

Currently, financial assets become an alternative choice for investors in Indonesia to get maximum profits. The Indonesia Stock Exchange is the official capital market in Indonesia which is a place for trading financial assets. Stocks are listed as the most preferred financial asset by investors. In reality, stock investment is not a risk-free investment. The main risk that investors should face is the loss risk. This kind of risk can occur at any time. From that problem, this study aims to do risk assessment on the Indonesian stock market. The evaluation will be started with stock price index prediction using the Stochastic model (Geometric Brownian Motion Model and Jump Diffusion). Then, the result from that processes will be used to get loss risk prediction through the Adjusted Expected Shortfall model. By using the historical price of JKSE index from 01/08/21 to 31/08/22, Jump Diffusion is the best model to predict the JKSE index with MAPE value is 1.08%. Then, at the 95% confidence level and 1-day holding period, the expected loss risk using Adjusted Expected Shortfall model on 09/01/2022 is -0.02978.
SPRATAMA MODEL FOR INDONESIAN PARAPHRASE DETECTION USING BIDIRECTIONAL LONG SHORT-TERM MEMORY AND BIDIRECTIONAL GATED RECURRENT UNIT

Paraphrasing is a way to write sentences with other words with the same intent or purpose. Automatic paraphrase detection can be done using Natural Language Sentence Matching (NLSM) which is part of Natural Language Processing (NLP). NLP is a computational technique for processing text in general, while NLSM is used specifically to find the relationship between two sentences. With the development Neural Network (NN), nowadays NLP can be done more easily by computers. Many models for detecting and paraphrasing in English have been developed compared to Indonesian, which has less training data. This study proposes SPratama Model, which models paraphrase detection for Indonesian using a Recurrent Neural Network (RNN), namely Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The data used is "Quora Question Pairs" taken from Kaggle and translated into Indonesian using Google Translate. The results of this study indicate that the proposed model has an accuracy of around 80% for the detection of paraphrased sentences.
Scene Change aware Inter-Frame Forgeries Detection Technique for Surveillance Videos Based on Similarities Analysis

Surveillance videos provide security and increases work efficiency in places of work and homes. as the most acceptable form of evidence, surveillance videos are now tampered to hide actions or convey wrong information. Researchers have proposed ways to mitigate the effect of activities of the attackers through checking the authenticity of the video. The proposed schemes suffer performance degradation in the presence of scene changes. Recently a scheme that addresses the effects of scene change on inter-frame forgery detection was developed where it detects scene changes and divides multiple scenes in to shots. The scheme improves the overall performance of the inter-frame forgery detection at the expense of high average computational time. In this research, a video scene change aware forgery detection scheme is proposed to mitigate the effect of scene change on inter-frame forgery detection with low average computational time. The proposed scheme utilizes the luminance level within frame region which is a more efficient feature to detect scene change. The experimental results show that the scheme has 57% decreases in computational average time and increased in accuracy to 99.03%.
Stochastic process analysis enhancement via quantum system parameter estimation

Abstract In this paper, we investigate a linear Ito stochastic differential equation in the vector space from every conceivable perspective. In this case, the potential vector describes the magnitude of the classical noise acting on a quantum system. This vector potential can be expressed as a linear function of its parameters, with Hermitian operators serving as its coefficients since its parameters are assumed to be unknown. To the second order of perturbation, the unitary evolution operator can be determined with the aid of a potential perturbation parameter. As for the second term, it is written as a double-iterated stochastic integral with respect to Brownian motion, while the first term is written as an Ito stochastic integral. When controlling quantum systems, noise from the environment can be a major hindrance; this technique can help. Improve the dependability and practicality of quantum technologies like computers by learning to detect and regulate noise. If the potential’s parameters are affected by noise, then their reliability is thought to decrease. We focus on the special case where the potential is a linear function of these parameters with Hermitian operators as the coefficients. To find the unitary evolution operator up to O (ε), we can write the O (ε) term as an ito stochastic integral with respect to Brownian motion, and the O (ε 2 ) term as a doubly iterated stochastic integral with respect to Brownian motion.
A Cloud-Based AI Way to deal with Phishing URL Location

Abstract: Phishing is constantly growing to be one of the most adopted tools for conducting cyber-attacks. Recent statistics indicated that 97% of users could not recognize a sophisticated phishing email. With over 1.5 million new phishing websites being created every month, legacy black lists and rule-based filters can no longer mitigate the increasing risks and sophistication level of phishing. Phishing can deploy various malicious payloads that compromise the network’s security. In this context, machine learning can play a crucial role in adapting the capabilities of computer networks to recognize current and evolving phishing patterns. In this paper, we present PhishNot, a phishing URL detection system based on machine learning. Hence, our work uses a primarily ”learning from data” driven approach, validated with a representative scenario and dataset. The input features were reduced to 14 to assure the system’s practical applicability. Experiments showed that Random Forest presented the best performance with a very high accuracy of 97.5%. Furthermore, the design of our system also lends itself to being more adoptable in practice through a combination of high phishing detection rate and high speed (an average of 11.5 per URL) when deployed on the cloud
“If we are simply creating techies who can only work with the technology, we’re in big trouble.”

Freeman Hrabowski III talks about the importance of the humanities, culture change at universities, and scientists’ involvement in civic life.