Reviewerly/exHarmony huggingface dataset:
Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery

In this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDIALOG curated in our work. The cur rent models indeed suffer from spurious correlations and have a tendency of generating irrelevant and generic responses. Inspired by causal discovery algorithms, we propose a novel model-agnostic method for training and inference of response generation model using a conditional independence classifier. The classifier is trained by a constrained self-training method, coined CONSTRAIN, to overcome data scarcity. The experimental results based on both human and automatic evaluation show that our method significantly outperforms the competitive baselines in terms of relevance, informativeness, and fluency.
QKD Based on Time-Entangled Photons and its Key-Rate Promise

For secure practical systems, quantum key distribution (QKD) must provide high key rates over long distances. Time-entanglement-based QKD promises to increase the secret key rate and distribution distances compared to other QKD implementations. This article describes the major steps in QKD protocols, focusing on the nascent QKD technology based on high-dimensional time-bin entangled photons. We overview state-of-the-art from the information and coding theory perspective. In particular, we discuss the key rate loss due to single-photon detector imperfections. We hope the open questions posed and discussed in this paper will inspire information and coding theorists to contribute to and impact fledgling quantum applications and influence future quantum communication systems.
3D-Aware Object Localization using Gaussian Implicit Occupancy Function

To automatically localize a target object in an image is crucial for many computer vision applications. To represent the 2D object, ellipse labels have recently been identified as a promising alternative to axis-aligned bounding boxes. This paper further considers 3D-aware ellipse labels, \textit{i.e.}, ellipses which are projections of a 3D ellipsoidal approximation of the object, for 2D target localization. Indeed, projected ellipses carry more geometric information about the object geometry and pose (3D awareness) than traditional 3D-agnostic bounding box labels. Moreover, such a generic 3D ellipsoidal model allows for approximating known to coarsely known targets. We then propose to have a new look at ellipse regression and replace the discontinuous geometric ellipse parameters with the parameters of an implicit Gaussian distribution encoding object occupancy in the image. The models are trained to regress the values of this bivariate Gaussian distribution over the image pixels using a statistical loss function. We introduce a novel non-trainable differentiable layer, E-DSNT, to extract the distribution parameters. Also, we describe how to readily generate consistent 3D-aware Gaussian occupancy parameters using only coarse dimensions of the target and relative pose labels. We extend three existing spacecraft pose estimation datasets with 3D-aware Gaussian occupancy labels to validate our hypothesis. Labels and source code are publicly accessible here: https://cvi2.uni.lu/3d-aware-obj-loc/.
Interruptions detection in video conferences

In recent years, video conferencing (VC) popularity has skyrocketed for a wide range of activities. As a result, the number of VC users surged sharply. The sharp increase in VC usage has been accompanied by various newly emerging privacy and security challenges. VC meetings became a target for various security attacks, such as Zoombombing. Other VC-related challenges also emerged. For example, during COVID lockdowns, educators had to teach in online environments struggling with keeping students engaged for extended periods. In parallel, the amount of available VC videos has grown exponentially. Thus, users and companies are limited in finding abnormal segments in VC meetings within the converging volumes of data. Such abnormal events that affect most meeting participants may be indicators of interesting points in time, including security attacks or other changes in meeting climate, like someone joining a meeting or sharing a dramatic content. Here, we present a novel algorithm for detecting abnormal events in VC data. We curated VC publicly available recordings, including meetings with interruptions. We analyzed the videos using our algorithm, extracting time windows where abnormal occurrences were detected. Our algorithm is a pipeline that combines multiple methods in several steps to detect users' faces in each video frame, track face locations during the meeting and generate vector representations of a facial expression for each face in each frame. Vector representations are used to monitor changes in facial expressions throughout the meeting for each participant. The overall change in meeting climate is quantified using those parameters across all participants, and translating them into event anomaly detection. This is the first open pipeline for automatically detecting anomaly events in VC meetings. Our model detects abnormal events with 92.3% precision over the collected dataset.
Who could be behind QAnon? Authorship attribution with supervised machine-learning

A series of social media posts signed under the pseudonym "Q", started a movement known as QAnon, which led some of its most radical supporters to violent and illegal actions. To identify the person(s) behind Q, we evaluate the coincidence between the linguistic properties of the texts written by Q and to those written by a list of suspects provided by journalistic investigation. To identify the authors of these posts, serious challenges have to be addressed. The "Q drops" are very short texts, written in a way that constitute a sort of literary genre in itself, with very peculiar features of style. These texts might have been written by different authors, whose other writings are often hard to find. After an online ethnology of the movement, necessary to collect enough material written by these thirteen potential authors, we use supervised machine learning to build stylistic profiles for each of them. We then performed a rolling analysis on Q's writings, to see if any of those linguistic profiles match the so-called 'QDrops' in part or entirety. We conclude that two different individuals, Paul F. and Ron W., are the closest match to Q's linguistic signature, and they could have successively written Q's texts. These potential authors are not high-ranked personality from the U.S. administration, but rather social media activists.
Eventual Discounting Temporal Logic Counterfactual Experience Replay

Linear temporal logic (LTL) offers a simplified way of specifying tasks for policy optimization that may otherwise be difficult to describe with scalar reward functions. However, the standard RL framework can be too myopic to find maximally LTL satisfying policies. This paper makes two contributions. First, we develop a new value-function based proxy, using a technique we call eventual discounting, under which one can find policies that satisfy the LTL specification with highest achievable probability. Second, we develop a new experience replay method for generating off-policy data from on-policy rollouts via counterfactual reasoning on different ways of satisfying the LTL specification. Our experiments, conducted in both discrete and continuous state-action spaces, confirm the effectiveness of our counterfactual experience replay approach.
Bell inequalities with overlapping measurements

Which nonlocal correlations can be obtained, when a party has access to more than one subsystem? While traditionally nonlocality deals with spacelike separated parties, this question becomes important with quantum technologies that connect devices by means of small shared systems. Here we study Bell inequalities where measurements of different parties can have overlap. This allows to accommodate problems in quantum information such as the existence of quantum error correction codes in the framework of non-locality. The scenarios considered show an interesting behaviour with respect to Hilbert space dimension, overlap, and symmetry.
Youtube dan Perbankan Syariah

The purpose of this research was to investigate how students majoring in subjects other than English, at the Syaria Banking Department in STES Manna Wa Salwa Tanah Datar Sumatera Barat, use social media, particularly YouTube, and to determine the impact it has on their speaking skills. The study was carried out using a descriptive qualitative design, with 25 participants selected from the Syaria Banking Department. The students were asked to speak on prepared topics in front of a phone camera, and five online meetings were held to assess their progress. A semi-structured interview was also conducted to gather more detailed supporting data. The findings indicate that using social media, particularly YouTube, had a positive impact on the speaking ability of non-English major students, as evidenced by iswsissefforts to improve their speaking skills, such as psychological factors (e.g. lack of motivation and confidence), environmental factors (e.g. relationship with their surroundings), and linguistic factors (e.g. finding references, selecting vocabulary, and using appropriate speaking style and gestures). The use of social media, especially YouTube, was found to be helpful in overcoming these obstacles and improving students' speaking ability.
STRATEGI PEMBELAJARAN KITAB KUNING DI MBI MAMBAUL FALAH SOKAONENG

This article aims to conduct a comprehensive study of the yellow book learning strategy applied at MBI Mambaul Falah Sokaoneng Tambak Gresik. It is common knowledge that studying and understanding the yellow book is not an easy thing. The difficulty is because the yellow book which is a religious source of Islam is written in Arabic and in a typical pre-modern format. However, these difficulties can be overcome if the asatidz have the right strategies so that they can make the learning process effective and efficient. It is MBI Mambaul Falah Sokaoneng, located in Tambak sub-district, Gresik district, who succeeded in sending some of its students to a book reading competition throughout the province of East Java and has been the best participant several times by winning the championship.&#x0D; This research is a type of field research and is carried out with a qualitative approach. The results of this study indicate that there are several strategies used by MBI Mambaul Falah to improve the mastery and understanding of the students in learning the yellow book. Some of the strategies in question are: choosing learning methods, bahtsul masail and online discussions attended by students, alumni, guardians of students and sympathizers, granting permission to use laptops and cell phones, adequate facilities, pleasant environment, musyrif standby, and building interest. and students' learning motivation.
Fold-unfold lemmas for reasoning about recursive programs using the Coq proof assistant – ERRATUM

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FORMS OF ASSESSMENT OF STUDENTS’ ABILITIES AND QUALITIES IN VIETNAMESE SUBJECTS

The article is based on Circulars, Regulations, General Training Program 2018 on the Vietnamese's objectives, content, and requirements in primary schools to present and clarify two forms of assessment of students’ abilities and qualities in Vietnamese including regular assessment and periodic assessment. From the analysis and affirmation: to confirm the progress of students, it is necessary to use the form of process assessment, also known as regular assessment. To confirm the student's learning results after a period, it is necessary to use a form of summative assessment, also known as periodical assessment. Through the assessment process, it helps teachers to assess the quality and assess the general capacity in Vietnamese integrated into the assessment of language ability, that is, to assess what students can do to solve the tasks in reading, writing, speaking, and listening.
A Privacy-Centered Protocol for Enhancing Security and Authentication of Academic Certificates

Academic certificate authentication is crucial in safeguarding the rights and opportunities of individuals who have earned academic credentials.This authentication helps prevent fraud and forgery, ensuring that only those who have genuinely earned certificates can use them for education and career opportunities.With the increased use of online education and digital credentials in the digital age, the importance of academic certificate authentication has significantly grown.However, traditional techniques for authentication, such as QR code, barcode, and watermarking, have limitations regarding security and privacy.Therefore, proposing a privacy-centred protocol to enhance the security and authentication of academic certificates is vital to improve the trust and credibility of digital academic certificates, ensuring that individuals' rights and opportunities are protected.In this context, we adopted the Challenge Handshake Authentication (CHA) protocol to propose the Certificate Verification Privacy Control Protocol (CVPC).We implemented it using Python and Flask with a Postgres database and an MVT structure for the application.The results of the implementation demonstrate that the proposed protocol effectively preserves privacy during the academic certificate issuance and verification process.Additionally, we developed a proof of concept to evaluate the proposed protocol, demonstrating its functionality and performance.The PoC provided insights into the strengths and weaknesses of the proposed protocol and highlighted its potential to prevent forgery and unauthorised access to academic certificates.Overall, the proposed protocol has the potential to significantly enhance the security and authenticity of academic certificates, improving the overall trust and credibility of the academic credentialing system.
Deep Study of CRF Models for Speech understanding in Limited Task

In this paper, we propose to evaluate in depth CRF models (Conditional Random Fields) for speech-understanding in limited task.To evaluate these models, we design several models that differ according to the level of integration of local dependencies in the same turn.As we propose to evaluate these models on different types of processed data.We perform our study on a corpus where turns are not segmented into utterances.In fact, we propose to use the whole turn as one unit during training and testing of CRF models.This represents the natural way of conversation.The language used in this work is the Tunisian Arabic dialect.The obtained results prove the robustness of CRF models when dealing with raw data.They are able to detect the semantic dependency between words in the same speech turn.Results are important when CRF models are designed to take into account the words with deep dependencies in the same turn and with advanced preprocessed data.
Semi-supervised Method to Detect Fraudulent Transactions and Identify Fraud Types while Minimizing Mounting Costs

Financial fraud is a complex problem faced by financial institutions, and existing fraud detection systems are often insufficient, resulting in significant financial losses.Researchers have proposed various machine learning-based techniques to enhance the performance of these systems.In this work, we present a semi-supervised approach to detect fraudulent transactions.First, we extract and select features, followed by the training of a binary classification model.Secondly, we apply a clustering algorithm to the fraudulent transactions and use the binary classification model with the SHAP framework to analyze the clusters and associate them with a particular fraud type.Finally, we present an algorithm to detect and assign a fraud type by leveraging a multi-fraud classification model.To minimize the mounting cost of the model, we propose an algorithm to choose an optimal threshold that can detect fraudulent transactions.We work with experts to adapt a risk cost matrix to estimate the mounting cost of the model.This risk cost matrix takes into account the cost of missing fraudulent transactions and the cost of incorrectly flagging a legitimate transaction as fraudulent.In our experiments on a real dataset, our approach achieved high accuracy in detecting fraudulent transactions, with the added benefit of identifying the fraud type, which can help financial institutions better understand and combat fraudulent activities.Overall, our approach offers a comprehensive and efficient solution to financial fraud detection, and our results demonstrate its effectiveness in reducing financial losses for financial institutions.
Breast Cancer Prediction using Machine Learning Models

Breast cancer is a type of cancer that develops in the cells of the breast.Treatment for breast cancer usually involves X-ray, chemotherapy, or a combination of both treatments.Detecting cancer at an early stage can save a person's life.Artificial intelligence (AI) plays a very important role in this area.Therefore, predicting breast cancer remains a very challenging issue for clinicians and researchers.This work aims to predict the probability of breast cancer in patients.Using machine learning (ML) models such as Multilayer Perceptron (MLP), K-Nearest Neightbot (KNN), AdaBoost (AB), Bagging, Gradient Boosting (GB), and Random Forest (RF).The breast cancer diagnostic medical dataset from the Wisconsin repository has been used.The dataset includes 569 observations and 32 features.Following the data analysis methodology, data cleaning, exploratory analysis, training, testing, and validation were performed.The performance of the models was evaluated with the parameters: classification accuracy, specificity, sensitivity, F1 count, and precision.The training and results indicate that the six trained models can provide optimal classification and prediction results.The RF, GB, and AB models achieved 100% accuracy, outperforming the other models.Therefore, the suggested models for breast cancer identification, classification, and prediction are RF, GB, and AB.Likewise, the Bagging, KNN, and MLP models achieved a performance of 99.56%, 95.82%, and 96.92%, respectively.Similarly, the last three models achieved an optimal yield close to 100%.Finally, the results show a clear advantage of the RF, GB, and AB models, as they achieve more accurate results in breast cancer prediction.
Sequence Recommendation based on Deep Learning

Sequence recommendation systems have become increasingly popular in various fields such as movies and social media.These systems aim to predict a user's preferences and interests based on their past behavior and provide them with personalized recommendations.Deep learning, particularly Recurrent Neural Networks (RNNs), have emerged as a powerful tool for sequence recommendation.In this research, we explore the effectiveness of RNNs in movie and Instagram recommendation systems.We investigate and compare the performance of different types of RNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in recommending movies and Instagram posts to users based on their browsing history.Additionally, we study the impact of incorporating additional information such as user's demographics and Instagram hashtags on the performance of the recommendation system.We also evaluate the performance of RNN-based movie and Instagram recommendation systems in comparison to traditional approaches, such as collaborative filtering and content-based filtering, in terms of accuracy and personalization.The findings of this research provide insights into the effectiveness of RNNs in movie and Instagram recommendation systems and contribute to the development of more accurate and personalized recommendations for users Keywords-Long short-term memory (LSTM) and
Privacy-Preserving and Trustless Verifiable Fairness Audit of Machine Learning Models

In the big data era, machine learning has developed prominently and is widely used in real-world systems.Yet, machine learning raises fairness concerns, which incurs discrimination against groups determined by sensitive attributes such as gender and race.Many researchers have focused on developing fairness audit technique of machine learning model that enable users to protect themselves from discrimination.Existing solutions, however, rely on additional external trust assumptions, either on third-party entities or external components, that significantly lower the security.In this study, we propose a trustless verifiable fairness audit framework that assesses the fairness of ML algorithms while addressing potential security issues such as data privacy, model secrecy, and trustworthiness.With succinctness and non-interactive of zero knowledge proof, our framework not only guarantees audit integrity, but also clearly enhance security, enabling fair ML models to be publicly auditable and any client to verify audit results without extra trust assumption.Our evaluation on various machine learning models and real-world datasets shows that our framework achieves practical performance.
Hierarchical Pretrained Deep Learning Features for the Breast Cancer Classification

Breast cancer is a common and fatal disease among women worldwide.Accurately and early diagnosing of breast cancer plays a pivotal role in improving the prognosis of patients.Recently, advanced techniques of artificial intelligence and natural image classification have been used for the breast cancer image classification task and have become a hot topic for research in machine learning.This paper proposes a fully automatic computerized method for breast cancer classification using two well-established pretrained CNN models, namely VGG16 and ResNet50.Next, the feature extraction process is used to extract features in a hierarchical manner to train a support vector machine classifier.Evaluating the proposed model shows achieving 92% accuracy.In addition, this paper investigates the effect of different factors, highlights its findings, and provides future directions for the research to develop more advanced models.
A Survey on Blockchain Technology Concepts, Applications and Security

In the past decade, blockchain technology has become increasingly prevalent in our daily lives.This technology consists of a chain of blocks that contains the history of transactions and information about its users.Distributed digital ledgers are used in blockchain.A transparent environment is created by using this technology, allowing encrypted secure transactions to be verified and approved by all users.As a powerful tool, blockchain can be utilized for a wide range of useful purposes in everyday life including cryptocurrency, Internet-of-Things (IoT), finance, reputation system, and healthcare.This paper aims to provide an overview of blockchain technology and its security issues for users and researchers.In particular, those who conduct their business using blockchain technology.This paper includes a comparison of consensus algorithms and a description of cryptography.Further, most applications used in blockchain are focused on in this paper also analyzing real attacks and then summarizing security measures in blockchain.Even though Blockchain holds a promising scope of development in several sectors, it is prone to several security and vulnerability issues that arise from different types of blockchain networks which represent a challenge to deal with blockchain.Finally, as a research community, we encourage future research challenges that can be addressed to improve security in blockchain systems.