Reviewerly/exHarmony huggingface dataset:
Explaining Cross-domain Recognition with Interpretable Deep Classifier

The recent advances in deep learning predominantly construct models in their internal representations, and it is opaque to explain the rationale behind and decisions to human users. Such explainability is especially essential for domain adaptation, whose challenges require developing more adaptive models across different domains. In this article, we ask the question: How much does each sample in the source domain contribute to the network’s prediction on the samples from the target domain? To address this, we devise a novel Interpretable Deep Classifier (IDC) that learns the nearest source samples of a target sample as evidence upon which the classifier makes the decision. Technically, IDC maintains a differentiable memory bank for each category, and the memory slot derives a form of key–value pair. The key records the features of discriminative source samples, and the value stores the corresponding properties, e.g., representative scores of the features for describing the category. IDC computes the loss between the output of IDC and the labels of source samples to back-propagate to adjust the representative scores and update the memory banks. Extensive experiments on Office-Home and VisDA-2017 datasets demonstrate that our IDC leads to a more explainable model with almost no accuracy degradation and effectively calibrates classification for optimum reject options. More remarkably, when taking IDC as a prior interpreter, capitalizing on 0.1% source training data selected by IDC still yields superior results than that uses full training set on VisDA-2017 for unsupervised domain adaptation.
Quantum LDPC Codes Based on Cocyclic Block Matrices

Motivated by a family of binary cocyclic block matrices over GF(2), we proposed a construction method to gain the stabilizer of long-length quantum error-correction codes (QECCs). Stabilizer quantum codes (SQCs) can be obtained by the different rows of the yielded circulant permutation matrices; hence, the quantum codes have the virtue of a fast construction algorithm. The recursive relation of a block matrix is employed in the proposed approach, so that the generator matrix of quantum cocyclic codes with long length can be constructed easily. Furthermore, the obtained quantum codes have the low-density advantage of there being no 4-cycles in the Tanner graph.
Teaching Data Science with Literate Programming Tools

This paper presents a case study on using Emacs and Org-mode for literate programming in undergraduate computer and data science courses. Over three academic terms, the author mandated these tools across courses in R, Python, C++, SQL, and more. The onboarding relied on simplified Emacs tutorials and starter configurations. Students gained proficiency after undertaking initial practice. Live coding sessions demonstrated the flexible instruction enabled by literate notebooks. Assignments and projects required documentation alongside functional code. Student feedback showed enthusiasm for learning a versatile IDE, despite some frustration with the learning curve. Skilled students highlighted efficiency gains in a unified environment. However, the uneven adoption of documentation practices pointed to a need for better incorporation into grading. Additionally, some students found Emacs unintuitive, desiring more accessible options. This highlights a need to match tools to skill levels, potentially starting novices with graphical IDEs before introducing Emacs. The key takeaways are as follows: literate programming aids comprehension but requires rigorous onboarding and reinforcement, and Emacs excels for advanced workflows but has a steep initial curve. With proper support, these tools show promise for data science education.
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning

This article proposes a sensor data anonymization model that is trained on decentralized data and strikes a desirable trade-off between data utility and privacy, even in heterogeneous settings where the sensor data have different underlying distributions. Our anonymization model, dubbed Blinder, is based on a variational autoencoder and one or multiple discriminator networks trained in an adversarial fashion. We use the model-agnostic meta-learning framework to adapt the anonymization model trained via federated learning to each user’s data distribution. We evaluate Blinder under different settings and show that it provides end-to-end privacy protection on two IMU datasets at the cost of increasing privacy loss by up to 4.00% and decreasing data utility by up to 4.24%, compared to the state-of-the-art anonymization model trained on centralized data. We also showcase Blinder’s ability to anonymize the radio frequency sensing modality. Our experiments confirm that Blinder can obscure multiple private attributes at once, and has sufficiently low power consumption and computational overhead for it to be deployed on edge devices and smartphones to perform real-time anonymization of sensor data.
Optimising a Higher Education Library Website to Improve Access to Information Resources

The purpose of the article is to summarise and present the peculiarities of optimising a university library website to improve access to information resources. Research methodology. The research uses general scientific (generalisation, comparison, induction, and deduction) and empirical-theoretical methods (analysis and synthesis). The use of system-structural analysis helped to distinguish the main features of optimising a university library website to improve access to information resources. The scientific novelty of the results obtained in the article is due to the solution of an important scientific task, which is to develop theoretical provisions and practical recommendations for the formation of a system to ensure the optimisation of the university library website to improve access to information resources. The article further develops the research on the strategy of optimising the university library website. Conclusions. It has been established that the website of a university library is an integrated resource that has information, search, educational, advertising, and navigation functions and can be a powerful tool for promoting information and library products and services, as well as for shaping the image of a university library. It has been determined that access to information resources of the university library is an essential component of the educational process and research, and optimisation of the university library website is a necessary element for improving the availability of information resources. It has been found that to ensure convenient communication with users, differentiated communication tools can be installed on the website of a university library, which should be accessible and visible to users on all pages of the site. It has been determined that an essential place in providing remote user service is given to the library website, since it contributes to the expansion of library capabilities in terms of informing about the range of information and library resources and services. It has been found that in order to optimise a university library website and improve access to information resources, the following measures should be implemented: improved navigation, fast loading, implementation of search and mobile optimisation, and data backup.&#x0D; Key words: university library, website, remote service, electronic resources, information space, information resources, optimisation, repository, digitalisation.
A Scalable Reduced-Complexity Compression of Hyperspectral Remote Sensing Images Using Deep Learning

Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing platforms and the high computational cost of compression algorithms for hyperspectral data, especially when the number of bands is large. To address these issues, a channel clusterisation strategy is proposed, which reduces the computational demands of learned compression methods for real scenarios and is scalable for different sources of data with varying numbers of bands. The proposed method is compatible with an embedded implementation for state-of-the-art on board hardware, a first for a ML hyperspectral data compression method. In terms of coding performance, our proposal surpasses established lossy methods such as JPEG 2000 preceded by a spectral Karhunen-Loève Transform (KLT), in clusters of 3 to 7 bands, achieving a PSNR improvement of, on average, 9 dB for AVIRIS and 3 dB for Hyperion images.
Safer Working at Heights: Exploring the Usability of Virtual Reality for Construction Safety Training among Blue-Collar Workers in Kuwait

Virtual Reality (VR) construction safety training modules have reached a level of maturity which renders them as a serious alternative to traditional safety training modules. The purpose of this study is to investigate the usability of a particular safety training module related to “Working at heights” for blue-collar construction workers in Kuwait. A mixed study approach was applied based on a semi-quasi experimental research design, utilizing a control group/experimental group with pre-/post-test measurements, supplemented by observations. The findings indicate a statistically insignificant higher learning effectiveness of the workers exposed to the VR approach. Observations confirmed that trainees require an extended time of preparation to become familiar with moving within the virtual environment and using the related hardware. Furthermore, younger users with less work experience reported a higher usability than older users with more work experience. VR content developers are encouraged to investigate the possibilities of simplifying the virtual environment to make it more relevant for blue-collar workers, reduce the complexity of the hardware, and intensify the feeling of the consequences resulting from users’ choices. Construction companies and educational institutions training construction blue-collar workers can benefit from the VR approach to safety training if they allow sufficient time for familiarization with the virtual training module.
Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices

Handwritten digit recognition has been used in many consumer electronic devices for a long time. However, we found that the recognition system used in current consumer electronics is sensitive to image or character rotations. To address this problem, this study builds a low-cost and light computation consumption handwritten digit recognition system. A Principal Component Analysis (PCA)-based logistic regression classifier is presented, which is able to provide a certain degree of robustness in the digit subject to rotations. To validate the effectiveness of the developed image recognition algorithm, the popular MNIST dataset is used to conduct performance evaluations. Compared to other popular classifiers installed in MATLAB, the proposed method is able to achieve better prediction results with a smaller model size, which is 18.5% better than the traditional logistic regression. Finally, real-time experiments are conducted to verify the efficiency of the presented method, showing that the proposed system is successfully able to classify the rotated handwritten digit.
An Integrated Support System for People with Intellectual Disability

People with Intellectual Disability (ID) encounter several problems in their daily living regarding their needs, activities, interrelationships, and communication. In this paper, an interactive platform is proposed, aiming to provide personalized recommendations for information and entertainment, including creative and educational activities, tailored to the special user needs of this population. Furthermore, the proposed platform integrates capabilities for the automatic recognition of health-related emergencies, such as fever, oxygen saturation decline, and tachycardia, as well as location tracking and detection of wandering behavior based on smartwatch/smartphone sensors, while providing appropriate notifications to caregivers and automated assistance to people with ID through voice instructions and interaction with a virtual assistant. A short-scale pilot study has been carried out, where a group of end-users participated in the testing of the integrated platform, verifying its effectiveness concerning the recommended services. The experimental results indicate the potential value of the proposed system in providing routine health measurements, identifying and managing emergency cases, and supporting a creative and qualitative daily life for people with disabilities.
Lower bounds for adiabatic quantum algorithms by quantum speed limits

We introduce a simple framework for estimating lower bounds on the runtime of a broad class of adiabatic quantum algorithms. The central formula consists of calculating the variance of the final Hamiltonian with respect to the initial state. After examining adiabatic versions of certain keystone circuit-based quantum algorithms, this technique is applied to adiabatic quantum algorithms with undetermined speedup. In particular, we analytically obtain lower bounds on adiabatic algorithms for finding $k$-clique in random graphs. Additionally, for a particular class of Hamiltonian, it is straightforward to prove the equivalence between our framework and the conventional approach based on spectral gap analysis.
Development of Educandy Platform as an Educational Game to Improve Arabic Language Learning Achievement

Game-based learning is certainly very favored by students, especially nowadays games in learning do not need to be done in the classroom using a cellphone. One application that can be developed to improve student achievement is the Educandy platform. The purpose of this research is to develop the Educandy platform as a learning media in improving Arabic language learning achievement. The method used in this research is research and development method with ADDIE model. The results of this study indicate that the Educandy platform is able to improve student achievement so that students are happy in learning Arabic. The conclusion of this study can be seen that the use of the Educandy platform as an educational game in learning Arabic can support the learning and teaching process. The limitation of this research is that researchers only make Arabic language games at the junior high school level so that the game cannot be developed as a whole, for that the researcher hopes that the next researcher can do the same research and be practiced at boarding schools where there is more knowledge about Arabic lessons.
Abnormal Human Behavior Detection from a Video Sequence Using Deep Learning

The utilization of cameras is becoming accepted society more frequently in a wide range of settings and contexts, including live traffic monitoring, vehicle parking, space surveillance, inside vehicles, and intelligent spaces. These cameras provide information concerning everyday activities that must be effectively examined. The preponderance of visual surveillance, regrettably, still relies on an operator to sort through this footage. In this paper, behavior of human is analyzed from a video sequence and accordingly the behavior is classified as dangerous or safe. Recurrent artificial neural networks are being used to evaluate those identify the features and foresee potential video activity, while throughout video sequences. Identifying the behavior of the person would help to take the necessary action on the immediate basis.
I-LAA: An Education Chabot

Chatbots are applications of artificial intelligence developed to build conversations and interact with human users. They are used in various applications, such as customer service, sales and marketing, virtual assistants, and more. Chatbots rely on technologies like artificial intelligence (AI) and natural language processing (NLP)., allowing them to understand and respond to user input, often in real-time these chatbots are widely built on the python platform all over the world. Chatbots are quickly becoming a key component of many digital user experiences. This chatbot helps to reduce the workload of the admission department. Provide necessary information to students and parents. Chatbots became popular because people cannot provide 24/7 assistance and may not be available continuously and as websites are vast in nature which makes people hard to find the exact information they required. To overcome this problem chatbots being the automated assistance came into the light.
A Novel Privacy-Centric Training Routine for Maintaining Accuracy in Traditional Machine Learning Systems

The increasingly prevalent concerns on user privacy and user’s abstinence on data sharing assuredly spells a decline in novel model development for machine learning. On-Device AI is an exciting paradigm with local training and no requirements of externalizing personal data. Hybridizing these techniques of traditional data upload and server-side training to maximize accuracy and an On-Device approach, protecting privacy standards yields us a novel training routine, which can find itself extensively invoked with increasing privacy concerns. We enumerate multiple use cases in addition to examining feasibility and an abstract implementation of this concept.
Deep Learning Framework for Speaker Verification Under Multi Sensor, Multi Lingual and Multi Session Conditions

Deep learning algorithms have significantly prevailed as a stand-in for i-vector in numerous applications such as speaker recognition, diarization, segmentation and verification. CNN when fed directly with raw voice fragments have resulted in propitious outcomes. Instead of using conventional hand-crafted features, CNNs learn low-level speech patterns using waveforms, which allows the network to better represent crucial narrow band speaker properties like pitch and formants. These narrow band features are less susceptible to surrounding noise. The design of neural network becomes significant to achieve this purpose. This study suggests the use of SincNet, an unique CNN architecture which enables the first convolutional layer to find deep relevant filters. Band-pass filters are implemented via parameterized sinc functions, on which SincNet is built. The standard CNNs normally learns all elements of each filter, where as the sincnet technique learns only high and low cutoff frequencies directly from raw fed data. This provides a highly compact and effective method to produce a unique filter bank that is precisely tuned for the particular application. Our research on the speaker verification task illustrate that the suggested architecture attains higher speed and performs better than the regular CNN.
The Opinions Imparted on Singular’s Face

The Opinions Imported On A Singular's Face is a significant computer vision and artificial intelligence topic due to its significant academic and commercial potential. Because facial expressions are one of the primary information channels in interpersonal communication, this review focuses on studies that only use facial images. Albeit the Sentiments Suggested On A Particular’s Face can be led utilizing different sensors, the latest Conclusions Granted On A Solitary’s Face research is summed up in this paper in a reasonable and succinct way. A summary of the primary algorithms for the various representative categories of the Opinions Imparted On A Singular’s Face systems and a description of conventional methods for The Feelings Expressed On A Person’s Face are first presented. After that, a demonstration of how “end-to-end” deep learning approaches make use of deep networks. Suppositions Given On A Particular’s Face, this survey likewise centers around another cross breed profound learning strategy that utilizes a convolutional brain organization (CNN) for individual edges spatial highlights and a long short-term memory (LSTM) for continuous casings’ worldly elements. To quantitatively compare the Opinions Imparted On A Singular’s Face research, benchmark results and a brief description of publicly available evaluation metrics are provided in the paper’s conclusion. Newcomers to The Opinions Imparted On A Singular’s Face can use this brief overview to get a general idea of the most recent cutting-edge research, as well as seasoned researchers looking for productive work directions in the future.
Content Based Document Image Retrieval Using Computer Vision and AI Techniques

This work presents a technique for finding if a same image is present in the database of PDF files. The objective of content-based document image retrieval is achieved by comparing a query image with the images from the PDF documents to check if similar image is present in the searched PDF documents. Typically, the ranking of the photos for retrieval is based on how closely the representative features of the query image and PDF images show similarity. The features such as texture features and Scale Invariant Feature Transform (SIFT) features, are extracted from the images of the pdf documents as well as from query image to determine whether the query images present in the PDF file or not. If the image from searched PDF matches with the query image, the result displays the searched PDF.
Generation of Historical Artwork Using GAN

Preserving cultural history and heritage improves the sense of belongingness and cooperation, which leads to a healthy and prosperous economy. Museums housing historically famous artworks are one of the key contributing factors to cultural preservation. However, reports suggest that museums’ footfall and general impression are declining and hence, acts as a catalyst towards cultural extinctions. It is imperative to explore and design new ways for people to discover, create and learn with historically famous art collections. The creation of art inspired by famous works leads to an innate level of connection and engagement that preserves cultural heritage and enriches it. This paper details the mechanisms and techniques involved in building an experience that allows users to instantly generate new artworks inspired by historically famous ones using an interface powered by a Generative Adversarial Network (GAN). It also covers a follow-up experience where users can visually search their newly generated artwork to find its closest visual matches within the complete collection and discover new areas of interest.
Navigating the Aisles: An Augmented Reality Solution for Gamified Indoor Grocery Store Navigation

In the world of e-commerce, there is a need for innovative technology to enhance the customer experience and drive more foot traffic to physical stores. Augmented reality (AR) is a promising technology that can provide a new level of engagement and interactivity. However, there is a challenge in developing an AR technology that encourages users to go to the grocery store to use it. The question is, how can we design an AR system that creates a compelling reason for users to visit a grocery store instead of simply shopping online? This problem statement seeks to explore the feasibility and effectiveness of an AR system that incentivizes users to visit a physical store by offering a unique, gamified immersive shopping experience that cannot be replicated online, with clear virtual navigation signs along the aisles to find all your favourite products.