Reviewerly/exHarmony huggingface dataset:
TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization

Recently, end-to-end neural diarization (EEND) is introduced and achieves promising results in speaker-overlapped scenarios. In EEND, speaker diarization is formulated as a multi-label prediction problem, where speaker activities are estimated independently and their dependency are not well considered. To overcome these disadvantages, we employ the power set encoding to reformulate speaker diarization as a single-label classification problem and propose the overlap-aware EEND (EEND-OLA) model, in which speaker overlaps and dependency can be modeled explicitly. Inspired by the success of two-stage hybrid systems, we further propose a novel Two-stage OverLap-aware Diarization framework (TOLD) by involving a speaker overlap-aware post-processing (SOAP) model to iteratively refine the diarization results of EEND-OLA. Experimental results show that, compared with the original EEND, the proposed EEND-OLA achieves a 14.39% relative improvement in terms of diarization error rates (DER), and utilizing SOAP provides another 19.33% relative improvement. As a result, our method TOLD achieves a DER of 10.14% on the CALLHOME dataset, which is a new state-of-the-art result on this benchmark to the best of our knowledge.
FaceXHuBERT: Text-less Speech-driven E(X)pressive 3D Facial Animation Synthesis Using Self-Supervised Speech Representation Learning

This paper presents FaceXHuBERT, a text-less speech-driven 3D facial animation generation method that allows to capture personalized and subtle cues in speech (e.g. identity, emotion and hesitation). It is also very robust to background noise and can handle audio recorded in a variety of situations (e.g. multiple people speaking). Recent approaches employ end-to-end deep learning taking into account both audio and text as input to generate facial animation for the whole face. However, scarcity of publicly available expressive audio-3D facial animation datasets poses a major bottleneck. The resulting animations still have issues regarding accurate lip-synching, expressivity, person-specific information and generalizability. We effectively employ self-supervised pretrained HuBERT model in the training process that allows us to incorporate both lexical and non-lexical information in the audio without using a large lexicon. Additionally, guiding the training with a binary emotion condition and speaker identity distinguishes the tiniest subtle facial motion. We carried out extensive objective and subjective evaluation in comparison to ground-truth and state-of-the-art work. A perceptual user study demonstrates that our approach produces superior results with respect to the realism of the animation 78% of the time in comparison to the state-of-the-art. In addition, our method is 4 times faster eliminating the use of complex sequential models such as transformers. We strongly recommend watching the supplementary video before reading the paper. We also provide the implementation and evaluation codes with a GitHub repository link.
disco: a toolkit for Distributional Control of Generative Models

Pre-trained language models and other generative models have revolutionized NLP and beyond. However, these models tend to reproduce undesirable biases present in their training data. Also, they may overlook patterns that are important but challenging to capture. To address these limitations, researchers have introduced distributional control techniques. These techniques, not limited to language, allow controlling the prevalence (i.e., expectations) of any features of interest in the model's outputs. Despite their potential, the widespread adoption of these techniques has been hindered by the difficulty in adapting complex, disconnected code. Here, we present disco, an open-source Python library that brings these techniques to the broader public.
On the Expressiveness and Generalization of Hypergraph Neural Networks

This extended abstract describes a framework for analyzing the expressiveness, learning, and (structural) generalization of hypergraph neural networks (HyperGNNs). Specifically, we focus on how HyperGNNs can learn from finite datasets and generalize structurally to graph reasoning problems of arbitrary input sizes. Our first contribution is a fine-grained analysis of the expressiveness of HyperGNNs, that is, the set of functions that they can realize. Our result is a hierarchy of problems they can solve, defined in terms of various hyperparameters such as depths and edge arities. Next, we analyze the learning properties of these neural networks, especially focusing on how they can be trained on a finite set of small graphs and generalize to larger graphs, which we term structural generalization. Our theoretical results are further supported by the empirical results.
Special Issue on Testability and Dependability of Artificial Intelligence Hardware

Artificial intelligence (AI) hardware, including AI accelerators and neuromorphic computing processors, emerges as one new frontier in the field of computing. There is an expedited paradigm shift in embracing bold and radical innovation of computer architectures, aiming at the continuation of computing performance improvement despite the slowed-down physical device scaling. Testability and dependability of AI hardware need to be addressed before mainstream adoption, especially in latency or throughput-critical, safety-critical, mission-critical, or remotely controlled applications (e.g., computer vision, autonomous driving, smart healthcare, IoTs, and robotics).
Special Issue on Testability and Dependability of Artificial Intelligence Hardware

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New method for detecting and removing random-valued impulse noise from images

The paper proposes a method for detecting and removing impulse noise in images, which determines the similarity between pixels by distance and the difference in brightness values in the local detector window. An impulse noise model is considered, where distorted pixels take random values and also randomly appear in the image. Pixels identified as pulses are recovered with an adaptive median filter. The impulses are determined in the detector window, whose size is calculated in the Euclidean metric and increases with increasing noise intensity in the image. In the experimental part, we discuss visual differences between familiar methods and the one proposed herein on three images for three different impulse noise intensities. In the approximation of image fragments, it is seen that the proposed method copes with the task in the best way, which is also confirmed by numerical estimates of the quality of image reconstruction from impulse noise based on the peak signal-to-noise ratio and the structural similarity index. The proposed method can be used when solving problems of cleaning images under conditions of distorting impulses and for eliminating distortions caused by adverse weather effects, such as raindrops and snow.
DEEP LEARNING PROJECT BASED ON HUMAN ACTIVITY RECOGNITION

- The main objective of this paper is to implement human activities by performing some of the specified tasks. In order to classify a time series, the challenge of human activity recognition requires data from a number of timesteps. The methodology that would be followed in the paper is using deep learning and classification methods to make predictions with the model. To extract the local features of each frame, a convolutional neural network and Long Short Term Memory (LSTM) of the deep learning algorithm will be used. This implementation will help to monitor human activity and ensure their safety by sending a message notification through their mobile phones using Twilio packages that is a library. This is a programmable messaging software application that will send notifications from the prevention of danger and the monitoring process will be easy to know and convenient. The project will be developed as an application for the users to interact as well as analyze real-time data analysis.In this paper,we will discuss about how human activity recognition can effectively be employees to ensure safety and security of elderly people, women and also in burglary prevention.The complete implementation proposal along with the technical specifications have been designed so as to implement best possible solutions. Keyword - CNN, LSTM, Deep Learning, Twilio packages, User Interface.
Using Learning Platforms to Support Online Learning in Junior High School

The online learning process can be done through various e-learning platforms. The determination of the platform should consider the dimensions of the teacher and those of the students. This study aims to analyze the use of the e-learning platform from the perspective of teachers and students in junior high school. This quantitative research design uses a descriptive method. Participants consisted of 91 teachers and 165 students in Medan City. Data were collected by questionnaire via Google Forms. Quantitative data analysis used descriptive statistics to obtain the percentages for each indicator. Then the presentation of data is done with tables and diagrams. The results showed that teachers and students have different perspectives on each e-learning platform supporting the learning process. Based on the philosophy of teachers and students, it can be stated that 1) teachers and students prefer Google Classroom to Edmodo as an LMS in Online Learning. This is because Google Classroom is easier to use, more interactive, and more efficient; 2) Google Meet is the application most often used and preferred by teachers and students in direct virtual Learning. Even though Zoom Cloud Meeting has a more attractive appearance and features, Google Meet is easier to access and considered to save internet quota; 3) As many as 43% of teachers and 49% of students prefer a combination of Google Classroom, Google Meet, WA Group to support the online learning process. Meanwhile, 41% of teachers and 42% of students prefer a combination of Google Classroom, Zoom Cloud Meeting, and WA Group as an e-learning platform. Only a few teachers and students choose the variety of Edmodo Zoom and WhatsApp groups and the combination of Edmodo, Google Meet, and WhatsApp groups.Keywords: E-Learning Platform, Student Perspective, Teacher Perspective
Power to the Data Defenders: Human-Centered Disclosure Risk Calibration of Open Data

The open data ecosystem is susceptible to vulnerabilities due to disclosure risks.Though the datasets are anonymized during release, the prevalence of the release-andforget model makes the data defenders blind to privacy issues arising after the dataset release.One such issue can be the disclosure risks in the presence of newly released datasets which may compromise the privacy of the data subjects of the anonymous open datasets.In this paper, we first examine some of these pitfalls through the examples we observed during a red teaming exercise and then envision other possible vulnerabilities in this context.We also discuss proactive risk monitoring, including developing a collection of highly susceptible open datasets and a visual analytic workflow that empowers data defenders towards undertaking dynamic risk calibration strategies.
Book Review: My Life as an Artificial Creative Intelligence by Mark Amerika

This tenth-anniversary volume is an occasion to read A Journal of Critical Thinking and Writing reflexively, by turning its focus on critical thinking and writing back on itself as an academic genre that organizes knowledge according to time.A journal assumes time to be a linear, unidirectional movement segmented into a past, which has already happened; a present, which is happening; and a future, which has yet to happen.This sequence is inscribed in a journal's paratextuality, such as a chronological division into annual volumes, which conforms reading to knowledge configured temporally: a reference to a text locates that text in the past; an argument against locates a text in the present; and questions for further research locate a text in the future.This commonplace notion of time informs a process of inquiry understandable across the disciplines, but it is also at odds with disciplinary theories of temporality.Consider, for a moment, how the visual arts can realign perception with uncommon patterns of time.When this volume's cover image is viewed according to its title, Counter-Curve (see Figure 1), it creates a curvilinear motion that either quickens along contracting concentric circles or slows along expanding concentric circles.Each motion, as a counter-curve, implies the other, inverse motion, but the either/or structure of perception limits our awareness of time's bidirectionality, described by the image's artist, Dana Karwas (n.d.), as "a single moment curving infinitely around on itself" (para.10).As a moment, time is undivided into past, present, and future.It is instead composed of recurrences that simultaneously spiral inwardly toward an infinitesimal center of nonrecurrence, a point of origin, and outwardly toward an infinite circumference that is the end of time.Because our perception can align with time's contraction or expansion, but not both at once, Counter-Curve can perhaps explain an otherwise paradoxical experience of the moment as ephemeral and durable.If the last ten years of Double Helix is telescoped to this swirl of time, then the practice of reading the journal changes.It locates one in the moment, a condition of "mindfulness" that Ellen Carillo (2016) has advocated for reading, in which one sustains attention to the text, undistracted by the past or the future, because, in this case, neither the past nor the future exists.A sequence of texts is thereby converted into an intertext, and a reference to, an argument against, and questions for further research become recurring features of intertextuality that advance an area of knowledge toward pure originality and the completion of knowledge as ideal and inverse limits of inquiry (see Figure 2).
Application of Airborne Geophysical Survey Data in a Logistic Regression Model to Improve the Predictive Power of Geogenic Radon Maps. A Case Study in Castleisland, County Kerry, Ireland

In this study, a novel methodology was investigated to improve the resolution and predictive power of geogenic radon maps. The method incorporates indoor radon measurements and seven geogenic factors including geological data (i.e. bedrock and Quaternary geology, aquifer type and soil permeability) and airborne geophysical parameters (i.e. magnetic field strength, gamma-ray radiation and electromagnetic resistivity). The methodology was tested in Castleisland (southwest Ireland), a radon-prone area identified based on the results of previous indoor radon surveys. The developed model was able to justify almost 75% of the variation in geogenic radon potential. It was found that the attributes with the greatest statistical significance were the equivalent uranium content (EqU) and the soil permeability. A new radon potential map was produced in a higher resolution and compared with the original map. In the final step, the activity of radon in soil gas was measured at 87 sites, and the correlation between the observed soil gas radon and geophysical properties was evaluated. The results indicate that any model using only geophysical data cannot accurately predict soil radon activity and that geological information should be integrated to achieve a successful prediction model. Furthermore, we found that EqU is a better indicator for predicting indoor radon potential than the measured soil radon concentrations.
The Analysis of Game Usage for Senior High School Students to Improve Their Vocabulary Mastery

Vocabulary plays an important role in learning English. Learning vocabulary in English is not easy, especially for students in vocational high school. In addition, they have special characteristics that are very different from adults. With emphasis on these different characteristics, it can be concluded that teachers who teach vocational learners have a challenging task to be able to motivate them in the learning process. In this case, teachers must be more creative and keep their students away from boredom while learning English vocabulary. They must prepare material to be given to students and also choose appropriate techniques to use. By doing these things, they can make students have a great interest in the learning process. They must be able to choose and use teaching techniques that can make the learning process more fun, interesting, and exciting for students. One such technique is to use games in the classroom. Using games can provide many benefits for teachers and students.This research aimed at identifying students’ improvement on their vocabulary mastery after learning English using game. The participant of this study is students of junior high school in Lampung province. Data gathered through questionnaire. The data then analyzed by using Creswell method. The result shows that using games in learning English vocabulary is very effective, because using games is very fun and matches the characteristics of children as learners.
STUDENTS’ PERSPECTIVE TOWARD THE USE OF DIGITAL COMIC IN LEARNING WRITING SKILL

Many instructors and students have used digital comic to enhance their English skills. This research aims to analyze students' perspectives on using digital comic in learning writing skill at the senior high school level. This research employed a qualitative design to gain the data through a questionnaire instrument. The sample of this research was the eleventh-grade students of SMAN 1 Padang Cermin, and 20 students participated in this research. Based on the data result of this study, students had positive feedback toward the use of digital comic to help them develop their multiple English skills and help the students enjoy and increase their interest, especially in learning writing skill of the English language through creating a short English story. As conclusion, the result proved that the use of digital comic could give positive effects on students’ writing skill in eleventh grade. It is also suggested that teachers can apply this learning medium to help them teach writing skill for their students at school.
Geospatial analysis of groundwater contamination by heavy oil in the Dammam aquifer-Middle of Iraq

This study aims to apply Artificial Neural Network (ANN) to map the groundwater contamination of the Dammam aquifer by heavy oil in the middle of Iraq. For this purpose, the inventory map of 139 groundwater wells (contaminated and non-contaminated with heavy oil) with the seven important factors playing a role in controlling contamination were used. The factors are distance to faults, faults density, groundwater depth, aquifer saturated thickness and hydraulic conductivity, elevation, and distance from Abu Jir fault. For the performance of ANN model, five statistical measures were used namely, accuracy, sensitivity, specificity, kappa, and the relative operating characteristic curve. Obtained results from applying the model in R statistical package indicated that ANN has a high accuracy (&gt; 90%) in training and testing phases. The probability prediction of ANN model was categorized into five groundwater contamination classes: very low-low, moderate, high-very high. The averages of areas occupied by these zones were 5267 km2 (65%), 488 km2 (6%), and 2362 km2 (29%), for very low-low, moderate, and high-very high, respectively. The contamination map developed in this study could be used to drill successful non-contaminated groundwater wells and avoid loss of many efforts in drilling contaminated wells in the study area.
Groundwater quality and origin within Dibdibba aquifer, near Jabel Sanam area southern of Basrah Governorate, Iraq

The study aims at assessing the groundwater quality and identifying its origin within the Dibdibba aquifer (Jabel Snam area) south of Basrah. Twenty wells were selected from the studied area and subjected to comprehensive physical and chemical analyses involving major cations (Ca2+, Mg2+, Na+ and K+), anions (HCO3-, Cl- and SO42-) and trace elements (Zn, Cd, Ni and Pb) besides general parameters (pH, EC, TDS and total hardness). The spatial distribution of parameters were drawin using geostatistic, interpolation technique, namely, ordinag Kriging in Arc GIS 10.2.2. Based on the results, the groundwater quality was classified to chloride group-sodium family-type (Na+ &gt;Ca2+ &gt;Mg2+). On the other hand, the groundwater origin investigated by Sullin diagram which showed old meteoric origin for all the studied wells.&#x0D;
Role of Ultrasound and Ultrasound Guided Interventional Procedures in the Evaluation of Mammographically Detected Breast Calcifications

Background: Breast calcifications may be the sole observable indicator of early breast cancer, making them a significant finding on a mammography. This research aimed to evaluate the usefulness of ultrasonography and ultrasound-guided interventional treatments in the assessment of breast calcification diagnosed by mammography. Methods: This investigation was done on thirty individuals whose mammograms revealed worrisome microcalcifications. Each patient had a comprehensive history, physical examination, laboratory analysis, mammography, ultrasound, core biopsy under sonographic guidance, and guide wire localisation under sonographic direction. Twenty patients got ultrasound-guided breast biopsies, and ten patients had ultrasound-guided wire localisation. Results: 75 percent of the 20 lesions that underwent guided biopsy were diagnosed sonographically as masses, while the other lesions were classified as parenchymal distortions or calcifications. 75% of the cases were malignant, whereas 25% were benign lesions. In 7/10 (70%) instances, wire localization was successful; in 2 cases, the wire reached the posterior side of the lesion but did not transfix it, and in 1 case, the wire reached only the anterior aspect of the lesion. Conclusions: Based on the findings of the current investigation, we have shown that targeted US is an excellent tool for the characterisation of microcalcification observed on mammography and may be useful for predicting invasive breast tumours. Sonographically detectable microcalcifications appear as small echogenic, non-shading foci, particularly if they are coupled with sonographic hypoechoic lesions.
Electronic Mannequin Innovation by Using Microcontrollers Technology

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