Reviewerly/exHarmony huggingface dataset:
A Search on the Importance of Forensic Voice Studies in Forensic and a Example Application

In recent years, with the enormous development of technology, some security problems have emerged. One of these security problems is undoubtedly identification. Forensic voice examinations are carried out to ensure security by identifying. Because the protection of personal data of individuals, prevention of sharing with others, ensuring privacy by preventing the loss of valuable data are very important for the prevention of fraud, forgery and judicial crimes. In this study, research has been conducted on the importance of forensic today, its usage areas and examination of forensic voice. The general definition of forensic voice, its subject, common areas of use, methods used, studies and recent studies on forensic are discussed in detail. Improvement on sound, examination, identification from voice, speaker recognition, determination of spoken text covers the basic issues of forensic voice. For this reason, in order to set an example for the subject of forensic voice informatics, a sample application has been made that defines the speaker. In this application, approximately 5000 different lengths and the same texts were read and audio recordings were taken from 1000 people (5 voice recordings from each person). The attributes of the voice samples were obtained using MFCC (Mel Frequency Cepstral Coefficients). In this study, accuracy rates were calculated using different MFCC coefficients. The feature vectors of the obtained voice samples are trained with Convolutional Neural Networks. In the test phase, it was tried to determine who the voice sample belonged to by looking at the trained voice samples. In addition, the results and performances of the study are presented.
An Approach to Analyzing the Layout of Unstructured Digital Documents

Extracting personal information from the documents is important to protect personal data. Personal information in a form or document with a standard structure can be determined by using the methods applied in the fields of machine learning, natural language processing, image processing, optical character recognition, etc. However, if the document does not have a standard structure, analyzing the layout of this document and processing the different document or form structures in the document separately may be necessary. There are different methods used for document layout analysis in the literature. However, each of these methods has its own parameters. In this study, a new approach for common parameter tuning is proposed. With the proposed approach, the number of required parameters is reduced and the adjustment range of the parameters is decreased. The proposed approach showed a recall of 94% in the experimental results.
Research on Personalized Service Path of Learning Resources by Data Driven

In the digital age, the trend of personalized learning requires learning resources to provide learners with more accurate and intelligent personalized services. However, current research is inadequate in meeting the need for personalized, intelligent, and accurate digital learning resource services for learners. This study is based on a large number of learning analytics and learning resources relevant to the analysis of literature studies and the corresponding theoretical framework, using over 1.4 million learner data collected and stored by H Publishing from real-life contexts and natural learning conditions as the basis of the study. This study uses learning analytics and artificial intelligence technologies to analyze learners' personalized learning needs and construct intelligent adaptive service path for learning resources. On the one hand, it reduces the unreasonable use of digital learning resources, saves learner's time costs, and improves learning efficiency. On the other hand, it provides effective references for digital learning resources can better serve learners.
Convolutional Neural Network as an Image Processing Technique for Classification of Bacilli Tuberculosis Extra Pulmonary (TBEP) Disease

Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting for a long time. Biopsy is one of the techniques used to take the patient's lung fluid and given Ziehl Neelsen chemical dye and then observed using a microscope to determine this TBEP disease. In this research, the TBEP detection process was developed using a classification method, namely CNN with feature extraction and feature selection. The feature uses 5 features where these features are a combination of shape features and texture features with the highest information gain value. From the results of research conducted through the training and testing stages of the classification method using feature selection, the accuracy rate is higher than not using feature selection with a comparison of the feature selection stage increasing 0.6536% for the training process, and 0.8942% for the testing process.
Fast Large-Integer Extended GCD Algorithm and Hardware Design for Verifiable Delay Functions and Modular Inversion

The extended GCD (XGCD) calculation, which computes Bézout coefficients ba, bb such that ba ∗ a0 + bb ∗ b0 = GCD(a0, b0), is a critical operation in many cryptographic applications. In particular, large-integer XGCD is computationally dominant for two applications of increasing interest: verifiable delay functions that square binary quadratic forms within a class group and constant-time modular inversion for elliptic curve cryptography. Most prior work has focused on fast software implementations. The few works investigating hardware acceleration build on variants of Euclid’s division-based algorithm, following the approach used in optimized software. We show that adopting variants of Stein’s subtraction-based algorithm instead leads to significantly faster hardware. We quantify this advantage by performing a large-integer XGCD accelerator design space exploration comparing Euclid- and Stein-based algorithms for various application requirements. This exploration leads us to an XGCD hardware accelerator that is flexible and efficient, supports fast average and constant-time evaluation, and is easily extensible for polynomial GCD. Our 16nm ASIC design calculates 1024-bit XGCD in 294ns (8x faster than the state-of-the-art ASIC) and constant-time 255-bit XGCD for inverses in the field of integers modulo the prime 2255−19 in 85ns (31× faster than state-of-the-art software). We believe our design is the first high-performance ASIC for the XGCD computation that is also capable of constant-time evaluation. Our work is publicly available at https://github.com/kavyasreedhar/sreedhar-xgcd-hardware-ches2022.
Protecting Critical Inter-Domain Communication through Flyover Reservations

To protect against naturally occurring or adversely induced congestion in the Internet, we propose the concept of flyover reservations, a fundamentally new approach for addressing the availability demands of critical low-volume applications. In contrast to path-based reservation systems, flyovers are fine-grained "hop-based" bandwidth reservations on the level of individual autonomous systems. We demonstrate the scalability of this approach experimentally through simulations on large graphs. Moreover, we bring the flyovers' potential to full fruition by introducing Helia, a protocol for secure flyover reservation setup and data transmission. We evaluate Helia's performance based on an implementation in DPDK, demonstrating authentication and forwarding of reservation traffic at 160 Gbps. Our security analysis shows that Helia can resist a large variety of powerful attacks against reservation admission and traffic forwarding. Despite its simplicity, Helia outperforms current state-of-the-art reservation systems in many key metrics.
Revealing Agile Mindset Using LEGO SERIOUS PLAY: Experience from an Online Agile Training Project

LEGO ® SERIOUS PLAY ® (LSP) is an effective methodology to enable the representation of abstract concepts and has been applied to teach several Software Engineering topics.However, there is limited evidence on how LSP can be used in training on agile mindset, which is the core and central element of agile methods.This paper demonstrates how LSP can be utilized in agile training to reveal the agile mindset of participants.We describe our experience of utilising LSP in an agile training project for local software companies.Since the project was run during the COVID-19 pandemic, we adapted the LSP methodology for online settings, which was not straightforward because face-to-face interactions and tangible objects are key characteristics of LSP.In this experience report, we describe the design of the online LSP workshops and explain how to analyze LSP models to reveal the agile mindset of the participants and to tailor the training accordingly.We also provide evidence of the effectiveness of the LSP methodology in our training project.Drawing upon our experience, we synthesize a set of lessons learned and sketch recommendations for educators who intend to apply LSP in their future endeavours.
Live Demonstration: Home Appliance Control System with Dynamic Hand Gesture Recognition base on 3D Hand Skeletons

In this paper, we present a two-stage lightweight convolutional neural network architecture on hand gesture recognition for home appliance control system. At the first stage, we utilize DetNet to detect the hand and generate 3D hand skeleton locations. At the second stage, a skeleton-based dynamic hand gesture recognition model is developed. We have 99.4% accuracy by the trained CNN model with our testing dataset. Besides, we implement this system on the Nvidia Jetson AGX Xavier to control the on/off of the fan and the light and the overall system achieve 15 fps.
Beta Distribution based XCS Classifier System

This paper proposes the Beta Distribution based XCS Classifier System (called j3-XCS) as the novel XCS having the new representation (1) that can handle curved surface hyperpolyhedral conditions, including hyperellipsoids, (2) that can “quickly” and “stably” evolve classifiers that appropri-ately classify the area, and (3) that is robust to the initial hyperparameters of the representation. Concretely, j3-XCS is composed of classifiers that condition part in each dimension is represented by the beta distribution that can change a flexible distribution shape according to its parameters. Through the intensive experiments of the different types of continuous space problems, the following implications have been revealed: (1) j3-X CS can show higher classification performance and function approximation performance with fewer classifiers than other XCSs with the conventional representations such as XCS with the hyperrectangular representation (i.e., XCSR) and XCS with the hyperellipsoidal representations (i.e., hyperellipsoid-based XCS); (2) fJ-XCS can quickly and stably evolve the classifiers that can appropriately match the line and curved shapes in comparison with XCSR and the ellipsoidal-based XCS; and (3) while the performance of the conventional XCSs is highly sensitive to the hyperparameter that defines the generality of the covering classifier, the performance of fJ-XCS is the most robust to its values.
Analysis of Neural Network Predictions for Entanglement Self-Catalysis

Machine learning techniques have been successfully applied to classifying an extensive range of phenomena in quantum theory. From detecting quantum phase transitions to identifying Bell non-locality, it has been established that classical machines can learn genuine quantum features via classical data. Quantum entanglement is one of the uniquely quantum phenomena in that range, as it has been shown that neural networks can be used to classify different types of entanglement. Our work builds on this topic. We investigate whether distinct models of neural networks can learn how to detect catalysis and self-catalysis of entanglement for pure states. Additionally, we also study whether a trained machine can detect another related phenomenon - which we dub transfer knowledge. As we build our models from scratch, besides making all the codes available, we can study a whole gamut of paradigmatic measures, including accuracy, execution time, training time, bias in the training data set and so on.
Fast and accurate measurement of the polarization-dependent detection efficiency of superconducting nanowire single photon detectors

Superconducting nanowire single photon detectors (SNSPDs) have been extensively investigated due to their superior characteristics, including high system detection efficiency, low dark count rate and short recovery time. The polarization sensitivity introduced by the meandering-type superconductor nanowires is an intrinsic property of SNSPD, which is normally measured by sweeping hundreds of points on the Poincaré sphere to overcome the unknown birefringent problem of the SNSPD's delivery fiber. In this paper, we propose an alternative method to characterize the optical absorptance of SNSPDs, without sweeping hundreds of points on the Poincaré sphere. It is shown theoretically that measurements on the system detection efficiencies (SDEs) subject to cases of four specific photon polarization states are sufficient to reveal the two eigen-absorptances of the SNSPD. We validate the proposed method by comparing the measured detection spectra with the spectra attained from sweeping points on the Poincaré sphere and the simulated absorption spectra.
Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments Using Acoustic Features

Standardized tests play a crucial role in the detection of cognitive impairment. Previous work demonstrated that automatic detection of cognitive impairment is possible using audio data from a standardized picture description task. The presented study goes beyond that, evaluating our methods on data taken from two standardized neuropsychological tests, namely the German SKT and a German version of the CERAD-NB, and a semi-structured clinical interview between a patient and a psychologist. For the tests, we focus on speech recordings of three sub-tests: reading numbers (SKT 3), interference (SKT 7), and verbal fluency (CERAD-NB 1). We show that acoustic features from standardized tests can be used to reliably discriminate cognitively impaired individuals from non-impaired ones. Furthermore, we provide evidence that even features extracted from random speech samples of the interview can be a discriminator of cognitive impairment. In our baseline experiments, we use OpenSMILE features and Support Vector Machine classifiers. In an improved setup, we show that using wav2vec 2.0 features instead, we can achieve an accuracy of up to 85%.
An Overview of The Revolution in Printmaking and Monoprint Application Techniques in Malaysia

Printmaking and monoprint is part of the practices that well established in fine art with different application techniques.Printmaking is an artistic process based on the principle of transferring images from a matrix onto another surface.On the other side, a monoprint is a unique creative process because of the single impression of an image made from a reprint able block.Relatively than printing various copies of a single image, only one impression is produced, either by painting or creation a collage on the block.To get a clear division between these two application techniques, this article provides a clear explanation based on printmaking and monoprint experts.An extensive literature review is conducted as the research methodology based on current and prior studies relating to printmaking and monoprint that require additional development and details.This article significantly contributes to the knowledge on the overview of the revolution in printmaking globally, subsequently focusing on monoprint application technique in Malaysia context.As a result, this research has critically reviewed on acceptable instructional approach, learning, historical background, intellectual development, knowledge, and distinguish between printmaking and monoprint.This study recommends a further study in other technique and art disciplines such as characterization and classification in art and design to enhance the art education globally, especially in Malaysia.
SpamDL: A High Performance Deep Learning Spam Detector Using Stanford Global Vectors and Bidirectional Long Short-Term Memory Neural Networks

Spam consists of unwanted messages that are often containers of malicious code and/or links pointing to shady sites or objects that pose real dangers to a company’s machines, software, or data. Spam detection is therefore a primary security objective. Nevertheless, the detection tools available on the market are few in number and their efficiency is often limited. In this paper, we propose a spam detection tool based on deep-learning. Our tool uses bidirectional Long-Short Term Memory networks while relying on Stanford Global Vectors for word representation. We present the techniques we use. Then, we conduct a series of experiments on a family of candidate detectors. Finally, we present the performance of the selected detector.
A Novel Secure Vector Product for Protecting the Privacy of Data in Vertically Partitioned Dataset

The contemporary advancements in the field of data collecting, storage and networking have resulted in data being saved and kept at many locations. Data mining methods allow hidden knowledge to be extracted from massive data sources. Data on the centralized server are not always present. Data might be obtained at individual or several places. Data are divided vertically, horizontally or at both sites and disseminated between places. The sharing of data repositories existing at many stages in the mining process leads to discomfort in data privacy. Data mining protection is a technology that prevents sensitive information from being disclosed to others during a mining operation. Simple multiparty calculations and anonymization are the most common privacy protecting technology for data mining. Secure multiparty (SMC) computing allows parties to do distributed data mining activities in separate places without disclosing more personal data. The SMC protocol employs mostly encryptions that offer high security and enhance the complexity of protocols. Before it is utilized for mining, perturbation alters the original sensitive data. Distortion of the data may diminish the mining performance accuracy to decrease the usefulness. A novel vector scalar product is suggested to save execution time and to protect privacy data privacy in the vertically partitioned dataset, retaining the induction of decision tree. A downward approach to the safe scalar product is taken. Each node is a radix or fewer party groups. The binary vector is divided as well. Each group discovers a safe sum of binary vectors with the secret sharing of Shamir. The running time of the CPU was established between sequences and when running downstream. The execution time was determined. The proposed bottom-up approach results in comparison with following sequential approach.
Applying autonomous hybrid agent-based computing to difficult optimization problems

Evolutionary multi-agent systems (EMASs) are very good at dealing with difficult, multi-dimensional problems, their efficacy was proven theoretically based on analysis of the relevant Markov-Chain based model. Now the research continues on introducing autonomous hybridization into EMAS. This paper focuses on a proposed hybrid version of the EMAS, and covers selection and introduction of a number of hybrid operators and defining rules for starting the hybrid steps of the main algorithm. Those hybrid steps leverage existing, well-known and proven to be efficient metaheuristics, and integrate their results into the main algorithm. The discussed modifications are evaluated based on a number of difficult continuous-optimization benchmarks.
Research on Construction of College English Education Mode Based on Artificial Intelligence

With the rapid development of artificial intelligence, artificial intelligence is no longer limited to the integration with classroom teaching, but is gradually integrated into Teachers’ scientific research, campus management, student evaluation, and even campus life. In this context, we analyze the current situation of college English education and discuss how artificial intelligence enables college English education. Finally, a new college English education model is constructed based on artificial intelligence. By analyzing the current situation of college English education and combining the advantages of artificial intelligence, we put forward the countermeasures of using artificial intelligence in college English education to provide a reference for educators.
An Atlas for the Pinhole Camera

We introduce an atlas of algebro-geometric objects associated with image formation in pinhole cameras. The nodes of the atlas are algebraic varieties or their vanishing ideals related to each other by projection or elimination and restriction or specialization, respectively. This atlas offers a unifying framework for the study of problems in 3D computer vision. We initiate the study of the atlas by completely characterizing a part of the atlas stemming from the triangulation problem. We conclude with several open problems and generalizations of the atlas.
PENGEMBANGAN KOMPETENSI ASN DI ERA NEW NORMAL: QUO VADIS WIDYAISWARA?

Distance learning by utilizing information technology has now become a necessity. This has been accelerated by the outbreak of the Covid 19 pandemic. The purpose of this study was to explore the readiness of learning facilitators in training for ASN in the face of changing the learning paradigm from traditional face-to-face to e-learning. This study used a qualitative approach. The data mining technique used was interviews with several Widyaiswaras from young, middle, and major positions. In addition, data mining is also carried out from relevant secondary sources. The results showed that not all Widyaiswara were ready to face the paradigm shift from conventional training to e-learning. Widyaiswara really needs the development of competencies related to digital literacy. The Widyaiswara Guiding Agency, namely the State Administration Institute, is expected to provide facilitation for widyaiswara and also encourage government training institutions to equip widyaiswara so that they are competent in the use of learning technology.
Speed of evolution in entangled fermionic systems

We consider the simplest identical-fermion system that exhibits the phenomenon of entanglement (beyond exchange correlations) to analyze its speed of evolution towards an orthogonal state, and revisit the relation between this latter and the amount of fermionic entanglement. A characterization of the quantum speed limit and the orthogonality times is performed, throwing light into the general structure of the faster and the slower states. Such characterization holds not only for fermionic composites, but apply more generally to a wide family of 6-dimensional states, irrespective of the specific nature of the system. Further, it is shown that the connection between speed of evolution and entanglement in the fermionic system, though more subtle than in composites of distinguishable parties, may indeed manifest for certain classes of states.