Reviewerly/exHarmony huggingface dataset:
Within-Class Covariance Normalization (WCCN) for Channel Normalization Based of Indonesian Speaker Recognition System

Speaker recognition system is a technology that can be used to verify the speaker's identity from an unknown speech sample. In Indonesia, this system is actively used to assist the speaker verification process as an evidence in court. The speaker recognition system developed in this study is based on i-vector modeling, along with the usage of cosine distance scoring. This system is trained and tested using a speech database spoken in Indonesian language. Previous study using same database result in 3.50% equal error rate (EER). This study perform an effort to improve system performance in the presence of mismatch channels using within-class covariance normalization (WCCN) technique. Through this, the system has successfully lower its EER value to 2.40%.
Blurryism

research-article Share on Blurryism: messing with dualisms through design Author: Matthew Lee-Smith Loughborough University Loughborough UniversityView Profile Authors Info & Claims InteractionsVolume 29Issue 1January - February 2022 pp 64–67https://doi.org/10.1145/3502298Online:06 January 2022Publication History 0citation351DownloadsMetricsTotal Citations0Total Downloads351Last 12 Months351Last 6 weeks51 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An Effective Keypoints Extraction Scheme for Image Tampering Detection

Digital image security has become a key and challenging social issue with the advent of sophisticated image editing tools. Copy move forgery is an ordinary and malicious image manipulation technique. Numerous keypoints-based forgery detection approaches have been proposed, however some algorithms report the difficulty of matched keypoints generation competency and performance. In this approach, an effective keypoints generation is offered centred on SIFT features. A robust feature point generator is presented to extract enough keypoints. Moreover, an active extracting step is applied to estimate keypoints of small or smooth textured forged regions. Experimental results illustrate that the proposed keypoints generation scheme can generate more keypoints in terms of different scales and filter sizes.
A Deep Supervised Hashing Algorithm for Image Retrieval

Traditional image retrieval algorithms, which are based on hand-crafted feature, are greatly influenced by the designed features and are not suitable for retrieving images with the same semantics. To solve this problem, an end-to-end deep learning network is designed to learn features automatically and to retrieve images with same semantics. A union loss function is designed to maximize the likelihood function and the discrete constraints, which result in hard optimization, are relaxed to be continuous by regularization. The experiments on CfF AR10 dataset showed that the proposed algorithm achieved high mean average precision and recall compared with several state-of-the-art algorithms.
MP-Mono: Monocular 3D Detection Using Multiple Priors for Autonomous Driving

Monocular 3D object detection is an important and challenging task in autonomous driving. Due to the ill-posed nature of 3D detection, recent studies use prior knowledge on object categories to estimate 3D parameters. However, for each object category in real driving scenes, there exist a couple of sub-categories with different shapes (i.e. length, width, and height). For example, vehicle generally contains the sub-categories of car, van, and truck. Obviously, single prior knowledge cannot cover such diverse sub-categories. In this paper, we propose MP-Mono that exploits multiple priors to improve object detection. Specifically, a data-heuristic strategy is presented to generate multiple 3D proposals, in which we leverage the unsupervised algorithm to cluster potential sub-categories from realistic datasets, and a height-guided inference policy is used to determine the initial distances of proposals, reducing the difficulty of network learning. Additionally, we propose a local-ground embedding method that learns local depth information to enhance monocular 3D detection. The experimental results on the KITTI dataset demonstrate that our MP-Mono achieves competitive performances compared to other monocular methods, verifying the effectiveness of multi-prior integration and local-ground embedding.
The Mathematical Form of Grover Algorithm and Its Search Success Rate for Composite Database

Grover proposed a database quantum search method with m capacity. In this paper, we extend the Grover search method in reference to the mixed state case, give a Grover search method based on mixed state, and analyze the upper bound of the success probability of the search method. It is further found that the success probability of the search method depends entirely on the initial state (mixed state) used. The conclusion provides a theoretical basis for understanding the influence of quantum noise on Grover search method. Finally, an example is given to illustrate how to implement Grover search method based on mixed states.
Sociopragmatic Differences and Behaviors in the Use of WhatsApp in Virtual Classrooms at a Public College of Education in Ghana

This study sought to establish how tutors at Komenda College of Education engaged students in virtual classroom during the COVID-19 pandemic. The study employed a qualitative approach through the descriptive design. Data was 462 conversations between tutors and students through the WhatsApp messaging application. To ensure anonymity and confidentiality, all personal names and other details were removed. The study established that while tutors posted more messages than students, messages of the tutors were made up of instructions, commands and directions. Students addressed their tutors with high respect. Female students sent more and longer messages and utilized more emoticons than their male counterparts. Contracted and non-conventional forms of speech were very common in students’ WhatsApp chats with their tutors. Both the tutors and students used persuasive words (e.g. please, and kindly) as a way of gaining approval from the other party. Verbal fillers in the WhatsApp group chats performed two macro-functions: interpersonal and textual. Based on the findings, the following recommendations were made. First, institutions of higher learning should provide the instructional support to teachers and students on how to minimize or maximize their own speech differences while engaged in a virtual classroom. There should be pedagogical models in virtual classrooms that allow learners and facilitators to engage in more informal ways. Finally, for an effective online learning to take place, tutors and students should appropriately accommodate each other.
Deep Learning-Based Smart Mask for Social Distancing

The COVID-19 is having a significant impact on our everyday lives. One of the measures put in place to slow the spread of the disease is social distancing. Because of the abrupt implementation of these laws, it become impossible for a person to follow this form of lifestyle. So we have developed a device that can assist both normal and visually disabled people in maintaining physical distance from other people by using deep learning to identify an individual and verify if the other person is six feet away from the user so that social distance can be sustained, and virus spread can be reduced. Our architecture includes a Webcam and a Raspberry Pi 4 model B to run an advanced image processing algorithm SSD MobileNet for human detection. SSD MobileNet is implemented to determine if the other individual is physically close to the user. If an individual does not adhere to social distancing, an automated reading aid is activated, and an audio warning is sent to the user. Since the whole setup is light and portable, it can be conveniently installed with any mask. The job is evaluated in real time. The results show that the proposed device improves mobility, convenience, and ease of navigation for both normal and visually disabled people in this pandemic situation.
Support Vector Classifier Trained by Gradient Descent

In this study, gradient descent (GD) based support vector classifier (SVC) is proposed for binary classification. There are two structures for the proposed method, i.e., the primal form and the dual form which will be trained using gradient descent. Comprehensive comparisons among GD based SVC, GD based full connected (FC), and traditional training method of SVC are conducted. Experimental results for six datasets demonstrate that the accuracy of GD based primal form SVC is better than FC, and close to traditional train method of SVC. The GD based dual form SVC performs better in small samples. While the proposed SVCs have better generalization ability than FC.
AntiDoteX: Attention-Based Dynamic Optimization for Neural Network Runtime Efficiency

Deep neural networks (DNNs) achieved great cognitive performance at the expense of a considerable computation workload. To relieve the computational burden, many optimization works are developed to reduce the model redundancy by identifying and removing insignificant model components, such as weight sparsity and filter pruning methods. However, these works only evaluate model components’ static significance with parameter information, ignoring their dynamic interaction with external inputs. Specifically, due to the difference in per-input features, the model components’ significance can dynamically change and, thus, the static methods can only achieve suboptimal performance. Focusing on this aspect, we propose a dynamic DNN optimization framework in this work. Based on the neural network attention mechanism, we propose a comprehensive dynamic optimization framework, including 1) testing-phase dynamic feature map pruning; 2) training-phase optimization by training with targeted dropout; and 3) deployment-phase one-for-all (OFA) model adaptability enhancement. By providing a holistic dynamic testing, training, and deployment co-optimization framework, our work has the following benefits: first, it can accurately identify and aggressively remove per-input feature redundancy by considering the model-input interaction and involving the channel/column-wise pruning flexibility; meanwhile, the training-testing co-optimization favors the dynamic pruning and helps maintain the model accuracy even with a very high feature pruning ratio. Finally, the deployment enhancement provides one unified OFA model to support full-spectrum feature sparsity ratios. The unified model can be dynamically reconfigured to meet different resource budgets without any retraining cost, and thus provide significant deployment flexibility. Extensive experiments show that our method could bring 37.4%–54.5% floating-point operations reduction with negligible accuracy drop on various test benchmarks. Meanwhile, the OFA deployment optimization enables us to use one model to support at most ten different resource constraints without any retraining cost.
An Image Depth Information Extraction Based on Parallel Random Evolutionary Algorithm

Computer vision is mainly used to study the application of computers to simulate human and biological visual system functions. Its research goal is to enable the computer to have the ability to recognize 3D scene information through one or more 2D images, that is, to use the computer to realize the perception, understanding and recognition of the objective world three-dimensional scene. Computer vision is not only an engineering field, but also a challenging research field in the scientific field. This paper introduces a parallel stochastic evolutionary algorithm, by proposing a new evolutionary algorithm for solving multi-objective minimum spanning tree, extracting image depth information.
TRANSIENT EARTH FAULT PROTECTION BASED ON INSTANTANEOUS VALUES

This paper presents the principle of operation of a new algorithm based on instantaneous values to detect Transient Earth Faults, which has been validated by RTDS simulations and real cases from the field. The short duration of the fault (between 2 and 10 ms), the high frequency of the fault, and the transitory state of the fault after it disappears, are conditions which cause trouble for conventional directional overcurrent units which are based on algorithms like Fourier Transform (DFT) or similar that do not have enough accuracy to detect properly this kind of faults and determine the direction of the fault (faulty and healthy feeders). The new algorithm works with the first half a cycle of the signal therefore it is independent of the short duration of the fault and the transitory state after the fault. On the other hand, it works with a sampling rate of 4800 Hz which allows it to detect properly the high frequency signal of the fault.
EStarGAN: Enhanced StarGAN for Multi-Style High-Definition Image Translation

Among the major remaining challenges in image-to-image translation is the capacity to generate multi-domain High-Definition images. Recently, the StarGAN is proposed to solve the multi-domain image translation problem. However, the StarGAN focuses on the low-resolution facial image synthesis, leaving some high-frequency information and unsuitable for HD image translation tasks. To address the issue, we propose the Enhanced StarGAN (called EStarGAN), aiming to generate multi-style High-Definition images with fine-grained details simultaneously. Specifically, we propose the Channel-and-Spatial Residual-in-Residual (CSRIR) module to learn high-frequency information in the deep network more effectively. Furthermore, we propose a new reconstruction loss, which consists of mean squared error and Structural Similarity loss to enhance the quality of the generated images. Extensive experiments on real-world datasets prove that our EStarGAN excels baselines with respect to both subjective and objective evaluations.
An Analysis of Machine Learning Risk Factors and Risk Parity Portfolio Optimization

Many strategies are used to optimize portfolios to achieve high-profit returns while minimizing risk. Portfolio building is critical to achieving this aim, and risk budgeting significantly influences portfolio optimization techniques. This research looks at how machine learning and factor-based portfolio optimization may create risk parity portfolios with risk factor contributions. In terms of risk reduction, auto-encoder neural networks and Minimum-variance portfolios with latent components outperform more straightforward benchmarks. Finally, the Mean Conditional Value at Risk (Mean-CVaR) is utilized to maximize returns and minimize risks. The findings show that our method works in a real-world scenario of constructing an essential resource distribution based on financial components for an annuity reserve with risk constraints. In times of high volatility or tail risk, these effects are amplified for financial supporters.
Web Crawler Development to Optimize the Sentiment Analysis Process of Indonesian News Article Opinions

The explosive growth of textual information on websites has brought about changes in human life over the past few decades. On websites, people share their opinions about products or services they know. Many opinions on the website create opportunities that can be utilized in the business world by conducting a sentiment analysis of the existing collection of opinions. The opinion data can determine the response and impact of a product or service in a marketing strategy. However, large and varied opinions make it difficult to conclude whether a news item is about a product or service negatively or positively. It becomes a challenge for archiving textual information on the website, extracting opinions on the textual information, and displaying the extraction results into tables and graphs containing negative and positive views. This research aims to develop a web crawler that could collect the data from several online news websites with various website structures without rebuilding the web crawler.
Combining PhoBERT and SentiWordNet for Vietnamese Sentiment Analysis

Sentiment analysis is one of the most important NLP tasks, where machine learning models are trained to classify text by polarity of opinion. Many models have been proposed to tackle this task, in which pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese. PhoBERT pre-training approach is based on RoBERTa which optimizes the BERT pre-training method for more robust performance. In this paper, we introduce a new approach to combine phoBERT and SentiWordnet for Sentiment Analysis of Vietnamese reviews. Our proposed sentiment analysis model using PhoBERT for Vietnamese, which is a robust optimization for Vietnamese of the prominent BERT model, and SentiWordNet, a lexical resource explicitly devised for supporting sentiment classification applications. Experimental results on the dataset VLSP 2016 and AIVIVN 2019 demonstrate that our sentiment analysis system has achieved good performance in comparison to other models.
ЗАСОБИ ОРГАНІЗАЦІЇ ОСВІТНЬОГО ПРОЦЕСУ В ЗАКЛАДАХ ВИЩОЇ ОСВІТИ ЕКОНОМІЧНОГО СПРЯМУВАННЯ США

The article reveals the means of organizing the educational process, which are actively used by higher education institutions in the United States. The necessity of implementation of interactive means and information and communication technologies in the educational process is substantiated. The practical training of students, which includes project and grant activities, internships, as well as research activities, has been studied
Information Feedback System for Multimedia Mobile English Teaching

The research content of this paper: Based on the development technology of B/S mode, this paper studies the theoretical learning and production methods of English network teaching system, and carries out the actual evaluation and development and application of the teaching system. By comparing the teaching effect between the computer network teaching mode and the traditional teaching mode, the network English multimedia is determined. The feasibility and effectiveness of teaching system. On the basis of web browser based network teaching mode, using Internet as a means of transmitting media teaching content, English synchronous courses and playback courses are carried out on the network, and relevant online tests and online Q & A can be carried out timely and efficiently. The teaching system is evaluated and applied.
A Modified Convolutional Neural Network for Nuclei Classification in Histopathology Image

Classification of nuclei in histopathology images is an important step in pathology workflow. Although deep learning methods have been extensively employed in this task, automatic nuclei classification is still a challenging task because of the large inter-and intra-class variability as well as the serious clustered together. To address this challenge, we propose a modified automatic nuclei classification network for histopathology images. The proposed method adopts the encoding-decoding structure and leverages the segmented instance to guide the classification with the same feature maps from an encoding branch. We mainly pay attention to enhancing the feature representation. We introduce a new module composed of a modified multi-layer perceptron(MLP) module and a multi-kernel convolution(MKC) module. The MLP module focuses on mining global contextual information of nuclei and their micro-environments, and the MKC module focuses on extracting local information through different receptive fields. We validate the proposed method on two large publicly available datasets collected from different tissues. In comparison with other state-of-the-art methods, the proposed method obtains the best performance of Accuracy 88.1 and 81.6, respectively. We also verify that the proposed module can be easily incorporated into other networks for improving feature representation.
Comparisons Between Median Filter and Proposed Morphological Operations Based on the Denoising Process

Imaging processing is important topic in graphics and imaging denoising has been widely studied from many perspectives. In this paper, Median Filter and Morphological Operators are introduced to study how their behaviors can affect the SNR and how reconstructed images will look like after applying these two methods to them individually in different noise density levels of 20%, 40%, 60% and 80%. These simulation procedures include preprocessing testing image and apply median filter/morphological operations with manually selected window sizes to reconstruct the image and make comparisons in each case.
Multi-dimensional sub/super-range signatures

In time-specific signatures (TSS) (Paterson & Quaglia, SCN’10 and Ishizaka & Kiyomoto, ISC’20) with T numerical values, each signer is given a secret-key associated with a numerical value t∈[0,T−1] and each signature on a message is generated under a numerical range [L,R] s.t. 0≤L≤R≤T−1. A signer with t can correctly generate a signature under [L,R] if t is truly included in [L,R], i.e., t∈[L,R]. As a generalized primitive of TSS, we propose multi-dimensional sub-range signatures (MDSBRS). As a related primitive, we also propose multi-dimensional super-range signatures (MDSPRS). In MDSBRS (resp. MDSPRS) with D∈N dimensions, each secret-key is associated with a set of D ranges {[li,ri]∣i∈[1,D]} s.t. 0≤li≤ri≤Ti−1 and a threshold value d∈[1,D], and it correctly produces a signature on any message under a set of D ranges {[Li,Ri]∣i∈[1,D]} s.t. 0≤Li≤Ri≤Ti−1, if and only if total number of key-ranges every one [li,ri] of which is a sub-range (resp. super-range) of the corresponded signature-range [Li,Ri], i.e., Li≤li≤ri≤Ri (resp. li≤Li≤Ri≤ri), is more than d−1. We show that, by extending (or generalizing) an existing TSS scheme, we obtain MDSBRS and MDSPRS schemes each one of which is secure, i.e., existentially unforgeable and perfectly (signer-)private, under standard assumption and asymptotically efficient.
Sentiment Analysis of Video Game Console Pre-launching Tweets Using Python

Sentiment analysis is an activity carried out to evaluate the level of public sentiment or public opinion related to something. This work observes the sentiment raised for Xbox Series X and PS5 on Twitter before the pre-launching date in Indonesia. The data itself is in the form of tweets from Indonesian people. We used Python for data scraping on Twitter API for September to November 2020 tweets. In this paper we do several methods for pre-processing such as data cleaning, transformation, reduction and implementation of machine learning such as Naïve Bayes and KNN algorithm, we tried to collect and classified the sentiment value for each tweet and performed the sentiment analysis. The result of this work shows the sentiment of Indonesian for both video game console before their release date. We compare both machine learning model to show that Naïve Bayes model is the best for this case.