Reviewerly/exHarmony huggingface dataset:
Early Warning System on Zakat Android Application with Hybrid Fuzzy Time Series Method

The countries with a majority Muslim population have an efficient solution to grow the national economy from zakat instruments divided into two main categories, i.e. zakat fitrah and zakat maal. In Indonesia, zakat maal, especially stock zakat, has great potential in raising public funds because stock trading continues to grow positively so that the results of the acquisition of stock zakat can be distributed and enjoyed directly by people in need. However, until now, the management of stock zakat is still lacking, i.e. the lack of Muslim discipline in paying stock zakat. It can happen because of Muslim negligence in paying stock zakat both in terms of knowledge, time and the amount of zakat that must be prepared. In this study, we propose an android application that implements the early warning system as a warning that it is time to pay stock zakat. This system will make stock price predictions determining the amount (nisab) and the time (haul) to pay stock zakat. We use the hybrid fuzzy time series method in predicting stock prices. Then the stock price predictions results are used to determine the nisab and haul stock zakat in this system. With this system, it is hoped that all Muslim and Muslimat are disciplined in paying stock zakat. So that they can indirectly help improve the welfare of a country. For our experiment, we used PT Astra, Tbk's daily stock trading data from July 15, 2020 to July 14, 2021. When people use the android platform, they will get a response from the early warning system that comes from the results of data processing by the database server. The experimental results show that this system provides value of MSE is 0.1843 and a value of RMSE of 0.4, which indicates good performance.
Evaluation of Auditory Saliency Model Based on Saliency Map

For the bottom-up auditory attention process, many auditory attention models have been proposed, including the earliest four auditory saliency models developed from visual saliency models, namely Kayser model, Kalinli model, Duangudom model and Kaya model. In order to compare the correlation between the output results of the four models and subjective perception, firstly the four models were evaluated by carrying out a subjective saliency evaluation experiment in this paper. In the subjective evaluation experiment, 20 kinds of sound scene materials were scored with relative saliency and absolute saliency, and two rankings were obtained. Secondly in the saliency model, the saliency scores were calculated for the same 20 kinds of sounds, and the saliency of the sounds were scored by extracting the mean, peak, variance and dynamic characteristics of the saliency score of each sound, and then correlations were calculated between model saliency scores and two subjective scores. The conclusion was that Kalinli model had the best effect among the four models and had the highest correlation with subjective perception; among the four features of the saliency score, the variance had the highest correlation with subjective perception. The main reason for the better results of Kalinli model was that the method of extracting auditory spectrograms and features was more consistent with the auditory characteristics of human ear and the extracted features were more comprehensive. By analyzing the structure and perceptual features of the models with high correlation between model output and subjective perception, we can improve the models in the future based on the conclusions drawn, so as to enhance their performance and make them more consistent with the auditory characteristics of the human ear.
A Causal Network Construction Algorithm Based on Partial Rank Correlation on Time Series

Identifying causal relationships from observational time-series data is a key problem in dealing with complex dynamical systems such as in the industrial or natural climate fields. Data-driven causal network construction in such systems is challenging since data sets are often high-dimensional and nonlinear. In response to this challenge, this paper combines partial rank correlation coefficients and proposes a new structure learning algorithm, TS-PRCS, suitable for time-series causal network models. In this article, we mainly make three contributions. First, we proved that partial rank correlation can be used as a standard of independence tests. Second, we combined partial rank correlation with constraint-based causality discovery methods, and proposed a causal network discovery algorithm (TS-PRCS) on time-series data based on partial rank correlation. Finally, the effectiveness of the algorithm is proven in experiments on time-series data generated by a time-series causal network model. Compared with an existing algorithm, the proposed algorithm achieves better results on high-dimensional and nonlinear data systems, and it also demonstrates good time performance. In particular, the algorithm has been applied to real data generated by a power plant. Experiments show that our method improves the ability to detect causality on time-series data, and further promotes the development of the field of causal network construction on time-series data.
Sign Language Dynamic Gesture Recognition System Leveraging Deep Learning and Computer Vision

The ability to communicate verbally and nonverbally is impacted by speech impediment, a disability. These individuals use sign language and other alternative forms of communication. Even though sign language has grown in popularity recently, it is still challenging for individuals who cannot connect with signers/sign users because they do not comprehend sign language. Motion & gesture identification utilizing deep learning as well as computer vision-based algorithms has made considerable development recently because of advancements in both domains. The novelty of the project is to develop a computer vision based deep learning application that delivers text translation of sign language gestures, encouraging communication among signers and non-signers. Video sequences are used in the proposed approach to extract temporal and spatial information. In the proposed model, it makes use of CNN (Convolutional Neural Network) that uses depth and point information, together with the successive application of GRU (Gated Recurrent Unit). The upgraded component uses the temporal tokenization information to extract deeper information with reasonable resource usage when training on temporal features. The massive Word-Level American Sign Language (WLASL) video collection was employed, which includes more than 2000 words signed by more than 100 signatories. It’s the biggest publicly available ASL dataset for word level sign recognition research. The proposed model identifies 20 gestures from video sequences accomplishing a web application with remarkable accuracy of 94% within a limited amount of time.
Cross-dataset performance evaluation of deep learning distracted driver detection algorithms

Deep learning has gained traction due its supremacy in terms of accuracy and ability to automatically learn features from input data. However, deep learning algorithms can sometimes be flawed due to many factors such as training dataset, parameters, and choice of algorithms. Few studies have evaluated the robustness of deep learning distracted driver detection algorithms. The studies evaluate the algorithms on a single dataset and do not consider cross-dataset performance. A problem arises because cross-dataset performance often implies model generalisation ability. Deploying a model in the real world without knowing its cross-dataset performance could lead to catastrophic events. The paper investigates the cross-dataset performance of deep learning distracted driver detection algorithms. Experimental results found reveal that deep learning distracted driver detection algorithms do not generalise well on unknown datasets for CNN models that use the whole image for prediction. The cross-dataset performance evaluations shed light on future research in developing robust deep learning distracted driver detection algorithms.
Service Analytics on ITSM Processes Using Time Series

Information technology service management (ITSM) consists of all the activities, processes and procedures in place to design, create and implement, deliver and support and continually improve and manage the IT services through its life cycle. ITSM plays a major role in aligning IT with the business and meeting the needs of the business. Service analytics has emerged in recent years as a new domain of IT service, with great potential for customer experience. In this work, a time series forecasting models are presented for predicting the expected demand from the customers for IT-managed and supported services in a given time. Two widely used forecast models—autoregressive integrated moving average (ARIMA) and time series linear model (TSLM) are compared and analysed for the IT services. For the evaluated performance of the incident testing data set, RMSE values of 8.78 and 13.55 are achieved for the ARIMA c (3,0,4) and TSLM methods, respectively. Overall, the results demonstrated here indicate that the proposed method TSLM can provide better performance than ARIMA method for real data.
MDF-Net: Multimodal Deep Fusion for Large-Scale Product Recognition

Large-scale production recognition systems are crucial for building efficient E-commerce platforms. However, various traditional product recognition approaches are based on single-modal data input (e.g., image or text), which limits recognition performance. To tackle this issue, in this paper, we propose a Multimodal Deep Fusion Network (MDF-Net) for accurate large-scale product recognition. The MDF-Net has a two-stream late fusion architecture, with a CNN model and a bi-directional language model that respectively extract semantic latent features from multimodal inputs. Image and text features are fused via Hadamard product, then jointly generate results. Further, we investigated the integration of attention mechanism and residual connection to respectively improve the text and image representations. We conduct experiments on a large-scale multimodal E-commerce product dataset MEP-3M, which consists of three million image-text product data. MDF-Net achieves a 93.72% classification accuracy over 599 fine-grained classes. Empirical results demonstrated that the MDF-Net yields better performance than traditional approaches.
Nearly Quantum Computing by Simulation

Quantum computing has ceased to be an exotic topic for researchers, moving its treatment today from theoretical physicists to computer scientists and engineers. Recently, several real quantum devices have become available through the cloud. On the other hand, different possibilities on-premises allow having quantum computing simulators using High-Performance Computing (HPC) capabilities. Nevertheless, they did not expect to be very limited, in the near term, the number and quality of the fundamental storage element, the qubit. Therefore, software quantum simulators are the only widely available tools to design and test quantum algorithms. However, the representation of quantum computing components in classical computers consumes significant resources. In quantum computing, a state composed of n qubits will be a union of all possible combinations of n 0s and 1s. That is to say, the size of the information is $$2^n$$ . The amplitude is the magnitude associated with every variety and is composed of a complex number. This paper shows a survey of different implementations to simulate quantum computing supported by classical computing, highlighting important considerations for implementing and developing solutions.
CMFT: Contrastive Memory Feature Transfer for Nonshared-and-Imbalanced Unsupervised Domain Adaption

Recently, nonshared-and-imbalanced unsupervised domain adaption has been proposed to fix domain shift from Big Data source domain with long-tail distribution to specific small target domain with imbalanced distribution, including two challenges: 1) nonshared classes sharing in big data with long-tail distribution; and 2) imbalanced domain adaptation. Prior approaches explore knowledge sharing between classes to improve performance of unsupervised domain adaption methods. However these methods have inductive bias for prior tree or graph. And previous contrastive domain adaptation methods take center-based prototypes as positive samples which only coarsely characterize the domain structure, and fail to depict the local data structure. To fix these problems, we propose a novel framework called contrastive memory feature transfer (CMFT). To solve nonshared data sharing without inductive bias, we build a centroid memory based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directed memory transfer</b> mechanism to enhance imbalanced class features with similar nonshared class centroid. To address the imbalanced domain adaptation, we design a fault-tolerant and fine-grained <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neighborhood prototype</b> for the contrastive learning which can narrow the domain shift. The proposed CMFT outperforms previous methods on most benchmarks.
A generic review of cyberbullying detection framework by machine learning approach

Research into cyberbullying detection has increased in recent years, this is due to the proliferation of cyberbullying across social media and its negative effect on the younger generation. The growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilize machine learning, natural language processing techniques and cyberbullying features to automatically detect cyberbullying. In this paper, a review of published research (as identifies via Scopus, ACM and IEEE bibliographic databases) on cyberbullying detection approaches will be presented. On the basis of the review that have been done the most suitable cyberbullying feature and the categories will be mapped out.
QoE Aware Video Adaptation For Video Streaming in 5G Networks

The popularity of video streaming has skyrocketed as an outcome of the increased demand for use of wireless devices such as smartphones and tablets etc. As a result, perplexing video applications like remote surgery, mobile broadcasting, real-time demand, delivery of Ultra High Quality, and Augmented Reality are predicted to control the traffic of mobile networks in the future generation (5G). This is because video applications currently account for more than 70% of IP-based internet traffic, and by 2021, they are expected to account for more than 80%. In addition, mobile device traffic is expected to increase by 10%. As the demand for mobile video consumption grows, 5G networks will require larger bandwidths, improved dependability, and reduced end-to-end delay. Despite considerable improvements in QoS, network operators will continue to face significant issues as 5G video traffic grows in volume. As a result, the focus of network quality has shifted in recent days from network provider QoS to Quality of Experience (QoE), culminating in the QoE predictive model. The assumption for 5G networks is that they shall be capable of delivering Ultra Hd video streaming and the QoE-aware techniques shall be able to match the user’s anticipated quality standard. The purpose of this research is to study, give a broad overview of the many QoE aware adaptive video streaming systems available today, as well as their current trends, and implement an adaptive video streaming system that could enhance the QoE and user perception using a SDN platform.
The Effect of Pair Programming on Code Maintainability

Software maintainability is an important key aspect in software development life cycle. It has a huge impact on the time, effort, and cost. This research investigated the effect of applying pair programming on software maintainability vs individual programming approach. We reviewed many related studies which talked about the two approaches, but few of them focused on maintainability. Therefore, we conducted an experiment in educational environment. The participants were novice students in their second year, who finished object oriented programming course. The participating students were divided into two groups, some worked as individual and the others worked as pairs. In order to get good results, we made the experiment among the students in a competitive way. Enabling those who produced the best code to win a cash prize. Finally, code obtained from both approaches was analyzed by software quality assurance expert, who has finished a master degree in software engineering. The results taken from pair groups showed a statistically significant difference in building software with 50% less errors, 30% less code, and 25% better code. Furthermore, there is a marginal reducing in programming time particularly in small size software compared to individual programming.
A Performance Evaluation Approach for n-tier Cloud-Based Software Services

Cloud computing and cloud testing are vast fields that have attracted significant attention recently. In addition, the need to find an approach for measuring cloud-based applications' effectiveness has also increased. In this work, we introduced an approach to testing the performance of the cloud software services on the Amazon cloud. We used two cloud-based applications hosted in the Amazon cloud to demonstrate the approach depending on five technical performance metrics. We applied the testing methodology using a JMeter test script. The two selected applications represent two different taxonomies: 2-tier and 3-tier architectures. Following the testing process, we found that the WordPress application (i.e., 3-tier architecture) performs better than Ghost and is more stable in terms of the selected performance metrics. Practitioners would benefit from this study by a better understanding of the assessment and testing of n-tier Cloud-Based Software Services using technical arguments.
Evaluation of importance for condition attributes based on quality of decision reducts

Relative or decision reducts belong with mechanisms dedicated to feature selection, and they are embedded in rough set approach to data processing. Algorithms for reduct construction typically aim at dimensionality reduction aspect, searching for smallest reducts, which are considered as the most advantageous from the point of view of knowledge representation. However, classifiers build on reduced data models, based on reducts, can significantly vary in performance. Therefore, to ensure quality of predictions, other characteristics of reducts, apart from their cardinalities, need to be taken into account. The paper presents research in which estimation of reduct quality through their characteristics was reflected in calculation of the proposed weighting factors leading to attribute rankings. These rankings were next employed in the process of filtering decision rules, inferred by classic rough set approach. Constructed rule-based classifiers were applied in the stylometric domain to solve a task of authorship attribution.
An Anomaly Detection Method for Metro Signal and Control Systems

Abnormal pattern detection is the base of automatic failure diagnosis and prediction in heavy equipment health management. Due to large-volume real-time data, high feature dimension, data uncertainty, and heavy dependence on prior knowledge, it is very demanding for traditional models to detect abnormal patterns in logs generated in metro signal and control systems. This paper aims to improve anomaly detection through inducing expert knowledge and to build a bridge between data-driven anomaly detection and rule-based fault detection. Therefore, a novel semi-supervised anomaly detection method is proposed. The method contains two main steps: 1) abnormal pattern mining, and 2) abnormal pattern refinement. Comparing to traditional anomaly detection models, the proposed method possesses three advantages: 1) features are generated through data mining algorithms rather than pre-defined ones; 2) a novel classification model is adapted from one-class support vector machine; 3) human experts can interact with the method by providing new abnormal templates and evaluating the generated rules and cases. The proposed method is implemented in a practical metro signal and control system and its performance is compared with traditional methods. The results demonstrate that the method is practically useful and outperforms the traditional ones in prediction accuracy and domain knowledge coherency.
Effortless Training of Joint Energy-Based Models with Sliced Score Matching

Standard discriminative classifiers can be upgraded to joint energy-based models (JEMs) by combining the classification loss with a log-evidence loss. Hence, such models intrinsically allow detection of out-of-distribution (OOD) samples, and empirically also provide better calibrated posteriors, i.e. prediction uncertainties. However, the training procedure suggested for JEMs (using stochastic gradient Langevin dynamics—or SGLD— to maximize the evidence) is reported to be brittle. In this work we propose to utilize score matching—in particular sliced score matching—to obtain a stable training method for JEMs. We observe empirically that the combination of score matching with the standard classification loss leads to improved OOD detection and better calibrated classifiers for otherwise identical DNN architectures. Additionally, we also analyze the impact of replacing the regular soft-max layer for classification with a gated soft-max one in order to improve the intrinsic transformation invariance and generalization ability. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
<i>LiveProbe</i>: Exploring Continuous Voice Liveness Detection via Phonemic Energy Response Patterns

Voice assistants support contactless smart device control and thus act as a holy grail of human–computer interaction. However, recent studies reveal that an adversary can manipulate devices by vicious voice commands. This security risk is caused by only executing one-time liveness detection and lacking safeguard modules after service activation. Therefore, identifying speaker type (i.e., human articulators or loudspeakers) is critical in protecting voice-driven services during an entire interaction session. In this article, we propose a continuous voice liveness detection approach LiveProbe, leveraging unique energy response patterns in frequency bands induced by distinct voice generation mechanisms. The rationality behind LiveProbe is presented in two aspects: human articulator reshapes initial voices by exquisitely coordinated movements of vocal organs, which act as band-pass filters generating unique energy responses; nevertheless, the internal modules of loudspeakers are position fixed and cannot reproduce this response characteristic. To that end, we first work on voice generation mechanisms behind two-type speakers that cause spectrum differences. Then, we elaborately construct signal processing and deep-learning modules to extract liveness features. Especially, our approach does not interfere with normal voice interaction and need not to carry customized sensors. The experiment presents its effectiveness against potential attacks with a false acceptance rate of 0.51%.