Reviewerly/exHarmony huggingface dataset:
Executing the Apriori Hybrid Algorithm in Semi-structured Mining Datasets and Comparison with HD Algorithm

The recent development in data mining in various fields and firms, for example, hospital, medicinal research, banking, government firm attracted many users. Data examination can be done in numerous possible ways quickly. But for the data examination in semi-structured datasets like XML format, json is little tricky than standard datasets. The easy examination of data in semi-structured datasets is possible by using Apriori hybrid algorithm. Hash tree algorithm is combined with weighed Apriori to get the Apriori hybrid algorithm and when compared with hybrid distribution algorithm (HD algorithm). The pseudo-code is then created and implemented for the proposed algorithm. The systematic graph illustrates to differentiate the Apriori hybrid algorithm and other algorithms like weighted, hash tree, and hybrid distribution algorithm.
Instruments for Monitoring the Quality of Higher Education: Structural Validation of the Ukrainian Version of the ETLQ Questionnaire

The problem of ensuring the quality of higher education and the formation of a culture of quality causes a significant interest. That determines the practice of collecting, analyzing, and taking into account information about the academic environment, requires identifying and constantly monitoring of factors that affect the effectiveness of curricula, and determines student success. To better monitor the effectiveness of the proposed approaches, keep up with emerging challenges, and adjust decisions, it is necessary to develop special tools to study participants’ opinions of the educational process. Such tools should have sufficient validity and meet current trends and approaches. The purpose of the study was to check the internal structure and validate the Ukrainian version of the content part “Experiences of teaching and learning” of the ETLQ tool. Materials and methods. The study involved students (n = 632, 59.81 % were women; age (M±SE) – 21.5±0.2 years old) of Ivan Boberskyj Lviv State University of Physical Culture, who studied in the specialty 227 “Physical therapy, occupational therapy”. Data collection was conducted during the 2019/2020 and 2020/2021 academic years upon completing mandatory special subjects. The convergent, discriminant, and criterion validity of the “Experiences of teaching and learning” part of the ETLQ tool were evaluated. Exploratory and confirmatory factor analyses were used in the research; α-Cronbach values, composite reliability coefficient, and average extracted variance were calculated. Results and discussion. Based on the results of the work, a modified version of the questionnaire was proposed, which contained 29 questions and had a sufficient level of validity. The final model consisted of four factors, which explained 51.353% of the total variance (the first factor – 39.339 %, the second – 5.224 %, the third – 4.281 %, the fourth – 2.509 %). The model was characterized by satisfactory indices: χ² / df (920/344) = 2.67, CFI = 0.923, TLI = 0.935, RMSEA = 0.055, SRMR = 0.058. Based on the validation results, a modified Ukrainian version of the questionnaire “Experiences of teaching and learning” was proposed, which is one of the content parts of the ETLQ tool. It consists of separate scales “Organization and structure,” “Teaching, learning, assessments and other set work”, “Teachers’ enthusiasm and responsiveness to students”, “Climate and support from peers”. The proposed questionnaire has sufficient validity to assess the quality of the academic environment, the effectiveness of specialists’ educational process in physical therapy, occupational therapy, and physical culture and sports
Algorithms Design for Tracking Moving Objects with Colored Process Noise

Tracking of moving objects is often accompanied with colored process noise (CPN) in the object speed. To reduce tracking errors, the Kalman and unbiased finite impulse response filtering algorithms are modified in this paper assuming the Gauss-Markov noise nature. The state differencing approach employed to derive the algorithms, requires solving a nonsymmetric algebraic Riccati equation that allows modifying the system matrix for CPN. Based on a simulated tracking example, a higher accuracy of the modified KF and unbiased finite impulse response (UFIR) filter is demonstrated. Extensive investigations of the walking human trajectories are provided for colored speed noise.
A novel hybrid feature extraction method using LTP, TFCM, and GLCM

Abstract Image classification and feature extraction have been studied extensively and used efficiently in several applications. This paper suggests a novel method by combining three main methods for texture feature extraction. The proposed method is based on Local Ternary Pattern (LTP), Texture Feature Coding Method (TFCM), and Gray Level Cooccurrence Matrix (GLCM). We have entitled our method as GCLTP which is stand for Gray Coding Local Ternary Pattern. The combination of LTP, TFCM, and GLCM is assigned a unique value used to extract the features of an image. GCLTP is tested using images are taken from the Brodatz database. A set of 22 features were extracted from images. GCLTP is experimentally accomplished a high accuracy in classification by using the most known classifiers.
Comparative Study of Traditional Techniques for Unsupervised Autonomous Intruder Detection

In this paper we investigate five traditional techniques to extract features within a face image, and we evaluate them by applying the Kernel-based Online Anomaly Detection (KOAD) algorithm. The main objective of this work is to explore the various fundamental feature extraction techniques that can be used to identify whether a person's face is covered by a mask or not. Although face covering or wearing a mask is recommended during this global COVID-19 pandemic, deliberate face occlusion is considered to be a suspicious activity in a normal scenario. Even during this pandemic, it may be considered suspicious if someone is covering his/her face inside an ATM booth or an apartment complex during odd hours, for instance. Our proposed framework detects such intrusion activity by combining a traditional face detection algorithm with KOAD. Comparative analysis is performed for each filter used and we show that our proposed system achieves high detection accuracy with low computational complexity, while also providing the added benefits of being adaptive, portable, and involving low infrastructural costs.
A fast RFID image matching algorithm for multi-tag identification and distribution optimization

We propose an improved image matching algorithm that combines the minimum feature value algorithm to extract feature points and the direction gradient histogram to calculate the description vector. This algorithm is oriented to RFID multi-tag identification and distribution optimization in the actual scenario, and the traditional SURF algorithm has the problems of low matching accuracy and high complexity in multi-tag matching. This algorithm effectively improves the positioning accuracy of the RFID multi-tag positioning system. The experimental results show that the matching success rate of the improved algorithm in this paper is 87.4%, which is 50% higher than the SURF algorithm. Not only the matching accuracy is greatly improved, but the running speed is also increased by 48%. The algorithm in this paper has high matching accuracy and real-time performance.It provides an effective way for RFID multi-tag real-time fast matching and precise positioning.
Accounting and Privacy Preserving of Data Owner in Cloud Storage

People use the support of distributed computing however can’t completely believe the cloud suppliers to have protection and confidential information. To guarantee secrecy, data owners relocate encoded information rather than plain texts. To divide the encoded documents with different clients, Ciphertext-Policy Attribute-based Encryption (CP-ABE) can be utilized. But this cannot become secure against some other assaults. Many other schemes did not gave guarantee that the cloud provider has the power to check whether a downloader can unscramble or not. Consequently, these files are accessible to everybody who is approachable to the cloud storage. An intentionally harmful assailant can download a great many records to start Economic Denial of Sustainability (EDoS) attacks, it will to a great extent expend the cloud asset. The owner will bear all the expenses for the cloud storage but the cloud provider doesn’t provide the whole information about the access or usage. There is no transparency for the owner. We have to solve these concerns. In order to this we are going to propose a solution for securing the encrypted data from EDoS attacks and providing the owner whole usage information about the cloud storage. We are implementing by using the arbitrary access policy of CP-ABE.
Implication of Mining to Health in Maiganga Coal Mine, Gombe State, Nigeria

Operations of mining are large industrial scale in nature, with significant environmental impacts. The mining industry generates and produces wastes containing high concentrations of metals and metalloids which are highly toxic and destructive to the immediate landscape and environment. In addition, the continued use of the traditional methods of mining intensifies the emission of toxic and products that are not friendly to the ecosystem.. Even regulated and controlled mining sites release toxins into the surrounding environment. While mining has prospects and benefits to the economy, both in terms of its own economic impact and the value to other industries of its product, it almost always has adverse environmental impacts and eventually health impacts. There are numerous ways in which mines impact the health of nearby environments and local communities. Mining involves some generally standard processes and practices. This paper considers the effects and harm done to living things around the mine location. By understanding mining’s threats to health and long-term well-being and by taking precautions to reduce harm in all mines, miners and other people in mining communities can better protect their health and improve their lives. This research will address questions like: which are the toxic substances causing health problems in Maiganga Coal Mine? What is the challenging impact of toxic substances in Maiganga coal Mine?
Research on data Traceability Method Based on blockchain Technology

Energy Internet is a major innovation to deal with the environmental crisis and efficient energy management and use in the current society. The important condition to achieve this goal is to summarize, integrate, process and apply the data of various industries in the energy field, and then support the relevant management and decision-making. In this process, how to ensure the authenticity and credibility of data is one of the keys in the construction of energy Internet. Therefore, this paper will study the application scenarios of blockchain technology in data traceability. With the help of the natural characteristics of blockchain, such as decentralized, distributed storage, tamper proof, open and transparent, combined with relevant national standards and international theoretical models, based on the needs of energy Internet data integration and management, this paper will develop a data traceability method suitable for the energy industry, and build a covering energy Data life cycle model of Internet. Through the research of this paper, we can help all localities to establish data traceability mechanism in the energy Internet, to help users to accurately grasp where the data is created, what systems have been transferred, which users have carried out query and modification, and so on, so as to realize the monitoring and control of the whole process of data flow, which helps to improve the credibility of data, and also helps to ensure the safety and quality of data and promote the construction of energy Internet huge data application.
Extracting Information from Food-Related Textual Sources

Often when we think of the world of analysis, we always think of numerical experimental data that will then have to be somehow processed and then visualized on a graph. In reality, analysis has no prejudice regarding the type of “data to be analyzed”. This can be a simple number and an image, a sound, or even a text. And it is precisely the analysis of texts, and in particular of the language used, that we will discuss in this article. This article aims to exploit Python libraries that allow Language Processing and text analysis in general to extract the information of interest.
Discursive Quads: New Kinds of Lexical Co‐occurrence Data With Linguistic Concept Modelling

Abstract This paper introduces linguistic concept modelling, a new computational approach to humanities‐driven analysis of meaning in large text collections, and presents illustrative examples of the approach applied to over one billion words of printed Early Modern English contained in Early English Books Online (Text Creation Partnership edition). Linguistic concept modelling methods and innovations are described in detail, and justified as a unique, new, powerful means for studying meaning in texts in relation to individual lexical items, sets of lexical items and entire text collections. Linguistic concept modelling is compared to established approaches of distributional semantics and topic modelling, to show how linguistic concept modelling overcomes the limitations of those approaches to support humanities research. Examples demonstrate how our approach can be used to explore and analyse semantic, pragmatic and discursive meaning in texts, highlighting the novel observations that our approach affords, and its benefits for humanities scholars.
PRArch: Pattern-Based Reconfigurable Architecture for Deep Neural Network Acceleration

Quantization is now widely used for Deep Neural Network (DNN) inference acceleration. While mixed-precision quantization achieve better compression rate as well as better accuracy compared to fixed-precision quantization, it is non-trivial and costly to make hardware accelerator like systolic array to support mixed-precision. In this paper, we propose a Pattern-based Mixed-precision Quantization algorithm, namely PMQ, to transform mixed-precision kernels into fix-precision which is hardware friendly, and we further observe the pattern-based sparsity existing in the high parts of transformed kernels, leading to a novel aggregated sparse kernel convolution. Based on the PMQ, we propose an accelerator PRArch supporting mixed-precision convolution neural networks using fix-precision systolic array with minimal overhead. Experiments on several typical convolution networks show a speedup of 1.86x in average compared to coarse-grained quantization accelerator Eyeriss using the same computation chip area, and the accuracy drop less than 1% without fine-tuning.
A Switched-Capacitor Power Side-Channel Attack Detection Circuit in 65-nm CMOS

Side-channel attacks (SCA) pose significant threats to the hardware security of embedded devices. Various logical and circuit-level countermeasures propose security, but they all come with different power, performance, and area overhead. Besides countermeasures cannot guarantee security, if the adversaries have enough time and computing power in hacking. Thus, highly accurate, real-time attack detection techniques are needed. This paper proposes a switched-capacitor circuit that can detect power SCAs by intrusion detection of a current sense resistor in the external power supply line. A pulsed current is pulled on the supply line to introduce a voltage difference proportional to the IR drop. This IR drop is then amplified through a parasitic- insensitive switched-capacitor amplifier and compared against a pre-determined threshold. The circuit achieved a 4.01us typical detection time and better than 72% accuracy, consuming 2.97 mW in 65-nm CMOS. This work is the first mixed-signal IC implementation for power SCA detection. Compared to prior approaches that monitor the entire on-chip power grid, this circuit is more computation- and energy-efficient. It uses a single sensor with no need for data mining or machine learning classification.
Improving auto-encoder novelty detection using channel attention and entropy minimization

Novelty detection is a important research area which mainly solves the classification problem of inliers which usually consists of normal samples and outliers composed of abnormal samples. Auto-encoder is often used for novelty detection. However, the generalization ability of the auto-encoder may cause the undesirable reconstruction of abnormal elements and reduce the identification ability of the model. To solve the problem, we focus on the perspective of better reconstructing the normal samples as well as retaining the unique information of normal samples to improve the performance of auto-encoder for novelty detection. Firstly, we introduce attention mechanism into the task. Under the action of attention mechanism, auto-encoder can pay more attention to the representation of inlier samples through adversarial training. Secondly, we apply the information entropy into the latent layer to make it sparse and constrain the expression of diversity. Experimental results on three public datasets show that the proposed method achieves comparable performance compared with previous popular approaches.
Analisa Model Evaporasi dan Evapotranspirasi Menggunakan Pemodelan Matematika pada Visual Basic di Kabupaten Maros

The approach to calculating evaporation and evapotranspiration, both potential and actual, varies widely. The models used to estimate the amount of evapotranspiration, particularly at the Maros Climatology Station, Maros Regency, South Sulawesi. Evaporation models use the Penman, Priestley, Bruin, and Valiantzas models while evapotranspiration models use the Penman, Hargreaves, Jensen-Haise, Penman-Monteith, Radiation, Turc, and Makkink models, where all of these methods use climate data, such as are the minimum temperature (Tn), maximum temperature (Tx), air temperature (Ta), average humidity (RH), rainfall (R), duration of sun exposure (SS), and maximum wind speed (U) in calculations using Visual basic program in Microsoft Excel in the form of code. Thus, it is necessary to conduct an analysis of the suitability of the model to the results of the observations in order to find out which model is suitable according to the results of the largest coefficient of determination (R2). Based on the results of the model suitability analysis, a selected model was obtained, namely the Valiantzas model with a value of 0.980 in the evaporation calculation and the Jensen-Haise model, namely 0.889.
An artificial intelligence-based real-time monitoring framework for time series

In the era of data technology, data growth is occurring at an unprecedented scale. Business data and information are among the most valuable assets. Massive data analysis now drives nearly every aspect of society and can facilitate informed decision-making by businesses. Fully automated data flow detection of anomalies plays a crucial role in maintaining data service stability and preventing malicious attacks. This paper presents an extensible and generic real-time monitoring system framework (EGRTMS) for large-scale time-series data. EGRTMS employs a prediction module and an anomaly detection module within an anomaly filtering layer for the accurate identification of anomalies. Moreover, the alarm module and anomaly handling module within an anomaly trace processing layer enables the system to respond swiftly to the detected threats. Our solution does not rely on the labelling of anomalies; instead, a predictor module with a deep learning attention-based mechanism learns the normal behaviour of the data, and an anomaly handling module determines the dynamic alarm-threshold by utilizing a sliding window. The results of this study demonstrate that our framework significantly outperforms other anomaly detection systems on most real and synthetic datasets.
Recognition of Degraded Bangla Documents Using Hybrid Deep Neural Network Model

Digitization of degraded document by Optical Character Recognition is an active research area in the context of document analysis. This will help to edit document electronically, to perform content based searching and finally to store it for easy document management. Considering the popularity and heritage, in this work degraded printed Bangla document has been considered as a source material to be digitized. The well known ISIDDI database of Bangla degraded document has been exploited in the present research. This database contains 535 images of printed Bengali pages. These pages are of different fonts, sizes, formats together with different levels of degradation, collected from various sources. From these page images character samples have been extracted and 336 character classes are identified which are now ready for classification. In this research work we have developed an CNN-XGBoost hybrid model for better classification. Here CNN extracts the features of the character images automatically and XGBoost technique is responsible for better classification and recognition. The classification accuracy thus obtained is 91.86%, which outperforms the accuracies of the classifiers exercised so far on the ISIDDI datasets.
"Manos que hablan": tool for teaching – learning the Mexican Sign Language for children with or without hearing disabilities

The benefits that ICT brings to education are becoming more evident every day. For teaching of Mexican sign language, it is one of a great benefit to have tools that support the teaching-learning processes. This paper presents a tool to teach children with or without hearing disabilities the Mexican Sign Language in order to communicate in a successfully way to get along and play nicely through the language. To guarantee the quality of the tool, it relied on Software Engineering, specifically on the agile Extreme Programming methodology. The main result is the tool named "Manos que hablan"; with it, through games, it is sought to achieve meaningful learning in the Mexican Sign Language in children with or without hearing disabilities of preschool age; at the same time, a better integration and inclusion is sought inside and outside the classroom with people with hearing disabilities. The tests carried out on children get positive results, achieving the validation of the tool. This tool is accessible through an electronic device connected to the Internet, which the child with or without hearing disabilities could play to learn the Mexican Sign Language.
Performance Enhancement of Hadoop MapReduce by Combining Data Inside the Mapper

Hadoop is a framework for storing and processing large scale data. Hadoop Distributed File System (HDFS) handles the storage part and MapReduce does the data processing while Yet Another Resource Negotiator(YARN) manages all the resources of the cluster. MapReduce is a programming framework for user defined Mapping and Reducing. Key-value pairs generated by Mapper are unfiltered and recurring, transferred to Reducer results in a huge unnecessary data throughput creating bandwidth overdraw. In this paper, a new algorithm called Inner Map Combining(IMC) for Map phase is proposed. The values of recurring keys get combined by this algorithm inside the Mapper. A successful testing was conducted in 12 different way to test the efficiency of the algorithm. The test shows that IMC implemented with Default Combiner(DC) of MapReduce is 65% more efficient than basic MapReduce program without any combiner. This work can be followed while MapReduce programming for significantly faster computation.