Reviewerly/exHarmony huggingface dataset:
Recent Development on Photovoltaic Parameters Estimation: Total Least Squares Approach and Metaheuristic Algorithms

Photovoltaic parameter extraction has been the focus of many studies, each one presenting a new optimization method to obtain them. To further enhance the optimization process, this work presents a new cost function based on Total Least Squares and compares its performance with the usual function which is based on the Ordinary Least Squares approach. In this paper, eleven different metaheuristic methods are used to compare the performance of the two functions for both single and double diode photovoltaic cell models. The results are presented in terms of mean values of the estimated parameters. Additionally, the two cost function values are also evaluated according to their convergence properties for the different optimization methods considered. The results showed that the Total Least Squares method performs better parameter estimation than the Ordinary Least Squares approach, when used with all the eleven methods, where the best values are obtained when it is coupled with Teaching Learning Based Optimization algorithm for double diode model, resulting in a mean error value of 5.0375e-04 compared to 7.6423e-04 when using Ordinary Least Squares. The big difference between the convergence of the two cost functions was with the Dragonfly method. With the single diode model, this method results in a 0.0016 difference in the mean value of RMSE, and with the double diode model, this difference decreased to 0.0014.
Automated Machine Learning (AutoML): an overview of opportunities for application and research

The wider adoption of AI and machine learning (ML) applications has been limited by the high costs of infrastructure and scarcity of ML experts and data scientists. To address some of these concerns, automated ML (AutoML) systems have been developed alongside cloud computing platforms to mitigate some of the constraints in the wider adoption of ML technologies, including by small and medium size organizations. In this paper, we introduce AutoML, identify some of the fundamental steps in model development, and currently available operationalizations of these systems, before concluding with an outline of potential research opportunities for IS researchers in the field.
A comprehensive overview of software product management challenges

Abstract The principal focus of software product management is to ensure the economic success of the product, which means to prolong the product life as much as possible with modest expenditures to maximizs profits. Software product managers play an important role in the software development organization while being responsible for the strategy, business case, product roadmap, high-level requirements, product deployment (release-management), and retirement plan. This article explores the problems that affect the software product management process, their perceived frequency and perceived severity. The data were collected by a systematic literature review (5 main databases were analyzed), interviews (10 software product managers from IT companies), and surveys (89 participants). 95 software product management problems assigned nonexclusively to 7 areas were identified. 27 commonly mentioned software product management problems were evaluated for their perceived frequency and perceived severity. The problems perceived as the most frequent are: determining the true value of the product that the customer needs, strategy and priorities change frequently, technical debt, working in silos, and balancing between reactive and proactive work. In total, 95 problems have been identified which have been narrowed down to 27 problems based on their occurrence in at least 3 interviews. These selected problems were prioritized by perceived frequency and perceived severity. Some of the identified problems spanned beyond the software product management process itself, but they all affect the work of software product managers.
An Ensemble-Based Model to Improve the Accuracy of Automatic Short Answer Grading

Since 1966 much research has been done on the automatic grading of student answers task, and it was divided into short answer grading and essay scoring. In this paper, on the short answer grading challenge, we are working with text similarity approaches that are being classified into string, semantic (corpus and knowledge-based), and embedding text similarity approaches are the three types of text similarity techniques. On the Texas data-structure data set, different experiments were examined individually before being merged to give a maximum correlation result of 0.65.
Work with Free Response Implementation Process Analysis Based on Sequential Pattern Mining in Engineering Education

Learning process, its dynamics are one of the points of focus of researchers which conduct studies in the field of education. The widespread use of learning management systems and MOOC platforms allows to create and store records of student activities in educational system (logs). In educational process special systems for implementing assignments of a particular type, for example, software development, construction of concept maps, can be used together with MOOC platforms or learning management systems. These special systems can also keep logs of student activities. The logs are an analytical issue in applying educational data mining, in particular in sequential pattern mining. The use of sequential pattern mining makes it possible to identify typical patterns of student activities during the learning process. These patterns may be used in defining potential problems for students and courses with a goal of improving educational process through course corrections or through providing special feedback to students with problems. However, the usage of sequential pattern mining approach is limited to the majority of works with free response, widely applied in engineering education. A method of work with free response implementation process analysis based on sequential pattern mining is proposed in the paper. The method was tested in training future engineers in the field of information technologies in Ufa State Aviation Technical University on the results of distance term paper preparation in the course "The design of automated systems for information processing and control". The use of the suggested method allows to extract undesirable patterns of implementing works with free response and reasons for their presence. Also, the method allows to define typical strategies of implementing such works and their proportions.
Search-based detection of code changes introducing performance regression

In contemporary software development, developers commonly conduct regression testing to ensure that code changes do not affect software quality. Conducting performance regression testing after every code change is known to be expensive which emigres the need to direct the performance regression testing efforts on code changes that are most likely introducing performance regression. In this paper, we exploit code change metrics to identify the group introducing the regression based on a pre-trained detection rule. We present PRICE approach as a new formulation of detecting code changes introducing performance regression as an optimization problem using multi-objective evolutionary algorithms. PRICE evaluated using a set of 8000 commits, extracted from the Git project. Results show the effectiveness of our approach in accurately detecting performance regression introducing code changes. The average regression detection PRICE provides is 77% which is 22% more than the state-of-the-art deterministic approach. This improvement didn’t compromise the detection of the contradicted code changes that are not introducing a regression. PRICE provides detection of code changes not introducing a regression averaged 63%, which is also an improvement of 14% than the comparative approach. Using PRICE, we were able to explore new search spaces and provide competing results.
Quantum speed limit for mixed states in a unitary system

Since the evolution of a mixed state in a unitary system is equivalent to the joint evolution of the eigenvectors contained in it, we could use the tool of instantaneous angular velocity for pure states to study the quantum speed limit (QSL) of a mixed state. We derive a lower bound for the evolution time of a mixed state to a target state in a unitary system, which automatically reduces to the quantum speed limit induced by the Fubini–Study metric for pure states. The computation of the QSL of a degenerate mixed state is more complicated than that of a non-degenerate mixed state, where we have to make a singular value decomposition (SVD) on the inner product between the two eigenvector matrices of the initial and target states. By combing these results, a lower bound for the evolution time of a general mixed state is presented. In order to compare the tightness among the lower bound proposed here and lower bounds reported in the references, two examples in a single-qubit system and in a single-qutrit system are studied analytically and numerically, respectively. All conclusions derived in this work are independent of the eigenvalues of the mixed state, which is in accord with the evolution properties of a quantum unitary system.
A performance evaluation of convolution neural networks for kinship discernment: An application in digital forensics

Kinship Verification from facial images is known to have attracted major attention since time immemorial. Identifying the underlying patterns that exist between images and analysing the relationship hidden between them have enabled the multitudes of applications to utilize kinship relationships. This work serves as a study on the amount of influence that hereditary features can exert on the families tied together by lineage in identifying the relationship prevailing between them and whether it holds true or not. The approach employed involves detecting the relationship existing between the provided facial images using Siamese Network, which comprises two identical convolutional neural networks that share common weight values. A difference vector is computed from this Siamese CNN, which is then fed into a network of fully connected linear layers. This extended layer will determine whether the two individuals in the input images are related to each other or not.
Human activity recognition via wearable devices using enhanced ternary weight convolutional neural network

Human activity recognition (HAR) has been known as an active area for more than a decade, and there are still crucial aspects that are intended as challenging problems. Providing detailed and appropriate information about the activities and behaviors of people is one of the most important fields in ubiquitous computing. There are numerous applications in this field, among which healthcare, security, and entertainment scenarios can be listed. Human activity recognition can be carried out with the assistance of smartphone sensors such as accelerometers and gyroscopes or images captured from webcams. Today, the application of deep neural networks in this domain has received much attention and has led to more accurate and effective results compared to traditional techniques. The deep neural network performs arithmetic operations on a various number of hidden layers. In this article, a new approach called HAR-CT is proposed to enhance the accuracy of human activity recognition in various classes by adopting a convolutional neural network (CNN). Subsequently, an optimization technique using the TWN model is also suggested to reduce the complexity of the deep neural network approach that decreases the energy consumption of mobile devices. To this end, the float precision weights of the convolutional neural network are quantized and converted into ternary weights, while the decline in the accuracy is very low compared to the initial deep neural network. The evaluation results of both networks demonstrate that the proposed methods outperform the recently published approaches in human activity recognition.
An Analysis of Various Techniques in Blockchain Applications

Satoshi Nakamoto invented blockchain technology in 2008. This cutting-edge characteristic of technology has given rise to intriguing notions and themes that developers and researchers can use in their work. Transactions in blocks were decentralized, immutable and digital. The blockchain is a chain of connected blocks created using cryptography and hashing techniques. The purpose of blockchain technology is to store and improve the value of data. Blockchain can store important information in blocks. It improves productivity in industries by utilizing Blockchain technique in a wide range of applications. Peer-to-peer networks are a type of network that helps with data storage. The blockchain technology has a real-time updating feature to keep the records of data up to date and consistent. This analysis started with a look at some of the most popular blockchain applications. Existing blockchain applications were examined and a list of its merits in various field was compiled as proof of concept.
Robust Image Matching Based on Image Feature and Depth Information Fusion

In this paper, we propose a robust image feature extraction and fusion method to effectively fuse image feature and depth information and improve the registration accuracy of RGB-D images. The proposed method directly splices the image feature point descriptors with the corresponding point cloud feature descriptors to obtain the fusion descriptor of the feature points. The fusion feature descriptor is constructed based on the SIFT, SURF, and ORB feature descriptors and the PFH and FPFH point cloud feature descriptors. Furthermore, the registration performance based on fusion features is tested through the RGB-D datasets of YCB and KITTI. ORBPFH reduces the false-matching rate by 4.66~16.66%, and ORBFPFH reduces the false-matching rate by 9~20%. The experimental results show that the RGB-D robust feature extraction and fusion method proposed in this paper is suitable for the fusion of ORB with PFH and FPFH, which can improve feature representation and registration, representing a novel approach for RGB-D image matching.
Design of virtual reality modules for multivariable calculus and an examination of student noticing within them

Virtual reality (VR) research in mathematics education has centred largely on geometry content. This paper contributes by describing VR modules developed for another area that heavily involves three-dimensional objects: multivariable calculus. This paper also contributes by describing an empirical study on students’ experiences inside the VR modules through student (conceptual) noticing. One finding was that colourful objects had moderate associated conceptual noticing, but that accompanying animation drastically improved the conceptual noticing. Symbolic and textual elements were conceptually noticed much less. While narration was meant to guide the students’ noticing, it often did not produce the conceptual noticing intended, though animation again was a key factor. The conceptual noticing was also found to be connected to the students’ emerging understandings of the mathematical ideas discussed in the modules. We end by discussing implications for (a) designing VR modules generally, and for (b) learning specific mathematical content within VR.
Benefits from Variational Regularization in Language Models

Representations from common pre-trained language models have been shown to suffer from the degeneration problem, i.e., they occupy a narrow cone in latent space. This problem can be addressed by enforcing isotropy in latent space. In analogy with variational autoencoders, we suggest applying a token-level variational loss to a Transformer architecture and optimizing the standard deviation of the prior distribution in the loss function as the model parameter to increase isotropy. The resulting latent space is complete and interpretable: any given point is a valid embedding and can be decoded into text again. This allows for text manipulations such as paraphrase generation directly in latent space. Surprisingly, features extracted at the sentence level also show competitive results on benchmark classification tasks.
A Preliminary Evaluation of “GenDAI”, an AI-Assisted Laboratory Diagnostics Solution for Genomic Applications

Genomic data enable the development of new biomarkers in diagnostic laboratories. Examples include data from gene expression analyses or metagenomics. Artificial intelligence can help to analyze these data. However, diagnostic laboratories face various technical and regulatory challenges to harness these data. Existing software for genomic data is usually designed for research and does not meet the requirements for use as a diagnostic tool. To address these challenges, we recently proposed a conceptual architecture called “GenDAI”. An initial evaluation of “GenDAI” was conducted in collaboration with a small laboratory in the form of a preliminary study. The results of this pre-study highlight the requirement for and feasibility of the approach. The pre-study also yields detailed technical and regulatory requirements, use cases from laboratory practice, and a prototype called “PlateFlow” for exploring user interface concepts.
Data Stream Oriented Fine-grained Sparse CNN Accelerator with Efficient Unstructured Pruning Strategy

Network pruning can effectively alleviate the excessive parameters and computation issues in CNNs. However, unstructured pruning is not hardware friendly, while structured pruning will result in a significant loss of accuracy. In this paper, an unstructured fine-grained pruning strategy is proposed and achieves a 16X compression ratio with a top-1 accuracy loss of 1.4% for VGG-16. Combined with the proposed hardware-oriented hyperparameter selection method, compression rates of up to 64X can be obtained while fully meeting the edge-side accuracy requirements. Further, a light-weight, high-performance sparse CNN accelerator with modified systolic array is proposed for pruned VGG-16. The experimental results show that compared with the most advanced design, the proposed accelerator can achieve 21 Frames Per Second (FPS) with 3X better power efficiency and 2.19X better calculation density.
Anomaly Detection using CNN with SVM

People within the present world and everybody worries about one thing that&#x0027;s security. Providing this security depends on various things. An anomaly is any practical action that might specify that someone could also be involved in a very crime or be ready to commit criminality. Anomaly detection is nothing but detecting abnormal activity. These cameras&#x0027; data analysis can play influential roles in event prediction and online monitoring applications, including anomalies. Nowadays, various machine learning techniques are accustomed detect anomalies like convolutional neural networks; among them, Support Vector Machine using machine learning techniques improved the detection accuracy significantly. The goal is to propose a brand new method that is supported SVM and CNN techniques for anomaly suspicious detection with higher accuracy. By using this method to Detect anomaly activity publicly, places like trying to kill someone, fighting with one another etc.
ATOVis – A visualisation tool for the detection of financial fraud

Fraud detection is related to the suppression of possible financial losses for institutions and their clients. It is a task of high responsibility and, therefore, an important phase of the decision-making chain. Nowadays, experts in charge base their analysis on tabular data, usually presented in spreadsheets and seldom supplemented with simple visualisations. However, this type of inspection is laborious, time-consuming, and may be of little use for the analysis and overview of complex transactional data. To aid in the inspection of fraudulent activities, we develop ATOVis – a visualisation tool that enables a fast analysis and detection of suspicious behaviours. We aim to ease and accelerate fraud detection by providing an overview of specific patterns within the data, and enabling details on demand. ATOVis focuses on applying visualisation techniques to the Finance domain, specifically e-commerce, contributing to the state-of-the-art as the first visualisation tool primarily specialised in Account Takeover (ATO) patterns. In particular, the present paper incorporates: a task abstraction for detecting a specific financial fraud pattern – ATO; two models for the visualisation of ATO; and a multiscale timeline to enable an overview of the data. We also validate our tool through user testing, with experts in fraud detection and experts from other fields of data science. Based on the feedback provided by the analysts, we could conclude that ATOVis is an efficient and effective tool in detecting specific patterns of fraud which can improve the analysts’ work.
INSPIRE

Private Information Retrieval (PIR) plays a vital role in secure, database-centric applications. However, existing PIR protocols explore a massive working space containing hundreds of GiBs of query and database data. As a consequence, PIR performance is severely bounded by storage communication, making it far from practical for real-world deployment.
Advancement on optical methods in stress dead-zone characterisation and SIF evaluation

The work described in this paper is a study of the stress dead zone (SDZ) concept with advanced full-field optical methods. The SDZ is a region located around the fatigue crack flanks, adding a negligible contribution to internal stresses on the global deformation energy calculation. In the analytical modelling, this area can be removed from the structural member, resulting in a geometry simplification, and in the possibility of using analytical formulations in the Stress Intensity Factor (SIF) calculation associated to the existing fatigue crack. In a fatigue-testing program, aluminium middle tension (MT) specimens were prepared and then subjected to a cyclic fatigue loading, in order to generate different crack lengths for each specimen. Then, the fatigued specimens are monotonically tested under a uniaxial tensile loading to assess the SDZ concept. The experimental data is collected and analysed through two full-field high-resolution optical techniques with different system resolutions, Digital Image Correlation (DIC), and Electronic Speckle Pattern Interferometry (ESPI). As a result, the SDZ is geometrically characterized through a set of mathematical formulations based on the compliance function for the studied specimens, and is experimentally identified through deformation fields obtained from both DIC and ESPI techniques. The obtained results from both high-resolution methods verify the validity of the concept and demonstrate that it can be used to simplify the analysis.