Reviewerly/exHarmony huggingface dataset:
Multiple Object Tracking using YOLO-based Detector

In computer vision, multiple object tracking (MOT) plays a crucial role in solving many important issues. A common approach of MOT is tracking by detection. Tracking by detection includes occlusions, motion prediction, and object re-identification. From the video frames, a set of detections is extracted for leading the tracking process. These detections are usually associated together for assigning the same identifications to bounding boxes holding the same target. In this article, MOT using YOLO-based detector is proposed. The authors’ method includes object detection, bounding box regression, and bounding box association. First, the YOLOv3 is exploited to be an object detector. The bounding box regression and association is then utilized to forecast the object’s position. To justify their method, two open object tracking benchmarks, 2D MOT2015 and MOT16, were used. Experimental results demonstrate that our method is comparable to several state-of-the-art tracking methods, especially in the impressive results of MOT accuracy and correctly identified detections.
Malicious URL detection Using majority vote method with machine learning and deep learning models

Website can be accessed by URL (Uniform Resource Locator) and hackers use it with unsolicited content, which leads to cyber-attacks known as malicious URL and causes economic loss around the world. Machine learning, deep learning and a lot of other mechanisms detect these malicious URLs. However, the combination of these models faces over-fitting problem if they do not follow the same law (binomial and multinomial laws combined). This paper suggests majority vote system of different machine learning and deep learning models. With 420 464 URLs, result is interesting for combination of Random Forest (RF), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models, giving an accuracy rate of 93% and solving over-fitting problem.
Enhancing users’ security engagement through cultivating commitment: the role of psychological needs fulfilment

Employee behaviour is fundamental to corporate information security (InfoSec) capabilities across the phases of prevention, detection, and response. Unfortunately, despite over a decade of research on the topic, the human aspect of security remains the most vulnerable in many companies today, often rooted in employee disinterest. Two traditions within the InfoSec research that may contribute to this disconnect are 1) emphasis on extrinsic manipulation of behaviour versus cultivation of internalised commitment to organisational InfoSec and 2) emphasis on isolated activities over more integrated perspectives of security behaviour. Addressing these gaps, the current study examines end user InfoSec behaviour through a distinct internal motivational lens. Rooted in Self-Determination Theory, a research model is introduced that highlights workplace factors which drive end users' internalised commitment to organisational InfoSec by fulfiling fundamental psychological needs (autonomy, competence, and relatedness) within this context. Commitment, which captures internally regulated motivation to contribute to organisational InfoSec performance, is then positioned as a driver of intention to engage in various security behaviours. Overall, the results support the study's hypotheses and underscore the important roles perceived behavioural control, IT competence, and user-IS department relations have on commitment to organisational InfoSec and resultant behavioural outcomes.
Post-processing integration and semi-automated analysis of eye-tracking and motion-capture data obtained in immersive virtual reality environments to measure visuomotor integration

Mobile eye-tracking and motion-capture techniques yield rich, precisely quantifiable data that can inform our understanding of the relationship between visual and motor processes during task performance. However, these systems are rarely used in combination, in part because of the significant time and human resources required for post-processing and analysis. Recent advances in computer vision have opened the door for more efficient processing and analysis solutions. We developed a post-processing pipeline to integrate mobile eye-tracking and full-body motion-capture data. These systems were used simultaneously to measure visuomotor integration in an immersive virtual environment. Our approach enables calculation of a 3D gaze vector that can be mapped to the participant's body position and objects in the virtual environment using a uniform coordinate system. This approach is generalizable to other configurations, and enables more efficient analysis of eye, head, and body movements together during visuomotor tasks administered in controlled, repeatable environments.
The Dissensus Protocol: Governing Differences in Online Peer Communities

Peer-to-peer networks and protocols have inspired new ideas and ideologies about governance, with the aim of using technology to enable horizontal and decentralized decision-making at scale. This article introduces the concept of “dissensus” from political theory to debates about peer governance in online communities. Dissensus describes the emergence of incompatible differences. Among peer-to-peer technologies, blockchain stands out as a set of ideas that explicitly seek to resolve dissensus through consensus protocols. In this article, we propose dissensus as a “protocol” for foregrounding the often sidelined yet productive aspects of incompatible differences. The concept highlights that there might not always be consensus about a consensus algorithm, and that indeed, dissensus is the precondition for new possibilities and perspectives to emerge. We discuss the concept in relation to the histories of governance ideas in blockchain, namely, a “materialist,” “design,” and “emergent” approach. We then describe moments of dissensus in practice through two cases of online communities, Genesis DAO and Ouishare, discussing their different ways of recognizing and navigating dissensus. Finally, we give a critical overview of consensus algorithms, voting, staking, and forking as the mechanisms that make out blockchain governance ideologies. In conclusion, we argue that dissensus can serve as a useful concept for pointing attention to governance as it is conducted in practice, as historically and culturally specific practices, rather than as a problem to be solved through supposedly universal mechanisms.
An Exploration of Augmented Reality in an Introductory Engineering Graphics Course

The purpose of the study was to explore the effects of augmented reality (AR) in an introductory engineering graphics course. The study specifically examined the potential for AR to affect the spatial visualization ability of students and influence student motivation. This study included 50 students from an introductory engineering graphics course at a large southeastern US public university. The AR intervention consisted of six weekly sessions in which students were required to complete an assignment with the assistance of AR. Two quantitative measurements were employed to measure the results of the implementation. The Purdue Spatial Visualization Test: Rotations (PSVT-R) was used to measure spatial visualization ability, and the Motivated Strategies for Learning Questionnaire (MSLQ) was used to measure student motivation. Both instruments used a pre- and post-test format and were analyzed with paired t-tests. The results of the PSVT-R (p &lt; .01) showed a significant difference between the pre- and post-test scores; however, this could not be solely attributed to the implementation of AR. The results of the overall MSLQ (p = 0.57) showed no significant difference between pre- and post-test scores.
A Neural State Pushdown Automata

To learn complex formal grammars, recurrent neural networks (RNNs) require sufficient computational resources to ensure correct grammar recognition. One approach to expand model capacity is to couple an RNN to an external stack memory. Here, we introduce a “neural state” pushdown automaton (NSPDA), which consists of a discrete stack instead of an continuous one and is coupled to a neural network state machine. We empirically show its effectiveness in recognizing various context-free grammars (CFGs). First, we develop the underlying mechanics of the proposed higher order recurrent network and its manipulation of a stack as well as how to stably program its underlying pushdown automaton (PDA). We also introduce a noise regularization scheme for higher-order (tensor) networks and design an algorithm for improved incremental learning. Finally, we design a method for inserting grammar rules into a NSPDA and empirically show that this prior knowledge improves its training convergence time by an order of magnitude and, in some cases, leads to better generalization. The NSPDA is also compared to a classical analog stack neural network pushdown automaton (NNPDA) as well as a wide array of first and second-order RNNs with and without external memory, trained using different learning algorithms. Our results show that for the Dyck languages, prior rule-based knowledge is critical for optimization convergence and for ensuring generalization to longer sequences at test time. We observe that many RNNs with and without memory, but no prior knowledge, struggle to converge and generalize on complex and longer CFGs.
Synthetic18K: Learning better representations for person re-ID and attribute recognition from 1.4 million synthetic images

Learning robust representations is critical for the success of person re-identification and attribute recognition systems.However, to achieve this, we must use a large dataset of diverse person images as well as annotations of identity labels and/or a set of different attributes.Apart from the obvious concerns about privacy issues, the manual annotation process is both time consuming and too costly.In this paper, we instead propose to use synthetic person images for addressing these difficulties.Specifically, we first introduce Synthetic18K, a large-scale dataset of over 1 million computer generated person images of 18K unique identities with relevant attributes.Moreover, we demonstrate that pretraining of simple deep architectures on Synthetic18K for person re-identification and attribute recognition and then fine-tuning on real data leads to significant improvements in prediction performances, giving results better than or comparable to state-of-the-art models.
In quest of goldilocks ranges in searching for information on the web

Purpose The quest to discover optimal conditions or amounts has been carried out in many scientific disciplines and practical fields. In astrophysics, biology, medicine, psychology and education, the quest has resulted in finding the right amount of something, a desirable middle between extremes, a balance between conditions or the optimal state of a system. The results are referred to as the Goldilocks principle, which is based on the idea of being “just right”. The aim of our study was to find out if there are any measures in information search that could be identified as Goldilocks ranges. Design/methodology/approach We conducted a user experiment in which 68 participants carried out a time-unlimited, topical search task involving finding relevant websites on the basis of which the participants were supposed to prepare a presentation on a given topic. We examined aspects of their search behavior. Findings We found that information search Goldilocks ranges can be identified for a length of a search session, number of relevant results, number of queries submitted and number of search engine results pages (SERPs) visited. This preliminary study has resulted in indicating the following dominant ranges: Number of relevant documents found: 5–8; Time spent searching: 21–35 min; Number of queries submitted: 3–7; Number of SERPs viewed: 1–3. Originality/value Till now, no one has studied Goldilocks ranges in information retrieval. The Goldilocks ranges have some practical implications for improving the effectiveness of web searching.
User Experience Research and Analysis Based on Usability Testing Methods

In recent years, the rapid development of the Internet makes all kinds of software products become an indispensable part of people’s daily life. In the process of using the software, users not only pay attention to the function of the software, but also pay more attention to the feeling of using. User experience has become a key factor for the success of the software, so the test from the perspective of user experience will become an operation that can’t be ignored in the software industry. This paper takes inspection application software as an example, UCD (user-centered) as the core idea, uses the usability test method based on eye-movement tracking and system availability scale to study the user experience problems of the software, gives suggestions for improvement, improves user experience, and shows the importance of user experience.
An Ensemble-Based Credit Card Fraud Detection Algorithm Using an Efficient Voting Strategy

Abstract The existence of fraud in credit card transactions causes many financial losses leading to customers’ loss of trust. Fraud detection methods based on machine learning techniques prevent such losses. Despite the literature on fraud detection, there is a lack of algorithms that detect fraud with acceptable performance in the credit card fraud detection field. Therefore, this study proposed a comprehensive ensemble-based method using an efficient weighted voting strategy for credit card fraud detection that can address the previous algorithms’ weaknesses. First, since the dataset is imbalanced, the proposed method balanced the dataset by stratifying it into three different proportions of normal and fraudulent transactions (1 to 1, 1 to 4 and 1 to 9 ratios). The features in each dataset are ranked by four feature-ranking methods, and the Random Forest classifier is applied to each of them for selecting the essential features. Afterward, using the seven base classifiers and the chosen features, 12 ensembles have been developed. Besides, a weighted voting strategy is proposed, and the fraudulent transactions are detected through voting based on the base classifiers’ and ensembles’ weights, which are calculated by their performance. The computational results indicated that the suggested Eclf10 is the best ensemble and its Logistic Regression classifier also has the best performance among other base classifiers. The Eclf10 leads to 99.97% accuracy, 87.78% precision, 97.70% recall, 92.21% F1-score and 95.634% F2-score, which has a superiority over the previous ensemble-based methods (e.g. majority voting ensemble, stacking classifier, Adaboost, Gradient Boosting).
Why Does Regularization Help with Mitigating Poisoning Attacks?

We use distributionally-robust optimization for machine learning to mitigate the effect of data poisoning attacks. We provide performance guarantees for the trained model on the original data (not including the poison records) by training the model for the worst-case distribution on a neighbourhood around the empirical distribution (extracted from the training dataset corrupted by a poisoning attack) defined using the Wasserstein distance. We relax the distributionally-robust machine learning problem by finding an upper bound for the worst-case fitness based on the empirical sampled-averaged fitness and the Lipschitz-constant of the fitness function (on the data for given model parameters) as regularizer. For regression models, we prove that this regularizer is equal to the dual norm of the model parameters.
Smilax: Statistical Machine Learning Autoscaler Agent for Apache FLINK

Smilax is a statistical machine learning autoscaler agent for applications running on Apache Flink. Smilax agent acts proactively by predicting the forthcoming workload in order to adjust the allocation of workers to the actual needs of an application ahead of time. During an online training phase, Smilax builds a model which maps the performance of the application to the minimum number of servers. During the work (optimal) phase, Smilax maintains the performance of the application within acceptable limits (i.e. defined in the form of SLAs) while minimizing the utilization of resources. The effectiveness of Smilax is assessed experimentally by running a data intensive fraud detection application.
Determination of Excavator Tool Position using Absolute Sensors

Automation techniques for controlled job execution in the excavation process are extremely useful for excavator tool trajectory generation, digging process simulation, and new tools or machines' developmental testing. Proper choice of sensors for excavator tool position determination is crucial for the successful implementation of automated systems. We used an industrial robotic arm to simulate excavator operation and determine the characteristics of available sensors. Four inertial measurement units (IMU) sensors mounted on the industrial robotic arm are used to estimate the joints positions and compare results with data from internal robot sensors. The obtained findings suggest that the chosen sensors are appropriate for the estimation of joint angle. It was found that the X axis error should be less than 2.27 degrees. If the Z axis is inevitable for some control task, additional sensors can be used to establish reference points for yaw angle calculation.
Generalized inductive item tree analysis

Inductive item tree analysis is an established method of Boolean analysis of questionnaires. By exploratory data analysis, from a binary data matrix, the method extracts logical implications between dichotomous test items. For example, assume that we have the problems i and j of a test that can be solved or failed by subjects. With inductive item tree analysis, an implication i→j between the items i and j can be uncovered, which has the interpretation ”If a subject is able to solve item i, then this subject is also able to solve item j”. Hence, in the current form of the method, solely dichotomous items are considered. In this paper, we extend this approach to the general case of polytomous items, when more than two answer categories are possible. Thus, we introduce extensions of inductive item tree analysis that can deal with nominal polytomous (including dichotomous) and ordinal polytomous, each with item-specific, answer scales. To show their usefulness, the extensions proposed in this paper are illustrated with empirical data examples.
Prediction of Daily Photovoltaic Energy Production Using Weather Data and Regression

Abstract This paper presents linear regression models to predict the daily energy production of three photovoltaic (PV) systems located in southeast Virginia. The prediction is based on daylight duration, sky index, average relative humidity, and the presence of fog or mist. No other daily weather report components were statistically significant. The proposed method is easy to implement, and it can be used in conjunction with other advanced methods in estimating any given future day’s energy production if weather prediction is available. Data from 2013 to 2015 were used in the model construction. Model validation was performed using newer (2016, 2017, 2020, and 2021) data not used in the model construction. Results show good prediction accuracy for a simple methodology, free of system parameters, that can be utilized by ordinary photovoltaic energy users. The majority of the data was collected at the Old Dominion University. The entire data set can be downloaded using the link provided.
A two-level deterministic reasoning pattern to curb the spread of COVID-19 in Africa

The emergence of the pandemic disease COVID-19, which keeps many nations on their toes to find a the solution for a cure, needs a more predetermined approach by investigating the pattern and speed with which the disease is spread from one individual to another. The predetermining method can also be used to solve the future occurrence of such diseases. The predetermined approach is s good reasoning model for proactive measures. This study presents a two-level deterministic reasoning model to curb the spread of COVID-19 in some populated and economically optimistic African countries. A Petri net was used as a predetermining model to ensure proactive measures for present and future control of the spread of deadly diseases such as COVID-19. Data were collected from a reliable organization, and the result of the use the normal distribution model on these sets of data was fed into the Petri net to determine the severity and rate at which people contract disease before and during lockdown in selected countries. The results from this model proved that the number of cases of COVID-19 is not a function of the death rate in the selected countries; the discharge rate had a stronger effect on the COVID-19 cases. The results of the normal statistical distribution of various instances of COVID-19 were compared with those of the Petri net and proved that the hybrid deterministic model is viable for future use on any pandemic disease.
A Bibliometric Statistical Analysis of the Fuzzy Inference System - based Classifiers

Nowadays, under the pressure of numerous research publications, researchers increasingly pay attention to writing survey papers to track and understand one research topic they are interested in, and then begin to conduct more in-depth research. Until this moment, there are two types of survey papers: traditional review analysis and bibliometric statistical analysis. Compared with traditional review analysis, due to the analysis of various bibliometric information that can be quickly summarized to assess and predict one research field's development, the bibliometric statistical analysis is progressively proposed. However, no research relied on the bibliometric approach to explore fuzzy inference system (FIS) -based classifiers. More importantly, since the current open-ended bibliometric analysis approaches have different assessment focuses, choosing a suitable approach is problematic. Therefore, based on the extraction, integration, and expansion of previous bibliometric analysis theories, this research proposes a new systematic and time-saving bibliometric statistical analysis approach. It is worth noting that the proposed approach eliminates the need to read the internal content of all publications. Two core parts (Publication Information and TOP 20 SETs) are generated by the proposed analysis approach. Among them, analyzing Author Keywords and TOP 20 SETs are unprecedented guiding features to assist researchers in exploring research topic. Significantly, this research relies on the proposed approach to explore FIS-based classifiers. Various assessments cover the bibliometric information of the entire related publications. In addition, these results may need to be considered to increase the citation rate of future publications.
Multi-signal spectroscopy of qubit-resonator systems

Some unique properties of superconducting devices are promising for the development of modern quantum technologies. Superconducting quantum circuits use large coupling constants and provide good scalability and controllability due to their macroscopic dimensions. Still, micro-fabrication methods have some hardship with reproducibility of identical superconducting quantum circuits. The dressed state approach presents some possibility to reduce influence of non-identity of qubits. We study a qubit-resonator system, when the qubit interacts with three signals. Such system configuration adds additional flexibility for circuit tunability. A particular realization of such a system is a superconducting flux qubit coupled to a transmission-line resonator driven by three signals. We describe this triply-driven system in terms of the dressed qubit states and conclude that using several signals can be beneficial for both system spectroscopy and tunability. Such study of a qubit-based system, coupled to both classical and quantum fields, can be useful for detection of individual itinerant microwave photons.