Reviewerly/exHarmony huggingface dataset:
Perception Visualization: Seeing Through the Eyes of a DNN

Artificial intelligence (AI) systems power the world we live in. Deep neural networks (DNNs) are able to solve tasks in an ever-expanding landscape of scenarios, but our eagerness to apply these powerful models leads us to focus on their performance and deprioritises our ability to understand them. Current research in the field of explainable AI tries to bridge this gap by developing various perturbation or gradient-based explanation techniques. For images, these techniques fail to fully capture and convey the semantic information needed to elucidate why the model makes the predictions it does. In this work, we develop a new form of explanation that is radically different in nature from current explanation methods, such as Grad-CAM. Perception visualization provides a visual representation of what the DNN perceives in the input image by depicting what visual patterns the latent representation corresponds to. Visualizations are obtained through a reconstruction model that inverts the encoded features, such that the parameters and predictions of the original models are not modified. Results of our user study demonstrate that humans can better understand and predict the system's decisions when perception visualizations are available, thus easing the debugging and deployment of deep models as trusted systems.
SOFTWARE AND HARDWARE FOR DETERMINING GAUSSIAN NOISE LEVEL IN IMAGES

Accurate determination of the noise level in digital images is required to obtain their maximum signal-to-noise ratio, which is a necessary condition for the effective performance of the following stages of image processing: visualization, segmentation, recognition, etc. The task of calculating the Gaussian noise level is quite common, because such noise appears in most experimental images taken by video cameras. However, existing high-speed noise detection methods have a significant error, and the fairly accurate LLROI method has a low speed. The LLROI method is based on Low-frequency filtering of the noise component and Low-frequency filtering when selecting the Region Of Interest (ROI). Therefore, it is proposed to determine the level of Gaussian noise by the exact LLROI method and to increase its speed by appropriate hardware and software. Based on the LLROI method, a program in the MATLAB system was created, the structure and Simulink-model of a computer system for determining of Gaussian noise level on digital images were synthesized. Hardware implementation of image filtering units is made by FPGA Artix-7, which allowed us to increase the speed of the system. The results of calculating the Gaussian noise level for test images by the LLROI method using the developed hardware and software proved the errors not to exceed those provided by analogous methods.&#x0D; The scientific novelty of the paper is to improve the LLROI method, namely to refine the threshold coefficient, which reduces the errors of calculating the noise level, even for images with clear contours and pronounced textures.&#x0D; The practical significance of the developed tools is that they can be used to build high-speed computer systems (or subsystems) designed to increase the signal-to-noise ratio on digital images.
Massive Trajectory Matching and Construction from Aerial Videos based on Frame-by-Frame Vehicle Detections

Vehicle trajectory data provides critical information for traffic flow modeling and analysis. Unmanned aerial vehicles (UAV) is an emerging technology for traffic data collection because of its flexibility and diversity on spatial and temporal coverage. Vehicle trajectories are constructed from frame-by-frame detections. The increase of vehicle counts makes multiple-target matching more challenging. Errors are caused by pixel jitter, vehicle shadows, road marks as well as some missing detections. This research proposes a novel framework for construction of massive vehicle trajectories from aerial videos by matching vehicle detections based on traffic flow dynamic features. The You Look Only Once (YOLO) v4 is used for vehicle detection in UAV videos based on Convolution Neural Network (CNN). Trajectory construction is proposed in detected bounding boxes with trajectory identification, integrity enhancement, and coordinate transformation from image coordinates to the Frenet coordinates. The raw trajectory obtained is then denoised by the ensemble empirical mode decomposition (EEMD). Our framework is tested on two aerial videos taken by a UAV on city expressway covering congested and free-flow traffic conditions. The results show that the proposed framework achieves a Recall of 93.00% and 86.69%, and a Precision of 98.86% and 98.83% for vehicle trajectories in the free-flow and congested traffic conditions.The trajectory processing speed is about 30s per track.
The Construction of Contolled S-box

The development of block cryptoalgorithms does not stand still. There are new architectures and increase of key information. But there is one primitive that will always be present in block cryptoalgorithms. This is a substitution table or S-box. At the same time, the development of cryptography also requires the improvement of S-box. The article discusses approaches to improve the S-box efficiency. The structure of the substitution table with a controlled operation logic is proposed. The effectiveness of the proposed approaches is assessed.
Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency

Approaches for single-view reconstruction typically rely on viewpoint annotations, silhouettes, the absence of background, multiple views of the same instance, a template shape, or symmetry. We avoid all such supervision and assumptions by explicitly leveraging the consistency between images of different object instances. As a result, our method can learn from large collections of unlabelled images depicting the same object category. Our main contributions are two ways for leveraging cross-instance consistency: (i) progressive conditioning, a training strategy to gradually specialize the model from category to instances in a curriculum learning fashion; and (ii) neighbor reconstruction, a loss enforcing consistency between instances having similar shape or texture. Also critical to the success of our method are: our structured autoencoding architecture decomposing an image into explicit shape, texture, pose, and background; an adapted formulation of differential rendering; and a new optimization scheme alternating between 3D and pose learning. We compare our approach, UNICORN, both on the diverse synthetic ShapeNet dataset - the classical benchmark for methods requiring multiple views as supervision - and on standard real-image benchmarks (Pascal3D+ Car, CUB) for which most methods require known templates and silhouette annotations. We also showcase applicability to more challenging real-world collections (CompCars, LSUN), where silhouettes are not available and images are not cropped around the object.
On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages

A recent study by Ahmed and Devanbu reported that using a corpus of code written in multilingual datasets to fine-tune multilingual Pre-trained Language Models (PLMs) achieves higher performance as opposed to using a corpus of code written in just one programming language. However, no analysis was made with respect to fine-tuning monolingual PLMs. Furthermore, some programming languages are inherently different and code written in one language usually cannot be interchanged with the others, i.e., Ruby and Java code possess very different structure. To better understand how monolingual and multilingual PLMs affect different programming languages, we investigate 1) the performance of PLMs on Ruby for two popular Software Engineering tasks: Code Summarization and Code Search, 2) the strategy (to select programming languages) that works well on fine-tuning multilingual PLMs for Ruby, and 3) the performance of the fine-tuned PLMs on Ruby given different code lengths. In this work, we analyze over a hundred of pre-trained and fine-tuned models. Our results show that 1) multilingual PLMs have a lower Performance-to-Time Ratio (the BLEU, METEOR, or MRR scores over the fine-tuning duration) as compared to monolingual PLMs, 2) our proposed strategy to select target programming languages to fine-tune multilingual PLMs is effective: it reduces the time to fine-tune yet achieves higher performance in Code Summarization and Code Search tasks, and 3) our proposed strategy consistently shows good performance on different code lengths.
On MDS color codes

In this note, we show that the unique class of color codes whose parameters attain the quantum Singleton bound (MDS codes) is the class of [[4g+2,4g,2]] color codes derived from the tiling of the 4g-gon by the regular {4g+2,3} tessellation. Such codes are obtained from the {4g,4g} tessellation whose Fuchsian group associated with the fundamental region consists of opposite edge-pairing transformations as the planar model of an arbitrary compact orientable surface of genus g.
Projective measurements are sufficient for recycling nonlocality

Unsharp measurements are widely seen as the key resource for recycling the nonlocality of an entangled state shared between several sequential observers. Contrasting this, we here show that nonlocality can be recycled using only standard projective measurements, without using quantum ancillas. Focusing on the CHSH inequality, we determine the optimal trade-off in the Bell parameters for a maximally entangled two-qubit state in the presence of shared classical randomness. We then find that non-maximally entangled states make possible larger sequential violations, which contrasts the standard CHSH scenario. Furthermore, we show that nonlocality can be recycled even when only using projective measurements and local randomness. We discuss the implications of our results for experimental implementations of sequential nonlocality.
Quasi-Reflective Chaotic Mutant Whale Swarm Optimization Fused with Operators of Fish Aggregating Device

To improve the performance of the whale optimization algorithm and further enhance the search accuracy, while increasing the convergence speed, a quasi-reflective chaotic mutant whale swarm optimization, namely QNWOA, is proposed, fused with an operator of Fish Aggregating Devices (FADs) in this paper. Firstly, the swarm diversity is increased by using logistic chaotic mapping. Secondly, a quasi-reflective learning mechanism is introduced to improve the convergence speed of the algorithm. Then, the FADs vortex effect and wavelet variation of the marine predator algorithm (MPA) are introduced in the search phase to enhance the stability of the algorithm in the early and late stages and the ability to escape from the local optimum by broking the symmetry of iterative routes. Finally, a combination of linearly decreasing and nonlinear segmentation convergence factors is proposed to balance the local and global search capabilities of the algorithm. Nine benchmark functions are selected for the simulation, and after comparing with other algorithms, the results show that the convergence speed and solution accuracy of the proposed algorithm are promising in this study.
A Multi-Person Video Dataset Annotation Method of Spatio-Temporally Actions

Spatio-temporal action detection is an important and challenging problem in video understanding. However, the application of the existing large-scale spatio-temporal action datasets in specific fields is limited, and there is currently no public tool for making spatio-temporal action datasets, it takes a lot of time and effort for researchers to customize the spatio-temporal action datasets, so we propose a multi-Person video dataset Annotation Method of spatio-temporally actions.First, we use ffmpeg to crop the videos and frame the videos; then use yolov5 to detect human in the video frame, and then use deep sort to detect the ID of the human in the video frame. By processing the detection results of yolov5 and deep sort, we can get the annotation file of the spatio-temporal action dataset to complete the work of customizing the spatio-temporal action dataset. https://github.com/Whiffe/Custom-ava-dataset_Custom-Spatio-Temporally-Action-Video-Dataset
comparative study of handwriting characteristics in three generation of a family in Haryanvi population

Background: Since evolution handwriting is one of the most efficient acquired characteristic by any individual. It is an amalgamation of nature and nurture. Learning begins prenatally, and children are not only “ready to learn” but already actively learning from the time they are born. Parents lay the foundation stone of their child’s pre-writing skills. Moreover, genes also play a crucial role in determining certain characteristics of handwriting. Result: The present study focuses on determination of similarities in handwriting among three generations in 100 families through analysis of pre-defined class characteristics. It was observed that the “Alignment” of handwriting show high level of similarity in two generations whereas the “Movement” was the only handwriting characteristics that show high level of similarity in all the three generations. Conclusion: Therefore, to conclude it can be inferred that the father-son relationship (grandfather &amp; father or father &amp; son) depicts majority of similarities in the handwriting characteristics which might be due to the dominance of male gene.
Making Progress Based on False Discoveries

The study of adaptive data analysis examines how many statistical queries can be answered accurately using a fixed dataset while avoiding false discoveries (statistically inaccurate answers). In this paper, we tackle a question that precedes the field of study: Is data only valuable when it provides accurate answers to statistical queries? To answer this question, we use Stochastic Convex Optimization as a case study. In this model, algorithms are considered as analysts who query an estimate of the gradient of a noisy function at each iteration and move towards its minimizer. It is known that $O(1/\epsilon^2)$ examples can be used to minimize the objective function, but none of the existing methods depend on the accuracy of the estimated gradients along the trajectory. Therefore, we ask: How many samples are needed to minimize a noisy convex function if we require $\epsilon$-accurate estimates of $O(1/\epsilon^2)$ gradients? Or, might it be that inaccurate gradient estimates are \emph{necessary} for finding the minimum of a stochastic convex function at an optimal statistical rate? We provide two partial answers to this question. First, we show that a general analyst (queries that may be maliciously chosen) requires $\Omega(1/\epsilon^3)$ samples, ruling out the possibility of a foolproof mechanism. Second, we show that, under certain assumptions on the oracle, $\tilde \Omega(1/\epsilon^{2.5})$ samples are necessary for gradient descent to interact with the oracle. Our results are in contrast to classical bounds that show that $O(1/\epsilon^2)$ samples can optimize the population risk to an accuracy of $O(\epsilon)$, but with spurious gradients.
Nurses’ perception on Initial Implementation of Early Warning System: A Mixed Method Study

The early warning system (EWS) has been decided as a new standard for Indonesian hospitals. The main objective of EWS implementation is to help nurses quickly recognize and react to deteriorating patients. This study explores how EWS contributes to nurses’ clinical decisions around patient deterioration. The research design for this study was mixed-method sequential explanatory. A purposive sampling approach was used to recruit the participants. Closed and open-ended questionnaires were distributed (n = 53) to adult unit nurses and the data was analyzed by using descriptive statistics. Focus group discussions were conducted to evaluate the implementation process and the results obtained were analyzed by using thematic analysis. Both data were integrated by using a joint display table. We found that 79% of nurses indicated that they needed education about how to use the EWS and 92% of nurses required education on physiology and management of the deteriorating patient. Three themes emerged: (1) the nurses’ experience of EWS Implementation, (2) the impact of EWS implementation, and (3) ameliorating the EWS Implementation. The participants showed that the EWS has been used for the assessment, documentation, and communication process of deteriorating patient management. The EWS is a complex tool for nurses, and they need support from stakeholders to maintain and optimize the advantages.
HIGH PERFORMANCE WORK SYSTEMS AND SERVICE DELIVERY: THE MEDIATING ROLE OF MANAGERIAL VALUES IN THE NIGERIAN TOURISM INDUSTRY

Research into high performance work systems appears congruent on its substantiality in driving outcomes of service offerings and related features. Recent changes in managerial values across the Tourism industry, particularly hotels, due to the emerging realities of business operations and survival of post COVID-19 and other crisis factors, however, suggest rising concerns over job insecurity, poor staff safety and health and other factors based on the new normal. This paper, in investigating the mediating effect of managerial values on the relationship between high performance work systems and service delivery in 4-star hotels in the South-south of Nigeria, revealed that while the relationship between high performance work systems (information sharing and decision-making) and service delivery is significant, managerial values does not significantly mediate or explain the relationship between the variables. The findings point to high performance work systems as a significant predictor of service delivery. Thus, it was affirmed that high performance work systems enables the effective control and health of related processes through information sharing and decentralized decision making such that enrich communications, creativity levels and thus facilitate features that are conducive and necessary for outcomes of service delivery. It is therefore recommended that managers create enabling environment for decentralised decision-making, information sharing links and creativity for efficient service delivery.
THE ACA-BASED PID CONTROLLER FOR ENHANCING A WHEELED-MOBILE ROBOT

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE.
Design of Three Control Algorithms for an Averaging Tank with Variable Filling

Abstract An averaging tank with variable filling is a nonlinear multidimensional system and can thus be considered a complex control system. General control objectives of such object include ensuring stability, zero steady-state error, and achieving simultaneously shortest possible settling time and minimal overshoot. The main purpose of this research work was the modeling and synthesis of three control systems for an averaging tank. In order to achieve the intended purpose, in the first step, a mathematical model of the control system was derived. The model was adapted to the form required to design two out of three planned control systems by linearization and reduction of its dimensions, resulting in two system variants. A multivariable proportional-integral-derivative (PID) control system for the averaging tank was developed using optimization for tuning PID controllers. State feedback and output feedback with an integral action control system for the considered control system was designed using a linear-quadratic regulator (LQR) and optimization of weights. A fuzzy control system was designed using the Mamdani inference system. The developed control systems were tested using theMATLAB environment. Finally, the simulation results for each control algorithm (and their variants) were compared and their performance was assessed, as well as the effects of optimization in the case of PID and integral control (IC) systems.
A New Model for Cognitive IVT based on loT for Critical Safety Solutions: Firefighter Use Case

Immersive visual technologies and Internet of things are considered the main paradigms in defining new models of immersive training for critical safety applications. Accordingly, matching appropriate devices and technologies with the training requirements and its individual, technological, and operational factors is mandatory to achieve the training goals. In this context, we investigate and classify different factors based on immersive technologies, emerging devices, and sensors. Furthermore, we propose a new model for cognitive immersive visual technologies for critical safety solutions. The proposed model is validated in a use case of a fire brigade intervention after an earthquake. The use of this model mitigates heterogeneity conflicts due to the variety of immersive technologies for a specific situation by respecting stakeholders&#x0027; requirements. This paper helps future researchers, industrial and enterprise stockholders to select adequate immersive visual technologies, devices, and engines for creating further critical safety solutions.
Penerapan Quality Function Deployment Untuk Meningkatkan EServequal (Implementation of Quality Function Deployment to Improve E-Servequal)

This study aims to identify the service attributes of the application needed by consumers of the Pegadaian Digital Service application. The research objective can be completed using the Quality Function Deployment method which is able to define consumer needs into the company's technical response in improving the quality of application services. The use of the Quality Function Deployment method in this study resulted in eight variables of consumer needs. The need variable with the highest weight value will be the priority. The product variables that are prioritized are Features, Applications, and Servers. The three highest priority consumer needs can be met by the company by carrying out 4 of the 18 technical responses that have the highest importance weight. The Technical Responses were "improvement of the database coding structure", "implementing the API system", "IT and Operational team training", and "adding Capacity Connecting Bandwidth".