Reviewerly/exHarmony huggingface dataset:
Simile on sense of self, virocell and the COVID19 pandemic

The virocell concept of a viral lifecycle maintains that there are two main stages in a virus’s existence. The one is a passive state of a virion, which is not a living entity, and the second one is the virocell. In the state of virocell, a virus infects a host’s cell and hijacks its cellular machinery to achieve its own replication. Depending on the way that a cell reacts to a viral infection, it can have its activities or character as an individual living organism, suppressed completely, to some extent or not at all. In the article, the authors propose that there is analogy between the virocell theory as applied to the SARS-CoV-2 virus lifecycle and the sense of self for humans in the coronavirus pandemic world, and the post-COVID19 space-time. Methodology adopted is a combination of simile of Wittgenstein’s parallel cases, autoethnography, the virocell concept of the SARV-CoV-2 lifecycle and a multi-individual nature of one’s self based on the principles of the assemblage theory. The nature of human existence and the sense of self is defined as a relational notion which is tied to the societal educational standards, phases of developments of a human being and their fluid sense of self. The COVID19 pandemic space-time provides, in fact it demands that humanity undergoes an ongoing education of itself to maintain a productive and resilient movement forward for self and society, as an equivalent of the ribovirocell of a human cell infected with the coronavirus. The notion of speed will have to be unpacked and investigated further, which is done in part in a follow up article and is anchored as the defining characteristics of the post-COVID19 world.
ZeroGrad: Costless conscious remedies for catastrophic overfitting in the FGSM adversarial training

Vulnerability of deep neural networks to small adversarial examples has recently attracted a lot of attention. As a result, making models robust to small adversarial noises has been sought in many safety critical applications. Adversarial training through iterative projected gradient descent (PGD) has been established as one of the mainstream ideas to achieve this goal. However, PGD is computationally demanding and often prohibitive in the case of large datasets and models. For this reason, the single-step PGD, also known as the Fast Gradient Sign Method (FGSM), has recently gained interest in the field. Unfortunately, FGSM-training leads to a phenomenon called "catastrophic overfitting," which is a sudden drop in the test adversarial accuracy under the PGD attack. In this paper, we propose new methods to prevent this failure mode of the FGSM-based attacks with almost no extra computational cost. The proposed methods are also backed up with theoretical insights into the causes of the catastrophic overfitting. Our intuition is that small input gradients play a key role in this phenomenon. The signs of such gradients are quite unstable and fragile from an epoch to the next, making the signed gradient method discontinuous along the training process. These instabilities introduce large weight updates by the stochastic gradient descent, and hence potentially cause overfitting. To mitigate this issue, we propose to simply identify such gradients and make them zero prior to taking the sign in the FGSM attack calculation that is used in the training. This remedy makes the training perturbations stable, while almost preserving the adversarial property of such perturbations. The idea while being simple and efficient, achieves competitive adversarial accuracy on various datasets and can be used as an affordable method to train robust deep neural networks2.
Multi-strategy Improved Sparrow Search Algorithm

Abstract The multi-strategy improved sparrow search algorithm (MSISSA) is proposed to address the problems that the sparrow search algorithm (SSA) is not rich in population diversity, and is prone to fall into local optimality and poor accuracy in solving multi-dimensional functions. Firstly, Cat mapping is used to initialize the SSA population. Secondly, an elite reverse learning strategy is introduced to increase the population diversity and improve the global search ability of SSA. Then, the number of discoverers and the number of aware-at-risk sparrows are dynamically adjusted by improving the scaling factor. Finally, individuals are subjected to Cauchy variation or Tent chaos perturbation according to their fitness values to effectively solve the problem of their falling into local optimality. Simulation results show that MSISSA has higher performance in finding the optimum compared with classical optimization algorithms such as SSA.
Optimization tool for operating isolated diesel-photovoltaic-battery hybrid power systems using day-ahead power forecasts

This paper presents a computational tool based on a genetic algorithm and artificial neural network for optimizing the operation of isolated diesel-photovoltaic-battery hybrid power systems using day-ahead power forecasts obtained with quantile random forests. The optimization tool was conceived to be flexible, i.e., it can be used to operate isolated power systems with multiple configurations of diesel generator sets (DGS), to work with a reduced number of input data, and to be as simple as possible to be used. The optimization relies on combining valley-filling and peak-shaving strategies using battery energy storage systems while considering the combined forecast of demand and photovoltaic (PV) generation. The tool also simulates the behavior of the DGS to define the optimum arrangement of diesel generators considering the variability of both demand and PV generation. The output consists of hourly values of energy storage power dispatch, DGS arrangement, and, if necessary, load shedding and/or PV curtailment. The algorithm that implements the optimization tool, which is currently in the phase of field-test in the isolated diesel-photovoltaic-battery hybrid power system of Fernando de Noronha, Brazil, demonstrated a good performance in computer simulations validated with real measured data.
Radial-based undersampling approach with adaptive undersampling ratio determination

Nowadays, machine learning techniques are employed in a wide range of applications, where classification is a common task in machine learning. It predicts the class label of a previously unseen example according to the decision of a classification model, which is learned by running a classifier learning algorithm on the collected training examples set. On the other hand, in many practical applications, the collected training sets are usually class imbalanced, that is, one class can have significantly more examples than the other class(es), but the minority class usually carries much valuable information and is more important than the majority class. However, most classifier learning algorithms are designed under the assumption that each class in a training set has approximately the same number of examples, leading to the consequence that they often can not achieve satisfactory classification performance on imbalanced data especially for the minority class examples. To solve this problem, a Radial-Based Undersampling approach with Adaptive undersampling Ratio (RBU-AR) is proposed in this paper. The main novelty of RBU-AR is that it attempts to determine the proper undersampling ratio according to the class overlap data complexity rather than adopting the default value 1 or using the empirical trial and error strategy as many existing undersampling approaches do. Experiments are conducted on 30 benchmark imbalanced datasets and 10 artificial datasets, the obtained results and corresponding statistical tests indicate that class overlap degree indeed has a great influence on the achievable classification performance and is usually more important than the class imbalance ratio IR, and our undersampling approach RBU-AR generally achieves highly competitive or better performance with respect to several state-of-the-art approaches. Therefore, this work provides a theoretical guideline in determining the proper extent of undersampling by utilizing the class overlap data complexity information.
Synthesizing research on programmers’ mental models of programs, tasks and concepts — A systematic literature review

Programmers' mental models represent their knowledge and understanding of programs, programming concepts, and programming in general. They guide programmers' work and influence their task performance. Understanding mental models is important for designing work systems and practices that support programmers. Although the importance of programmers' mental models is widely acknowledged, research on mental models has decreased over the years. The results are scattered and do not take into account recent developments in software engineering. In this article, we analyze the state of research on programmers' mental models and provide an overview of existing research. We connect results on mental models from different strands of research to form a more unified knowledge base on the topic. We conducted a systematic literature review on programmers' mental models. We analyzed literature addressing mental models in different contexts, including mental models of programs, programming tasks, and programming concepts. Using nine search engines, we found 3678 articles (excluding duplicates). Of these, 84 were selected for further analysis. Using the snowballing technique, starting from these 84, we obtained a final result set containing 187 articles. We show that the literature shares a kernel of shared understanding of mental models. By collating and connecting results on mental models from different fields of research, we provide a comprehensive synthesis of results related to programmers' mental models. The research field on programmers' mental models faces many challenges arising from a lack of a shared knowledge base and poorly defined constructs. By creating a unified knowledge base on the topic, this work provides a basis for future work on mental models. We also point to directions for future studies. In particular, we call for studies that examine programmers working with modern practices and tools.
Tight bound on tilted CHSH inequality with measurement dependence

Measurement dependence is the property that the distribution of the underlying variable is correlated with the measurement settings. If there is measurement dependence in a Bell test, the upper bound of Bell inequality will be affected. In the framework of device-independent quantum random number generation, the violation of Bell inequality indicates the existence of certified randomness. A tight upper bound on Bell inequality is critical to the amount of certified randomness. Recently, Sadhu and Das (2023) obtained a modified tilted CHSH inequality when measurement dependence exists. However, the upper bound of the modified tilted CHSH inequality they obtained is not tight. In this paper, we propose a method to quantify the measurement dependence applicable to the tilted CHSH inequality based on its structure. After that, we obtain a tight upper bound of modified tilted CHSH inequality and construct a local deterministic strategy to reach the upper bound.
Continuous Filament Network of the Local Universe

Simulated galaxy distributions are suitable for developing filament detection algorithms. However, samples of observed galaxies, being of limited size, cause difficulties that lead to a discontinuous distribution of filaments. We created a new galaxy filament catalog composed of a continuous cosmic web with no lone filaments. The core of our approach is a ridge filter used within the framework of image analysis. We considered galaxies from the HyperLeda database with redshifts $0.02\leqslant z\leqslant 0.1$, and in the solid angle $120^\circ\leqslant {\rm RA}\leqslant 240^\circ$, $0^\circ\leqslant {\rm DEC}\leqslant 60^\circ$. We divided the sample into 16 two-dimensional celestial projections with redshift bin $\Delta z=0.005$, and compared our continuous filament network with a similar recent catalog covering the same region of the sky. We tested our catalog on two application scenarios. First, we compared the distributions of distance to nearest filament of various astrophysical sources (Seyfert galaxies and other active galactic nuclei, radio galaxies, low surface brightness galaxies, and dwarf galaxies), and found that all source types trace the filaments well, with no systematic differences. Next, among the HyperLeda galaxies, we investigated the dependence of $g-r$ color distribution on distance to nearest filament, and confirmed that early type galaxies are located on average further from the filaments than late type ones.
A bioinspired discrete heuristic algorithm to generate the effective structural model of a program source code

When the source code of a software is the only product available, program understanding has a substantial influence on software maintenance costs. The main goal in code comprehension is to extract information that is used in the software maintenance stage. Generating the structural model from the source code helps to alleviate the software maintenance cost. Software module clustering is thought to be a viable reverse engineering approach for building structural design models from source code. Finding the optimal clustering model is an NP-complete problem. The primary goals of this study are to minimize the number of connections between created clusters, enhance internal connections inside clusters, and enhance clustering quality. The previous approaches' main flaws were their poor success rates, instability, and inadequate modularization quality. The Olympiad optimization algorithm was introduced in this paper as a novel population-based and discrete heuristic algorithm for solving the software module clustering problem. This algorithm was inspired by the competition of a group of students to increase their knowledge and prepare for an Olympiad exam. The suggested algorithm employs a divide-and-conquer strategy, as well as local and global search methodologies. The effectiveness of the suggested Olympiad algorithm to solve the module clustering problem was evaluated using ten real-world and standard software benchmarks. According to the experimental results, on average, the modularization quality of the generated clustered models for the ten benchmarks is about 3.94 with 0.067 standard deviations. The proposed algorithm is superior to the prior algorithms in terms of modularization quality, convergence, and stability of results. Furthermore, the results of the experiments indicate that the proposed algorithm can be used to solve other discrete optimization problems efficiently.
A Survey and Risk Assessment on Virtual and Augmented Reality Cyberattacks

Nowadays, Virtual Reality (VR) and Augmented Reality (AR) systems are not exclusively associated with the gaming industry. Their potential is also useful for other business areas such as healthcare, automotive, and educational domains. Companies need to accompany technological advances and enhance their business processes and thus, the adoption of VR or AR technologies could be advantageous in reducing resource usage or improving the overall efficiency of processes. However, before implementing these technologies, companies must be aware of potential cyberattacks and security risks to which these systems are subject. This study presents a survey of attacks related to VR and AR scenarios and their risk assessment when considering healthcare, automation, education, and gaming industries. The main goal is to make companies aware of the possible cyberattacks that can affect the devices and their impact on their business domain.
Matrix Profile based Anomaly Detection in Streaming Gait Data for Fall Prevention

The automatic detection of gait anomalies can lead to systems that can be used for fall detection and prevention. In this paper, we present a gait anomaly detection system based on the Matrix Profile (MP) algorithm. The MP algorithm is exact, parameter free, simple and efficient, making it a perfect candidate for on the edge deployment. We propose a gait anomaly detection system that is able to adapt to an individual’s gait pattern and successfully detect anomalous steps with short latency. To evaluate the system we record a small database of enacted anomalous steps. The results show the system outperforms a more complex Neural Network baseline.
Occlusion-Aware Pedestrian Detection and Tracking

This paper proposes an occlusion-aware mechanism, used on a framework for detecting and tracking pedestrians in videos acquired from surveillance cameras, which includes the extraction of trajectory points, estimation of walking velocities, detection of groups, and projection of the final trajectories into a 2D plan. The occlusion-aware mechanism is introduced in order to manage irregularities in the pedestrian trajectory data derived from occlusions. This mechanism is able to identify the parts of the human body that are occluded, using skeleton data generated by human pose estimation algorithms, and adjust the dimensions of the bounding boxes of the occluded pedestrians.
Predicting QoE for Delay-critical services in Mobile Networks: A video conferencing case study

There is an increasing demand to support high-quality data communications for delay-critical services such as Augmented/Virtual Reality (ARNR), cloud gaming, drone control or video conferencing. Mobile network operators need to estimate quality of experience (QoE) values based on available QoS parameters such as radio environment and core network metrics, to respond to performance degradations and maximize revenue, as the QoE of users is more important than the QoS metrics. To develop accurate QoE models, operators need a large amount of versatile data under different network conditions. We propose a standardized and automated data collection framework to gather fine-grained transport and radio data, along with user experience values assigned through a standardized survey, for model training purposes in delay-critical services. Our study identifies valuable QoS parameters and relationships between mobile network degradations and service quality impacts, demonstrating the possibility of building accurate QoE models with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.78 based solely on transport and radio data. Our finding can be beneficial to MNOs in designing new data collection and QoE estimation services for various delay-critical services.
Measuring and Predicting Perceptions of Video Quality Across Screen Sizes with Crowdsourcing

A large-scale crowdsourcing experiment was carried out to study how changes in screen size affect perceptions of video quality. By rescaling video stimuli to different canvas sizes on participants' devices, we collected responses that enabled us to study how encoding artifact visibility is influenced by changes to the screen size. We collected ratings on 1,674 distorted videos from 14,450 participants from four countries and found the data to be reliable and largely consistent. With these data, we evaluated state-of-the-art subjective modeling techniques and benchmarked several objective quality models across screen sizes.
Are Quality and Sustainability Reconcilable? A Subjective Study on Video QoE, Luminance and Resolution

The increasing use of ICT has raised concerns about its negative impact on energy consumption and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$CO_{2}$</tex> emissions. To address this issue, there is a need to better understand the trade-off between Quality of Experience (QoE) and sustainable video streaming services. In this study, we designed and conducted a subjective assessment to investigate the impact of video resolution, different types of luminance, and different end devices on the QoE and energy consumption of video streaming services. Then, we applied statistical models (Analysis of Variance and t-test) to subjective data to find out what factors influence the QoE the most and consume more energy. The obtained results suggest that under specific conditions (e.g., dark or bright ambient, low device backlight luminance, small-screen device) the users could be encouraged towards a trade-off between acceptable QoE and sustainable (green) choices because spending more energy (e.g., streaming higher-quality video) would not provide noticeable QoE enhancement.
Human Interaction in Industrial Tele-Operated Driving: Laboratory Investigation

Tele-operated driving enables industrial operators to control heavy machinery remotely. By doing so, they could work in improved and safe workplaces. However, some challenges need to be investigated while presenting visual information from on-site scenes for operators sitting at a distance in a remote site. This paper discusses the impact of video quality (spatial resolution), field of view, and latency on users' depth perception, experience, and performance in a lab-based tele-operated application. We performed user experience evaluation experiments to study these impacts. Overall, the user experience and comfort decrease while the users' performance error increases with an increase in the glass-to-glass latency. The user comfort reduces, and the user performance error increases with reduced video quality (spatial resolution).
Appeal and quality assessment for AI-generated images

Recently AI-generated images gained in popularity. A critical aspect of AI-generated images using, e.g., DALL-E-2 or Midjourney, is that they may look artificial, be of low quality, or have a low appeal in contrast to real images, depending on the text prompt and AI generator. For this reason, we evaluate the quality and appeal of AI-generated images using a crowdsourcing test as an extension of our recently published AVT-AI-Image-Dataset. This dataset consists of a total of 135 images generated with five different AI-text-to-image generators. Based on the collected subjective ratings in the crowdsourcing test, we evaluate the different used AI generators in terms of image quality and appeal of the AI-generated images. We also link image quality and image appeal also with SoA objective models. The extension will be made publicly available for reproducibility.
Relaxed forced choice improves performance of visual quality assessment methods

In image quality assessment, a collective visual quality score for an image or video is obtained from the individual ratings of many subjects. One commonly used format for these experiments is the two-alternative forced choice method. Two stimuli with the same content but differing visual quality are presented sequentially or side-by-side. Subjects are asked to select the one of better quality, and when uncertain, they are required to guess. The relaxed alternative forced choice format aims to reduce the cognitive load and the noise in the responses due to the guessing by providing a third response option, namely, “not sure”. This work presents a large and comprehensive crowdsourcing experiment to compare these two response formats: the one with the “not sure” option and the one without it. To provide unambiguous ground truth for quality evaluation, subjects were shown pairs of images with differing numbers of dots and asked each time to choose the one with more dots. Our crowdsourcing study involved 254 participants and was conducted using a within-subject design. Each participant was asked to respond to 40 pair comparisons with and without the “not sure” response option and completed a questionnaire to evaluate their cognitive load for each testing condition. The experimental results show that the inclusion of the “not sure” response option in the forced choice method reduced mental load and led to models with better data fit and correspondence to ground truth. We also tested for the equivalence of the models and found that they were different. The dataset is available at http://database.mmsp-kn.de/cogvqa-database.html.