Reviewerly/exHarmony huggingface dataset:
Serious Games for Vision Training Exercises with Eye-Tracking Technologies: Lessons from Developing a Prototype

Eye-tracking technologies (ETs) and serious games (SGs) have emerged as new methods promising better support for vision screening and training. Previous research has shown the practicality of eye-tracking technology for vision screening in health care, but there remains a need for studies showing that the effective utilization of SGs and ETs are beneficial for vision training. This study investigates the feasibility of SGs and ETs for vision training by designing, developing, and evaluating a prototype influenced by commercially available games, based on a battery of exercises previously defined by vision experts. Data were collected from five participants, including a vision teacher, through a user experience questionnaire (UEQ) following a mixed method. Data analysis of the UEQ results and interviews highlighted the current challenges and positive attitudes in using SGs and ET for vision training. In conjunction with UEQ indicators such as attractiveness and perspicuity, the stimulation of the vision training battery based on the user experience provided insights into using ETs and further developing SGs to better approach different eye movements for vision training.
The Epistemological Compass and the (Post)Truth about Objectivity

Massimo Dell'Utri proposes the idea of an 'epistemological compass', which he alleges provides a common intuitive sense of objectivity, the existence of which defenders of 'post-truth' positions would perversely try to deny. I argue that Dell'Utri's choice of a compass – metaphorical or otherwise – is unfortunate because it is a device that presupposes that what appears plain to the senses is directed by hidden forces emanating from distant sources, such as the stars. More generally, the post-truth condition is not about the denial of facts as ordinarily understood. Rather, it is about the denial of a privileged context in terms of which the significance of the facts should be understood. In Dell'Utri's terms, it implies a plurality of epistemological compasses, which in turn undermines the effectiveness of the metaphor. I have described this situation as a struggle over 'the name of the game'. In terms of philosophical logic, it is about which metalanguage provides the semantics for expressions in the object language the people try to deploy to their advantage.
Prior’s big Y and the Idea of Branching Time

In his famous letter to A. N. Prior dated 3 September 1958, Saul Kripke suggested the use of branching time in temporal logic. In this paper, however, it is argued that Prior worked with an idea close to the notion of branching time (‘the big Y’) already the year before he received Kripke’s letter. It is likely that Prior’s findings based on this early study can explain why Prior so quickly accepted the idea of branching time when he received Kripke’s letter.
Computational Strategies and Estimation Performance With Bayesian Semiparametric Item Response Theory Models

Item response theory (IRT) models typically rely on a normality assumption for subject-specific latent traits, which is often unrealistic in practice. Semiparametric extensions based on Dirichlet process mixtures (DPMs) offer a more flexible representation of the unknown distribution of the latent trait. However, the use of such models in the IRT literature has been extremely limited, in good part because of the lack of comprehensive studies and accessible software tools. This article provides guidance for practitioners on semiparametric IRT models and their implementation. In particular, we rely on NIMBLE, a flexible software system for hierarchical models that enables the use of DPMs. We highlight efficient sampling strategies for model estimation and compare inferential results under parametric and semiparametric models.
Parity Check Based Fault Detection against Timing Fault Injection Attacks

Fault injection technologies can be utilized to steal secret information inside integrated circuits (ICs), and thus cause serious information security threats. Parity check has been adopted as an efficient method against fault injection attacks. However, the contradiction between security and overhead restricts the further development and applications of parity check in fault injection detection. This paper proposes two methods, mixed-grained parity check and word recombination parity check, based on parity check for the trade-off between security and overhead. The efficiency of the proposed approaches is verified on RC5, AES, and DES encryption implementations by clock glitch attack. Compared with the traditional parity check, the fault coverage rate of the mixed-grained approach can be increased by up to 53.69% by consuming 13.2% registers more. Against the traditional parity check, the fault coverage rate of the word recombination approach can be increased by up to 47.16% by using only 2.35% register more. The proposed approaches provide IC designers with countermeasure options targeting different design skills and design specifications.
Bi-LSTM-Based Neural Source Code Summarization

Code summarization is a task that is often employed by software developers for fixing code or reusing code. Software documentation is essential when it comes to software maintenance. The highest cost in software development goes to maintenance because of the difficulty of code modification. To help in reducing the cost and time spent on software development and maintenance, we introduce an automated comment summarization and commenting technique using state-of-the-art techniques in summarization. We use deep neural networks, specifically bidirectional long short-term memory (Bi-LSTM), combined with an attention model to enhance performance. In this study, we propose two different scenarios: one that uses the code text and the structure of the code represented in an abstract syntax tree (AST) and another that uses only code text. We propose two encoder-based models for the first scenario that encodes the code text and the AST independently. Previous works have used different techniques in deep neural networks to generate comments. This study’s proposed methodologies scored higher than previous works based on the gated recurrent unit encoder. We conducted our experiment on a dataset of 2.1 million pairs of Java methods and comments. Additionally, we showed that the code structure is beneficial for methods’ signatures featuring unclear words.
Research on a Map-Based Cooperative Navigation System for Spraying–Dosing Robot Group

To solve the problem encountered when the spraying robot has run out of medicine even though the spraying task on the field is not complete, we developed a spraying–dosing robot group and proposed a collaborative navigation system based on an orchard map. Firstly, we constructed a 3D orchard point cloud map and set up navigation path points on the projected map. Secondly, we developed a master–slave command-based cooperative navigation strategy, where the spraying robot was the master and the dosing robot was the slave. Finally, the spraying robot and the dosing robot completed the cooperative navigation on the constructed map by using the pure pursuit algorithm and D-A control algorithm, respectively. To validate the cooperative navigation system, we conducted field tests on the separate communication and navigation control. The results of communication experiments demonstrated that the packet loss rate was less than 5%, which satisfied communication requirements. The experimental results of the navigation control demonstrated that the maximum value of the absolute lateral error is 24.9 cm for the spraying robot and 29.7 cm for the dosing robot. The collaborative navigation system proposed in this research can meet the automatic navigation requirements of the spraying–dosing robot group for collaborative tasks in traditional orchards.
View-Invariant Spatiotemporal Attentive Motion Planning and Control Network for Autonomous Vehicles

Autonomous driving vehicles (ADVs) are sleeping giant intelligent machines that perceive their environment and make driving decisions. Most existing ADSs are built as hand-engineered perception-planning-control pipelines. However, designing generalized handcrafted rules for autonomous driving in an urban environment is complex. An alternative approach is imitation learning (IL) from human driving demonstrations. However, most previous studies on IL for autonomous driving face several critical challenges: (1) poor generalization ability toward the unseen environment due to distribution shift problems such as changes in driving views and weather conditions; (2) lack of interpretability; and (3) mostly trained to learn the single driving task. To address these challenges, we propose a view-invariant spatiotemporal attentive planning and control network for autonomous vehicles. The proposed method first extracts spatiotemporal representations from images of a front and top driving view sequence through attentive Siamese 3DResNet. Then, the maximum mean discrepancy loss (MMD) is employed to minimize spatiotemporal discrepancies between these driving views and produce an invariant spatiotemporal representation, which reduces domain shift due to view change. Finally, the multitasking learning (MTL) method is employed to jointly train trajectory planning and high-level control tasks based on learned representations and previous motions. Results of extensive experimental evaluations on a large autonomous driving dataset with various weather/lighting conditions verified that the proposed method is effective for feasible motion planning and control in autonomous vehicles.
Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering

Text document clustering is one of the data mining techniques used in many real-world applications such as information retrieval from IoT Sensors data, duplicate content detection, and document organization. Swarm intelligence (SI) algorithms are suitable for solving complex text document clustering problems compared to traditional clustering algorithms. The previous studies show that in SI algorithms, particle swarm optimization (PSO) provides an effective solution to text document clustering problems. This PSO still needs to be improved to avoid the problems such as premature convergence to local optima. In this paper, an approach called dynamic sub-swarm of PSO (subswarm-PSO) is proposed to improve the results of PSO for text document clustering problems and avoid the local optimum by improving the global search capabilities of PSO. The results of this proposed approach were compared with the standard PSO algorithm and K-means algorithm. As for performance assurance, the evaluation metric purity is used with six benchmark data sets. The experimental results of this study show that our proposed subswarm-PSO algorithm performs best with high purity comparing the standard PSO and K-means traditional algorithms and also the execution time of subswarm-PSO comparatively takes a little less than the standard PSO algorithm.
Technology recognition and traffic characterization for wireless technologies in ITS band

The rapid advancement of wireless technologies requires efficient spectrum management considering issues such as interference management and fair coexistence between different technologies. Wireless technology recognition is one of the approaches used to enable intelligent spectrum management. This work proposes a technology classification and traffic characterization system that can recognize and characterize a wide range of wireless technologies that may coexist in the ITS 5.9 GHz band, namely LTE, Wi-Fi, 5G NR, C-V2X PC5, and ITS-G5 technologies. Compared to current state-of-the-art technology recognition solutions, a short time resolution window is selected based on the shortest possible frame duration of the considered technologies. We carried out a “complexity and accuracy trade-off” analysis for six distinct technology recognition models trained and validated at different sampling rates, including 1, 5, 10, 15, 20, and 25 Msps. In addition, the performance of the technology recognition models was evaluated under different channel conditions. For average to high SNR, a less complex CNN model with lower sampling rates (e.g., 5 Msps) can effectively distinguish the signal with 96% classification accuracy. On the other hand, high classification accuracy is obtained using complex, high sampling rate-based CNN models (e.g., 20 Msps) for low (less than 0 dB) SNR channels. A traffic characterization process is also proposed, where the output of the technology recognition is used to identify the traffic characteristics of the technologies in terms of channel occupancy time, transmission pattern, and frame count. The obtained results show that the proposed solution can be used to effectively characterize the identified traffic.
Traffic Optimization Algorithms in Optical Networks For Real Time Traffic Analysis

The rapid growth of metropolitan areas poses significant traffic control issues. Advanced telecommunication structures, such as the IoT (Internet of Things) technology, including wireless connections, produce enormous amounts of diverse traffic information. Conventional networking control approaches for tracking and information analysis confront certain obstacles and problems in specific systems, like reliability and efficient actual-time analysis of massive datasets. Furthermore, due to variables like gadget portability and connection diversity, the overall structure of internet activity, particularly in wireless networks, exhibits extremely complicated activity. The article aims to create a method for collecting high-quality, comprehensive information like transportation flowing strength, travel routes, and mean vehicle velocity. Simultaneously, information is gathered throughout the whole operational zone of junctions and neighboring highway segments under the city video security sensor perspective. More profound learning methods will be used within the communication domain for Networking Transport Management and Analytics (NTMA) activities such as traffic categorization and forecasting. The YOLOv3 artificial routing topology, as well as a SORT-accessible monitor, are used in our approach. Utilizing extended masking branches and improving its geometry of anchoring, overall essential efficiency in YOLO has been enhanced. We employed a technique of viewpoint conversion using parameters from actual pictures to global dimensions for computing overall vehicular velocities.
An effective and secured authentication and sharing of data with dynamic groups in cloud

Cloud computing framework facilitates users to save data in the cloud server for using desired and scalable services. The data sharing in the cloud facilitates several contributors to generously distribute group data. However, how to facilitate data security in group and how to effective outsourced data are major issues in the existing methods. The aim of this research is to devise a technique for privacy preservation with data sharing in the cloud. The method includes nine phases, like set-up phase, user-registration phase, key generation phase, revocation phase, file generation, authentication, encryption, file access and verification, and decryption. In setup, the group manager performs the initialization of the system. In user registration, the registration of group members is done with group manager. In the key generation phase, a common key is generated amongst group members. In the user revocation phase, the user revocation is done with the group manager. In the file encryption phase, the member encrypts the data file. In the file generation phase, the data file are accumulated in the cloud. The file access phase helps to access the file from the cloud. Here, the authentication phase helps to access the cloud and the decryption phase helps to verify authorized users using the signature. The proposed privacy-based group data sharing outperformed with the highest detection rate of 95%, smallest computation cost of 719 s and smallest memory of 56991123 bytes.
Effects of YouTube Culture on Academic Performance among Students in Jordan: A Structural Equation Modeling Study

The global technological transformation has greatly changed and improved almost every part of our life. Notably, today, social media influences us even for the basic necessities of life, such as education. By keeping the importance of social media in education, this research also focuses on the effects of YouTube culture in improving students' academic performance in Jordan. The researchers used Structural Equation Modeling (SEM)as the study relies on a self-proposed conceptual model. Results revealed that social network usage significantly affects Perceived Usefulness, which further leads to accepting YouTube educational channels. Besides, these YouTube educational channels significantly affect the academic performance of Jordanian students. Finally, demographic variables are found to have a significant indirect effect on the student's academic performance, indicating that YouTube channels as benefiting Jordanian students. Thus, it is concluded that YouTube significantly affects academic performance among Jordanian students. Additionally, the effect of demographical factors is also strong on YouTube usage and academic performance. Here it is concluded that the negativity or positivity of these impacts can be determined by the purpose of usage and the extent to which it is used. Further, the researchers have highlighted and discussed the study's limitations.
MTSCANet: Multi temporal resolution temporal semantic context aggregation network

Abstract Temporal action localisation is a challenging task, and video context is crucial to localisation actions. Most existing cases that incorporate temporal and semantic contexts into video features suffer from single contextual representation and blurred temporal boundaries. In this study, a multi‐temporal resolution pyramid structure model is proposed. Firstly, a temporal‐semantic context aggregation module (TSCF) is designed to assign different attention weights to temporal contexts and combine them with multi‐level semantics into video features. Secondly, for the problem of large differences in the time span between different actions in the video, a local‐global attention module is designed to combine local and global temporal dependencies for each temporal point to obtain a more flexible and robust representation of contextual relations. The redundant representation of the convolution kernel is reduced by modifying the convolution and the arithmetic power is redeployed at a microscopic granularity. To verify the effectiveness of the model, extensive experiments on three challenging datasets are performed. On THUMOS14, the best performance is obtained in IoU@0.3–0.6 with an average mAP of 47.02%. On ActivityNet‐1.3, an average mAP of 34.94% was obtained. On HACS, an average mAP of 28.46% was achieved.
Low Code for Smart Software Development

The more we know about patterns in code, the better we can support those patterns. In this article, Jordi Cabot and Robert Claris ´o discuss the promise and perils of AI enhanced low-code environments that allow programmers and non=programmers alike to quickly deliver software solutions. They offer a “wish list” that outlines what developers need to watch for in low-code tools for smart software. Got anything else you want to say about SE+AI? For SE+AI applications, you have a surprising result or industrial experience? Something that challenges decades of conventional thinking in software engineering? If so, email a one paragraph synopsis to tim@menzies.us (use the subject line “SE for AI: Idea: [your idea]”). If that looks interesting, I’ll ask you to submit a 1,000- to 2,400-word article (where each graph, table, or figure is worth 250 words) for review for IEEE Software. Note: Heresies are more than welcome (if supported by well-reasoned industrial experiences, case studies, or other empirical results).—Tim Menzies
Multi-label feature selection using density-based graph clustering and ant colony optimization

Abstract Multi-label learning is a machine learning subclass that aims to assign more than one label simultaneously for each instance. Many real-world tasks include high-dimensional data which reduces the performance of machine learning methods. To solve this issue, a filter and multi-label feature selection is proposed in this paper. The main idea of the proposed method is to choose highly relevant and non-redundant features with the lowest information loss. The proposed method first uses a novel graph-based density peaks clustering to group similar features to reach this goal. It then uses the ant colony optimization search process to rank features based on their relevancy to a set of labels and also their redundancy with the other features. A graph first represents the feature space, and then a novel density peaks clustering is used to group similar features. Then, the ants are searched through the graph to select a set of non-similar features by remaining in the clusters with a low probability and jumping among the clusters with a high probability. Moreover, in this paper, to evaluate the solutions found by the ants, a novel criterion based on mutual information was used to assign a high pheromone value to highly relevant and non-redundant features. Finally, the final features are chosen based on their pheromone values. The results of experiments on a set of real-world datasets show the superiority of the proposed method over a set of baseline and state-of-the-art methods.
Introduction to the thematic collection: applications of innovations in geochemical data analysis

Geochemistry: Exploration, Environment, Analysis (GEEA) as the scientific journal of the AAG focuses on the use of geochemistry in mineral exploration/resource development. Since the first issue of GEEA was published in 2001, there have been hundreds of papers regarding methodologies for geochemical data analysis. In 2017, a thematic issue: analysis of exploration geochemical data for mapping of anomalies (Carranza and Zuo, 2017) was published on the development of methods and techniques for exploration geochemical data analysis. As a successor, this current thematic set of papers introduces some new progress and advancements in geochemical data analysis, e.g., nonlinear, fractal/multifractal, multi-statistical, machine learning methods and their applications in mineral exploration. Twelve papers from Professor Qiuming Cheng's colleagues in China and Canada are organized in this thematic set in honor of his Gold Medal Award. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/topic/collections/applications-of-innovations-in-geochemical-data-analysis
Stochastic Model of Sub-Poissonian Quantum Light in an Interband Cascade Laser

This work theoretically investigates the possibility of generating amplitude-squeezed light with high-quantum-efficiency interband cascade lasers. Based on a stochastic approach, we show that, by employing the suppressed-pump-noise configuration, this kind of midinfrared source enables operation with considerable amplitude squeezing over a large bandwidth of several GHz. Our results facilitate future midinfrared quantum photonic applications such as free-space secure communications.