Reviewerly/exHarmony huggingface dataset:
Towards JPEG AIC part 3: visual quality assessment of high to visually-lossless image coding

Due to the increasing number of pictures captured and stored every day by and on digital devices, lossy image compression has become inevitable to limit the needed storage requirement. As a consequence, these compression methods might introduce some visual artifacts, whose visibility depends on the chosen bitrate. Modern applications target images with high to near-visually lossless quality, in order to maximize the visual quality while still reducing storage space consumption. In this context, subjective and objective image quality assessment are essential tools in order to develop compression methods able to generate images with high visual quality. While a large variety of subjective quality assessment protocols have been standardized in the past, they have been found to be imprecise in the quality interval from high to near-visually lossless. Similarly, an objective quality metric designed to work specifically in the mentioned range has not been designed yet. As current quality assessment methodologies have proven to be unreliable, a renewed activity on the Assessment of Image Coding, also referred to as JPEG AIC, was recently launched by the JPEG Committee. The goal of this activity is to extend previous standardization efforts, i.e. AIC Part 1 and AIC Part 2 (also know as AIC-1 and AIC-2), by developing a new standard, known as AIC Part 3 (or AIC-3). Notably, the goal of the activity is to standardize both subjective and objective visual quality assessment methods, specifically targeting images with quality in the range from high to near-visually lossless. Two Draft Calls for Contributions on Subjective Image Quality Assessment1, 2 were released, aiming at collecting contributions on new methods and best practices for subjective image quality assessment in the target quality range, while a Call for Proposals on Objective Image Quality Assessment is expected to be released at a later date. This paper aims at summarizing past JPEG AIC efforts and reviewing the main objectives of the future activities, outlining the scope of the activity, the main use cases and requirements, and call for contributions. Finally, conclusions on the activity are drawn.
Green image codec: a lightweight learning-based image coding method

Image compression has experienced a new revolution with the success of deep learning, which yields superior rate-distortion (RD) performance against traditional codecs. Yet, high computational complexity and energy consumption are the main bottlenecks that hinder the practical applicability of deep-learning-based (DL-based) codecs. Inspired by the neural network’s hierarchic structure yet with lower complexity, we propose a new lightweight image coding framework and name it the ”Green Image Codec” (GIC) in this work. First, GIC down-samples an input image into several spatial resolutions from fine-to-coarse grids and computes image residuals between two adjacent grids. Then, it encodes the coarsest content, interpolates content from coarse-tofine grids, encodes residuals, and adds residuals to interpolated images for reconstruction. All coding steps are implemented by vector quantization while all interpolation steps are conducted by the Lanczos interpolation. To facilitate VQ codebook training, the Saab transform is applied for energy compaction and, thus, dimension reduction. A simple rate-distortion optimization (RDO) is developed to help select the coding parameters. GIC yields an RD performance that is comparable with BPG at significantly lower complexity.
Complexity Measures for IoT Network Traffic

The coming era of widespread integration of Internet of Things (IoT) devices to all areas of society has facilitated a fundamental transformation of local and global communication networks, giving rise to novel issues relating to capacity planning, network administration, and cybersecurity. Accurate network traffic prediction is one of the key enablers for addressing these challenges. While there are methods for quantifying the complexity (predictability in timing, shape, and volume) of wide-scale aggregate traffic, they cannot be directly applied to IoT traffic as they do not account for the heterogeneity of IoT devices. Lacking an effective complexity characterization for IoT traffic, network traffic administrators are under-informed on the impacts of IoT device-type traffic on their networks. In this work, the complexity of IoT traffic is examined from two novel perspectives, the information-theoretic approach of Lempel–Ziv, a foundational algorithm in lossless data compression, and in the distribution of spectral components of the Fourier transform. Based on these perspectives, two new measures of IoT network traffic complexity are proposed. Furthermore, we introduce a novel mathematical framework to permit a formal comparison of new and existing methods. The new framework additionally verifies that the new metrics satisfy desirable properties for a measure of complexity. In a comprehensive empirical study, our results, when compared with existing approaches, exceed all others in behavioral resolution, convergence rate, physical interpretability, and algorithmic stability, under severely heterogeneous conditions. Benchmark experiments demonstrate substantial run-time improvements over existing approaches, creating a strong case for their use in online, real-time settings.
Automated evolution of CNN with 3PGA for geographical landmark recognition

Computer Vision (CV) is constantly inundated with massive volumes of data. One of the most challenging types of data for an Artificial Intelligence (AI) system is imagery data. Convolutional neural networks (CNNs) are utilized to cope with Big Data of such type, but progress is gradual. The 3 Parent Genetic Algorithm (3PGA), an evolutionary computation method, is employed to evolve a default CNN in this study. 3PGA is an extension of GA which has been developed further for better optimization. We observed from the literature that 3PGA is giving excellent results on standard benchmark functions as compared to other recent soft-computing-based approaches. The accuracy of the evolved CNN increased from 53% to 75%, resulting in a net improvement of more than 40%. Furthermore, it was noted that the hyperparametric combinations or features of a CNN, which are very distinct from those commonly utilized, appear to perform better. A geographical landmarks dataset from Google was used for testing purposes. Landmark recognition is one of the most time-consuming jobs for an AI system, and the optimization of a network on a landmarks dataset shows that evolutionary computation can be substantially used in the future for the evolution of Artificial Neural Networks (ANNs).
Configuration-aware model predictive motion planning for Tractor–Trailer Mobile Robot

This study proposes a motion planning method with strict obstacle avoidance constraints among polygonal-shaped objects for a tractor–trailer mobile robot. The conventional method generally expresses collision-free constraints by approximating a robot as one circle or several circles or ellipsoids. However, accomplishing moving tasks with minimum margins is desirable, with improved efficiency of space availability when multiple robots operate in one area such as a factory, restaurant, office, or warehouse. The target of this study is a tractor–trailer mobile robot that can transport more goods efficiently, while significantly reducing margins during movement. To control the target, multiple convex polygonal objects expressing the mobile robot and surroundings are transformed into linear simultaneous inequalities using ‘Farkas’ lemma.' Embedding inequalities into model predictive control yields a motion planner that allows tractor–trailer mobile robot movement and possesses non-holonomic constraints. The proposed model is validated through simulation and real machine experiments.
The “ultimate empathy machine” as technocratic solutionism? Audience reception of the distant refugee crisis through virtual reality

This article aims to deconstruct the myth of technological utopianism which contends that immersive virtual reality (VR) can inevitably lead to a more moral and egalitarian world due to its promises of copresence, immediacy and transcendence in humanitarian communication. The problématique we explore is whether existing VR artifacts, as exemplars of the "ultimate empathy machine," construct a technocratic solutionism which becomes constitutive of humanitarian crises themselves. Drawing upon empirical material from focus group discussions and in-depth interviews with VR audiences in China, Germany, and the UK, the findings show that VR may easily construct a depoliticized hyperreality of intense spectacularity and trap audiences within an improper distance, thereby reworking the colonial legacies of humanitarianism while also obfuscating complex asymmetries of power and structural political exclusion. These findings have important implications for reminding humanitarian news organizations and aid agencies that they should not rely entirely on the particular affordances of VR to gain a moral bond with the distant refugee crisis.
Integrated geophysical, remote sensing and geochemical investigation to explore gold-mineralizations and mapping listvenites at Wadi Haimur, Eastern Desert, Egypt

Although the continuity of gold mining work since Pharaonic times till today, Wadi Haimur is still promised source for gold. Wadi Haimur is located in west Allaqi-Heinai-Suture (AHS), south Eastern Desert of Egypt. Gold mineralization is mainly confined within quartz-carbonate and sulphide-bearing quartz veins within sheared, altered rocks, while listvenitization plays a significant role in gold concentration along the Haimur area. Therefore, critical analyses of remote sensing data were applied to map alteration zones and delineate listvenites. Spatially enhanced images were processed to trace lineaments and weakness zones which may represent pathways of mineralized, ascended hydrothermal fluids. Ground geophysical, magnetic and geoelectric surveys were performed to study the depth-extension of mineralized bodies in the vicinity of the historical mines within Wadi Haimur. Interpretation of land-magnetic data indicates the presence of mineralized veins associated with narrow shear zones. Derivatives and high-pass filters were utilized to map shallow mineralized sources and deduce their trends. Upward continuation and low-pass filters were used to detect the deep sources. Depths to magnetic bodies were estimated using Power-Spectrum, Euler and inversion of single anomalies. Geochemical sampling was performed to test the results. According to the remarkable variation in resistivity and chargeability values of resistivity and induced-polarization tomography profiles, the subsurface lithology was classified and probable mineralized zones within the subsurface were detected. Encouraging results from regolith, trenching, rock-chip sampling and Reverse-Circulation (RC) drilling indicated the extension of auriferous anomalies within the bedrock. Good correlation of RC-holes auriferous anomalies depths and deduced depths from magnetic interpretation was revealed.HIGHLIGHTSIntegrated geophysical, geochemistry and remote sensing data are used to delineate mineralized ore deposits.The estimated average depths of mineralized sources are 12.5, 48 and 463 m for shallow, intermediate and deep magnetic bodies, respectively.Massive sulphide-bearing zones ± gold concentrations were suggested for zones with low-resistivity and high-chargeability signatures.
PAV-SOD: A New Task towards Panoramic Audiovisual Saliency Detection

Object-level audiovisual saliency detection in 360° panoramic real-life dynamic scenes is important for exploring and modeling human perception in immersive environments, also for aiding the development of virtual, augmented, and mixed reality applications in fields such as education, social network, entertainment, and training. To this end, we propose a new task, p anoramic a udio v isual s alient o bject d etection, ( PAV-SOD 1 ), which aims to segment the objects grasping most of the human attention in 360° panoramic videos reflecting real-life daily scenes. To support the task, we collect PAVS10K , the first p anoramic video dataset for a udio v isual s alient object detection, which consists of 67 4K-resolution equirectangular videos with per-video labels including hierarchical scene categories and associated attributes depicting specific challenges for conducting PAV-SOD , and 10,465 uniformly sampled video frames with manually annotated object-level and instance-level pixel-wise masks. The coarse-to-fine annotations enable multi-perspective analysis regarding PAV-SOD modeling. We further systematically benchmark 13 state-of-the-art salient object detection (SOD)/video object segmentation (VOS) methods based on our PAVS10K . Besides, we propose a new baseline network, which takes advantage of both visual and audio cues of 360° video frames by using a new conditional variational auto-encoder (CVAE). Our C VAE-based a udio v isual net work, namely, CAV-Net , consists of a spatial-temporal visual segmentation network, a convolutional audio-encoding network, and audiovisual distribution estimation modules. As a result, our CAV-Net outperforms all competing models and is able to estimate the aleatoric uncertainties within PAVS10K . With extensive experimental results, we gain several findings about PAV-SOD challenges and insights towards PAV-SOD model interpretability. We hope that our work could serve as a starting point for advancing SOD towards immersive media.
SAT-Inspired Eliminations for Superposition

Optimized SAT solvers not only preprocess the clause set, they also transform it during solving as inprocessing. Some preprocessing techniques have been generalized to first-order logic with equality. In this article, we port inprocessing techniques to work with superposition, a leading first-order proof calculus, and we strengthen known preprocessing techniques. Specifically, we look into elimination of hidden literals, variables (predicates), and blocked clauses. Our evaluation using the Zipperposition prover confirms that the new techniques usefully supplement the existing superposition machinery.
Comparative analysis of React, Next and Gatsby programming frameworks for creating SPA applications

&#x0D; &#x0D; &#x0D; This article presents a performance analysis of some of the most popular development frameworks based on the React library. The aim of the study was to show which of the technologies used to create the visual parts of web applications is the most efficient. The research was conducted with the use of 3 applications representing the same research content but based on the above-mentioned frontend technologies. In order to evaluate the performance, web browser development tools and React library were used, which proved that vanilla React is the most efficient for rendering pages with a lot of data.&#x0D; &#x0D; &#x0D;
Termino-ontology Resources of Endogenous Agro-Sylvo-Pastoral Practices for the Adaptation to Climate Change

For the construction of the ontology of endogenous technical or practices for the adaptation of the agro-sylvo-pastoral domain to climate change, an important step is to identify the Termino-Ontological Resources (TOR). This article presents an overview of the main resources identified in the agro-sylvo-pastoral domain in general and the agricultural domain in particular. This will facilitate the knowledge acquisition in the construction process of this ontology.
The spatial organization of hybrid Scrum meetings: A multimodal conversation analysis study

Bridging the physical distance between remote locations, video mediated interaction has long been an integral part of workplace activities. Hybrid meetings are instances of video-mediated interaction combining in-person and virtual attendance with some of the participants being co-located, and others joining from remote locations with real-time streaming. This study sets out to explore the ways in which participants co-construct space across multiple sites by coordinating their presence and participation in hybrid daily Scrum meetings. We draw on multimodal conversation analysis to closely examine video recorded data from hybrid Scrum meetings of a software development team. Our analysis shows how participants orient to multiple spaces and their efforts in establishing and maintaining visibility and audibility. The findings have implications for an understanding of hybrid interactions in workplace meetings informing future designs of meetings and collaborative technologies for hybrid collaboration.
An empirical analysis of vulnerability information disclosure impact on patch R&amp;D of software vendors

The vulnerability patch R&amp;D has become an important part of information security governance. An effective collaboration with software vendors in patch R&amp;D is of great significance to reduce the existence time of information security risks. This works aims to explore the relationship between vulnerability information disclosure and patch R&amp;D of software vendors. The data regarding the vulnerability and software vendors is gathered from third-party vulnerability sharing platforms, including (China’s national information security vulnerability database, CNNVD) and Tianyacha.com. Based on the theory of organizational information processing, linear regression model and Cox proportional risk regression model are built for appropriately addressing the research questions. The results show that the vulnerability disclosure of the third-party sharing platform can improve the patch R&amp;D probability of software vendors. The information processing requirements, such as vulnerability information attention, vulnerability score and whether vulnerabilities are disclosed in advance accelerate the vulnerability patch R&amp;D. The enterprise information processing capability indicators, including the industry dependence of software product customers and the staff size of software vendors accelerate the patch R&amp;D. The number of products affected by the vulnerabilities and the number of software copyrights of software vendors have no significant impact on patch R&amp;D.
Evolving data stream clustering based on constant false clustering probability

Today’s world needs new methods to deal with and analyze the ever-increasingly generated data streams. Two of the most challenging aspects of data streams are (i) concept drift, i.e. evolution of data stream over time, which requires the ability to make timely decisions against the high speed of receiving new data; (ii) limited memory storage and the impracticality of using memory due to the large amount of data. Clustering is one of the common methods to process data streams. In this paper, we propose a novel, fully-online, density-based method for clustering evolving data streams. In recent years, a number of methods have been proposed, which also have the ability to cluster data streams. The main limitation of these methods is the use of parameters based on knowledge-expert. This work is among the first works to address this issue. Constant False Clustering Probability, CFCP, has tried to choose the algorithm’s parameters based on Statistics. The proposed method has also the ability to identify clusters with arbitrary shapes. It is robust to noise and offers high accuracy and efficiency in both low and high dimensions. In this method, we determine the value of the parameters by using statistical theories and do not require more information, taking advantage of expert-knowledge. The presented experimental results show that the method performs data clustering at high speeds without reducing the quality compared to the state-of-the-art algorithms.
Learning the cross-modal discriminative feature representation for RGB-T crowd counting

To reduce the interference of the arbitrary crowd distribution and complex background on the counting accuracy in the unconstrained scene, this paper presents a novel RGB-T crowd counting approach based on the cross-modal discriminative feature representation learning, including two main stages. Specially, the first stage explicitly establishes a non-linear mapping from the RGB domain to the thermal domain so as to learn the cross-modal discriminative feature representation about the crowd distribution, given that the thermal image of the crowded scene presents more intuitive information related to the crowd and would be more insensitive to the background element than the conventional optical image. The second stage incorporates the complementary features of the RGB and thermal image pair based on combining the learned feature representation from the first stage to yield the final counting result. Experiments on the RGB-T crowd counting benchmark verify the superiority of the proposed approach with the state-of-the-art methods. The ablation studies on RGB-T and the evaluation on RGB crowd counting benchmarks validate the effectiveness of the designed cross-modal discriminative feature representation learning. Experimental results demonstrate that the proposed approach could obviate the need of relying on the specific structure of modality-specific feature extraction parts, rather than the conventional works of treating the paired RGB-thermal image as the indiscriminative information source and fusing them directly. The proposed approach in this study could realize the cross-modal discriminative feature representation learning in an efficient way of establishing cross-modal domain explicitly for the algorithm research of the intelligent surveillance system development.
A graph signal processing approach to Fourier-like number-theoretic transforms

In this paper, we employ a graph signal processing approach to redefine Fourier-like number-theoretic transforms, which includes the Fourier number transform itself, the Hartley number transform and specific types of the cosine number transform and the sine number transform. Our strategy basically consists in identifying graphs whose Laplacian or adjacency matrix has an eigenbasis coinciding with the basis employed to define each of the aforementioned transforms. We then demonstrate how to extend this idea to multidimensional cases and provide a general definition, which corresponds to the graph Fourier number transform (GFNT). We develop illustrative examples and perform a preliminary investigation regarding the use of the GFNT in an image encryption scenario.
A Novel Image Encryption Algorithm by Delay Induced Hyper-chaotic Chen System

A novel image encryption algorithm based on delay induced hyper-chaos (hyper-chaotic multi-attractors Chen system with time delay, HCMACS-TD), Arnold map and Multi-shift cipher function is proposed. To prevent private images from being stolen or used by unauthorized accesses, protecting images is very important. HCMACS-TD generating by linear time delay feedback, which possesses more than one positive Lyapunov exponent, the infinite dimensional, multi-attractors, and a wider chaotic parameter range is used in image encryption algorithm. Arnold map is used to complete the shuffle of the image and the multi-shift cipher function is used to encrypt the shuffled and confused image. In addition, the shuffled image except the first pixel as the initial element of the confusion process, enhances the relationship between the encrypted image and the original image. The performances of the proposed image encryption algorithm are evaluated, and the results show that the proposed image encryption algorithm has considerable advantages over some existing algorithms.