Reviewerly/exHarmony huggingface dataset:
An Image Encryption Scheme Based on DNA Computing and Multiple Chaotic Systems

A number of techniques for securing plaintext, images and video frames have been developed in cryptography using jointly DNA computing and Chaos Theory.With the advancement of DNA/quantum computing, the threats of security breaches to information have an increasing possibility.In this paper, we propose a symmetric encryption algorithm for color images by extending the current encryption/decryption techniques.Our encryption algorithm is based on three chaotic systems (PWLCM, Lorenz and 4D Lorenz-type), a Secure Hash Algorithm, a scrambler, a chaotic generator and DNA sequence based Linear Feedback Shift Register.We introduce multilevel security to increase the degree of diffusion and confusion.Through experiments, we present security analysis for key irreproducibility and sensitivity, Gray Level Co-occurrence Matrix based analysis, maximum deviation, irregular deviation, entropy, histogram, variance and correlation, number of pixel change rate, unified average cipher intensity, known/chosenplaintext attacks, mean absolute error, robustness against noises of various types using PSNR and occlusion attacks.It is demonstrated that mostly our proposed encryption algorithm has enhanced performance as compared to contemporary works in information security, while comparable in other cases.
Multimedia Intelligence: When Multimedia Meets Artificial Intelligence

Owing to the rich emerging multimedia applications and services in the past decade, super large amount of multimedia data has been produced for the purpose of advanced research in multimedia. Furthermore, multimedia research has made great progress on image/video content analysis, multimedia search and recommendation, multimedia streaming, multimedia content delivery etc. At the same time, Artificial Intelligence (AI) has undergone a “new” wave of development since being officially regarded as an academic discipline in 1950s, which should give credits to the extreme success of deep learning. Thus, one question naturally arises: What happens when multimedia meets Artificial Intelligence? To answer this question, we introduce the concept of Multimedia Intelligence through investigating the mutual-influence between multimedia and Artificial Intelligence. We explore the mutual influences between multimedia and Artificial Intelligence from two aspects: i) multimedia drives Artificial Intelligence to experience a paradigm shift towards more explainability and ii) Artificial Intelligence in turn injects new ways of thinking for multimedia research. As such, these two aspects form a loop in which multimedia and Artificial Intelligence interactively enhance each other. In this paper, we discuss what and how efforts have been done in literature and share our insights on research directions that deserve further study to produce potentially profound impact on multimedia intelligence.
Fractal Coding-Based Robust and Alignment-Free Fingerprint Image Hashing

Biometric image hashing techniques have been widely studied and seen progressive advancements. However, only a handful of available solutions provide two-factor cancelability while simultaneously satisfying the tradeoff among all criteria of template protection mechanisms. In this paper, we propose a novel scheme for generating a secure and robust hash from a fingerprint image using Fourier-Mellin transform and fractal coding. First, due to its invariance property, Fourier-Mellin transform is incorporated into the domain fingerprint minutiae blocks to provide feature alignment, therein generating a fixed-length minutiae representation for comparison. Then, dimensionality reduction and texture compression are exploited using fractal coding to generate a robust and compact hash for improved security and recognition. The experimental results demonstrate a favorable recognition performance on benchmarked state-of-the-art schemes from FVC2002 and FVC2004 fingerprint databases. The analyses prove our method's robustness and resiliency to security and privacy attacks. Our method also satisfies the revocability and unlinkability criteria of cancelable biometrics.
Disaggregated Data Centers: Challenges and Trade-offs

Disaggregated data centers (DCs) can offer high flexibility for resource allocation; hence, their resource utilization can be significantly improved. However, communications between different types of resources, in particular between CPU and memory, in fully disaggregated DCs face severe problems in terms of stringent requirements for ultra-low latency and ultra-high transmission bandwidth. Optical fiber communication is promising to provide high capacity and low latency, but it is still challenging for the state-of-the-art optical technologies to meet the requirements of fully disaggregated DCs. In this article, different levels of resource disaggregation are investigated. For fully disaggregated DCs, two architectural options are presented, where optical interconnects are necessary for the CPU-memory communications. We review the state-of-the-art optical transmission and switching technologies, and analyze pros and cons of their applicability in the disaggregated DCs. The results reveal that resource disaggregation does improve the resource utilization in DCs. However, the bandwidth provided by the state-of-the-art technologies is not always sufficient for fully disaggregated DCs. It calls for further advances in optical communications to fully utilize the advantages of fully disaggregated DCs.
Robust image watermarking using invariant accurate polar harmonic Fourier moments and chaotic mapping

Polar harmonic Fourier moments (PHFMs) are very effective global image descriptors and can be used in many areas of digital image processing. However, the numerical integration error of traditional computation methods significantly affects its computation accuracy. This paper proposed an accurate PHFMs computation method based on Gaussian numerical integration (GNI), which effectively mitigated the numerical integration error, and proposed a novel watermarking algorithm resistant to geometric attacks based on accurate PHFMs and chaotic mapping. This algorithm improves its robustness against geometric attacks by relying on the geometric invariance of accurate PHFMs and scrambles the watermark image by relying on the sensitivity of the Tent chaotic system to the initial value, enhancing its security. The experimental results show that the proposed algorithm can effectively resist geometric attacks, such as scaling, rotation, length-width ratio change and flipping, and is superior to other watermarking algorithms.
Hateful Speech Detection in Public Facebook Pages for the Bengali Language

Online hateful speech detection and classification in social media for the various major languages other than English has drawn the attention of researchers recently. In this paper, we develop Machine Learning (ML) algorithms based model, as well as Gated Recurrent Unit (GRU), based deep neural network model for classifying users' comments on Facebook pages. We have collected, annotated 5,126 Bengali comments and classified them into six classes - Hate Speech, Communal Attack, Inciteful, Religious Hatred, Political Comments, and Religious Comments. The produced corpus is the first contribution to the field of hateful speech detection in the Bengali language for social media. Finally, we employ several machine learning algorithms, compare the performance, and attained 52.20% accuracy in Random Forest. The accuracy is improved in the case of GRU based model (70.10% accuracy) about 18%.
Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm

Sampling-based methods are popular in the motion planning of robots, especially in high-dimensional spaces. Among the many such methods, the Rapidly-exploring Random Tree (RRT) algorithm has been widely used in multi-degree-of-freedom manipulators and has yielded good results. However, existing RRT planners have low exploration efficiency and slow convergence speed and have been unable to meet the requirements of the intelligence level in the Industry 4.0 mode. To solve these problems, a general autonomous path planning algorithm of Node Control (NC-RRT) is proposed in this paper based on the architecture of the RRT algorithm. Firstly, a method of gradually changing the sampling area is proposed to guide exploration, thereby effectively improving the search speed. In addition, the node control mechanism is introduced to constrain the extended nodes of the tree and thus reduce the extension of invalid nodes and extract boundary nodes (or near-boundary nodes). By changing the value of the node control factor, the random tree is prevented from falling into a so-called “local trap” phenomenon, and boundary nodes are selected as extended nodes. The proposed algorithm is simulated in different environments. Results reveal that the algorithm greatly reduces the invalid exploration in the configuration space and significantly improves planning efficiency. In addition, because this method can efficiently use boundary nodes, it has a stronger applicability to narrow environments compared with existing RRT algorithms and can effectively improve the success rate of exploration.
Incremental learning imbalanced data streams with concept drift: The dynamic updated ensemble algorithm

Learning nonstationary data streams has been well studied in recent years. However, most of the researches assume that the class imbalance of data streams is relatively balanced. Only a few approaches tackle the joint issue of concept drift and class imbalance due to its complexity. Meanwhile, the existing chunk ensembles for classifying imbalanced nonstationary data streams always need to store previous data, which consumes plenty of memory usage. To overcome these issues, we propose a chunk-based incremental ensemble algorithm called Dynamic Updated Ensemble (DUE) for learning imbalanced data streams with concept drift. Compared to the existing techniques, its merits are five-fold: (1) it learns one chunk at a time without requiring access to previous data; (2) it emphasizes misclassified examples in the model update procedure; (3) it can timely react to multiple kinds of concept drifts; (4) it can adapt to the new condition when switching majority class to minority class; (5) it keeps a limited number of classifiers to ensure high efficiency. Experiments on synthetic and real datasets demonstrate the effectiveness of DUE in learning nonstationary imbalanced data streams.
Semisupervised Adaptive Symmetric Non-Negative Matrix Factorization

As a variant of non-negative matrix factorization (NMF), symmetric NMF (SymNMF) can generate the clustering result without additional post-processing, by decomposing a similarity matrix into the product of a clustering indicator matrix and its transpose. However, the similarity matrix in the traditional SymNMF methods is usually predefined, resulting in limited clustering performance. Considering that the quality of the similarity graph is crucial to the final clustering performance, we propose a new semisupervised model, which is able to simultaneously learn the similarity matrix with supervisory information and generate the clustering results, such that the mutual enhancement effect of the two tasks can produce better clustering performance. Our model fully utilizes the supervisory information in the form of pairwise constraints to propagate it for obtaining an informative similarity matrix. The proposed model is finally formulated as a non-negativity-constrained optimization problem. Also, we propose an iterative method to solve it with the convergence theoretically proven. Extensive experiments validate the superiority of the proposed model when compared with nine state-of-the-art NMF models.
Evaluation of Stream Processing Frameworks

The increasing need for real-time insights in data sparked the development of multiple stream processing frameworks. Several benchmarking studies were conducted in an effort to form guidelines for identifying the most appropriate framework for a use case. In this article, we extend this research and present the results gathered. In addition to Spark Streaming and Flink, we also include the emerging frameworks Structured Streaming and Kafka Streams. We define four workloads with custom parameter tuning. Each of these is optimized for a certain metric or for measuring performance under specific scenarios such as bursty workloads. We analyze the relationship between latency, throughput, and resource consumption and we measure the performance impact of adding different common operations to the pipeline. To ensure correct latency measurements, we use a single Kafka broker. Our results show that the latency disadvantages of using a micro-batch system are most apparent for stateless operations. With more complex pipelines, customized implementations can give event-driven frameworks a large latency advantage. Due to its micro-batch architecture, Structured Streaming can handle very high throughput at the cost of high latency. Under tight latency SLAs, Flink sustains the highest throughput. Additionally, Flink shows the least performance degradation when confronted with periodic bursts of data. When a burst of data needs to be processed right after startup, however, micro-batch systems catch up faster while event-driven systems output the first events sooner.
An efficient deep learning-based scheme for web spam detection in IoT environment

From the last few years, Internet of Things has revolutionized the entire world. In this, various smart objects perform the tasks of sensing and computing to provide uninterrupted services to the end users in different applications such as smart transportation, e-healthcare to name a few. With the inherent capabilities of these objects to take adaptive intelligent decisions, Cognitive Internet of Things is another paradigm of Internet of Things which emerges during this era. However, while accessing data from the Internet, web spam is one of the challenges to be handled. It has been observed from the literature review that for accessing data, search engines are preferred mostly by an individual. The search engine’s effective ranking can decrease the computational cost of objects during the data access. The current solutions to this issue are aimed to discover the spam in the search engine after its occurrence. So, in this proposal, we present a cognitive spammer framework that removes spam pages when search engines calculate the web page rank score. The framework detects web spam with the support of Long Short-Term Memory network by training the link features. This training resulted with an accuracy of 95.25, as more than 1,11,000 hosts are being correctly classified. However, the content features are trained by neural network. The proposed scheme has been validated with the WEBSPAM-UK 2007 dataset. Prior to processing, the dataset is pre-processed using a new technique called ‘Split by Over-sampling and Train by Under-fitting’. The ensemble and cross validation approach has been used for optimization of results with an accuracy of 96.96%. So, the proposed scheme outperforms the existing techniques.
Anchor Box Optimization for Object Detection

In this paper, we propose a general approach to optimize anchor boxes for object detection. Nowadays, anchor boxes are widely adopted in state-of-the-art detection frameworks. However, these frameworks usually pre-define anchor box shapes in heuristic ways and fix the sizes during training. To improve the accuracy and reduce the effort of designing anchor boxes, we propose to dynamically learn the anchor shapes, which allows the anchors to automatically adapt to the data distribution and the network learning capability. The learning approach can be easily implemented with stochastic gradient descent and can be plugged into any anchor box-based detection framework. The extra training cost is almost negligible and it has no impact on the inference time or memory cost. Exhaustive experiments demonstrate that the proposed anchor optimization method consistently achieves significant improvement (≥ 1% mAP absolute gain) over the baseline methods on several benchmark datasets including Pascal VOC 07+12, MS COCO and Brainwash. Meanwhile, the robustness is also verified towards different anchor initialization methods and the number of anchor shapes, which greatly simplifies the problem of anchor box design.
A Context-Aware Loss Function for Action Spotting in Soccer Videos

In video understanding, action spotting consists in temporally localizing human-induced events annotated with single timestamps. In this paper, we propose a novel loss function that specifically considers the temporal context naturally present around each action, rather than focusing on the single annotated frame to spot. We benchmark our loss on a large dataset of soccer videos, SoccerNet, and achieve an improvement of 12.8% over the baseline. We show the generalization capability of our loss for generic activity proposals and detection on ActivityNet, by spotting the beginning and the end of each activity. Furthermore, we provide an extended ablation study and display challenging cases for action spotting in soccer videos. Finally, we qualitatively illustrate how our loss induces a precise temporal understanding of actions and show how such semantic knowledge can be used for automatic highlights generation.
Wingsuit Flying Search—A Novel Global Optimization Algorithm

In this paper, a novel global optimization algorithm -Wingsuit Flying Search (WFS) is introduced.It is inspired by the popular extreme sport -wingsuit flying.The algorithm mimics the intention of a flier to land at the lowest possible point of the Earth surface within their range, i.e., a global minimum of the search space.This is achieved by probing the search space at each iteration with a carefully picked population of points.Iterative update of the population corresponds to the flier progressively getting a sharper image of the surface, thus shifting the focus to lower regions.The algorithm is described in detail, including the mathematical background and the pseudocode.It is validated using a variety of classical and CEC 2020 benchmark functions under a number of search space dimensionalities.The validation includes the comparison of WFS to several nature-inspired popular metaheuristic algorithms, including the winners of CEC 2017 competition.The numerical results indicate that WFS algorithm provides considerable performance improvements (mean solution values, standard deviation of solution values, runtime and convergence rate) with respect to other methods.The main advantages of this algorithm are that it is practically parameter-free, apart from the population size and maximal number of iterations.Moreover, it is considerably ''lean'' and easy to implement.
A Novel Hierarchical Secret Image Sharing Scheme with Multi-Group Joint Management

With the spread of the Internet, the speed of data spread is getting faster and faster. It benefits us a lot but also brings us many potential security problems, especially the problem of privacy leakage. For example, more and more people choose to store their private images in the cloud. Secret image sharing as a significant method has been widely applied in protecting images in the cloud, which reduces the risks of data leakage and data loss. Generally, the secret image sharing scheme would encrypt the secret image into a series of shares and then stored these shares in a cloud. However, when this cloud has been attacked, the secret may meet a risk of leakage. A solution to solve the problem is that the generated shares are distributed storage in multiple clouds. Each cloud is independent and all clouds can have a collaboration to manage the secret image. To address this issue, a novel hierarchical secret image sharing scheme with multi-group joint management is proposed in this paper, which is suitable for protecting the security of the secret image by distributed storage over multiple clouds. In the proposed scheme, the secret image would be shared among multiple groups with different thresholds. The number of each group’s shareholders is determined by a sequence of thresholds. Therefore, the proposed scheme is a hierarchical secret image sharing scheme in which the secret image can be reconstructed if and only if the number of shares has met all threshold conditions. In addition, the generated shares have the same weight, which is more suitable for universal applicability. Both the system analysis and the simulation results prove that the proposed scheme is efficient and practical.
How agile software development methods reduce work exhaustion: Insights on role perceptions and organizational skills

Abstract Agile methods are widely used in the software industry as a way to more rapidly develop and deliver new software. They define iterative work processes, advocate self‐organization and openness for change, and prescribe how software developers interact with each other and external stakeholders. Despite their popularity, it is unclear how agile methods influence work exhaustion in software developers and how developer skills play into this effect. On the one hand, agile methods may reduce software developers' work exhaustion by levelling out their workload across the entire duration of a project. On the other hand, agile methods exert a high level of pressure on software developers to continuously deliver working software, create many intensive social interactions, and to frequently adapt to changes. In light of these effects, prior research could not explain why some software developers become less exhausted from using agile methods, whereas others perceive the exact opposite. Based on the job demand‐control model, we develop a theoretical model connecting agile method use to individual developer skills and to two established determinants of employee exhaustion: role conflict and role ambiguity. We tested our research model in a field study among 1894 software developers in 217 project teams that used agile methods. The random coefficient modelling results show that agile method use facilitates the achievement of clear and unambiguous role perceptions and thereby reduces work exhaustion in developers, particularly if developers possess the organizational skills to effectively interact with others in their organization. We highlight implications for theory on the individual‐level effects of software development methods and provide practical insights for software companies.