Reviewerly/exHarmony huggingface dataset:
Quantitative Evaluation of Time‐Dependent Multidimensional Projection Techniques

Abstract Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time‐dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time‐dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 9 methods, 10 datasets, and 12 quality metrics, and elect the best‐suited methods for projecting time‐dependent multivariate data, exploring the design choices and characteristics of each method. Additional results can be found in the online benchmark repository. We designed our evaluation pipeline and benchmark specifically to be a live resource, open to all researchers who can further add their favorite datasets and techniques at any point in the future.
AccQOC: Accelerating Quantum Optimal Control Based Pulse Generation

In the last decades, we have witnessed the rapid growth of Quantum Computing. In the current Noisy Intermediate-Scale Quantum (NISQ) era, the capability of a quantum machine is limited by the decoherence time, gate fidelity and the number of Qubits. Current quantum computing applications are far from the real “quantum supremacy” due to the fragile physical Qubits, which can only be entangled for a few microseconds. Recent works use quantum optimal control to reduce the latency of quantum circuits, thereby effectively increasing quantum volume. However, the key challenge of this technique is the large overhead due to long compilation time. In this paper, we propose AccQOC, a comprehensive static/dynamic hybrid workflow to transform gate groups (equivalent to matrices) to pulses using QOC (Quantum Optimal Control) with a reasonable compilation time budget. AccQOC is composed of static pre-compilation and accelerated dynamic compilation. After the quantum program is mapped to the quantum circuit with our heuristic mapping algorithm considering crosstalk, we leverage static pre-compilation to generate pulses for the frequently used groups to eliminate the dynamic compilation time for them. The pulse is generated using QOC with binary search to determine the latency. For a new program, we use the same policy to generate groups, thus avoid incurring overhead for the “covered” groups. The dynamic compilation deals with “un-covered” groups with accelerated pulse generation. The key insight is that the pulse of a group can be generated faster based on the generated pulse of a similar group. We propose to reduce the compilation time by generating an ordered sequence of groups in which the sum of similarity among consecutive groups in the sequence is minimized. We can find the sequence by constructing a similarity graph - a complete graph in which each vertex is a gate group and the weight of an edge is the similarity between the two groups it connects, then construct a Minimum Spanning Tree (MST) for SG. With the methodology of AccQOC, we reached a balanced point of compilation time and overall latency. The results show that accelerated compilation based on MST achieves 9.88× compilation speedup compared to the standard compilation of each group while maintaining an average 2.43× latency reduction compared with gate-based compilation.
Believe it when you see it: Dyadic embeddedness and reputation effects on trust in cryptomarkets for illegal drugs

Large-scale online marketplace data have been repeatedly used to test sociological theories on trust between strangers. Most studies focus on sellers’ aggregate reputation scores, rather than on buyers’ individual decisions to trust. Theoretical predictions on how repeated exchanges affect trust within dyads and how buyers weigh individual experience against reputation feedback from other actors have not been tested directly in detail. What do buyers do when they are warned not to trust someone they have trusted many times before? We analyze reputation effects on trust at the dyadic and network levels using data from an illegal online drug marketplace. We find that buyers’ trust decisions are primarily explained by dyadic embeddedness - cooperative sellers get awarded by repeated exchanges. Although buyers take third-party information into account, this effect is weaker and more important for first-time buyers. Buyers tend to choose market exit instead of retaliation against sellers after negative experiences.
Analyzing the Behaviour of Applying Agile Methodologies &amp; DevOps Culture in e-Commerce Web Application

Agile and DevOps both can inspire IT industries with better means of achieving business goals. Agile is evolutionary, allowing teams to effectively prioritize work and feature and deliver a prototype that increases the visibility of the software development process. DevOps has cognitive adaptability in IT projects where rapid development of products is on an apical priority. It's a bunch of principles and practices having an objective to collaborate between Development and Operation teams. Due to these lucrative factors, agile and DevOps are trendy in IT industries nowadays. The project development through agile and DevOps may include any genre of software sphere ranging from traditional business software to web-based applications. Through this paper, some of the functional facets of agile methodologies and DevOps processes are acquainted in e-commerce application.
B-Box - A Decentralized Storage System Using IPFS, Attributed-based Encryption, and Blockchain

In recent years, centralized storage systems have been extensively adopted by many companies, organizations, and individuals for storing and sharing data. These systems, however, make concerns for users of a single point of failure and the involvement of a centralized entity or third party. Therefore, there is a need for developing decentralized storage systems to overcome the drawbacks of traditional approach. In order to enhance secure and transparent characteristics of decentralized storage systems, in this paper, we present a combination of IPFS (InterPlanetary File System), ABE (Attribute-based Encryption), Multi-Authority ABE (MA-ABE), and Ethereum blockchain. In particular, we facilitate the advantages of IPFS network to store user's data in a distributed manner. Furthermore, we make the use of MA-ABE to encrypt a document, which an user needs to share it among multiple organizations. The hash returned by the IPFS network will be stored in the Ethereum blockchain network to provide trustworthy for all users participating in our system. To the best of our knowledge, it is the first storage system using IPFS, ABE, MA-ABE, and blockchain technologies together to ensure decentralized, secure, and transparent characteristics for storing and sharing data.
Memory-aware resource management algorithm for low-energy cloud data centers

The continuous advancement of cloud computing technology has driven the vigorous development of cloud data centers. This manifests itself not only in increasing numbers, but also in rapid expansion scale. At the same time, the problems of high energy consumption, high cost and high carbon emissions began to stand out. These factors have become a bottleneck restricting the further development of cloud computing technology. As a result of the application of virtualization technology, mature cloud service providers use virtual machines instead of physical machines to provide users with computing, storage and other services. Therefore, the scheduling algorithm of cloud tasks and virtual machines has been widely studied by academia as a core problem. However, most of the current cloud data center resource management algorithms take CPU utilization as the main consideration, and increase the CPU utilization through virtual machine integration to reduce the energy consumption of cloud data centers. However, these algorithms will cause waste and low utilization of other resources in the cloud data center due to excessive consideration of CPU utilization. This paper systematically analyzes the mapping relationship between cloud tasks, virtual machines and physical machines. At the same time, the performance of cloud tasks, virtual machines, and physical machines is modeled in the cloud data center. By comprehensively considering the CPU and memory characteristics of cloud tasks, the memory priority cloud task mapping rule is established. Based on the rule, the memory-aware resource management algorithm for low-energy cloud data centers (MALE) is proposed. The algorithm maps cloud tasks and deploys virtual machines according to the memory requirements of the cloud tasks, thereby achieving the goal of simultaneously reducing the total fee of cloud users and the energy consumption of cloud data centers. Finally, the algorithm is compared with the other three algorithms. The experimental simulation results show that the effect of the proposed algorithm in this paper is significantly better than the comparison algorithm for the total fee of cloud users and the energy consumption of the cloud data center.
Dual-Branch CNNs for Vehicle Detection and Tracking on LiDAR Data

We present a novel vehicle detection and tracking system that works solely on 3D LiDAR information. Our approach segments vehicles using a dual-view representation of the 3D LiDAR point cloud on two independently trained convolutional neural networks, one for each view. A bounding box growing algorithm is applied to the fused output of the networks to properly enclose the segmented vehicles. Bounding boxes are grown using a probabilistic method that takes into account also occluded areas. The final vehicle bounding boxes act as observations for a multi-hypothesis tracking system which allows to estimate the position and velocity of the observed vehicles. We thoroughly evaluate our system on the KITTI benchmarks both for detection and tracking separately and show that our dual-branch classifier consistently outperforms previous single-branch approaches, improving or directly competing to other state of the art LiDAR-based methods.
Detection of Electric Network Frequency in Audio Recordings–From Theory to Practical Detectors

Recently, it has been discovered that the electric network frequency (ENF) could be captured by digital audio, video, or even image files, and could further be exploited in forensic investigations. However, the existence of the ENF in multimedia content is not a sure thing, and if the ENF is not present, ENF-based forensic analysis would become useless or even misleading. In this paper, we address the problem of ENF detection in digital audio recordings, which is modeled as the detection of a weak (ENF) signal contaminated by unknown colored wide-sense stationary (WSS) Gaussian noise, while the signal also contains multiple unknown random parameters. We first derive three Neyman-Pearson (NP) detectors, i.e., general matched filter (GMF), matched filter (MF)-like detector, and the asymptotic approximation of the GMF, and choose the MF-like detector as the clairvoyant detector. For practical detectors, we show that the generalized likelihood ratio test (GLRT) could not be efficiently obtained due to the unknown noise and large matrix inversion. Alternatively, we propose two least-squares (LS)-based time domain detectors termed as LS-likelihood ratio test (LRT) and naive-LRT. Further, we propose a time-frequency (TF) domain detector, termed as TF detector, which exploits the a priori knowledge of the ENF. The performances of the derived detectors are extensively analyzed in terms of test statistic distributions, threshold selection, and computational complexity. The naive-LRT detector is found to be only effective for very short recordings. As the data recording length increases, both LS-LRT and TF detectors yield effective detection results, while the latter is approximately a constant false alarm rate (CFAR) detector. Practical experiments using real audio recordings justify the effectiveness of the proposed detectors and our analysis.
On Training Traffic Predictors via Broad Learning Structures: A Benchmark Study

A fast architecture for real-time (i.e., minute-based) training of a traffic predictor is studied, based on the so-called broad learning system (BLS) paradigm. The study uses various traffic datasets by the California Department of Transportation, and employs a variety of standard algorithms (LASSO regression, shallow and deep neural networks, stacked autoencoders, convolutional, and recurrent neural networks) for comparison purposes: all algorithms are implemented in MATLAB on the same computing platform. The study demonstrates a BLS training process two-three orders of magnitude faster (tens of seconds against tens-hundreds of thousands of seconds), allowing unprecedented real-time capabilities. Additional comparisons with the extreme learning machine architecture, a learning algorithm sharing some features with BLS, confirm the fast training of least-square training as compared to gradient training.
How Could Blockchain Transform 6G towards Open Ecosystemic Business Models?

Future 6G scenarios in 2030 envision society that will be data-driven, enabled by near-instant and unlimited wireless connectivity to intelligence. This calls for a multidisciplinary approach and a re-imagining of how we create, deliver and consume network resources, data and services. This development will change the traditional business models and ecosystem roles, as well as open the market for new stakeholders like micro-operators, edge cloud operators and resource brokers. This paper discusses unprecedented challenges of enabling and stimulating multiple stakeholders to have a more active participation in the future 6G ecosystem and gives a brief outline of key implications of blockchain technologies for related business model transformations. The research extends the existing archetypes of closed and supply focused mobile broadband business models and proposes the novel open ecosystem-focused scenario in which value configuration is leveraging distributed ledger technologies. This expands the architecture from centralized innovation and transaction platforms towards decentralization without a focal resource-orchestrating entity. Results showed that blockchain enabled 6G business can be built on novel business opportunities, value generation and competitive advantage that have positive strategic consequences on scalability, replicability and sustainability.
Semantic-Aware Real-Time Correlation Tracking Framework for UAV Videos

Discriminative correlation filter (DCF) has contributed tremendously to address the problem of object tracking benefitting from its high computational efficiency. However, it has suffered from performance degradation in unmanned aerial vehicle (UAV) tracking. This article presents a novel semantic-aware real-time correlation tracking framework (SARCT) for UAV videos to enhance the performance of DCF trackers without incurring excessive computing cost. Specifically, SARCT first constructs an additional detection module to generate ROI proposals and to filter any response regarding the target irrelevant area. Then, a novel semantic segmentation module based on semantic template generation and semantic coefficient prediction is further introduced to capture semantic information, which can provide precise ROI mask, thereby effectively suppressing background interference in the ROI proposals. By sharing features and specific network layers for object detection and semantic segmentation, SARCT reduces parameter redundancy to attain sufficient speed for real-time applications. Systematic experiments are conducted on three typical aerial datasets in order to evaluate the performance of the proposed SARCT. The results demonstrate that SARCT is able to improve the accuracy of conventional DCF-based trackers significantly, outperforming state-of-the-art deep trackers.
Energetic Perspective on Rapid Quenches in Quantum Annealing

There are well developed theoretical tools to analyse how quantum dynamics can solve computational problems by varying Hamiltonian parameters slowly, near the adiabatic limit. On the other hand, there are relatively few tools to understand the opposite limit of rapid quenches, as used in quantum annealing and (in the limit of infinitely rapid quenches) in quantum walks. In this paper, we develop several tools which are applicable in the rapid quench regime. Firstly, we analyse the energy expectation value of different elements of the Hamiltonian. From this, we show that monotonic quenches, where the strength of the problem Hamiltonian is consistently increased relative to fluctuation (driver) terms, will yield a better result on average than random guessing. Secondly, we develop methods to determine whether dynamics will occur locally under rapid quench Hamiltonians, and identify cases where a rapid quench will lead to a substantially improved solution. In particular, we find that a technique we refer to as "pre-annealing" can significantly improve the performance of quantum walks. We also show how these tools can provide efficient heuristic estimates for Hamiltonian parameters, a key requirement for practical application of quantum annealing.
Efficient image dataset classification difficulty estimation for predicting deep-learning accuracy

Abstract In the deep-learning community, new algorithms are published at a very fast pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, as it requires to re-evaluate published algorithms and their different configurations in order to find a close to optimal classifier. To facilitate this process, before biasing our decision toward a class of neural networks or running an expensive search over the network space, we propose to estimate the classification difficulty of the dataset. Our method computes a single number that characterizes the dataset difficulty $$97\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>97</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math> faster than training state-of-the-art networks. The proposed method can be used in combination with network topology and hyper-parameter search optimizers to efficiently drive the search toward promising neural network configurations.
A Survey of Blockchain-based Cybersecurity for Vehicular Networks

The development of vehicular networks has greatly improved the efficiency and safety of intelligent traffic systems. However, it also introduces additional security threats into the system. The special characteristics of vehicular networks, such as dynamic topology, huge network scale and so on, make it difficult to adopt the traditional security solutions directly into this scenario. In addition, the single point failure problem of the centralized security mechanisms is also a big challenge. Recently, blockchain technology, which is a distributed database, is a potential approach to address these security issues. In this paper, a comprehensive review of the existing blockchain-based cybersecurity mechanisms is presented with the corresponding performance analysis. The purpose of this work is to provide a guideline for the further study in the application of blockchain in the vehicular network security area.
A Novel Camera-Free Eye Tracking Sensor for Augmented Reality Based on Laser Scanning

Next generation AR glasses require a highly integrated, high-resolution near-eye display technique such as focus-free retinal projection to enhance usability. Combined with low-power eye-tracking, such glasses enable better user experience and performance. We propose a novel and robust low-power eye-tracking sensor for integration into retinal projection systems. In our approach, a MEMS micro mirror scans an IR laser beam over the eye region and the scattered light is received by a photodiode. The advantages of our approach over typical VOG systems are its high integration capability and low-power consumption, which qualify our approach for next generation AR glasses. Here, we present a mathematical framework to estimate the achievable gaze angle resolution of our approach. We further show the viability of the proposed eye-tracking sensor based on a laboratory setup and discuss power consumption and gaze angle resolution compared to typical eye-tracking techniques.
Secret sharing based reversible data hiding in encrypted images with multiple data-hiders

The existing models of reversible data hiding in encrypted images (RDH-EI) are based on single data-hider, where the original image cannot be reconstructed when the data-hider is damaged. To address this issue, this article proposes a novel model with multiple data-hiders for RDH-EI based on secret sharing. It divides the original image into multiple different encrypted images with the same size of the original image and distributes them to multiple different data-hiders for data hiding. Each data-hider can independently embed data into the encrypted image to obtain the corresponding marked encrypted image. The original image can be losslessly recovered by collecting sufficient marked encrypted images from undamaged data-hiders when individual data-hiders are subjected to potential damage. This further protects the security of the original image. We provide four cases of the proposed model, namely, two joint cases and two separable cases. From the proposed model, we derive a separable RDH-EI method with high-capacity. Experimental results are presented to illustrate the effectiveness of the proposed method.
<i>ElectroBlocks</i>: A blockchain‐based energy trading scheme for smart grid systems

Summary Smart grid systems are widely used across the world for providing demand response management between users and service providers. In most of the energy distributions scenarios, the traditional grid systems use the centralized architecture, which results in large transmission losses and high overheads during power generation. Moreover, owing to the presence of intruders or attackers, there may be a mismatch between demand and supply between utility centers (suppliers) and end users. Thus, there is a need for an automated energy exchange to provide secure and reliable energy trading between users and suppliers. We found, from the existing literature, that blockchain can be an effective solution to handle the aforementioned issues. Motivated by these facts, we propose a blockchain‐based smart energy trading scheme, ElectroBlocks , which provides efficient mechanisms for secure energy exchanges between users and service providers. In ElectroBlocks , nodes in the network validate the transaction using two algorithms that are cost aware and store aware. The cost‐aware algorithm locates the nearest node that can supply the energy, whereas the store‐aware algorithm ensures that the energy requests go to the node with the lowest storage space. We evaluated the performance of the ElectroBlocks using performance metrics such as mining delay, network exchanges, and storage energy. The simulation results obtained demonstrate that ElectroBlocks maintains a secure trade‐off between users and service providers when using the proposed cost‐aware and store‐aware algorithms.