Reviewerly/exHarmony huggingface dataset:
Network Recovery from Unlabeled Noisy Samples

There is a growing literature on the statistical analysis of multiple networks in which the network is the fundamental data object. However, most of this work requires networks on a shared set of labeled vertices. In this work, we consider the question of recovering a parent network based on noisy unlabeled samples. We identify a specific regime in the noisy network literature for recovery that is asymptotically unbiased and computationally tractable based on a three-stage recovery procedure: first, we align the networks via a sequential pairwise graph matching procedure; next, we compute the sample average of the aligned networks; finally, we obtain an estimate of the parent by thresholding the sample average. Previous work on multiple unlabeled networks is only possible for trivial networks due to the complexity of brute-force computations.
Meta-heuristics for generation of linguistic descriptions of weather data: Experimental comparison of two approaches

In this paper we experimentally assess, from both algorithmic and pragmatic perspectives, the adequacy of linguistic descriptions of real data generated by two metaheuristics: simulated annealing and genetic algorithm meta-heuristics. The type of descriptions we consider are fuzzy quantified statements (both Zadeh's type-1 and type-2) involving three well-known quantification models (Zadeh's scalar and fuzzy and Delgado's GD). We conducted an empirical validation using real observation and prediction meteorological data, where both automatic (metrics-based) and manual (human experts-based) assessment on the adequacy of the generated descriptions was assessed. Results indicate that, overall, the genetic approach performs better than simulated annealing in terms of quality of the obtained descriptions and time execution. Significance of this outperforming depends on the type of meteorological data and the quantification model selected. Tests of statistical significance point out that for type-1 descriptions no significant differences exist between the two meta-heuristics in the prediction case. For type-2 descriptions, significant differences exist for Delgado's GD model for both types of data. For Zadeh's scalar and fuzzy quantification significance depends on the type of data (observation or prediction). Globally, outperforming of the genetic approach over simulated annealing i) is significant in 4 out of 12 scenarios considered (all of them type-2), and ii) is not significant in the other 8 out 12 scenarios (all type-1 and two type-2). Also human expert assessment on the adequacy of the descriptions was conducted, showing that both meta-heuristics behave similarly for type-1 descriptions, while genetic algorithms produce more suitable type-2 linguistic descriptions.
Augmenting Depth Estimation with Geospatial Context

Modern cameras are equipped with a wide array of sensors that enable recording the geospatial context of an image. Taking advantage of this, we explore depth estimation under the assumption that the camera is geocalibrated, a problem we refer to as geo-enabled depth estimation. Our key insight is that if capture location is known, the corresponding overhead viewpoint offers a valuable resource for understanding the scale of the scene. We propose an end-to-end architecture for depth estimation that uses geospatial context to infer a synthetic ground-level depth map from a co-located overhead image, then fuses it inside of an encoder/decoder style segmentation network. To support evaluation of our methods, we extend a recently released dataset with overhead imagery and corresponding height maps. Results demonstrate that integrating geospatial context significantly reduces error compared to baselines, both at close ranges and when evaluating at much larger distances than existing benchmarks consider.
Optimization of quantum secret sharing communication using corresponding bits

A three-users quantum secret sharing procedure by means of secure direct communication is proposed. The procedure is an improved version of Wang et al.’s in terms of efficiency. The improvement is achieved by introducing a new concept of "encoded classical bits" in the decoding phase. Using the proposed protocol, treble amount of message at a time is transmitted instead of doing J. Wang et al.’s protocol thrice. No qubit is transferred to send the message. Because of using quantum network in this communication, the procedure is fully safe.
Comparison of the Representational Power of Random Forests, Binary Decision Diagrams, and Neural Networks

In this letter, we compare the representational power of random forests, binary decision diagrams (BDDs), and neural networks in terms of the number of nodes. We assume that an axis-aligned function on a single variable is assigned to each edge in random forests and BDDs, and the activation functions of neural networks are sigmoid, rectified linear unit, or similar functions. Based on existing studies, we show that for any random forest, there exists an equivalent depth-3 neural network with a linear number of nodes. We also show that for any BDD with balanced width, there exists an equivalent shallow depth neural network with a polynomial number of nodes. These results suggest that even shallow neural networks have the same or higher representation power than deep random forests and deep BDDs. We also show that in some cases, an exponential number of nodes are required to express a given random forest by a random forest with a much fewer number of trees, which suggests that many trees are required for random forests to represent some specific knowledge efficiently.
Prefixed-threshold real-time selection for free-space phase-matching quantum key distribution

Abstract Phase-matching quantum key distribution (PM-QKD), a practical version of twin-field quantum key distribution (TF-QKD), is able to surpass the linear bound without quantum repeaters. Implementing PM-QKD through free space can guarantee two parties to share unconditionally secure keys between ground stations and mobile platforms. However, atmospheric turbulence (AT) in the free-space channel would decrease the secure key rate (SKR) since it would cause fluctuations of transmittances. Two works we do are presented in this paper. First, for simplicity and generality, we derive the SKR for 4-intensity PM-QKD with asymmetric channel transmittances. Secondly, we apply the observable model of the prefixed-threshold real-time selection (P-RTS) method to mitigate the effect of AT in PM-QKD. Here, the authorized parties predetermine the threshold of transmittances and use it to select the signals while monitoring the real-time transmittances. The results show that the performance of PM-QKD in free space is severely degraded by AT, while P-RTS can reduce the impact of AT, making it transmit more than 3 times as long the distance and have a 3.37 times as large SKR at a distance of 50 km.
Group isomorphism is nearly-linear time for most orders

We show that there is a dense set of group orders such that for every such order we can decide in nearly-linear time whether two multiplication tables describe isomorphic groups. This improves significantly over the general quasi-polynomial time complexity and shows that group isomorphism can be tested efficiently for almost all group orders. We also show that in nearly-linear time it can be decided whether a multiplication table describes a group; this improves over the known super-linear complexity. Our complexities are calculated for a deterministic multi-tape Turing machine model, but we give the implications to a RAM model in the promise hierarchy as well.
Descriptive Analysis of Built-in Security Features in Web Development Frameworks

Many challenges are facing modern web applications, and security is a major concern for web applications developers especially when today's web applications are interactive and support user collaboration. SQL injections, cross-site scripting, cross-site request forgery, and broken authentication are examples of these common security vulnerabilities. Several web development frameworks (e.g., Laravel, Spring Boot, Django, Ruby on Rails, and ASP.NET Core) provide out-of-the-box security features to protect modern web applications against the above-mentioned vulnerabilities. Developers usually use manual protection mechanisms to secure modern web applications. Although, manual protection increases the probability of web application attacks. This study focuses on reviewing and comparing and analyzing the most common vulnerabilities found in modern web applications and the web development frameworks built-in security features. This study aid software developers and organizations in selecting the most effective protection methods which are provided by web application frameworks.
Nonclassicality dynamics of a dissipative cavity field containing two qubits with Kerr medium: Linear and Wehrl phase entropies

An analytical solution for a dissipative cavity field interacting with two qubits (TQs) in presence of Kerr-like medium is obtained. The super-operator method is used to obtaining the field density matrix elements under the dispersive approximation. Linear entropy is calculated to determine the entanglement between the cavity field and the TQs. Moreover, the Wehrl entropy and Wehrl phase distribution are discussed to discover the nonclassicality dynamics of a dissipative cavity field. It is found that the Kerr medium and the dissipation reduce the generated quantum coherence due to the qubit–cavity interactions. It is possible to control the ability of cavity–qubit interactions to generate quantum coherence by the mean photon number of the initial coherent cavity state. Moreover, the nonclassicality of the initial coherent state reduces the dissipative and the Kerr medium effects.
Neural Network Approach to Program Synthesis for Tabular Transformation by Example

Data transformation is a laborious and time-consuming task for analysts. Programming by example (PBE) is a technique that can simplify this difficult task for data analysts by automatically generating programs for data transformation. Most of the previously proposed PBE methods are based on search algorithms, but recent improvements in machine learning (ML) have led to its application in PBE research. For example, RobustFill was proposed as an ML-based PBE method for string transformation by using long short-term memory (LSTM) as the sequential encoder&#x2013;decoder model. However, an ML-based PBE method has not been developed for tabular transformations, which are used frequently in data analysis. Thus, in the present study, we propose an ML-based PBE method for tabular transformations. First, we consider the features of tabular transformations, which are more complex and data intensive than string transformations, and propose a new ML-based PBE method using the state-of-the-art Transformer sequential encoder&#x2013;decoder model. To our knowledge, this is the first ML-based PBE method for tabular transformations. We also propose two decoding methods comprising multistep beam search and program validation-beam search, which are optimized for program generation, and thus generate correct programs with higher accuracy. Our evaluation results demonstrated that the Transformer-based PBE model performed much better than LSTM-based PBE when applied to tabular transformations. Furthermore, the Transformer-based model with the proposed decoding method performed better than the conventional PBE model using the search-based method.
Evolutionary Optimization Algorithm on Content based Image Retrieval System using Handcrafted features with Squeeze Networks

Content based image retrieval (CBIR) is commonly utilized in several application areas due to the rising significance of images in day to day lives. On comparing with textual data, images require high storage area and processing complexity. The latest advances in machine (ML) and deep learning (DL) models can be utilized for the design of effective CBIR In this view, this paper presents an evolutionary optimization algorithm on CBIR system using handcrafted features with squeeze networks (EOCBIR-HFSN) technique. The goal of the EOCBIR-HFSN technique is to proficiently retrieve the related images based on the query image (Q1). The proposed EOCBIR-HFSN technique involves the feature extraction process by the use of local binary patterns (LBP) based handcrafted features and SqueezeNet based deep features. Besides, the hyper-parameter tuning of the SqueezeNet model is performed by the grasshopper optimization algorithm (GOA), shows the novelty of the work. Finally, Euclidean distance metric is used to determine the highly similar images from the database. The comprehensive result analysis of the EOCBIR-HFSN technique take place on benchmark database reported the enhanced outcomes over the other techniques.
Entanglement teleportation in anisotropic Heisenberg XY spin model with Herring–Flicker coupling

In this paper, the features of quantum teleportation of a maximally entangled input thermal state using a system, consisting of two qubits (spin-1/2) anisotropic XY Heisenberg model with Herring–Flicker coupling in the presence of a magnetic field, are discussed. So, we study the influence on the thermal entanglement of different physical parameters such as temperature [Formula: see text], magnetic field [Formula: see text], degree of anisotropy [Formula: see text] and distance [Formula: see text] between the two spins. The goal is to determine the conditions of use of the system as a good channel to promote quantum teleportation. The amount of output entanglement is quantified by the concurrence [Formula: see text], and the quality of teleportation is analyzed using the concept of fidelity [Formula: see text]. It is shown that [Formula: see text] presents a maximum at extremely low temperature for a large range of the parameter [Formula: see text]. Also, when we increase the temperature, we observe a dissipation of the appropriate range of [Formula: see text] and the decrease of the amplitude of [Formula: see text]. Besides, under an external magnetic field [Formula: see text], we show the appearance of quantum phase transitions with the entanglement vanishing when the condition [Formula: see text] is fulfilled, then the system can ensure the ideal communication transmission. Concerning the Ising model [Formula: see text] in the ground state, the output entanglement is nonzero only in the presence of the magnetic field [Formula: see text], and this model is useful for ensuring a distorted transmission. At finite temperature, the fidelity [Formula: see text] is less than the value 2/3, so that the system is useless as a quantum channel for providing quantum teleportation.
A robust packet‐dropping covert channel for mobile intelligent terminals

Covert communication in this regard has been widely used for protecting the secrecy of communication. Voice over Long Term Evolution (VoLTE) is a packet-switched core network solution for high-speed and high-quality end-to-end services which usually applied to the communication between intelligent systems. However, covert channels using inter-packet delays and packet order in VoLTE services are limited by specific rules. Since minor modifications to overt traffic can be detected, existing covert channel solutions cannot be directly applied to VoLTE. Therefore, this study presents a robust packet loss covert timing channel by cascade hash coding with intelligent system. To ensure robustness and undetectability, we design hash-based inter-codeword verification, codeword self-verification based on cyclic redundancy check, and adaptive mapping matrix. The sender modulates the covert messages according to the sequence number of the actively dropped packets, and the receiver can retrieve the covert messages using a specialized verification method. To evaluate undetectability, robustness, throughput, and construction costs, a large number of experiments in mobile intelligent terminals have been conducted. The experimental results prove that the proposed scheme is feasible for VoLTE communication, as the covert message is shown to be transmitted secretly, and the bit error and throughput are within acceptable ranges.
On the effectiveness of same-domain memory deduplication

Memory deduplication, an OS memory optimization technique that merges identical pages into a single Copy-on-Write (CoW) page, has been shown to be susceptible to a variety of timing side channel attacks, all of which stem from the differences between write times to the CoW page and to the normal page. To mitigate this issue, operating systems only merge pages from the same security domain (e.g., from the same process); moreover, browsers can piggyback on this defense with the recent adoption of site isolation. This was all considered sufficient, because it thwarts existing attacks, which have all relied upon separate domain (e.g., cross-process) scenarios.
A Visual Translation of the Pandemic

Abstract In 1923, Walter Benjamin published translations of Baudelaire’s poetry with a prefatory essay developing the idea that translation is not only the practice of addressing foreign readerships, but rather a process of authorship in which the original text is amplified with further significance. The authors use the term translation with a meaning that is not only linguistic but also visual. They analyze the coronavirus pandemic by translating scientific literacy through the techniques of natural language processing and data visualization. The Cartography of COVID-19 results from a visual translation that invites readers to explore the current pandemic from a different point of view that extends their perception.
Low-Cost Data Acquisition System for Solar Thermal Collectors

Solar thermal collectors are among the most popular renewable energy research subjects. Automatic Data Acquisition Systems (ADAS) have greatly facilitated their experimental testing, but their high cost is a drawback. In this paper, we present the design and testing of a decentralized, low-cost alternative ADAS based on the ESP32 microcontroller and on open-source software. The proposed system can be used for the experimental characterization of water (or air) operated solar thermal collectors in accordance with the ISO 9806:2017 requirements, but it is also compatible with sensors with lower specifications. We present also its performance results when applied for the testing of a solar thermal collector.
Object Detection in Video Surveillance Based on Multiscale Frame Representation and Block Processing by a Convolutional Neural Network

A method for detecting objects in high-resolution images is proposed that is based on representing an image as a set of its copies of decreasing scale, splitting it into blocks with overlap at each level of the image pyramid except for the top one, detecting objects in the blocks, and analyzing objects at the boundaries of adjacent blocks to merge them. The number of pyramid layers is determined by the size of the image and the input layer of the convolutional neural network (CNN). At all levels except for the top one, a block splitting is performed, and the use of overlap allows one to improve the correct classification of objects, which are divided into fragments and located in adjacent blocks. The decision to merge such fragments is made based on the analysis of the metric of intersection over union and membership in the same class. The proposed approach is evaluated for 4K and 8K images. To carry out experiments, a database is prepared with objects of two classes, person and vehicle, marked in such images. Networks of the You Only Look Once (YOLO) family of the third and fourth versions are used as CNNs. A quantitative assessment of the detection efficiency of objects is performed using the mAP metric for various combinations of parameters such as the degree of threshold confidence of the CNN and the percentage of intersection of blocks in the hierarchical representation of images. The results of the investigations are presented.
Fireworks

Serverless computing is a new paradigm that is rapidly gaining popularity in Cloud computing. One unique property in serverless computing is that the unit of deployment and execution is a serverless function, which is much smaller than a typical server program. Serverless computing introduces a new pay-as-you-go billing model and provides a high economic benefit from highly elastic resource provisioning. However, serverless computing also brings new challenges such as (1) long start-up times compared to relatively short function execution times, (2) security risks from a highly consolidated environment, and (3) memory efficiency problems from unpredictable function invocations. These problems not only degrade performance but also lower the economic benefits of Cloud providers.
Blockchain Technology in Post-Covid Agriculture

The Covid-19 pandemic poses great challenges to the resilience of global agriculture in response to disasters or crises. The pandemic mitigation measures prevent the spread of coronavirus but bring disruptions to the agriculture sector. Smallholder farmers and small agricultural producer particularly showed their vulnerability to these disruptions amid the pandemic. Technology innovations can be adopted to develop resilient, flexible and inclusive agriculture. Blockchain enables autonomous and decentralized coordination of trust in a distributed network without any trusted third-party intermediaries that can vastly reduce the transaction costs of exchange and trade. This paper examines the viable applications of blockchain technology in enhancing the resilience of agriculture and the food supply chain, facilitating trades and financing, and driving value allocation from a perspective of institutional economics.
Ethical Hacking and Web Security: Approach Interpretation

The crucial elements emerged for cyber security training is ethics of hacking and Web safety. It would be hard to create effective web security without identifying the flaws of the computer technology to prohibit intruders in the actual world. Thus, the teaching of ethics of hacking and weakness assessment is a crucial element of a good program for cyber security. Growth hacking became one of the renowned marketing mantras. This study explores both the hacking process and the trend of hacking. In each of these sectors a single failure might lead to cyber-vandalism, disappointment, revenue or loss of mind or worse. Any new technology has advantages and disadvantages. While ethical hackers assist consumers understand their security requirements more effectively, consumers are obliged to remain cautious. Ethical Hacking is intended to identify shortcomings and notify the administrator while web security helps to protect the information and the computer from dangerous threats through recognition and resolution of any vulnerability as computer networking increased, the Internet enlarged the computer and network vulnerability also.
Quasi-Unimodal Distributions for Ordinal Classification

Ordinal classification tasks are present in a large number of different domains. However, common losses for deep neural networks, such as cross-entropy, do not properly weight the relative ordering between classes. For that reason, many losses have been proposed in the literature, which model the output probabilities as following a unimodal distribution. This manuscript reviews many of these losses on three different datasets and suggests a potential improvement that focuses the unimodal constraint on the neighborhood around the true class, allowing for a more flexible distribution, aptly called quasi-unimodal loss. For this purpose, two constraints are proposed: A first constraint concerns the relative order of the top-three probabilities, and a second constraint ensures that the remaining output probabilities are not higher than the top three. Therefore, gradient descent focuses on improving the decision boundary around the true class in detriment to the more distant classes. The proposed loss is found to be competitive in several cases.