Reviewerly/exHarmony huggingface dataset:
A hybrid classical-quantum algorithm for digital image processing

A hybrid classical-quantum approach for evaluation of multi-dimensional Walsh–Hadamard transforms and its applications to quantum image processing are proposed. In this approach, multi-dimensional Walsh–Hadamard transforms are obtained using quantum Hadamard gates (along with state preparation, shifting, scaling and measurement operations). The proposed approach for evaluation of multi-dimensional Walsh–Hadamard transform has a considerably lower computational complexity (involving $$O(N^d)$$ operations) in contrast to classical Fast Walsh–Hadamard transform (involving $$O(N^d~\log _2 N^d)$$ operations), where d and N denote the number of dimensions and degrees of freedom along each dimension. Unlike many other quantum image representation and quantum image processing frameworks, our proposed approach makes efficient use of qubits, where only $$\log _2 N $$ qubits are sufficient for sequential processing of an image of $$ N \times N $$ pixels. Selected applications of the proposed approach (for $$ d=2 $$ ) are demonstrated via computational examples relevant to basic image filtering and periodic banding noise removal, and the results were found to be satisfactory.
Homodyne measurement with a Schrödinger cat state as a local oscillator

Homodyne measurements are a widely used quantum measurement. Using a coherent state of large amplitude as the local oscillator, it can be shown that the quantum homodyne measurement limits to a field quadrature measurement. In this work, we give an example of a general idea: injecting nonclassical states as a local oscillator can lead to nonclassical measurements. Specifically, we consider injecting a superposition of coherent states, a Schr\"odinger cat state, as a local oscillator. We derive the Kraus operators and the positive operator-valued measure in this situation.
Universal quantum computation with symmetric qubit clusters coupled to an environment

One of the most challenging problems for the realization of a scalable quantum computer is to design a physical device that keeps the error rate for each quantum processing operation low. These errors can originate from the accuracy of quantum manipulation, such as the sweeping of a gate voltage in solid state qubits or the duration of a laser pulse in optical schemes. Errors also result from decoherence, which is often regarded as more crucial in the sense that it is inherent to the quantum system, being fundamentally a consequence of the coupling to the external environment. Grouping small collections of qubits into clusters with symmetries may serve to protect parts of the calculation from decoherence. In this work, we use four-level cores with a straightforward generalization of discrete rotational symmetry, called $\ensuremath{\omega}$-rotation invariance, to encode pairs of coupled qubits and universal two-qubit logical gates. We include quantum errors as a main source of decoherence, and show that symmetry makes logical operations particularly resilient to untimely anisotropic qubit rotations. We propose a scalable scheme for universal quantum computation where cores play the role of quantum-computational transistors, or quansistors for short. Initialization and readout are achieved by tunnel-coupling the quansistor to leads. The external leads are explicitly considered and are assumed to be the other main source of decoherence. We show that quansistors can be dynamically decoupled from the leads by tuning their internal parameters, giving them the versatility required to act as controllable quantum memory units. With this dynamical decoupling, logical operations within quansistors are also symmetry-protected from unbiased noise in their parameters. We identify technologies that could implement $\ensuremath{\omega}$-rotation invariance. Many of our results can be generalized to higher-level $\ensuremath{\omega}$-rotation-invariant systems, or adapted to clusters with other symmetries.
Artificial Neural Network Hyperparameters Optimization: A Survey

Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics. Neural Networks (NNs) are one of the most es-sential ways to ML; the most challenging element of designing a NN is de-termining which hyperparameters to employ to generate the optimal model, in which hyperparameter optimization improves NN performance. This study includes a brief explanation regarding a few types of NN as well as some methods for hyperparameter optimization, as well as previous work results in enhancing ANN performance using optimization methods that aid research-ers and data analysts in developing better ML models via identifying the ap-propriate hyperparameter configurations.
Keyword Extraction: A Modern Perspective

Abstract The goal of keyword extraction is to extract from a text, words, or phrases indicative of what it is talking about. In this work, we look at keyword extraction from a number of different perspectives: Statistics, Automatic Term Indexing, Information Retrieval (IR), Natural Language Processing (NLP), and the emerging Neural paradigm. The 1990s have seen some early attempts to tackle the issue primarily based on text statistics [13, 17]. Meanwhile, in IR, efforts were largely led by DARPA’s Topic Detection and Tracking (TDT) project [2]. In this contribution, we discuss how past innovations paved a way for more recent developments, such as LDA, PageRank, and Neural Networks. We walk through the history of keyword extraction over the last 50 years, noting differences and similarities among methods that emerged during the time. We conduct a large meta-analysis of the past literature using datasets from news media, science, and medicine to business and bureaucracy, to draw a general picture of what a successful approach would look like.
The influence of the Internet of things on pharmaceutical inventory management

The primary goal of this research is to examine the influence of the Internet of Things on Pharmaceutical Inventory Management in Jordan. The employees of the pharmaceutical companies in Jordan represent the population of the study. Accordingly, the study instrument was distributed to an appropriate sample of 620 employees of pharmaceutical companies in Jordan; the questionnaires used in the analysis were 432. Structural equation modeling (SEM) was used to test the study hypotheses. The study results indicated that all dimensions of the Internet of Things had an influence on inventory management. The greatest influence was for cloud computing. Based to the study findings, pharmaceutical companies in Jordan should provide high-quality Internet coverage in their offices and stores, as well as to invest in Internet of things technologies that allow the company to remotely monitor and control the physical components of the stores, attract employees with knowledge of modern Internet technologies, and provide the necessary training and development for their employees.
Improved Differential and Linear Trail Bounds for ASCON

Ascon is a family of cryptographic primitives for authenticated encryption and hashing introduced in 2015. It is selected as one of the ten finalists in the NIST Lightweight Cryptography competition. Since its introduction, Ascon has been extensively cryptanalyzed, and the results of these analyses can indicate the good resistance of this family of cryptographic primitives against known attacks, like differential and linear cryptanalysis.Proving upper bounds for the differential probability of differential trails and for the squared correlation of linear trails is a standard requirement to evaluate the security of cryptographic primitives. It can be done analytically for some primitives like AES. For other primitives, computer assistance is required to prove strong upper bounds for differential and linear trails. Computer-aided tools can be classified into two categories: tools based on general-purpose solvers and dedicated tools. General-purpose solvers such as SAT and MILP are widely used to prove these bounds, however they seem to have lower capabilities and thus yield less powerful bounds compared to dedicated tools.In this work, we present a dedicated tool for trail search in Ascon. We arrange 2-round trails in a tree and traverse this tree in an efficient way using a number of new techniques we introduce. Then we extend these trails to more rounds, where we also use the tree traversal technique to do it efficiently. This allows us to scan much larger spaces of trails faster than the previous methods using general-purpose solvers. As a result, we prove tight bounds for 3-rounds linear trails, and for both differential and linear trails, we improve the existing upper bounds for other number of rounds. In particular, for the first time, we prove bounds beyond 2−128 for 6 rounds and beyond 2−256 for 12 rounds of both differential and linear trails.
Wideband and high-dimensional chaos generation using optically pumped spin-VCSELs

We propose and numerically demonstrate wideband and high-dimensional chaos signal generation based on optically pumped spin-polarized vertical-cavity surface-emitting lasers (spin-VCSELs). Here, we focus on the chaotic characteristics of spin-VCSELs under two scenarios: one is a spin-VCSEL with optical feedback and the other is optical heterodyning the outputs of two free-running spin-VCSELs. Specifically, we systematically investigate the influence of some key parameters on the chaotic properties, i.e., bandwidth, spectral flatness (SF), time delay signature (TDS), correlation dimension (CD), and permutation entropy (PE), and reveal the route to enhance these properties simultaneously. Our simulation results demonstrate for the first time that spin-VCSELs with simple auxiliary configurations allow for chaos generation with desired properties, including effective bandwidth up to 30 GHz and above, no TDS of greater than 0.2, the flatness of 0.75 and above, and the high complexity/dimensionality over a wide range of parameters under both schemes. Therefore, our study may pave the way for potential applications requiring wideband and high-dimensional chaos.
A hybrid encryption system for communication and financial transactions using RSA and a novel symmetric key algorithm

Today's digital data transmission over unsecured wired and wireless communication channels is making encryption algorithms an increasingly important tool for securing data and information. Hybrid encryption techniques combine encryption schemes of either two symmetric keys or both symmetric and asymmetric encryption methods, and that provides more security than public or private key single encryption models. Currently, there are many techniques on the market that use a combination of cryptographic algorithms and claim to provide higher data security. Many hybrid algorithms have failed to satisfy customers in securing data and cannot prevent all types of security threats. To improve the security of digital data, it is essential to develop novel and resilient security systems as it is inevitable in the digital era. The recommended algorithm scheme is a combination of the well-known Rivest Shamir Adleman (RSA) algorithm and a simple symmetric key (SSK) algorithm. The aim of this study is to develop a better encryption method using RSA and a newly proposed symmetric SSK algorithm. We believe that the proposed hybrid cryptographic algorithm provides more security and privacy.
The Bonsai algorithm: grow your own fermion-to-qubit mapping

Fermion-to-qubit mappings are used to represent fermionic modes on quantum computers, an essential first step in many quantum algorithms for electronic structure calculations. In this work, we present a formalism to design flexible fermion-to-qubit mappings from ternary trees. We discuss in an intuitive manner the connection between the generating trees' structure and certain properties of the resulting mapping, such as Pauli weight and the delocalisation of mode occupation. Moreover, we introduce a recipe that guarantees Fock basis states are mapped to computational basis states in qubit space, a desirable property for many applications in quantum computing. Based on this formalism, we introduce the Bonsai algorithm, which takes as input the potentially limited topology of the qubit connectivity of a quantum device and returns a tailored fermion-to-qubit mapping that reduces the SWAP overhead with respect to other paradigmatic mappings. We illustrate the algorithm by producing mappings for the heavy-hexagon topology widely used in IBM quantum computers. The resulting mappings have a favourable Pauli weight scaling $\mathcal{O}(\sqrt{N})$ on this connectivity, while ensuring that no SWAP gates are necessary for single excitation operations.
The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results

This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LIB) were defined, tools were evaluated on equal-cost hardware (using an automatic evaluation pipeline based on AWS instances), and tool parameters were chosen by the participants before the final test sets were made public. In the 2022 iteration, 11 teams participated on a diverse set of 12 scored benchmarks. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.
Virtual Reality Applications Market Analysis—On the Example of Steam Digital Platform

This paper presents research on the topic of virtual reality (VR) applications. It conducts a quantitative analysis of virtual reality applications available in the international market using the example of a digital platform, which was the Steam platform. The study presents and analyzes data on the number of applications in the selected categories, such as genres, types of headsets, and language. The research also includes the analysis of the top-rated VR applications, their reviews, and their features, recognized based on the tags describing them. Additionally, the article provides and systematizes new knowledge about the VR applications environment. Based on the results, it was concluded that the most numerous group of VR applications was action applications, and they account for more than half of all VR apps (51.22%). Following this, there were casual games (40.78%) and then simulation VR apps (37.35%). Referring to the results of the top-rated VR applications (‘overwhelmingly positive’ status on Steam), there were only two apps with a result of 98% (the highest rated) positive feedback: Half-Life: Alyx, the action and adventure app, which is a shooter described as zombie horror, and Walkabout Mini Golf VR, a casual and minimalist sport application. When it comes to the analysis of the tags of the top-rated VR applications, the most repeated tags, despite the ‘VR’ tag, included ‘first-person’ and ‘singleplayer’ (occurred in the descriptions of 68% of the applications).
Roles of Library in the Metaverse

The library is an important institution throughout society. Library roles change over time, from the custodian of records to addressing information needs of the society in the digital age of the Internet. Tech companies around the world are start investing in the next version of the Internet called the Metaverse. The key elements of Metaverse are Virtual Reality, Augmented Reality, Real-Time, and Immersive experience in the common cyberspace. The Metaverse is not fully existed yet. It is predicted that it will change business disruptively. This paper discusses the roles of the library may play in the Metaverse. Keywords: Metaverse, Virtual Reality, Augmented Reality, Roles of the library, Information Science, Information Management eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by E-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under the responsibility of AMER (Association of Malaysian Environment-Behavior Researchers), ABRA (Association of Behavioral Researchers on Asians), and cE-Bs (Centre for Environment-Behavior Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia.
Tractability of explaining classifier decisions

Explaining decisions is at the heart of explainable AI. We investigate the computational complexity of providing a formally-correct and minimal explanation of a decision taken by a classifier. In the case of threshold (i.e. score-based) classifiers, we show that a complexity dichotomy follows from the complexity dichotomy for languages of cost functions. In particular, submodular classifiers allow tractable explanation of positive decisions, but not negative decisions (assuming P≠NP). This is an example of the possible asymmetry between the complexity of explaining positive and negative decisions of a particular classifier. Nevertheless, there are large families of classifiers for which explaining both positive and negative decisions is tractable, such as monotone or modular (e.g. linear) classifiers. We extend the characterisation of tractable cases to constrained classifiers (when there are constraints on the possible input vectors) and to the search for contrastive rather than abductive explanations. Indeed, we show that tractable classes coincide for abductive and contrastive explanations in the constrained or unconstrained settings. We show the intractability of returning a set of k diverse explanations even for linear classifiers and k=2. Finding a minimum-cardinality explanation is tractable for the family of modular classifiers, i.e. when the score function is the sum of unary functions, but becomes intractable when any non-modular function is also allowed.
Computing Without Borders: The Way Towards Liquid Computing

Despite the de-facto technological uniformity fostered by the cloud and edge computing paradigms, resource fragmentation across isolated clusters hinders the dynamism in application placement, leading to suboptimal performance and operational complexity. Building upon and extending these paradigms, we propose a novel approach envisioning a transparent continuum of resources and services on top of the underlying fragmented infrastructure, called liquid computing. Fully decentralized, multi-ownership-oriented and intent-driven, it enables an overarching abstraction for improved applications execution, while at the same time opening up for new scenarios, including resource sharing and brokering. Following the above vision, we present liqo, an open-source project that materializes this approach through the creation of dynamic and seamless Kubernetes multi-cluster topologies. Extensive experimental evaluations have shown its effectiveness in different contexts, both in terms of Kubernetes overhead and compared to other open-source alternatives.
CamNuvem: A Robbery Dataset for Video Anomaly Detection

(1) Background: The research area of video surveillance anomaly detection aims to automatically detect the moment when a video surveillance camera captures something that does not fit the normal pattern. This is a difficult task, but it is important to automate, improve, and lower the cost of the detection of crimes and other accidents. The UCF-Crime dataset is currently the most realistic crime dataset, and it contains hundreds of videos distributed in several categories; it includes a robbery category, which contains videos of people stealing material goods using violence, but this category only includes a few videos. (2) Methods: This work focuses only on the robbery category, presenting a new weakly labelled dataset that contains 486 new real-world robbery surveillance videos acquired from public sources. (3) Results: We have modified and applied three state-of-the-art video surveillance anomaly detection methods to create a benchmark for future studies. We showed that in the best scenario, taking into account only the anomaly videos in our dataset, the best method achieved an AUC of 66.35%. When all anomaly and normal videos were taken into account, the best method achieved an AUC of 88.75%. (4) Conclusion: This result shows that there is a huge research opportunity to create new methods and approaches that can improve robbery detection in video surveillance.
An IoT-Based Wristband for Automatic People Tracking, Contact Tracing and Geofencing for COVID-19

The coronavirus disease (COVID-19) pandemic has triggered a huge transformation in the use of existing technologies. Many innovations have been made in the field of contact tracing and tracking. However, studies have shown that there is no holistic system that integrates the overall process from data collection to the proper analysis of the data and actions corresponding to the results. It is critical to identify any contact with infected people and to ensure that they do not interact with others. In this research, we propose an IoT-based system that provides automatic tracking and contact tracing of people using radio frequency identification (RFID) and a global positioning system (GPS)-enabled wristband. Additionally, the proposed system defines virtual boundaries for individuals using geofencing technology to effectively monitor and keep track of infected people. Furthermore, the developed system offers robust and modular data collection, authentication through a fingerprint scanner, and real-time database management, and it communicates the health status of the individuals to appropriate authorities. The validation results prove that the proposed system identifies infected people and curbs the spread of the virus inside organizations and workplaces.
Path of modern natural sciences: from discovery of realities to study of their attributes

We analyze the development of the natural sciences according to the scheme: “… reality – attributes – reality – attributes”. Any material reality is interpreted as a carrier of attributes manifested in a certain range of its experimental study and at a certain theoretical level of data understanding. More sophisticated experiments and refined theories lead to a more detailed and correct vision of realities and their attributes. The latter initiates new experiments and creation of new theories. These cognitive processes lead to the discovery of new material realities with their additional or refined attributes compared to those previously known. The objectivity and relative truth of scientific statements about the realities under study and their attributes are based on a qualitative or quantitative correspondence between theoretically calculated values of attributes and their experimentally measured values. We emphasize that the isolation and adequate historical and philosophical analysis of those cycles requires professional knowledge of the analyzed complex scientific material and cannot be implemented within the framework of oversimplified ideas about science and the role, which theories play in it. Examples of such ideas are the understanding of theories solely in terms of their refutation and confirmation or even the replacement of theories with fuzzy sociological concepts such as a paradigm and sociocultural determination of the scientific results. Those views often become the source of idealistic, irrational, and postmodernist interpretations of science and its history.