Reviewerly/exHarmony huggingface dataset:
Deep self-supervised clustering with embedding adjacent graph features

Deep clustering uses neural networks to learn the low-dimensional feature representations suitable for clustering tasks. Numerous studies have shown that learning embedded features and defining the clustering loss properly contribute to better performance. However, most of the existing studies focus on the deep local features and ignore the global spatial characteristics of the original data space. To address this issue, this paper proposes deep self-supervised clustering with embedding adjacent graph features (DSSC-EAGF). The significance of our efforts is three-fold: 1) To obtain the deep representation of the potential global spatial structure, a dedicated adjacent graph matrix is designed and used to train the autoencoder in the original data space; 2) In the deep encoding feature space, the KNN algorithm is used to obtain the virtual clusters for devising a self-supervised learning loss. Then, the reconstruction loss, clustering loss, and self-supervised loss are integrated, and a novel overall loss measurement is proposed for DSSC-EAGF. 3) An inverse-Y-shaped network model is designed to well learn the features of both the local and the global structures of the original data, which greatly improves the clustering performance. The experimental studies prove the superiority of the proposed DSSC-EAGF against a few state-of-the-art deep clustering methods.
The Virtual Theremin: Designing an Interactive Digital Music Instrument for Film Scene Scoring

Abstract This paper presents a first prototype of a virtual Theremin instrument for accompanying film scenes with sound. The virtual Theremin is implemented as a hybrid application for the web. Sound control is achieved by capturing user gestures with a webcam and mapping the gestures to the corresponding virtual Theremin parameters pitch and volume. Different sound types can be selected. The application’s underlying research is part of the multi-modal digital heritage project KOLLISIONEN which targets to open up the private archive of the Russian film maker Sergej Eisenstein to a broader public in digital form. Eisenstein, a film theorist and pioneer of film montage, was particularly intrigued by the Theremin as an instrument for film sound design. The virtual Theremin presented here is therefore linked to a film scene from the 1929 Soviet drama “The General Line” by Sergej Eisenstein which was never set to music originally. In its first implementation state, the application connects music interaction design with digital heritage in a modular, flexible and playful way and uses contemporary web technologies to enable easy operation and the greatest possible accessibility.
Quantum coherence of an orbital angular momentum multiplexed continuous-variable entangled state

Orbital angular momentum (OAM) multiplexed entangled state is an important quantum resource for high dimensional quantum information processing. In this paper, we experimentally quantify quantum coherence of OAM multiplexed continuous-variable (CV) entangled state and characterize its evolution in a noisy environment. We show that the quantum coherence of the OAM multiplexed CV entangled state carrying topological charges l = 1 and l = 2 are the same as that of the Gaussian mode with l = 0 in a noisy channel. Furthermore, we show that the quantum coherence of OAM multiplexed entangled state is robust to noise, even though the sudden death of entanglement is observed. Our results provide reference for applying quantum coherence of OAM multiplexed CV entangled state in a noisy environment.
Comparing the frequentist and Bayesian periodic signal detection: rates of statistical mistakes and sensitivity to priors

We perform extensive Monte Carlo simulations to systematically compare the frequentist and Bayesian treatments of the Lomb--Scargle periodogram. The goal is to investigate whether the Bayesian period search is advantageous over the frequentist one in terms of the detection efficiency, how much if yes, and how sensitive it is regarding the choice of the priors, in particular in case of a misspecified prior (whenever the adopted prior does not match the actual distribution of physical objects). We find that the Bayesian and frequentist analyses always offer nearly identical detection efficiency in terms of their tradeoff between type-I and type-II mistakes. Bayesian detection may reveal a formal advantage if the frequency prior is nonuniform, but this results in only $\sim 1$ per cent extra detected signals. In case if the prior was misspecified (adopting nonuniform one over the actual uniform) this may turn into an opposite advantage of the frequentist analysis. Finally, we revealed that Bayes factor of this task appears rather overconservative if used without a calibration against type-I mistakes (false positives), thereby necessitating such a calibration in practice.
Confidence Calibration for Object Detection and Segmentation

Calibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. However, although the task of confidence calibration has been investigated on classification problems, thorough investigations on object detection and segmentation problems are still missing. Therefore, we focus on the investigation of confidence calibration for object detection and segmentation models in this chapter. We introduce the concept of multivariate confidence calibration that is an extension of well-known calibration methods to the task of object detection and segmentation. This allows for an extended confidence calibration that is also aware of additional features such as bounding box/pixel position, shape information, etc. Furthermore, we extend the expected calibration error (ECE) to measure miscalibration of object detection and segmentation models. We examine several network architectures on MS COCO as well as on Cityscapes and show that especially object detection as well as instance segmentation models are intrinsically miscalibrated given the introduced definition of calibration. Using our proposed calibration methods, we have been able to improve calibration so that it also has a positive impact on the quality of segmentation masks as well.
Energy-efficient Resource Allocation in Multi-Tenant Edge Computing using Markov Decision Processes

We address the problem of a Network Operator (NO) owning limited resources at the network edge. The NO wishes to enable advanced services, by virtualizing and allocating such resources among multiple tenants, i.e., third-party Service Providers, co-existing at the edge, with different Quality of Service (QoS) constraints. The NO applies a resource allocation policy with the objective of minimizing energy consumption via switching off non-used resources while guaranteeing tenants QoS requirements. We propose a resource allocation policy based on Markov Decision Processes (MDP). In simulation we show that our policy is able to reduce energy consumption, by turning off unused resources, while meeting heterogeneous SP requirements. Our code is available as open source.
Shortcuts to Adiabaticity for Fast Qubit Readout in Circuit Quantum Electrodynamics

We propose how to engineer the longitudinal coupling to accelerate the measurement of a qubit longitudinally coupled to a cavity, motivated by the concept of shortcuts to adiabaticity. Different modulations are inversely designed from two methods of inverse engineering and counter-diabatic driving, for achieving larger values of the signal-to-noise ratio (SNR) at nanosecond scale. By comparison, we demonstrate that our protocols outperform the usual periodic modulations on the pointer state separation and SNR. Finally, we show a possible implementation considering state-of-the-art circuit quantum electrodynamics architecture, estimating the minimal time allowed for the measurement process.
Design and Application of Improved Ant Colony Algorithm in E-Commerce System

Since the 21st century, artificial intelligence and e-commerce have taken the lead. The two are close to life. Data show that, in the digital economy era, the two have developed rapidly and are widely used; however, they also have some problems. E-commerce logistics system has always been the first problem mentioned by relevant personnel and users. The continuous increase in transaction volume is worth celebrating, but the logistics efficiency continues to be flat or even declining, which has become a serious problem. This shows that, for the e-commerce platform, the investment in science and technology is not comprehensive and in depth, and the artificial intelligence technology has not been deeply applied for the logistics industry. Understanding needs, finding problems, and solving them in time are the way to develop this industry. Pareto is chosen by most logistics systems, but in fact, its efficiency is not as high as imagined. In view of this, this paper proposes an innovative algorithm, based on ant colony algorithm, with optimization mechanism to strengthen the shortcomings, thus improving the premature problem of ant colony algorithm, and at the same time, a series of performances such as convergence speed, accuracy, and stability have been strengthened because of this improvement. Experiments show that the optimization ability of this algorithm is better than the other two.
The Impact of Workplace Interpersonal Conflict on Job Performance, Job Depression and Turnover Intention

The objective of the current study is to check the impact of workplace interpersonal conflict’s impact on job performance, job depression and turnover intention. The current study is been set to make cross sectional analysis of employees working in both public and private organizations. A pre-planned and well-structured questioner is been designed and provided to 350 respondents. 215 respondents returned the fill questioners. Simple statistical tools are been used to find the demographic features and average behavior of the respondents. Regression technique is been used for hypothesis testing and inferential statistics. The findings of regression analysis showed the interpersonal conflicts reduces job performance. Increase in interpersonal conflict increase the job depression. The impact of interpersonal conflict on turnover intention is been observed positive and statistically significant at 1%. All three-null hypotheses are been rejected while alternative hypotheses are been accepted. The moderating and mediating factors i.e. organizational maturity and supervisor behavior help reducing the interpersonal conflicts. It is been suggested that the organization need to promote employees frankly strategies and policies.
Error rate reduction of single-qubit gates via noise-aware decomposition into native gates

In the current era of Noisy Intermediate-Scale Quantum (NISQ) technology, the practical use of quantum computers remains inhibited by our inability to aptly decouple qubits from their environment to mitigate computational errors. In this paper, we introduce an approach by which knowledge of a qubit's initial quantum state and the standard parameters describing its decoherence can be leveraged to mitigate the noise present during the execution of a single-qubit gate. We benchmark our protocol using cloud-based access to IBM quantum processors. On ibmq_rome, we demonstrate a reduction of the single-qubit error rate by 38%, from [Formula: see text] to [Formula: see text], provided the initial state of the input qubit is known. On ibmq_bogota, we prove that our protocol will never decrease gate fidelity, provided the system's [Formula: see text] and [Formula: see text] times have not drifted above 100 times their assumed values. The protocol can be used to reduce quantum state preparation errors, as well as to improve the fidelity of quantum circuits for which some knowledge of the qubits' intermediate states can be inferred. This paper presents a pathway to using information about noise levels and quantum state distributions to significantly reduce error rates associated with quantum gates via optimized decomposition into native hardware gates.
Recommendation System Issues, Approaches and Challenges Based on User Reviews

With the ever-increasing volume of online information, recommender systems have been effective as a strategy to overcome information overload. They have a wide range of applications in many fields, including e-learning, e-commerce, e-government and scientific research. Recommender systems are search engines that are based on the user’s browsing history to suggest a product that expresses their interests. Being usually in the form of textual comments and ratings, such reviews are a valuable source of information about users’ perceptions. Recommender systems (RSs) apply various approaches to predict users’ interest on information, products and services among a huge amount of available items. In this paper, we will describe the recommender system, discuss ongoing research in this field, and address the challenges, limitations and the techniques adopted. This paper also discusses how review texts are interpreted to solve some of the major problems with traditional recommendation techniques. To assess the value of a recommender system, qualitative evaluation measures are discussed as well in this research. Based on a series of selected articles published between 2008 and 2020, the study allowed us to conclude that the efficiency of RSs is strongly centered on the control of information context, the operated exploration algorithm, the method, and the type of processed data in addition to the information on users’ trust.
Optimized Feature Selection in Software Product Lines using Discrete Bat Algorithm

Software Product Lines (SPLs) are one of the ways to develop software products by increasing productivity and reducing cost and time in the software development process. In SPLs, each product has many features and it is necessary to consider the optimal and custom features of the products. In fact, selecting key features in SPLs is a challenging process. Feature selection in SPLs is an optimization problem and an NP-Hard problem. One way to select a feature is to use meta-heuristic algorithms modeled on nature, i.e., Bat Algorithm. By modeling bat behavior in prey hunting, a suitable meta-innovative algorithm is considered. This algorithm has important advantages that make it more accurate than conventional methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In this paper, to select software product features, idol binary algorithm and artificial neural network are used to identify important features of software products that reduce production costs and time. The experiments in MATLAB software and datasets related to software production lines show that the rate of reduction of target performance error or feature selection cost in software production lines in the proposed method has decreased by 64.17% with increasing population.
Broadcast of a restricted set of qubit and qutrit states

The no-cloning theorem forbids the distribution of an unknown state to more than one receiver. However, if the sender knows the state, and the state is chosen from a restricted set of possibilities, a procedure known as remote state preparation can be used to broadcast a state. Here we examine a remote state preparation protocol that can be used to send the state of a qubit, confined to the equator of the Bloch sphere, to an arbitrary number of receivers. The entanglement cost is less than that of using teleportation to accomplish the same task. We present a number of variations on this task, probabilistically sending an unknown qubit state to two receivers, sending different qubit states to two receivers, and sending qutrit states to two receivers. Finally, we discuss some applications of these protocols.
Principled or Partisan? The Effect of Cancel Culture Framings on Support for Free Speech

Political scientists have long been interested in the effects that media framings have on support or tolerance for controversial speech. In recent years, the concept of cancel culture has complicated our understanding of free speech. In particular, the modern Republican Party under Donald Trump has made “fighting cancel culture” a cornerstone of its electoral strategy. We expect that when extremist groups invoke cancel culture as a reason for their alleged censorship, support for their free speech rights among Republicans should increase. We use a nationally representative survey experiment to assess whether individuals’ opposition to cancel culture is principled or contingent on the ideological identity of the speaker. We show that framing free speech restrictions as the consequence of cancel culture does not increase support for free speech among Republicans. Further, when left-wing groups utilize the cancel culture framing, Republicans become even less supportive of those groups’ free speech rights.
ECC, RSA, and DSA analogies in applied mathematics

The use of resource constrained devices is on the rise these days, and they are largely used to work with sensitive data. Data security becomes increasingly important for both producers and users nowadays, the main problem that renders these devices vulnerable is a lack of resources. Attackers could take advantage of these flaws to get access to sensitive information. Improving the speed of the devices and avoiding data loss, a carefully chosen, and well-verified mathematical encryption algorithm should be used. Because of its simplicity, the RSA (Rivest, Shamir, and Adleman) algorithm is a well-known public key cryptography used by lots of people. The digital signature algorithm (DSA) is the industry standard for digital signature algorithms. Elliptic curve cryptography (ECC) uses elliptic curves on limited region to encrypt public keys. A comparison of several of these cryptographic algorithms will be presented in this paper, to determine whether one has a better chance of succeeding.
Return Probability of Quantum and Correlated Random Walks

The analysis of the return probability is one of the most essential and fundamental topics in the study of classical random walks. In this paper, we study the return probability of quantum and correlated random walks in the one-dimensional integer lattice by the path counting method. We show that the return probability of both quantum and correlated random walks can be expressed in terms of the Legendre polynomial. Moreover, the generating function of the return probability can be written in terms of elliptic integrals of the first and second kinds for the quantum walk.
The precision paradox

Precision medicine—or personalized medicine—promises greater precision and certainty in medicine. This paper highlights a ‘precision paradox’: that it may, in important ways, also create more uncertainty and imprecision. First, the aim of precision medicine is to tailor treatment to the individual. However, when the number of individuals that are seen to have a specific health problem moves towards one (<italic>n</italic> = 1), it becomes hard to employ randomized control trials or other statistically based methods. This creates uncertainty about what kind of rationality and evidence will be seen as validating what works in medicine. Just as biomedicine aims to turn personalization into science, the art of medicine resurfaces. Second, one strategy in precision medicine is to make models more complex in order to make them more accurate and thus certain. However, there is a fundamental tension between complexity and precision. Beyond a certain threshold, as more variables are included, models paradoxically become less precise. Third, in the preventive vision of precision medicine, a massive, new form of screening is proposed where DNA is sequenced, and multiple variables of human health are monitored through life. However, as each body is rendered ‘transparent’ by multiple screening technologies, there is a risk of labelling each person with multiple health problems. Contrary to its promise of medicine ‘at the right time in the right person’, precision medicine, as proposed, will paradoxically be present ‘all the time’ in ‘everybody’.
Federated Learning-Based Privacy Preservation with Blockchain Assistance in IoT 5G Heterogeneous Networks

In the area where privacy is of greater concern, federated learning,a distributed machine learning strategy for preserving privacy,is widely employed in several privacy concern applications. In the meantime, neural architectures became familiar with deep learning approaches for automatic tuning of the architecture of deep neural networks (DNN). While searching with neural architecture and federated learning has experienced several challenges, optimized neural architecture research in federated learning is extensively on demand. DNN faces numerous issues while training such user privacy and ensuring the integrity of the aggregated results obtained from a server. To provide solutions for the above-mentioned issues, enormous federated learning techniques worked towards preserving privacy and were applied in different situations. Still, it is an open challenge that enables users to verify if the cloud server functions appropriately while ensuring users’ privacy while training. Federated Learning Method is a new way to improve the accuracy and precision, since the previous approach failed to opt the solutions. Here, Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL)is proposed to ensure the confidentiality of users’ local gradients while performing federated learning. The parameters such as classification accuracy, running time, Communication overhead, Computation overhead, and transaction speed are considered. The values obtained for these parameters are compared against three standard methods, namely Biparing Method (BM) Homomorphic Cryptosystem (HC), and Multiple Authorities with Attribute-Based Signature scheme (MA-ABS)against proposed Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL). As a result, the proposed ECC-BFL achieved 95% of classification accuracy, 65 sec of running time, 76% of communication overhead, 63% of computation overhead, and 92% of transaction speed.
Hate speech in social media: personal experiences and perceptions of university students in Bangladesh

Purpose Social media (SM) is a new communication tool that substantially contribute to facilitating online hate speech (OHS). In emphasis of the question “what role can SM play in an individual’s life?”, this study aims to understand Bangladeshi university students’ personal experiences and opinions of OHSs related to SM. Design/methodology/approach The authors used an online survey method to collect data and retrieved responses from 410 students. Mann–Whitney U test, Kruskal–Wallis test and Spearman’s rank correlation analysis were used to test the hypotheses. Findings This study found that hate speech is a familiar term among students. Students’ political views or opinions, religion and gender have become the most targeted instruments for OHSs. Comparing students’ use of SM, the authors found that Facebook was the most used SM site to spread hate speech in Bangladesh. In terms of personal experiences, the findings indicated that 45.6% of students became victims of OHSs at least once or more times, and the majority of students tended to simply avoid OHSs. Another significant finding was that OHS has real-life effects on the students, resulting in various personal and psychological distress. Originality/value Although some research has been conducted on hate speech at the local level, to the best of the authors’ knowledge, no study has focused on the student community. To the best of the authors’ knowledge, this study is the first attempt in Bangladesh to focus on OHSs from a student’s personal viewpoint.