Reviewerly/exHarmony huggingface dataset:
Future of Quantum Computing in Cyber Security

Quantum computing leverages the probabilistic nature of the universe to harness computing capabilities, superseding classical and even supercomputers to solve complex problems in areas including drug development, financial modelling, etc. It is all about metadata and algorithms. This chapter per the authors aims to examine the field quantum computing in the context of cybersecurity. Through a thorough study of the timeline of developments in cybersecurity, modern cybersecurity schemes have been examined and conclusions pertaining to their vulnerabilities due to the emergence of quantum computers have been drawn. Breaking modern cryptographic schemes is equivalent to solving the underlying mathematical problems that these schemes are based on, which can be significantly sped up with a quantum computer. Hence, this chapter conveys the need for enterprises to adopt post quantum cryptographic schemes, which are not easily vulnerable to attacks by a quantum computer.
Efficient blockchain addresses classification through cascading ensemble learning approach

Bitcoin is a pseudonymous, decentralised cryptocurrency that has become one of the most widely utilised digital assets to date. Because of its uncontrolled nature and Bitcoin users' inherent anonymity, it has seen a significant surge in its use for illegal operations. This necessitates the use of unique methods for categorising the addresses of Bitcoin users. This research classifies and predicts the portion of users' activities that are lawful and unlawful on the Bitcoin blockchain. The dataset contains almost 27 billion samples that are divided into nine user acts, five of which were unlawful. To predict cross-validation (CV) accuracy, ensemble learning algorithms are trained and tested. With cross-validation accuracy of 68.63% and 49.64%, respectively, gradient boosting emerged as the best ensemble learning algorithm for classification and prediction, while bagging emerged as the worst. To get the best classification and prediction, hyperparameter tuning is used to find the optimal parameters, which helped to enhance the cross-validation accuracy of the bagging algorithm to 67.70%, with moderate improvements in the rest of the learning algorithms.
A Deep Neural Network Model for the Task of Named Entity Recognition

One of the most important factors which directly and significantly affects the quality of the neural sequence labeling is the selection and encoding the input features to generate rich semantic and grammatical representation vectors.In this paper, we propose a deep neural network model to address a particular task of sequence labeling problem, the task of Named Entity Recognition (NER).The model consists of three sub-networks to fully exploit character-level and capitalization features as well as word-level contextual representation.To show the ability of our model to generalize to different languages, we evaluated the model in Russian, Vietnamese, English and Chinese and obtained state-of-the-art performances: 91.10%, 94.43%, 91.22%, 92.95% of F-Measure on Gareev's dataset, VLSP-2016, CoNLL-2003 and MSRA datasets, respectively.Besides that, our model also obtained a good performance (about 70% of F1) with using only 100 samples for training and development sets.
Incentive-compatible and budget balanced AGV mechanism for peer-to-peer energy trading in smart grids

Peer-to-peer (P2P) energy trading refers to a type of decentralized transaction, where the energy from distributed energy resources is directly traded between peers. A key challenge in peer-to-peer energy trading is designing a safe, efficient, and transparent trading model and operating mechanism. In this study, we consider a P2P trading environment based on blockchain technology, where prosumers can submit bids or offers without knowing the reports of others. We propose an Arrow-d’Aspremont-Gerard-Varet (AGV)-based mechanism to encourage prosumers to submit their real reserve price and determine the P2P transaction price. We demonstrate that the AGV mechanism can achieve Bayesian incentive compatibility and budget balance. Kernel density estimation (KDE) is used to derive the prior distribution from the historical bid/offer information of the agents. Case studies are carried out to analyze and evaluate the proposed mechanism. Simulation results verify the effectiveness of the proposed mechanism in guiding agents to report the true reserve price while maximizing social welfare. Moreover, we discuss the advantages of budget balance for decentralized trading by comparing the Vickrey-Clarke-Groves (VCG) and AGV mechanisms.
The role of absorptive capacity, technological capability, and firm performance in Indonesia’s high-tech industry

The aim of this research is to examine the role of Absorptive capacity as an important ability to absorb technology and as a key to the firm's innovation capabilities. Technological capability is an important resource that companies must own to increase competitiveness because with these core resources and technology competencies, the firm can create values. Technological capabilities can be developed from internal or external sources. This study focuses on the external source in the form of royalty expense by presenting foreign experts/technology/copyrighted works/trademark/formulas to obtain technology and knowledge transfer. The research uses Large and Medium-scale Manufacturing Survey data that is taken from the Indonesian Central Bureau of Statistics (CBS). This is descriptive research that aims to produce an accurate mechanism of process relationship among variables. The results show that royalty expenses have a different relationship with firm performance. Some industries like electrical equipment and motor vehicles, as well as trailers and semi-trailers, have a very high royalty expense accompanied by the firm’s performance which is also the highest among all industries in the high-tech industry. This proves that several firms in the high-tech industry in Indonesia have a high absorptive capacity. However, different findings are seen in the chemicals and pharmaceutical industry. Firms do not spend above the industry’s average on royalty expenses, but their performance remains above the average performance of the high-tech industry in Indonesia.
RL-Chord: CLSTM-Based Melody Harmonization Using Deep Reinforcement Learning

Automatic music generation is the combination of artificial intelligence and art, in which melody harmonization is a significant and challenging task. However, previous recurrent neural network (RNN)-based work fails to maintain long-term dependency and neglects the guidance of music theory. In this article, we first devise a universal chord representation with a fixed small dimension, which can cover most existing chords and is easy to expand. Then a novel melody harmonization system based on reinforcement learning (RL), RL-Chord, is proposed to generate high-quality chord progressions. Specifically, a melody conditional LSTM (CLSTM) model is put forward that learns the transition and duration of chords well, based on which RL algorithms with three well-designed reward modules are combined to construct RL-Chord. We compare three widely used RL algorithms (i.e., policy gradient, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -learning, and actor–critic algorithms) on the melody harmonization task for the first time and prove the superiority of deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -network (DQN). Furthermore, a style classifier is devised to fine-tune the pretrained DQN-Chord for zero-shot Chinese folk (CF) melody harmonization. Experimental results demonstrate that the proposed model can generate harmonious and fluent chord progressions for diverse melodies. Quantitatively, DQN-Chord achieves better performance than the compared methods on multiple evaluation metrics, such as chord histogram similarity (CHS), chord tonal distance (CTD), and melody–chord tonal distance (MCTD).
Learning Long-Horizon Robot Exploration Strategies for Multi-object Search in Continuous Action Spaces

Recent advances in vision-based navigation and exploration have shown impressive capabilities in photorealistic indoor environments. However, these methods still struggle with long-horizon tasks and require large amounts of data to generalize to unseen environments. In this work, we present a novel reinforcement learning approach for multi-object search that combines short-term and long-term reasoning in a single model while avoiding the complexities arising from hierarchical structures. In contrast to existing multi-object search methods that act in granular discrete action spaces, our approach achieves exceptional performance in continuous action spaces. We perform extensive experiments and show that it generalizes to unseen apartment environments with limited data. Furthermore, we demonstrate zero-shot transfer of the learned policies to an office environment in real world experiments.
Estimation of Global Irradiation on Horizontal Plane Using Artificial Neural Network

All studies concerning solar thermal or photovoltaic systems require the knowledge of solar radiation data but measurements of these radiation are in most cases not available. Consequently, the objective of this work is to establish a prediction model of the global solar irradiation on a daily scale using the artificial neural network (ANN). For this purpose, using the meteorological data collected in the Solar Energy and Environment Laboratory (LESE), five models were tested in order to find the combinations of input variables that lead to an efficient prediction. As a result, it was found that the fifth model (M5) is the most valid one because the obtained errors are 1.32 for RMSE, 1.2 for MAE and 0.94 for R2.
Large-Scale Reconfigurable Integrated Circuits for Wideband Analog Photonic Computing

Photonic integrated circuits (PICs) have been a research hotspot in recent years. Programmable PICs that have the advantages of versatility and reconfigurability that can realize multiple functions through a common structure have been especially popular. Leveraging on-chip couplers and phase shifters, general-purpose waveguide meshes connected in different topologies can be manipulated at run-time and support a variety of applications. However, current waveguide meshes suffer from relatively a low cell amount and limited bandwidth. Here, we demonstrate a reconfigurable photonic integrated computing chip based on a quadrilateral topology network, where typical analog computing functions, including temporal differentiation, integration, and Hilbert transformation, are implemented with a processing bandwidth of up to 40 GHz. By configuring an optical path and changing the splitting ratio of the optical switches in the network, the functions can be switched and the operation order can be tuned. This approach enables wideband analog computing of large-scale PICs in a cost-effective, ultra-compact architecture.
Analysis and Design of Student Guidance Information System through Software Development Life Cycle (SDLC) dan Waterfall Model

Universities play an essential role in encouraging digital literacy as a form of support to accelerate digital transformation in Indonesia. One of the strategic steps to support the acceleration of digital transformation is to carry out organizational change in the academic scope. One of the innovative ideas to facilitate the thesis registration process, selection of supervisors, and thesis consultation is the existence of an integrated and accountable control system. This research proposes an idea to analyze and design the Information System for Thesis Consultation Process known as SIBIMA using the Software Life Cycle Development (SDLC) method according to the Waterfall model. SIBIMA is intended to support services in the education sector by adjusting the context of the values of the Atma Jaya Catholic University of Indonesia, namely Kristiani, Unggul, Peduli, and Profesional. Thus, students, lecturers, and academic staff complement each other in improving the quality of education. The case study of application design is adjusted to the registration process, selection of supervisors, and thesis consultation at the Head of Tourism Department, Faculty of Business Administration and Communication Sciences, Atma Jaya Catholic University of Indonesia.
“Oh, happy day!” Examining the role of AI-powered voice assistants as a positive technology in the formation of brand loyalty

Purpose This study examines how the locus of agency of brands' artificial intelligence (AI)–powered voice assistants (VAs) could lead to brand loyalty through perceived control, flow and consumer happiness under the moderating influences of brand image and voice congruity. Design/methodology/approach This study conducted a 2 (locus of agency: high vs. low) by 2 (brand image-voice congruity: congruent vs. incongruent) between-subjects experimental design. MANOVA, ANOVA and structural equation modeling (SEM) were conducted to test the hypothesized model. Findings ANOVA results revealed that human-centric (vs. machine-centric) agency led to higher perceived control. The interaction effect was significant, indicating the importance of congruency between brand image and VAs' voices. SEM results confirmed that perceived control predicted brand loyalty fully mediated by flow experience and consumer happiness. Originality/value This study provides evidence that the positive technology paradigm could carve out a new path in existing literature on AI-powered devices by showing the potential of a smart device as a tool for improving consumer–brand relationships and enriching consumers' well-being.
Learning From COVID-19 to Improve Surveillance for Emerging Threats

Learning From COVID-19 to Improve Surveillance for Emerging Threats Daniel B. Jernigan MD, MPH, Dylan George PhD, and Marc Lipsitch DPhil Affiliation Daniel B. Jernigan is the director for the National Center for Emerging and Zoonotic Infectious Diseases, US Centers for Disease Control and Prevention (CDC), Atlanta, GA, and is former deputy director for public health science and surveillance, CDC. Dylan George is the director of operations for the Center for Forecasting and Outbreak Analytics (CFA), CDC. Marc Lipsitch is the director of science for the CFA, CDC, and is director for the Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA. CopyRightCorrespondence should be sent to Daniel B. Jernigan, MD, MPH, Director, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, 1600 Clifton Rd NE, MS H16-5, Atlanta, GA 30329 (e-mail: [email protected]gov). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. CONTRIBUTORS All authors contributed equally to this article. https://doi.org/10.2105/AJPH.2023.307261 Accepted: February 12, 2023 Published Online: March 16, 2023
Advancement of Digital Image Steganography using Deep Convolutional Neural Networks

Steganography is a means of secretly concealing information in which the act of steganography itself is hidden in plain sight. Steganography is also known as “stealth writing.” Steganography is not a suitable replacement for cryptography; nevertheless, it can be used in conjunction with cryptography to further strengthen the information hiding mechanism. Steganography is not a good replacement for cryptography. The cover media for a steganographic method can be anything that can be represented digitally, such as a text file, image, audio file, video file, Transmission Control Protocol/Internet Protocol packet, or any other entity. Steganography can be used to hide information in these and other formats. First, the Robust Multitier Spatial Domain Secured Color Image Steganography in Server Environment (MSS-SE) approach is created as part of an effort to lower the possibility of compromising confidential communications. MSS-SE steganography methods, rather than transmitting the original picture from the server, send a randomized mix of a cover image and a confused image from the same server. This is done in place of delivering the original image. A unique approach that is based on deep hierarchical spectral spatial feature fusion has been developed by us in order to improve the classification of HSIs (DHSSFF). The pooling method, which is considered by CNN to be one of the most important regularization procedures, has been the primary focus of our attention prior to the construction of the model. Following an examination of the relevant published material, we have reached the conclusion that a wide variety of pooling algorithms have been applied to carry out fruitful analyses of remote sensing data.
Taleblazer vs. Metaverse: a comparative analysis of two platforms for building AR location-based educational games

Location-based AR games are becoming increasingly popular in education. With location-based AR games, learners can obtain knowledge by visiting places of educational value through informative digital content that is activated and displayed on their mobile devices when specific locations are reached. To create location-based AR games, there are several available authoring tools. Taleblazer and Metaverse Studio are two popular platforms that are used nowadays by many educators. This study aims to perform a comparative analysis between these platforms to provide educators interested in developing location-based AR experiences with all the information needed to make an informed decision on which platform to use. The analysis examines the designer environment and its available features, the end-user interface, the documentation that accompanies each platform, and third-party applications that are developed by these tools. Furthermore, two game prototypes have been developed to better understand the two platforms' functionality.
Is open science a double-edged sword?: data sharing and the changing citation pattern of Chinese economics articles

Data sharing is an important part of open science (OS), and more and more institutions and journals have been enforcing open data (OD) policies. OD is advocated to help increase academic influences and promote scientific discovery and development, but such a proposition has not been elaborated on well. This study explores the nuanced effects of the OD policies on the citation pattern of articles by using the case of Chinese economics journals. China Industrial Economics (CIE) is the first and only Chinese social science journal so far to adopt a compulsory OD policy, requiring all published articles to share original data and processing codes. We use the article-level data and difference-in-differences (DID) approach to compare the citation performance of articles published in CIE and 36 comparable journals. Firstly, we find that the OD policy quickly increased the number of citations, and each article on average received 0.25, 1.19, 0.86, and 0.44 more citations in the first four years after publication respectively. Furthermore, we also found that the citation benefit of the OD policy rapidly decreased over time, and even became negative in the fifth year after publication. In conclusion, this changing citation pattern suggests that an OD policy can be double edged sword, which can quickly increase citation performance but simultaneously accelerate the aging of articles.
Lower Bound for the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>T</mml:mi></mml:math> Count Via Unitary Stabilizer Nullity

We introduce magic measures to quantify the nonstabilizerness of multiqubit quantum gates and establish lower bounds on the $T$ count for fault-tolerant quantum computation. First, we introduce the stabilizer nullity of multiqubit unitary, which is based on the subgroup of the quotient Pauli group associated with the unitary. This unitary stabilizer nullity extends the state-stabilizer nullity by Beverland et al. [Quantum Sci. Technol. 5, 035009 (2020)] to a dynamic version. In particular, we show this nonstabilizerness measure has desirable properties, such as subadditivity under composition and additivity under tensor product. Second, we prove that a given unitary's stabilizer nullity is a lower bound for the $T$ count, utilizing the above properties in gate synthesis. Third, we compare the state and the unitary stabilizer nullity, proving that the lower bounds for the $T$ count obtained by the unitary stabilizer nullity are never less than the state-stabilizer nullity. Moreover, we show an explicit $n$-qubit unitary family of unitary stabilizer nullity $2n$, which implies that its $T$ count is at least $2n$. This gives an example where the bounds derived by the unitary stabilizer nullity strictly outperform the state-stabilizer nullity by a factor of 2. We finally showcase the advantages of unitary stabilizer nullity in estimating the $T$ count of quantum gates with interests.
Building a Chatbot System to Analyze Opinions of English Comments

Chatbot research has advanced significantly over the years. Enterprises have been investigating how to improve these tools’ performance, adoption, and implementation to communicate with customers or internal teams through social media. Besides, businesses also want to pay attention to quality reviews from customers via social networks about products available in the market. From there, please select a new method to improve the service quality of their products and then send it to publishing agencies to publish based on the needs and evaluation of society. Although there have been numerous recent studies, not all of them address the issue of opinion evaluation on the chatbot system. The primary goal of this paper’s research is to evaluate human comments in English via the chatbot system. The system’s documents are preprocessed and opinion-matched to provide opinion judgments based on English comments. Based on practical needs and social conditions, this methodology aims to evolve chatbot content based on user inter-actions, allowing for a cyclic and human-supervised process with the following steps to evaluate comments in English. First, we preprocess the input data by collecting social media comments, and then our system parses those comments according to the rating views for each topic covered. Finally, our system will give a rating and comment result for each comment entered into the system. Experiments show that our method can improve accuracy better than the referenced methods by 78.53%.
The Flow of Trust: A Visualization Framework to Externalize, Explore, and Explain Trust in ML Applications

We present a conceptual framework for the development of visual interactive techniques to formalize and externalize trust in machine learning (ML) workflows. Currently, trust in ML applications is an implicit process that takes place in the user’s mind. As such, there is no method of feedback or communication of trust that can be acted upon. Our framework will be instrumental in developing interactive visualization approaches that will help users to efficiently and effectively build and communicate trust in ways that fit each of the ML process stages. We formulate several research questions and directions that include: 1) a typology/taxonomy of trust objects, trust issues, and possible reasons for (mis)trust; 2) formalisms to represent trust in machine-readable form; 3) means by which users can express their state of trust by interacting with a computer system (e.g., text, drawing, marking); 4) ways in which a system can facilitate users’ expression and communication of the state of trust; and 5) creation of visual interactive techniques for representation and exploration of trust over all stages of an ML pipeline.