Reviewerly/exHarmony huggingface dataset:
Solving the Shipment Rerouting Problem with Quantum Optimization Techniques

In this work we develop methods to optimize an industrially-relevant logistics problem using quantum computing. We consider the scenario of partially filled trucks transporting shipments between a network of hubs. By selecting alternative routes for some shipment paths, we optimize the trade-off between merging partially filled trucks using fewer trucks in total and the increase in distance associated with shipment rerouting. The goal of the optimization is thus to minimize the total distance travelled for all trucks transporting shipments. The problem instances and techniques used to model the optimization are drawn from real-world data describing an existing shipment network in Europe. We show how to construct this optimization problem as a quadratic unconstrained binary optimization (QUBO) problem. We then solve these QUBOs using classical and hybrid quantum-classical algorithms, and explore the viability of these algorithms for this logistics problem.
An Article-Oriented Framework for Automatic Semantic Analysis of COVID-19 Researches

In this work, we propose an article-oriented framework for automatically extract semantic topics from scientific articles related to COVID-19 researches. Our framework has four key building blocks (i) pre-processing, (ii) topic modeling, (iii) correlating topics, authors, and institutions, and (iv) a summarization interface. The first one corresponds to apply traditional textual pre-processing strategies in the texts extracted from articles and constructing their data representations. The topic modeling block aims at finding semantic topics from articles based on the most relevant words of each discovered topic. The third block correlates these discovered topics with the articles, authors (researchers), and institutions. The summarization interface provides an intuitive visualization for these results. Our evaluation shows that our framework can automatically extract relevant features from the articles, identifying the key topics covered by them, as well as the contribution of researchers, institutions, and countries to the topics. Our framework can help research institutions and companies to form multidisciplinary teams and funding agencies to identify more promising research approaches regarding COVID-19.
Coin Drop—A Decentralised Exchange Platform

In today’s world, cryptocurrency has seen a boom in users’ number, and the numbers are increasing day by day. There are multiple cryptocurrencies, so there must be a platform to provide an exchange of cryptocurrencies. These days, many platforms provide users with the service, but they lack speed and are limited to some cryptocurrencies. Thus, we have proposed and developed a system that will increase the transactions rate by performing it off-chain to increase the transactions’ speed and perform exchange between any cryptocurrencies. The proposed system combines the best practices of both the decentralised and centralised exchange platforms.
Accredited Undergraduate Cybersecurity Degrees: Four Approaches

In response to the demand for cybersecurity professionals, schools are developing new academic programs in cybersecurity. The first four cybersecurity programs accredited by the ABET illustrate different ways to offer a four-year undergraduate cybersecurity degree.
Development of Fabric Feed Mechanism Using Horizontal Articulated Dual Manipulator for Automated Sewing

This study addresses the problem of automating the sewing process in the garment industry. While automatic feeding during sewing has been achieved, overlapping and feeding two a pair of cloths still require a human operator. The difficulty lies in manipulating a soft material. Instead of using a generic robot finger, we use a horizontal dual-arm manipulator with rollers as end effectors, which always hold down the cloth during manipulation. We validated our system through experiments with a prototype that can feed and sew a single piece of cloth in a single operation.
Efficient Novel Privacy Preserving PoS Protocol Proof-of-concept with Algorand

Proof of Stake (PoS) emerged to replace and tackle the problem of vast energy consumption in Proof of Work (PoW) consensus. PoS is based on the assumption that the majority of the stake is owned by honest participants. Consequently, instead of solving a computationally hard puzzle to propose the next block in the blockchain, PoS selects a participant with probability proportional to its stake in the network. In contrast to the solution to the puzzle, the proof of selection in PoS has inherent privacy issues. The identity of the selected participant is revealed to other participants to verify the proof, and the stake of the selected can be deducted by frequency analysis. Therefore, Private Proof of Stake (PPoS) emerged to provide a valid alternative to PoW, aiming to tackle the energy consumption in PoW while preserving the privacy of the selected participant in a consensus round. Recent PPoS protocols by Baldimtsi et al. and Ganesh et al., rely on an anonymous broadcast channel and have a large proof size that hinders the practical implementation of the protocols.
The future of the printed book in the era of technological advancement: an imperative for digital innovation and engagement

Purpose The purpose of this paper is to establish the place of the printed book in the era of technological advancement with the assumption that the print media is facing imminent death in the face of readily available and convenient online information. Also the paper aims to assess how the development of new technologies have affected the production, circulation and readership of the printed book, especially among the young generation. Design/methodology/approach Explanatory study was used with closed-ended approach to collect data from 50 students of the Technical University of Kenya and 5 key informant interviews with selected book publishers in Nairobi. The uses and gratification theory was used to explore the knowledge-seeking behavior among the respondents. Findings Findings showed that more than 80% of the respondents preferred the internet to the printed book, which, according to them, has no future in the face of technological advancement. Book publishers, on the other hand, felt that the printed book has a bright future among specific audiences who are committed to it, and especially those in the rural areas who have no access to the internet. While they agreed that the internet has posed a major challenge to the sales and readership of the printed book significantly, it is helping in marketing the printed book as opposed to killing it. New bookshops in Nairobi and modern libraries in high schools, tertiary institutions and universities demonstrate that the printed book is not dying soon. Research limitations/implications The researcher experienced challenges in data collection as the respondents were busy preparing for final examinations and hence many of them were not willing to spare time to fill the questionnaire. To solve this, the researcher had to spend more time to collect data as opposed to if the students were free and ready to participate in the study without any pressure. Practical implications The findings can be used as a basis for further research to widen the scope that can help bring a wider perspective to the topic. The results can also inform policy guidelines on the topic and also contribute to the body of knowledge. Social implications The topic touches on social phenomena that are affecting a number of young people and their information-seeking habits in the era of digital revolution. The way the young generation seek and use information should be of interest not only to academic staff but also to policymakers. Originality/value The paper is original based on primary data that was collected by the researcher from the respondents. It is backed by secondary data to bridge the gap between theory and practice.
The LCOE Evolution and Grid Parity Analysis of Centralized Solar Photovoltaic: A Case Study of Ningxia, China

Achieving grid parity in 2021 is the goal of China’s photovoltaic development, which is not only on the user side but also on the generation side. Relevant studies indicated that distributed PV has realized grid parity basically in China, while centralized PV, which belongs to the generation side, still has some difficulties in achieving grid parity. Therefore, this paper takes Ningxia Province, which is abundant in solar resources, as the research object and compares LCOE with the traditional coal-fired price to analyze the situation of grid parity of the Pingluo project. It is found that this project cannot reach the goal of grid parity. Then, the future evolution of the local LCOE is analyzed, so as to determine the time of grid parity of Ningxia’s centralized PV power stations. In the calculation of LCOE, the presence and absence of environmental benefits and the general and optimistic forecast of cumulative installed capacity are combined into four scenarios. The results show that the centralized PV in Ningxia cannot achieve grid parity in 2021 under the four scenarios. However, in addition to the scenario that there are no environmental benefits and the cumulative installed capacity is generally forecasted and will reach grid parity of the generation side in 2023, the other three situations can achieve the goal in 2022. Moreover, the LCOE value is the lowest under the scenario of considering environmental benefits and the optimistic forecast of future installed capacity.
Governance Mechanisms for Fourth Industrial Revolution Artifacts in Higher Education Institutions: A Systems Perspective

This conceptual contribution explores the relevance of the Fourth Industrial Revolution (4IR) and its artifacts to the transformation and redefinition of corporate governance in general, and specifically to Information Technology Governance (ITG). We have premised our contribution on the ‘open’ nature of the contemporary society founded on open digital infrastructures, with the Internet as the backbone, with various 4IR technologies reliant on this evolving and dynamic digital infrastructure. Our perspective and conceptualization of the emerging 4IRG draws from systems thinking, and specifically management cybernetics from which Stafford Beer evolved the Viable Systems Model (VSM) as a theory for diagnosing organizations. From a Critical Realist philosophical foundation, our contribution is contextualized in Higher Education Institutions (HEIs) to aid in motivating governance mechanisms that can be anchored on 4IR and VSM. We offer a conceptualization of digital governance mechanisms that call for algorithmic accountability of 4IR technologies. Specific research questions are proposed that can guide future studies on Fourth Industrial Revolution Governance (4IRG).
Rank & Sort Loss for Object Detection and Instance Segmentation

We propose Rank & Sort (RS) Loss, as a ranking-based loss function to train deep object detection and instance segmentation methods (i.e. visual detectors). RS Loss supervises the classifier, a sub-network of these methods, to rank each positive above all negatives as well as to sort positives among themselves with respect to (wrt.) their continuous localisation qualities (e.g. Intersection-over-Union - IoU). To tackle the non-differentiable nature of ranking and sorting, we reformulate the incorporation of error-driven update with backpropagation as Identity Update, which enables us to model our novel sorting error among positives. With RS Loss, we significantly simplify training: (i) Thanks to our sorting objective, the positives are prioritized by the classifier without an additional auxiliary head (e.g. for centerness, IoU, mask-IoU), (ii) due to its ranking-based nature, RS Loss is robust to class imbalance, and thus, no sampling heuristic is required, and (iii) we address the multi-task nature of visual detectors using tuning-free task-balancing coefficients. Using RS Loss, we train seven diverse visual detectors only by tuning the learning rate, and show that it consistently outperforms baselines: e.g. our RS Loss improves (i) Faster R-CNN by ~ 3 box AP and aLRP Loss (ranking-based baseline) by ~ 2 box AP on COCO dataset, (ii) Mask R-CNN with repeat factor sampling (RFS) by 3.5 mask AP (~ 7 AP for rare classes) on LVIS dataset; and also outperforms all counterparts. Code available at this https URL
Stereotype-aware collaborative filtering

In collaborative filtering, recommendations are made using user feedback on a few products.In this paper, we show that even if sensitive attributes are not used to fit the models, a disparate impact may nevertheless affect recommendations.We propose a definition of fairness for the recommender system that expresses that the ranking of items should be independent of sensitive attribute.We design a coclustering of users and items that processes exogenous sensitive attributes to remove their influence to return fair recommendations.We prove that our model ensures approximately fair recommendations provided that the classification of users approximately respects statistical parity.
Acmeological technologies of development and assessment of professional and educational activity productivity

The article describes the results of scientific study reflection on acmeological technologies, which are the main mechanisms of adoption of a uniquely designed activity systems aimed at achieving a highly productive level of development of artistic potential in all participants. Significance of the study demonstrates that, in the context of professional education globalization, a teacher must apply highly productive educational technologies, perfecting the process of their professional and educational activities and developing mental work – creative readiness of a student for their individual professional activity. Hypothesis of the study: knowledge and introduction of highly productive acmeological technologies to professional and educational activity facilitate creative readiness in students for future activity and successful entry into forthcoming educational and professional system. According to acmeological theory of fundamental education, productive activity laws, an effort was made to classify acmeological technologies of professional and educational activity, definition and description of their features, results of their application taking into consideration a criterion of education quality in an educational system of different levels. Summarizing the results of the study 1) features of acmeological technologies as a method of productivity development of main subjects of educational system were theoretically proved and defined; 2) features of acmeological technologies of different levels of the educational system were selected and defined; 3) structural and functional elements of acmeological technologies in pedagogic activity were specified.
Finding the Hardest Formulas for Resolution

A CNF formula is harder than another CNF formula with the same number of clauses if it requires a longer resolution proof. In this paper we introduce resolution hardness numbers; they give for m=1,2,... the length of a shortest proof of a hardest formula on m clauses. We compute the first ten resolution hardness numbers, along with the corresponding hardest formulas. To achieve this, we devise a candidate filtering and symmetry breaking search scheme for limiting the number of potential candidates for hardest for- mulas, and an efficient SAT encoding for computing a shortest resolution proof of a given candidate formula.
The nexus of trans collegians’ pronouns and name practices navigating campus space(s): Beyond the binary.

The purpose of our study is to explore the nexus between trans students’ experiences with pronoun usage and naming practices through campus interactions and navigations of campus. Grounded in a Deleuzian conception of becoming, our analysis explores the multiple and simultaneous ways that pronoun use effects trans student’s experiences on campus. As a theoretical orientation, becoming centers the multiplicitous and entangled narratives, experiences, and negotiations of pronoun usage and naming practices with the trans students in our studies, disrupting the dominant culture. Our study’s findings contribute to the literature on trans collegians’ experiences navigating genderist campus environments, as well as theoretical and methodological considerations for centering trans, nonbinary, and agender student narratives. Specifically, our analysis of student narratives emphasized the ways in which students simultaneously experience isolation and affirmation, the ways in which policies designed to affirm and support trans students could also work to silence them, as well as how students practiced resilience and built relationships within these contexts. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell

A code smell is a surface indicator of an inherent problem in the system, most often due to deviation from standard coding practices on the developer’s part during the development phase. Studies observe that code smells made the code more susceptible to call for modifications and corrections than code that did not contain code smells. Restructuring the code at the early stage of development saves the exponentially increasing amount of effort it would require to address the issues stemming from the presence of these code smells. Instead of using traditional features to detect code smells, we use user comments (given on the packages’ repositories) to manually construct features to predict code smells. We use three Extreme learning machine kernels over 629 packages to identify eight code smells by leveraging feature engineering aspects and using sampling techniques. Our findings indicate that the radial basis functional kernel performs best out of the three kernel methods with a mean accuracy of 98.52.
Superpixel Segmentation Based on Grid Point Density Peak Clustering

Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem. That would produce redundant complex jagged textures. In this paper, the density peak clustering will be used to reduce the redundant superpixels and highlight the primary textures and contours of the salient objects. Firstly, the grid pixels are extracted as feature points, and the density of each feature point will be defined. Secondly, the cluster centers are extracted with the density peaks. Finally, all the feature points will be clustered by the density peaks. The pixel blocks, which are obtained by the above steps, are superpixels. The method is carried out in the BSDS500 dataset, and the experimental results show that the Boundary Recall (BR) and Achievement Segmentation Accuracy (ASA) are 95.0% and 96.3%, respectively. In addition, the proposed method has better performance in efficiency (30 fps). The comparison experiments show that not only do the superpixel boundaries have good adhesion to the primary textures and contours of the salient objects, but they can also effectively reduce the redundant superpixels in the homogeneous region.
Learning a Belief Representation for Delayed Reinforcement Learning

This paper considers sequential decision-making problems where the interactions between an agent and its environment are affected by delays. Delays may be present in the state observation, in the action execution, or in the reward collection. We consider the delayed Markov Decision Process (MDP) framework both in the case of deterministic and stochastic delays. Given the hardness of the delayed MDP problem, we use a heuristic approach to design an algorithm that uses the belief over the current unobserved state to select its action. We design a self-attention prediction module which, given the last observed state and the following sequence of actions, estimates the beliefs over the following states. This algorithm is able to deal with deterministic delays and could potentially be extended to stochastic delays. We empirically evaluate the effectiveness of the proposed approach in both deterministic and stochastic control problems affected by deterministic delays.
Machine Learning View on Blockchain Parameter Adjustment

A fundamental problem in distributed computing is achieving agreement among many parties for a single data value in the presence of faulty processes–to get consensus. The consensus mechanism is an underlying part of blockchain design and commits new blocks and changes protocol itself. In addition to classic correctness requirements, blockchains need specific ones: high performance regarding transactions per second, fast transaction confirmation, etc. Blockchains control the requirements with parameters. But how to meet qualitative and optimize quantitative requirements? Typically we have the main blockchain network without access to try different parameters and the test network to do whatever we want. In the paper, we provide a machine learning view on the blockchain parameter adjustment. We list the blockchain parameters for Solana blockchain and apply feature importance to select the most significant parameters during the forthcoming optimization.
State Transfer of Two-level Quantum System Feedback Control Based on Online State Estimation

A quantum state feedback control method is proposed in this paper. The state of a two-level open quantum system is estimated online based on the continuous weak measurement and the compressed sensing theory. Based on the state estimated online and the Lyapunov stability theorem, the state feedback control law used to transfer the quantum state is designed. Moreover, three numerical simulation experiments are implemented in the MATLAB environment: the state transfer from eigenstates to eigenstates, superposition states to superposition states, and superposition states to mixed states. The experimental results verify high performance of the proposed feedback control based on the state estimated online.
Sketch Your Own GAN

Can a user create a deep generative model by sketching a single example? Traditionally, creating a GAN model has required the collection of a large-scale dataset of exemplars and specialized knowledge in deep learning. In contrast, sketching is possibly the most universally accessible way to convey a visual concept. In this work, we present a method, GAN Sketching, for rewriting GANs with one or more sketches, to make GANs training easier for novice users. In particular, we change the weights of an original GAN model according to user sketches. We encourage the model's output to match the user sketches through a cross-domain adversarial loss. Furthermore, we explore different regularization methods to preserve the original model's diversity and image quality. Experiments have shown that our method can mold GANs to match shapes and poses specified by sketches while maintaining realism and diversity. Finally, we demonstrate a few applications of the resulting GAN, including latent space interpolation and image editing.
Data Centric Domain Adaptation for Historical Text with OCR Errors

We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French. For the cross-domain case, we address domain shift by integrating unsupervised in-domain data via contextualized string embeddings; and OCR errors by injecting synthetic OCR errors into the source domain and address data centric domain adaptation. We propose a general approach to imitate OCR errors in arbitrary input data. Our cross-domain as well as our in-domain results outperform several strong baselines and establish state-of-the-art results. We publish preprocessed versions of the French and Dutch Europeana NER corpora.