Reviewerly/exHarmony huggingface dataset:
Certified quantum gates

High quality, fully-programmable quantum processors are available with small numbers (<1000) of qubits, and the scientific potential of these near term machines is not well understood. If the small number of physical qubits precludes practical quantum error correction, how can these error-susceptible processors be used to perform useful tasks? We present a strategy for developing quantum error detection for certain gate imperfections that utilizes additional internal states and does not require additional physical qubits. Examples for adding error detection are provided for a universal gate set in the trapped ion platform. Error detection can be used to certify individual gate operations against certain errors, and the irreversible nature of the detection allows a result of a complex computation to be checked at the end for error flags.
Decentralized Identifiers and Verifiable Credentials for Smartphone Anticounterfeiting and Decentralized IMEI Database

The smartphone industry is lucrative for device counterfeiting with over 1.5 billion devices sold annually in the last three years. In 2017, it is estimated that there were around 184 million counterfeit devices, valued at 45.3 billion EUR, 12.9% of total sales. Beyond its economic impact, smartphone counterfeiting affects various aspects of user security and privacy, harms manufacturer reputation, and degrades service quality. Furthermore, since smartphone devices are attached to different mobile networks globally, challenges arise on how devices’ identities are maintained and verified and how the supply chain actors can access the device identity throughout its life cycle with less control from third parties. Decentralized identifiers (DIDs) and verifiable claims implemented on a distributed ledger technology present a powerful candidate to address this challenge. Thanks to Blockchain’s use of cryptographic identifiers, record immutability, and provenance, and the features provided by the DIDs and verifiable claims that enable identity management decentralization, portability, and discoverability. This article proposes a smartphone anticounterfeiting system based on an integrated approach of the technologies mentioned above. The proposed system eliminates the need for a central authority and provides the features of identity creation, transfer of ownership, and the capability of fast and secure reporting of stolen devices.
Reasonableness discussion and analysis for Hyperledger Fabric configuration

Blockchain, as a distributed ledger technology, becomes more and more popular in both industry and academia. Hyperledger Fabric is permissioned blockchain platform hosted by Linux foundation. Fabric has various components such as peer, ordering service, chaincode and state database. The structure of Fabric network is very complicated to provide reliable permissioned blockchain service. Generally, developers must deal with hundreds of parameters to configure a network. That will cause many reasonableness problems in configurations. In this paper, we focus on how to detect reasonableness problems in Fabric configurations. Firstly, we discuss and provide a reasonableness problem knowledge database based on the perspectives of functionality, security and performance. Secondly, we implemented a detect tool for reasonableness check to Fabric. Finally, we collect 108 sample networks as the testing dataset in the experiment. The result shows our tool can help developers to locate reasonableness problems and understand their network better.
How to overcome lockdown: selective isolation versus contact tracing

At this stage of the COVID-19 pandemic, two policy aims are imperative: avoiding the need for a general lockdown of the population, with all its economic, social and health costs, and preventing the healthcare system from being overwhelmed by the unchecked spread of infection. Achieving these two aims requires the consideration of unpalatable measures. Julian Savulescu and James Cameron argue that mandatory isolation of the elderly is justified under these circumstances, as they are at increased risk of becoming severely ill from COVID-19, and are thus likely to put disproportionate strain on limited healthcare resources. However, their arguments for this strategy are contingent on the lack of viable alternatives. We suggest that there is a possible alternative: a mandatory, centralised contact-tracing app. We argue that this strategy is ethically preferable to the selective isolation of the elderly, because it does not target members of a certain group, relying instead on the movements of each individual, and because it avoids the extended isolation of certain members of the society. Although this type of contact-tracing app has its drawbacks, we contend that this measure warrants serious consideration.
A Unified Taylor Framework for Revisiting Attribution Methods

Attribution methods have been developed to understand the decision making process of machine learning models, especially deep neural networks, by assigning importance scores to individual features. Existing attribution methods often built upon empirical intuitions and heuristics. There still lacks a general and theoretical framework that not only can unify these attribution methods, but also theoretically reveal their rationales, fidelity, and limitations. To bridge the gap, in this paper, we propose a Taylor attribution framework and reformulate seven mainstream attribution methods into the framework. Based on reformulations, we analyze the attribution methods in terms of rationale, fidelity, and limitation. Moreover, We establish three principles for a good attribution in the Taylor attribution framework, i.e., low approximation error, correct contribution assignment, and unbiased baseline selection. Finally, we empirically validate the Taylor reformulations, and reveal a positive correlation between the attribution performance and the number of principles followed by the attribution method via benchmarking on real-world datasets.
Stably Adaptive Anti-Occlusion Siamese Region Proposal Network for Real-Time Object Tracking

Siamese region proposal network has made remarkable achievements in visual object tracking because of its balanced accuracy and speed. However, it regards tracking as a local one-shot detection task, which lose the power of updating the appearance model online thereby cannot handle the object-occlusion, fast motion and out-of-view situations. To tackle this problem, we propose a method that combines adaptive Kalman filter with Siamese region proposal network (Anti-occlusion-SiamRPN) to make full use of the object spatial-temporal information. Specifically we first extract target features through deep network and then uses adaptive Kalman filter to predict target trajectory in these difficult scenarios. Further this trajectory is used to select the candidate area of the next frame for Siamese region proposal network, which improve the searching mechanism. In this way, the introduction of adaptive Kalman filter makes the tracking process online learning which makes up for the disadvantage that Siamese region proposal network can only track offline. In addition, a hard example discrimination method (HEDM) is proposed to estimate whether the occlusion occurs and how seriously it is, which also improve Kalman filtering mechanism to make it update adaptively. Our method being evaluated with the speed of 80 FPS on five widely-applied challenging benchmarks including OTB2013, OTB2015, OTB50, VOT2016 and VOT2018. The extensive experimental results demonstrate our method achieves state-of-the-art effects and great improvement in comparison to other trackers.
Smart Contract-Based Blockchain Solution to Reduce Supply Chain Risks

Companies are becoming aware of the potential risks arising from sustainability aspects in supply chains. These risks can affect ecological, economic or social aspects. One important element in managing those risks is improved transparency in supply chains by means of digital transformation. Innovative technologies like blockchain technology can be used to enforce transparency. In this paper, we present a smart contract-based Supply Chain Control Solution to reduce risks. Technological capabilities of the solution will be compared to a similar technology approach and evaluated regarding their benefits and challenges within the framework of supply chain models. As a result, the proposed solution is suitable for the dynamic administration of complex supply chains.
The Determinants of Employee’s Performance: A Literature Review

This piece of research highlights a contextual understanding of employee performance’s concept by identifying factors affecting employee performance in the organization. This achieved through analyzing literature in ISI (Web of Knowledge) from 2015 until 2019, after that determine factors influencing employee performance. The definition of employee performance is given, furthermore, the description of each factor which has an influence on employee performance. The result indicates that knowledge management, information and communication technology, employee’s empowerment, innovation and creativity and organization culture have a significant impact on employee performance. On the other hand, the correlation between these factors has a vital role in maintaining employee’s performance and attitude.
Utilizing Fuzzy Logic in Developing Reversible Data Hiding Method

The development of information technology has brought an avoidable consequence for data security.It is because data, specifically those are private or confidential, are vulnerable to illegal access.Therefore, a protection method, such as data hiding, is essential.This protection is done by inserting a secret message (payload) into a medium (cover).One of the media commonly used in this method is audio, where a payload is embedded in audio samples.Nevertheless, this common technique has several disadvantages, including the relatively small insertion space and the significant dissimilarity between stego-audio and its original audio cover.Moreover, there is a risk of payload that cannot be returned to the original file.To overcome these problems, we propose to employ the fuzzification stage in the fuzzy logic algorithm that the samples are grouped into five categories.The binary payload is evenly distributed to the available sample space, which previously was calculated by considering the total payload and the number of interpolated samples.In the case that space is not enough to carry the payload, it is dynamically increased.The results show an increase of about 48% in stego audio quality measured by the Peak Signal-to-Noise Ratio (PSNR).Moreover, the method is reversible, and the size of the sample space can be controlled to specific values.
Simultaneous Representation of Knowledge and Belief for Epistemic Planning with Belief Revision

We propose a novel approach to the problem of false belief revision in epistemic planning. Our state representations are pointed Kripke models with two binary relations over possible worlds: one representing agents' necessarily true knowledge, and one representing agents' possibly false beliefs. State transition functions maintain S5n properties in the knowledge relation and KD45n properties in the belief relation. When new information contradicts an agent's beliefs, belief revision draws new possible worlds from the agent's knowledge relation. Our method also improves upon prior work by accommodating false announcements. We develop our system as an extension to the mA* action language, presenting transition functions for ontic, sensing, and announcement actions.
ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate and Compare Multi-class Classifiers

A major challenge during the development of Machine Learning systems is the large number of models resulting from testing different model types, parameters, or feature subsets. The common approach of selecting the best model using one overall metric does not necessarily find the most suitable model for a given application, since it ignores the different effects of class confusions. Expert knowledge is key to evaluate, understand and compare model candidates and hence to control the training process. This paper addresses the research question of how we can support experts in the evaluation and selection of Machine Learning models, alongside the reasoning about them. ML-ModelExplorer is proposed – an explorative, interactive, and model-agnostic approach utilising confusion matrices. It enables Machine Learning and domain experts to conduct a thorough and efficient evaluation of multiple models by taking overall metrics, per-class errors, and individual class confusions into account. The approach is evaluated in a user-study and a real-world case study from football (soccer) data analytics is presented. ML-ModelExplorer and a tutorial video are available online for use with own data sets: www.ml-and-vis.org/mex
High-Frequency Path Mining-Based Reward and Punishment Mechanism for Multi-Colony Ant Colony Optimization

To solve the problem of falling into local optimum and poor convergence speed of traditional ant colony algorithm, this paper proposes a High-frequency path mining-based Reward and Punishment mechanism for multi-colony Ant Colony Optimization (HRPACO). Firstly, the pheromone concentration on the path of effective strong association is rewarded adaptively according to the lift of association rules to accelerate the convergence speed. Secondly, the pheromone concentration on the path of minimum spanning tree is punished adaptively according to the support of association rules to improve the diversity of the colony. The interaction of reward and punishment mechanism can effectively balance the diversity and convergence. Finally, a self-evolutionary mechanism based on Gaussian filter is proposed to adaptively adjust the pheromone concentration by dynamic smoothing of the pheromone matrix, so as to help the colony jump out of the local optimum. The TSP is used to verify the performance of the algorithm. The simulation results show that the proposed algorithm can effectively accelerate the convergence speed and improve the accuracy of solution, especially for large-scale problems. Meanwhile, path planning is used to verify the feasibility of the proposed algorithm. The simulation results show that the algorithm can find an effective and better path even in the environment of complex obstacles.
SCALEUS-FD: A FAIR Data Tool for Biomedical Applications

The Semantic Web and Linked Data concepts and technologies have empowered the scientific community with solutions to take full advantage of the increasingly available distributed and heterogeneous data in distinct silos. Additionally, FAIR Data principles established guidelines for data to be Findable, Accessible, Interoperable, and Reusable, and they are gaining traction in data stewardship. However, to explore their full potential, we must be able to transform legacy solutions smoothly into the FAIR Data ecosystem. In this paper, we introduce SCALEUS-FD, a FAIR Data extension of a legacy semantic web tool successfully used for data integration and semantic annotation and enrichment. The core functionalities of the solution follow the Semantic Web and Linked Data principles, offering a FAIR REST API for machine-to-machine operations. We applied a set of metrics to evaluate its “FAIRness” and created an application scenario in the rare diseases domain.
Creating a Learning Management System at the Smart Bangun Negeri Community Learning Activity Center

In accordance with the Education Service Circular No. 4 of 2020 related to Learning during the Covid-19 Pandemic Period, PKBM Smart Bangun Negeri developed learning methods through online methods, which in this case were accompanied by a community service team from the Indraprasta PGRI university. The abdimas team created a virtual learning room with the Moodle platform that was tailored to the learning needs of the Smart Bangun Negeri PKBM. As a result of this abdimas, PKBM Smart Bangun Negeri can organize learning in accordance with the learning standards of the BSNP (National Education Standards Agency) through virtual learning spaces on the Moodle platform.
Visualizing stemming techniques on online news articles text analytics

Stemming is the process to convert words into their root words by the stemming algorithm. It is one of the main processes in text analytics where the text data needs to go through stemming process before proceeding to further analysis. Text analytics is a very common practice nowadays that is practiced toanalyze contents of text data from various sources such as the mass media and media social. In this study, two different stemming techniques; Porter and Lancaster are evaluated. The differences in the outputs that are resulted from the different stemming techniques are discussed based on the stemming error and the resulted visualization. The finding from this study shows that Porter stemming performs better than Lancaster stemming, by 43%, based on the stemming error produced. Visualization can still be accommodated by the stemmed text data but some understanding of the background on the text data is needed by the tool users to ensure that correct interpretation can be made on the visualization outputs.
Beyond Cross-Validation—Accuracy Estimation for Incremental and Active Learning Models

For incremental machine-learning applications it is often important to robustly estimate the system accuracy during training, especially if humans perform the supervised teaching. Cross-validation and interleaved test/train error are here the standard supervised approaches. We propose a novel semi-supervised accuracy estimation approach that clearly outperforms these two methods. We introduce the Configram Estimation (CGEM) approach to predict the accuracy of any classifier that delivers confidences. By calculating classification confidences for unseen samples, it is possible to train an offline regression model, capable of predicting the classifier’s accuracy on novel data in a semi-supervised fashion. We evaluate our method with several diverse classifiers and on analytical and real-world benchmark data sets for both incremental and active learning. The results show that our novel method improves accuracy estimation over standard methods and requires less supervised training data after deployment of the model. We demonstrate the application of our approach to a challenging robot object recognition task, where the human teacher can use our method to judge sufficient training.
Making a case for Cultural Historical Activity Theory: Examples of CHAT in practice

This paper argues that Cultural Historical Activity Theory (CHAT) provides a practical framework for theory-based research into Technology Enhanced Learning (TEL).Far from being inaccessible, CHAT provides researchers with the concepts, language and tools to create meaningful and manageable data sets that allow insights into complex social situations such as education.Within the context of a pre-university English language program at a Federal institution in the United Arab Emirates (UAE), the paper describes two recent examples of the author's research into laptop-mediated English language classrooms using CHAT as the theoretical framework.The paper then goes on to describe a theory-based intervention currently in progress that has grown out of the two examples described.This paper aims to demonstrate that theory and practicality need not be separate.CHAT can be used to guide and design research into not only innovation and best practice in TEL but also the actual day-today realities of technology usage in real classroom settings, bringing much-needed criticality to the field.The paper concludes by arguing that CHAT can also provide a framework to drive theory-based interventions via the Change Laboratory.Through CHAT, findings from theory-based research need not remain at a conceptual level but can actually be used to create concrete solutions to problems and actual improved educational practice and policy.
Teacher-student relationships: An influence on the English teaching-learning process

There are two main purposes navigated in this study; first of all, it was carried out to investigate the teacher-student relationship; secondly, it was also conducted to find out whether there was a correlation between teacher-student relationship and English teaching-learning process. A number of 14 teachers and 43 students participated in this study; they are from two publish junior high schools in Aceh, Indonesia. Questionnaires for the students and interviews with the teachers were used to discover the influence of the relationship between English teachers and students, and the problems that students faced during their interactions with the teachers. There were twenty-two items in the questionnaire and five questions in the interviews that the participants needed to answer. The collected data from the questionnaire were then analysed by using a Likert scale and the Pearson product-moment correlation coefficient, meanwhile, the data from the interviews were analysed by following the stages of Miles and Huberman‘s(1994) model. In the first junior high school, a significant correlation appeared between the teacher-student relationship and teaching-learning process, with the raw score of 0.745 that refers to a high correlation. The tcount was higher than the ttable (4.784≥2.100). However, at second junior high school, had no significant correlation detected between the two variables with the raw score of 0,401 that referred to moderate correlation, where tcount was lower than ttable (2.004≤2.097). This research is expected to have a beneficial impact on the English teaching-learning process especially in the areas of the two schools, where this study took place, as well as the teachers and educators from other institutions spread in Aceh and Indonesia in general.
Feature Selection Optimization in Software Product Lines

Feature modeling is a common approach for configuring and capturing commonalities and variations among different Software Product Lines (SPL) products. This process is carried out by a set of SPL design teams, each working on a different configuration of the desired product. The integration of these configurations leads to inconsistencies in the final product design. The typical solution involves extensive deliberation and unnecessary resource usage, which makes SPL inconsistency resolution an expensive and unoptimized process. We present the first comprehensive evaluation of swarm intelligence (using Particle Swarm Optimization) to the problem of resolving inconsistencies in a configured integrated SPL product. We call it o-SPLIT (optimization-based Software Product LIne Tool) and validate o-SPLIT with standard ERP, SPLOT (Software Product Lines Online Tools), and BeTTy (BEnchmarking and TesTing on the analYsis) product configurations along with diverse feature set sizes. The results show that Particle Swarm Optimization can successfully optimize SPL product configurations. Finally, we implement o-SPLIT as a decision-support tool in a real, local SPL setting and acquire subjective feedback from SPL designers which shows that the teams are convinced of the usability and high-level decision support provided by o-SPLIT.