Reviewerly/exHarmony huggingface dataset:
Genetic algorithms with permutation-based representation for computing the distance of linear codes

Finding the minimum distance of linear codes is an NP-hard problem. Traditionally, this computation has been addressed by means of the design of algorithms that find, by a clever exhaustive search, a linear combination of some generating matrix rows that provides a codeword with minimum weight. Therefore, as the dimension of the code or the size of the underlying finite field increase, so it does exponentially the run time. In this work, we prove that, given a generating matrix, there exists a column permutation which leads to a reduced row echelon form containing a row whose weight is the code distance. This result enables the use of permutations as representation scheme, in contrast to the usual discrete representation, which makes the search of the optimum polynomial time dependent from the base field. In particular, we have implemented genetic and CHC algorithms using this representation as a proof of concept. Experimental results have been carried out employing codes over fields with two and eight elements, which suggests that evolutionary algorithms with our proposed permutation encoding are competitive with regard to existing methods in the literature. As a by-product, we have found and amended some inaccuracies in the Magma Computational Algebra System concerning the stored distances of some linear codes.
Intelligent Process Quality Management for Supporting Collaboration of Mold Manufacturing SMEs

A mold is based on a core root technology for shaping materials into parts or products, and the mold industry is an essential production-based industrial field for mass production of various products. In Korea, most of mold manufacturing companies are small and medium-sized companies, and therefore, it is necessary to establish and use a collaboration system among companies to improve mold quality while reducing costs and delivery time. The collaboration system, based on core technologies of the 4th Industrial Revolution such as Artificial Intelligence (AI), Internet-of-things (IoT), and big data, is expected to have a high impact on the competitiveness of the mold industry in terms of quality, cost, and delivery. It is also essential to introduce data collection and analysis technologies that consider collaborative process characteristics throughout the mold production. The intelligent process quality management proposed in this paper is defined by an AI-based statistical analysis technique and consists of process quality management module and operational scenario design. It is expected that the proposed process quality management will enhance competitiveness of the mold industry and facilitate innovation in value chains by maximizing the use of field information for corporate collaboration.
Symmetry‐Protected Quantum Adiabatic Evolution in Spontaneous Symmetry‐Breaking Transitions

Quantum adiabatic evolution, an important fundamental concept inphysics, describes the dynamical evolution arbitrarily close to the instantaneous eigenstate of a slowly driven Hamiltonian. In most systems undergoing spontaneous symmetry-breaking transitions, their two lowest eigenstates change from non-degenerate to degenerate. Therefore, due to the corresponding energy-gap vanishes, the conventional adiabatic condition becomes invalid. Here we explore the existence of quantum adiabatic evolutions in spontaneous symmetry-breaking transitions and derive a symmetry-dependent adiabatic condition. Because the driven Hamiltonian conserves the symmetry in the whole process, the transition between different instantaneous eigenstates with different symmetries is forbidden. Therefore, even if the minimum energy-gap vanishes, symmetry-protected quantum adiabatic evolutioncan still appear when the driven system varies according to the symmetry-dependent adiabatic condition. This study not only advances our understandings of quantum adiabatic evolution and spontaneous symmetry-breaking transitions, but also provides extensive applications ranging from quantum state engineering, topological Thouless pumping to quantum computing.
A Scientometric Review of Digital Currency and Electronic Payment Research: A Network Perspective

The potential implications of digital currencies and electronic payment (DC/EP) have become a hot global research area. To present the knowledge bases and research fronts of this field, we apply a scientometric approach to analyze 454 publications obtained from the Web of Science core collection. Results show that, first, the knowledge bases can be classified into three main topics: (1) the usage and diversification effect of private digital currencies from the point of investment and asset allocation; (2) the price dynamics and market efficiency of private digital currencies; and (3) other economic roles of digital currencies and corresponding change brought into the monetary system. Second, several research trends can be inferred using sliding window analysis and burst detection, namely, how the introduction of digital currencies changes consumers’ choice in payment instrument and their money demand; how social media and investor sentiment affect the market of digital currencies; and the impact of digital currencies on the central bank, monetary policy, and central bank digital currency. Third, core scholars and countries involved in the research of DC/EP are identified, and it is found that collaboration has been rising especially among European scholars and countries. With these systematic analyses, we offer recommendations for scholars and practitioners in future research of DC/EP.
Artificial Intelligence Cyber Security Strategy

Nowadays, STEM (science, technology, engineering and mathematics) have never been treated so seriously before. Artificial Intelligence (AI) has played an important role currently in STEM. Under the 2020 COVID-19 pandemic crisis, coronavirus disease across over the world we are living in. Every government seek advices from scientist before making their strategic plan. Most of countries collect data from hospitals (and care home and so on in the society), carried out data analysis, using formula to make some AI models, to predict the potential development patterns, in order to make their government strategy. AI security become essential. If a security attack make the pattern wrong, the model is not a true prediction, that could result in thousands life loss. The potential consequence of this non-accurate forecast would be even worse. Therefore, take security into account during the forecast AI modelling, step-by-step data governance, will be significant. Cyber security should be applied during this kind of prediction process using AI deep learning technology and so on. Some in-depth discussion will follow.AI security impact is a principle concern in the world. It is also significant for both nature science and social science researchers to consider in the future. In particular, because many services are running on online devices, security defenses are essential. The results should have properly data governance with security. AI security strategy should be up to the top priority to influence governments and their citizens in the world. AI security will help governments' strategy makers to work reasonably balancing between technologies, socially and politics. In this paper, strategy related challenges of AI and Security will be discussed, along with suggestions AI cyber security and politics trade-off consideration from an initial planning stage to its near future further development.
Quantum-inspired Fredkin gate based on spatial modes of light

Insights gained from quantum physics can inspire novel classical technologies. These quantum-inspired technologies are protocols that aim at mimicking particular features of quantum algorithms. They are generally easier to implement and make use of intense beams. Here we demonstrate in a proof-of-concept experiment a quantum-inspired protocol based on the idea of quantum fingerprinting (Phys. Rev. Lett. 87 , 167902, 2001).The carriers of information are optical beams with orbital angular momentum (OAM). These beams allow the implementation of a Fredkin gate or polarization-controlled SWAP operation that exchanges data encoded on beams with different OAM. We measure the degree of similarity between waveforms and strings of bits without unveiling the information content of the data.
Long-Range Visual UAV Detection and Tracking System with Threat Level Assessment

Unmanned aerial vehicles (UAVs) can pose a serious threat to critical infrastructure which has motivated researchers to develop solutions for early detection. Nevertheless, the problem remains unsolved due to the limitations of the current detection techniques. In this paper, a vision-based approach using deep learning and a pan-tilt-zoom camera is proposed. In addition to detecting and tracking UAVs at long distances, the approach also assesses the threat level of the intruder UAVs based on their orientation. The proposed system offers long-range coverage while being cheap and practically feasible.
Virtual Reality Training in Electric Utility Sector - An Underground Application Study Case

Virtual reality is being considered as an innovative way to perform training in many industry sector. Even though it has being developed in many aspects, the final users still resistant for its full use in electrical power sector. This paper evaluate the opinion of expert professionals in the electrical utility sector to analyses an underground activity in virtual reality. The main goal is to observe what is needed to have a complete application of Virtual Reality in a risk operation present in the electrical sector, and to propose functionalities that may improve the immersion and satisfaction in the Virtual Reality training procedures.
A Comparative Analysis of Cryptographic Algorithms in the Internet of Things

Wireless Sensor Network (WSN) technology is an integral part of Internet of Things (IoT) and the security of IoT will play a pivotal role in its deployment and commercialization. To secure communication in WSN, selecting appropriate encryption algorithm is essentially important because IoT devices are inherently low-powered and have limited resources. In this regard, in this paper, we compare diverse symmetric block-based cryptographic algorithms to comment on their capabilities that will aid in selecting the right algorithm for a specific application. We select widely used algorithms such as AES, CLEFIA, DES, Triple DES, IDEA, PRESENT, SEA, SPECK, TEA, XTEA and TWOFISH with different block and key lengths. The algorithms are compared from the perspective of energy, power and memory consumption, and throughput. The comparison is performed in Cooja simulator using z1 motes and the source code is published in the GitHub repository.
The use of a Blockchain-based System in Traffic Operations to promote Cooperation among Connected Vehicles

This paper intends to present some ideas for the implementation of cooperative ITS systems based on the Blockchain Technology (BT) concept. Blockchain technology has been recently introduced and, in this paper, we discuss a system that is based on a dedicated blockchain, able to involve both drivers and city administrations in the adoption of promising and innovative technologies that will create cooperation among connected vehicles. The proposed blockchain-based system can allow city administrators to reward drivers when they are willing to share travel data. The system manages in a special way the creation of new coins which are assigned to drivers and institutions participating actively in the system. Moreover, the system allows keeping a complete track of all transactions and interactions between drivers and city management on a completely open and shared platform. The main idea is to combine connected vehicles with BT to promote Cooperative ITS use and a better use of infrastructures.
Automatic recognition of abdominal lymph nodes from clinical text

Lymph node status plays a pivotal role in the treatment of cancer.The extraction of lymph nodes from radiology text reports enables large-scale training of lymph node detection on MRI.In this work, we first propose an ontology of 41 types of abdominal lymph nodes with a hierarchical relationship.We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports.We demonstrate the superior performance of a model fine-tuned on MRI reports using BlueBERT, called MriBERT.We find that MriBERT outperforms the rule-based labeler (0.957 vs 0.644 in micro weighted F1-score) as well as other BERT-based variations (0.913 -0.928).We make the code and MriBERT publicly available at https://github.com/ncbi-nlp/bluebert, with the hope that this method can facilitate the development of medical report annotators to produce labels from scratch at scale.
Quantum public-key encryption schemes based on conjugate coding

We present several quantum public-key encryption (QPKE) protocols designed using conjugate coding single-photon strings; thus, they may be realized in the laboratory using current techniques. The first two schemes can encrypt one-bit messages; these are then extended to two kinds of QPKE schemes oriented toward multi-bit messages. In these schemes, Boolean functions are used as private keys and classical-quantum pairs as public keys, where one private key corresponds to an exponential number of public keys. Later, we discuss some issues related to authentication and a possible way to improve the security of the proposed schemes in this paper. The novel structure of the protocols presented here ensures that most of them are information-theoretic secure under attacks on the private key and on the encryption, while they can be realized more easily compared to other protocols.
New Classes of Finite Dimensional Filters with Nonmaximal Rank Estimation Algebra on State Dimension $n$ and Linear Rank $n-2$

Ever since the Kalman filter technique was popularized, there has been an abundance of interest in finding new classes of finite dimensional recursive filters. In this paper, by applying Wong's theorem [W. S. Wong, Systems Control Lett, 9 (1987), pp. 79--83], we construct a new class of finite dimensional filters with arbitrary state space dimension $n$ and linear rank $n-2$. Importantly, we show that in the new class of nonlinear filtering systems, the entries of Wong's $\Omega$-matrix need not be constants or polynomials and can be $C^\infty$ functions.
Gender Detection on Social Networks Using Ensemble Deep Learning

Analyzing the ever-increasing volume of posts on social media sites, such as Facebook and Twitter requires improved information processing methods for profiling authorship. Document classification is central to this task, but the performance of traditional supervised classifiers has degraded as the volume of social media has increased. This paper addresses this problem in the context of gender detection through ensemble classification that employs multi-model deep learning architectures to generate specialized understanding from different feature spaces.
Event-Based Path-Planning and Path-Following in Unknown Environments for Underactuated Autonomous Underwater Vehicles

The paper addresses path planning and path-following problems in an unknown complex environment for an underactuated autonomous underwater vehicle (AUV). The AUV is required to follow a given reference path represented as a sequence of smoothly joined lines and arcs, bypassing obstacles encountered on the path. A two-level control system is proposed with an upper level for event-driven path planning and a lower level for path-following. A discrete event system is designed to identify situations that require planning a new path. An improved waypoint guidance algorithm and a Dubins curves based algorithm are proposed to build paths that allow the AUV to avoid collision with obstacles and to return to the reference path respectively. Both algorithms generate paths that meet the minimum turning radius constraint. A robust parameter-varying controller is designed using sublinear vector Lyapunov functions to solve the path-following problem. The performance of the developed event-based control system is demonstrated in three different simulation scenarios: with a sharp-edged obstacle, with a U-shaped obstacle, and with densely scattered obstacles. The proposed scheme does not require significant computing resources and allows for easy implementation on board.
Towards the Issue of the Cybersecurity Analysis of a Significant Object of Critical Information Infrastructure

The number of cybersecurity events in the world has grown significantly, and the most significant number of events falls on the objects of critical information infrastructure. The article considers the issues of risk formation and its assessment.
Protect Your Smart Contract Against Unfair Payment

While smart contracts have enabled a wide range of applications in many public blockchains, e.g., Ethereum, their security issues have been raising an increasing number of threats on the stability of blockchain ecosystem. In practice, many external attacks on smart contracts result from broken payments with digital assets, e.g., cryptocurrencies. While an increasing number of research works have been focusing on such problems, many of them adopted pattern-based heuristics (e.g., reentrancy) to find payment-related attacks thus can incur a considerably large portion of both false positives and negatives. To overcome these limitations and achieve better payment security on blockchain, we introduced a new class of payment attacks in this paper, i.e., unfair payment (UP). Compared to existing heuristics, UP semantically captures a wider range of payment attacks. Furthermore, we highlighted the general framework SAFEPAY to systematically detect UP. The key insight behind is a novel security invariant, i.e., fair value exchange (FVE), which models the fairness for blockchain payments between multiple parties. More specifically, SAFEPAY systematically explores the transaction space of a given smart contract and generates a bounded set of transaction sequences. For each of the sequence, SAFEPAY reports a UP attack once a violation on FVE is confirmed. We have further instantiated SAFEPAY for Ethereum and applied it in real-world smart contracts. In the empirical evaluation, SAFEPAY managed to identify previously unreported UP attacks and effectively avoid false alarms compared to analyzers in the literature as well.
Rule-Based Conflict Management for Unmanned Traffic Management Scenarios

The growing use of Unmanned Aerial Vehicles (UAVs) operations will require effective conflict management to keep the shared airspace safe and avoid conflicts among airspace users. Conflicts pose high risk and hazard to human lives and assets as they ma may result in financial and human loss. The proposed rule-based conflict management model consists of three main stages. The first stage includes strategic deconfliction during the flight plan generation. The second stage, pre-tactical deconfliction, applies a ground delay to the agent to resolve the conflict. The third stage corresponds to the tactical deconfliction, where the drone hovers or loiter in the last waypoint before the conflict area until the conflict time window passes. The proposed method differs from most existing conflict management approaches in that it applies deconfliction methods sequentially using a rule-based strategy. Furthermore, a high number of published studies do not consider realistic airspace constraints and potential airspace modernization concepts such as dynamic flight restrictions Assessment and validation are performed in three simulation scenarios that consider different patterns of the airspace availability in the areas where flights may be restricted, such as airfields, recreational areas, and prisons. The Particle Swarm Optimization (PSO) algorithm was used for drone path planning. For the simulated scenarios all of the conflicts were resolved after implementation of the proposed method. The implemented method is simple, flexible and suitable for the management of more complex and dense airspaces.
Effect of imperfect homodyne visibility on multi-spatial-mode two-mode squeezing measurements

We study the effect of homodyne detector visibility on the measurement of quadrature squeezing for a spatially multi-mode source of two-mode squeezed light. Sources like optical parametric oscillators (OPO) typically produce squeezing in a single spatial mode because the nonlinear medium is within a mode-selective optical cavity. For such a source, imperfect interference visibility in the homodyne detector couples in additional vacuum noise, which can be accounted for by introducing an equivalent loss term. In a free-space multi-spatial-mode system imperfect homodyne detector visibility can couple in uncorrelated squeezed modes, and hence can cause faster degradation of the measured squeezing. We show experimentally the dependence of the measured squeezing level on the visibility of homodyne detectors used to probe two-mode squeezed states produced by a free space four-wave mixing process in 85Rb vapor, and also demonstrate that a simple theoretical model agrees closely with the experimental data.
A New IoT Application for Dynamic WiFi based Wireless Sensor Network

The Internet of Things is the technology that allows the exchange of data between objects through various communication channels. With the development of wireless communication technology, data can be controlled remotely from the devices. Thanks to the dynamic structure of the Internet, the Internet of Things makes an important contribution to the growth of the IT sector and the development of the technology world. In this study, a wireless sensor network based method is recommended for internet of objects. Wireless access devices in the campus networks and ESP8266 devices are connected to each other and a large network of wireless sensors have been developed. Data from the temperature and humidity sensor connected to ESP8266 devices can be monitored via the wireless network in the campus area. The data from the ESP8266 modules used in the study are collected on a server and monitored and reported in real time. It is a significant advantage that the proposed method is a dynamic wireless sensor network and low cost. Considering the advantages that the developed system can be easily used and low cost in existing Wi-Fi networks, it is seen that there is an important study in the literature.
Flexeme: untangling commits using lexical flows

Today, most developers bundle changes into commits that they submit to a shared code repository. Tangled commits intermix distinct concerns, such as a bug fix and a new feature. They cause issues for developers, reviewers, and researchers alike: they restrict the usability of tools such as git bisect, make patch comprehension more difficult, and force researchers who mine software repositories to contend with noise. We present a novel data structure, the 𝛿-NFG, a multiversion Program Dependency Graph augmented with name flows. A 𝛿-NFG directly and simultaneously encodes different program versions, thereby capturing commits, and annotates data flow edges with the names/lexemes that flow across them. Our technique, Flexeme, builds a 𝛿-NFG from commits, then applies Agglomerative Clustering using Graph Similarity to that 𝛿-NFG to untangle its commits. At the untangling task on a C# corpus, our implementation, Heddle, improves the state-of-the-art on accuracy by 0.14, achieving 0.81, in a fraction of the time: Heddle is 32 times faster than the previous state-of-the-art.