Reviewerly/exHarmony huggingface dataset:
RAST: Risk-Aware Spatio-Temporal Safety Corridors for MAV Navigation in Dynamic Uncertain Environments

Autonomous navigation of Micro Aerial Vehicles (MAVs) in dynamic and unknown environments is a complex and challenging task. Current works rely on assumptions to solve the problem. The MAV's pose is precisely known, the dynamic obstacles can be explicitly segmented from static ones, their number is known and fixed, or they can be modeled with given shapes. In this letter, we present a method for MAV navigation in dynamic uncertain environments without making any of these assumptions. The method employs a particle-based dynamic map to represent the local environment and predicts it to the near future. Collision risk is defined based on the predicted maps and a series of risk-aware spatio-temporal (RAST) safety corridors are constructed, which are finally used to optimize a dynamically-feasible collision-free trajectory for the MAV. We compared our method with several state-of-the-art works in 12000 simulation tests in Gazebo with the physical engine enabled. The results show that our method has the highest success rate at different uncertainty levels. Finally, we validated the proposed method in real experiments.
An RF SQUID readout for a flux qubit-based microwave single photon counter

Abstract An analysis of the measurement of the magnetic flux in superconducting qubits based on RF SQUIDs was carried out with an 800 MHz bandwidth low-power-consumption cryogenic high-electron-mobility transistor amplifier. The preliminary experimental results obtained at temperatures 2 K and 4 K for RF SQUIDs in hysteretic, and in two non-hysteretic, regimes with a pump frequency of about 30 MHz are discussed. Parameters of RF SQUIDs in the hysteretic and non-hysteretic modes are analyzed within the framework of the resistively and capacitively shunted junction model for Josephson junctions. Its sensitivity at a temperature of 30 mK and frequency band (speed) are calculated and optimized to read the states of a flux qubit used as a single microwave photon counter. It is shown that an RF SQUID, operated in an adiabatic non-hysteretic mode for qubit readout, allows us to minimize its back-action effect and the dark count rate. This is due to the absence of Josephson generation, the small amplitude of the resonator electromagnetic field, and the selection of the pump frequency that does not coincide with the characteristic frequencies of the flux qubit.
A Study of the New Role of Blockchain in the Indian Education System

Blockchain is the foundational technology that allows cryptocurrencies like bitcoin to exist. Blockchain technology has been used in numerous domains such as banking, justice, and commerce as part of the fourth industrial revolution since the invention of the steam engine, electricity, and computer technology. People's willingness to adopt technology has been influenced by rapid technological improvement. The traditional education system in developing nations has lately been improved through the implementation of distributed ledger technology. Disruptive technology in education is a key prerequisite for better accountability and exposure. The authors investigated the key factors influencing educational institutions' (knowledge providers) and learners' (knowledge recipients) intentions to use blockchain technology.
Clustering of Relevant Documents Based on Findability Effort in Information Retrieval

A user expresses their information need in the form of a query on an information retrieval (IR) system that retrieves a set of articles related to the query. The performance of the retrieval system is measured based on the retrieved content to the query, judged by expert topic assessors who are trained to find this relevant information. However, real users do not always succeed in finding relevant information in the retrieved list due to the amount of time and effort needed. This paper aims 1) to utilize the findability features to determine the amount of effort needed to find information from relevant documents using the machine learning approach and 2) to demonstrate changes in IR systems' performance when the effort is included in the evaluation. This study uses a natural language processing technique and unsupervised clustering approach to group documents by the amount of effort needed. The results show that relevant documents can be clustered using the k-means clustering approach, and the retrieval system performance varies by 23%, on average.
Are quantum computers about to break online privacy?

A new algorithm is probably not efficient enough to crack current encryption keys — but that’s no reason for complacency, researchers say. A new algorithm is probably not efficient enough to crack current encryption keys — but that’s no reason for complacency, researchers say.
Multiple Elite Individual Guided Piecewise Search-Based Differential Evolution

The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters' selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.
Optical Neural Network With Complementary Decomposition to Overcome the Phase Insensitive Constrains

Photonic integrated circuits have potentials to advance artificial neural networks by providing ultra-fast and high power efficient computation resources. In many of optical neural network (ONN) architectures, data are encoded in light amplitudes, using magnitude and phase to denote the absolute value and the sign. However, the phase insensitivity of optical components such as saturable absorbers, semiconductor optical amplifiers and photodetectors can lead to limitations in ONN designs, including nonlinear activation and inference results readout. In this work, we propose a complementary decomposition approach to overcome the phase insensitive problem. Based on that, we further present a optical ReLU function implementation. The proposed designs can scale to multi-layer neural networks by cascading linear and nonlinear units. These optical neural networks do not require repeated bidirectional digital-analog conversions and optical-electronic conversions between each layers as well as reference signals for homodyne detection. These features could provide a potential implementation of compact, scalable, high efficiency ONNs. We evaluate the concept with a machine learning task. The numerical results demonstrate that the proposed design can tackle the aforementioned phase insensitive problem.
An Adaptive Low-Light Image Enhancement Using Canonical Correlation Analysis

Images captured under low-illumination conditions exhibit numerous unwanted appearances such as color distortion, low brightness, a narrow gray range, low contrast, and considerable noise. An excellent low-light image enhancement framework overcomes all undesirable attributes of low-light images. Here, a new fusion technique is introduced based on the canonical correlation analysis of two proposed adaptive Retinex and beta-hyperbolic secant distribution (BHSD) models. Firstly, an adaptive enhanced reflectance image is obtained by a new weighted multiscale Gaussian function and proposed adaptive reflectance map. For the second enhanced image, logarithmic image processing is modified according to the illuminance's factor, and the exponential function is incorporated for contrast boosting. The error function is used for the modified cumulative distribution function of BHSD to obtain the second enhanced image. The proposed framework is compared with state-of-the-art low-light methods for visual and parametric comparison. The results show that the designed framework produces the best results in terms of high contrast, natural brightness, vivid color combination, perfect structure, and texture information with preserving naturalness. Among all comparable methods, the designed technique produces a good result in terms of rich scene details and color reproduction in dark regions.
Optimizing the Numbers of Queries and Replies in Convex Federated Learning With Differential Privacy

Federated learning (FL) empowers distributed clients to collaboratively train a shared machine learning model through exchanging parameter information. Despite the fact that FL can protect clients’ raw data, malicious users can still crack original data with disclosed parameters. To amend this flaw, differential privacy (DP) is incorporated into FL clients to disturb original parameters, which however can significantly impair the accuracy of the trained model. In this work, we study an imperative question which has been vastly overlooked by existing works: what are the optimal numbers of queries and replies in FL with DP so that the final model accuracy is maximized. In FL, the parameter server (PS) needs to query participating clients for multiple global iterations to complete training. Each client responds a query from the PS by conducting a local iteration. We consider FL that will uniformly and randomly select participating clients to conduct local iterations with the FedSGD algorithm. Our work investigates how many times the PS should query clients and how many times each client should reply the PS by incorporating two most extensively used DP mechanisms (i.e., the Laplace mechanism and Gaussian mechanisms). Through conducting convergence rate analysis, we can determine the optimal numbers of queries and replies in FL with DP so that the final model accuracy can be maximized. Finally, extensive experiments are conducted with publicly available datasets: MNIST and FEMNIST, to verify our analysis and the results demonstrate that properly setting the numbers of queries and replies can significantly improve the final model accuracy in FL with DP.
Children’s digital competence in early childhood education: A comparative analysis of curricula

Children’s digital competence (DC) is often poorly supported in early childhood education (ECE). Furthermore, common definition of DC is difficult to find. Therefore, the aim of this comparative curriculum study was to better understand how objectives and content of DC are defined in ECE. Australia and Finland curricula were analyzed applying theory-driven content analysis. The results indicated that in both countries theoretical basis of DC was present: objectives and content of DC referred to (1) technical skills and practices, (2) applying digital technologies, (3) evaluate digital technologies critically, and (4) motivation to participate in a digital culture. However, aims, content, and practical guidelines on the curricula were unclear. This study recommends that ECE curricula should better emphasis and make explicit the key elements of DC and how to holistically foster children’s DC in practice. Further studies to clarify the important elements of DC in ECE curricula frameworks is required.
Prototype-Based Intent Perception

Intent perception is a novel task that aims to understand the intention of images, regular classification methods usually perform unsatisfactorily on intent perception due to the semantic ambiguity problem,i.e. the intra-class variety problem in which images of the same intent class may contain objects of different semantic categories and the inter-class confusion problem in which images of different intent classes may contain objects of similar semantic categories. To address this problem, this paper introduces prototype learning into the intent perception and proposes a unified framework named PIP-Net to reduce the influence of semantic ambiguity. Specifically, for each intent class, we first filter semantic ambiguity samples which are far away from the cluster center. Then we use features of the filtered samples to generate prototypes via clustering algorithm. Besides, we enhance the diversity between prototypes of different classes to better handle the inter-class confusion problem. To update the prototypes in the training process, we introduce a global matching algorithm to holistically match each feature with class prototypes, and use the momentum update strategy to stably update prototypes. Experimental results on the Intentonomy dataset demonstrate that our method can consistently outperform the traditional classification paradigm in multiple baseline models, and verify the effectiveness of our proposed prototype learning paradigm in addressing the intent perception problem. Our proposed PIP-Net achieves a new state-of-the-art performance on Intentonomy, including Macro F1 score of 31.57% and averaging F1 score of 41.85%. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CodeMonsterPHD/PIP-Net</uri> .
Channel Equalization Through Reservoir Computing: A Theoretical Perspective

Deep learning practice, including in wireless communications, often relies on trial and error to optimize neural network (NN) structures and their corresponding hyperparameters. We show that Reservoir Computing, especially the Echo State Network (ESN), is an ideal learning-based equalizer for a general fading channel and for an ESN equalizing a channel with known statistics, theoretically derive its optimum reservoir weights which are randomly initialized in state-of-the-art and lack interpretability. The theoretical results are validated with simulations. In contrast to existing literature, this letter analytically adapts the NN structure to the problem being addressed, guaranteeing optimum equalization under known channel statistics.
How SonarQube-identified technical debt is prioritized: An exploratory case study

Repaying all technical debt (TD) in a system may be unviable, as there is typically a shortage of resources allocated for TD repayment activities. Therefore, TD prioritization is essential to best allocate such limited resources. Fortunately, one can utilize a static code analysis tool, such as SonarQube, to aid in expediting the TD prioritization process. Given that SonarQube is one of the most utilized tools in the context of TD, this exploratory case study seeks to explore how SonarQube-identified TD items are perceived and prioritized for repayment. The study was designed, replicated, and conducted in four companies and a master’s level course, with a total of 89 participants. The participants were requested to select TD items to include for repayment under a resources constraint. The results revealed that the overwhelming majority of participants prioritized TD by factoring in a TD item’s value and cost, a smaller number prioritized higher value TD items, and only one participant prioritized lower cost TD items. Furthermore, it was revealed that the value of a TD item is subjective and context-dependent, and the majority of participants perceive the cost estimations provided by SonarQube for repaying TD items to be reliable and trustworthy when prioritizing TD. Based on the results, one can conclude that there is no silver bullet TD prioritization approach that addresses all of a developer’s objectives and needs. New TD prioritization approaches should be designed without concentrating on a specific prioritization perspective and should be independent of value estimation methods.
Primal Topologies on Finite-Dimensional Vector Spaces Induced by Matrices

Given an matrix <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>A</mi> </math> , considered as a linear map <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>A</mi> <mo>:</mo> <msup> <mrow> <mi>ℝ</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msup> <mo>⟶</mo> <msup> <mrow> <mi>ℝ</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msup> </math> , then <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>A</mi> </math> induces a topological space structure on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msup> <mrow> <mi>ℝ</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msup> </math> which differs quite a lot from the usual one (induced by the Euclidean metric). This new topological structure on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msup> <mrow> <mi>ℝ</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msup> </math> has very interesting properties with a nice special geometric flavor, and it is a particular case of the so called “primal space,” In particular, some algebraic information can be shown in a topological fashion and the other way around. If <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>X</mi> </math> is a non-empty set and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>f</mi> <mo>:</mo> <mi>X</mi> <mo>⟶</mo> <mi>X</mi> </math> is a map, there exists a topology <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </math> induced on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi>X</mi> </math> by <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <mi>f</mi> </math> , defined by <math xmlns="http://www.w3.org/1998/Math/MathML" id="M11"> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> <mo>=</mo> <mfenced open="{" close="}" separators="|"> <mrow> <mi>U</mi> <mo>⊂</mo> <mi>X</mi> <mo>:</mo> <msup> <mrow> <mi>f</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mfenced open="(" close=")" separators="|"> <mrow> <mi>U</mi> </mrow> </mfenced> <mo>⊂</mo> <mi>U</mi> </mrow> </mfenced> </math> . The pair <math xmlns="http://www.w3.org/1998/Math/MathML" id="M12"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>X</mi> <mo>,</mo> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </mfenced> </math> is called the primal space induced by <math xmlns="http://www.w3.org/1998/Math/MathML" id="M13"> <mi>f</mi> </math> . In this paper, we investigate some characteristics of primal space structure induced on the vector space <math xmlns="http://www.w3.org/1998/Math/MathML" id="M14"> <msup> <mrow> <mi>ℝ</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msup> </math> by matrices; in particular, we describe geometrical properties of the respective spaces for the case.
Network-aware compute and memory allocation in optically composable data centers with deep reinforcement learning and graph neural networks

Composable data center architectures promise a means of pooling resources remotely within data centers, allowing for both more flexibility and resource efficiency underlying the increasingly important infrastructure-as-a-service business. This can be accomplished by means of using an optically circuit switched backbone in the data center network (DCN), providing the required bandwidth and latency guarantees to ensure reliable performance when applications are run across non-local resource pools. However, resource allocation in this scenario requires both server-level and network-level resources to be co-allocated to requests. The online nature and underlying combinatorial complexity of this problem, alongside the typical scale of DCN topologies, make exact solutions impossible and heuristic-based solutions sub-optimal or non-intuitive to design. We demonstrate that deep reinforcement learning , where the policy is modeled by a graph neural network , can be used to learn effective network-aware and topologically scalable allocation policies end-to-end. Compared to state-of-the-art heuristics for network-aware resource allocation, the method achieves up to a 20% higher acceptance ratio, can achieve the same acceptance ratio as the best performing heuristic with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>3</mml:mn> <mml:mo>×<!-- × --></mml:mo> </mml:math> less networking resources available, and can maintain all-around performance when directly applied (with no further training) to DCN topologies with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> </mml:math> more servers than the topologies seen during training.
A Recommender System for Robust Smart Contract Template Classification

IoT environments are becoming increasingly heterogeneous in terms of their distributions and included entities by collaboratively involving not only data centers known from Cloud computing but also the different types of third-party entities that can provide computing resources. To transparently provide such resources and facilitate trust between the involved entities, it is necessary to develop and implement smart contracts. However, when developing smart contracts, developers face many challenges and concerns, such as security, contracts' correctness, a lack of documentation and/or design patterns, and others. To address this problem, we propose a new recommender system to facilitate the development and implementation of low-cost EVM-enabled smart contracts. The recommender system's algorithm provides the smart contract developer with smart contract templates that match their requirements and that are relevant to the typology of the fog architecture. It mainly relies on OpenZeppelin, a modular, reusable, and secure smart contract library that we use when classifying the smart contracts. The evaluation results indicate that by using our solution, the smart contracts' development times are overall reduced. Moreover, such smart contracts are sustainable for fog-computing IoT environments and applications in low-cost EVM-based ledgers. The recommender system has been successfully implemented in the ONTOCHAIN ecosystem, thus presenting its applicability.
Bounds in the Lee metric and optimal codes

In this paper we investigate known Singleton-like bounds in the Lee metric and characterize their extremal codes, which turn out to be very few. We then focus on Plotkin-like bounds in the Lee metric and present a new bound that extends and refines a previously known, and out-performs it in the case of non-free codes. We then compute the density of extremal codes with regard to the new bound. Finally we fill a gap in the characterization of Lee-equidistant codes.
Corpus-based bundle analysis to disciplinary variations: Relocating the role of bundle extraction criteria

Previous lexical bundle research has stimulated heated discussions on disciplinary variations and disciplinary specificity-generality spectrum. The current study explores whether (and how) bundle extraction criteria (i.e. frequency, dispersion, and bundle length) may affect the conclusions on disciplinary variations and specificity-generality. Focusing on eight disciplines, it used an 11-million corpus of academic journal articles with a balanced design (the same number of texts and similar average text length for each subcorpus). The results indicated a clear picture of disciplinary variations and a strong tendency towards disciplinary specificity. More importantly, the results suggested that different methodological criteria played an important part, especially concerning the analysis of disciplinary specificity or generality. The choice of ‘4-word’ bundles over ‘3-word’ bundles, in particular, would tend to be associated with a result of a higher degree of disciplinary specificity. Regarding disciplinary variations, it was found that the effect from different choices of bundle extraction criteria was relatively smaller. Only the choice on different dispersion requirements could predict the significant differences between certain disciplines. These findings provide support for disciplinary variations and new perspectives on the disciplinary specificity-generality debate.