Reviewerly/exHarmony huggingface dataset:
Super-resolution reconstruction of depth image based on edge-selected deep residual network

In recent years, depth images have been widely used in human daily life and production, but the quality of the images obtained is often poor and the resolution is low. The existing improved methods often assume that there are common edges between the color image and the depth image, and then super-resolution the depth image under the guidance of the color image. When the part of depth image is smooth and the high-frequency details of the corresponding part of color image are rich, the color details that should not exist will be introduced into depth image. In this paper, an edge-selected deep residual network is proposed for depth image super-resolution. The scheme can effectively extract the common features of color and depth images, reduce the influence of irrelevant color details on super-resolution images, and ultimately improve the resolution of depth images. In the experiment of super-resolution, this method is effective and reliable.
A Proposed Framework for Research Data Management Services in Research Institutions in Zimbabwe

The chapter documents the proposed framework for the establishment of research data management services in research institutions in Zimbabwe. It has been indicated that there are no formal research data management services taking place in Zimbabwe as researchers are managing their own data. It is against such a background that a literature review was undertaken to understand how research institutions in other countries are engaging in research data services. E-mails were sent to the pioneers of research data services. It was discovered that there are challenges that are faced when establishing research data management services and it is important to consult all stakeholders at the planning stage. The framework consists of strategies, policies, guidelines, processes, technologies, and services.
Accessibility of Research Data at Academic Institutions in Zimbabwe

This chapter presents the findings of an online survey that was carried out to assess research data accessibility at research and academic institutions in Zimbabwe. The study primarily sought to ascertain the custodianship, storage and accessibility of research data at these institutions. The chapter also highlights the challenges associated with accessing research data in Zimbabwe and proposes mechanisms that can be put in place to address these challenges.
Impact of Stack Overflow Code Snippets on Software Cohesion: A Preliminary Study

Developers frequently copy code snippets from publicly-available resources such as Stack Overflow (SO). While this may lead to a 'quick fix' for a development problem, little is known about how these copied code snippets affect the code quality of the recipient application, or how the quality of the recipient classes subsequently evolve over the time of the project. This has an impact on whether such code copying should be encouraged, and how classes that receive such code snippets should be monitored during evolution. To investigate this issue, we used instances from the SOTorrent database where Java snippets had been copied from Stack Overflow into GitHub projects. In each case, we measured the quality of the recipient class just prior to the addition of the snippet, immediately after the addition of the snippet, and at a later stage in the project. Our goal was to determine if the addition of the snippet caused quality to improve or deteriorate, and what the long-term implications were for the quality of the recipient class. Code quality was measured using the cohesion metrics Low-level Similarity-based Class Cohesion (LSCC) and Class Cohesion (CC). Over a random sample of 378 classes that received code snippets copied from Stack Overflow to GitHub, we found that in almost 70% of the cases where the copied snippet affected cohesion, the effect was to reduce the cohesion of the recipient class. Furthermore, this deterioration in cohesion tends to persist in the subsequent evolution of the recipient class. In over 70% of cases the recipient class never fully regained the cohesion it lost in receiving the snippet. These results suggest that when copying code snippets from external repositories, more attention should be paid to integrating the code with the recipient class.
Multiple hypothesis method for tracking move‐stop‐move target

This study presents a new approach to track the move-stop-move targets. In the presence of the radar Doppler blind area, a move-stop-move target may lead to track breakages, which has some adverse effects on estimating the number of targets in the surveillance area. The authors develop a mode switching-based multiple hypothesis tracking (MHT) method to solve this problem. In each association, MHT generates three types of hypotheses: moving and detected, moving and undetected and stopped. In the first two types of hypotheses, the target is in the mode of move and tracked with interacting multiple model (IMM)-extended Kalman filter (EKF) estimator, while in the third kind of hypotheses, the target is in the mode of stop and tracked by EKF with a stop model. The mode probabilities are calculated using the kinematic state statistics from the estimator and the Markov transfer probability of the target movement stage. The track discontinuity event in the classical log-likelihood ratio of MHT is modified as well. The results of simulation experiment show that the proposed MHT-based tracker outperforms the conventional MHT-based method when tracking move-stop-move target.
Deep neural network based two-stage Indian language identification system using glottal closure instants as anchor points

This paper presents a two-stage Indian language identification (TS-LID) system which is made up of a tonal/non-tonal pre-classification and individual language identification modules. It studies the effectiveness of Mean Hilbert envelope coefficients (MHEC) and Mel-frequency cepstral coefficients (MFCCs), and their combinations with prosody in TS-LID context. Both glottal closure instants (GCIs)-based approaches and the block processing (BP) approach have been explored. It also explores different types of analysis units, such as whole utterance and syllable. Various state-of-art modeling techniques have been analyzed in this work. Experiments have been carried out for the NIT Silchar language database (NITS-LD) and OGI-Multilingual database (OGI-MLTS). The results suggest that at the pre-classification stage, for NITS-LD, the deep neural network (DNN) with syllable-level features, using GCI-based approaches, provides the highest accuracies of 90.6%, 85% and 81.3% for 30 s, 10 s and 3 s test data respectively. The GCI-based approaches outperform the BP method by as much as 7.5%, 6.2%, and 5.7%. The pre-classification module helps to improve the performance of the LID system by as much as 5.7%, 4.4% and 2.2% for 30 s, 10 s and 3 s test data respectively. The corresponding improvements for OGI-MLTS database are 7.4%, 6.8%, and 5%.
IMPLEMENTATION OF THE PERSONAL ACCOUNT OF THE EMPLOYER IN THE INFORMATION SYSTEM FOR ASSESSING STUDENTS’ ACTIVITY

The article describes the mechanism of formation of electronic portfolio and assessment of the student team, discusses the relevance of portfolio's database by employers and proposes the development of a personal account of the employer. It describes the components of the main business process for the collection and use of data and the implementation of the project, indicating the technologies and approaches used in the development of the software product. The inheritance of data processing mechanisms and the server part of the functional on the basis of service oriented architecture from the index-rating system of Ukhta State Technical University is noted, which allows using a significant part of the previously implemented opportunities for data processing and analysis. An example of the implementation of a comparative assessment of student activity based on the data accumulated in the previous version of the system in 7 areas of student activity based on the data of 8000 students for 5 years of the system in one of universities.
Ensemble transform particle filter using regularized optimal transport and measure of nonlinearity

In this paper, the proposed method replaces a linear transformation step in an ensemble transform particle filter (ETPF) with an algorithm based on regularized optimal transport (ROT) resulting in improved performance. The linear transformation in the ETPF leads to warping of a posteriori weights into desired weights. However, it is computationally expensive and robust processing entails relaxation matching between posterior weights and desired weights. Therefore, a ROT method was used to address the ETPF challenges using a modified Sinkhorn-Knopp algorithm via the convergence of the process iteration rate. In addition, the proposed algorithm is correlated with the degree of nonlinearity such that the ROT method is only used for nonlinear measurement models to increase the efficiency of the proposed algorithm. In this paper, the proposed method is evaluated using 2D LiDAR via simulations and single object tracking experiments. Accordingly, the proposed method enhances the estimation of the target position compared to conventional methods.
Reducing the Cost of Authenticity with Leakages: a $$\mathsf {CIML2}$$ -Secure $$\mathsf {AE}$$ Scheme with One Call to a Strongly Protected Tweakable Block Cipher

This paper presents $$\mathsf {CONCRETE}$$ ( $$Commit-Encrypt-Send-the-Key$$ ) a new Authenticated Encryption mode that offers $$\mathsf {CIML2}$$ security, that is, ciphertext integrity in the presence of nonce misuse and side-channel leakages in both encryption and decryption. $$\mathsf {CONCRETE}$$ improves on a recent line of works aiming at leveled implementations, which mix a strongly protected and energy demanding implementation of a single component, and other weakly protected and much cheaper components. Here, these components all implement a tweakable block cipher $$\mathsf {TBC}$$ . $$\mathsf {CONCRETE}$$ requires the use of the strongly protected $$\mathsf {TBC}$$ only once while supporting the leakage of the full state of the weakly protected components – it achieves $$\mathsf {CIML2}$$ security in the so-called unbounded leakage model. All previous works need to use the strongly protected implementation at least twice. As a result, for short messages whose encryption and decryption energy costs are dominated by the strongly protected component, we halve the cost of a leakage-resilient implementation. $$\mathsf {CONCRETE}$$ additionally provides security when unverified plaintexts are released, and confidentiality in the presence of simulatable leakages in encryption and decryption.
Having a look at the Bayes Blind Spot

Abstract The Bayes Blind Spot of a Bayesian Agent is, by definition, the set of probability measures on a Boolean $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>σ</mml:mi></mml:math> -algebra that are absolutely continuous with respect to the background probability measure (prior) of a Bayesian Agent on the algebra and which the Bayesian Agent cannot learn by a single conditionalization no matter what (possibly uncertain) evidence he has about the elements in the Boolean $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>σ</mml:mi></mml:math> -algebra. It is shown that if the Boolean algebra is finite, then the Bayes Blind Spot is a very large set: it has the same cardinality as the set of all probability measures (continuum); it has the same measure as the measure of the set of all probability measures (in the natural measure on the set of all probability measures); and is a “fat” (second Baire category) set in topological sense in the set of all probability measures taken with its natural topology. Features of the Bayes Blind Spot are determined from the perspective of repeated Bayesian learning when the Boolean algebra is finite. Open problems about the Bayes Blind Spot are formulated in probability spaces with infinite Boolean $$\sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>σ</mml:mi></mml:math> -algebras. The results are discussed from the perspective of Bayesianism.
Guided, Automated Testing of Blockchain-Based Decentralized Applications

As blockchain-based decentralized applications (DApp) become increasingly popular recently, there is an urgent demand for effective testing schemes and tools. Due to the complexity introduced by application-blockchain interaction, existing testing methods become non-applicable. In this paper, we present a guided and automated framework called Sungari to test DApps. The insights behind Sungari are two-fold. First, we employ random events to infer an abstract relation between frontend and blockchain. Second, we use the relation to generate event sequnces in a guided manner that can cover blockchain smart contracts as quick as possible. In a real-world DApp case study, Sungari outperformed random approach by covering 33% more application use cases.
Topologies of the quotient spaces induced by the M-topological plane and the infinite M-topological sphere

The present paper studies two topologies of the quotient spaces related to the Alexandroff one point compactification of the Marcus-Wyse (M-, for brevity) topological plane. The topologies are exactly proved to be the cofinite particular point and the excluded two points topology. We also concern separation axioms and nowhere dense subset properties of the quotient spaces of the M-topological plane. Furthermore, we investigate both a particular point and an excluded point topological structure associated with the quotient spaces of the M-topological plane. Finally, we investigate the fixed point property of the compactification of the M-topological plane. Unlike the non-fixed point property of the Hausdorff compactification of the Euclidean topological plane, we prove that every continuous self-bijection of the compactification of the M-topological plane has a fixed point.
Diversity space of multicarrier continuous‐variable quantum key distribution

Summary The diversity space of multicarrier continuous‐variable quantum key distribution (CVQKD) is defined. The diversity space utilizes the resources that are injected into the transmission by the additional degrees of freedom of the multicarrier modulation. We prove that the exploitable extra degree of freedom in a multicarrier CVQKD scenario significantly extends the possibilities of single‐carrier CVQKD. The manifold extraction allows for the parties to reach decreased error probabilities by utilizing those extra resources of a multicarrier transmission that are not available in a single‐carrier CVQKD setting. We define the multidimensional manifold space of multicarrier CVQKD and the optimal tradeoff between the available degrees of freedom of the multicarrier transmission. We extend the manifold extraction for the multiple‐access AMQD‐MQA (multiuser quadrature allocation) multicarrier protocol. The additional resources of multicarrier CVQKD allow the achievement of significant performance improvements that are particularly crucial in an experimental scenario.
Sora: A Decentralized Autonomous Economy

Money, when viewed as a social construct, can be a powerful tool for organizing society and allocating finite resources. However, existing monetary systems are highly centralized and this centralization has caused many crises in recent times. We therefore propose a token-based economic system that is self-regulating, can adjust the quantity of tokens to provide liquidity as needed, and avoids moral hazards. An early version of this system has been implemented on a blockchain platform as the Sora decentralized autonomous economy (DAE). Through allocating votes in a Sybil-resistant way, the Sora DAE democratically allocates new purchasing power according to open referendums. A dual-token system allows both stability of value for payments and creation of tokens for economic growth.
A Measure of Structural Complexity of Hierarchical Fuzzy Systems Adapted from Software Engineering

Hierarchical fuzzy systems (HFSs) have been seen as an effective approach to reduce the complexity of fuzzy logic systems (FLSs), largely as a result of reducing the number of rules. However, it is not clear completely how complexity of HFSs can be measured. In FLSs, complexity is commonly expressed using a multi-factorial approach, taking into consideration the number of rules, variables, and fuzzy terms. However, this may not be the best way to assess complexity in HFSs that have structures involving multiple subsystems, layers and different topologies. Thus far, structural complexity associated with the structure of HFSs has not been discussed. In the field of software engineering (SE), a complexity measure has been proposed to measure program complexity. This measure uses the concept of graph theory complexity, which considers the control structure complexity. The measure can also be applied to assess the complexity of a collection of programs known as a hierarchical nest. In this paper, we present an approach to mapping an SE complexity measure to HFS design. The approach includes several mapping alternatives that are outlined and illustrated using different HFS designs. This study contributes a new approach for the first time to assessing structural complexity in HFSs based on an approach from SE complexity measure.
Estimation of component values of filter design using optimization algorithms

In this paper, component values of analog active filters are selected based on the manufacturer’s values of E series. The selection is based on optimization algorithms and here one is nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), and another one i s the physics-based method called Sine Cosine Algorithm (SCA), are used for active filter design. The capability of optimization of the above algorithms is evaluated by considering the two active filters of a 4th order Butterworth and State variable filter. The performances of each algorithm are analyzed by applying to above two different filter structures, where the component values are determined by making compatible with different E series manufacturer.
A Generic Machine Learning Based Approach for Addressee Detection In Multiparty Interaction

Addressee detection is one of the most fundamental tasks for seamless dialogue management and turn taking in human-agent interaction. Whereas addressee detection is implicit in dyadic interaction, it becomes a challenging task in multiparty interactions when more than two participants are involved. Existing research works employ either rule-based or statistical approaches for addressee detection. However, most of these works either have been tested on a single data set or only support a fixed number of participants. In this article, we propose a model based on generic features to predict the addressee in data sets with varying number of participants. The results tested on two different corpora show that the proposed model outperforms existing baselines.
Privacy-Preserving Subgraph Matching Protocol for Two Parties

Graph data structure has been widely used across many application areas, such as web data, social network, and cheminformatics. The main benefit of storing data as graphs is there exists a rich set of graph algorithms and operations that can be used to solve various computing problems, including pattern matching, data mining, and image processing. Among these graph algorithms, the subgraph isomorphism problem is one of the most fundamental algorithms that can be utilized by many higher level applications. The subgraph isomorphism problem is defined as, given two graphs [Formula: see text] and [Formula: see text], whether [Formula: see text] contains a subgraph that is isomorphic to [Formula: see text]. In this paper, we consider a special case of the subgraph isomorphism problem called the subgraph matching problem, which tests whether [Formula: see text] is a subgraph of [Formula: see text]. We propose a protocol that solve the subgraph matching problem in a privacy-preserving manner. The protocol allows two parties to jointly compute whether one graph is a subgraph of the other, while protecting the private information about the input graphs. The protocol is secure under the semi-honest setting, where each party performs the protocol faithfully.
Diagnostics of Industrial Electrical Equipment Using Modern Information Technologies

The article dwells upon the feasibility of using modern information technologies for solving problems on technical diagnostics of electrical equipment under uncertainty in the oil industry. The problems arising during the process of diagnostics and prediction of electrical equipment operation are considered. As an example, a fragment of a fuzzy model (a fuzzy directed hypergraph) is proposed. It reflects a full list of factors affecting the technical condition of the equipment. A transition algorithm from a hypergraph to a fuzzy cognitive model (FCM) in the form of a weighted directed graph is developed in order to use fuzzy cognitive modeling for identifying faulty equipment and the main factors characterizing the normal operation of the electrical equipment. The system indicators of a fuzzy cognitive model are calculated, the search for the best values of the factors indicating the operational condition of the equipment in the presence of existing resources under the conditions of lack of information is carried out. We hope the results obtained will allow increasing the validity of decision making in relation to the faulty equipment and improving recommendations for such faults' elimination.
The Path Planning of Mobile Robots Based on an Improved A* Algorithm

Path planning with the traditional A* search algorithm for mobile robots has the disadvantages of too close distance from the obstacle, no consideration of the robot size and much redundant turning points. These disadvantages make it difficult to guide the robot movement in practice. This paper proposes a smoothing A* algorithm based on map compression. The map is compressed to make the planned path avoid the narrow position, and the smoothing algorithm is adopted to effectively reduce the turning points and the length of the path. The simulation is carried out on a grid map with typical narrow positions, and its results show that the obtained path is feasible, safe and efficient. The improved A* algorithm is applied to the XQ-4 Pro robot for comparative experiments. The experimental results show that the path of the improved A* algorithm is obviously better than the former algorithm, and can guide the robot to the target in practice.
An Optimal Data Fusion Algorithm in the Presence of Unknown Cross Covariances

This paper presents an optimal data fusion formulation and algorithm in the sense of minimum mean-square error when some or all cross covariances are unknown. This algorithm is generic in that it is capable of processing any number of measurement vectors of any dimension with any pattern of unknown cross covariances. Closed-form solution is provided for the case when all cross covariances are unknown and all measurement covariance matrices are diagonal. Numerical projected subgradient optimal fusion algorithm is provided for the most generic case. The well known covariance intersection method is shown to have a weaker upper bound of this formulation.