Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Consensus Algorithms in Private Blockchain Networks

Blockchain, one of the modern technologies, has received significant attention recently. The blockchain is an immutable ledger which records the transaction in a decentralized manner that ensures high security. Blockchain-based applications are popular in numerous fields like financial services, cryptocurrencies, cybersecurity, supply chain, health care, E-Governance, asset management, Internet of Things (IoT), and so on. In order to validate a transaction within a ledger, blockchain uses the concept of consensus. Consensus guarantees fault tolerance, reliability and high security of the blockchain systems. The most widely used consensus algorithms in the modern blockchains are the Proof of Work (PoW), the Proof of Stake (PoS), the Proof of Activity(PoA) and the Practical Byzantine Fault Tolerance(PBFT). In this work, we conduct a performance evaluation of these four consensus algorithms using Naive implementation of a blockchain network. We also conduct experiments with two popular consensus mechanisms in Ethereum platform. We study the characteristics of transaction and query latencies by varying the number of complexity and transactions. Finally, we present the conclusion on the performance consensus algorithms.
Automatic Voltage Regulator Design Using Particle Swarm Optimization Technique

The generation of energy in large power systems and islanded networks is ensured by the synchronous machine, the enhancement of dynamic performance during disturbances is increasingly required. This research work aims to maintain the terminal voltage constant of a 1.5 kVA synchronous laboratory power machine with salient pole under different loads. Then, a second generator of $187\ast 10^{3}\,\mathrm{k}$ VA with different exciting system is studied. A voltage regulation is ensured via a well-chosen controller named 'automatic voltage regulator’ that is based on proportional integral controller (PI). The adopted optimization method that has been used to determine the regulator parameters is the particle swarm optimization algorithm (PSO). The automatic voltage regulator (AVR) is tested under three different conditions. The obtained simulation results are encouraged the validation of the use of the designed AVR.
MQT-TZ: Hardening IoT Brokers Using ARM TrustZone : (Practical Experience Report)

The publish-subscribe paradigm is an efficient communication scheme with strong decoupling between the nodes, that is especially fit for large-scale deployments. It adapts natively to very dynamic settings and it is used in a diversity of real-world scenarios, including finance, smart cities, medical environments, or IoT sensors. Several of the mentioned application scenarios require increasingly stringent security guarantees due to the sensitive nature of the exchanged messages as well as the privacy demands of the clients/stakeholders/receivers. MQTT is a lightweight topic-based publish-subscribe protocol popular in edge and IoT settings, a de-facto standard widely adopted nowadays by the industry and researchers. However, MQTT brokers must process data in clear, hence exposing a large attack surface. This paper presents MQT-TZ, a secure MQTT broker leveraging ARM TRUSTZONE, a trusted execution environment (TEE) commonly found even on inexpensive devices largely available on the market (such as Raspberry Pi units). We define a mutual TLS-based handshake and a two-layer encryption for end-to-end security using the TEE as a trusted proxy. The experimental evaluation of our fully implemented prototype with micro-, macro-benchmarks, as well as with real-world industrial workloads from a MedTech use-case, highlights several tradeoffs using TRUSTZONE TEE. We report several lessons learned while building and evaluating our system. We release MQT-TZ as open-source.
Importance of Cross-Correlation of QoS metrics in Network Emulators to Evaluate QoE of Video Streaming Applications

Most of the existing studies on mapping of Quality of Service (QoS) to Quality of Experience (QoE) ignores any correlation between the QoS metrics. Network emulators such as NetEm that are used to study various network situations, have similar limitations. NetEm's inability to emulate realistic long-tail packet latency and correlation between QoS metrics may have already resulted in doubtful QoS to QoE functions. In this paper, we analysed real-time cellular QoS metrics data, and observed that there is significant correlation between several QoS parameters. We used them to create bespoke distributions for NetEm using different statistical methods such as maximum likelihood estimation and cumulative distribution functions. These distributions were used to modify NetEm to address the lack of representative packet waiting-time distributions. In addition, the bespoke distributions were also used to provide a way to emulate correlated packet delay and loss in NetEm. This bespoke emulator was then used to observe the impact of correlated delay-loss metric on end user QoE of video streaming applications using PSNR, SSIM and VMAF objective evaluation techniques. The emulation results showed that bespoke emulator gives better QoE compared to default NetEm configuration for same Packet Loss Ratio (PLR). In this paper, we analysed real-time cellular QoS metrics data, and observed that there is significant correlation between several QoS parameters. We used them to create bespoke distributions for NetEm using different statistical methods such as maximum likelihood estimation and cumulative distribution functions. These distributions were used to modify NetEm to address the lack of representative packet waiting-time distributions. In addition, the bespoke distributions were also used to provide a way to emulate correlated packet delay and loss in NetEm. This bespoke emulator was then used to observe the impact of correlated delay-loss metric on end user QoE of video streaming applications using PSNR, SSIM and VMAF objective evaluation techniques. The emulation results showed that bespoke emulator gives better QoE compared to default NetEm configuration for same Packet Loss Ratio (PLR).
Coherence of quantum Gaussian channels

Coherence is a basic feature for quantum states. Instead of quantum states, in this work, we establish a resource theory for quantifying the coherence of Gaussian channels. To do this, we propose the definitions of incoherent Gaussian channels and incoherent Gaussian superchannels.
Graph Diffusion Kernel LMS using Random Fourier Features

This work introduces kernel adaptive graph filters that operate in the reproducing kernel Hilbert space. We propose a centralized graph kernel least mean squares (GKLMS) approach for identifying the nonlinear graph filters. The principles of coherence-check and random Fourier features (RFF) are used to reduce the dictionary size. Additionally, we leverage the graph structure to derive the graph diffusion KLMS (GDKLMS). The proposed GDKLMS requires only single-hop communication during successive time instants, making it viable for real-time network-based applications. In the distributed implementation, usage of RFF avoids the requirement of a centralized pre-trained dictionary in the case of coherence-check. Finally, the performance of the proposed algorithms is demonstrated in modeling a nonlinear graph filter via numerical examples. The results show that centralized and distributed implementations effectively model the nonlinear graph filters, whereas the random-feature-based solutions are shown to outperform coherence-check based solutions.
A differential evolution-based approach for effort-aware just-in-time software defect prediction

Software defect prediction technology is an effective method to improve software quality. Effort-aware just-in-time software defect prediction (JIT-SDP) aims to identify more defective changes in limited effort. Although many methods have been proposed for JIT-SDP, the prediction performance of existing prediction models still needs to be improved. To improve the effort-aware prediction performance, we propose a new method called DEJIT based on differential evolution algorithm. First, we propose a metric called density-percentile-average (DPA), which is used as the optimization objective of models on the training set. Then, we use logistic regression to build models and use the differential evolution algorithm to determine coefficients of logistic regression. We conduct empirical research on six open source projects. Empirical results demonstrate that the proposed method significantly outperforms the state-of-the-art 4 supervised models and 4 unsupervised models.
Analyzing the period distribution of digital chaos with graph theory

Abstract The related characteristics of chaos are closely related to the basic concepts of confusion and diffusion in cryptography, which makes chaotic systems widely used in chaotic secure communication. However, when a chaotic system is implemented in a hardware device with finite calculation precision, its dynamic characteristics will be degraded to varying degrees due to the different calculation precision of the hardware device. The dynamic degradation of digital chaos will threaten the security of chaotic secure communication. To evaluate the performance of various digital chaotic maps, we proposed a universal period search algorithm (UPSA) that is based on graph theory with the purpose of analyzing the period distribution of digital chaos from a new perspective. The algorithm can calculate the maximal transient length, fixed points and periodic limit cycles (i.e. attractive basins) of digital chaos in finite-precision domains accurately. Furthermore, in order to test the versatility and effectiveness of our proposed algorithm, the periodic distribution and security of several typical digital chaotic maps are analyzed in detail. To a certain degree, UPSA can reduce the difficulty of cryptanalysis of digital chaotic ciphers and understand the network structures of functional graphs of digital chaos.
CoRelatE: Learning the correlation in multi-fold relations for knowledge graph embedding

Existing approaches for knowledge graph embedding usually represent knowledge as triples of binary relations between entities and embed them into continuous vector space, which are not practical for the complex facts in the form of multi-fold relations in real life. In this work, we address the problem of multi-fold relation embedding in knowledge graph and propose a new framework CoRelatE that learns correlations between entities, facts and relations from the instances. We first model the entity-relation correlation directly via combinational operator, then utilize a graph convolutional network to model the correlation between entities and their related facts. Finally, the facts are forced to embed close to their corresponding relations to learn the fact-relation correlation. We formulate the objective as a joint optimization problem and introduce an efficient algorithm to solve it. We compare the proposed CoRelatE model with several state-of-the-art models including HypE, NaLP and RAE, m-TransH. Experimental results on four datasets of multi-fold relations and two datasets of binary relations for link prediction and instance classification tasks validate the effectiveness and merits of our model.
Being a permutation is also orthogonal to one-wayness in quantum world: Impossibilities of quantum one-way permutations from one-wayness primitives

In this paper, three impossible results for one-way permutations in the quantum world are obtained. The first one is the impossibility of fully black-box reduction from one-way permutations to one-way functions in a quantum setting. The two-oracle method is the main technique adopted in our proof that was originally proposed by Hsiao and Reyzin, and extended later to a quantum setting by Hosoyamada and Yamakawa. Informally this technique involves a pair of oracles, and in our proof, one oracle is chosen as a random function, and the other one is devised for breaking the security of quantum one-way permutation. We show, according to the first oracle, that there is an oracle-aided circuit that is a quantum-secure one-way function. However, we also show, according to the second oracle, that there is no one-way permutation relative to those oracles. It hence claims the impossibility in the quantum setting to construct one-way permutations from one-way functions (even if both input and output are classical). The rest two impossibilities are essential enhancements of the first impossibility: We successfully obtain that it is impossible, in the quantum world, to obtain one-way permutations in a black-box manner from injective adaptive one-way functions, and (respectively) even from injective adaptive trapdoor functions. In order to reach the latter two results, we manage to get over the obstacle of finding the inverse of random injective functions even given the partial invert oracle in the quantum setting. As a result, we get a quantum lower bound of inverting random injective functions in random case. That should be of independent interest in this paper, which might, to authors' knowledge, be the first quantum lower bound of inverting random injective functions given the partial invert oracle.
Attention Based Recurrent Neural Network for Lung Cancer Detection

Lung cancer detection is a significant technique needed in the current medical field. Moreover, it causes the larger death rate because of late identification which then delays the diagnosis process. Hence, to provide the earlier detection of lung cancer, Attention-based recurrent neural network (ARNN) is designed in our work. This proposed approach is framed for the motive of obtaining the increased accuracy and faster prediction of the cancer spots even before the cancer symptoms recognized by the patients. The proposed ARNN has an attention layer which performs the encoding and decoding process within single sequences using variable-length vector and also has an advantage that it performs the automatic feature extraction. Finally, the classification is done using the proposed ARNN and the classification results are obtained with great accuracy and within less computation time.
A New PyROOT: Modern, Interoperable and More Pythonic

Python is nowadays one of the most widely-used languages for data science. Its rich ecosystem of libraries together with its simplicity and readability are behind its popularity. HEP is also embracing that trend, often using Python as an interface language to access C++ libraries for the sake of performance. PyROOT, the Python bindings of the ROOT software toolkit, plays a key role here, since it allows to automatically and dynamically invoke C++ code from Python without the generation of any static wrappers beforehand. In that sense, this paper presents the efforts to create a new PyROOT with three main qualities: modern, able to exploit the latest C++ features from Python; pythonic, providing Python syntax to use C++ classes; interoperable, able to interact with the most important libraries of the Python data science toolset.
Matrix factorization based Bayesian network embedding for efficient probabilistic inferences

Bayesian network (BN) is a well adopted framework for representing and inferring uncertain knowledge. By the existing methods, multiple probabilistic inferences on the same BN are often fulfilled one by one via repeated searches and calculations of probabilities. However, lots of intermediate results of probability calculations cannot be shared and reused among different probabilistic inferences. It is necessary to improve the overall efficiency of multiple probabilistic inferences on the same BN by incorporating an easy-to-calculate representation of BN and an easy-to-reuse technique for common calculations in multiple inferences. In this paper, we first propose the method of Bayesian network embedding to generate the easy-to-reuse node embeddings. Specifically, we transform BN into the point mutual information (PMI) matrix to simultaneously preserve the directed acyclic graph (DAG) and conditional probability tables (CPTs). Then, we give the singular value decomposition (SVD) based method to factorize the PMI matrix for generating node embeddings. Secondly, we propose a novel method of random sampling to make multiple probabilistic inferences via similarity calculation between node embeddings. Experimental results show that the runtime of our proposed BNERS performing 10 times of inferences is 30% faster than Gibbs sampling (GS) and 50% faster than forward sampling (FS) on LINK BN (very large network), while maintaining almost the same results as GS and FS.
Path Planning Algorithm for Unmanned Ground Vehicles (UGVs) in Known Static Environments

Unmanned ground vehicles (UGVs) have been utilized in many civilian fields in addition to the traditional military field. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. Many applications require the UGV to autonomously navigate static environments while taking into consideration obstacle avoidance. Autonomous path planning is one of the key challenges and issues related to UGVs. Generally, robotic path planning is an optimization search problem that comes in different forms. Some of its forms have been solved by different classical algorithms such as A*, but these algorithms are computationally inefficient. In contrast, the emerging nature-inspired algorithms outperform the classical ones since the computational overhead is reduced. Nature-inspired algorithms are among the most common heuristic algorithms. This paper proposes a near-optimal algorithm to find a feasible path for UGV in a static environment. The performance of the proposed algorithm was compared with other well-established algorithms in path planning literature such as A* using a simulator developed for this purpose. The simulator tests three performance measures path length, farness from obstacles, and running time. The simulator results revealed that the length of the path generated by this algorithm is near-optimal, however, the generated path is kept far from obstacles.
An analysis of use and performance data aggregated from 35 institutional repositories

Purpose This study demonstrates that aggregated data from the Repository Analytics and Metrics Portal (RAMP) have significant potential to analyze visibility and use of institutional repositories (IR) as well as potential factors affecting their use, including repository size, platform, content, device and global location. The RAMP dataset is unique and public. Design/methodology/approach The webometrics methodology was followed to aggregate and analyze use and performance data from 35 institutional repositories in seven countries that were registered with the RAMP for a five-month period in 2019. The RAMP aggregates Google Search Console (GSC) data to show IR items that surfaced in search results from all Google properties. Findings The analyses demonstrate large performance variances across IR as well as low overall use. The findings also show that device use affects search behavior, that different content types such as electronic thesis and dissertation (ETD) may affect use and that searches originating in the Global South show much higher use of mobile devices than in the Global North. Research limitations/implications The RAMP relies on GSC as its sole data source, resulting in somewhat conservative overall numbers. However, the data are also expected to be as robot free as can be hoped. Originality/value This may be the first analysis of aggregate use and performance data derived from a global set of IR, using an openly published dataset. RAMP data offer significant research potential with regard to quantifying and characterizing variances in the discoverability and use of IR content. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0328
Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges

Fall detection (FD) has been the focus of many research studies during the last years. Developing reliable FD systems is relevant, for instance, to provide support to the elderly population in their everyday life. Besides, the generalization of the use of wearable devices (and more specifically, on-wrist devices) to measure the daily activity strongly suggests that in a short period of time, the elderly people will be making use of this type of devices. On-wrist devices can be used as the FD basic sensing unit; while the intelligent classification can be obtained either autonomously (on the device) or requested to a remote service (via the paired smartphone or via web services). This study tries to analyze the current challenges in autonomous on-wrist wearable devices for producing a reliable and robust FD system. To do so, we analyze the related work; one of the possible solutions is implemented with several alternatives and evaluated with publicly available simulated falls data sets. The most remarkable findings in this research are that i) real fall data sets are needed, at least, a valid merging method to produce real fall like Time Series, ii) generalized solutions might not be enough and research is needed in models that learns from the user, iii) the need of tuning and fitting to the current user performance, iv) the amount of fall types suggests that hybrid and ensemble approaches might be interesting.
Texture image classification based on a pseudo-parabolic diffusion model

This work proposes a novel method based on a pseudo-parabolic diffusion process to be employed for texture recognition. The proposed operator is applied over a range of time scales giving rise to a family of images transformed by nonlinear filters. Therefore each of those images are encoded by a local descriptor (we use local binary patterns for that purpose) and they are summarized by a simple histogram, yielding in this way the image feature vector. Three main novelties are presented in this manuscript: (1) The introduction of a pseudo-parabolic model associated with the signal component of binary patterns to the process of texture recognition and a real-world application to the problem of identifying plant species based on the leaf surface image. (2) We also introduce a simple and efficient discrete pseudo-parabolic differential operator based on finite differences as texture descriptors. While the work in [26] uses complete local binary patterns, here we use the original version of the local binary pattern operator. (3) We also discuss, in more general terms, the possibilities of exploring pseudo-parabolic models for image analysis as they balance two types of processing that are fundamental for pattern recognition, i.e., they smooth undesirable details (possibly noise) at the same time that highlight relevant borders and discontinuities anisotropically. Besides the practical application, the proposed approach is also tested on the classification of well established benchmark texture databases. In both cases, it is compared with several state-of-the-art methodologies employed for texture recognition. Our proposal outperforms those methods in terms of classification accuracy, confirming its competitiveness. The good performance can be justified to a large extent by the ability of the pseudo-parabolic operator to smooth possibly noisy details inside homogeneous regions of the image at the same time that it preserves discontinuities that convey critical information for the object description. Such results also confirm that model-based approaches like the proposed one can still be competitive with the omnipresent learning-based approaches, especially when the user does not have access to a powerful computational structure and a large amount of labeled data for training.