Reviewerly/exHarmony huggingface dataset:
A comparative analysis of machine learning models for quality pillar assessment of SaaS services by multi-class text classification of users’ reviews

Software as a Service (SaaS) has emerged as the most widely used of all the current software delivery models. With the growth of edge computing, as SaaS services increasingly become distributed, selecting the best SaaS provider from those available is challenging but it is of critical importance. In the recent past, well-known cloud service providers such as Amazon Web Services and Microsoft have developed frameworks and service quality pillars for cloud applications. However, there are currently no mechanisms for product users to know if and to what extent a service satisfies the defined service pillar. Having such information would enable users to form trustworthy associations in edge computing. In this paper, we address this drawback by adopting a systematic approach of analysing customer reviews related to SaaS products and ascertain to which service quality pillar they refer. We use eleven traditional machine learning classification approaches and a weighted voting ensemble of these classifiers to achieve this task and test the performance of each of them. Since the dataset is unbalanced in terms of sample distribution per class, we use 10-fold cross-validation on the training dataset to determine the best parameters for each machine learning algorithm to achieve optimal performance. Friedman test and Nemenyi's post hoc test is applied to identify the significant difference among the classifiers performance during cross-validation. Based on the experimental results, a comparative analysis is conducted to identify the best performing machine learning classification model on the SaaS reviews. The results show that the performance of the logistic regression model has a higher performance among the individual classifiers and the weighted voting ensemble shows minimal improvement in overall performance.
A fair contract signing protocol with blockchain support

Electronically signing contracts is fundamental for e-commerce transactions. The main property that contract signing protocols must achieve is fairness of the exchange. The solutions presented to date are divided into two major types: those that have a trusted third party (TTP) to achieve fairness and those that do not. In the literature, we find more than 40 published proposals, but none of these proposed protocols has become a recognized or de facto standard in the market. Blockchain has provided a new way to address classic problems such as double spending, as well as problems such as fairness. In this article, we present a protocol for contract signing based on blockchain. Our proposed protocol does not require a conventional TTP, and it does not present the disadvantages of solutions without a TTP (computational and/or communication cost). The protocol satisfies the necessary security requirements: fairness, timeliness and non-repudiation. We demonstrate the feasibility of the protocol with a cost analysis and a proof of concept implementation. In addition, we show how Ethereum can be integrated in our solution as an alternative platform to the use of Bitcoin. Finally, we show how our proposal improves previous solutions for contract signing based on blockchain in terms of cost, efficiency and security.
Reusable Non-Interactive Secure Computation

We consider the problem of Non-Interactive Two-Party Secure Computation (NISC), where Rachel wishes to publish an encryption of her input x, in such a way that any other party, who holds an input y, can send her a single message which conveys to her the value f(x, y), and nothing more. We demand security against malicious parties. While such protocols are easy to construct using garbled circuits and general non-interactive zero-knowledge proofs, this approach inherently makes a non-black-box use of the underlying cryptographic primitives and is infeasible in practice. Ishai et al. (Eurocrypt 2011) showed how to construct NISC protocols that only use parallel calls to an ideal oblivious transfer (OT) oracle, and additionally make only a black-box use of any pseudorandom generator. Combined with the efficient 2-message OT protocol of Peikert et al. (Crypto 2008), this leads to a practical approach to NISC that has been implemented in subsequent works. However, a major limitation of all known OT-based NISC protocols is that they are subject to selective failure attacks that allows a malicious sender to entirely compromise the security of the protocol when the receiver's first message is reused. Motivated by the failure of the OT-based approach, we consider the problem of basing reusable NISC on parallel invocations of a standard arithmetic generalization of OT known as oblivious linear-function evaluation (OLE). We obtain the following results:
A Novel Algorithm for Initial Cluster Center Selection

As one of the most important techniques in data mining, clustering has always been highly concerned. Most clustering algorithms have encountered challenges, such as the difficulty of cluster centers selection, the artificial determination of the number of clusters K, low accuracy of clustering, and uneven clustering efficiency of different data sets. Considering the difficulty of cluster centers chosen, a new algorithm of fast selecting the initial cluster centers is proposed in this paper. Generally, cluster centers are those data points with higher density, smaller radius threshold and far away from each other, this method uses MNN (M nearest neighbors), density and distance to determine the initial cluster centers. First, the neighborhood radius r of each point is measured by MNN based on distance, and the average value of all r is marked as r̅; second, the densities ρ of each point in the region within r̅ are calculated; and then, factor f is defined to describe the probability that points become cluster centers, based on which, the initial cluster centers are determined by the candidates with bigger f . In the end, the method proposed in this paper is tested by using 12 groups of typical benchmark data sets and applied in the stellar spectral data of LAMOST survey. The experiment results compared with the other six algorithms indicate that the initial cluster centers obtained by this method are of higher quality than that of the six algorithms. Meanwhile, the initial cluster centers of spectral data are of good agreement with the actual stellar classifications.
Event Detection Performance of the PLUM Earthquake Early Warning Algorithm in Southern California

Abstract We test the Japanese ground‐motion‐based earthquake early warning (EEW) algorithm, propagation of local undamped motion (PLUM), in southern California with application to the U.S. ShakeAlert system. In late 2018, ShakeAlert began limited public alerting in Los Angeles to areas of expected modified Mercalli intensity (IMMI) 4.0+ for magnitude 5.0+ earthquakes. Most EEW systems, including ShakeAlert, use source‐based methods: they estimate the location, magnitude, and origin time of an earthquake from P waves and use a ground‐motion prediction equation to identify regions of expected strong shaking. The PLUM algorithm uses observed ground motions directly to define alert areas and was developed to address deficiencies in the Japan Meteorological Agency source‐based EEW system during the 2011 Mw 9.0 Tohoku earthquake sequence. We assess PLUM using (a) a dataset of 193 magnitude 3.5+ earthquakes that occurred in southern California between 2012 and 2017 and (b) the ShakeAlert testing and certification suite of 49 earthquakes and other seismic signals. The latter suite includes events that challenge the current ShakeAlert algorithms. We provide a first‐order performance assessment using event‐based metrics similar to those used by ShakeAlert. We find that PLUM can be configured to successfully issue alerts using IMMI trigger thresholds that are lower than those implemented in Japan. Using two stations, a trigger threshold of IMMI 4.0 for the first station and a threshold of IMMI 2.5 for the second station PLUM successfully detect 12 of 13 magnitude 5.0+ earthquakes and issue no false alerts. PLUM alert latencies were similar to and in some cases faster than source‐based algorithms, reducing area that receives no warning near the source that generally have the highest ground motions. PLUM is a simple, independent seismic method that may complement existing source‐based algorithms in EEW systems, including the ShakeAlert system, even when alerting to light (IMMI 4.0) or higher ground‐motion levels.
Reporting Hate Comments: Investigating the Effects of Deviance Characteristics, Neutralization Strategies, and Users’ Moral Orientation

Norm violations in comments on social networking sites have raised concerns about the climate of political discourse. In this article, we analyze how comment characteristics and neutralization techniques in potential hate speech comments influence users’ ascription of deviance by reporting negative comments about refugees. We focus on the type of norm violation, subtlety, type of victim, and justifications as comment characteristics. Moreover, we examine the influence of descriptive and injunctive norms on reporting behavior of potential hate comments. Here, we focus on individual’s moral orientation and the perceived level of peer deviance. Results of an online survey ( n = 457) that included a choice-based conjoint design suggest that incitements for violence and direct offenses are more likely to be reported than other types of norm violation and indirect offenses. Moral foundations, indicative of liberal political orientations, fostered reporting behavior, but conservative orientations reduced reporting behavior.
Automatic detection of inconsistencies between numerical scores and textual feedback in peer-assessment processes with machine learning

The use of peer assessment for open-ended activities has advantages for both teachers and students. Teachers might reduce the workload of the correction process and students achieve a better understanding of the subject by evaluating the activities of their peers. In order to ease the process, it is advisable to provide the students with a rubric over which performing the assessment of their peers; however, restricting themselves to provide only numerical scores is detrimental, as it prevents providing valuable feedback to others peers. Since this assessment produces two modalities of the same evaluation, namely numerical score and textual feedback, it is possible to apply automatic techniques to detect inconsistencies in the evaluation, thus minimizing the teachers' workload for supervising the whole process. This paper proposes a machine learning approach for the detection of such inconsistencies. To this end, we consider two different approaches, each of which is tested with different algorithms, in order to both evaluate the approach itself and find appropriate models to make it successful. The experiments carried out with 4 groups of students and 2 types of activities show that the proposed approach is able to yield reliable results, thus representing a valuable approach for ensuring a fair operation of the peer assessment process.
A domain theory for statistical probabilistic programming

We give an adequate denotational semantics for languages with recursive higher-order types, continuous probability distributions, and soft constraints. These are expressive languages for building Bayesian models of the kinds used in computational statistics and machine learning. Among them are untyped languages, similar to Church and WebPPL, because our semantics allows recursive mixed-variance datatypes. Our semantics justifies important program equivalences including commutativity. Our new semantic model is based on `quasi-Borel predomains'. These are a mixture of chain-complete partial orders (cpos) and quasi-Borel spaces. Quasi-Borel spaces are a recent model of probability theory that focuses on sets of admissible random elements. Probability is traditionally treated in cpo models using probabilistic powerdomains, but these are not known to be commutative on any class of cpos with higher order functions. By contrast, quasi-Borel predomains do support both a commutative probabilistic powerdomain and higher-order functions. As we show, quasi-Borel predomains form both a model of Fiore's axiomatic domain theory and a model of Kock's synthetic measure theory.
Flow Field Reduction Via Reconstructing Vector Data From 3-D Streamlines Using Deep Learning

We present a new approach for streamline-based flow field representation and reduction. Our method can work in the in situ visualization setting by tracing streamlines from each time step of the simulation and storing compressed streamlines for post hoc analysis where users can afford longer reconstruction time for higher reconstruction quality using decompressed streamlines. At the heart of our approach is a deep learning method for vector field reconstruction that takes the streamlines traced from the original vector fields as input and applies a two-stage process to reconstruct high-quality vector fields. To demonstrate the effectiveness of our approach, we show qualitative and quantitative results with several data sets and compare our method against the de facto method of gradient vector flow in terms of speed and quality tradeoff.
Fuzzy Clustering to Identify Clusters at Different Levels of Fuzziness: An Evolutionary Multiobjective Optimization Approach

Fuzzy clustering methods identify naturally occurring clusters in a dataset, where the extent to which different clusters are overlapped can differ. Most methods have a parameter to fix the level of fuzziness. However, the appropriate level of fuzziness depends on the application at hand. This paper presents an entropy c-means (ECM), a method of fuzzy clustering that simultaneously optimizes two contradictory objective functions, resulting in the creation of fuzzy clusters with different levels of fuzziness. This allows ECM to identify clusters with different degrees of overlap. ECM optimizes the two objective functions using two multiobjective optimization methods, nondominated sorting genetic algorithm II (NSGA-II) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). We also propose a method to select a suitable tradeoff clustering from the Pareto front. Experiments on challenging synthetic datasets as well as real-world datasets show that ECM leads to better cluster detection compared to the conventional fuzzy clustering methods as well as previously used multiobjective methods for fuzzy clustering.
Unraveling Transparency and Accountability in Blockchain

Blockchain technology is heralded for improving trust and can provide a new approach for creating transparency and promoting accountability of government activities. However, it is still not clear how and in what ways blockchain technologies can improve this. This study examines the mechanisms and capability of blockchain technology to contribute to improved transparency and accountability in government. We use a set of system transparency and accountability concepts and mechanisms to critically assess the capabilities of blockchain. By means of a land registration case in Indonesia, we investigate the effects of blockchain on the transparency and accountability of the system. Creating transparency and accountability might be more difficult than expected, as non-technical issues need to be addressed. Based on our assessment we discuss key issues, including digital ID, privacy, interoperability, connectivity and technology aware population, computational efficiency and storage size, acceptability, check and control mechanism, data validity, digital signature, algorithm transparency, law and regulation support, and dispute resolution, that must be considered in developing a transparent and accountable blockchain-based e-Government system.
A Survey on Path Planning Algorithms for Mobile Robots

The use of mobile robots is growing every day. Path planning algorithms are needed to allow the coordination of several robots, and make them travel with the least cost and without collisions. With this emerged the interest in studying some path planning algorithms, in order to better understand the operation of each one when applied in this type of robots. The objective of this paper is to present a state of the art survey of some algorithms of path planning for mobile robots. A brief introduction on mobile robots and trajectory planning algorithms is made. After, the basis of each algorithm is explained, their relative advantages and disadvantages are presented and are mentioned areas of application for each of them. This study was developed in order to implement some of these algorithms in the near future, with the objective to find out their relative advantages and disadvantages, and in which situations their implementation is more adequate.
Emergency Service for Smart Home System Using Ethereum Blockchain: System and Architecture

Due to emerging disruptive technologies, Internet of Things (IoT) play a vital role for smart living domains, for examples, elderly and disabilities healthcare services and home safety and security monitoring and automation control services. These systems will send automatically an emergency call with home user information and location as a privacy data to public services like hospitals, police offices, or fire departments. This paper introduces emergency service for a Smart Home System (SHS) based on Ethereum blockchain with smart contract for decentralized handling access control among untrusted public services so called Home Service Providers (HSPs) and smart home IoT devices. Our SHS testbed consists of (1) smart home sensor manger equipped with Raspberry Pi (RPi) represented as an edge IoT gateway for gathering environmental sensor data, (2) HSP miners deployed Meteor and Ethereum platform, and (3) web-based applications for home users and HSP staffs. Furthermore, our contribution includes the integration of digital signature for the IoT device authentication, the One Time Passcode with QR code for HSP staff access control, and IPFS for manipulating emergency call from SHS in peers. Our implementation results focusing on HSP miners will be presented and analysed.
A bi-population QUasi-Affine TRansformation Evolution algorithm for global optimization and its application to dynamic deployment in wireless sensor networks

In this paper, we propose a new Bi-Population QUasi-Affine TRansformation Evolution (BP-QUATRE) algorithm for global optimization. The proposed BP-QUATRE algorithm divides the population into two subpopulations with sort strategy, and each subpopulation adopts a different mutation strategy to keep the balance between the fast convergence and population diversity. What is more, the proposed BP-QUATRE algorithm dynamically adjusts scale factor with a linear decrease strategy to make a good balance between exploration and exploitation capability. We compare the proposed algorithm with two QUATRE variants, PSO-IW, and DE algorithms on the CEC2013 test suite. The experimental results demonstrate that the proposed BP-QUATRE algorithm outperforms the competing algorithms. We also apply the proposed algorithm to dynamic deployment in wireless sensor networks. The simulation results show that the proposed BP-QUATRE algorithm has better coverage rate than the other competing algorithms.
Understanding Natural Language Instructions for Fetching Daily Objects Using GAN-Based Multimodal Target–Source Classification

In this letter, we address multimodal language understanding with unconstrained fetching instruction for domestic service robots. A typical fetching instruction such as “Bring me the yellow toy from the white shelf” requires to infer the user intention, i.e., what object (target) to fetch and from where (source). To solve the task, we propose a multimodal target-source classifier model (MTCM), which predicts the region-wise likelihood of target and source candidates in the scene. Unlike other methods, MTCM can handle region-wise classification based on linguistic and visual features. We evaluated our approach that outperformed the state-of-the-art method on a standard dataset. We also extended MTCM with generative adversarial nets, and enabled simultaneous data augmentation and classification.
Velocity: Scalability Improvements in Block Propagation Through Rateless Erasure Coding

Blockchain technology and other distributed ledger systems fill an important role in resilient data storage and publication. However, their normally-decentralized methods also bring unique challenges distinct from more traditional approaches. One lies in the replication of data between participating nodes; since no individual node is more trusted than any other, each node maintains its own copy of the entire ledger for the purposes of validating new transactions. Improving how this information is stored and more importantly, how it propagates across the network, are open research questions. In this work, we propose Velocity, a novel block propagation approach based on fountain codes, allowing for better decentralized delivery of blocks and reduced network bottlenecks without sacrificing the security guarantees of the blockchain ledger itself. We also provide an assessment of economic incentives and their impact on participant behavior, showing that the proposed approach is financially beneficial to rational actors. We conclude by showing experimentally that this approach permits for the mining of even larger blocks, thereby increasing transaction throughput of the system compared to existing state of the art methods.
Understanding Motivators, Constraints, and Practices of Sharing Internet of Things

Smart devices such as mobile phones, tablets, and smart watches are designed under the assumption that they will be used by a single user. In contrast, many other devices, such as smart thermostats and smart speakers, are inherently sharable. This paper presents results from a diary study that we conducted with 20 participants to gain a nuanced understanding of the purposes, motivators, and constraints involved in the sharing of smart devices, which are cumulatively referred to as the Internet of Things. We also report on users' practices of coordinating their shared use with sharees/co-users, the impact of not understanding a smart device's behavior and the context of shared use, the differences between sharing personal and inherently sharable devices in terms of content that is available and accessible, trust between sharees, and measures taken to ensure accountable use. Finally, we discuss the implications of our findings and provide guidelines for the design of future smart devices.
Statistical ZAP Arguments

Dwork and Naor (FOCS'00) first introduced and constructed two message public coin witness indistinguishable proofs (ZAPs) for NP based on trapdoor permutations. Since then, ZAPs have also been obtained based on the decisional linear assumption on bilinear maps, and indistinguishability obfuscation, and have proven extremely useful in the design of several cryptographic primitives. However, all known constructions of two-message public coin (or even publicly verifiable) proof systems only guarantee witness indistinguishability against computationally bounded verifiers. In this paper, we construct the first public coin two message witness indistinguishable (WI) arguments for NP with statistical privacy, assuming quasi-polynomial hardness of the learning with errors (LWE) assumption. We also show that the same protocol has a super-polynomial simulator (SPS), which yields the first public-coin SPS statistical zero knowledge argument. Prior to this, there were no known constructions of two-message publicly verifiable WI protocols under lattice assumptions, even satisfying the weaker notion of computational witness indistinguishability.