Reviewerly/exHarmony huggingface dataset:
A reactive model based on neighborhood consensus for continuous optimization

Evolutionary Computation (EC) algorithms have been proposed as stochastic methods to solve complex optimization problems. The design of EC methods typically involves the construction of empirical operators based on abstractions of animal behaviors or physical and biological phenomena. Through its operators, every EC approach proposes a particular solution to the exploration-exploitation balance which is currently considered an unsolved problem within EC literature. On the other hand, multi-agent systems have been utilized as intelligent, cooperative and self-organized structures where the synergy of simple rules creates complex interactions among agents. In this paper, a novel EC algorithm called Neighborhood-based Consensus for Continuous Optimization (NCCO) is presented. NCCO is based on typical processes present in multi-agent systems, such as local consensus formulations and reactive responses. These operations are conducted by using appropriate operators that are applied in each evolutionary stage. A traditional EC algorithm considers in its operation the application of every operator without examining its final impact in the searching process. In contrast to other EC techniques, the proposed method uses additional operators to avoid the undesirable effects produced by the over-exploitation or suboptimal exploration of conventional operations. In order to illustrate the performance and accuracy of the proposed NCCO approach, it is compared to several well-known, state-of-the-art algorithms over a set of benchmark functions and real-world design applications. The experimental results demonstrate that NCCO's performance is superior to the test algorithms.
Generating mutually recursive definitions

Many functional programs — state machines [Krishnamurthi 2006], top-down and bottom-up parsers [Hinze and Paterson 2003; Hutton and Meijer 1996], evaluators [Abelson et al. 1984], GUI initialization graphs [Syme 2006], &c. — are conveniently expressed as groups of mutually recursive bindings. One therefore expects program generators, such as those written in MetaOCaml, to be able to build programs with mutual recursion.
The talk scandal as mediatized event and communicative resource in far-right populist talk

This paper examines the discursive realizations and media representations of scandalous talk with reference to a scandalous 2018 parliamentary speech performance by a representative of the far-right Golden Dawn (GD) party in Greece. By examining the recontextualization of the talk scandal in on-line news articles, it is shown that the original speech event gives rise to other speech events related to it, creating a ‘talk scandal universe’. Viewing talk scandals as building blocks upon which engaging media narratives are built, it is argued that they can serve as communicative resources in far-right populist performances, enacting a clean break with established norms and values of the political establishment. Moreover, talk scandals project perpetrators as candid, outspoken and defiant of the status quo, even in the face of damaging face or suffering legal penalties. Finally, as scandalous utterances and subsequent retractions contain political positioning statements, talk scandals in effect help spread and reproduce populist agendas.
Zero shot learning by partial transfer from source domain with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si43.gif" overflow="scroll"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> norm constraint

Current zero shot learning methods mostly focus on applying the knowledge learnt by seen images to the unseen images. However, there is a big distribution difference between seen and unseen data, also called source and target domain. Thus, there are many irrelevant seen samples for unseen samples. We want to partially transfer the seen samples to target domain by selecting relevant seen samples. In this paper, we propose a method, zero shot learning by partial transfer from source domain with L2,1 norm constraint, called ZSLPT which embeds visual similarity and semantic similarity to transfer partial source samples. The relevant source samples are selected, while the irrelevant are eliminated. What’s more, we train source classification model used for transferring to target domain with the selected source samples, making the transferred target model more accurate. We have experimented on the state-of-the-art zero shot learning datasets, demonstrating that ZSLPT has good performance.
Markov Chain Based Efficient Defense Against Adversarial Examples in Computer Vision

Adversarial examples are the inputs to machine learning models that result in erroneous outputs, which are usually generated from normal inputs via subtle modification and seem to remain unchanged to human observers. They have severely threatened the applications of machine learning, especially in the areas with high-security requirements. Unfortunately, for this issue, there is neither unambiguous interpretation about the causes nor almighty defenses in spite of the increasing attention and discussions. Based on the distinguished statistical feature of Markov chain, an effective defense method is proposed in this paper by exploring the differences in the probability distributions of adjacent pixels between normal images and adversarial examples. Specifically, the concept of overall probability value (OPV) is defined to estimate the modification to an input, which can be used to preliminarily determine whether the input is an adversarial example or not. Furthermore, by calculating the OPV of an input and modifying its pixel value to destroy the potential adversarial characteristics, the proposed method can efficiently purify adversarial examples. A series of experiments demonstrate the effectiveness of the defense method. When facing various attacks, it obtains excellent performance with accuracy over 92% for MNIST and 70% for ImageNet.
Fast Data Reduction With Granulation-Based Instances Importance Labeling

Data reduction has become greatly significant prior to applying instance-based machine learning algorithms in the Big Data era. Data reduction is used to reduce the size of data sets while retaining representative data. Existing algorithms, however, suffer from heavy computational cost and in having tradeoff in size reduction rate and learning accuracy. In this paper, we propose a fast data reduction approach by using granular computing to label important instances, i.e., instances with higher contributions to the learning task. The original data set is first granulated into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> granules by applying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means to a mapped lower-dimension space. Then, the importance of each instance in every granule is labeled based on its Hausdorff distance. Those instances whose importance values are lower than an experimentally tuned threshold are eliminated. The presented algorithm is applied to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN classification tasks with eighteen different sizes of data sets from the UCI repository, and its outstanding performance in classification accuracy, size reduction rate, and runtime is illustrated by comparing with seven data reduction methods. The experimental results demonstrate that the proposed algorithm can greatly reduce the computational cost and achieve a higher classification accuracy when the reduction size is the same for all the compared algorithms.
Natural Language Generation Using Dependency Tree Decoding for Spoken Dialog Systems

In this paper, we propose a new natural language generation (NLG) method for spoken dialog systems and demonstrate its capacity. Studies on NLG often employ sequence decoding, which generates the words comprising a sentence in sequential order and uses the input generated by each word in the previous step. In contrast, we propose a decoding method that employs a sequence generated by traversing a dependency tree with feed input to a pair consisting of a parent and sibling in the dependency tree. As a result, the most important words are generated first, thereby enabling words with greater relevance to be fed into the process. At prediction time, our model generates dependency trees and converts the trees into sentences. The proposed decoding method was evaluated by re-implementing a semantically controlled long short-term memory structure for NLG, and the input and predicted sequence were converted to allow dependency tree decoding. The experimental results indicated that our suggested approach, i.e., dependency tree decoding, dramatically elevates the BLEU-score and naturalness. Furthermore, when creating sentences with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -best using dependency tree decoding, the word diversity of the output sentences was increased by approximately 6%, offering a more diverse sentence pattern.
Clustering Through Probability Distribution Analysis Along Eigenpaths

Data clustering is one of the most fundamental techniques in exploratory data analysis. It is widely used for determining the underlying data structure, classifying natural data and compressing data in engineering, business management, social statistics, computer science, and medicine. Under the assumption that clusters are high density regions in the feature space separated by relatively low density neighbors, a novel approach is proposed for modeling any high dimensional clustering problem as a one-dimensional analysis of the probability distribution. First, a special path between two vertexes, namely eigenpath, is defined in this paper to represent their close connection. Second, we propose the connectedness index based on the eigenpath for quantitatively describing the connection between two vertexes. Third, the connectedness index is applied to the candidates of cluster centers and measures the connection between different candidates. Then an indicative curve can be drawn with the knowledge of connectedness index. This approach not only provides effective indicative curve for unknown data sets but also facilitates eliminating the curse of dimensionality partly as well as correctly recognizes arbitrary cluster forms and automatically excludes outliers. Extensive experiments showed the effectiveness and efficiency of the proposed approach.
Inline detection and reconstruction of multiphoton quantum states

Integrated single-photon detectors open new possibilities for monitoring inside quantum photonic circuits.We present a concept for the in-line measurement of spatially-encoded multi-photon quantum states, while keeping the transmitted ones undisturbed.We theoretically establish that by recording photon correlations from optimally positioned detectors on top of coupled waveguides with detuned propagation constants, one can perform robust reconstruction of the density matrix describing the amplitude, phase, coherence and quantum entanglement.We report proof-of-principle experiments using classical light, which emulates single-photon regime.Our method opens a pathway towards practical and fast in-line quantum measurements for diverse applications in quantum photonics.
Critical professional skills of MIS graduates: Practitioner vs. Academician perspectives

Since enterprises have become aware of technological developments; they have started to give significant importance to information technology investments. As in all university departments, also management information systems (MIS) and business informatics programs analyze the change in the industry and aim to graduate students that fit the current needs. The authors aim to evaluate the critical skills of MIS graduates and industry based on industry demand. Providing information systems support, knowledge on latest systems, managerial knowledge, systems development, project management, and teamwork comprise the six critical factors for becoming a successful MIS professional.
An Efficient Strong Designated Verifier Signature Based on &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$\mathcal{R}-$ &lt;/tex-math&gt; &lt;/inline-formula&gt;SIS Assumption

The designated verifier signature (DVS), introduced by Jakobsson <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , has the property that only the designated verifier can verify the generated signature. In order to prevent an eavesdropper to get the signature on-line before the designated verifier receives it, they also proposed strong designated verifier signature (SDVS). In this paper, according to an efficient SDVS proposed by Saeednia <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , we present a post-quantum SDVS in the random oracle model based on lattice assumption. The unforgeability is based on the hardness of the average-case hard problem <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {R}-$ </tex-math></inline-formula> SIS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{q,n,m,\beta }$ </tex-math></inline-formula> , which is at least as hard as worst-case SVP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\gamma }$ </tex-math></inline-formula> over ideal lattices. In addition, compared with existing lattice-based SDVS schemes, our scheme cuts by more than 50 percent repetitions and the size of signature is shorter with 256 bits security.
A Taxonomy of Side Channel Attacks on Critical Infrastructures and Relevant Systems

Information disclosure leads to serious exploits, disruption or damage of critical operations and privacy breaches, both in Critical Infrastructures (CIs) and Industrial Control Systems (ICS) and in traditional IT systems. Side channel attacks in computer security refer to attacks on data confidentiality through information gained from the physical implementation of a system, rather an attack on the algorithm or software itself. Depending on the source and the type of information leakage, certain general types of side channel attacks have been established: power, electromagnetic, cache, timing, sensor-based, acoustic and memory analysis attacks. Given the sensitive nature of ICS and the vast amount of information stored on IT systems, consequences of side channel attacks can be quite significant. In this paper, we present an extensive survey on side channel attacks that can be implemented either on ICS or traditional systems often used in Critical Infrastructure environments. Presented taxonomies try to take into consideration all major publications of the last decade and present them using three different classification systems to provide an objective form of multi-level taxonomy and a potentially profitable statistical approach. We conclude by discussing open issues and challenges in this context and outline possible future research directions.
Opportunities, Challenges, and Future Extensions for Smart-Contract Design Patterns

Blockchains enable the trustless establishment of long-term consensus. The primary paradigm for extending this capability to generalized use cases is smart contracts. Smart contracts have the advantages of trustlessness, immutability, transparency, censorship-resistance, and DDoS resistance, but suffer from immutability, chain-boundedness, high cost of storage and execution, and poor parallelizability. While the advantages of smart contracts create many opportunities, their unique properties impose important constraints. A suite of design patterns are therefore proposed as one methodology for addressing these constraints while taking full advantage of the opportunities that smart contracts provide.
Chapter 9. Relational practices on commercial Facebook wall interactions

This chapter explores mediated service encounters in social networking sites, as examples of the new (or reconfigured) forms of social organization and interaction brought about by technology. Social networking sites afford multidirectional communication, i.e. encounters between companies and consumers, as well as interactions among consumers, which are impacting previously stable corporate and consumer roles and are transforming efficiency and sociability management in various ways. This chapter specifically investigates relational practices on commercial Facebook pages. To carry out this research, a reference corpus of commercial wall interactions, comprising over 900 comments, was compiled. A representative sample (n = 290 posts) was quantitatively analyzed to identify consumers’ alignment with the corporation. Drawing on a revised framework of relational work and extant second-order taxonomies of im/politeness, a qualitative analysis unveiled a number of relational practices that emerged in supportive and unsupportive interactions. This chapter unveiled specific interactional patterns and revealed the predominance of both textual and visual relational practices on Facebook.
Long-term learning for type-2 neural-fuzzy systems

The development of a new long-term learning framework for interval-valued neural-fuzzy systems is presented for the first time in this article. The need for such a framework is twofold: to address continuous batch learning of data sets, and to take advantage the extra degree of freedom that type-2 Fuzzy Logic systems offer for better model predictive ability. The presented long-term learning framework uses principles of granular computing (GrC) to capture information/knowledge from raw data in the form of interval-valued sets in order to build a computational mechanism that has the ability to adapt to new information in an additive and long-term learning fashion. The latter, is to accommodate new input–output mappings and new classes of data without significantly disturbing existing input–output mappings, therefore maintaining existing performance while creating and integrating new knowledge (rules). This is achieved via an iterative algorithmic process, which involves a two-step operation: iterative rule-base growth (capturing new knowledge) and iterative rule-base pruning (removing redundant knowledge) for type-2 rules. The two-step operation helps create a growing, but sustainable model structure. The performance of the proposed system is demonstrated using a number of well-known non-linear benchmark functions as well as a highly nonlinear multivariate real industrial case study. Simulation results show that the performance of the original model structure is maintained and it is comparable to the updated model's performance following the incremental learning routine. The study is concluded by evaluating the performance of the proposed framework in frequent and consecutive model updates where the balance between model accuracy and complexity is further assessed.
Method of identifying the lengths of equivalent clear-sky periods in the time series of DNI measurements based on generalized atmospheric turbidity

In photovoltaic (PV) power plant construction, solar resources need to be evaluated using some criteria. Besides considering the classical total amount of solar resources at the considered location, the lengths and distributions of equivalent clear-sky periods have also been taken into account in recent years to consider the stability of both the PV system output power and other connected grids. We present a method of identifying the lengths and distributions of equivalent clear-sky periods using time series of direct normal irradiance (DNI) measurements based on generalized atmospheric turbidity. This method can be employed not only to calculate the lengths and distributions of historical equivalent clear-sky periods, but also for real-time equivalent clear-sky condition detection. In this report, we demonstrate the validity our algorithm using data from the Solar Radiation Research Laboratory (39.74°N, 105.18°W) and compare our results with those obtained using the Reno and Hansen algorithm.
Frequency Hopping Sequence Sets With Good Aperiodic Hamming Correlation Properties and Large Family Size

In this letter, two constructions of frequency hopping (FH) sequence sets with respect to aperiodic Hamming correlation (AHC) are presented. They have optimal or near optimal family size according to the upper bound on the family size of FH sequence sets with respect to AHC which was derived by Liu et al. Meanwhile, the parameters of the two classes of FH sequence sets are new. The FH sequence sets obtained have large family size and are applicable to the large-capacity systems.
Face Detection for Privacy Protected Images

In recent years, privacy has received increasing attention to the development of the Internet.Yet, the problem still exists in the field of image processing that needs to be solved urgently.The cloud server has an abundant resource on computation for the specified computing tasks.With the rapid growth of personal and enterprise image data, more and more computing tasks are difficult to handle locally and are usually be outsourced to the cloud server for computing.Therefore, private image data protection while image processing has become our main concern.To solve this problem, we propose a scheme to extract secure Haar feature for privacy-preserving images, which can be applied to the face detection task.This paper solves the problem of extracting Haar feature from privacy-preserved images for the first time.Besides, we compare the numerical values in the ciphertext domain with double encryption protocols based on homomorphic encryption.In this scheme, we adopt Li et al.'s homomorphic encryption algorithm to protect image privacy.The security analysis of Li et al.'s encryption algorithm shows that the scheme we proposed is secure under the known plaintext attack and ciphertext attack.The experimental results show that the scheme can be implemented in polynomial time.
A Unified Preprocessing Technique for Enhancement of Degraded Document Images

The field of Document Image Processing has encountered sensational development and progressively across the board relevance lately. Luckily, propels in PC innovation have kept pace with the fast development in the volume of picture information in different applications. One such utilization of Document picture preparing is OCR (Optical Character Recognition). Pre-preparing is one of the pre-imperative stages in the handling of record pictures which changes the archive to a frame reasonable for ensuing stages. In this paper, various preprocessing techniques are proposed for the enhancement of degraded document images. The algorithms implemented are adept at handling variety of noises that include foxing effect, illumination correction, show through effect, stain marks, and pen and other scratch marks removal. The techniques devised works based on noise degradation models generated from the attributes of noisy pixels which are commonly found in degraded or ancient document images. Further, these noise models are employed for the detection of noisy regions in the image to undergo the enhancement process. The enhancement procedures employed include the local normalization, convolution using central measures like mean and standard deviation, and Sauvola's adaptive binarization technique. The outcomes of the preprocessing procedure is very promising and are adaptable to various degraded document scenarios.