Reviewerly/exHarmony huggingface dataset:
Malicious Website Detection using Machine Learning on Apache Spark

Malicious websites considered as critical threats to the users’ systems that access these websites as hackers use these websites to steal users’ personal information or account information or even harms the users’ systems. Many solutions have been developed to detect and prevent these malicious websites, but these solutions are not fully effective as these websites are changed continuously. This paper evaluates various classification algorithms to predict malicious and non- malicious web sites, based on various feature selection scenarios. Reasonable results are reached with 100% accuracy, recall, and precision when applying Logistic Regression and Decision Tree algorithms while 95% when applying Naïve Bayes algorithm with good timing.
Visual contour tracking based on inner-contour model particle filter under complex background

Abstract In this paper, a novel particle filter–based visual contour tracking method is proposed, which uses inner-contour model to track contour object under complex background. The purpose is to achieve effectiveness and robustness against complex background. To that end, the proposed method first utilized Sobel edge detector to detect the edge information along the normal line of the contour. Then, it sampled the inner part of the normal line to get the local color information, which was then combined with the edge information to construct new normal line likelihood. After that, all the inner color information was used to construct global color likelihood. Finally, the edge information, local color information, and global color information were fused into new observation likelihood. Experimental results showed that the proposed method was robust for contours tracking under complex background, and it was also computationally efficient and can run in real-time completely.
Words that Hurt (1): Normative and Institutional Considerations in the Regulation of Hate Speech in Europe

Europe is experiencing at present intense dilemmas in regulating hate speech and online harassment. Free speech exercise can be offensive and even contribute to a climate of prejudice and discrimination against minorities. Often, the media exacerbate the tendency by reporting negatively about minorities. The first working paper on this topic engages with the normative dimensions of the balance between the need to control and limit incitement to violence in reconciliation with the fundamental right to freedom of expression. Three distinct aspects of hate speech are covered: the first relates to the role of freedom of expression as a tool of inclusiveness. With the limits of liberal tolerance being unclear, just like the definition of hate speech itself, legal actors and systems are torn between criminalising the speaker’s motive alone or in conjunction with the effects of the speech. A survey of recent related European Court of Human Rights case-law demonstrates these ambiguities. The second aspect covered looks at the challenges of the regulation of the freedom of expression in the digital age, with emphasis of the online dimensions of the phenomenon from a legal perspective. The final aspect of the paper proposes an actor-based analysis of hate speech, as it emerges from the current regulatory frameworks applied. This section deals not only with the role of the State but also with that of equality bodies, political parties and private businesses in providing more efficient networks of protection of minorities from such violent expressions of hatred.
Optimal Strategies Against Generative Attacks

Generative neural models have improved dramatically recently. With this progress comes the risk that such models will be used to attack systems that rely on sensor data for authentication and anomaly detection. Many such learning systems are installed worldwide, protecting critical infrastructure or private data against malfunction and cyber attacks. We formulate the scenario of such an authentication system facing generative impersonation attacks, characterize it from a theoretical perspective and explore its practical implications. In particular, we ask fundamental theoretical questions in learning, statistics and information theory: How hard is it to detect a ``fake reality''? How much data does the attacker need to collect before it can reliably generate nominally-looking artificial data? Are there optimal strategies for the attacker or the authenticator? We cast the problem as a maximin game, characterize the optimal strategy for both attacker and authenticator in the general case, and provide the optimal strategies in closed form for the case of Gaussian source distributions. Our analysis reveals the structure of the optimal attack and the relative importance of data collection for both authenticator and attacker. Based on these insights we design practical learning approaches, and show that they result in models that are more robust to various attacks on real-world data. Code will be made publicly available upon publication.
Cross-media Image-Text Retrieval Based on Two-Level Network

Cross-media retrieval is to find the relationship between different modal samples, and to use some modal samples to search for other modal samples of approximate semantics. The existing cross-media retrieval method only utilizes the information of the image and part of the text, that is, the whole image and the whole sentence are matched, or some image areas and some words are matched. In order to make better use of the integrated features of image and text, this paper proposes a cross-media image-text retrieval method that integrates two-level similarity to explore better matching between image and text semantics. Specifically, in this method, the image is divided into the whole picture and some image areas, the text is divided into the whole sentences and some words, to study respectively, to explore the full potential alignment of images and text, and then a two-level alignment framework is used to promote each other, fusion of two kinds of similarity can learn to complete representation of cross-media retrieval. Experimental results on the Flickr30K and MS-COCO datasets show that this model has a better recall rate than many of the current internationally advanced cross-media retrieval models.
The Discriminatory Power of Lexical Context for Alternations: An Information-theoretic Exploration

This paper makes a very exploratory, tentative, and thinking-aloud kind of suggestion for the corpus-based analysis of alternation data. I start from the observation that studies of alternations/choices in particular in corpus linguistics have become increasingly sophisticated in terms of the statistical methods they employ and the number of predictors they involve. While the predictors employed come from many different levels of linguistic analysis – phonology, morphosyntax, semantics, pragmatics/ discoursal, textual, psycholinguistic, sociolinguistic, and others – they are usually contextual in nature, meaning they characterize the context of the choice the language user needs to make or has just made. However, one aspect of the context seems to be crucially underutilized when it comes to modeling speakers’ choices: the lexical context. In this paper, I build on recent work in computational psycholinguistics to: (a) define a lexical-distribution prototype of each of the (typically, but not necessarily, two) alternants of an alternation; and (b) compute the degree to which each instance of the alternation in question diverges from each of the prototypes. Then, (c) the values that all choices score on the divergences from each of the prototypes are entered as predictors to all others in statistical models to, minimally, serve as a variable that controls for whatever information is contained in the lexical context of an instance of speaker’s choice. I exemplify the approach and its sometimes amazing predictive power on the basis of a choice between near synonyms, two morphosyntactic alternations (preposition stranding vs. pied-piping and of- vs. s genitives), and a distinction between the functions of well.
A multi-level network for radio signal modulation classification

A new multi-level network combined the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is proposed to classify modulation types of signals. The network takes the advantage of CNN in feature extraction and robust to noise and the advantage of LSTM in time sequence analysis. The experiments based on a GNU radio data set shows that the classification accuracy is about 93% for ten different modulation type signals at SNR above 2 dB, which is much better than the conventional CNN. The classification accuracy is about 95% for 8 digital modulation types at SNR above 2dB.
Fuzzy Adaptation of Surveillance Plans of Patients with Diabetes

Reuse past experiences in solving new problems is common in the everyday lives, since it is well obvious and legitimate that similar problems have similar solutions and we are often confronted with a problem already met. The case-based reasoning (CBR) as a powerful design methodology of intelligent systems can be strengthened in the different stages of process by techniques for optimization. In a previous work [10], an approach guided by case-based reasoning based on retrieval by fuzzy decision tree has been proposed. In this paper, we propose a medical decision support system to assist physicians by adapting monitoring plans. The case-based reasoning process involves through several steps, we are interested in the adaptation phase which is to reuse totally or partially a solution of the selected case to solve the new problem. FUZZY DTA offers a medical decision making support system using a fuzzy reasoning for diabetes surveillance plans.
Discovering objects and services in context-aware IoT environments

The future IoT is expected to connect trillions of objects across infrastructures and domains worldwide. Compared to traditional web applications, the context of objects, such as dynamic object property values, may rapidly change. Browsing web pages with hyperlinks is unsuitable for representing and discovering the dynamic state of objects and services in the IoT, as the dynamic context is less keyword-rich than the relatively static contents on traditional web pages. This paper reviews state-of-the-art in discovering objects and services in the current IoT, and analyses the constraints. This paper also proposes a discovery mechanism, which is extended from previous work GGIoT. The proposed discovery mechanism is aware of dynamic context change in the IoT, and can improve the discovery process using automation.
Can Neural Image Captioning be Controlled via Forced Attention?

Learned dynamic weighting of the conditioning signal (attention) has been shown to improve neural language generation in a variety of settings.The weights applied when generating a particular output sequence have also been viewed as providing a potentially explanatory insight into the internal workings of the generator.In this paper, we reverse the direction of this connection and ask whether through the control of the attention of the model we can control its output.Specifically, we take a standard neural image captioning model that uses attention, and fix the attention to predetermined areas in the image.We evaluate whether the resulting output is more likely to mention the class of the object in that area than the normally generated caption.We introduce three effective methods to control the attention and find that these are producing expected results in up to 27.43% of the cases.
Modeling and simulation of agriculture production system considering seasonal variable information using big data analysis

The agricultural sector is required to produce a steady, year-round supply of crops to maintain the quality of life of consumers.The organization of agricultural production has, therefore, become increasingly important.This study aims to evaluate the agricultural production systems for storage shortages and quality loss while considering seasonal changes in crop growth.We first discuss the occurrence of storage shortages and quality loss.Next, we investigate the production and crop growth processes in fields used by an agricultural production corporation.We examined the statistical treatment of growth and the relationship between crop growth and the field environment using the data collected from field sensors.We then analyzed the data obtained in the observation.Based on the results of the analysis, we proposed a model for the work process in the field, which is a prediction algorithm for crop growth that considers seasonal changes.We then implemented the simulation model.We verified the accuracy of the proposed growth prediction algorithm through a case study and used the simulation to evaluate storage shortages and quality loss.
Cryptanalysis of Raindrop and FBC

This paper introduces the results of several different security analysis of two new block ciphers: Raindrop and FBC, which are the two candidates of block cipher designs submitted to the Chinese Cryptographic Algorithms Design Competition in 2019. Raindrop applies two-branch Feistel structure, while FBC block cipher is based on the four-way dual Feistel structure design. We give detailed security evaluation on Raindrop and FBC, using differential, linear, impossible difference and boomerang cryptanalysis approaches. For Raindrop, we achieved the results as follows: differential attack on 12-round Raindrop based on the 11-round distinguisher with the computational complexity of $$2^{62.41}$$ ; linear attack on 13-round Raindrop based on 12-round distinguisher with the computational complexity of $$2^{106.3}$$ ; impossible differential attack on 18-round Raindrop based on 12-round distinguisher with the computational complexity of $$2^{102.83}$$ ; and boomerang attack on 15-round Raindrop based on 14-round distinguisher with the computational complexity of $$2^{224.6}$$ . For FBC, results are as follows: differential attack on 12-round FBC based on 11-round distinguisher with the computational complexity of $$2^{93.41}$$ ; linear attack on 11-round FBC based on 10-round distinguisher with the computational complexity of $$2^{112.54}$$ ; impossible differential attack on 11-round FBC based on 7-round distinguisher with the computational complexity of $$2^{94.54}$$ ; and boomerang attack on 13-round FBC based on 12-round distinguisher with the computational complexity of $$2^{247.67}$$ . At present, the best records achieved are 18-round impossible differential attack for Raindrop-128-128 and 13-round boomerang attack for FBC128-256. The statistical distinguishers we built are similar to the proposals but we provide the concrete key recovery attacks in this study.
A Blockchain-Based IoT Data Management System for Secure and Scalable Data Sharing

At present, the centralized architecture for the Internet of Things (IoT) data management faces the risks of data leakage, single-point failure, and vulnerability to malicious attacks. Recently, many researchers and security analysts are focusing on the blockchain technology to solve the security and privacy issues of the IoT data management. However, current blockchain technologies are unable to meet the highly concurrent IoT data transactions and the fast expansion of IoT devices due to the decentralized architecture. Moreover, there is a lack of useful solutions for effective data sharing between public and private networks, and networks with different services (e.g., smart grid and intelligent transportation). In this paper, we propose a decentralized data access model for IoT data storage and sharing based on the FISCO-BCOS blockchain. In the model, we divide the IoT devices that handle different services into different set-chains. These set-chains share data with each other through an elaborate access control model. Experimental results show that the proposed model fits the characteristics of the IoT data management, guaranteeing the privacy and security requirements with better scalability. Our model can be used as a solution for smart cities.
Towards a Proactive MWE Terminological Platform for Cross-Lingual Mediation in the Age of Big Data

The emergence of China as a global economic power in the 21st Century has brought about surging needs for cross-lingual and cross-cultural mediation, typically performed by translators. Advances in Artificial Intelligence and Language Engineering have been bolstered by Machine learning and suitable Big Data cultivation. They have helped to meet some of the translator’s needs, though the technical specialists have not kept pace with the practical and expanding requirements in language mediation. One major technical and linguistic hurdle involves words outside the vocabulary of the translator or the lexical database he/she consults, especially Multi-Word Expressions (Compound Words) in technical subjects. A further problem is in the multiplicity of renditions of a term in the target language. This paper discusses a proactive approach following the successful extraction and application of sizable bilingual Multi-Word Expressions (Compound Words) for language mediation in technical subjects, which do not fall within the expertise of typical translators, who have inadequate appreciation of the range of new technical tools available to help him/her. Our approach draws on the personal reflections of translators and teachers of translation and is based on the prior R&D efforts relating to 300,000 comparable Chinese-English patents. The subsequent protocol we have developed aims to be proactive in meeting four identified practical challenges in technical translation (e.g. patents). It has broader economic implication in the Age of Big Data (Tsou et al, 2015) and Trade War, as the workload, if not, the challenges, increasingly cannot be met by currently available front-line translators. We shall demonstrate how new tools can be harnessed to spearhead the application of language technology not only in language mediation but also in the “teaching” and “learning” of translation. It shows how a better appreciation of their needs may enhance the contributions of the technical specialists, and thus enhance the resultant synergetic benefits.
A Strategy Using Continuous Simulation to Mitigate Effort Estimation Risks in Software Projects

The role of project managers has become crucial to the success of software development companies, it is imperative to understand customer requirements while address business needs. In this context, the present work aims to present simulation as a strategy to mitigate fails in software development effort estimation. In doing so, it will activate an outstanding project manager's asset for risk management. This study used historical data of 181 project from a software company and the simulation package ARENA R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> , to model and evaluation. Results point that the current method of project effort estimation - based on empirical feeling-based estimates of its employees - should be improved with the approach of continuous simulation. The modeling of this simulation provided a better understanding of efforts ranges applied in software developments on this company, and, most important, the amount of risk taken on each total effort estimative. This simulator attends the prerogatives of the project risk management area, being able to support decisions regarding schedule, resources bottlenecks and risk taken.
An Adaptive Hardware and Software Based Human Computer Interface for People with Motor Disabilities

This paper introduces IOM (an acronym for Interface Oculos Mouse, in Portuguese), an adaptive human-computer interface for people with motor disabilities in the upper limbs. The device, designed to be a low-cost hardware solution, consists of an eye frame glasses with integrated sensors responsible for detecting head movements and eyes blinks. IOM is accompanied by a user-friendly software interface that allows its users to adjust configuration options according to their needs and preferences. The goal of the interface is to enable the users to control the computer entirely. The paper presents the main components of both hardware architecture and software design that make up the developed prototype. At the end, the paper also reports the first evaluation process of the IOM device. Initial experiments performed using the AttrakDiff tool evaluate IOM, specifically from the end user's perspective, emphasizing desirable characteristics related to both hedonic and attractiveness dimensions. A comparison between some same purpose devices and the IOM using the Fitt’s Law is presented in the paper. The preliminary experimental results show that the proposed platform attends to the usability aspect to computer requisites, in special for people with disabilities.
Data Modeling and Aggregation

In Chapter 2 , we discussed MongoDB CRUD operations, embedded documents, and arrays. In this chapter, we cover the following topics.
An Encoder with non-Sequential Dependency for Neural Data-to-Text Generation

Data-to-text generation aims to generate descriptions given a structured input data (i.e., a table with multiple records).Existing neural methods for encoding input data can be divided into two categories: a) pooling based encoders which ignore dependencies between input records or b) recurrent encoders which model only sequential dependencies between input records.In our investigation, although the recurrent encoder generally outperforms the pooling based encoder by learning the sequential dependencies, it is sensitive to the order of the input records (i.e., performance decreases when injecting the random shuffling noise over input data).To overcome this problem, we propose to adopt the self-attention mechanism to learn dependencies between arbitrary input records.Experimental results show the proposed method achieves comparable results and remains stable under random shuffling over input data.
Unified Framework for Visual Domain Adaptation Using Globality-Locality Preserving Projections

Domain Adaptation is a segment of machine learning that allows us to learn from a labelled source data distribution to classify different but related unlabelled target data distribution. In this paper, we propose a novel framework called Unified Framework for Visual Domain Adaptation using Globality-Locality Preserving Projections (UFVDA) that reduces the divergence between source and target domain both statistically and geometrically. In this framework, we use Globality-Locality Preserving Projections (GLPP) instead of primitive methods such as Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) for dimensionality reduction and two projection vectors to project the source and the target domain data onto a common subspace. The better performance of our proposed framework than other state-of-the-art visual domain adaptation and the primitive dimensional reduction methods on real-world domain adaptation data-sets has been verified by extensive experiments. Our proposed approach UFVDA achieved a mean accuracy of 84.09% and 79.35% for all tasks of Office-Caltech data-set with VGG-Net features and PIE Face Recognition data-set respectively.
(When) inflection needs derivation: a word formation lexicon for Latin

The Word Formation Latin (WFL) project has been awarded a Marie Curie Individual Fellowship to create a language resource consisting of a derivational morphological lexicon of the Latin language, which connects lexical elements on the basis of word formation rules. In WFL, lexemes are segmented and analysed into their derivational morphological components, in order to establish relationships between them on the basis of derivational or compounding processes. This chapter illustrates the methodology lying behind WFL, its lexical basis and the choices taken in order to maintain consistency in the resource, when encountering a number of cases raising theoretical issues. The online graphical query system used to access WFL is described, as well as a few examples of linguistic investigations that are made easier through the use of such a resource.