Reviewerly/exHarmony huggingface dataset:
Scenario and Architecture for Intelligent Decision Support Based on User Digital Life

Human digital traces as a source of personal information are becoming increasingly popular in personalized systems. Such traces collected for a user organize the user digital life. The paper proposes a scenario of intelligent decision support based on user digital life and service-oriented architecture of a decision support system that provide recommendations based on context-sensitive user types. A user ontology represents the user types and infers a type of the user based on the information identified in the user’s decision-making activities contained in the digital life model of this user. Recommended decisions are proposed based on the information about the user preferences and the kinds of decisions that the users of the same type as the active user make. DSS behaviour is demonstrated by providing recommendation to a user that searches for a book.
The Impact of Instance Selection Algorithms on Maintenance Effort Estimation for Open-Source Software

Open-source software are very used nowadays in the industry, and the performance of the estimation of their maintenance effort becomes an interesting research topic. In this context, researchers have conducted many open-source software maintenance effort estimation (O-MEE) studies based on statistical and machine learning (ML) techniques for better estimation. This study focuses on the impact of instance selection on the performance of ML techniques in O-MEE, mainly for bug resolution. An empirical study was conducted using three techniques: K-nearest neighbor (kNN), support vector machine (SVM), and multinomial naïve Bayes (MNB) using all-kNN instance selection algorithms on three datasets: Eclipse JDT, Eclipse Platform, and Mozilla Thunderbird datasets. This study reports on a set of 18 experiments and a comparison of the results. The results of this study show that instance selection helped make ML techniques more performant.
FAIRification of Citizen Science Data Through Metadata-Driven Web API Development

Citizen Science (CS) implies a collaborative process to encourage citizens to collect data in CS projects and platforms. Unfortunately, these CS initiatives do not follow metadata nor data-sharing standards, which hampers their discoverability and reusability. To improve this scenario in CS is crucial to consider FAIR (Findability, Accessibility, Interoperability and Reusability) guidelines. Therefore, this paper defines a FAIRification process (i.e. make CS initiatives more FAIR compliant) which maps metadata of CS platforms’ catalogues to DCAT and generates Web Application Programming Interfaces (APIs) for improving CS data discoverability and reusability in an integrated approach. An experiment in a CS platform with different CS projects shows the performance and suitability of our FAIRification process. Specifically, the validation of the DCAT metadata generated by our FAIRification process was conducted through a SHACL standard validator, which emphasises how the process could boost CS projects to become more FAIR compliant.
$$D^3$$: A Novel Face Forgery Detector Based on Dual-Stream and Dual-Utilization Methods

The rapid development of computer vision and deep learning techniques make it easy for non-professionals to create realistic fake faces. As a result, a large number of fake faces are spread on the Internet. Therefore, accurately identifying fake faces is of great significance for protecting citizens’ legal rights and maintaining social stability. In this light, a novel face forgery detector named $$D^3$$ based on dual-stream and dual-utilization methods is proposed. Specifically, considering the subtle differences in the inter-frame correlation and edge details between real and fake faces, temporal stream and spatial stream are used comprehensively. In the temporal stream, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are introduced and trained to learn the inter-frame correlation. In the spatial stream, the Multi-Angle Filters (MAF) and CNN are introduced and trained to learn the edge details of the image. Moreover, to further improve the detection ability, the dual-utilization method is considered. That is to say, the relevant network model is used twice in each stream, the first time the initial network is trained to learn classification, and the second time the pre-trained network is used to extract features. Finally, the temporal and spatial features output by the two streams are spliced into spatio-temporal features, which are used to train a SVM-based classifier to achieve the final classification of true and fake faces. Compared with some state-of-the-art methods, the superiority of the proposed algorithm in face forgery detection is validated by related experiments.
Modeling IsA Relations via Box Structure for Knowledge Graph Embedding

Knowledge graph completion (KGC) aims to predict missing connections by mining information already present in a knowledge graph (KG). Predicting such connections is heavily dependent on the inference patterns. IsA relations (i.e., instanceOf and subclassOf) play an essential part in inferencing the composition pattern. Some existing methods already exploit isA relations. However, most of them learn insufficient representations, which may limit the performance. To address this issue, we propose a box-based knowledge graph embedding model called IBKE, in which concepts are embedded as boxes, and instances are represented by vectors in the same semantic space. According to the relative positions of elements, IBKE can naturally formulate isA relations. In addition, we introduce a random update strategy (RUS) for optimizing training, which updates embeddings in a probability pattern. Experimental results on benchmark datasets show that IBKE outperforms most existing state-of-the-art methods, and demonstrate the effectiveness of RUS.
Deep Sentiment Extraction using Fuzzy-Rule Based Deep Sentiment Analysis

In the world of social media, the amount of textual data is increasing exponentially on the internet, and a large portion of it expresses subjective opinions.Sentiment Analysis (SA) also named as Opinion mining, which is used to automatically identify and extract the subjective sentiments from text.In recent years, the research on sentiment analysis started taking off because of a huge of amount of data is available on the social media like twitter, machine learning algorithms popularity is increased in IR (Information Retrieval) and NLP (Natural Language Processing).In this work, we proposed three phase systems for sentiment classification in twitter tweets task of SemEval competition.The task is predicting the sentiment like negative, positive or neutral of a twitter tweets by analyzing the whole tweet.The first system used Artificial Bee Colony (ABC) optimization technique is used with Bag-of-words (BoW) technique in association with Naive Bayes (NB) and k-Nearest Neighbor (kNN) classification techniques with combination of various categories of features in identifying the sentiment for a given twitter tweet.The second system used to preserve the context a Rider Feedback Artificial Tree Optimization-enabled Deep Recurrent neural networks (RFATO-enabled Deep RNN) is developed for the efficient classification of sentiments into various grades.Further to improve the accuracy of classification on n-valued scale Adaptive Rider Feedback Artificial Tree (Adaptive RiFArT)-based Deep Neuro fuzzy network is devised for efficient sentiment grade classification.Finally, this research work proposed a Fuzzy-Rule Based Deep Sentiment Extraction (FBDSE) Algorithm with Deep Sentiment Score computation.Accuracy measure is considered to test the proposed systems performance.It was observed that the fuzzy-rule based system achieved good accuracy compared with machine learning and deep learning based approaches.
Enhanced Attention Framework for Multi-Interest Sequential Recommendation

Sequential recommendation tasks predict items to be interacted at the next moment according to users' historical behavior sequences.A large number of studies have shown that accuracy is not the only evaluation metric in the sequential recommendation tasks.Diversity can measure homogeneity of items to capture the changes of users' interests in the sequences.Integrating multiple interests of users has become the focus of current research.The existing multi-interest sequential recommendation methods adopt the method of self-attention, but it is based on the self-attention of transformer, which lacks the consideration of the correlation between different samples.Therefore, we propose an Enhanced Attention (EA) framework, which is based on two linear layers and two norm layers.Compared with self-attention, it not only reduces the high computational complexity, but also obtains the correlation between different samples.Multi-head mechanism is also applied to EA framework.We conduct experiments for the sequential recommendation on three real-world datasets, Amazon, Taobao and MovieLens-1M.The experimental results show that the EA framework is significantly improved compared with the current state-of-the-art models.
Definitional Quantifiers Realise Semantic Reasoning for Proof by Induction

Proof assistants offer tactics to apply proof by induction, but these tactics rely on inputs given by human engineers. To automate this laborious process, we developed SeLFiE, a boolean query language to represent experienced users’ knowledge on how to apply the induct tactic in Isabelle/HOL: when we apply an induction heuristic written in SeLFiE to an inductive problem and arguments to the induct tactic, the SeLFiE interpreter judges whether the arguments are plausible for that problem according to the heuristic by examining both the syntactic structure of the problem and definitions of the relevant constants. To examine the intricate interaction between syntactic analysis and analysis of constant definitions, we introduce definitional quantifiers. For evaluation we build an automatic induction prover using SeLFiE. Our evaluation based on 347 inductive problems shows that our new prover achieves $$1.4 \cdot {} 10^{3}\%$$ improvement over the corresponding baseline prover for 1.0 s of timeout and the median value of speedup is 4.48x.
Special issue: informatics &amp;amp; data-driven medicine-2021

Modern medical diagnosis, treatment, or rehabilitation problems of the patient reach completely different levels due to the rapid development of artificial intelligence tools. Methods of machine learning and optimization based on the intersection of historical data of various volumes provide significant support to physicians in the form of accurate and fast solutions of automated diagnostic systems. It significantly improves the quality of medical services. This special issue deals with the problems of medical diagnosis and prognosis in the case of short datasets. The problem is not new, but existing machine learning methods do not always demonstrate the adequacy of prediction or classification models, especially in the case of limited data to implement the training procedures. That is why the improvement of existing and development of new artificial intelligence tools that will be able to solve it effectively is an urgent task. The special issue contains the latest achievements in medical diagnostics based on the processing of small numerical and image-based datasets. Described methods have a strong theoretical basis, and numerous experimental studies confirm the high efficiency of their application in various applied fields of Medicine.
Writing Machine Learning Code More Productively

Data scientists often come from a background quite far removed from traditional computer science/software engineering, such as physics, biology, statistics, economics, and electrical engineering. Unfortunately, there are not a lot of tutorials geared towards data scientists and machine learning practitioners who do not come from a software engineering background. Writing high-quality, production-level code is a skill to be learned and honed over a lifetime. It’s the bread and butter of software engineers and developers. Not all data scientists will have the motivation and drive to acquire these skills. However, some simple good practices can be learned and applied in your everyday work. This chapter will take you through that journey with some hands-on examples.
Usability and User Experience

Usability tests are a means to evaluate whether a product is usable and how the users experience its use. But what are usability and user experience? In this chapter, we define these two concepts. The definitions are adopted from the International Organization for Standardization and have, thus, gone through a process of proposal, comments, and refinement among experts in the field. In spite of this consensus process, alternative conceptualizations abound. To contextualize the definitions of usability and user experience, we discuss some of these alternative conceptualizations.
The effect of epigenetic blocking on dynamic multi-objective optimisation problems

Hundreds of Evolutionary Computation approaches have been reported. From an evolutionary perspective they focus on two fundamental mechanisms: cultural inheritance in Swarm Intelligence and genetic inheritance in Evolutionary Algorithms. Contemporary evolutionary biology looks beyond genetic inheritance, proposing an "Extended Evolutionary Synthesis". Many concepts from the Extended Evolutionary Synthesis have been left out of Evolutionary Computation as interest has moved towards specific implementations of the same general mechanisms. One such concept is epigenetic inheritance, which is increasingly considered central to evolutionary thinking. Epigenetic mechanisms allow quick non- or partially-genetic adaptations to environmental changes. Dynamic multi-objective optimisation problems represent similar circumstances to the natural world where fitness can be determined by multiple objectives (traits), and the environment is constantly changing.
Black-box adversarial attack via overlapped shapes

Deep neural networks (DNNs) have achieved state-of-the-art performance in many tasks but have shown extreme vulnerabilities to attacks generated by adversarial examples. Many works assume an attacker has total access to the targeted model. A realistic assumption is that an attacker has access to the targeted model only by querying some input and observing its predicted class probabilities. In this paper we propose a concept of applying techniques similar to those used within evolutionary-art to generated adversarial images.
Geodesics, non-linearities and the archive of novelty search

The Novelty Search (NS) algorithm was proposed more than a decade ago. However, the mechanisms behind its empirical success are still not well formalized/understood. This short note focuses on the effects of the archive on exploration. Experimental evidence from a few application domains suggests that archive-based NS performs in general better than when Novelty is solely computed with respect to the population. An argument that is often encountered in the literature is that the archive prevents exploration from backtracking or cycling, i.e. from revisiting previously encountered areas in the behavior space. We argue that this is not a complete or accurate explanation as backtracking - beside often being desirable - can actually be enabled by the archive. Through low-dimensional/analytical examples, we show that a key effect of the archive is that it counterbalances the exploration biases that result, among other factors, from the use of inadequate behavior metrics and the non-linearities of the behavior mapping. Our observations seem to hint that attributing a more active role to the archive in sampling can be beneficial.
ANALYSIS OF THE RESULTS ACHIEVED BY DIFFERENT FORMS OF TEACHING COMPUTER SCIENCE AT SECONDARY SCHOOLS

ANALYSIS OF THE RESULTS ACHIEVED BY DIFFERENT FORMS OF TEACHING COMPUTER SCIENCE AT SECONDARY SCHOOLS
Robust 3D Object Detection in Cold Weather Conditions

Adverse weather conditions can negatively affect LiDAR-based object detectors. In this work, we focus on the phenomenon of vehicle gas exhaust condensation in cold weather conditions. This everyday effect can influence the estimation of object sizes, orientations and introduce ghost object detections, compromising the reliability of the state of the art object detectors. We propose to solve this problem by using data augmentation and a novel training loss term. To effectively train deep neural networks, a large set of labeled data is needed. In case of adverse weather conditions, this process can be extremely laborious and expensive. We address this issue in two steps: First, we present a gas exhaust data generation method based on 3D surface reconstruction and sampling which allows us to generate large sets of gas exhaust clouds from a small pool of labeled data. Second, we introduce a point cloud augmentation process that can be used to add gas exhaust to datasets recorded in good weather conditions. Finally, we formulate a new training loss term that leverages the augmented point cloud to increase object detection robustness by penalizing predictions that include noise. In contrast to other works, our method can be used with both grid-based and point-based detectors. Moreover, since our approach does not require any network architecture changes, inference times remain unchanged. Experimental results on real data show that our proposed method greatly increases robustness to gas exhaust and noisy data.
Report on the First DES Fixed Points for Non-Weak Keys: Case-Study of Hacking an IoT Environment

Fixed points can be defined as <i>E<sub>K</sub></i>(<i>P</i>) = <i>P</i> for a key <i>K</i> and plaintext <i>P</i> where <i>E</i> is the encryption function. For the Data Encryption Standard (DES) algorithm there are four weak keys, such that <i>E<sub>K</sub></i>(<i>E<sub>K</sub></i>(<i>P</i>)) = <i>P</i> for all <i>P</i>. For each weak key there are 2<sup>32</sup> fixed points where all the sixteen round keys are the same. While there is a high probability that DES has fixed points for non-weak keys, nobody has reported such (<i>P,K</i>) pairs until now. Performing 1000 &#x00D7; 2<sup>56</sup> DES encryption operations using 256 Xlinix Spartan-6 parallel cost-effective FPGAs we answer the question of Judy H. Moore and Gustavus J. Simmons: we report the first known DES fixed points where all round keys are different. A case study is presented where an IoT based Sensor Device Authentication Algorithm (<i>SDAA</i>) can be exploited using DES fixed points.
Shoot360: Normal View Video Creation from City Panorama Footage

We present Shoot360, a system that efficiently generates multi-shot normal view videos with desired content presentation and various cinematic styles, given a collection of 360 video recordings on different environments. The core of our system is a three-step decision process: 1) It firstly semantically analyzes the contents of interest from each panorama environment based on shot units, and produces a guidance that specifies the semantic focus and movement type of its output shot according to the user specification on content presentation and cinematic styles. 2) Based on the obtained guidance, it generates video candidates for each shot with shot-level control parameters for view projections following the filming rules. 3) The system further aggregates the projected normal view shots with the imposed local and global constraints, which incorporates the external knowledge learned from exemplar videos and professional filming rules. Extensive experiments verify the effectiveness of our system design, and we conclude with promising extensions for applying it to more generalized scenarios.
Comparing Two Samples Through Stochastic Dominance: A Graphical Approach

Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples in which unpredictable outcomes are common.These measures can be modeled as random variables and compared among each other via their expected values or more sophisticated tools such as null hypothesis statistical tests.In this paper, we propose an alternative framework to visually compare two samples according to their estimated cumulative distribution functions.First, we introduce a dominance measure for two random variables that quantifies the proportion in which the cumulative distribution function of one of the random variables stochastically dominates the other one.Then, we present a graphical method that decomposes in quantiles i) the proposed dominance measure and ii) the probability that one of the random variables takes lower values than the other.With illustrative purposes, we re-evaluate the experimentation of an already published work with the proposed methodology and we show that additional conclusions-missed by the rest of the methods-can be inferred.Additionally, the software package RVCompare was created as a convenient way of applying and experimenting with the proposed framework.
APPLICATION OF NATURE-BASED SOLUTIONS AND USE OF MONITORING AIR&amp;WATER POLLUTION SENSORS THROUGH LOW-COST MINI-PORTABLE MEASUREMENT STATIONS TO PROMOTE ENVIRONMENTAL AWARENESS IN SECONDARY EDUCATION

APPLICATION OF NATURE-BASED SOLUTIONS AND USE OF MONITORING AIR&WATER POLLUTION SENSORS THROUGH LOW-COST MINI-PORTABLE MEASUREMENT STATIONS TO PROMOTE ENVIRONMENTAL AWARENESS IN SECONDARY EDUCATION
Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption

A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.94% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.
Messages from Within

In the book’s final chapter, “Messages from Within,” Serenella Iovino analyzes the work of the Italian writer Primo Levi, particularly the short story “Man’s Friend,” which imagines a genetic poetics produced by a tapeworm infestation. Iovino then uses the story to explore the emergent field of biosemiotics. The chapter traces a genealogy of biosemiotics in the work of Charles Sanders Peirce, Jakob von Uexküll, Denis Noble, and Wendy Wheeler (to whom the chapter is dedicated). Iovino also connects Levi’s writing to his experience as a prisoner in a Nazi concentration camp – a dehumanizing experience that deprived him of his freedom as well as his ability to communicate. Framing the chapter within reflections on the novel coronavirus pandemic, Iovino posits semiotic freedom as a fundamental imperative of all life.