Reviewerly/exHarmony huggingface dataset:
Social Recommender Systems

Due to the incredible growth of information on the World Wide Web in the recent years, searching and finding contents, products or services that may be of interest for users has become a very difficult task. Recommender systems (RSs) help overcome the information overload problem by studying the preferences of online users and suggesting items they might like. Many companies and Web sites have implemented these systems to recommend products/information/services to their users in a more accurate manner, therefore improving the company’s profits. In this chapter, first we give a brief review on social recommender systems and then we introduce sevela learning automata-based recommended systems.
With a Little Help from My Friends (and Their Friends): Influence Neighborhoods for Social Recommendations

Social recommendations have been a very intriguing domain for researchers in the past decade. The main premise is that the social network of a user can be leveraged to enhance the rating-based recommendation process. This has been achieved in various ways, and under different assumptions about the network characteristics, structure, and availability of other information (such as trust, content, etc.) In this work, we create neighborhoods of influence leveraging only the social graph structure. These are in turn introduced in the recommendation process both as a pre-processing step and as a social regularization factor of the matrix factorization algorithm. Our experimental evaluation using real-life datasets demonstrates the effectiveness of the proposed technique.
An improved multi-branch residual network based on random multiplier and adaptive cosine learning rate method

Deep residual networks have emerged as a leading technique showing the accuracy and excellent convergence performance. However, because of overfitting and the vanishing gradient problems, we cannot achieve better results by increasing the model parameters. In this study, we propose a novel architecture that has a multi-branch residual network. The shortcut branch of residual networks can ease off the vanishing gradient. The random function and the structure of a multi-branch network improve the fitting ability. The dropout function can weaken overfitting. The proposed method also adopts the adaptive cosine learning rate method and variate batch size to improve the test accuracy. Some experimental investigations are set up to explore the impact of the following factors on the performance: the sequence of the layers, random numbers and different batch sizes, etc. We report that the results achieve 2.63% and 14.2% Top-1 error on CIFAR-10 and CIFAR-100.
A Case Study of Research Improvements in an Service Industry Upgrading the Knowledge Base of the Information System and the Process Management: Data Flow Automation, Association Rules and Data Mining

In this paper is analyzed a case study of an upgrade of an industry communication system developed by following 'Frascati' research guidelines.The goal of the proposed model is to enhance the industry knowledge Base -KB-by acting directly on information communication system improvements and data system integration, enabling automated process and data processing.The paper follow all the steps performed during the project development: the preliminary data infrastructure design, the information infrastructure improvements, and data processing.Data processing is performed by a calculus engine embedding data mining association rules and Artificial Neural Network -ANN-predictive algorithms thus improving the research.The calculus engine has been implemented by a multiple variables model where the contract data are preliminary processed in order to define functions classifying the operation processes and activating automatically the service process management.The business intelligence -BI-operations are performed mainly by the calculus engine optimizing industry performances.The goal of the paper is to show how research and development -R&D-can be applied by gaining and optimizing the knowledge and processes of an Italian industry working in car services.The project has been developed with the collaboration of the industry ACI Global working in roadside assistance services.By means of a research project resources, the information technology -IT-infrastructure has been improved by new solutions of the communication system and of the data transfer.The proposed case of study provides a model and a guideline to follow in order to apply research in industry acting directly on data and information network.
Data Analytics: Intelligent Anti-Phishing Techniques Based on Machine Learning

According to the international body Anti-Phishing Work Group (APWG), phishing activities have skyrocketed in the last few years and more online users are becoming susceptible to phishing attacks and scams. While many online users are vulnerable and naive to the phishing attacks, playing catch-up to the phishers’ evolving strategies is not an option. Machine Learning techniques play a significant role in developing effective anti-phishing models. This paper looks at phishing as a classification problem and outlines some of the recent intelligent machine learning techniques (associative classifications, dynamic self-structuring neural network, dynamic rule-induction, etc.) in the literature that is used as anti-phishing models. The purpose of this review is to serve researchers, organisations’ managers, computer security experts, lecturers, and students who are interested in understanding phishing and its corresponding intelligent solutions. This will equip individuals with knowledge and skills that may prevent phishing on a wider context within the community.
Linking Stam’s Bounds with Generalized Truncation

One of the most prominent PRP-to-PRF designs is truncation, a method that found renewed interest with the GCM-SIV authenticated encryption scheme. A long line of research (from 1998 to 2018) shows that truncating an n-bit random permutation to m bits achieves tight $$n-m/2$$ security. However, it appeared that the result was a direct consequence of a statistical result of Stam from 1978. In this work, we aim to gain better understanding in the possibilities and impossibilities of truncation. We take a closer look at the ancient result, observe that it is much more general, and link it with a generalized truncation function that uses an arbitrary post-processing function after the evaluation of the permutation. The main conclusion is that generalized truncation with any balanced post-processing achieves the same security bound as plain truncation. For unbalanced post-processing, security degrades gradually with the amount of unbalancedness. The results in particular exhibit a use of the Kullback-Leibler divergence for cryptographic indistinguishability proofs, without resorting to the recently popularized chi-squared method.
Video Object Segmentation with Referring Expressions

Most semi-supervised video object segmentation methods rely on a pixel-accurate mask of a target object provided for the first video frame. However, obtaining a detailed mask is expensive and time-consuming. In this work we explore a more practical and natural way of identifying a target object by employing language referring expressions. Leveraging recent advances of language grounding models designed for images, we propose an approach to extend them to video data, ensuring temporally coherent predictions. To evaluate our approach we augment the popular video object segmentation benchmarks, $$\text {DAVIS}_{\text {16}}$$ and $$\text {DAVIS}_{\text {17}}$$ , with language descriptions of target objects. We show that our approach performs on par with the methods which have access to the object mask on $$\text {DAVIS}_{\text {16}}$$ and is competitive to methods using scribbles on challenging $$\text {DAVIS}_{\text {17}}$$ .
Pruning of genetic programming trees using permutation tests

Abstract We present a novel approach based on statistical permutation tests for pruning redundant subtrees from genetic programming (GP) trees that allows us to explore the extent of effective redundancy . We observe that over a range of regression problems, median tree sizes are reduced by around 20% largely independent of test function, and that while some large subtrees are removed, the median pruned subtree comprises just three nodes; most take the form of an exact algebraic simplification. Our statistically-based pruning technique has allowed us to explore the hypothesis that a given subtree can be replaced with a constant if this substitution results in no statistical change to the behavior of the parent tree—what we term approximate simplification. In the eventuality, we infer that more than 95% of the accepted pruning proposals are the result of algebraic simplifications, which provides some practical insight into the scope of removing redundancies in GP trees.
Discovery of Multimodal Sensor Data Through Webpage Exploration

Technological advances allow perceptual physical objects to be connected to the Internet and share their information over webpages. As a predominant source of public sensor data, automatic discovery of these data is critical and desirable for general Internet of Things search service, which is fundamental to many intelligent applications. However, discovering sensor data from webpages is quite challenging, since there are diverse data presentation modes, and webpage layouts and structures are complicated and heterogeneous. To this end, we explore webpages to discover and collect sensor data under a hierarchical mechanism. In this paper, we first devise novel textual features (TFs) to recognize potential webpages that may contain sensor data; specifically, we construct sensing information corpus to provide keyword reference for the features. Then to get the position of sensor data, we develop granularity adaptive page segmentation (GAPS) algorithm to segment potential webpages into a set of informative blocks; and accordingly, we extract several visual features (VFs) of the blocks so that sensor data can be identified via a block classifier. Based on the novel created sensing information pages dataset, which consists of webpages and manual markings about sensing data, extensive experiments are conducted to evaluate the performance of our exploration methods. Results demonstrate that the TFs achieve supreme performance in sensing data recognition when compared to the state-of-the-art approaches, and GAPS is efficient to locate multimodal sensor data in cooperation with the VFs.
Deep Learning for Automated Tagging of Fashion Images

We present 9 deep learning classifiers to predict Fashion attributes in 4 different categories: apparel (dresses and tops), shoes, watches and luggages. Our prediction system hosts several classifiers working at scale to populate a catalogue of millions of products. We provide details of our models as well as the challenges involved in predicting Fashion attributes in a relatively homogeneous problem space.
Discovering Latent Topics by Gaussian Latent Dirichlet Allocation and Spectral Clustering

Today, diversifying the retrieval results of a certain query will improve customers’ search efficiency. Showing the multiple aspects of information provides users an overview of the object, which helps them fast target their demands. To discover aspects, research focuses on generating image clusters from initially retrieved results. As an effective approach, latent Dirichlet allocation (LDA) has been proved to have good performance on discovering high-level topics. However, traditional LDA is designed to process textual words, and it needs the input as discrete data. When we apply this algorithm to process continuous visual images, a common solution is to quantize the continuous features into discrete form by a bag-of-visual-words algorithm. During this process, quantization error will lead to information that inevitably is lost. To construct a topic model with complete visual information, this work applies Gaussian latent Dirichlet allocation (GLDA) on the diversity issue of image retrieval. In this model, traditional multinomial distribution is substituted with Gaussian distribution to model continuous visual features. In addition, we propose a two-phase spectral clustering strategy, called dual spectral clustering , to generate clusters from region level to image level. The experiments on the challenging landmarks of the DIV400 database show that our proposal improves relevance and diversity by about 10% compared to traditional topic models.
Renal structural image processing techniques: a systematic review

Renal disease, such as nephritis and nephropathy, is very harmful to human health. Accordingly, how to achieve early diagnosis and enhance treatment for kidney disorders would be the important lesion. Nevertheless, the clues from the clinical data, such as biochemistry examination, serological examination, and radiological studies are quite indirect and limited. It is no doubt that pathological examination of kidney will supply the direct evidence. There is a requirement for greater understanding of image processing techniques for renal diagnosis to optimize treatment and patient care.This study aims to systematically review the literature on publications that has been used image processing methods on pathological microscopic image for renal diagnosis.Nine included studies revealed image analysis techniques for the diagnosis of renal abnormalities on pathological microscopic image, renal image studies are clustered as follows: Glomeruli Segmentation and analysis of the Glomerular basement membrane (55/55%), Blood vessels and tubules classification and detection (22/22%) and The Grading of renal cell carcinomas (22/22%).A medical image analysis method should provide an auto-adaptive and no external-human action dependency. In addition, since medical systems should have special characteristics such as high accuracy and reliability then clinical validation is highly recommended. New high-quality studies based on Moore neighborhood contour tracking method for glomeruli segmentation and using powerful texture analysis techniques such as the local binary pattern are recommended.
A word-building method based on neural network for text classification

Text classification is a foundational task in many natural language processing applications. All traditional text classifiers take words as the basic units and conduct the pre-training process (like word2vec) to directly generate word vectors at the first step. However, none of them have considered the information contained in word structure which is proved to be helpful for text classification. In this paper, we propose a word-building method based on neural network model that can decompose a Chinese word to a sequence of radicals and learn structure information from these radical level features which is a key difference from the existing models. Then, the convolutional neural network is applied to extract structure information of words from radical sequence to generate a word vector, and the long short-term memory is applied to generate the sentence vector for the prediction purpose. The experimental results show that our model outperforms other existing models on Chinese dataset. Our model is also applicable to English as well where an English word can be decomposed down to character level, which demonstrates the excellent generalisation ability of our model. The experimental results have proved that our model also outperforms others on English dataset.
HTN-like solutions for classical planning problems: An application to BDI agent systems

In this paper we explore the question of what characterises a desirable plan of action and how such a plan could be computed, in the context of systems that already possess a certain amount of hierarchical domain knowledge. In contrast to past work in this setting, which focuses on generating low-level plans, losing much of the domain knowledge inherent in such systems, we argue that plans ought to be HTN-like or abstract , i.e., re-use and respect the user-supplied know-how in the underlying domain. In doing so, we recognise an intrinsic tension between striving for abstract plans but ensuring that unnecessary actions, not linked to the specific goal to be achieved, are avoided. We explore this tension by characterising the set of “ideal” abstract plans that are non-redundant but maximally abstract, and then develop a more limited yet feasible account in which a given (arbitrary) abstract plan is “specialised” into one such non-redundant plan that is as abstract as possible. We present an algorithm that can compute such specialisations, and analyse the theoretical properties of our proposal.
An efficient 3D face recognition approach using Frenet feature of iso-geodesic curves

Extracting efficient features from the large volume of 3D facial data directly is extremely difficult in 3D face recognition (3D-FR) with the latest methods, which mostly require heavy computations and manual processing steps. This paper presents a computationally efficient 3D-FR system based on a novel Frenet frame-based feature that is derived from the 3D facial iso-geodesic curves. In terms of the evaluation of the proposed method, we conducted a number of experiments on the CASIA 3D face database, and a superior recognition performance has been achieved. The performance evaluation suggests that the pose invariance attribute of the features relieves the need of an expensive 3D face registration in the face preprocessing procedure, where we take less time to process conversely. Our experiments further demonstrate that the proposed method not only achieves competitive recognition performance when compared with some existing techniques for 3D-FR, but also is computationally efficient.
Behavioural threshold analysis: methodological and practical considerations for applications in information security

The application of behavioural threshold analysis to analyse group behaviour in information security presents a unique challenge in terms of the measurement instruments and methodology used to gather relevant attitude data. This paper presents an analysis of the specialised requirements for such a measurement instrument and makes methodological recommendations on the content and especially presentation of information security topics in a measurement instrument for this context. A comparison between existing methods and the specific requirements for threshold analysis is presented and serves as the main rationale for the suggested methodology. The recommended methodology and subsequent measurement instrument were implemented and experimentally tested in case studies to gauge their feasibility. Applications of behavioural threshold analysis in information security that follow the recommended methodology suggested in this article performed satisfactorily and elicits cause for further real-world experimentation.
Knowledge-Enhanced Ensemble Learning for Word Embeddings

Representing words as embeddings in a continuous vector space has been proven to be successful in improving the performance in many natural language processing (NLP) tasks. Beyond the traditional methods that learn the embeddings from large text corpora, ensemble methods have been proposed to leverage the merits from pre-trained word embeddings as well as external semantic sources. In this paper, we propose a knowledge-enhanced ensemble method to combine both knowledge graphs and pre-trained word embedding models. Specifically, we interpret relations in knowledge graphs as linear translation from one word to another. We also propose a novel weighting scheme to further distinguish edges in the knowledge graph with same type of relation. Extensive experiments demonstrate that our proposed method is up to 20% times better than state-of-the-art in word analogy task and up to 16% times better than state-of-the-art in word similarity task.
Generation of entanglement between two three-level atoms interacting with a time-dependent damping field

In this article, the interaction between two Λ-type three-level atoms and a single-mode cavity field is discussed. The damping parameter is taken into account. Also, the field and the atoms are assumed to be coupled with modulated coupling parameter which depends explicitly on time. Even though the system seems complicated, the analytical form of the wave function and the probability amplitudes are exactly obtained. To comprehend the degree of entanglement between subsystems (field-atom and atom-atom), the dynamics of entanglement through various measures, namely, concurrence and negativity is evaluated. Also, the Mandel parameter and normal squeezing of the resultant state have been calculated. In each case, the influences of damping parameter on the above criteria are numerically analyzed, in detail. It is illustrated that the degree of entanglement can be generated by taking the evolved parameters, appropriately.
Fast conversion of Dicke states <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mrow><mml:mo>|</mml:mo></mml:mrow><mml:msubsup><mml:mi>D</mml:mi><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mn>2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msubsup><mml:mrow><mml:mo>〉</mml:mo></mml:mrow></mml:mrow></mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mrow><mml:mo>|</mml:mo></mml:mrow><mml:msubsup><mml:mi>D</mml:mi…

A scheme is proposed for fast conversion of two-atom Dicke states to three-atom Dickes state and three-atom Dicke states to four-atom Dicke states based on transitionless quantum driving. This scheme can be achieved within one step through appropriately regulating the Rabi frequencies of classical fields. The effects of decoherence induced by atomic spontaneous emission and leakage of the cavity are also taken into account. The numerical simulation result shows that this scheme is robust against both atomic spontaneous emission and cavity leakage. Furthermore, when multiple atoms are trapped in a cavity, the ($n+1$)-atom Dicke states can be derived directly from the $n$-atom Dicke states with the same principle, which provides a scalable way for the preparation of Dicke states in theory.
CV Retrieval System based on job description matching using hybrid word embeddings

We use the Average Word Embeddings (AWE) model for retrieving relevant CVs based on a job description. We designed experiments to demonstrate that the trained vectors, obtained from a balanced domain corpus, are better than using pre-trained word embeddings. We also present some experiments to show that different combinations of both word embeddings spaces increase the overall accuracy of the retrieval task compared to using only the pre-trained vectors. However an issue arised when both embeddings spaces are not sharing the same dimensions and terms, as shown in our case. In order to handle this situation, we suggest to use a method to reduce dimensions of pre-trained vectors (e.g. PCA), and combine them with our trained vectors. This improves the accuracy of the retrieval task for unseen CVs. Our main contribution is to create a model that detects which embeddings need to be used in order to maximize the relevant retrieval results.