Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of Advice Solicitation and Social Peers in an Energy Recommender System

In face-to-face interactions, advice acceptance depends on how it is presented, as well as a number of social factors. For example, some persons are inclined to accept advice from an expert if they possess little domain knowledge. In contrast, if such advice is unsolicited, persons might only accept advice from a trusted source, such as a family member. Whether these mechanisms also play a role in the recommender context is unknown, even though advice solicitation may be particularly important in domains where a recommender user seeks behavioral change (e.g. energy conservation, healthy eating). This study examines the role of advice solicitation (i.e. whether one asks for advice or simply receives it) and advice source (i.e. either explained in social terms or not) in our 'Saving Aid' energy recommender system. Through a web-based user study with 252 participants, we find that allowing users to solicit advice themselves increases their perceived level of trust with our energy recommender system, compared to users that are presented unsolicited advice. In turn, we find that trust positively affects user satisfaction levels, as well as the number of chosen energy-saving measures. We discuss how system designers should consider how advice is presented and in which context.
Ghost routers: energy-efficient asymmetric multicore processors with symmetric NoCs

Asymmetric multicore architectures have been proposed to exploit the benefits of heterogeneous cores. However, asymmetric cores present challenge to network-on-chip (NoC) designers since the floorplan is not necessarily regular with "nodes" being different size. In contrast, most of the previously proposed NoC topologies commonly assume a regular or symmetric floorplan with equal size nodes. In this work, we first describe how asymmetric floorplan leads to asymmetric topology and can limit overall performance. To overcome the asymmetric floorplan, we present Ghost Routers - extra "dummy" routers that are added to the NoC to create a symmetric NoC architecture for asymmetric multicore architectures. Ghost router provides higher network path diversity and provides higher network performance that leads to higher system performance. Ghost routers also enable simpler routing algorithms because of the symmetric NoC architecture. While ghost routers is a simplistic modification to the NoC architecture, it does increase NoC cost. However, ghost routers exploit the observations that in realistic systems, the cost of NoC is not a significant fraction of overall system cost. Our evaluations show that ghost routers can improve performance by up to 21% while improving overall energy-efficiency of the system by up to 26%.
PLFCN: Pyramid Loss Reinforced Fully Convolutional Network

In the field of remote sensing, the semantic segmentation network for orthophotos has received widely attention. However, it is usually impossible to achieve high accuracy and high efficiency at the same time. In this paper, we propose a novel pyramid loss reinforced fully convolutional network (PLFCN) to address this issue. By introducing deep pyramid supervisions, the network explores multi-scale spatial context information to improve performance of semantic segmentation. And the auxiliary pyramid loss structure can be ignored during testing, so that the network can inference as fast as FCN. The main contributions of this paper are as follows: 1) auxiliary pyramid loss structure is proposed to enhance the performance of FCN by multiscale and deep supervisions; 2) the advantages of multi scale structures and auxiliary loss is combined to improve the performance and maintain the efficiency at the same time. The results show that the semantic segmentation performance is significantly improved, while achieves the high effeciency as FCN.
Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems

The effectiveness of recommender system algorithms varies in different real-world scenarios. It is difficult to choose a best algorithm for a scenario due to the quantity of algorithms available, and because of their varying performances. Furthermore, it is not possible to choose one single algorithm that will work optimally for all recommendation requests. We apply meta-learning to this problem of algorithm selection for scholarly article recommendation. We train a random forest, gradient boosting machine, and generalized linear model, to predict a best-algorithm from a pool of content similarity-based algorithms. We evaluate our approach on an offline dataset for scholarly article recommendation and attempt to predict the best algorithm per-instance. The best meta-learning model achieved an average increase in F1 of 88% when compared to the average F1 of all base-algorithms (F1; 0.0708 vs 0.0376) and was significantly able to correctly select each base-algorithm (Paired t-test; p < 0.1). The meta-learner had a 3% higher F1 when compared to the single-best base-algorithm (F1; 0.0739 vs 0.0717). We further perform an online evaluation of our approach, conducting an A/B test through our recommender-as-a-service platform Mr. DLib. We deliver 148K recommendations to users between January and March 2019. User engagement was significantly increased for recommendations generated using our meta-learning approach when compared to a random selection of algorithm (Click-through rate (CTR); 0.51% vs. 0.44%, Chi-Squared test; p < 0.1), however our approach did not produce a higher CTR than the best algorithm alone (CTR; MoreLikeThis (Title): 0.58%).
Review-Based Cross-Domain Collaborative Filtering: A Neural Framework

Cross-domain collaborative filtering recommenders exploit data from other domains (e.g., movie ratings) to predict users’ interests in a different target domain (e.g., suggest music). Most current crossdomain recommenders focus on modeling user ratings but pay limited attention to user reviews. Additionally, due to the complexity of these recommender systems, they cannot provide any information to users to support user decisions. To address these challenges, we propose Deep Hybrid Cross Domain (DHCD)model, a cross-domain neural framework, that can simultaneously predict user ratings, and provide useful information to strengthen the suggestions and support user decision across multiple domains. Specifically, DHCD enhances the predicted ratings by jointly modeling two crucial facets of users’ product assessment: ratings and reviews. To support decisions, it models and provides natural review-like sentences across domains according to user interests and item features. This model is robust in integrating user rating and review information from more than two domains. Our extensive experiments show that DHCD can significantly outperform advanced baselines in rating predictions and review generation tasks. For rating prediction tasks, it outperforms cross-domain and single-domain collaborative filtering as well as hybrid recommender systems. Furthermore, our review generation experiments suggest an improved perplexity score and transfer of review information in DHCD.
Smartphone-Based Point-of-Care Cholesterol Blood Test Performance Evaluation Compared with a Clinical Diagnostic Laboratory Method

Managing blood cholesterol levels is important for the treatment and prevention of diabetes, cardiovascular disease, and obesity. An easy-to-use, portable cholesterol blood test could accelerate more frequent testing by patients and at-risk populations. We aim to evaluate the performance of smartphone-based point-of-care cholesterol blood tests compared to that of hospital-grade laboratory tests. We used smartphone systems that are already familiar to many people. Because smartphone systems can be carried around everywhere, blood can be measured easily and frequently. We compared the results of cholesterol tests with those of existing clinical diagnostic laboratory methods. We found that smartphone-based point-of-care lipid blood tests were as accurate as hospital-grade laboratory tests (N = 116, R > 0.97, p < 0.001 for all three cholesterol blood tests, i.e., total cholesterol, high density lipoprotein, and triglyceride). Our system could be useful for those who need to manage blood cholesterol levels to motivate them to track and control their behavior.
Multi-Criteria Knowledge-Based Recommender System for Decision Support in Complex Business Processes

In this paper, we present a concept of a multi-criteria knowledgebased Recommender System (RS) designed to provide decision support in complex business process (BP) scenarios. The developed approach is based on the knowledge aspects of Stylistic Patterns, Business Sentiment and Decision-Making Logic extracted from the BP unstructured texts. This knowledge serves as an input for a multi-criteria RS algorithm. The output is prediction of the BP complexity, based on which the algorithm modifies the type and the way of decision support, ranging from full to minimal automation. We show how the algorithm can be applied in the real-life scenarios by the example of the IT ticketing case study. We also evaluate the BP complexity prediction quality using both quantitative (databased) and qualitative (interview-based) approach in the case study.
A survey on FPGA-based high-resolution TDCs

Time-to-digital converters based on Nutt method are especially suitable for FPGA implementation. They are able to provide high resolution, range and linearity with low resources usage. The core of this architecture consist in a coarse counter for long range, a fine time interpolator for high resolution and real-time calibration for high linearity. This paper reviews different time interpolation and real-time calibration techniques. Moreover, a comparison of state-of-the-art FPGA-based TDCs is presented as well.
Adapted Psychological Profiling Verses the Right to an Explainable Decision

Copyright 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. Adaptive Psychological Profiling is the process of determining a person’s internal mental state through the analysis of a person’s non-verbal behaviour. Silent Talker is a pioneering psychological profiling system which was developed by experts in behavioural neuroscience and computational intelligence. Designed for use in natural conversation, Silent Talker combines image processing and artificial intelligence to classify multiple visible non-verbal signals of the face during verbal communication to produce an accurate and comprehensive time-based profile of a subject’s psychological state. Silent Talker uses a unique configuration of artificial neural networks, hence, it is difficult to understand how the classification of a person’s behaviour is obtained. New legislation in the form of GDPR, now requires individuals whom are automatically profiled, to have the right to an explanation of how the “machine” reached its decision and receive meaningful information on the logic involved. This is difficult in practice, both from a technical and legal point of view. This paper, uses an application of psychological profiling within a pilot system known as iBorderCtrl, which detects deception through an avatar border guard interview during a travellers pre-registration to demonstrate the challenges faced in trying to obtain explainable decisions from models derived through Computational Intelligence techniques.
Semantic interpretability of latent factors for recommendation

Model-based approaches to recommendation have proven to be very accurate. Unfortunately, exploiting a latent space we miss references to the actual semantics of recommended items. In this extended abstract, we show how to initialize latent factors in Factorization Machines by using semantic features coming from a knowledge graph in order to train an interpretable model. Finally, we introduce and evaluate the semantic accuracy and robustness for the knowledge-aware interpretability of the model.
Quantum-inspired Multimodal Representation

We introduce our work in progress that targets on building multimodal representation under quantum inspiration. The challenge for multimodal representation falls on a fusion strategy to capture the interaction between different modalities of data. As the most successful approaches, neural networks lack a mechanism of explicitly showing how different modalities are related to each other. We address this issue by seeking inspirations from Quantum Theory (QT), which has been demonstrated advantageous in explicitly capturing the correlations between textual features. In this paper, we give an overview of the related works and present the proposed methodology.
Using Facial Recognition Services as Implicit Feedback for Recommenders

User authentication and feedback gathering are crucial aspects for recommender systems. The most common implementations, a username / password login and star rating systems, require user interaction and a cognitive effort from the user. As a result, users opt to save their password in the interface and optional feedback with a star rating system is often skipped, especially for applica- tions such as video watching in a home environment. In this article, we propose an alternative method for user authentication based on facial recognition and an automatic feedback gathering method by detecting various face characteristics. Using facial recognition with a camera in a tablet, smartphone, or smart TV, the persons in front of the screen can be identified in order to link video watch- ing sessions to their user profile. During video watching, implicit feedback is automatically gathered through emotion recognition, attention measurements, and behavior analysis. An emotion finger- print, which is defined as a unique spectrum of expected emotions for a video scene, is compared to the recognized emotions in order to estimate the experience of a user while watching. An evaluation with a test panel showed that happiness can be most accurately detected and the recognized emotions are correlated with the user’s star rating.
Emoji-Aware Attention-based Bi-directional GRU Network Model for Chinese Sentiment Analysis

Nowadays, social media has become the essential part of our lives. Pictograms (emoticons/emojis) have been widely used in social media as a medium for visually expressing emotions. In this paper, we propose a emoji-aware attention-based GRU network model for sentiment analysis of Weibo which is the most popular Chinese social media platform. Firstly, we analyzed the usage of 67 emojis with facial expression. By performing a polarity annotation with a new “humorous type” added, we have confirmed that 23 emojis can be considered more as humorous than positive or negative. On this basis, we applied the emojis polarity to a attentionbased GRU network model for sentiment analysis of undersized labelled data. Our experimental results show that the proposed method can significantly improve the performance for predicting sentiment polarity on social media.
A survey of intrusion detection on industrial control systems

The modern industrial control systems now exhibit an increasing connectivity to the corporate Internet technology networks so as to make full use of the rich resource on the Internet. The increasing interaction between industrial control systems and the outside Internet world, however, has made them an attractive target for a variety of cyber attacks, raising a great need to secure industrial control systems. Intrusion detection technology is one of the most important security precautions for industrial control systems. It can effectively detect potential attacks against industrial control systems. In this survey, we elaborate on the characteristics and the new security requirements of industrial control systems. After that, we present a new taxonomy of intrusion detection systems for industrial control systems based on different techniques: protocol analysis based, traffic mining based, and control process analysis based. In addition, we analyze the advantages and disadvantages of different categories of intrusion detection systems and discuss some future developments of intrusion detection systems for industrial control systems, in order to promote further research on intrusion detection technology for industrial control systems.
Processing Data Where It Makes Sense in Modern Computing Systems: Enabling In-Memory Computation

Today's systems are overwhelmingly designed to move data to computation. This design choice goes directly against at least three key trends in systems that cause performance, scalability and energy bottlenecks: 1) data access from memory is already a key bottleneck as applications become more data-intensive and memory bandwidth and energy do not scale well, 2) energy consumption is a key constraint in especially mobile and server systems, 3) data movement is very expensive in terms of bandwidth, energy and latency, much more so than computation [1-3]. These trends are especially severely-felt in the data-intensive server and energy-constrained mobile systems of today. At the same time, conventional memory technology is facing many scaling challenges in terms of reliability, energy, and performance [4-5]. As a result, memory system architects are open to organizing memory in different ways and making it more intelligent, at the expense of slightly higher cost. The emergence of 3D-stacked memory plus logic [6], the adoption of error correcting codes inside the latest DRAM chips [7-8], and intelligent memory controllers to solve the RowHammer problem [9-11] are an evidence of this trend. In this talk, I will discuss some recent research that aims to practically enable computation close to data. After motivating trends in applications as well as technology, we will discuss at least two promising directions: 1) performing massively-parallel bulk operations in memory by exploiting the analog operational properties of DRAM, with low-cost changes [3, 12-14, 23], 2) exploiting the logic layer in 3D-stacked memory technology in various ways to accelerate important data-intensive applications [2, 15-22]. In both approaches, we will discuss relevant cross-layer research, design, and adoption challenges in devices, architecture, systems, applications, and programming models [1]. Our focus will be the development of in-memory processing designs that can be adopted in real computing platforms and real data-intensive applications, spanning machine learning, graph processing, data analytics, and genome analysis, at low cost [26]. If time permits, we will also discuss simulation and evaluation infrastructures that can enable exciting and forward-looking research in future memory systems, including Ramulator [24] and SoftMC [25].
Generalized Stokes parameter approach for analysis of perfect scattering experiments on impact excitation by spin polarized particles

The authors define 'generalized Stokes parameters' for scattered-projectile-polarized-photon coincidence experiments after impact excitation of unpolarized heavy atoms by spin polarized projectiles. Using electron-impact excitation of the (6s2)1SOe to (6s6p)3P1o transition in Hg as the primary example, the authors demonstrate how a 'perfect scattering experiment' without analysis of the projectile spin after the collision can provide the eleven independent parameters that determine the six independent scattering amplitudes.
Real-time tracking for the moving object using multiple moving cameras and MDNet

This article researches the problem of accurately tracking a target that is moving in an environment where there are a large number of disturbing factors, using multiple cameras for a larger observation field and better positioning accuracy than that of using a single camera. This paper proposes a method based on the common features learned through multidomain networks to reduce the interference of the external environment and thus more accurately and steadily track the target. The image model proposed by Multi-domain Network has significantly improved the fusion and utilization efficiency of multi-scale features. We also establish a consistent calibration of the same target between cameras by making full use of the overlapping visual field of multiple cameras and counting the number of similar pairs between the template and the target image search window to achieve target association and handover between cameras, making the cameras’ vision field expanded. By extracting the geometric constraint relationship between the cameras and combining the views of the target from multiple angles, the spatial positioning of the target object is finally achieved. Experiments show that the proposed tracking and positioning method can accurately track the moving target and can accurately position it.
Itinerários formativos e curriculares na educação de jovens e adultos

Esta tese de doutorado sobre a Educacao de Jovens e Adultos (EJA) surge com o objetivo de analisar itinerarios formativos de adultos, atraves de uma analise aberta, dialetica e relacional no campo da educacao de jovens e adultos. Como itinerarios formativos estamos compreendendo os processos constituintes de um percurso de formacao que estao presentes nas praticas educativas envolvendo professores, adultos, jovens, instituicoes e sistema escolar. Algumas questoes acompanham este trabalho: como vem se constituindo o campo da educacao de jovens e adultos a partir da construcao de seus projetos politicos pedagogicos e das praticas educativas. Mais especificamente, como se dao as relacoes de poder e de saber nas praticas educativas da educacao de jovens e adultos? Que papeis desempenham nessas relacoes os curriculos que sao estabelecidos nas escolas de EJA? Buscamos estabelecer um dialogo entre os atores sociais da EJA com outros pesquisadores como Paulo Freire, Gaston Bachelar, Gimeno Sacristan, Carlos Rodrigues Brandao, Miguel Arroyo e outros autores que pensam a educacao e a EJA, realizando assim uma pesquisa de cunho qualitativo. Analisamos, em especial, o Projeto de Educacao de Trabalhadores (PET) da Escola Municipal Uniao Comunitaria e o trabalho de EJA da Escola Municipal Caio Libano Soares, na Rede de Ensino de Belo Horizonte. Educacao de Jovens e Adultos: seu nome e sua insercao no sistema de ensino brasileiro trazem de inicio uma contradicao, ou pelo menos, indica uma tensao entre educar e ensinar. Ela tem sua origem na Educacao Popular, mas aventura-se nos espacos e tempos escolares. A Educacao popular quer ensinar, mas seu objetivo e educar. A EJA quer educar, mas, enquanto escola, objetiva ensinar. Assim, curriculos e metodologias sao construidos na Educacao Popular de forma mais livre e aberta, na EJA e uma construcao negociada com professores, alunos, jovens, adultos e, principalmente, com o sistema oficial de ensino, que busca enquadrar a Educacao nos trilhos do ensino ou das diretrizes curriculares. No entanto, a EJA pode ser o campo de construcao de uma utopia possivel na busca de uma educacao de direito e de qualidade para jovens, adolescentes e adultos.