Reviewerly/exHarmony huggingface dataset:
Learning Attention-Enhanced Spatiotemporal Representation for Action Recognition

Learning spatiotemporal features via 3D-CNN (3D Convolutional Neural Network) models has been regarded as an effective approach for action recognition. In this paper, we explore visual attention mechanism for video analysis and propose a novel 3D-CNN model, dubbed AE-I3D (Attention-Enhanced Inflated-3D Network), for learning attention-enhanced spatiotemporal representation. The contribution of our AE-I3D is threefold: First, we inflate soft attention in spatiotemporal scope for 3D videos, and adopt softmax to generate probability distribution of attentional features in a feedforward 3D-CNN architecture; Second, we devise an AE-Res (Attention-Enhanced Residual learning) module, which learns attention-enhanced features in a two-branch residual learning way, also the AE-Res module is lightweight and flexible, so that can be easily embedded into many 3D-CNN architectures; Finally, we embed multiple AE-Res modules into an I3D (Inflated-3D) network, yielding our AE-I3D model, which can be trained in an end-to-end, video-level manner. Different from previous attention networks, our method inflates residual attention from 2D image to 3D video for 3D attention residual learning to enhance spatiotemporal representation. We use RGB-only video data for evaluation on three benchmarks: UCF101, HMDB51, and Kinetics. The experimental results demonstrate that our AE-I3D is effective with competitive performance.
An Interval-Valued Data Classification Method Based on the Unified Representation Frame

Interval-valued data (IVD) is a kind of data where each feature is an interval.The midpoint and boundary are the two commonly used methods for representing IVD.However, their structure information (such as location, size) may be incomplete because only midpoint or endpoint is adopted which will lead to poor results of data processing.To better depict the structural information of IVD, a unified representation frame (URF) for IVD is proposed.It not only takes into account the size and location information, but the relationship between them as well.This frame can also represent the midpoint and boundary methods in a unified way.Besides, symmetrical uncertainty (SU) is adopted to measure the relationship between features and classes quantitatively, and irrelevant features will be eliminated based on SU.The proposed URF_ SU is applied in some traditional classifiers like LIBSVM, CART Tree and KNN.The experimental results on synthetic and real-world datasets demonstrate that the proposed approach is more effective than other representation methods of IVD in classification tasks.
Can Trust be Trusted in Cybersecurity?

Human compliance in cybersecurity continues to be a persistent problem for organizations.This researchin-progress advances theoretical understanding of the negative effects of trust formed between individuals and the cybersecurity function (i.e., those responsible for protection), cybersecurity system (i.e., the protective technologies), and organization (i.e., those verifying (e.g., hiring, championing, vouching.)the cybersecurity department) that leads to suboptimal compliance behaviors.In contrast to the current information security literature that focuses on how organizations can induce compliance, this study begins to provide understanding into the degradation of compliance through organizational actions.Additionally, understanding is provided on how to combat the negative effects of trust.An integrated model is conceptualized using the theories of trust and attention.This model provides the theoretical foundation to study the role of dark side trust in the context of cybersecurity and provides initial mechanisms to reduce it.By developing this conceptualization of dark side trust and model, this study contributes to the general study of trust in information systems research outside of the domain of cybersecurity.
My Virtual Colleague: A State-of-the-Art Analysis of Conversational Agents for the Workplace

Conversational interfaces at the workplace are not a new idea, but it is only the recent technological advancements that turned what was once a vision into near-future reality.Improved reliability and accuracy enable conversational systems to be used in higher stake environments, such as the workplace.In this work, we perform a literature review on concepts proposed to incorporate Conversational Agents (CA) into the workplace.We found 29 workplace CAs designed for workers that contribute to eight different application domains.Based on the studies of these CAs, we compiled a list of aspects to be considered when designing such CAs and identified starting points for further research.URI: https://hdl.handle.net/10125/63759978-0-9981331-3-3 (CC BY-NC-ND 4.0) ResultsIn this section, we describe our observations in our result set, then we summarize and classify the WCA concepts.Eventually, we summarize design aspects and other findings from those WCA concepts.Based on the 81 publications in our result set, we identified 29 WCAs 4 https:
Concurrent optimization of multiple base learners in neural network ensembles: An adaptive niching differential evolution approach

Neural network ensemble (NNE) exhibits improved performance when compared with a single neural network (NN) in most cases. Traditionally, each base network in an NNE is trained individually, which may result in network redundancy and expensive training overhead. This paper proposes a new adaptive niching evolutionary algorithm, which possesses promising performance in finding multiple optima in terms of good accuracy and diversity. By means of this algorithm, all NNs in an NNE can be trained simultaneously. In particular, the proposed algorithm is named adaptive niching differential evolution (ANDE), which is characterized by a heuristic clustering method to enable iteratively cluster subpopulations that track and locate multiple optima, a parameter adaptation strategy to adaptively adjust parameters according to the subpopulation states, and an auxiliary movement scheme to promote the equilibrium between exploration and exploitation. Experimental results validate the efficiency and effectiveness of the proposed ANDE on the benchmark test suite of multimodal optimization. Furthermore, ANDE is extended to concurrently train multiple base NNs for ensemble and the experiments show a promising performance of ANDE-NNE.
Leveled Certificateless Fully Homomorphic Encryption Schemes From Learning With Errors

Fully homomorphic encryption (FHE) is a form of public-key encryption that allows the computation of arbitrary functions on encrypted data without decrypting the data.As a result, it is a useful tool with numerous applications.Certificateless encryption (CLE) is a type of public-key encryption that combines the advantages of PKI-based public-key encryption with those of identity-based encryption (IBE).Thus, certificateless fully homomorphic encryption (CLFHE) has aroused considerable research interest.Recently, Chen, Hu, and Lian proposed a leveled certificateless homomorphic encryption (CLHE) scheme and proved its semantic security based on the learning with errors (LWE) problem in the random oracle model.However, their scheme supports only homomorphic addition, but not homomorphic multiplication.In this work, we construct two leveled CLFHE schemes using the approximate eigenvector method presented by Gentry, Sahai, and Waters.Based on the hardness of the LWE problem, we prove that one scheme satisfies adaptive semantic security and anonymity in the random oracle model, whereas the other satisfies selective semantic security and anonymity in the standard model.
Visualization and Interaction for Knowledge Discovery in Simulation Data

Discrete-event simulation is an established and popular technology for investigating the dynamic behavior of complex manufacturing and logistics systems.Besides traditional simulation studies that focus on single model aspects, data farming describes an approach for using the simulation model as a data generator for broad scale experimentation with a broader coverage of the system behavior.On top of that we developed a process called knowledge discovery in simulation data that enhances the data farming concept by using data mining methods for the data analysis.In order to uncover patterns and causal relationships in the model, a visually guided analysis then enables an exploratory data analysis.While our previous work mainly focused on the application of suitable data mining methods, we address suitable visualization and interaction methods in this paper.We present those in a conceptual framework followed by an exemplary demonstration in an academic case study.
Efficiency of SVM classifier with Word2Vec and Doc2Vec models

Abstract Support Vector Machine model is one of the most intensive used text data classifiers ever since the moment of its development. However, its performance depends not only on its features but also on data preprocessing and model tuning. The main purpose of this paper is to compare the efficiency of more Support Vector Machine models using both TF-IDF approach and Word2Vec and Doc2Vec neural networks for text data representation. Besides the data vectorization process, I try to enhance the models’ efficiency by identifying which kind of kernel fits better the data or if it is just better to opt for the linear case. My results prove that for the “Reuters 21578” dataset, nonlinear Support Vector Machine is more efficient when the conversion of text data into numerical attributes is realized using Word2Vec models instead of TF-IDF and Doc2Vec representations. When it is considered that data meet linear separability requirements, TF-IDF representation outperforms all other options. Surprisingly, Doc2Vec models have the lowest performance and only in terms of computational cost they provide satisfactory results. This paper proves that while Word2Vec models are truly efficient for text data representation, Doc2Vec neural networks are unable to exceed even TF-IDF index representation. This evidence contradicts the common idea according to which Doc2Vec models should provide a better insight into the training data domain than Word2Vec models and certainly than the TF-IDF index.
Efficient, Ultra-facile Breast Cancer Histopathological Images Classification Approach Utilizing Deep Learning Optimizers

Conventional approaches to breast cancer diagnosis are associated with drawbacks that ultimately affect the quality of diagnosis and subsequent treatment, pushing for the need for automatic and precise classification of breast cancer tumors.The advent of deep learning methods has witnessed an increasing interest in their applications in many tasks.The specific case of using convolutional neural networks with transfer learning has witnessed tremendous successes in many classification tasks.Nonetheless, with transfer learning, the sheer number of parameters associated with deep networks coupled with the distance disparity between source data and target data leave networks prone to overfitting, particularly in the case of limited data.Also, negative transfer may occur in the situation where the source and target domains are not related.This work proposes a simple convolutional neural network model trained from scratch for discriminating benign and malignant breast cancer tumors in histopathological images.Four deep learning optimization algorithms are leveraged and explored to ascertain how optimizers aid in finding good sets of parameters that help minimize loss and increase overall classification accuracy.By adopting a polynomial learning rate decay scheduling and implementing several data augmentation techniques that regulate overfitting and improve the generalization ability of the proposed model, accuracy, sensitivity, specificity, and Area Under the Curve values of 89.92%, 94.02%, 86.42%, and 0.884 (88.4%), respectively are reported.
Accelerating Binary String Comparisons with a Scalable, Streaming-Based System Architecture Based on FPGAs

This paper is concerned with Field Programmable Gate Arrays (FPGA)-based systems for energy-efficient high-throughput string comparison. Modern applications which involve comparisons across large data sets—such as large sequence sets in molecular biology—are by their nature computationally intensive. In this work, we present a scalable FPGA-based system architecture to accelerate the comparison of binary strings. The current architecture supports arbitrary lengths in the range 16 to 2048-bit, covering a wide range of possible applications. In our example application, we consider DNA sequences embedded in a binary vector space through Locality Sensitive Hashing (LSH) one of several possible encodings that enable us to avoid more costly character-based operations. Here the resulting encoding is a 512-bit binary signature with comparisons based on the Hamming distance. In this approach, most of the load arises from the calculation of the O ( m ∗ n ) Hamming distances between the signatures, where m is the number of queries and n is the number of signatures contained in the database. Signature generation only needs to be performed once, and we do not consider it further, focusing instead on accelerating the signature comparisons. The proposed FPGA-based architecture is optimized for high-throughput using hundreds of computing elements, arranged in a systolic array. These core computing elements can be adapted to support other string comparison algorithms with little effort, while the other infrastructure stays the same. On a Xilinx Virtex UltraScale+ FPGA (XCVU9P-2), a peak throughput of 75.4 billion comparisons per second—of 512-bit signatures—was achieved, using a design with 384 parallel processing elements and a clock frequency of 200 MHz. This makes our FPGA design 86 times faster than a highly optimized CPU implementation. Compared to a GPU design, executed on an NVIDIA GTX1060, it performs nearly five times faster.
A Cost-sensitive Genetic Programming Approach for High-dimensional Unbalanced Classification

Many real-world classification problems encounter the problem of class imbalance. Genetic programming (GP) is one of the most important evolutionary algorithms, making crucial contributions to classification, particularly in the high-dimensional case. However, like other classification algorithms, GP may also develop biased classifiers when the class distribution is unbalanced or skewed. This is because standard GP treats each instance equally and assumes the same cost of different misclassification. In unbalanced classification, misclassification cost of the minority class is often serious than that of the majority class. Cost-sensitive learning has been successfully applied to solve the problem of class imbalance for many classification algorithms, but it has not been heavily investigated in GP. This paper investigates how cost-sensitive learning can be effectively used by GP to address the problem of class imbalance in high-dimensional unbalanced classification. Experimental results on six high-dimensional unbalanced datasets show the better performance of the proposed methods than the compared methods.
VulnerCheck: A Content-Agnostic Detector for Online Hatred-Vulnerable Videos

With the increasing popularity of online video platforms (e.g., YouTube, Vimeo), the spread of hateful videos and the lack of rigorous hateful content control have become a critical issue. This paper focuses on the problem of identifying online hatred-vulnerable videos where the videos themselves do not contain any hateful content but unexpectedly trigger hateful comments from the audience. It is suboptimal to simply treat the hatred-vulnerable videos as hateful ones and remove them from the sharing platforms. This will discourage the uploaders of such videos from sharing valid and informative videos in the future. However, treating these hatred-vulnerable videos as hatred-free ones will provide undesirable opportunities for hateful users to spread their toxic comments and extreme ideology. In this paper, we develop VulnerCheck, an end-to-end supervised learning approach to effectively classify hatred-vulnerable videos from hateful and hatred-free ones by exploring the structure and semantics features of audience's comment networks. VulnerCheck is content-agnostic in the sense that it does not analyze the content of the video and is therefore robust against sophisticated content creators who craft hateful videos to bypass the current content censorship. We evaluate VulnerCheck on a real-world dataset collected from YouTube. Results demonstrate that our scheme is both effective and efficient in identifying hatred-vulnerable videos and significantly outperforms the state-of-the-art baselines.
A Two-Stage Genetic Programming Hyper-Heuristic for Uncertain Capacitated Arc Routing Problem

Genetic Programming Hyper-heuristic (GPHH) has been successfully applied to automatically evolve effective routing policies to solve the complex Uncertain Capacitated Arc Routing Problem (UCARP). However, GPHH typically ignores the interpretability of the evolved routing policies. As a result, GP-evolved routing policies are often very complex and hard to be understood and trusted by human users. In this paper, we aim to improve the interpretability of the GP-evolved routing policies. To this end, we propose a new Multi-Objective GP (MOGP) to optimise the performance and size simultaneously. A major issue here is that the size is much easier to be optimised than the performance, and the search tends to be biased to the small but poor routing policies. To address this issue, we propose a simple yet effective Two-Stage GPHH (TS-GPHH). In the first stage, only the performance is to be optimised. Then, in the second stage, both objectives are considered (using our new MOGP). The experimental results showed that TS-GPHH could obtain much smaller and more interpretable routing policies than the state-of-the-art single-objective GPHH, without deteriorating the performance. Compared with traditional MOGP, TS-GPHH can obtain a much better and more widespread Pareto front.
Learning to be human with sociable robots

Abstract This essay examines the debate over the status of sociable robots and relational artifacts through the prism of our relationship to television. In their work on human-technology relations, Cynthia Breazeal and Sherry Turkle have staked out starkly different assessments. Breazeal’s work on sociable robots suggests that these technological artifacts will be human helpmates and sociable companions. Sherry Turkle argues that such relational artifacts seduce us into simulated relationships with technological others that largely serve to exploit our emotional vulnerabilities and undermine authentic human relationships. Drawing on an analysis of the television as our first relational artifact and on the AMC television show Humans , this essay argues that in order to intervene in this debate we need a multimediated theory of technology that situates our technical artifacts in the domestic realm and examines their impact on those populations especially impacted by such technologies, including women, children, and the elderly. It is only then that we will be able to take the full measure of the impact of such sociable technologies on our being human.
Qualitative photo collage by quartet analysis and active learning

In order to select representative images from a large image set to construct an album, we propose a new album management method in a comic-like layout based on images classification. We mainly focus on three key problems: how to organize the input set of images reasonably; how to classify and select representative images rapidly and accurately according to the user intent; how to display the representative images clearly. For the first problem, we use the model organization idea of Quartet Analysis, analyzing the set of images by the multiple global features, and we realize the effective organization of the images through the construction of the categorization tree (C-tree). For the second problem, in order to express the user intent better, we perform fine classification by using active learning method based on the C-tree. Then we recommend representative images for the user to select. For the third problem, we display representative images in a compact comic-like layout with hierarchical ordering information. To enrich layout styles, we create a template library based on enumeration by comic layout rules and complete the template selection and automatic collage of the images based on the principle of presenting the maximum information content of the images to be displayed. Experiments demonstrate that our method can not only assist the user with album production but also involve users in the whole process of making an album.
Can the women's parliamentary representation reduces corruption and informal sector in Africa? Empirical analysis

The purpose of this work is to contribute to literature by showing explicitly the impact of women's promotion policies on the reduction of the level of corruption and informal activities. For this goal to be achieved, data from 25 African countries obtained from different sources will be empirically analyzed (ICRG, Medina and Schneider2018). These data were tested according to a linear regression model and the results obtained established a negative relation between the change in the proportion of women parliamentarians, the level of corruption and the size of the informal sector.
Explaining AI

Explainable AI (XAI) has started experiencing explosive growth, echoing the explosive growth that has preceded it of AI becoming used for practical purposes that impact the general public. This spread of AI into the world outside of research labs brings with it pressures and requirements that many of us have perhaps not thought about deeply enough. In this keynote address, I will explain why I think we have a very long way to go.
Automatic detection and classification of abnormal tissues on digital mammograms based on a bag-of-visual-words approach

Breast cancer represents the most common type of cancer worldwide among women. One of the most important diagnostic methods of this disease are mammograms, however, the high prevalence of breast cancer has not been reduced due to the incorrect diagnosis of these images, since they can be complex to interpret. An approach that represents a fundamental process for the improvement of this diagnosis is digital image processing, since it can facilitate the interpretation of the images for the specialists. In this work is proposed the implementation of a new multilevel segmentation approach based on the minimum cross-entropy threshold - Harris Hawks Optimization (MCET-HHO) metaheuristic algorithm, identifying regions within the breast that have abnormal tissue. Then, these regions are subjected to an automatic classification system based on a bag-of-visual-words (BoVW) approach to identify healthy tissue, benign tumors, and malignant tumors. According to the results, the classifier reached an average accuracy of 0.86 in the training stage and 0.73 in the testing, proving to be statistically significant in the automatic classification of mammograms, presenting a preliminary tool for the support of specialists in the diagnosis of mammography images.