Reviewerly/exHarmony huggingface dataset:
Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects

Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones, which are common in real-world settings. In this paper, we present a framework for discovering DNN failures that harnesses 3D renderers and 3D models. That is, we estimate the parameters of a 3D renderer that cause a target DNN to misbehave in response to the rendered image. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97% of their pose space. In addition, DNNs are highly sensitive to slight pose perturbations. Importantly, adversarial poses transfer across models and datasets. We find that 99.9% and 99.4% of the poses misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image classifiers trained on the same ImageNet dataset, respectively, and 75.5% transfer to the YOLOv3 object detector trained on MS COCO.
Open Research Knowledge Graph

Despite improved digital access to scholarly knowledge in recent decades, scholarly communication remains exclusively document-based. In this form, scholarly knowledge is hard to process automatically. We present the first steps towards a knowledge graph based infrastructure that acquires scholarly knowledge in machine actionable form thus enabling new possibilities for scholarly knowledge curation, publication and processing. The primary contribution is to present, evaluate and discuss multi-modal scholarly knowledge acquisition, combining crowdsourced and automated techniques. We present the results of the first user evaluation of the infrastructure with the participants of a recent international conference. Results suggest that users were intrigued by the novelty of the proposed infrastructure and by the possibilities for innovative scholarly knowledge processing it could enable.
Multifaceted trust in tourism service robots

In recognizing the increase in the use of service robots by service industries, identifying the structure of trust in intelligent robots is crucial for tourism studies. This paper first proposes a model of multifaceted trust in service robots comprised of three constructs – performance, process, and purpose – and, second, tests the trust model that considers institution-based trust, trusting belief, and intention. As a result, this paper identified a higher-order formative construct of trust in service robots with the highest importance for a performance construct (Study 1). The antecedents of the multifaceted trust in tourism service robots are then identified (Study 2). This study provides important theoretical and methodological contributions to the fields of information technology and tourism.
Benchmarking Adversarial Robustness on Image Classification

Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning. While a lot of efforts have been made in recent years, it is of great significance to perform correct and complete evaluations of the adversarial attack and defense algorithms. In this paper, we establish a comprehensive, rigorous, and coherent benchmark to evaluate adversarial robustness on image classification tasks. After briefly reviewing plenty of representative attack and defense methods, we perform large-scale experiments with two robustness curves as the fair-minded evaluation criteria to fully understand the performance of these methods. Based on the evaluation results, we draw several important findings that can provide insights for future research, including: 1) The relative robustness between models can change across different attack configurations, thus it is encouraged to adopt the robustness curves to evaluate adversarial robustness; 2) As one of the most effective defense techniques, adversarial training can generalize across different threat models; 3) Randomization-based defenses are more robust to query-based black-box attacks.
FineGym: A Hierarchical Video Dataset for Fine-Grained Action Understanding

On public benchmarks, current action recognition techniques have achieved great success. However, when used in real-world applications, e.g. sport analysis, which requires the capability of parsing an activity into phases and differentiating between subtly different actions, their performances remain far from being satisfactory. To take action recognition to a new level, we develop FineGym, a new dataset built on top of gymnasium videos. Compared to existing action recognition datasets, FineGym is distinguished in richness, quality, and diversity. In particular, it provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy. For example, a “balance beam” activity will be annotated as a sequence of elementary sub-actions derived from five sets: “leap-jump-hop”, “beam-turns”, “flight-salto”, “flight-handspring”, and “dismount”, where the sub-action in each set will be further annotated with finely defined class labels. This new level of granularity presents significant challenges for action recognition, e.g. how to parse the temporal structures from a coherent action, and how to distinguish between subtly different action classes. We systematically investigates different methods on this dataset and obtains a number of interesting findings. We hope this dataset could advance research towards action understanding.
AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation

Video frame interpolation is one of the most challenging tasks in video processing research. Recently, many studies based on deep learning have been suggested. Most of these methods focus on finding locations with useful information to estimate each output pixel using their own frame warping operations. However, many of them have Degrees of Freedom (DoF) limitations and fail to deal with the complex motions found in real world videos. To solve this problem, we propose a new warping module named Adaptive Collaboration of Flows (AdaCoF). Our method estimates both kernel weights and offset vectors for each target pixel to synthesize the output frame. AdaCoF is one of the most generalized warping modules compared to other approaches, and covers most of them as special cases of it. Therefore, it can deal with a significantly wide domain of complex motions. To further improve our framework and synthesize more realistic outputs, we introduce dual-frame adversarial loss which is applicable only to video frame interpolation tasks. The experimental results show that our method outperforms the state-of-the-art methods for both fixed training set environments and the Middlebury benchmark. Our source code is available at https://github.com/HyeongminLEE/AdaCoF-pytorch
Emerging Smart Logistics and Transportation Using IoT and Blockchain

Transportation and logistics management play a vital role in the development of a country. With the advancement of the Internet of Things (IoT) devices, smart transportation is becoming a reality. However, these abundant connected IoT devices are vulnerable to security attacks. Recently, Blockchain has emerged as one of the most widely accepted technologies for trusted, secure and decentralized intelligent transportation systems. This research study aims to contribute to the field of logistics and transportation by exploring the potential of IoT and Blockchain technology in smart logistics and transportation. We propose a layered framework, namely BCTLF, for smart logistics and transportation that integrates IoT and Blockchain to provide an intelligent logistics and transportation system. Finally, we present two real-life IoT and Blockchain-based case studies to highlight the contribution of IoT and Blockchain in logistics and transportation.
Context-Aware Attentive Knowledge Tracing

Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications. Recent developments in KT using flexible deep neural network-based models excel at this task. However, these models often offer limited interpretability, thus making them insufficient for personalized learning, which requires using interpretable feedback and actionable recommendations to help learners achieve better learning outcomes. In this paper, we propose attentive knowledge tracing (AKT), which couples flexible attention-based neural network models with a series of novel, interpretable model components inspired by cognitive and psychometric models. AKT uses a novel monotonic attention mechanism that relates a learner's future responses to assessment questions to their past responses; attention weights are computed using exponential decay and a context-aware relative distance measure, in addition to the similarity between questions. Moreover, we use the Rasch model to regularize the concept and question embeddings; these embeddings are able to capture individual differences among questions on the same concept without using an excessive number of parameters. We conduct experiments on several real-world benchmark datasets and show that AKT outperforms existing KT methods (by up to $6%$ in AUC in some cases) on predicting future learner responses. We also conduct several case studies and show that AKT exhibits excellent interpretability and thus has potential for automated feedback and personalization in real-world educational settings.
Forecasting of COVID19 per regions using ARIMA models and polynomial functions

COVID-2019 is a global threat, for this reason around the world, researches have been focused on topics such as to detect it, prevent it, cure it, and predict it. Different analyses propose models to predict the evolution of this epidemic. These analyses propose models for specific geographical areas, specific countries, or create a global model. The models give us the possibility to predict the virus behavior, it could be used to make future response plans. This work presents an analysis of COVID-19 spread that shows a different angle for the whole world, through 6 geographic regions (continents). We propose to create a relationship between the countries, which are in the same geographical area to predict the advance of the virus. The countries in the same geographic region have variables with similar values (quantifiable and non-quantifiable), which affect the spread of the virus. We propose an algorithm to performed and evaluated the ARIMA model for 145 countries, which are distributed into 6 regions. Then, we construct a model for these regions using the ARIMA parameters, the population per 1M people, the number of cases, and polynomial functions. The proposal is able to predict the COVID-19 cases with a RMSE average of 144.81. The main outcome of this paper is showing a relation between COVID-19 behavior and population in a region, these results show us the opportunity to create more models to predict the COVID-19 behavior using variables as humidity, climate, culture, among others.
Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy

The adoption of technologies by the operations and supply chain management (OSCM) field is leading to extraordinary disruptions. And with the rapid emergence of cutting-edge and more disruptive technologies, the OSCM is striving to take advantage of such innovations, but they are bringing in their wake a number of challenges. One of those disruptive technologies is blockchain, which is increasingly accepted in virtually all industries. This study aims to investigate the blockchain technology (BCT) adoption behaviour and possible barriers in the Brazilian OSCM context. We developed a model drawing on the unified theory of acceptance and use of technology (UTAUT) model, the supply chain literature, and the emerging literature on BCT. We empirically validated the proposed model with Brazilian operations and supply chain professionals by using the partial least squares structural equation modelling (PLS-SEM). Our findings revealed that facilitating conditions, trust, social influence, and effort expectancy are the most critical constructs that directly affect BCT adoption. Unexpectedly, performance expectancy appeared not decisive in terms of predicting BCT adoption. This study contributes to advancing and stimulating the theory about BCT adoption behaviour in supply chains, as well as important managerial implications, which may be more critical for emerging economies.
Two-Branch Recurrent Network for Isolating Deepfakes in Videos

The current spike of hyper-realistic faces artificially generated using deepfakes calls for media forensics solutions that are tailored to video streams and work reliably with a low false alarm rate at the video level. We present a method for deepfake detection based on a two-branch network structure that isolates digitally manipulated faces by learning to amplify artifacts while suppressing the high-level face content. Unlike current methods that extract spatial frequencies as a preprocessing step, we propose a two-branch structure: one branch propagates the original information, while the other branch suppresses the face content yet amplifies multi-band frequencies using a Laplacian of Gaussian (LoG) as a bottleneck layer. To better isolate manipulated faces, we derive a novel cost function that, unlike regular classification, compresses the variability of natural faces and pushes away the unrealistic facial samples in the feature space. Our two novel components show promising results on the FaceForensics+ +, Celeb-DF, and Facebook’s DFDC preview benchmarks, when compared to prior work. We then offer a full, detailed ablation study of our network architecture and cost function. Finally, although the bar is still high to get very remarkable figures at a very low false alarm rate, our study shows that we can achieve good video-level performance when cross-testing in terms of video-level AUC.
Atomic orbital search: A novel metaheuristic algorithm

In this paper, the Atomic Orbital Search (AOS) is proposed as a novel metaheuristic algorithm for optimization purposes. The main concept of this algorithm is based on some principles of quantum mechanics and the quantum-based atomic model in which the general configuration of electrons around nucleus is in perspective. In order to evaluate the performance of this algorithm, a total number of 20 unconstrained mathematical test functions are utilized with different dimensions of 2–100 while a maximum number of 150,000 function evaluations is considered with 100 independent optimization runs for statistical purposes. A complete statistical analysis is also conducted by utilization of the Kolmogorov Smirnov, Wilcoxon and the Kruskal Wallis tests while 8 metaheuristics are also utilized as alternatives for comparative purposes. The latest Competitions on Evolutionary Computation (CEC) regarding the single objective real-parameter numerical optimization (CEC 2017) including 30 benchmark test functions is also considered in which the capability of the proposed algorithm is compared to the most state−of-the−art algorithms in the optimization field. In addition, a total number of 5 constrained engineering design problems are utilized as design examples including some of the constrained optimization problems of the recent Competitions on Evolutionary Computation (CEC 2020). The obtained results of the AOS algorithm in dealing with the constraint problems are compared to the results of different standard, improved and hybrid metaheuristic algorithms from the literature. The obtained results demonstrate that the proposed AOS algorithm provides very outstanding results in dealing with the mathematical and engineering design problems.
Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization

This paper presents a novel bio-inspired algorithm inspired by starlings’ behaviors during their stunning murmuration named starling murmuration optimizer (SMO) to solve complex and engineering optimization problems as the most appropriate application of metaheuristic algorithms. The SMO introduces a dynamic multi-flock construction and three new search strategies, separating, diving, and whirling. The separating search strategy aims to enhance the population diversity and local optima avoidance using a new separating operator based on the quantum harmonic oscillator. The diving search strategy aims to explore the search space sufficiently by a new quantum random dive operator, whereas the whirling search strategy exploits the vicinity of promising regions using a new operator called cohesion force. The SMO strikes a balance between exploration and exploitation by selecting either a diving strategy or a whirling strategy based on the flocks’ quality. The SMO was tested using various benchmark functions with dimensions 30, 50, 100. The experimental results prove that the SMO is more competitive than other state-of-the-art algorithms regarding solution quality and convergence rate. Then, the SMO is applied to solve several mechanical engineering problems in which results demonstrate that it can provide more accurate solutions. A statistical analysis shows that SMO is superior to the other contenders.
Happy and Mindless, But Sad and Smart? The Impact of Affective States on Analytic Reasoning

This chapter provides a selective review of some of the available evidence, focusing on the impact of affective states on analytic reasoning. It offers a theoretical framework for the conceptualization of affective influences on information processing. To begin with the most fundamental issue, the chapter considers why one might expect that individuals' affective state influences their mode of information processing, before he turn to some selective evidence. While the preceding discussion pertained to subjects' reasoning about the situation that elicited the affective state to begin with, the chapter provides information on the more intriguing possibility that the impact of affective states may generalize to other tasks that individuals work on while in that state. Evidence for the assumption that different affective states are associated with different styles of organizing information is also provided by Bless et al.
Blockchain and the future of energy

This paper discusses the emergence of blockchain technology in the energy sector in the light of ongoing energy market transformation. The study builds on literature research and expert interviews, and provides insights into the future energy landscape in the context of the blockchain advent. While the interviewees acknowledge the great, though disruptive, potential of blockchain technology for the primary activities in the electricity sector, there is agreement that inflexible regulatory frameworks impose the biggest challenge. The widest impact which blockchain technology will have in the short-term will be in electric vehicle integration, while in the long-term blockchain will enable peer-to-peer microgrids. The role that the blockchain will play, though, relies mainly on the business model innovation in energy. While a growing body of literature discusses specific blockchain applications and solutions in an advanced technological set-up, this paper presents a holistic picture of the blockchain applicability in the energy sector and thematises this very powerful and versatile technology against the background of two emerging economies: South Africa and Russia.
Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions

Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior. As an image can be described in infinite ways depending on the goal and the context at hand, a higher degree of controllability is needed to apply captioning algorithms in complex scenarios. In this paper, we introduce a novel framework for image captioning which can generate diverse descriptions by allowing both grounding and controllability. Given a control signal in the form of a sequence or set of image regions, we generate the corresponding caption through a recurrent architecture which predicts textual chunks explicitly grounded on regions, following the constraints of the given control. Experiments are conducted on Flickr30k Entities and on COCO Entities, an extended version of COCO in which we add grounding annotations collected in a semi-automatic manner. Results demonstrate that our method achieves state of the art performances on controllable image captioning, in terms of caption quality and diversity. Code and annotations are publicly available at: https://github.com/aimagelab/show-control-and-tell.
Making Knowledge Tradable in Edge-AI Enabled IoT: A Consortium Blockchain-Based Efficient and Incentive Approach

Nowadays, benefit from more powerful edge computing devices and edge artificial intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find the knowledge derived from massive sensory data, such as cyber results or models of classification, and detection and prediction from physical environments. Heterogeneous edge-AI devices in IoT will generate isolated and distributed knowledge slices, thus knowledge collaboration and exchange are required to complete complex tasks in IoT intelligent applications with numerous selfish nodes. Therefore, knowledge trading is needed for paid sharing in edge-AI enabled IoT. Most existing works only focus on knowledge generation rather than trading in IoT. To address this issue, in this paper, we propose a peer-to-peer (P2P) knowledge market to make knowledge tradable in edge-AI enabled IoT. We first propose an implementation architecture of the knowledge market. Moreover, we develop a knowledge consortium blockchain for secure and efficient knowledge management and trading for the market, which includes a new cryptographic currency knowledge coin, smart contracts, and a new consensus mechanism proof of trading. Besides, a noncooperative game based knowledge pricing strategy with incentives for the market is also proposed. The security analysis and performance simulation show the security and efficiency of our knowledge market and incentive effects of knowledge pricing strategy. To the best of our knowledge, it is the first time to propose an efficient and incentive P2P knowledge market in edge-AI enabled IoT.
A novel deep multi-criteria collaborative filtering model for recommendation system

Recommender systems have been in existence everywhere with most of them using single ratings in prediction. However, multi-criteria predictions have been proved to be more accurate. Recommender systems have many techniques; collaborative filtering is one of the most commonly used. Deep learning has achieved impressive results in many domains such as text, voice, and computer vision. Lately, deep learning for recommender systems began to gain massive interest, and many recommendation models based on deep learning have been proposed. However, as far as we know, there is not yet any study which gathers multi-criteria recommendation and collaborative filtering with deep learning. In this work, we propose a novel multi-criteria collaborative filtering model based on deep learning. Our model contains two parts: in the first part, the model obtains the users and items’ features and uses them as an input to the criteria ratings deep neural network, which predicts the criteria ratings. Those criteria ratings constitute the input to the second part, which is the overall rating deep neural network and is used to predict the overall rating. Experiments on a real-world dataset demonstrate that our proposed model outperformed the other state-of-the-art methods, and this provides evidence pointing to the success of employing deep learning and multi-criteria in recommendation systems.