Reviewerly/exHarmony huggingface dataset:
Deep Multi-Critic Network for accelerating Policy Learning in multi-agent environments

Humans live among other humans, not in isolation. Therefore, the ability to learn and behave in multi-agent environments is essential for any autonomous system that intends to interact with people. Due to the presence of multiple simultaneous learners in a multi-agent learning environment, the Markov assumption used for single-agent environments is not tenable, necessitating the development of new Policy Learning algorithms. Recent Actor-Critic algorithms proposed for multi-agent environments, such as Multi-Agent Deep Deterministic Policy Gradients and Counterfactual Multi-Agent Policy Gradients, find a way to use the same mathematical framework as single agent environments by augmenting the Critic with extra information. However, this extra information can slow down the learning process and afflict the Critic with Curse of Dimensionality. To combat this, we propose a novel Deep Neural Network configuration called Deep Multi-Critic Network. This architecture works by taking a weighted sum over the outputs of multiple critic networks of varying complexity and size. The configuration was tested on data collected from a real-world multi-agent environment. The results illustrate that by using Deep Multi-Critic Network, less data is needed to reach the same level of performance as when not using the configuration. This suggests that as the configuration learns faster from less data, then the Critic may be able to learn Q-values faster, accelerating Actor training as well.
Towards Adversarial Robustness via Feature Matching

Image classification systems are known to be vulnerable to adversarial attacks, which are imperceptibly perturbed but lead to spectacularly disgraceful classification. Adversarial training is one of the most effective defenses for improving the robustness of classifiers. We introduce an enhanced adversarial training approach in this work. Motivated by human's consistently accurate perception of surroundings, we explore the artificial attention of deep neural networks in the context of adversarial classification. We begin with an empirical analysis of how the attention of artificial systems will change as the model undergoes adversarial attacks. Observation is that the class-specific attention gets diverted and subsequently induces wrong prediction. To that end, we propose a regularizer encouraging the consistency in the artificial attention on the clean image and its adversarial counterpart. Our method shows improved empirical robustness over the state-of-the-art, secures 55.74% adversarial accuracy on CIFAR-10 with perturbation budget of 8/255 under the challenging untargeted attack in white-box settings. Further evaluations on CIFAR-100 also show our potential for a desirable boost in adversarial robustness for deep neural networks. Code and trained models of our work are available at: https://github.com/lizhuorong/Towards-Adversarial-Robustness-via-Feature-matching.
Discrete and Fuzzy Models of Time Series in the Tasks of Forecasting and Diagnostics

The development of the economy and the transition to industry 4.0 creates new challenges for artificial intelligence methods. Such challenges include the processing of large volumes of data, the analysis of various dynamic indicators, the discovery of complex dependencies in the accumulated data, and the forecasting of the state of processes. The main point of this study is the development of a set of analytical and prognostic methods. The methods described in this article based on fuzzy logic, statistic, and time series data mining, because data extracted from dynamic systems are initially incomplete and have a high degree of uncertainty. The ultimate goal of the study is to improve the quality of data analysis in industrial and economic systems. The advantages of the proposed methods are flexibility and orientation to the high interpretability of dynamic data. The high level of the interpretability and interoperability of dynamic data is achieved due to a combination of time series data mining and knowledge base engineering methods. The merging of a set of rules extracted from the time series and knowledge base rules allow for making a forecast in case of insufficiency of the length and nature of the time series. The proposed methods are also based on the summarization of the results of processes modeling for diagnosing technical systems, forecasting of the economic condition of enterprises, and approaches to the technological preparation of production in a multi-productive production program with the application of type 2 fuzzy sets for time series modeling. Intelligent systems based on the proposed methods demonstrate an increase in the quality and stability of their functioning. This article contains a set of experiments to approve this statement.
Striving for a Safer and More Ergonomic Workplace: Acceptability and Human Factors Related to the Adoption of AR/VR Glasses in Industry 4.0

The word smart is very popular these days, as a result of nearly everything being digital today. Background information: In this digital world, everything is interconnected by smart devices. The driving force behind this is today’s Industry 4.0 environment, which is affected by many factors, including the ergonomic and safe design of new technology, ensuring the safety of factory operators, whilst increasing productivity and profits. The authors answer the following research questions: Are AR/VR systems or devices proposed for industrial use capable of meeting the needs of the industry (including sustainability)? Are these AR/VR devices designed to ensure easy use and efficient training of factory operators? Do the proposals of the reviewed research papers place sufficient emphasis on creating ergonomic workplaces? These publications were categorized into three subcategories based on the used key technology, research or application area, and their main purposes. Conclusion: Virtual reality, augmented reality, and IoT are becoming increasingly more suitable for industrial use, despite facing scrutiny and criticism.
Facial Expression Recognition Based on Local Features of Transfer Learning

Due to the difficulty and low accuracy of facial expression recognition caused by facial occlusion issue, a local-face expression recognition model based on migration learning was proposed and Inception-v3 pre-training model was used to build the network. Histogram equalization, ROF denoising, affine change, image correction and other image preprocessing were carried out on the image data. Extract the data characteristics of preprocessed data by using the Inception-v3 model, and put the extracted feature information into a new classifier for classification; The accuracy of the proposed model was tested by CK+ and Jaffe data sets, and compared with other models, the accuracy was up to 98.2%, which proved that the proposed model had strong robustness for facial expression recognition of face data covered by the face.
A unified robust framework for multi-view feature extraction with L2,1-norm constraint

Multi-view feature extraction methods mainly focus on exploiting the consistency and complementary information between multi-view samples, and most of the current methods apply the F-norm or L2-norm as the metric, which are sensitive to the outliers or noises. In this paper, based on L2,1-norm, we propose a unified robust feature extraction framework, which includes four special multi-view feature extraction methods, and extends the state-of-art methods to a more generalized form. The proposed methods are less sensitive to outliers or noises. An efficient iterative algorithm is designed to solve L2,1-norm based methods. Comprehensive analyses, such as convergence analysis, rotational invariance analysis and relationship between our methods and previous F-norm based methods illustrate the effectiveness of our proposed methods. Experiments on two artificial datasets and six real datasets demonstrate that the proposed L2,1-norm based methods have better performance than the related methods.
DOES ECONOMIC FREEDOM MATTER TO FOREIGN DIRECT INVESTMENT IN SUB-SAHARAN AFRICA?

This paper examines whether economic freedom matters to inward Foreign Direct Investment in Sub-Saharan Africa. A key contribution of this study is that it uses disaggregated measures of economic freedom from the Heritage Foundation and the Fraser Institute to unveil which components of economic freedom matter for inward FDI in a sample of 40 SSA countries during the 1997-2016 period. The estimation is done using panel data models with fixed and random effects. The results indicate that higher overall economic freedom is a key determinant of inward FDI in SSA. However, there is clear evidence that it is regulatory efficiency (business, labor and monetary freedom), fiscal freedom (low tax burden), market openness, market size, trade openness, and strong telecommunications infrastructure that are the key factors in attracting FDI in SSA. While monetary freedom (price stability) is important, financial freedom (banking and financial sector independence) or investment freedom seem less critical. Results on trade freedom suggest that SSA tends to attract tariff-jumping and efficiency-seeking FDI. There is no consistent evidence that stronger rule of law and property rights really matter for inward FDI in SSA. It appears that FDI tends to flow to SSA countries that have lax property rights protection.Keywords: Foreign Direct Investment, Economic Freedom, Sub-Saharan AfricaJEL Classifications: C33, F21, O43, N27DOI: https://doi.org/10.32479/ijefi.9597
Measuring Coarse-to-Fine Texture and Geometric Distortions for Quality Assessment of DIBR-Synthesized Images

A synthesized view can be generated via Depth-Image-Based Rendering (DIBR) technique using one (or more) color images and the associated depth maps. However, several artifacts may occur in the synthesized views due to the imperfect color images, depth maps or texture inpainting techniques, which cannot be effectively estimated by the conventional quality metrics designed for natural images. In this paper, a new quality metric is proposed to evaluate DIBR-synthesized images by measuring texture and geometric distortions. The artifacts are first analyzed on different phases of the synthesis process, and the associated features are extracted to estimate the degree of texture and geometric distortions from both coarse and fine scales. Finally, individual quality scores are aggregated into an overall quality via regression. Experimental results on three publicly available DIBR datasets demonstrate the superiority of the proposed method over the state-of-the-art quality models.
Fully-Unsupervised Embeddings-Based Hypernym Discovery

The hypernymy relation is the one occurring between an instance term and its general term (e.g., “lion” and “animal”, “Italy” and “country”). This paper we addresses Hypernym Discovery, the NLP task that aims at finding valid hypernyms from words in a given text, proposing HyperRank, an unsupervised approach that therefore does not require manually-labeled training sets as most approaches in the literature. The proposed algorithm exploits the cosine distance of points in the vector space of word embeddings, as already proposed by previous state of the art approaches, but the ranking is then corrected by also weighting word frequencies and the absolute level of similarity, which is expected to be similar when measuring co-hyponyms and their common hypernym. This brings us two major advantages over other approaches—(1) we correct the inadequacy of semantic similarity which is known to cause a significant performance drop and (2) we take into accounts multiple words if provided, allowing to find common hypernyms for a set of co-hyponyms—a task ignored in other systems but very useful when coupled with set expansion (that finds co-hyponyms automatically). We then evaluate HyperRank against the SemEval 2018 Hypernym Discovery task and show that, regardless of the language or domain, our algorithm significantly outperforms all the existing unsupervised algorithms and some supervised ones as well. We also evaluate the algorithm on a new dataset to measure the improvements when finding hypernyms for sets of words instead of singletons.
String Sanitization: A Combinatorial Approach

String data are often disseminated to support applications such as location-based service provision or DNA sequence analysis. This dissemination, however, may expose sensitive patterns that model confidential knowledge (e.g., trips to mental health clinics from a string representing a user’s location history). In this paper, we consider the problem of sanitizing a string by concealing the occurrences of sensitive patterns, while maintaining data utility. First, we propose a time-optimal algorithm, TFS-ALGO, to construct the shortest string preserving the order of appearance and the frequency of all non-sensitive patterns. Such a string allows accurately performing tasks based on the sequential nature and pattern frequencies of the string. Second, we propose a time-optimal algorithm, PFS-ALGO, which preserves a partial order of appearance of non-sensitive patterns but produces a much shorter string that can be analyzed more efficiently. The strings produced by either of these algorithms may reveal the location of sensitive patterns. In response, we propose a heuristic, MCSR-ALGO, which replaces letters in these strings with carefully selected letters, so that sensitive patterns are not reinstated and occurrences of spurious patterns are prevented. We implemented our sanitization approach that applies TFS-ALGO, PFS-ALGO and then MCSR-ALGO and experimentally show that it is effective and efficient.
A Novel Correction Algorithm for Distorted QR-code Image

In this paper, A correction algorithm is proposed for QR-code that cannot be scanned by common tools. First, we use the concept of contour tree to filter the contours and cluster them to extract the required positioning markers. Secondly, the three methods of slope comparison, coordinate comparison and angle rotation are using to determine the correction point. Finally, the image is corrected using an affine transformation. The experimental results show that the QR-code corrected by the algorithm proposed in this paper can be scanned by common tools, which shows that the proposed algorithm has certain useful value.
k-means Cluster Shape Implications

We present a novel justification why k-means clusters should be (hyper)ball-shaped ones. We show that the clusters must be ball-shaped to attain motion-consistency. If clusters are ball-shaped, one can derive conditions under which two clusters attain the global optimum of k-means. We show further that if the gap is sufficient for perfect separation, then an incremental k-means is able to discover perfectly separated clusters. This is in conflict with the impression left by an earlier publication by Ackerman and Dasgupta. The proposed motion-transformations can be used to the new labeled data for clustering from existent ones.
Handwriting Recognition with Artificial Neural Networks a Decade Literature Review

Deep Learning Artificial Neural Networks has pushed forward researches in the field of pattern recognition, furthermore in human handwriting recognition. From online to offline approach, signature verification, writing or writer identification, segmentation or features extraction, a multitude of Artificial Neural Networks (ANNs) models are applied in the process. This paper focuses on the literature review of human handwriting recognition with ANN's over the last decade. We propose an exploratory analysis of 294 research papers collected from five indexed research engines: ACM Digital Library, IEEE digital library, Science Direct, Scopus and Web of Science. Our aim is to provide a research papers distribution across years and journals, a Keywords frequency analysis using cloud visualization, and a Natural Language Processing Topic Modeling using Non-Negative Matrix Factorization (NMF). The results of this study show that the number of research papers reached noticeably a peak in the 2010 with 44 published papers; also Pattern Recognition was the top publishing journal with 12 published papers. As for the topic modeling using NMF we obtained 3 topics listed as follows: 1) Feature Extraction and segmentation techniques for Handwritten Texts Recognition; 2) Signature Verification in Biometric security for Off-line Authentication; 3) Assessment Systems for Student Identification
An Augmented Reality System for Operator Training in the Footwear Sector

Reverse engineering is of great interests for computer aided verification of freeform shapes.Accurate model is crucial to assure the validity of geometry assessment.Freeform reconstruction is especially challenging when the parts are thin and/or self-occlusion.An integrated measuring system was proposed by incorporating laser scanner into hardware mechanism on reconstructing model of complex shape.Mathematical model mapping the transformation relationship between system components and cylinder-based algorithm for mechanism calibration was presented.The proposed system was validated on blade measurement.Experimental results showed that the proposed system and calibration method were able to provide a reliable measurement on freeform shape.
Press RESET: Digitalising Education in Disruptive Times

The world is going through pandemic times, and about 85% of learners are affected by the fact that educational institutions have suspended their face to face classes. However, teachers are transitioning towards online education and the current paper looks at an example of good practice in finding a unitary teaching strategy during this crisis. Our aim is to review the practices and instruments used by colleagues from the West University of Timisoara, Romania, as well as to group together some solutions to potential issues arising from the sudden changes in pedagogical approach. We would like to propose seven steps “in resetting” education in disruptive times, which can be followed for a smooth and efficient digital transformation.
Heritage education experience supported in augmented reality

Heritage Education is a process that traditionally takes place within the classroom, where the teacher transmits some knowledge to the student; however, new ways have been implemented to develop Heritage Education processes supported by different technologies where Augmented Reality is included. This paper describes the process of implementing the “Framework for Heritage Education supported by Augmented Reality”, which proposes guidelines for the development of technology solutions based on Augmented Reality in the context of Heritage Education, through the deployment of an application called “Social Heritage ”developed under the guidelines of this Framework. The paper describes the implementation process of the Framework in the city of Cartagena - Colombia and the validation process with the end users. For the evaluation of the experience, the motivation test was used for the instructional design that measures the categories: Attention, Relevance, Confidence and Satisfaction. This test was applied to a group of students and a group of tourists. The validation results show that the use of augmented reality motivated both students and tourists in the development of heritage education processes. This allowed us to conclude that augmented reality technology is pertinent when promoting learning processes in the context of heritage.
A Traffic Signal Recognition Algorithm Based on Self-paced Learning and Deep Learning

Traffic signal recognition is a critical function of the intelligent vehicle system (IVS).Many algorithms can achieve a high accuracy in traffic signal recognition.But these algorithms have poor generalization ability, and their recognition rates vary greatly with datasets.These defects hinder their application in unmanned driving.To solve the problem, this paper introduces self-paced learning (SPL) to the image recognition of traffic signs.Based on complexity, the SPL automatically classifies samples into multiple sets.If machine learning (ML) algorithm is trained by the sample sets in ascending order of complexity, a universal computing model will be obtained, and the ML algorithm will have a better generalization ability.Here, the support vector machine (SVM) is adopted as the classifier for traffic sign detection, and the convolutional neural network (CNN) is employed as the classifier for traffic sign recognition.Then, the two classifiers were trained by the SPL on two public datasets: German Traffic Sign Detection Benchmark (GTSDB) and German Traffic Sign Recognition Benchmark (GTSRB).The model obtained through the training was tested on Belgium Traffic Sign Detection Benchmark (BTSDB) and KITTI datasets.The results show that the obtained computing model achieved similar accuracy on the training sets and test sets.Hence, the SPL can indeed enhance the generalization ability of ML algorithms, and promote the application of CNN, SVM, and other ML algorithms in unmanned driving.
Explicit Semantic Decomposition for Definition Generation

Definition generation, which aims to automatically generate dictionary definitions for words, has recently been proposed to assist the construction of dictionaries and help people understand unfamiliar texts.However, previous works hardly consider explicitly modeling the "components" of definitions, leading to under-specific generation results.In this paper, we propose ESD, namely Explicit Semantic Decomposition for definition generation, which explicitly decomposes meaning of words into semantic components, and models them with discrete latent variables for definition generation.Experimental results show that ESD achieves substantial improvements on WordNet and Oxford benchmarks over strong previous baselines.
Convergence Rates of Variational Inference in Sparse Deep Learning

Variational inference is becoming more and more popular for approximating intractable posterior distributions in Bayesian statistics and machine learning. Meanwhile, a few recent works have provided theoretical justification and new insights on deep neural networks for estimating smooth functions in usual settings such as nonparametric regression. In this paper, we show that variational inference for sparse deep learning retains the same generalization properties than exact Bayesian inference. In particular, we highlight the connection between estimation and approximation theories via the classical bias-variance trade-off and show that it leads to near-minimax rates of convergence for Holder smooth functions. Additionally, we show that the model selection framework over the neural network architecture via ELBO maximization does not overfit and adaptively achieves the optimal rate of convergence.
A secure VM allocation scheme to preserve against co-resident threat

Preserving the secrecy in cloud system is one of the biggest concerns for the cloud customers who faces security risks in the context of load balancing. The co-resident attacks are widely used by attackers, where malicious users build side channels and extract private information from VMs. The proposed model evaluates the possibility of VM co-residency and success rate of an attack. The emphasis of this paper is to reduce the possibility of co-resident attacks among different users. When cloud data centres receive requests for the tasks deployment, then the proposed system will find out the secure physical machine under VM allocation policies while avoiding the threats. The performance is calculated by these metrics: makespan, resource utilisation, co-residency probability and co-resident success rate. The results show that the most virtual machine allocation policy (MVMP) effectively reduces the risk under the safe states. The framework significantly improves the security by reducing the shared servers up to 32.2% and enhances the resource utilisation up to 44.14% over least VM allocation policy (LVMP), round robin VM allocation policy (RRVMP) allocation schemes.