Reviewerly/exHarmony huggingface dataset:
User Discrimination of Content Produced by Generative Adversarial Networks

Artificial Intelligence (AI) is increasingly being introduced in several domains for classification and clustering of different types of existing information (e.g., text, images, audio, and video). Recently, improvements to Machine Learning (ML) and new approaches to the design and use of Neural Networks (NNs) enabled the development of algorithms that generate new content that mimics the features of the training dataset. Specifically, Generative Adversarial Networks (GANs) are particularly effective in producing content with unprecedented levels of fidelity. As a result, they can generate realistic images of people, vehicles, and nature. In this paper, we discuss the results of a study that investigated user perception of pictures generated using GANs, with specific regard to portraits featuring faces. Specifically, our experiment involved 551 participants who were asked to classify over 7000 real images and pictures generated by ML algorithms. Our findings show that users show low accuracy in discriminating images and, thus, demonstrate the effectiveness of GANs in producing content that can be perceived as realistic.
Medical Image Labelling and Semantic Understanding for Clinical Applications

Semantic concept detection contributes to machine understanding and learning from medical images; it also plays an important role in image reading and image-assisted diagnosis. In this study, the problem of detecting high-frequency concepts from medical images was transformed into a multi-label classification task. The transfer learning method based on convolutional neural networks (CNNs) was used to recognize high-frequency medical concepts. The image retrieval-based topic modelling method was used to obtain the semantically related concepts from images similar to the given medical images. Our group participated in the concept detection subtasks that were launched by ImageCLEFcaption 2018 and ImageCLEFmed Caption 2019. In the 2018 task, the CNN-based transfer learning method achieved an F1 score of 0.0928, while the retrieval-based topic model achieved an F1 score of 0.0907. Although the latter method recalled some low-frequency concepts, it heavily depended on the image retrieval results. For the latter 2019 task, we proposed body part-based pre-classification strategies and achieved an F1 score of 0.2235. The results indicated that the transfer learning-based multi-label classification method was more robust in high-frequency concept detection across different data sets, but there is still much room for improvement in large-scale open semantic concept detection research.
Divergence-Augmented Policy Optimization

In deep reinforcement learning, policy optimization methods need to deal with issues such as function approximation and the reuse of off-policy data. Standard policy gradient methods do not handle off-policy data well, leading to premature convergence and instability. This paper introduces a method to stabilize policy optimization when off-policy data are reused. The idea is to include a Bregman divergence between the behavior policy that generates the data and the current policy to ensure small and safe policy updates with off-policy data. The Bregman divergence is calculated between the state distributions of two policies, instead of only on the action probabilities, leading to a divergence augmentation formulation. Empirical experiments on Atari games show that in the data-scarce scenario where the reuse of off-policy data becomes necessary, our method can achieve better performance than other state-of-the-art deep reinforcement learning algorithms.
Universal Weak Variable-Length Source Coding on Countably Infinite Alphabets

Motivated from the fact that universal source coding on countably infinite alphabets (∞-alphabets) is not feasible, this work introduces the notion of “almost lossless source coding”. Analog to the weak variable-length source coding problem studied by Han (IEEE Trans. Inf. Theory, vol. 46, no. 4, pp. 1217-1226, Jul. 2000), almost lossless source coding aims at relaxing the lossless block-wise assumption to allow an average per-letter distortion that vanishes asymptotically as the block-length tends to infinity. In this setup, we show on one hand that Shannon entropy characterizes the minimum achievable rate (similarly to the case of finite alphabet sources) while on the other that almost lossless universal source coding becomes feasible for the family of finite-entropy stationary memoryless sources with ∞-alphabets. Furthermore, we study a stronger notion of almost lossless universality that demands uniform convergence of the average per-letter distortion to zero, where we establish a necessary and sufficient condition for the so-called family of “envelope distributions” to achieve it. Remarkably, this condition is the same necessary and sufficient condition needed for the existence of a strongly minimax (lossless) universal source code for the family of envelope distributions. Finally, we show that an almost lossless coding scheme offers faster rate of convergence for the (minimax) redundancy compared to the well-known information radius developed for the lossless case at the expense of tolerating a non-zero distortion that vanishes to zero as the block-length grows. This shows that even when lossless universality is feasible, an almost lossless scheme can offer different regimes on the rates of convergence of the (worst case) redundancy versus the (worst case) distortion.
Semi-supervised Sentiment Classification Method Based on Weibo Social Relationship

Sentiment classification of microblog text is one of the hotspots and important research issues in text sentiment analysis. Aiming at the problem that the existing researches mostly assume that the micro-blog sentiments are independent of each other and have strong dependence on the training set, a semi-supervised sentiment classification method based on Weibo social relationship is proposed. The method utilizes the user’s theme sentimental consistency and the approval of social relationships (like and repost) in Weibo to establish the sentimental relationship between microblogs to solve the problem that microblog sentiments are independent of each other. Semi-supervised sentimental classification model is constructed by establishing the sentimental relationship between labeled micro-blog and unlabeled micro-blog, which reduced the dependence on training set. Specifically, the semi-supervised sentiment classification method was constructed by constructing a microblog sentimental relationship matrix using the Laplacian matrix of the above microblog social relationship graph, and adding to the text content based classification model. Climbing the real dataset of Sina Weibo for experiment, the experimental results showed that the method is superior to other typical sentiment classification methods in terms of accuracy and recall rate. The validity of this method is verified and the dependence on training data set is reduced to a certain extent.
iStar-p: A Modelling Language for Requirements Prioritization

This paper proposes the iStar-p model aiming to provide a visual requirements modelling language with prioritization information. This model extends i*, a goal-oriented modeling language, by including essential prioritization information, such as prioritization technique, prioritization criteria, the involved stakeholders in the prioritization and their weight, as well as the requirements priority. Early evaluation of the proposal indicates that not only it is easy to be applied and useful, but it also increases the transparency of the prioritization process, by explicitly expressing the factors used to calculate priorities.
EmbNum+: Effective, Efficient, and Robust Semantic Labeling for Numerical Values

Abstract In recent years, there has been an increasing interest in numerical semantic labeling, in which the meaning of an unknown numerical column is assigned by the label of the most relevant columns in predefined knowledge bases. Previous methods used the p value of a statistical hypothesis test to estimate the relevance and thus strongly depend on the distribution and data domain. In other words, they are unstable for general cases, when such knowledge is undefined. Our goal is solving semantic labeling without using such information while guaranteeing high accuracy. We propose EmbNum+, a neural numerical embedding for learning both discriminant representations and a similarity metric from numerical columns. EmbNum+ maps lists of numerical values of columns into feature vectors in an embedding space, and a similarity metric can be calculated directly on these feature vectors. Evaluations on many datasets of various domains confirmed that EmbNum+ consistently outperformed other state-of-the-art approaches in terms of accuracy. The compact embedding representations also made EmbNum+ significantly faster than others and enable large-scale semantic labeling. Furthermore, attribute augmentation can be used to enhance the robustness and unlock the portability of EmbNum+, making it possible to be trained on one domain but applicable to many different domains.
Sequential Learning with Active Partial Labeling for Building Metadata

Modern buildings are instrumented with thousands of sensing and control points. The ability to automatically extract the physical context of each point, e.g., the type, location, and relationship with other points, is the key to enabling building analytics at scale. However, this process is costly as it usually requires domain expertise with a deep understanding of the building system and its point naming scheme. In this study, we aim to reduce the human effort required for mapping sensors to their context, i.e., metadata mapping. We formulate the problem as a sequential labeling process and use the conditional random field to exploit the regular and dependent structures observed in the metadata. We develop a suite of active learning strategies to adaptively select the most informative subsequences in point names for human labeling, which significantly reduces the inputs from domain experts. We evaluated our approach on three different buildings and observed encouraging performance in metadata mapping from the proposed solution.
Efficient Adversarial Training with Transferable Adversarial Examples

Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training. In this paper, we first show that there is high transferability between models from neighboring epochs in the same training process, i.e., adversarial examples from one epoch continue to be adversarial in subsequent epochs. Leveraging this property, we propose a novel method, Adversarial Training with Transferable Adversarial Examples (ATTA), that can enhance the robustness of trained models and greatly improve the training efficiency by accumulating adversarial perturbations through epochs. Compared to state-of-the-art adversarial training methods, ATTA enhances adversarial accuracy by up to 7.2% on CIFAR10 and requires 12~14x less training time on MNIST and CIFAR10 datasets with comparable model robustness.
Performance of Internet of Things (IOT) Based Healthcare Secure Services and Its Importance: Issue and Challenges

The IoT has a variety of application domains, including medical assistance. The IoT revolution is redesigning modern medical care with promising technological, economic and social prospects. This article shared short advances in IoT-based healthcare technologies and reviewed cutting-edge networking architectures / platforms, industrial applications and trends in IoT-based healthcare solutions. In addition, this article has analyzed other features in addition to the security and privacy of the IoT, including security requirements, threat models and attack taxonomies from the point of view of medical care. Furthermore, this article also proposed a smart collaborative security model to minimize security risk; will di scuss different innovations, such as the widespread use of big data, artificial intelligence in the context of medical care, various IoT and e-Health policies and regulations have been addressed worldwide to determine how economies and societies can facilitate sustainable development terms and provides some avenues for future research on health care based on the IoT based on a series of open problems and challenges.
A Two-Stage Multi-task Learning-Based Method for Selective Unsupervised Domain Adaptation

Multi-task learning and domain adaption has been successfully applied in a number of real-world NLP applications, such as sentiment analysis. However, major challenges rising from real-world applications include that there may be little or even no labels in some domains, and there are large shifts between domains. In this paper, we consider the multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA), which is a popular application scenario in MS-UDA. In general, the variance among different domains can result in huge semantic gaps among domains. To alleviate this issue for MS-UDA, we propose a two-stage domain adaptation framework: (1) a supervised multi-task learning is applied to extract domain-common features and domain-specific features from all the available source domains; (2) an adversarial training over the target domain will be conducted to generate target-specific features from the one of the most similar source domain. Experimental results on two sentiment analysis datasets have demonstrated that the promising performance of our framework, which not only outperforms unsupervised state-of-the-art competitors but also is very close to supervised methods and even better than supervised methods on some tasks.
Sankhya: An Unbiased Benchmark for Bangla Handwritten Digits Recognition

The rise of artificial intelligence technology along with machine and deep learning are opening up almost limitless possibilities. In recent years, application-based researchers in machine learning and deep learning have started developing solutions for many practical problems. Handwriting recognition is one such area of interest. Bangla, being the seventh most spoken language in the world, is not an exception. However, unlike English, there have not been concerted formal attempts in building a benchmark in comparing the different approaches reported in the literature, mainly because of the lack of openly and freely available datasets and diversity of the approaches without formal comparative studies. In this research paper, we seek to rectify this gap. We have focused on benchmarking five robust algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), on all publicly available Bangla handwriting digits datasets, including Ekush, NumtaDB, CMARTdb, and BDRW. NumtaDB itself is a collection of five handwriting datasets. We have worked on fine-tuning these algorithms by finding the best possible hyper-parameters of these algorithms. It is our hope that Sankhya will work as a beginning point of an open and verifiable benchmarking process that we plan to repeat every two years for now on and set a standard for testing and validating newer and novel algorithms that will be reported in this area in the future. In addition, we have extensively compared our research with other states of the art research and our versions of these algorithms are now outperforming every reported result on these datasets.All the datasets we used are open-sourced. In addition, we are making the.csv version of these datasets available in public GitHub. Of all the models we tested, the Sankhya CNN model performed the best for all these datasets, which we fine-tuned specifically for Bangla character recognition. We are making this CNN model available in public GitHub.
Codes from incidence matrices of (<i>n</i>, 1)-arrangement graphs and (<i>n</i>, 2)-arrangement graphs

We examine the p-ary linear codes from incidence matrix of the (n, k)-arrangement graphs for k = 2, n –2, n – 1. All the main parameters of the codes are obtained as respectively. We examine also the p-ary linear codes from incidence matrix of graphs such as Ljubljana graph, Heawood graph and the main parameters of the codes are [168, 111, 3]p, [21, 13, 3]p respectively. Any transitive subgroup of automorphism groups of these graphs can be used for full permutation decoding using the corresponding codes. All the above codes can be used for full error correction by permutation decoding.
Cybersecurity and its integration with safety for transport systems: not a formal fulfillment but an actual commitment

Vulnerabilities and hacking cases in transport systems are already documented. In the last two or three years guidelines and methods for cybersecurity in transport systems have been drafted and the importance of convergence of safety and cybersecurity has been stressed. Many companies today offer products and services for this field. There is the risk, however, that such an important matter will be considered as a formal fulfillment. In order to promote a real awareness about vulnerabilities affecting transport systems technologies, which rely mostly on embedded devices, and about the attacks that can exploit them, we use as an example a real incident involving a safety-critical subsystem of a railway vehicle (that we think of as a “cyber-physical system”) to develop a case study concerning a cyberattack, aiming to show that devices and technological systems vulnerabilities must not be neglected in risk analysis. Conversely, they must be taken into account the same way as hazards in safety assessments for an effective integration between cybersecurity and safety. Therefore we propose an attack-fault tree for the case study as an example of integrated risk analysis.
Causal Relational Learning

Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard in causal inference is performing randomized controlled trials; unfortunately these are not always feasible due to ethical, legal, or cost constraints. As an alternative, methodologies for causal inference from observational data have been developed in statistical studies and social sciences. However, existing methods critically rely on restrictive assumptions such as the study population consisting of homogeneous elements that can be represented in a single flat table, where each row is referred to as a unit. In contrast, in many real-world settings, the study domain naturally consists of heterogeneous elements with complex relational structure, where the data is naturally represented in multiple related tables. In this paper, we present a formal framework for causal inference from such relational data. We propose a declarative language called CaRL for capturing causal background knowledge and assumptions and specifying causal queries using simple Datalog-like rules.CaRL provides a foundation for inferring causality and reasoning about the effect of complex interventions in relational domains. We present an extensive experimental evaluation on real relational data to illustrate the applicability of CaRL in social sciences and healthcare.
The Design of S-box Based on Combined Chaotic Map

The strength of the substitution box (S-box) determines the security of the cryptographic algorithm because it's the only nonlinear component in the block cipher. Because of the disadvantages of non-uniformity sequence and limited range in the one-dimension (1D) chaotic map, this paper constructs the logistic map and the sine map into a combined chaotic map, and a new S-box construction method based on this combined chaotic map is presented. Performance tests were performed on the S-box, including nonlinearity, linear probability, differential probability, strict avalanche criterion, bits independence criterion. Compared with others S-box, this result indicates that the S-box has more excellent cryptographic performance and can be used as a nonlinear component in the lightweight block cipher algorithm.
Detecting Human-Object Interactions with Action Co-occurrence Priors

A common problem in human-object interaction (HOI) detection task is that numerous HOI classes have only a small number of labeled examples, resulting in training sets with a long-tailed distribution. The lack of positive labels can lead to low classification accuracy for these classes. Towards addressing this issue, we observe that there exist natural correlations and anti-correlations among human-object interactions. In this paper, we model the correlations as action co-occurrence matrices and present techniques to learn these priors and leverage them for more effective training, especially in rare classes. The utility of our approach is demonstrated experimentally, where the performance of our approach exceeds the state-of-the-art methods on both of the two leading HOI detection benchmark datasets, HICO-Det and V-COCO.
Procedure for detecting the shape and size of defects on metallic substrates under composite repairs using shearography

This paper presents a numerical-experimental procedure to characterize through-holes with arbitrary shapes present on metallic substrates under composite material sleeves using pulsed laser shearography and dynamic excitation combined to the finite element method. The so-called fitting process consists of matching experimental and numerical results in order to determine the shape and dimensions of the holes under the composite repair, or even quantify defects between layers of the composite laminate. The results show that the technique is capable of characterizing, in the worst case, the geometry of a hole with 83% accuracy and its respective area with a maximum error of approximately 20%. The advantageous results achieved in this research show that the fitting process can be very useful for real applications in the oil and petroleum industry.
Associating Images with Sentences Using Recurrent Canonical Correlation Analysis

Associating images with sentences has drawn much attention recently. Existing methods commonly represent an image by indistinctively describing all its contents in an one-time static way, which ignores two facts that (1) the association analysis can only be effective for partial salient contents and the associated sentence, and (2) visual information acquisition is a dynamical rather than static process. To deal with this issue, we propose a recurrent canonical correlation analysis (RCCA) method for associating images with sentences. RCCA includes a contextual attention-based LSTM-RNN which can selectively attend to salient regions of an image at each time step, and then represent all the salient contents within a few steps. Different from existing attention-based models, our model focuses on the modelling of contextual visual attention mechanism for the task of association analysis. RCCA also includes a conventional LSTM-RNN for sentence representation learning. The resulting representations of images and sentences are fed into CCA to maximize linear correlation, where parameters of LSTM-RNNs and CCA are jointly learned. Due to the effective image representation learning, our model can well associate images with sentences with complex contents, and achieve better performance in terms of image annotation and retrieval.