Reviewerly/exHarmony huggingface dataset:
A Privacy-Preserving Machine Learning Scheme Using EtC Images

We propose a privacy-preserving machine learning scheme with encryption-then-compression (EtC) images, where EtC images are images encrypted by using a block-based encryption method proposed for EtC systems with JPEG compression. In this paper, a novel property of EtC images is first discussed, although EtC ones was already shown to be compressible as a property. The novel property allows us to directly apply EtC images to machine learning algorithms non-specialized for computing encrypted data. In addition, the proposed scheme is demonstrated to provide no degradation in the performance of some typical machine learning algorithms including the support vector machine algorithm with kernel trick and random forests under the use of z-score normalization. A number of facial recognition experiments with are carried out to confirm the effectiveness of the proposed scheme.
Optimal Quantum Control with Poor Statistics

Control of quantum systems is a central element of high-precision experiments and the development of quantum technological applications. Control pulses that are typically temporally or spatially modulated are often designed based on theoretical simulations. As we gain control over larger and more complex quantum systems, however, we reach the limitations of our capabilities of theoretical modeling and simulations, and learning how to control a quantum system based exclusively on experimental data can help us to exceed those limitations. Due to the intrinsic probabilistic nature of quantum mechanics, it is fundamentally necessary to repeat measurements on individual quantum systems many times in order to estimate the expectation value of an observable with good accuracy. Control algorithms requiring accurate data can thus imply an experimental effort that negates the benefits of avoiding theoretical modeling. We present a control algorithm based on Bayesian optimization that finds optimal control solutions in the presence of large measurement shot noise and even in the limit of single-shot measurements. With several numerical and experimental examples we demonstrate that this method is capable of finding excellent control solutions with minimal experimental effort
Distributed Multi-Object Tracking Under Limited Field of View Sensors

We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">novel</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed multi-object tracking algorithm</i> . To accomplish this, we first formalise the concept of label consistency, determine a sufficient condition to achieve it and develop a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">label consensus approach</i> that reduces label inconsistency caused by objects’ movements from one node’s limited FoV to another. Second, we develop a distributed multi-object fusion algorithm that fuses local multi-object state estimates instead of local multi-object densities. This algorithm: i) requires significantly less processing time than multi-object density fusion methods; ii) achieves better tracking accuracy by considering Optimal Sub-Pattern Assignment (OSPA) tracking errors over several scans rather than a single scan; iii) is agnostic to local multi-object tracking techniques, and only requires each node to provide a set of estimated tracks. Thus, it is not necessary to assume that the nodes maintain multi-object densities, and hence the fusion outcomes do not modify local multi-object densities. Numerical experiments demonstrate our proposed solution’s real-time computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios.
Detecting the Security Level of Various Cryptosystems Using Machine Learning Models

With recent advancements in multimedia technologies, the security of digital data has become a critical issue. To overcome the vulnerabilities of current security protocols, researchers tend to focus their efforts on modifying existing protocols. Over the last few decades, though, several proposed encryption algorithms have been proven insecure, leading to major threats against important data. Using the most appropriate encryption algorithm is a very important means of protection against such attacks, but which algorithm is most appropriate in any particular situation will also be dependent on what sort of data is being secured. However, testing potential cryptosystems one by one to find the best option can take up an important processing time. For a fast and accurate selection of appropriate encryption algorithms, we propose a security level detection approach for image encryption algorithms by incorporating a support vector machine (SVM). In this work, we also create a dataset using standard encryption security parameters, such as entropy, contrast, homogeneity, peak signal to noise ratio, mean square error, energy, and correlation. These parameters are taken as features extracted from different cipher images. Dataset labels are divided into three categories based on their security level: strong, acceptable, and weak. To evaluate the performance of our proposed model, we have performed different analyses (f1-score, recall, precision, and accuracy), and our results demonstrate the effectiveness of this SVM-supported system.
Camouflaged Object Detection and Tracking: A Survey

Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and there exist several essential survey papers. However, the research on camouflage object detection and tracking is limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this paper, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This paper also addresses several issues of interest as well as future research direction in this area. We hope this paper will help the reader to learn the recent advances in camouflaged object detection and tracking.
The DLPS: A New Framework for Benchmarking Blockchains

Distributed Ledger Technologies (DLT) promise to revolutionize business ecosystems by permitting secure transactions without intermediaries.A widely recognized challenge that inhibits the uptake of DLT is scalability and performance.Hence, quantifying key metrics such as throughput and latency is crucial for designing DLT-based infrastructures, applications, and ecosystems.However, current benchmarking frameworks for blockchains 1 do not cover the whole benchmarking process; impeding transparent comparisons of different DLT networks.In this paper, we present the Distributed Ledger Performance Scan (DLPS), an open-source 2 framework for end-to-end performance characterizations of blockchains, addressing the need to transparently and automatically evaluate the performance of highly customizable configurations.We describe our new framework and argue that it significantly improves existing DLT benchmarking solutions.To demonstrate the capabilities of the DLPS, we also summarize the main results obtained from a series of experiments that we have conducted with it, giving a first comprehensive comparison of essential scalability properties of several commonly used enterprise blockchains.
Automatic whole slide pathology image diagnosis framework via unit stochastic selection and attention fusion

Pathology tissue slides are taken as the gold standard for the diagnosis of most cancer diseases. Automatic pathology slide diagnosis is still a challenging task for researchers because of the high-resolution, significant morphological variation, and ambiguity between malignant and benign regions in whole slide images (WSIs). In this study, we introduce a general framework to automatically diagnose different types of WSIs via unit stochastic selection and attention fusion. For example, a unit can denote a patch in a histopathology slide or a cell in a cytopathology slide. To be specific, we first train a unit-level convolutional neural network (CNN) to perform two tasks: constructing feature extractors for the units and for estimating a unit’s non-benign probability. Then we use our novel stochastic selection algorithm to choose a small subset of units that are most likely to be non-benign, referred to as the Units Of Interest (UOI), as determined by CNN. Next, we use the attention mechanism to fuse the representations of the UOI to form a fixed-length descriptor for the WSI’s diagnosis. We evaluate the proposed framework on three datasets: histological thyroid frozen sections, histological colonoscopy tissue slides, and cytological cervical pap smear slides. The framework achieves diagnosis accuracies higher than 0.8 and AUC values higher than 0.85 in all three applications. Experiments demonstrate the generality and effectiveness of the proposed framework and its potentiality for clinical applications.
Minibatch Forward-Backward-Forward Methods for Solving Stochastic Variational Inequalities

We develop a new stochastic algorithm for solving pseudomonotone stochastic variational inequalities. Our method builds on Tseng’s forward-backward-forward algorithm, which is known in the deterministic literature to be a valuable alternative to Korpelevich’s extragradient method when solving variational inequalities over a convex and closed set governed by pseudomonotone Lipschitz continuous operators. The main computational advantage of Tseng’s algorithm is that it relies only on a single projection step and two independent queries of a stochastic oracle. Our algorithm incorporates a minibatch sampling mechanism and leads to almost sure convergence to an optimal solution. To the best of our knowledge, this is the first stochastic look-ahead algorithm achieving this by using only a single projection at each iteration.
The Relationship Between Referral of Touch and the Feeling of Ownership in the Rubber Hand Illusion

The rubber hand illusion (RHI) is one of the most commonly used paradigms to examine the sense of body ownership. Touches are synchronously applied to the real hand, hidden from view, and a false hand in an anatomically congruent position. During the illusion one may perceive that the feeling of touch arises from the false hand (referral of touch), and that the false hand is one's own. The relationship between referral of touch and body ownership in the illusion is unclear, and some articles average responses to statements addressing these experiences, which may be inappropriate depending on the research question of interest. To address these concerns, we re-analyzed three freely available datasets to better understand the relationship between referral of touch and feeling of ownership in the RHI. We found that most participants who report a feeling of ownership also report referral of touch, and that referral of touch and ownership show a moderately strong positive relationship that was highly replicable. In addition, referral of touch tends to be reported more strongly and more frequently than the feeling of ownership over the hand. The former observations confirm that referral of touch and ownership are related experiences in the RHI. The latter, however, indicate that when pooling the statements one may obtain a higher number of illusion ‘responders’ compared to considering the ownership statements in isolation. These results have implications for the RHI as an experimental paradigm.
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years. In particular, several Class Activation Mapping (CAM) methods have been proposed to discover the connection between CNN's decision and image regions. In spite of the reasonable visualization, lack of clear and sufficient theoretical support is the main limitation of these methods. In this paper, we introduce two axioms -- Conservation and Sensitivity -- to the visualization paradigm of the CAM methods. Meanwhile, a dedicated Axiom-based Grad-CAM (XGrad-CAM) is proposed to satisfy these axioms as much as possible. Experiments demonstrate that XGrad-CAM is an enhanced version of Grad-CAM in terms of conservation and sensitivity. It is able to achieve better visualization performance than Grad-CAM, while also be class-discriminative and easy-to-implement compared with Grad-CAM++ and Ablation-CAM. The code is available at https://github.com/Fu0511/XGrad-CAM.
Monthly, Seasonal and Yearly Assessments of Global Solar Radiation, Clearness Index and Diffuse Fractions in Alice, South Africa

The constant scheduled load shedding in South Africa has commonly been executed in an attempt to maintain the long aging coal power plants in the country. With the rise in the reduction of fossil fuels, efforts to eradicate environmental hazards of carbon through solar photovoltaic (PV) resources to their complete prospect are in progress. South Africa, and in particular the town Alice, acquires sunshine annually, making it appropriate to harvest solar energy. This work aims to characterize solar radiation, clearness index (Kt), and diffuse fraction (Kd) in Alice, South Africa. Hourly global and diffuse solar irradiance were estimated into monthly, seasonal, and yearly variations of Kt and Kd for the years 2017–2020. The range of values for describing the daily classification of sky condition was centered on earlier studies. The cumulative frequency and frequency distribution of daily Kt was analyzed statistically in an individual month. The analyses show that the average percentage frequency of Kt within the period is 11.72% of the cloudy days, 57% of partially cloudy days, and 31.28% of clear sky days. The findings of this research show that Alice remains a key contender for solar energy conversion location, owing to its reasonably high frequency (Kt &gt; 0.40) of clear and partially cloudy skies. Hence, it is essential to establish energy-efficiency for energy consumption and also for daily performances.
DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR

Scene flow is the dense 3D reconstruction of motion and geometry of a scene. Most state-of-the-art methods use a pair of stereo images as input for full scene reconstruction. These methods depend a lot on the quality of the RGB images and perform poorly in regions with reflective objects, shadows, ill-conditioned light environment and so on. LiDAR measurements are much less sensitive to the aforementioned conditions but LiDAR features are in general unsuitable for matching tasks due to their sparse nature. Hence, using both LiDAR and RGB can potentially overcome the individual disadvantages of each sensor by mutual improvement and yield robust features which can improve the matching process. In this paper, we present DeepLiDARFlow, a novel deep learning architecture which fuses high level RGB and LiDAR features at multiple scales in a monocular setup to predict dense scene flow. Its performance is much better in the critical regions where image-only and LiDAR-only methods are inaccurate. We verify our DeepLiDARFlow using the established data sets KITTI and FlyingThings3D and we show strong robustness compared to several state-of-the-art methods which used other input modalities. The code of our paper is available at https://github.com/dfki-av/DeepLiDARFlow.
Improving Document-Level Sentiment Classification Using Importance of Sentences

Previous researchers have considered sentiment analysis as a document classification task, in which input documents are classified into predefined sentiment classes. Although there are sentences in a document that support important evidences for sentiment analysis and sentences that do not, they have treated the document as a bag of sentences. In other words, they have not considered the importance of each sentence in the document. To effectively determine polarity of a document, each sentence in the document should be dealt with different degrees of importance. To address this problem, we propose a document-level sentence classification model based on deep neural networks, in which the importance degrees of sentences in documents are automatically determined through gate mechanisms. To verify our new sentiment analysis model, we conducted experiments using the sentiment datasets in the four different domains such as movie reviews, hotel reviews, restaurant reviews, and music reviews. In the experiments, the proposed model outperformed previous state-of-the-art models that do not consider importance differences of sentences in a document. The experimental results show that the importance of sentences should be considered in a document-level sentiment classification task.
Three-dimensional modeled environments versus 360 degree panoramas for mobile virtual reality training

Abstract Virtual field trip is a way of providing users with some knowledge and exposure of a facility without requiring them to physically visit the location. Due to the high computational costs that are necessary to produce virtual environments (VEs), the potential for photorealism is sacrificed. Often these three-dimensional (3D) modeled applications use an unrealistic VE and, therefore, do not provide a full depiction of real-world environments. Panoramas can be used to showcase complex scenarios that are difficult to model and are computationally expensive to view in virtual reality (VR). Utilizing 360° panoramas can provide a low-cost and quick-to-capture alternative with photorealistic representations of the actual environment. The advantages of photorealism over 3D models for training and education are not clearly defined. This paper initially summarizes the development of a VR training application and initial pilot study. Quantitative and qualitative study then was conducted to compare the effectiveness of a 360° panorama VR training application and a 3D modeled one. Switching to a mobile VR headset saves money, increases mobility, decreases set-up and breakdown time, and has less spatial requirements. Testing results of the 3D modeled VE group had an average normalized gain of 0.03 and the 360° panorama group, 0.43. Although the 3D modeled group had slightly higher realism according to the presence questionnaire and had slightly higher averages in the comparative analysis questionnaire, the 360° panorama application has shown to be the most effective for training and the quickest to develop.
A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

Abstract In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach consists of constructing and applying a set of deep learning algorithms to classify the sentiment of Arabic messages as positive or negative. It was applied on Facebook messages written in Modern Standard Arabic (MSA) as well as in Algerian dialect (DALG, which is a low resourced-dialect, spoken by more than 40 million people) with both scripts Arabic and Arabizi. To handle Arabizi, we consider both options: transliteration (largely used in the research literature for handling Arabizi) and translation (never used in the research literature for handling Arabizi). For highlighting the effectiveness of a semi-supervised approach, we carried out different experiments using both corpora for the training (i.e. the corpus constructed automatically and the one that was reviewed manually). The experiments were done on many test corpora dedicated to MSA/DALG, which were proposed and evaluated in the research literature. Both classifiers are used, shallow and deep learning classifiers such as Random Forest (RF), Logistic Regression(LR) Convolutional Neural Network (CNN) and Long short-term memory (LSTM). These classifiers are combined with word embedding models such as Word2vec and fastText that were used for sentiment classification. Experimental results (F1 score up to 95% for intrinsic experiments and up to 89% for extrinsic experiments) showed that the proposed system outperforms the existing state-of-the-art methodologies (the best improvement is up to 25%).
Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents

As modern games continue growing both in size and complexity, it has become more challenging to ensure that all the relevant content is tested and that any potential issues are properly identified and fixed. Attempting to maximize testing coverage using only human participants, however, results in a tedious and hard to orchestrate process which normally slows down the development cycle. Complementing playtesting via autonomous agents has shown great promise accelerating and simplifying this process. This paper addresses the problem of automatically exploring and testing a given scenario using reinforcement learning agents trained to maximize game state coverage. Each of these agents is rewarded based on the novelty of its actions, thus encouraging a curious and exploratory behaviour on a complex 3D scenario where previously proposed exploration techniques perform poorly. The curious agents are able to learn the complex navigation mechanics required to reach the different areas around the map, thus providing the necessary data to identify potential issues. Moreover, the paper also investigates different visualization strategies and evaluates how to make better use of the collected data to drive design decisions and to recognize possible problems and oversights.
SC-Com: Spotting Collusive Community in Opinion Spam Detection

In many cases, our decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers’ opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, we can spot the possibility to detect them from their collusiveness. In this paper, we propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, we extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. We show that our method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, our approach showed prominent performance while only considering primary data such as time and ratings.