Reviewerly/exHarmony huggingface dataset:
Design of Painting Art Style Rendering System Based on Convolutional Neural Network

Convolutional Neural Network- (CNN-) based GAN models mainly suffer from problems such as data set limitation and rendering efficiency in the segmentation and rendering of painting art. In order to solve these problems, this paper uses the improved cycle generative adversarial network (CycleGAN) to render the current image style. This method replaces the deep residual network (ResNet) of the original network generator with a dense connected convolutional network (DenseNet) and uses the perceptual loss function for adversarial training. The painting art style rendering system built in this paper is based on perceptual adversarial network (PAN) for the improved CycleGAN that suppresses the limitation of the network model on paired samples. The proposed method also improves the quality of the image generated by the artistic style of painting and further improves the stability and speeds up the network convergence speed. Experiments were conducted on the painting art style rendering system based on the proposed model. Experimental results have shown that the image style rendering method based on the perceptual adversarial error to improve the CycleGAN + PAN model can achieve better results. The PSNR value of the generated image is increased by 6.27% on average, and the SSIM values are all increased by about 10%. Therefore, the improved CycleGAN + PAN image painting art style rendering method produces better painting art style images, which has strong application value.
Biomedical Document Clustering Based on Accelerated Symbiotic Organisms Search Algorithm

Clustering is an important unsupervised analysis technique for big data mining. It finds its application in several domains including biomedical documents of the MEDLINE database. Document clustering algorithms based on metaheuristics is an active research area. However, these algorithms suffer from the problems of getting trapped in local optima, need many parameters to adjust, and the documents should be indexed by a high dimensionality matrix using the traditional vector space model. In order to overcome these limitations, in this paper a new documents clustering algorithm (ASOS-LSI) with no parameters is proposed. It is based on the recent symbiotic organisms search metaheuristic (SOS) and enhanced by an acceleration technique. Furthermore, the documents are represented by semantic indexing based on the famous latent semantic indexing (LSI). Conducted experiments on well-known biomedical documents datasets show the significant superiority of ASOS-LSI over five famous algorithms in terms of compactness, f-measure, purity, misclassified documents, entropy, and runtime.
Revocable Identity-Based Encryption with Server-Aided Ciphertext Evolution from Lattices

Revocable identity-based encryption (RIBE) with server-aided ciphertext evolution (RIBE-CE), recently proposed by Sun et al. at TCS 2020, offers significant advantages over previous identity (or key) revocation mechanisms when considering the scenario of a secure data sharing in the cloud setting. In this new system model, the user (i.e., a recipient) can utilize the current short-term decryption key to decrypt all ciphertexts sent to him, meanwhile, the ciphertexts in the cloud evolve to new ones with the aided of the cloud server and the old ones are completely deleted, and thus, the revoked users cannot access to both the previously and subsequently shared data.In this paper, inspired by Sun et al.’s work, we propose the first lattice-based RIBE-CE. Our scheme is more efficient and secure than the existing constructions of lattice-based RIBE. Simultaneously, the private key generator (PKG) maintains a binary tree (BT) to handle key revocation only with a logarithmic complexity workload in time key update, not growing linearly in the numbers of system users N, which serves as one solution to the challenge proposed by Sun et al. and based on the hardness of the learning with errors (LWE) problem, we prove that our first scheme is selectively secure in the standard model. Subsequently, based on the main techniques for lattice basis delegation with hierarchical IBE (HIBE), we construct our second lattice-based RIBE-CE scheme with decryption key exposure resistance (DKER), a default security requirement for RIBE, which has not been considered by Sun et al.
Rapid and Efficient Medical Image Segmentation Using Thresholding and CLAHE with 3-Level FCM Clustering

The segmentation of medical images is a method to examine and extract different portions of medical input data. Several ways have been devel-oped to improve the efficiency of these segmentation techniques. The main goal of the proposed method is to develop a rapid and efficient 3-level FCM cluster-ing algorithm and which is based on the 3 level threshold technique. The intro-duced method uses pseudo-random numbers generator which is normally dis-tributed for initial fuzzy estimates, so it acquires threshold faster than the regu-lar method. Before image segmentation, we use CLAHE to enhance the quality and resolution of the noise image which significantly improves segmentation performance. The proposed method evaluates performance for different expo-nential values of partitioned metrics for better segmentation.
Bringing trust and transparency to the opaque world of waste management with blockchain: a Polkadot parathread application

The majority of countries are currently struggling to achieve sustainable levels of recycling and waste treatment, particularly relating to household waste, and this area is in urgent need of new solutions. In general, the waste management sector has struggled with low consumer trust, fraud, manipulation, significant manual processes, and low levels of information and control. Here we propose a hybrid blockchain solution called a Polkadot parachain. Polkadot is a blockchain technology that allows for the development of a network of blockchains, each called a parachain that can be customised to the business needs of a given application. This solution provides the cost benefits, scalability, and control of a permissioned blockchain while providing the security, verifiability, and trust of a public blockchain. The solution combines three typically separate blockchain use cases: supply chain tracking, incentivisation through a payment system, and gamification to achieve a complete solution for waste management. We provide a detailed discussion on the design of this blockchain solution with the use of blockchain functionality assessed against the criteria and development approaches found in the literature. Finally, we demonstrate how such a blockchain can be implemented with the Substrate blockchain development framework and detail a pilot project where this system will be implemented.
Components of Inductive Reasoning

A now-recognized serious shortcoming of research on aptitude and intelligence is the lack of strong theoretical foundations based upon knowledge of human cognition. The theoretical deficit in this field was pointed out by McNemar in 1964 when he emphasized the failure of individual-difference research to “come to grips with the process, or operation, by which a given organism achieves an intellectual response [p. 881].” More recent critiques of psychometric research have underscored the necessity for understanding the cognitive processes that are assessed in aptitude measurement (e.g., Estes, 1974; Glaser, 1972; Tyler, 1976).
Badminton Recognition and Tracking System

Human–computer interaction systems have been developed in large numbers and quickly applied to sports. Badminton is the best sport for applying robotics because it requires quick recognition and fast movement. For the development of badminton recognition and tracking systems, it is important to accurately identify badminton, venues, and opponents. In this paper, we designed and developed a badminton recognition and tracking system using two 2 000 000-pixel high-speed cameras. The badminton tracking system has a transmission speed of 250[Formula: see text]fps and the maximum speed of the badminton resonator is 300[Formula: see text]km/h. The system uses the camera link interface Camera Link to capture images of high-speed cameras and process all captured images in real time using different regions of interest settings. In order to improve accuracy, we propose a new method for judging the center point of badminton. We have proposed a detector that detects the four corner points of the field by using the contour information of the badminton court when the approximate position of the badminton court is known. We set the sensing area according to the approximate position of the badminton court and use the histogram in the sensing area to select the point closest to the contour. Specify the intersection of the line as the corner point of the badminton court. The proposed angle detector has a high detection rate. It is more than 10 times more accurate than traditional detectors. The moving badminton is detected by an elliptical detector. We propose a method to find the center of the correct ellipse from the four candidates by selecting the four candidate contours of the ellipse. Compared to conventional circular detectors and points on three-dimensional coordinates, the proposed elliptical detector reduces the error by about 3[Formula: see text]mm.
Disguise Resilient Face Verification

With increasing usage of face recognition algorithms, it is well established that external artifacts and makeup accessories can be applied to different facial features such as eyes, nose, mouth, and cheek, to obfuscate one’s identity or to impersonate someone else’s identity. Recognizing faces in the presence of these artifacts comprises the problem of disguised face recognition, which is one of the most arduous covariates of face recognition. The challenge becomes exacerbated when disguised faces are captured in real-time environment, with low resolution images. To address the challenge of disguised face recognition, this paper first proposes a novel multi-objective encoder-decoder network, termed as DED-Net. DED-Net attempts to learn the class variations in the feature space generated by both disguised as well non-disguised images, using a combination of Mahalanobis and Cosine distance metrics, along with Mutual Information based supervision. The DED-Net is then extended to learn from the local and global features of both disguised and non-disguised face images for efficient face recognition, and the complete framework is termed as Disguise Resilient (D-Res) framework. The efficacy of the proposed framework has been demonstrated on two real-world benchmark datasets: Disguised Faces in the Wild (DFW) 2018 and DFW2019 competition datasets. In addition, this research also emphasizes on the importance of recognizing disguised faces in low resolution settings and proposes three experimental protocols to simulate the real-world surveillance scenario. To this effect, benchmark results have been shown on seven protocols for three low resolution settings (32×32, 24×24, and 16×16) of the two DFW benchmark datasets. The results demonstrate superior performance of the D-Res framework, in comparison with benchmark algorithms. For example, an improvement of around 3% is observed on the Overall protocol of the DFW2019 dataset, where the D-Res framework achieves 96.3%. Experiments have also been performed on benchmark face verification datasets (LFW, YTF, and IJB-B), where the D-Res framework achieves improved verification accuracy.
Causal constraint pruning for exact learning of Bayesian network structure

How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue.In this paper, four different causal constraints algorithms are added into score calculations to prune possible parent sets, improving state-ofthe-art learning algorithms' efficiency.Experimental results indicate that exact learning algorithms can significantly improve the efficiency with only a slight loss of accuracy.Under causal constraints, these exact learning algorithms can prune about 70% possible parent sets and reduce about 60% running time while only losing no more than 2% accuracy on average.Additionally, with sufficient samples, exact learning algorithms with causal constraints can also obtain the optimal network.In general, adding max-min parents and children constraints has better results in terms of efficiency and accuracy among these four causal constraints algorithms.
Guarantees of Differential Privacy in Cloud of Things: A Multilevel Data Publication Scheme

Nowadays, the cloud computing technology combined with the new generation networks and internet of things facilitate the networking of numerous smart devices. Moreover, the advent of the smart web requires massive data backup from the smart connected devices to the cloud. Unfortunately, the publication of several of these data, such as medical information and financial transactions, could lead to serious privacy breaches, which is becoming the most serious issue in cloud of things. For instance, passive attacks can launched in order to get access to private information. For this reason, several data anonymization techniques have emerged in order to keep data as confidential as possible. However, these different techniques are making the data unusable the most of time. Meanwhile, differential privacy that has been used in a number of cyber physical systems recently emerged as an efficient technique for ensuring the privacy of cloud of things stored data. In this exploratory paper, we study the guarantees of differential privacy of a multi-level anonymization scheme of data graphs. The considered scheme disturbs the structure of the graph by adding false edges, groups the vertices in distinct sets and permutes the vertices in these groups. Particularly, we demonstrated the guarantees that the anonymized data by this algorithm remain exploitable while guaranteeing the anonymity of users.
IF-Over-Fiber Transmission of OFDM Quantum-Noise Randomized PSK Cipher for Physical Layer Encryption of Wireless Signals

This study proposes an intensity modulation/direct detection intermediate frequency-over-fiber (IFoF) system that generates a microwave orthogonal frequency-division multiplexing (OFDM) quantum-noise randomized phase-shift-keying (PSK) cipher. The system directly prevents wireless interception by an eavesdropper without a private key. We theoretically analyze the system and derive a fundamental tradeoff between the security and signal quality at an intermediate frequency (IF). The system achieves balanced security and signal quality. We experimentally demonstrate 4.09-Gbit/s PSK cipher generation at an IF of 3.6 GHz. The cipher has 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sup> phase levels, and more than 100 adjacent signals are masked by quantum noise. The quantum-noise signal masking imposes irreducible uncertainty on illegitimate reception of the cipher, and a symbol error ratio (SER) that an eavesdropper can reach approaches 1. A lower bound of the SER is promised because truly random signal masking by quantum noise cannot be reduced. The error vector magnitude of the IF signals after decryption is maintained at a low value for wireless transmission. The cipher directly and effectively protects the wireless transmission of personal data from interception.
From Disparate Disciplines to Unity in Diversity: How the PARTHENOS Project Has Brought European Humanities Research Infrastructures Together

Since the first ESFRI roadmap in 2006, multiple humanities Research Infrastructures (RIs) have been set up all over the European continent, supporting archaeologists (ARIADNE), linguists (CLARIN-ERIC), Holocaust researchers (EHRI), cultural heritage specialists (IPERION-CH) and others. These examples only scratch the surface of the breadth of research communities that have benefited from close cooperation in the European Research Area. While each field developed discipline-specific services over the years, common themes can also be distinguished. All humanities RIs address, in varying degrees, questions around research data management, the use of standards and the desired interoperability of data across disciplinary boundaries. This article sheds light on how cluster project PARTHENOS developed pooled services and shared solutions for its audience of humanities researchers, RI managers and policymakers. In a time where the convergence of existing infrastructure is becoming ever more important – with the construction of a European Open Science Cloud as an audacious, ultimate goal – we hope that our experiences inform future work and provide inspiration on how to exploit synergies in interdisciplinary, transnational, scientific cooperation.
Learning from Suboptimal Demonstration via Self-Supervised Reward Regression.

Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration. However, modern LfD techniques, such as inverse reinforcement learning (IRL), assume users provide at least stochastically optimal demonstrations. This assumption fails to hold in all but the most isolated, controlled scenarios, reducing the ability to achieve the goal of empowering real end-users. Recent attempts to learn from sub-optimal demonstration leverage pairwise rankings through Preference-based Reinforcement Learning (PbRL) to infer a more optimal policy than the demonstration. However, we show that these approaches make incorrect assumptions and, consequently, suffer from brittle, degraded performance. In this paper, we overcome the limitations of prior work by developing a novel computational technique that infers an idealized reward function from suboptimal demonstration and bootstraps suboptimal demonstrations to synthesize optimality-parameterized training data for training our reward function. We empirically validate we can learn an idealized reward function with $\sim0.95$ correlation with the ground truth reward versus only $\sim 0.75$ for prior work. We can then train policies achieving $\sim 200\%$ improvement over the suboptimal demonstration and $\sim 90\%$ improvement over prior work. Finally, we present a real-world implementation for teaching a robot to hit a topspin shot in table tennis better than user demonstration.
What Is Urgent Publishing?

Publishing is increasingly being challenged through instantaneous social media publish- ing, even in the fields of scholarship and cultural, philosophical and political debate. Memetic self-publishers, such as the right-wing 'YouTube intellectual' Jordan Peterson and his left-wing counterpart Natalie Wynn, seem to tap into urgent needs that traditional publishing fails to identify and address. Does their practice amount to a new form of urgent publishing? How is it different from non-urgent publishing on the one hand and from propaganda on the other? Which urgencies can be addressed by urgent publishing? What is the role of artists and designers in it?
Learning 3D Dynamic Scene Representations for Robot Manipulation

3D scene representation for robot manipulation should capture three key object properties: permanency -- objects that become occluded over time continue to exist; amodal completeness -- objects have 3D occupancy, even if only partial observations are available; spatiotemporal continuity -- the movement of each object is continuous over space and time. In this paper, we introduce 3D Dynamic Scene Representation (DSR), a 3D volumetric scene representation that simultaneously discovers, tracks, reconstructs objects, and predicts their dynamics while capturing all three properties. We further propose DSR-Net, which learns to aggregate visual observations over multiple interactions to gradually build and refine DSR. Our model achieves state-of-the-art performance in modeling 3D scene dynamics with DSR on both simulated and real data. Combined with model predictive control, DSR-Net enables accurate planning in downstream robotic manipulation tasks such as planar pushing. Video is available at this https URL.
Combination method of accelerated metric data search in image classification problems

The subject of research of the paper is the methods of image classification on a set of key point descriptors in computer vision systems. The goal is to improve the performance of structural classification methods by introducing indexed hash structures on the set of the dataset reference images descriptors and a consistent chain combination of several stages of data analysis in the classification process. Applied methods: BRISK detector and descriptors, data hashing tools, search methods in large data arrays, metric models for the vector relevance estimation, software modeling. The obtained results: developed an effective method of image classification based on the introduction of high-speed search using indexed hash structures, that speeds up the calculation dozens of times; the gain in computing time increases with an increase of the number of reference images and descriptors in descriptions; the peculiarity of the classifier is that not an exact search is performed, but taking into account the permissible deviation of data from the reference; experimentally verified the effectiveness of the classification, which indicates the efficiency and effectiveness of the proposed method. The practical significance of the work is the construction of classification models in the transformed space of the hash data representation, the efficiency confirmation of the proposed classifiers modifications on image examples, development of applied software models implementing the proposed classification methods in computer vision systems.
A Query Expansion Method Using Multinomial Naive Bayes

Information retrieval (IR) aims to obtain relevant information according to a certain user need and involves a great diversity of data such as texts, images, or videos. Query expansion techniques, as part of information retrieval (IR), are used to obtain more items, particularly documents, that are relevant to the user requirements. The user initial query is reformulated, adding meaningful terms with similar significance. In this study, a supervised query expansion technique based on an innovative use of the Multinomial Naive Bayes to extract relevant terms from the first documents retrieved by the initial query is presented. The proposed method was evaluated using MAP and R-prec on the first 5, 10, 15, and 100 retrieved documents. The improved performance of the expanded queries increased the number of relevant retrieved documents in comparison to the baseline method. We achieved more accurate document retrieval results (MAP 0.335, R-prec 0.369, P5 0.579, P10 0.469, P15 0.393, P100 0.175) as compared to the top performers in TREC2017 Precision Medicine Track.
Quantifying Information via Structural Complexity in Optical Beams Using Shannon Entropy

We propose a new definition of classical Shannon information that is detached from a particular alphabet and scales with the complexity of the optical field. We experimentally validate these results by reconstructing the wavefront topology.