Reviewerly/exHarmony huggingface dataset:
IOTA-BT: A P2P File-Sharing System Based on IOTA

BitTorrent (BT) is the most popular peer-to-peer file-sharing system. According to official BT information, more than 100 million active users use BT for file transfers every month. However, BT mainly relies on either a central tracker (tracker) or distributed hash table (DHT) for locating file seeders while it runs a risk of a single point of failure or cyber-attacks such as Sybil and Eclipses. To attack this problem, we proposed a Peer-to-Peer (P2P) file-sharing system called IOTA-BT by integrating BitTorrent with IOTA in this paper. The advantages of IOTA over blockchain include scalability for high throughput, compatibility with Internet of Things (IoT) footprints, zero transaction fees, partition-tolerant, and quantum-resistant cryptography. The autopeering and neighbor selection of the Coordicide of IOTA is aimed at defending a Sybil or Eclipse attack. IOTA-BT inherits these advantages from IOTA. Moreover, our experimental results have shown that the cost of executing BT functions, such as releasing torrent files and retrieving seeder information on IOTA-BT, is acceptable for improving the security of BT. IOTA-BT can indeed efficiently provide users with a P2P file-sharing environment of higher security.
Improved C-Space Exploration and Path Planning for Robotic Manipulators Using Distance Information

We present a simple method to quickly explore C-spaces of robotic manipulators and thus facilitate path planning. The method is based on a novel geometrical structure called generalized bur. It is a star-like tree, rooted at a given point in free C-space, with an arbitrary number of guaranteed collision-free edges computed using distance information from the workspace and simple forward kinematics. Generalized bur captures large portions of free C-space, enabling accelerated exploration. The workspace is assumed to be decomposable into a finite set of (possibly overlapping) convex obstacles. When plugged in a suitable RRT-like planning algorithm, generalized burs enable significant performance improvements, while at the same time enabling exact collision-free paths.
Looc: Localize Overlapping Objects with Count Supervision

Acquiring count annotations generally requires less human effort than point-level and bounding box annotations. Thus, we propose the novel problem setup of localizing objects in dense scenes under this weaker supervision. We propose LOOC, a method to Localize Overlapping Objects with Count supervision. We train LOOC by alternating between two stages. In the first stage, LOOC learns to generate pseudo point-level annotations in a semi-supervised manner. In the second stage, LOOC uses a fully-supervised localization method that trains on these pseudo labels. The localization method is used to progressively improve the quality of the pseudo labels. We conducted experiments on popular counting datasets. For localization, LOOC achieves a strong new baseline in the novel problem setup where only count supervision is available. For counting, LOOC outperforms current state-of-the-art methods that only use count as their supervision.
Superpixel based robust reversible data hiding scheme exploiting Arnold transform with DCT and CA

The emergence of data hiding schemes was to provide multimedia security besides the sharing of information in a confidential manner. Since then, various techniques have cropped up to meet the objective of secured data communication using multimedia documents. In this paper, a novel data hiding approach has been designed to exploit the superpixel of the image to identify the nature of the blocks for embedding data using spatial and transform domain techniques such as Cellular Automata (CA) and Discrete Cosine Transform (DCT) respectively. In addition, an Arnold transform is used to provide a blanket of security through a random selection of blocks via sharable keys. The proposed technique obtains YCbCr color model of an RGB image to use superpixel on only the Y color component. Thereafter, the acquired labeled image of superpixel is partitioned into (8×8) blocks which are to be categorised as homogeneous and heterogeneous blocks. Based on the categorization, DCT and CA are applied for the embedding of the secret data in the Cb and Cr color components. The experimental results and its comparison with state-of-the-art schemes uphold the effectiveness of the demonstrated scheme. Various analysis have been conducted to showcase the degree of robustness, imperceptibility, and visual quality attained by the proposed scheme.
Equipment Anomaly Detection for Semiconductor Manufacturing by Exploiting Unsupervised Learning from Sensory Data

In-line anomaly detection (AD) not only identifies the needs for semiconductor equipment maintenance but also indicates potential line yield problems. Prompt AD based on available equipment sensory data (ESD) facilitates proactive yield and operations management. However, ESD items are highly diversified and drastically scale up along with the increased use of sensors. Even veteran engineers lack knowledge about ESD items for automated AD. This paper presents a novel Spectral and Time Autoencoder Learning for Anomaly Detection (STALAD) framework. The design consists of four innovations: (1) identification of cycle series and spectral transformation (CSST) from ESD, (2) unsupervised learning from CSST of ESD by exploiting Stacked AutoEncoders, (3) hypothesis test for AD based on the difference between the learned normal data and the tested sample data, (4) dynamic procedure control enabling periodic and parallel learning and testing. Applications to ESD of an HDP-CVD tool demonstrate that STALAD learns normality without engineers' prior knowledge, is tolerant to some abnormal data in training input, performs correct AD, and is efficient and adaptive for fab applications. Complementary to the current practice of using control wafer monitoring for AD, STALAD may facilitate early detection of equipment anomaly and assessment of impacts to process quality.
Substitute Model Generation for Black-Box Adversarial Attack Based on Knowledge Distillation

Although deep convolutional neural network (CNN) performs well in many computer vision tasks, its classification mechanism is very vulnerable when it is exposed to the perturbation of adversarial attacks. In this paper, we proposed a new algorithm to generate the substitute model of black-box CNN models by using knowledge distillation. The proposed algorithm distills multiple CNN teacher models to a compact student model as the substitution of other black-box CNN models to be attacked. The black-box adversarial samples can be consequently generated on this substitute model by using various white-box attacking methods. According to our experiments on ResNet18 and DenseNet121, our algorithm boosts the attacking success rate (ASR) by 20% by training the substitute model based on knowledge distillation.
Blockchain based Smart Model for Agricultural Food Supply Chain

Ensuring food safety requires to fully monitor the overall process of handling food, preparation as well as storage of food in such a way that it reduces the risk of people getting sick due to undue cleanliness or any mismanagement in the overall process. Smart agricultural includes an efficient, clean and safe food supply chain system that can tackle these issues more smartly than that of existing system. Agricultural food supply chain denotes the mechanism explaining how the farm food comes to our tables. The research presents a smart model for the transformation of traditional food supply chain considering Blockchain Technology. The model promises to give all stakeholders participating in the agricultural food supply chain equal opportunities even if they are not familiar to each other but without the help of trustworthy third party service provider. We validate proposed blockchain based smart model with our own scheme without using blockchain.
A Blockchain-Based Configuration for Balancing the Electricity Grid with Distributed Assets

This paper explores a future perspective to foster the provision of balancing services to the electricity grid by distributed assets. One recent test case, initiated by the Dutch Transmission System Operator (TSO), was to operate an Electric Vehicle (EV) fleet on the automatic Frequency Restoration Reserve (aFRR) market, which entails fast and automated reserves. To achieve that in a decentralised, automated and transparent manner, the role of blockchain technology for this specific application is explored. We propose a novel configuration that can serve as a basis for deploying distributed assets for aFRR markets using blockchain or any alternative Distributed Ledger Technology (DLT). Automation can be achieved via the deployment of smart contracts, which also results in transparency in the system. The blockchain configurations are designed for three phases in the aFRR market, namely: (i) Operational planning and scheduling by a balancing service provider (i.e., formulation and submission of aFRR bid), (ii) Real-time operations (i.e., activation and measurements), and (iii) Verification and settlement (i.e., imbalance correction and financial settlement). The paper concludes that the scalability of distributed assets that can participate in the system, combined with the large transaction times and energy consumption of some consensus mechanisms, could put limitations on the proposed architecture. Future research should address benchmarking studies of other alternatives (e.g., DLTs, such as the ones based on directed acyclic graphs, and non-DLT solutions) with the proposed blockchain solution.
Persona aware Response Generation with Emotions

Conversational systems are the perfect examples of human-machine interactions. The conversational agents while interacting with humans lack the ability to express emotions and behave inconsistently, making the conversations boring and non-interactive. In this work, we propose the task of persona aware emotional response generation in which the system can generate specific and consistent responses in accordance to the provided personality information and the conversational history. To make the responses interactive and interesting we intend to infuse the emotions in the responses that help in making the responses more human-like. We propose a persona aware attention framework employing an encoder-decoder approach. We investigate different ways to include the desired emotions in the responses. Experimental results on the PersonaChat dataset shows that our proposed framework outperforms the baseline models and can generate interactive and emotional responses.
Solving Raven’s Progressive Matrices with Multi-Layer Relation Networks

Raven's Progressive Matrices are a benchmark originally designed to test the cognitive abilities of humans. It has recently been adapted to test relational reasoning in machine learning systems. For this purpose the so-called Procedurally Generated Matrices dataset was set up, which is so far one of the most difficult relational reasoning benchmarks. Here we show that deep neural networks are capable of solving this benchmark, reaching an accuracy of 98.0 percent over the previous state-of-the-art of 62.6 percent by combining Wild Relation Networks with Multi-Layer Relation Networks and introducing Magnitude Encoding, an encoding scheme designed for late fusion architectures.
Single Image Haze Removal using a Generative Adversarial Network

Traditional methods to remove haze from images rely on estimating a transmission map. When dealing with single images, this becomes an ill-posed problem due to the lack of depth information. In this paper, we propose an end-to-end learning based approach which uses a modified conditional Generative Adversarial Network to directly remove haze from an image. We employ the usage of the Tiramisu model in place of the classic U-Net model as the generator owing to its higher parameter efficiency and performance. Moreover, a patch based discriminator was used to reduce artefacts in the output. To further improve the perceptual quality of the output, a hybrid weighted loss function was designed and used to train the model. Experiments on synthetic and real world hazy images demonstrates that our model performs competitively with the state of the art methods.
A Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty

Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, hit, or push, and solves the resulting smooth trajectory optimization. The expressive power of logic allows LGP for handling complex, large-scale sequential manipulation and tool-use planning problems. In this paper, we extend the LGP formulation to stochastic domains. Based on the control-inference duality, we interpret LGP in a stochastic domain as fitting a mixture of Gaussians to the posterior path distribution, where each logic pro le defines a single Gaussian path distribution. The proposed framework enables a robot to prioritize various interaction modes and to acquire interesting behaviors such as contact exploitation for uncertainty reduction, eventually providing a composite control scheme that is reactive to disturbance.
Applying Class-to-Class Siamese Networks to Explain Classifications with Supportive and Contrastive Cases

Case-based classification is normally based on similarity between a query and class members in the case base. This paper proposes a difference-based approach, class-to-class siamese network (C2C-SN) classification, in which classification is based on learning patterns of both similarity and difference between classes. A C2C-SN learns patterns from one class \(C_i\) to another class \(C_j\). The network can then be used, given two cases, to determine whether their similarity and difference conform to the learned patterns. If they do, it provides evidence for their belonging to the corresponding classes. We demonstrate the use of C2C-SNs for classification, explanation, and prototypical case finding. We demonstrate that C2C-SN classification can achieve good accuracy for case pairs, with the benefit of one-shot learning inherited from siamese networks.
On Ensemble Techniques for Data Stream Regression

An ensemble of learners tends to exceed the predictive performance of individual learners.This approach has been explored for both batch and online learning.Ensembles methods applied to data stream classification were thoroughly investigated over the years, while their regression counterparts received less attention in comparison.In this work, we discuss and analyze several techniques for generating, aggregating, and updating ensembles of regressors for evolving data streams.We investigate the impact of different strategies for inducing diversity into the ensemble by randomizing the input data (resampling, random subspaces and random patches).On top of that, we devote particular attention to techniques that adapt the ensemble model in response to concept drifts, including adaptive window approaches, fixed periodical resets and randomly determined windows.Extensive empirical experiments show that simple techniques can obtain similar predictive performance to sophisticated algorithms that rely on reactive adaptation (i.e., concept drift detection and recovery).
Improving 3D Brain Tumor Segmentation With Predict-Refine Mechanism Using Saliency And Feature Maps

This paper demonstrates the use of 3D Anisotropic Convolutional Neural Network (CNN) with predict-refine mechanism for 3D brain tumor segmentation. We propose two networks that utilize multi-scale feedback and saliency maps respectively to segment three critical regions involved in automated brain tumor segmentation. The proposed networks are formulated to predict feature maps at different resolutions during the prediction phase. These networks perform refinement process using the saliency or feature maps as feedback information for the refinement process. The recurrent architecture allows the network to automatically rectify errors in saliency map of the previous prediction phase resulting in more reliable final predictions. Our experimental results on the BraTS2017 dataset demonstrate the superior performance of our proposed predict-refine architecture than current state of the art approaches improving results by up to 8% without any additional increase in the 1.9M model parameters.
An Analytical Computing Infrastructure for Monitoring Dynamic Networks Based on Knowledge Graphs

Dynamic network monitoring systems are typically designed to solve a predefined number of tasks, new requirements lead to expensive development efforts and sometimes even require changes in the system architecture. Knowledge graphs are powerful and flexible tools for information integration and supported by a set of standardized vocabularies and languages (the “Sematic Web” toolset). In this work, we discuss the application of knowledge graphs to develop and analyze an analytical computing infrastructure for a dynamic network monitoring system. As a typical dynamic network, a multiservice telecommunication network is considered. The presented system combines static models of a telecommunication network and dynamic monitoring data and makes it possible to obtain complex analytical reports using SPARQL queries over the knowledge graph. Those reports are of crucial importance to network stakeholders for improving the network services and performance. First, we analyze problems solved by traditional monitoring systems, and identify the classes of problems such systems cannot solve. Then we propose an analytical monitoring system architecture based on knowledge graphs to address these classes of problems. We present the system structure and detailed descriptions of the ontological and mathematical models of the resulting knowledge graph. In order to test the architecture discussed, we create an example task of the analytical monitoring system and analyze system performance depending on the size of the knowledge graph. The results of the analysis are presented using a number of SPARQL queries.
On the Adaptive Security of MACs and PRFs

We consider the security of two of the most commonly used cryptographic primitives—message authentication codes (MACs) and pseudorandom functions (PRFs)—in a multi-user setting with adaptive corruption. Whereas is it well known that any secure MAC or PRF is also multi-user secure under adaptive corruption, the trivial reduction induces a security loss that is linear in the number of users. Our main result shows that black-box reductions from "standard" assumptions cannot be used to provide a tight, or even a linear-preserving, security reduction for adaptive multi-user secure deterministic stateless MACs and thus also PRFs. In other words, a security loss that grows with the number of users is necessary for any such black-box reduction.
Disentangling The Spatial Structure And Style In Conditional VAE

This paper proposes a structure in conditional variation autoencoder (cVAE) to disentangle the latent vector into a spatial structure and a style code, complementary to each other, with the one $( z_{s})$ being label relevant and the other $( z_{u})$ irrelevant. Different from traditional cVAE, our network maps the condition label into its relevant code z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> through a separated module. Depending on whether the label directly relates to the image spatial structure or not, z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> output from the condition mapping module is used either as the style code with the two spatial dimension of $1 \times 1$, or as the spatial structure code with a single channel. Based on the input image and its corresponding z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> , the encoder provides the posterior distribution close to a common prior regardless of its label, thus z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</inf> sampled from it becomes label irrelevant. The decoder employs z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> and z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</inf> by two typical adaptive normalization modules to reconstruct the input image. Results on two datasets with different types of labels show the effectiveness of our method.
MT-UNET: A Novel U-Net Based Multi-Task Architecture For Visual Scene Understanding

We tackle the problem of deep end-to-end multi-task learning (MTL) for jointly performing image segmentation and depth estimation from monocular images. It is proven already that learning several related tasks together helps in attaining improved performance per task than training them autonomously. To this end, we follow the typical U-Net based encoder-decoder architecture (MT-UNet) where the densely connected deep convolutional neural network (CNN) based feature encoder is shared among the tasks while the soft attention based task-specific decoder modules produce the desired outputs. Additionally, we encourage cross-talk (CT) between the tasks by introducing cross-task skip connections at the decoder end with adaptive weight learning for the task-specific loss functions in the final cost measure. We validate the proposed framework on the challenging CityScapes and NYUv2 datasets, where our method sharply outperforms the current state-of-the-art.
Augmented reality in robot programming

Industrial robots have been traditionally programmed using teaching pendants, whereas offline programming methods are getting increasingly popular in recent years. Although the above two methods are widely-used in the industry, they both have certain disadvantages. For instance, the teaching pendant method requires a shutdown of the production line during the programming process, while offline programming method requires 3D CAD models of both the robot and the workpiece. In this paper, an augmented reality (AR) application which alleviates the aforementioned problems was proposed for robot programming purposes. The application is created using commercially available AR software, with the addition of our JavaScript code. The use of commercially available software allows an easier sharing and widespread adoption of the application.
Improving the Performance of Neural Networks with an Ensemble of Activation Functions

Activation functions in the neural networks play an important role by introducing non-linear properties to the neural networks. Thus it is considered as one of the essential ingredients among other building blocks of a neural network. But the selection of the appropriate activation function for the enhancement of model accuracy is strenuous in a sense; the performance of the NN-model is influenced by a proper selection of activation function for a dataset. Proper activation function selection is still a trial and error method for which the model accuracy improves for classification. As a solution to this problem, we have proposed an activation function ensembling by majority voting that has significantly improved the model accuracy in a classification context. The proposed model is tested on four benchmark datasets such as MNIST, Fashion MNIST, Semeion, and ARDIS IV datasets. The result shows that the performance of the proposed model is appreciably better than other traditional methods such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Recurrent Neural Network (RNN).