Reviewerly/exHarmony huggingface dataset:
Clustering radar pulses with Bayesian nonparametrics: a case for online processing

In a radar environment, cognitive receivers typically process raw IQ samples into sequences of pulse descriptor words (PDWs). When there are multiple emitters present in the signal, their respective PDWs are interleaved in time, necessitating specialized signal processing to deinterleave the pulse trains. Many deinterleaving solutions in the literature today utilize clustering algorithms to summarize the potentially massive amount of PDW data. Often, these clustering algorithms do not account for the fact that the number of clusters can change as a function of time. Additionally, PDW data arrives in a continuous stream, necessitating an efficient implementation. In this paper, we use Bayesian nonparametrics to address a dynamically changing radar scenario and implement an efficient online algorithm. We show that this algorithm can provide an effective solution to radar pulse clustering.
Cofree coalgebras and differential linear logic

We prove that the semantics of intuitionistic linear logic in vector spaces which uses cofree coalgebras is also a model of differential linear logic, and that the Cartesian closed category of cofree coalgebras is a model of the simply-typed differential lambda calculus.
Robust image completion and masking with application to robotic bin picking

Automated image completion and masking have been emerged as a subject of keen interest due to their impact on image modification and interpretation. The current state-of-the-art approaches require a fixed format of missing parts and are ineffective for handling corrupted images. Besides, they focus exclusively on the image completion without taking into consideration the image masking as an inverse process of completion. This paper proposes a deep learning approach to an integrated framework of image completion and masking based on the cross-mapping generative adversarial network or CM-GAN, in short. CM-GAN offers the robustness in image completion under corruptions as well as the capability of synthesizing various masked images with arbitrary mask locations and shapes. In particular, the capability of CM-GAN in image masking is shown to be extended into the removal of unwanted backgrounds in images. We verify the superior performance of CM-GAN for image completion and masking based on extensive experiments. Furthermore, we implement a deep learning based robotic bin picking to demonstrate that the background removal capability of CM-GAN plays a key role for estimating the 3D pose of randomly filed multiple industrial parts in a bin.
t-PINE: tensor-based predictable and interpretable node embeddings

Graph representations have increasingly grown in popularity during the last years. Existing representation learning approaches explicitly encode network structure. Despite their good performance in downstream processes (e.g., node classification, link prediction), there is still room for improvement in different aspects, such as efficacy, visualization, and interpretability. In this paper, we propose, t-PINE, a method that addresses these limitations. Contrary to baseline methods, which generally learn explicit graph representations by solely using an adjacency matrix, t-PINE avails a multi-view information graph—the adjacency matrix represents the first view, and a nearest neighbor adjacency, computed over the node features, is the second view—in order to learn explicit and implicit node representations, using the Canonical Polyadic (a.k.a. CP) decomposition. We argue that the implicit and the explicit mapping from a higher-dimensional to a lower-dimensional vector space is the key to learn more useful, highly predictable, and gracefully interpretable representations. Having good interpretable representations provides a good guidance to understand how each view contributes to the representation learning process. In addition, it helps us to exclude unrelated dimensions. Extensive experiments show that t-PINE drastically outperforms baseline methods by up to 351.5% with respect to Micro-F1, in several multi-label classification problems, while it has high visualization and interpretability utility.
Character Region Awareness Network For Scene Text Recognition

Recognizing text in natural scenes is still a very challenging task, due to arbitrary shapes, varying fonts, complex backgrounds and so on. Recently, some recognizers utilize Spatial Transform Network (STN) to rectify irregular text instances and achieve promising results. However, their robustness and accuracy are still limited, since rectification performance can be easily degraded by challenging samples. To tackle this issue, we propose a simple yet effective two-dimensional (2D) character attention module, which can enhance foreground text instances via character region awareness. By incorporating this with existing rectification pipeline, we build a novel scene text recognizer named Character Region Awareness Network (CRAN). Extensive experiments demonstrate that our CRAN outperforms previous methods nearly on all benchmarks of both regular and irregular text, particularly on SVT (+2.0%), SVTP (+1.5%) and CUTE80 (+2.1%).
Automatic cyber security risk assessment based on fuzzy fractional ordinary differential equations

Real-time network security risk detection is of great significance to the research of dynamic network security, and it is one of the current research hotspots of network security. Based on the idea of artificial immune, a dynamic network intrusion detection and prediction model based on fuzzy fractional ordinary differential equations is proposed. The uniqueness of the solution is studied in the square integrable equation space using the principle of compression operator, and then the second type of solution is proposed. The differential-type piecewise Taylor series expansion method of the linear Fredholm integral equation is used to obtain the approximate solution expressions and perform convergence and error estimation. The process of network attack detection, antibody concentration calculation method and risk prediction process based on time series autoregressive moving average model are given. The experimental results show that the model can quantitatively analyze the current security situation of the network in real time and make predictions about the risks faced by the network. The prediction effect of catastrophe network risk is better than the GM (1, 1) model, and it is close to the actual risk situation, with high prediction accuracy.
ReSprop: Reuse Sparsified Backpropagation

The success of Convolutional Neural Networks (CNNs) in various applications is accompanied by a significant increase in computation and training time. In this work, we focus on accelerating training by observing that about 90% of gradients are reusable during training. Leveraging this observation, we propose a new algorithm, Reuse-Sparse-Backprop (ReSprop), as a method to sparsify gradient vectors during CNN training. ReSprop maintains state-of-the-art accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets with less than 1.1% accuracy loss while enabling a reduction in back-propagation computations by a factor of 10x resulting in a 2.7x overall speedup in training. As the computation reduction introduced by Re-Sprop is accomplished by introducing fine-grained sparsity that reduces computation efficiency on GPUs, we introduce a generic sparse convolution neural network accelerator (GSCN), which is designed to accelerate sparse back-propagation convolutions. When combined with ReSprop, GSCN achieves 8.0x and 7.2x speedup in the backward pass on ResNet34 and VGG16 versus a GTX 1080 Ti GPU.
State estimate for stochastic systems with dual unknown interference inputs

Stochastic system state estimation subject to the unknown interference input widely exists in many fields, such as the control, communication, signal processing, and fault diagnosis. However, the research results are mostly limited to the stochastic system in which only the dynamic state model or the measurement model concerns the individual unknown interference input, and the state model and the measurement model are both with the same unknown interference input. State estimate of the stochastic systems where the state model and the measurement model contain dual Unknown Interference inputs (dual-UI) with different physical meanings and mathematical definitions is concerned here. Firstly, the decoupling condition with the Unknown Interference input in the State model (S-UI) is shown, which introduces the decoupled system with the adjacent Measurement concerned Unknown Interference inputs (M-UI) appearing in the state model and the measurement model. Then, through defining the Differential term of the adjacent M-UI (M-UID), the equivalent system with only M-UID in the state model is obtained. Finally, considering the design freedom of the equivalent system, the decoupling filter in the minimum mean square error sense and the adaptive minimum upper filter with different applicable conditions are represented to obtain the optimal and sub-optimal state estimate, respectively. Two simulation cases verify the effectiveness and superiority compared with the traditional methods.
Open-Set Radar Waveform Classification: Comparison of Different Features and Classifiers

Performing open set classification of radar waveforms is a difficult problem due to issues including varying signal to noise ratio (SNR), complexity of the data, lack of separability between classes of interest, as well as the crowded nature of the spectrum. In addition, the evolving spectrum may lead to a situation where not every waveform is present in the training library. This paper addresses these challenges by the combination of obtaining machine learning features directly from the waveform, subsequently followed by a classification algorithm. The machine learning technique used in this paper is a discriminative network, specifically a convolutional neural network (CNN), for feature extraction. The classifier employed is SV-Means, a quantile one-class support vector machine-based algorithm (q-OCSVM), with the ability to reject unknown waveform classes while also providing an estimation of the likelihood of the class of interest being a member of the waveform library. A combination of these two methods results in a system of high credibility taking into account the challenges noted.
Learning to Observe: Approximating Human Perceptual Thresholds for Detection of Suprathreshold Image Transformations

Many tasks in computer vision are often calibrated and evaluated relative to human perception. In this paper, we propose to directly approximate the perceptual function performed by human observers completing a visual detection task. Specifically, we present a novel methodology for learning to detect image transformations visible to human observers through approximating perceptual thresholds. To do this, we carry out a subjective two-alternative forced-choice study to estimate perceptual thresholds of human observers detecting local exposure shifts in images. We then leverage transformation equivariant representation learning to overcome issues of limited perceptual data. This representation is then used to train a dense convolutional classifier capable of detecting local suprathreshold exposure shifts - a distortion common to image composites. In this context, our model can approximate perceptual thresholds with an average error of 0.1148 exposure stops between empirical and predicted thresholds. It can also be trained to detect a range of different local transformations.
Neural Network Compression Using Higher-Order Statistics and Auxiliary Reconstruction Losses

In this paper, the problem of pruning and compressing the weights of various layers of deep neural networks is investigated. The proposed method aims to remove redundant filters from the network to reduce computational complexity and storage requirements, while improving the performance of the original network. More specifically, a novel filter selection criterion is introduced based on the fact that filters whose weights follow a Gaussian distribution correspond to hidden units that do not capture important aspects of data. To this end, Higher Order Statistics (HOS) are used and filters with low cumulant values that do not deviate significantly from Gaussian distribution are identified and removed from the network. In addition, a novel pruning strategy is proposed aiming to decide on the pruning ratio of each layer using the Shapiro-Wilk normality test. The use of auxiliary MSE losses (intermediate and after the softmax layer) during the fine-tuning phase further improves the overall performance of the compressed network. Extensive experiments with different network architectures and comparison with state-of-the-art approaches on well-known public datasets, such as CIFAR-10, CIFAR-100 and ILSCVR-12, demonstrate the great potential of the proposed approach.
Information and knowledge assessment – Results from a multinational automotive company

This article reports the results from an information and knowledge assessment (IKA) to identify information and knowledge needs and, their coverage by information resources to derive recommendations for improvement and proposes a contingeny framework. The approach is based on a review of audit methods from information sciences and management, knowledge management and the engineering discipline and tested with data (N = 580) collected from the engineering domain within an automotive supplier over six European sites. The integrated assessment uses content needs profiles from two complementary perspectives, the coverage of needs by various internal information sources and data on awareness and usage of these information sources. The employment of content categories on a more granular analytical level than information systems sources opens up new possibilities to derive improvement measures and requirements for the design of information systems within an organization. The brief data-gathering instrument also reduces the required resources to implement this approach considerably overcoming weaknesses previously identified in case studies and the IA literature. It makes a contribution to research bridging the gap between research and practice and opens up options to design contingency frameworks for a specific domain.
Abusive Text Examination Using Latent Dirichlet Allocation, Self Organizing Maps and K Means Clustering

The widespread misuse of social media to disseminate hate speech targeted at a particular individual, community, religion, race, sex or caste has engendered researchers from across the world to formulate strategies and methodologies to counter this menace. Without detection and analysis of hate speech, one cannot imagine the social media to be free of malicious content. This paper proposes a methodology which employs a combination of popular topic modeling technique i.e. Latent Dirichlet Allocation (LD A) and an unsupervised machine learning technique i.e. self-organizing maps (SOM) to analyze hate speech spread over social media. This method is compared to K means clustering used after the application of LDA. Both the techniques used together provide a powerful analysis. The proposed LDA model outputted ten topics for features and had a low perplexity with a higher negative log-likelihood score.
BNNsplit: Binarized Neural Networks for embedded distributed FPGA-based computing systems

In the past few years, Convolutional Neural Networks (CNNs) have seen a massive improvement, outperforming other visual recognition algorithms. Since they are playing an increasingly important role in fields such as face recognition, augmented reality or autonomous driving, there is the growing need for a fast and efficient system to perform the redundant and heavy computations of CNNs. This trend led researchers towards heterogeneous systems provided with hardware accelerators, such as GPUs and FPGAs. The vast majority of CNNs is implemented with floating-point parameters and operations, but from research, it has emerged that high classification accuracy can be obtained also by reducing the floating-point activations and weights to binary values. This context is well suitable for FPGAs, that are known to stand out in terms of performance when dealing with binary operations, as demonstrated in FINN, the state-of-the-art framework for building Binarized Neural Network (BNN) accelerators on FPGAs. In this paper, we propose a framework that extends FINN to a distributed scenario, enabling BNNs implementation on embedded multi-FPGA systems.
Privacy Preserving Classification using 4-Dimensional Rotation Transformation

This paper focuses on privacy-preserving of the data while extracting some meaningful information from the large lumps of data available. Many techniques have been used for privacy-preserving of the data while mining but to balance it with data utility has always been a difficult task. In this paper four-dimensional rotational transformation to transform the numeric data into disguised format to preserve its privacy by hiding its sensitivity has been used. The non-numeric data could be preserved using cryptographic algorithms. The data before transformation is normalized using min-max normalization and Geometric Data Perturbation method, which fractionates the numeric data into 2 groups and rotates these groups simultaneously along two planes vis-a-vis xy-plane and zw-plane using four-dimensional rotational matrices. To rigidify the security level, the angle whose variance value is high is selected. By rigidifying the security level it becomes difficult to extort the actual data from the transformed data format. In the proposed method the privacy and utility of data are preserved using 4D Rotation Transformation.
Active knowledge graph completion

Enterprise and public Knowledge Graphs (KGs) are known to be incomplete. Methods for automatic completion, sometimes by rule learning, scale well. While previous rule-based methods learn closed (non-existential) rules, we introduce Open Path (OP) rules that are constrained existential rules. We present a novel algorithm, OPRL, for learning OP rules. Closed rules complete a KG by answering queries of unclear origin, usually derived from a holdback test set in experimental settings. However, OP rules can generate relevant queries for KG completion. OPRL generates queries even when there is no closed rule to answer the query, or when the correct answer is a missing entity that is not present in the KG. For OPRL to scale well, we propose a novel embedding-based fitness function to efficiently estimate rule quality. Additionally, we introduce a novel, efficient vector computation to formally assess rule quality. We evaluate OPRL using adaptations of Freebase, YAGO2, Wikidata, and a synthetic Poker KG. We find that OPRL mines hundreds of accurate rules from massive KGs with up to 8 M facts. The OP rules generate queries with precision as high as 98% and recall of 62% on a complete KG, demonstrating the first solution for active knowledge graph completion.
A Detail Dominant Approach for IoT and Blockchain with their Research Challenges

Internet of things (IoT) is a pool of various devices, objects, humans, services those might be interconnected and that can communicate with each other, share data and information to succeed a common aim in different applications and areas. The idea of the IoT is to allow devices to work together on the Internet. In IoT now days the main issue is the security, in our paper we have found many research challenges related to our area and the security challenges of IoT faces are becoming more and more severe as IoT uses is growing in almost all applications. If IoT devices are not secured properly, the privacy and security of data will remain major issues in the present world in which things are interconnected. The main motive of our paper is to review the present research challenges related to IoT and the developments in the field of IoT security with Blockchain. Blockchain can be a very good option, it provides wide range of security solutions for IoT ecosystems, including trustworthiness, privacy, integrity, transparency, data availability. The Blockchain technology is basically based on the concept of decentralization, security and transparency of the system that has developed as a proficient technology to resolve the serious issues those generally occurred in the fundamental centralized networks and secure the IoT technologies. Some interesting research directions of the use of blockchain in IoT can be found in our very recent survey paper. In this work, the effects of the blockchain on an extensive range of IoT security from the latest research papers and at last a model has been provided for the security of IoT using blockchain.
Anonymous Bitcoin Mixing Scheme Based on Semi-Trusted Supervisor

As Bitcoin users grow, the protection of the Bitcoin privacy protection becomes an important issue. Bitcoin mixing technology can cut off the connection between buyers and sellers. Now, a more effective method is used to carry out a coin mixing operation. The existing flat structure and two-layer structure have their own deficiencies. Therefore, anonymous Bitcoin mixing scheme is proposed. In order to increase the anonymity of coin mixing, group blind signature technology is used to expand the anonymous set of users. Moreover, a Supervisor for supervising Mixes is introduced to ensure the security of the system and the threshold signature is also used to ensure the security of the Mix's Bitcoin to prevent the Supervisor from stealing Bitcoin. Finally, the security analysis and experiment are given to conclude that our scheme is secure and efficient.
Theories and cases in decisions under uncertainty

We present and axiomatize a model combining and generalizing theory-based and analogy-based reasoning in decision under uncertainty. An agent has beliefs over a set of theories describing the data generating process, given by decision weights. She also puts weight on similarity to past cases. When a case is added to her memory and a new problem is encountered, two types of learning take place. First, the decision weight assigned to each theory is multiplied by its conditional probability. Second, subsequent problems are assessed for their similarity to past cases, including the newly-added case. If no weight is put on past cases, the model is equivalent to Bayesian reasoning over the theories. However, when this weight is positive, the learning process continually adjusts the balance between case-based and theory-based reasoning. In particular, a "black swan" which is considered a surprise by all theories would shift the weight to case-based reasoning.
Development of Algorithmic Thinking by Means of Lego Mindstorms Ev3 on Robotics

The article presents approaches to the formation of algorithmic thinking, as the most important component of the intellectual development of engineering personnel in the conditions of digital development of Russia. These approaches are related to the implementation of STEAM education, as well as ideological standards of CDIO engineering training. Solved the following problems: selected methods of evaluation of algorithmic thinking in adolescents; designed practical tasks for teaching course on robotics in the environment of Lego Mindstorms Education EV3; implemented training course in the school curriculum of adolescents; carried out diagnostics of algorithmic thinking in students before the course and after the event; analyzes information gained in the course of. An experimental study based on the developed evaluation methodology confirms that the implementation of these approaches allows to effectively form the algorithmic thinking of future engineers, starting from adolescence, by introducing robotics classes in the Lego Mindstorms Education EV3 environment into the school curriculum.