Reviewerly/exHarmony huggingface dataset:
Online Variational Learning of Shifted Scaled Dirichlet Mixture

Clustering is a typical unsupervised learning method applied to discover and analyze group structure in a set of observations. Among all of the clustering methods, mixture models have become one of the principal data mining approaches to explore various types of data and find the hidden structures. In real-time and large-scale applications, data instances are generated sequentially. Due to the nature of such data, online algorithms have grabbed lots of attention. In this paper, we propose a novel online clustering approach based on finite shifted scaled Dirichlet mixture models. We use a variational approach with an online setting for learning our model. This proposed algorithm is validated through two real-world applications related to smart buildings.
Recovery Attack on Bob’s Reused Randomness in CRYSTALS-KYBER and SABER

Quantum computing capability outperforms that of the classic computers overwhelmingly, which seriously threatens modern public-key cryptography. For this reason, the National Institute of Standards and Technology (NIST) and several other standards organizations are progressing the standardization for post-quantum cryptography (PQC). There are two contenders among those candidates, CRYSTALS-KYBER and SABER, lattice-based encryption algorithms in the third round finalists of NIST’s PQC standardization project. At the current phase, it is important to evaluate their security, which is based on the hardness of the variants of Ring Learning With Errors (Ring-LWE) problem. In ProvSec 2020, Wang et al. introduced a notion of “meta-PK” for Ring-LWE crypto mechanism. They further proposed randomness reuse attacks on NewHope and LAC cryptosystems which meet the meta-PKE model. In their attacks, the encryptor Bob’s partial (or even all) randomness can be recovered if it is reused. In this paper, we propose attacks against CRYSTALS-KYBER and SABER crypto schemes by adapting the meta-PKE model and improving Wang et al.’s methods. Then, we show that our proposed attacks cost at most 4, 3, and 4 queries to recover Bob’s randomness for any security levels of I (AES-128), III (AES-192), and V (AES-256), respectively in CRYSTALS-KYBER. Simultaneously, no more than 6, 6, and 4 queries are required to recover Bob’s secret for security levels I, III, and V in SABER.
Improvement of Data Stream Decision Trees

The classification of data streams has become a significant and active research area. The principal characteristics of data streams are a large amount of arrival data, the high speed and rate of its arrival, and the change of their nature and distribution over time. Hoeffding Tree is a method to, incrementally, build decision trees. Since its proposition in the literature, it has become one of the most popular tools of data stream classification. Several improvements have since emerged. Hoeffding Anytime Tree was recently introduced and is considered one of the most promising algorithms. It offers a higher accuracy compared to the Hoeffding Tree in most scenarios, at a small additional computational cost. In this work, the authors contribute by proposing three improvements to the Hoeffding Anytime Tree. The improvements are tested on known benchmark datasets. The experimental results show that two of the proposed variants make better usage of Hoeffding Anytime Tree’s properties. They learn faster while providing the same desired accuracy.
A sweeping optimization algorithm for the global cosine fitting energy image segmentation model

Abstract Image segmentation plays a pivotal role in image processing. Level set model is a traditional variation image segmentation method. In order to achieve level set evolution equation, the level set energy functionals are minimized with the gradient descent methods and then the partial differential equation (PDE) was solved by finite difference scheme. Slow speed is one of its disadvantages. We propose a sweep optimization algorithm based on global cosine fitting (GCF) energy. Instead of calculating the PDE and the curvature, the proposed sweeping algorithm directly calculates the energy change when a pixel moves from one side of evolving contour to the other. It checks whether the GCF energy is decreased or not. The proposed algorithm has many advantages. For example, independent of initial level set contour positions and parameters, need not consider the Courant Friedrichs Lew condition and the regularization energy term. The proposed algorithm can be easily extended to high dimension image segmentation. The experiments on synthetic images, noise images and real images demonstrate the effectiveness of the proposed sweeping optimization algorithm.
Object Detection of Basketball Robot Based on MobileNet-SSD

Object detection is one of the research hotspots in the field of computer vision. In this paper, we use the lightweight network MobileNet combined with Single Shot Multibox Detector (SSD) to realize the object detection of the robot. SSD combined with MobileNet can effectively compress the size of the network model and improve the detection rate. The method does automatic extraction on the image features first, and add different size feature maps after the basic network, and then do convolution filtering on the multi dimension feature maps to get the object coordinate value and the object category. In the experiment, compared with the original vision method based on OpenCV, the MobileNet-SSD algorithm was less affected by illumination conditions in the object recognition process, and achieved the rapid and accurate recognition of the basketball robot on the ball.
A Structured Analysis of the Video Degradation Effects on the Performance of a Machine Learning-enabled Pedestrian Detector

Machine Learning (ML)-enabled software systems have been incorporated in many public demonstrations for automated driving (AD) systems. Such solutions have also been considered as a crucial approach to aim at SAE Level 5 systems, where the passengers in such vehicles do not have to interact with the system at all anymore. Already in 2016, Nvidia demonstrated a complete end-to-end approach for training the complete software stack covering perception, planning and decision making, and the actual vehicle control. While such approaches show the great potential of such ML-enabled systems, there have also been demonstrations where already changes to single pixels in a video frame can potentially lead to completely different decisions with dangerous consequences in the worst case. In this paper, a structured analysis has been conducted to explore video degradation effects on the performance of an ML-enabled pedestrian detector. Firstly, a baseline of applying “You only look once” (YOLO) to 1,026 frames with pedestrian annotations in the KITTI Vision Benchmark Suite has been established. Next, video degradation candidates for each of these frames were generated using the leading video compression codecs libx264, libx265, Nvidia HEVC, and AV1: 52 frames for the various compression presets for color frames, and 52 frames for gray-scale frames resulting in 104 degradation candidates per original KITTI frame and in 426,816 images in total. YOLO was applied to each image to compute the intersection-over-union (IoU) metric to compare the performance with the original baseline. While aggressively lossy compression settings result in significant performance drops as expected, it was also observed that some configurations actually result in slightly better IoU results compared to the baseline. Hence, while related work in literature demonstrated the potentially negative consequences of even simple modifications to video data when using ML-enabled systems, the findings from this work show that carefully chosen lossy video configurations preserve a decent performance of particular ML-enabled systems while allowing for substantial savings when storing or transmitting data. Such aspects are of crucial importance when, for example, video data needs to be collected from multiple vehicles wirelessly, where lossy video codecs are required to cope with bandwidth limitations for example.
Image Recognition of Pledges of Capital Stock in Small- and Medium-Sized Enterprises Based on Partial Differential Equations

Image recognition is one of the core research directions in the field of computer vision research, which can be divided into general image recognition and fine-grained image recognition. General image recognition refers to the recognition of different types of objects; fine-grained image recognition refers to the recognition of different subclasses in the same broad class of objects, such as SME financing inventory pledge image recognition. In this paper, we propose a partial differential equation-based image recognition method for SME financing inventory pledges and conduct detailed analysis and experiments. Compared with general images, partial differential equation-based SME financing inventory pledges image recognition is difficult to recognize due to data characteristics such as small differences in features between classes, large differences in features within classes, and a small percentage of targets in the image. To address the problem that existing methods ignore the role of shallow features on fine-grained image recognition, this paper proposes a fine-grained image recognition method based on partial differential equations. By analyzing the important role of shallow features for fine-grained image recognition, a feature fusion method with adaptive weights is proposed. Using this method to fuse shallow and high-level semantic features for recognition, the role of shallow features in fine-grained image recognition is fully exploited. In addition, the proposed method does not change the order of magnitude of the model parameters and is highly transferable. The relevant experimental results verify the effectiveness of the proposed method.
Beyond the Keyboard, Mouse, and Screen: New Paradigms in Interface Design

Since Douglas Engelbart’s famous demo in 1968 - the so-called “Mother of All Demos” – the keyboard, mouse, screen triad has been a fixed convention in interface design. To this day, most desktop computers on the market use a combination of these three elements as part of their basic interface. The prototypes featured in this paper explore new design typologies that challenge these conventions and sketch new directions for the development of related design research. Furthermore, they demonstrate the application of research through design as a viable methodology for the development of new HCI modalities. By developing design typologies that are inconspicuously integrated into public artifacts and the built environment, we demonstrate new paradigms for ubiquitous computing.
NQuAD: 70,000+ Questions for Machine Comprehension of the Numerals in Text

Numeral information plays an important role in narratives of several domains such as medicine, engineering, and finance. Previous works focus on the foundation exploration toward numeracy and show that fine-grained numeracy is a challenging task. In machine reading comprehension, our statistics show that only a few numeral-related questions appear in previous datasets. It indicates that few benchmark datasets are designed for numeracy learning. In this paper, we present a Numeral-related Question Answering Dataset, NQuAD, for fine-grained numeracy, and propose several baselines for future works. We compare NQuAD with three machine reading comprehension datasets and show that NQuAD is more challenging than the numeral-related questions in other datasets. NQuAD is published under the CC BY-NC-SA 4.0 license for academic purposes.
Automated Environmental Mapping with Behavior Trees

Automated exploration, environmental measuring and mapping using robotic systems is highly relevant in post-disaster scenarios. Challenging conditions and presence of unknown hazardous substances might disturb rescuers, affecting their performance or even endanger their lives. Under these circumstances, the deployment of robotic systems can reduce the risk and workload of rescuers and improve the situational awareness of emergency services. To accomplish this, multiple algorithms need to be synchronized on the robots. Behavior Trees offer a simple and modular structure for task execution, and provide a deep insight into complex task flows at first glance. The main contribution of this work is the development and integration of methods for environmental measuring and robot exploration using Behavior Trees. These methods include robot navigation, complete coverage planning, 2D/3D obstacle mapping and localization of hazard substances. Our solution is tailored for post-disaster scenarios and focuses on providing remote situational awareness for rescuers. Our system was successfully tested at the European Robotics Hackathon 2019, where a hazardous materials incident response operation was staged. With this implementation, we aim to simplify the system design and application of robotic systems used for autonomous operation.
Research on the Upgrading Method of Electronic Equipment

This paper introduces the method of electronic equipment upgrading in detail, including how to upgrade the emergency equipment or software maintenance, through a detailed analysis of the advantages and disadvantages of the existing electronic equipment, and finally gives the system of penis upgrading. And the method and specific process of software upgrading and transformation, for the upgrading and transformation of other electronic equipment, including software and hardware has a guiding significance.
Fine-Grained Semantic Segmentation of National Costume Grayscale Image Based on Human Parsing

In order to enhance the image understanding of different regions for national costume grayscale image automatic colorization, let coloring tasks take advantage of semantic conditions, also let it can apply the human parsing semantic segmentation method to the national costume grayscale image for semantic segmentation task. This paper proposes a semantic segmentation model for context embedding based on edge perceiving. Aiming at the features of national costume grayscale image, more optimizing the model and loss function. The national costume grayscale image semantic segmentation is different from semantic segmentation of the color image, this task is more difficult for the grayscale image has no color feature. In this paper, edge information and edge consistency constraints are used to improve the national costume grayscale image coloring effect. The experimental results show that the model designed in this paper can obtain more accurate fine-grained semantic segmentation results for the national costume grayscale image.
Modular Multiplier for Digital Quantum Coprocessor

The article continues research on the creation of digital quantum coprocessors. A quantum computer has a heterogeneous structure. The quantum (probabilistic) part of a quantum computer is considered as a coprocessor in relation to a classical computer (as its accelerator). The digital quantum coprocessor itself has a heterogeneous structure. It consists of a probabilistic part, which implements the actual quantum computations, and a digital part, which performs complex mathematical calculations necessary for the implementation of quantum algorithms. Organization of modular multiplication and exponentiation, which is used in Shor's algorithm, is discussed in this article. It is proposed to perform modular multiplication and exponentiation using well-known logic circuits, namely, sequential multi-bit multipliers. The structure of a serial modular multiplier and its components is considered. The technical characteristics of the components of the modular multiplier are presented. Further work is outlined in the direction of creating a modular multiplier for implementing Shor's algorithm in a digital quantum coprocessor.
Bitcoin: The Most Law-Abiding System Ever Created.

It has been frequently argued that bitcoin is a “censorship resistant” immutable ledger that precludes law enforcement or government from acting against illicit actors in the system and precludes the freezing or seizing of assets. However, in this paper, it will be demonstrated that contrary to popular mythology, bitcoin can be easily frozen and seized under proceeds of crime legislation or even under the civil forfeiture rules. Furthermore, through the ability to enact punitive restraints against organizations that facilitate the operation of illicit transactions, law enforcement can ensure that the necessary controls that are designed to implement methodologies to ensure that financial crime using digital cash fails and that where criminal activity has been associated with digital currency or digital cash that the proceeds of crime can be removed from the benefit of the criminal actors. Importantly, as the bitcoin whitepaper notes, nodes enforce rules. Therefore, “any needed rules and incentives can be enforced with this consensus mechanism” (Wright, 2008b, p. 8).
An Improvement in Key Domain Maximization Technique by Entropy Maximization

Encryption–decryption method is the soul of cryptography, and their secrecy is not just the need of hour in modern era. In this paper, we improve the key domain maximization technique (Uniyal et al. in Int J Recent Technol Eng (IJRTE) 8(5):1385–1388 (2020), []) by working on the entropy parameters in cipher text. Albeit, KDMT uses a unique and eternal decomposition which is indiscriminate in the key selection inside key space, and here, a specific secondary key is used to achieve maximum entropy in the encrypted data. In the context of key secrecy, the enhanced security is justified by comparing it with LU—decomposition.
Efficient Adaptation of TFHE for High End-to-End Throughput

Homomorphic Encryption (HE) allows to process over user’s encrypted data. There are various HE schemes including TFHE, which offers homomorphic binary gate operation combined with bootstrapping. In most used case of TFHE’s gate operations, however, they encrypted a single bit of message in a single ciphertext, resulting in low throughput when sending ciphertexts.In this paper, we present a simple solution for the user to increase the throughput by packing multiple bits inside a single ciphertext using a ring based message space in TFHE. With the packing method, we propose a modified gate bootstrapping procedure to operate the same binary circuits on the server. Finally, without any additional public keys, we propose a new Keyswitching algorithm that we call PackKS, which enables even the server to pack multiple messages, thereby achieving high end-to-end throughput for TFHE.
Object Detection and Recognition for Visually Impaired Users: A Transfer Learning Approach

Vision is one of the most important human senses, and it plays a critical role in understanding the surrounding environment. However, millions of people in the world are experiencing visual impairment. They are facing difficulties in their daily navigations since they cannot see the obstacles in their surroundings. In this paper, we applied transfer learning to two pre-trained models, Single Shot Detector Mobilenet V1 (SSD) and Faster Region-Convolutional Neural Network Inception V2 (Faster R-CNN), to detect and recognise 40 classes of common objects in surroundings. The performance of these two trained models was subsequently compared. The fine-tuned Faster R-CNN model was able to achieve an mAP (Mean Average Precision) score of 0.8961 with an inference time of 7.2 seconds. On the other hand, the SSd model achieved an mAP score of 0.5708 with an inference time of 1.8 seconds. In consludion, Faster R-CNN appears as the suitable model to be implemented in the mobile application to help visually impaired people as it produces a good mAP result.
CPC-H2: Convolution Power-based Cryptosystem and Digital Signature

ElGamal system was introduced by Taher ElGamal in his landmark paper in 1985 [1]. ElGamal is an important asymmetric cryptosystem that is based on the discrete logarithm problem. In brief, this cryptosystem relies on the difficulty of computing discrete logarithms over finite fields [1]. Towards solving discrete logarithms, Shor demonstrated the existence of a polynomial algorithm that can solve these logarithms on quantum machines [2]. In this paper we propose the Convolution Power Cryptosystem (CPC-H2); an extension to the ElGamal cryptosystem that is based on a novel hard problem. CPC-H2 is designed to defend against a Chosen Ciphertext Attack. We show that CPC-H2 is less complicated and has a higher security level than the Number Theory Research Unit (NTRU).
Background Subtraction Using A Hybrid Modelling Based Technique

In recent years, Background Subtraction (BGS) has become a major topic of study in the field of Computer Vision for performing moving object detection. BGS is typically utilized in video surveillance cameras to monitor moving objects. To accomplish the aforementioned job, this study provides a new method based on Hybrid modelling technique that combines two models, namely Robust Principal Component Analysis (RPCA) using Principal Component Pursuit (PCP) and randomized Singular Value Decomposition (rSVD). Video sequence is decomposed into frames and a data matrix is created with these frames as a column vector and the above-mentioned methods are then applied on the data matrix to achieve the backdrop and foreground of the video. Experiments are run on two well-known datasets, CDnet (2014) and BMC, as well as a random YouTube video sequence. In comparison to the classic PCP model, experimental results reveal that the proposed model has lower computing complexity and produces results in less time.
Online System for Identifying Need of Machine Maintenance by Mining Data Streams and Handling Concept Drifts

Data streams represent an ongoing stream of data, in many forms, coming from different sources. In real time data often comes in streams and is changing over time. Concept drift in supervised learning means that the data is going through a change. While solving predictive maintenance tasks on the streaming data, traditional models, trained on historical data, may become invalid, when such change occurs. Hence, the learning models need to adapt to changes very quick and accurately. Adaptive ensemble models are used for classification on data streams. In this paper, we implemented the modifications of the adaptive bagging methods, which uses internal class-weighting schemes for the model adaptation. Implemented models were evaluated on manually created data streams with ensemble methods and analyzed performance evaluation of different classifiers. This performance is greatly differed than the traditional model and hence handles the drift in much more effective way.