Reviewerly/exHarmony huggingface dataset:
Comparative Study of Cooling Solutions of a Drone Based on Raspberry Pi Deducing the Most Efficient Cooling Method

Automation and robotics are recently being included in diverse applications, as they have numerous advantages and extensive research options. IoT applied in a vast area of projects and researches is very useful. One of the most talked-about robotic equipments for a diverse field of applications is the drone. Here, our research paper examines a drone based on a Raspberry Pi 3B+ model. Cooling the heat produced in the CPU is done via several methods. Concerning the heating, four cooling methods are compared and studied. The best one out of some considered active and passive cooling devices is attempted to find. The heating of the CPU is studied because heat can damage the components of the Raspberry Pi. Heat can also cause efficiency issues. The damage to Raspberry Pi from the heat will also affect the drone. Finally, from our study, it is deduced that a 40 × 40 × 20 mm heatsink applied with a thermal solution proves to be most effective in cooling the CPU, which is also indicated by our calculations.
MGEL: A Robust Malware Encrypted Traffic Detection Method Based on Ensemble Learning with Multi-grained Features

As the use of encryption protocols increase, so does the challenge of identifying malware encrypted traffic. One of the most significant challenges is the robustness of the model in different scenarios. In this paper, we propose an ensemble learning approach based on multi-grained features to address this problem which is called MGEL. The MGEL builds diverse base learners using multi-grained features and then identifies malware encrypted traffic in a stacking way. Moreover, we introduce the self-attention mechanism to process sequence features and solve the problem of long-term dependence. We verify the effectiveness of the MGEL on two public datasets and the experimental results show that the MGEL approach outperforms other state-of-the-art methods in four evaluation metrics.
Tai Le Character Recognition Using Deep Convolution Neural Networks

Tai Le characters are used widely in Southwest China and Southeast Asia, and it is necessary to recognize Tai Le characters. In this paper, a Tai Le characters recognition model base on the deep convolution neural network (DCNN) has been established. In addition, to recognize Tai Le characters, the printed Tai Le database YDH2019.1 has been constructed, and image digital processing technology has been used to preprocess the sample images of YDH2019.1. In the modeling of DCNN, 50% data of YDH2019.1 has been used as train data, and the performance of the recognition, model has been evaluated on the remaining 50%. The results show that the recognition accuracy of Tai Le characters reaches 99.98%, which proved the effectiveness of DCNN as a printed Tai Le character recognition model.
Educational Reform and Practice of Cultivating Unity3D+VR Somatosensory Interaction System Based on the Dual-Creation Ability Mode

Virtual reality technology is one of the frontier sciences in the world today. Since the 21st century, there have been precedents in the application of virtual reality in military and industrial fields. Based on this, a design method of somatosensory interactive system in digital campus based on Unity 3D and VR technology is proposed. Through the development and research of Kinect somatosensory device in windows platform, the depth image data and bone tracking data of human body are obtained, and the virtual roaming system is interacted with in Unity3D virtual reality platform. The flexibility and scalability of the system are improved through the layered and modular design scheme. The system has the characteristics of strong sense of reality, friendly interface and interaction, and meets the needs of modern campus in external publicity, internal guidance, campus planning, information management and so on.
Dynamic Resources Allocation among Collocated Applications via Reinforcement Learning

Cloud servers usually face low utilization. One method to mitigate such problem is to collocate multiple applications onto a single server to improve the resources exploitation. However, due to the workload variations and resources constrains, collocating applications interfere with each other, and this incurs performance degradation. The lack of effective resources managing mechanism further drops the utilization as the number of latency-sensitive user-facing applications, such as search engines and online shopping, increases. In this paper, we assume the strategy is to collocate one latency-critical task with one or more batch tasks. We design a reinforcement learning based controller that can simultaneously meet the performance target of the latency-critical task and improve the throughput of batch tasks. We combine the concept of workload characterization to make our design non-intrusive and widely applicable to any applications. The online reinforcement learning helps with capturing workload variations. We implement a prototype of our design and evaluate on real-world applications. The result indicates its effectiveness compared with uncontrolled collocation and the static feedback-based controller.
A Survey on Writer Identification System for Indic Scripts

Handwriting is a skill, expression of someone’s personality that cannot be imitated. It is a physical way of communicating thoughts and ideas and a method for speaking with others. Handwriting is considered as the behavioural characteristic of a person. It is concerned with the sentiments, attitude and brain of a person. Thus no two persons can have the same handwriting. Indeed, even indistinguishable twins who share appearance and hereditary qualities do not have the same handwriting. Handwriting based writer identification has attracted a great deal of attention by researchers to prove someone’s authenticity. It is the process of identifying a person through his/her handwriting. This paper introduces a survey on the work accomplished for writer identification for Indic scripts. The work done on Indic scripts is confined as compared to the non-Indic scripts due to non-availability of the dataset. The anatomy of the paper comprises introduction, stages of writer identification system, applications, reported work and conclusion. The primary focus of this paper is to present the review on writer identification system for Indic scripts, mainly, Devanagari, Gurumukhi, Tamil, Telugu, Bengali, Kannada, Gujarati, Oriya, and Malayalam. This study provides guidance to the research analysts in this area by giving the studies of work done on Indic scripts.
Principal Component Properties of Adversarial Samples

Deep Neural Networks for image classification have been found to be vulnerable to adversarial samples, which consist of sub-perceptual noise added to a benign image that can easily fool trained neural networks, posing a significant risk to their commercial deployment. In this work, we analyze adversarial samples through the lens of their contributions to the principal components of each image, which is different than prior works in which authors performed PCA on the entire dataset. We investigate a number of state-of-the-art deep neural networks trained on ImageNet as well as several attacks for each of the networks. Our results demonstrate empirically that adversarial samples across several attacks have similar properties in their contributions to the principal components of neural network inputs. We propose a new metric for neural networks to measure their robustness to adversarial samples, termed the (k, p) point. We utilize this metric to achieve 93.36% accuracy in detecting adversarial samples independent of architecture and attack type for models trained on ImageNet.
A Refinement of Key Mismatch Attack on NewHope

Abstract NewHope cryptosystem is one of the second-round submissions of the National Institute of Standards and Technology post-quantum cryptography standardization process, which is a suite of two key encapsulation mechanisms based on the ring-learning with errors (LWE) problem. It has received much attention from the research community due to its small key size and high efficiency. Recently, three key mismatch attacks are proposed against NewHope under the condition of key reuse. They do not solve the ring-LWE instance directly but exploit the leakage of secret information. As far as we know, the best result is given by Okada et al. ((2020) Improving Key Mismatch Attack on NewHope with Fewer Queries. In Proc. of the 25th Australasian Conf. on Information Security and Privacy, Perth, WA, Australia, November 30–December 2, pp. 505–524. Springer Cham, Switzerland), which recovers the whole secret with a success probability of $97\%$ and $233,803$ average queries. In this paper, we further improve the key mismatch attack of NewHope by reducing the average queries to $106,577$ and raising the success probability to $100\%$. Moreover, we analyze the key mismatch attack without key reuse for the first time and we propose a combinatorial attack against NewHope1024. The total complexity of the combinatorial attack is $2^{253}$, which is lower than the complexity of primal attack and the claimed security strength of NewHope1024.
Comprehensive opinion analysis on recent QUAD formation in Indo Pacific region using Twitter corpus

India is known for its highly disciplined foreign policies, strategic location, vibrant and massive Diaspora. India envisages enhancing its scope of cooperation, trade and widens its sphere of relations with the Pacific. As a result, the world is witnessing the rise of Indo-Pacific ties. Before the 1980's the keystone of the universe was called the Atlantic, but now a radical shift to the east is noticed by the term “Indo-Pacific". In this respect, a recent development occurred as a partnership, the Malabar exercise, in the waters of the Pacific and the Indian Oceanic region, which supports and proclaim free, open, and comprehensive Indo-Pacific and stays committed on a rule-based order. Considering the mass inclusion of people on social media platforms and contemplating their opinion has been of much interest in the research. Understanding and categorizing a large dataset of ideas into positive, negative, and neutral aspects is challenging. Our motive is to elucidate how the world, especially the citizens, is welcoming this strategic and influencing partnership. Also, Could social media influence the meaning of partnership? Our paper is divided into two aspects, one dealing with the comparison of various techniques and the other telling opinions of people regarding Indo-pacific relations. We used a geo-specific dataset obtained in various languages. Also, we applied a blend of various ML and DL techniques, feature extraction models, and opinion-based classification, which not only gives an analysis of opinions regarding “Indo-pacific” but also provides an in-depth insight to the comparison of various state-of-the-art sentiment analysis techniques, which is provided as a continuation of earlier review presented on sentiment analysis. The word-cloud visualization system assists people in understanding the changes of public sentiment reactions better.
A Simulation-based Study on the Reliability of Data Center Upgrades

Reliability is a critical concern for data centers and has attracted extensive studies. Prior studies mostly focused on static data centers, assuming that the software and hardware components remain unchanged for a long time. However, today's data centers have to upgrade due to the increasing requirements of security, performance, or capacity, and it remains unclear how the data center upgrades affect data reliability. In this paper, we particularly focus on studying typical upgrading policies that are widely adopted in data centers and present a simulation-based study to reveal the effects of different policies on data reliability. We first design and implement an event-based reliability simulator called SIMDDC, which simulates different system organizations and failure/recovery models, and takes the probability of data loss ( PDL) and the probability of data unavailability ( PUA) as the metrics of reliability. By using SIMDDC, we then conduct a set of experiments. Based on our quantitative analysis of the experimental results, our study has revealed several interesting and important findings related to software and hardware upgrades in terms of upgrade domain settings, upgrade checks, and upgrade with different storage devices (HDDs and SSDs) and different data redundancy schemes. Our findings are helpful to determine proper upgrade policies while guaranteeing the data reliability in modern data centers. For public use, we have open sourced our simulator SMIDDC at http://github.com/yichuan0707/SIMDDC.
Vision-Text Time Series Correlation for Visual-to-Language Story Generation

Automatic generation of textual stories from visual data representation, known as visual storytelling, is a recent advancement in the problem of images-to-text. Instead of using a single image as input, visual storytelling processes a sequential array of images into coherent sentences. A story contains non-visual concepts as well as descriptions of literal object(s). While previous approaches have applied external knowledge, our approach was to regard the non-visual concept as the semantic correlation between visual modality and textual modality. This paper, therefore, presents new features representation based on a canonical correlation analysis between two modalities. Attention mechanism are adopted as the underlying architecture of the image-to-text problem, rather than standard encoder-decoder models. Canonical Correlation Attention Mechanism (CAAM), the proposed end-to-end architecture, extracts time series correlation by maximizing the cross-modal correlation. Extensive experiments on VIST dataset ( http://visionandlanguage.net/VIST/dataset.html ) were conducted to demonstrate the effectiveness of the architecture in terms of automatic metrics, with additional experiments show the impact of modality fusion strategy.
Improved Mushroom Reproduciton Optimization for Robot Path Planning

In order to improve the performance of mushroom reproduction algorithm in path planning, an improved mushroom reproduction algorithm is proposed. Chaotic map is used to initialize to improve the ergodicity of the initial population. Introducing levy flight to avoid the algorithm falling into local optimum.To make the algorithm have adaptive local search ability, chaotic weight mechanism is proposed. Judgment factors are optimized to make the artificial wind judgment conditions change with iteration. The path planning is tested in navigation point model, and the path is smoothed by cubic spline interpolation. Simulation experiments are carried out in different obstacle maps, the results show that the improved mushroom reproduction algorithm has better effect than the mushroom reproduction algorithm and other swarm intelligence algorithm in path planning.
Both Comparison and Induction are Indispensable for Cross-Domain Few-Shot Learning

Few-shot learning (FSL), aiming to extract new knowledge from very small amount of labeled samples, has attracted noticeable attentions recently. However, most of existing methods often fail when facing huge domain shift between seen and unseen classes. We think this should be attributed to the episode strategy which ignore utilizing support samples to induct the test classes. So in this paper, for the first time, we propose a bilevel episode strategy (BL-ES) to train a inductive graph network (IGN) that learn to both comparison and induction. Specifically, first, outer episodes in BL-ES simulate the cross-domain few-shot tasks constantly, while inner episodes learn to drive IGN to induct the common features of test classes. Then, the propsoed IGN captures the correlation among all samples to update meta points of each category in induction module. Finally, we introduce a geometrical constraint term utilizing meta points into the training loss, to update the nodes and edges in feature space. This way improves the robustness of training process. Extensive experiments show that our framework outperforms the state-of-the-art FSL alternatives, and are more suitable for real-world applications.
Modulating Localization and Classification for Harmonized Object Detection

Object detection involves two sub-tasks, i.e. localizing objects in an image and classifying them into various categories. For existing CNN-based detectors, we notice the widespread divergence between localization and classification, which leads to degradation in performance. In this work, we propose a mutual learning framework to modulate the two tasks. In particular, the two tasks are forced to learn from each other with a novel mutual labeling strategy. Besides, we introduce a simple yet effective IoU rescoring scheme, which further reduces the divergence. Moreover, we define a Spearman rank correlation-based metric to quantify the divergence, which correlates well with the detection performance. The proposed approach is general-purpose and can be easily injected into existing detectors such as FCOS and RetinaNet. We achieve a significant performance gain over the baseline detectors on the COCO dataset.
Toward Stable Asymptotic Learning with Simulated Learners

Simulations of human learning have shown potential for supporting ITS authoring and testing, in addition to other use cases. To date, simulated learner technologies have often failed to robustly achieve perfect performance with considerable training. In this work we identify an impediment to producing perfect asymptotic learning performance in simulated learners and introduce one significant improvement to the Apprentice Learner Framework to this end.
LRD-Net: A Lightweight Deep Network for Low-light Image Enhancement

The computer vision algorithms demonstrate superior performance in the real world when provided with high-quality image or video inputs. However, low-light or non-uniformly illuminated conditions result in low visibility in the captured images, thus demanding low-light image enhancement. Therefore, the paper presents a lightweight deep learning-based method, referred to as LRD-Net, for enhancing low-light images. The proposed approach initially estimates the illumination map using maximum of RGB color channels and then utilizes the retinex theory to derive the corresponding reflectance. Further, we propose a lightweight deep network for refining the estimated reflectance, which is capable of restoring the lost structural and textural details. Our proposed approach employs a lightweight structure yet demonstrates competitive performance to various state-of-the-art methods. The fast computation speed escalates the practical usage of our method without compromising on quality, as demonstrated by extensive experiments.
Breaking and Fixing Third-Party Payment Service for Mobile Apps

Riding on the widespread user adoption of mobile payment, a growing number of mobile apps have integrated the service from third-party payment service providers or so-called Cashiers. Despite its prevalence and critical nature, no existing standard can guide the secure deployment of mobile payment. Thus, the protocol designs and implementations from different Cashiers are diverse. Given the complicated multi-party interactions in mobile payment, either the Cashiers or the apps may not fully consider various threat models, which enlarges the attack surface and causes the exploits with severe consequences, ranging from financial loss to privacy violations. In this paper, we perform an in-depth security analysis of real-world third-party payment services for mobile apps. Specifically, we examine the mobile payment systems from five top-tier Cashiers that serve over one billion users globally. Leveraging insecure protocol designs and practical implementation flaws, e.g., vulnerable backend SDKs for mobile apps, we have discovered six types of exploits. These exploits enable the attacker to violate user privacy and shop for free in the victim apps, affecting millions of users. Finally, we propose the fixings to defend against these exploits. We have shared our findings with the affected Cashiers and got their positive responses.
Numerical simulation of the workspace of robots with moving bases in the multi-agent system

Multi-robotic systems are used to solve many practical tasks in wide areas, including military surveillance, reconnaissance, rescue operations, warehousing, etc. Such systems can be represented as groups of intelligent robots capable of interacting with each other and jointly implementing various tasks. One of the problems in the development of such systems is the safe navigation of a group of robots. The restrictions imposed by the structural elements of its agents are determined to solve it. The article presents the structure of a multi-robotic system consisting of parallel and serial robots installed on mobile platforms. The parallel robot is made based on a tripod with the possibility of rotating the robot base about the horizontal axis. Determining the workspace boundaries of robots eliminates collisions between agents and external objects. The developed algorithms are based on deterministic methods for approximating the solutions set of nonlinear inequalities systems with restrictions in spaces of coordinates in the form of n-dimensional boxes. The relative positions of mobile platforms with manipulators, at which collisions are possible, are determined.
A Hybrid approach to identify Software Reusable Components in Software Intelligence

Reusability is demarcated as the way of utilizing existing software components in software development. It plays a significant role in component-based software engineering. Extracting the components from the source code and checking the reusability factors is the most crucial part. Software Intelligence, a combination of data mining and artificial intelligence, helps to cope with the extraction and detection of reusability factor of the component. In this work prediction of reusability factor is considered. This paper proposes a hybrid PSO-NSGA III approach to detect whether the extracted component is reusable or not. The existing models lack in tuning the hyper parameters for prediction, which is considered in this work. The proposed approach was compared with four models, showing better outcomes in terms of performance metrics.
Improved Meet-in-the-Middle Attacks on Reduced-Round Tweakable Block Cipher Deoxys-BC

Abstract Deoxys-BC is an internal tweakable block cipher of the authenticated encryption algorithm Deoxys, which is a third-round finalist in the CAESAR competition. In this paper, we study the property of Deoxys-BC, such as the subtweakey difference cancelation and the freedom of the tweak. Combining the differential enumeration technique with these properties, the authors achieve the key-recovery attacks on Deoxys-BC under the meet-in-the-middle attack. As a result, we get an attack on 9-round Deoxys-BC-128-128 by constructing a 6-round meet-in-the-middle distinguisher with $2^{113}$ plaintext–tweak combinations, $2^{97}$ Deoxys-BC blocks and $2^{121.6}$ 9-round Deoxys-BC-128-128 encryptions. We also present an attack on 11-round Deoxys-BC-256-128 for the first time by constructing a 7-round meet-in-the-middle distinguisher with $2^{113}$ plaintext-tweak combinations, $2^{226}$ Deoxys-BC blocks and $2^{251}$ 11-round Deoxys-BC-256-128 encryptions.