Reviewerly/exHarmony huggingface dataset:
YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection

Object detection remains an active area of research in the field of computer vision, and considerable advances and successes has been achieved in this area through the design of deep convolutional neural networks for tackling object detection. Despite these successes, one of the biggest challenges to widespread deployment of such object detection networks on edge and mobile scenarios is the high computational and memory requirements. As such, there has been growing research interest in the design of efficient deep neural network architectures catered for edge and mobile usage. In this study, we introduce YOLO Nano, a highly compact deep convolutional neural network for the task of object detection. A human-machine collaborative design strategy is leveraged to create YOLO Nano, where principled network design prototyping, based on design principles from the YOLO family of single-shot object detection network architectures, is coupled with machine-driven design exploration to create a compact network with highly customized module-level macroarchitecture and microarchitecture designs tailored for the task of embedded object detection. The proposed YOLO Nano possesses a model size of ~4.0MB (>15.1x and >8.3x smaller than Tiny YOLOv2 and Tiny YOLOv3, respectively) and requires 4.57B operations for inference (>34% and ~17% lower than Tiny YOLOv2 and Tiny YOLOv3, respectively) while still achieving an mAP of ~69.1% on the VOC 2007 dataset (~12% and ~10.7% higher than Tiny YOLOv2 and Tiny YOLOv3, respectively). Experiments on inference speed and power efficiency on a Jetson AGX Xavier embedded module at different power budgets further demonstrate the efficacy of YOLO Nano for embedded scenarios.
Complexity of controlled bad sequences over finite sets of Nd

We provide upper and lower bounds for the length of controlled bad sequences over the majoring and the minoring orderings of finite sets of Nd. The results are obtained by bounding the length of such sequences by functions from the Cichon hierarchy. This allows us to translate these results to bounds over the fast-growing complexity classes.
Short Paper: An Empirical Analysis of Monero Cross-chain Traceability

Monero is a privacy-centric cryptocurrency that makes payments untraceable by adding decoys to every real input spent in a transaction. Two studies from 2017 found methods to distinguish decoys from real inputs, which enabled traceability for a majority of transactions. Since then, a number protocol changes have been introduced, but their effectiveness has not yet been reassessed. Furthermore, little is known about traceability of Monero transactions across hard fork chains. We formalize a new method for tracing Monero transactions, which is based on analyzing currency hard forks. We use that method to perform a (passive) traceability analysis on data from the Monero, MoneroV and Monero Original blockchains and find that only a small amount of inputs are traceable. We then use the results to estimate the effectiveness of known heuristics for recent transactions and find that they do not significantly outperform random guessing. Our findings suggest that Monero is currently mostly immune to known passive attack vectors and resistant to tracking and tracing methods applied to other cryptocurrencies.
Decaying momentum helps neural network training

Momentum is a simple and popular technique in deep learning for gradient-based optimizers. We propose a decaying momentum (Demon) rule, motivated by decaying the total contribution of a gradient to all future updates. Applying Demon to Adam leads to significantly improved training, notably competitive to momentum SGD with learning rate decay, even in settings in which adaptive methods are typically non-competitive. Similarly, applying Demon to momentum SGD improves over momentum SGD with learning rate decay in most cases. Notably, Demon momentum SGD is observed to be significantly less sensitive to parameter tuning than momentum SGD with learning rate decay schedule, critical to training deep neural networks in practice.Results are demonstrated across a variety of settings and architectures, including image classification, generative models, and language models. Demon is trivial to implement, easy to tune, and incurs limited extra computational overhead, compared to the vanilla counterparts. Code is readily available.
Advanced ATM System Using Iris Scanner

Nowadays we are experiencing a radical increase in skimming in the Automated Teller Machine (ATM) systems. So, actuation in advancement and security of the ATM machines is required. An automated teller machine (ATM) is an electronic telecommunications device that helps customers of banking departments in transactions and transfer of money in their accounts. The customer enters their unique personal identification number (PIN), i.e. stored in the chip of the card. Due to an increase in the installation of ATM and the number of ATM cardholders, the number of cases of fraudulence has also increased radically. The advancement in technology has resulted in an increase in various skimming activities. So, developments are incorporated in the existing systems to make it more secure, convenient and reliable. The employed secured system must have high speed and must be durable. The presented design is unique because of biometric scanners such as Iris scanner and the two-way check with fingerprint scanner makes it more reliable. The iris scanner being the primary security check lets the system access the further steps for transaction. Fingerprint scanner embedded in the ATM card acts as the secondary security check for the system. The transaction procedure is successful only if the input data by the card holder matches with the database. It consumes less energy that makes it suitable for use. The suggested modified system is pragmatic moreover economical when correlating to the alternative existing classification and affirmation processes of ATMs.
Generative collaborative networks for single image super-resolution

A common issue of deep neural networks-based methods for the problem of Single Image Super-Resolution (SISR), is the recovery of finer texture details when super-resolving at large upscaling factors. This issue is particularly related to the choice of the objective loss function. In particular, recent works proposed the use of a VGG loss which consists in minimizing the error between the generated high resolution images and ground-truth in the feature space of a Convolutional Neural Network (VGG19), pre-trained on the very “large” ImageNet dataset. When considering the problem of super-resolving images with a distribution “far” from the ImageNet images distribution (e.g., satellite images), their proposed fixed VGG loss is no longer relevant. In this paper, we present a general framework named Generative Collaborative Networks (GCN), where the idea consists in optimizing the generator (the mapping of interest) in the feature space of a features extractor network. The two networks (generator and extractor) are collaborative in the sense that the latter “helps” the former, by constructing discriminative and relevant features (not necessarily fixed and possibly learned mutually with the generator). We evaluate the GCN framework in the context of SISR, and we show that it results in a method that is adapted to super-resolution domains that are “far” from the ImageNet domain.
Source code analysis dataset

The data in this article pair source code with three artifacts from 108,568 projects downloaded from Github that have a redistributable license and at least 10 stars. The first set of pairs connects snippets of source code in C, C++, Java, and Python with their corresponding comments, which are extracted using Doxygen. The second set of pairs connects raw C and C++ source code repositories with the build artifacts of that code, which are obtained by running the make command. The last set of pairs connects raw C and C++ source code repositories with potential code vulnerabilities, which are determined by running the Infer static analyzer. The code and comment pairs can be used for tasks such as predicting comments or creating natural language descriptions of code. The code and build artifact pairs can be used for tasks such as reverse engineering or improving intermediate representations of code from decompiled binaries. The code and static analyzer pairs can be used for tasks such as machine learning approaches to vulnerability discovery.
Learning chinese word embeddings from character structural information

Word embedding is a basic task in natural language processing area. Unlike English, Chinese subword units, such as characters, radicals, and components, contain rich semantic information which can be used to enhance word embeddings. However, existing methods neglect the semantic contribution of corresponding subword units to the word. In this work, we employ attention mechanism to capture the semantic structure of Chinese words and propose a novel framework, named Attention-based multi-Layer Word Embedding model(ALWE). We also design an asynchronous strategy for updating embedding and attention efficiently. Our model learns to share subword information between distinct words selectively and adaptively. Experimental results on the word similarity, word analogy, and text classification show that the proposed model outperforms all baselines, especially when words do not appear frequently. Qualitative analysis further demonstrates the superiority of ALWE.
Research on campus network cloud storage open platform based on cloud computing and big data technology

Based on the analysis of campus cloud storage requirements, the paper plans a cloud storage structure model to achieve the goal of breaking the bottleneck of traditional campus network resource utilization. The model established in the thesis fully considers the problems of cluster data security an d big data analysis and utilization, and proposes an optimization scheme for cloud storage open platform system design. Plan the HDFS + HBase distributed storage deployment in the Hadoop cluster, elaborate the process of behavior analysis, and find a way to optimize and improve the cloud storage in colleges and universities. HBase technology implements operations on HDFS distributed storage systems in Hadoop clusters, effectively improving the efficiency of resource access and resource analysis. The cloud platform storage platform designed by the thesis solves the problem of integrating the superior resources of colleges and universities.
Detection of Flood Images Posted on Online Social Media for Disaster Response

User-generated content on online social media (OSM) has several data mining applications, such as extracting useful information during disaster events. In this paper, we propose a CNN based model to detect flood images among several images posted on OSM. The proposed system consists of two levels of identification scheme. At the first level, a CNN model is used for detecting flood images, followed by a color based filtering to obtain the final detection. The proposed system is tested using real Twitter images collected during a recent flood event and the results are encouraging.
Permissioned Blockchain-Based Agriculture Network in Rootnet Protocol

The agriculture sectorSaji, Amal C. has seen considerate changes in its framework and working methodologies sinceVijayan, Akshay its inception. Current trend in supply chain management includesSundar, Ann Jerin theBaby Syla, L. interception of prejudiced middlemen taking advantage of the anonymity in relationships existing between the actual producers and ultimate consumers. This trend puts two main areas in the supply chain at risk, the profit factor of the cultivators and quality of the final product delivered to the consumers. This paper aims at enhancing the supply chain performance by incorporating blockchain technology to solve the aforementioned problems. It includes the creation of a blockchain network encompassing the producers and consumers, and an automated Distributed Digital Ledger mechanism that effectuates two-way product traceability. It will consequently reduce the gap between the market price and the farmer’s selling price to a great extent. The consumers on the other hand will receive better and healthier agro products that will eventually eradicate deadly ailments from the ecosystem.
Gaussian Quantum Bat Algorithm with Direction of Mean Best Position for Numerical Function Optimization

Quantum-behaved bat algorithm with mean best position directed (QMBA) is a novel variant of bat algorithm (BA) with good performance. However, the QMBA algorithm generates all stochastic coefficients with uniform probability distribution, which can only provide a relatively small search range, so it still faces a certain degree of premature convergence. In order to help bats escape from the local optimum, this article proposes a novel Gaussian quantum bat algorithm with mean best position directed (GQMBA), which applies Gaussian probability distribution to generate random number sequences. Applying Gaussian distribution instead of uniform distribution to generate random coefficients in GQMBA is an effective technique to promote the performance in avoiding premature convergence. In this article, the combination of QMBA and Gaussian probability distribution is applied to solve the numerical function optimization problem. Nineteen benchmark functions are employed and compared with other algorithms to evaluate the accuracy and performance of GQMBA. The experimental results show that, in most cases, the proposed GQMBA algorithm can provide better search performance.
Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.
An efficient and secure identity based multiple signatures scheme based on RSA

Information security is one of the main concerns today. Digital signature plays an important role to ensure authentication, non-integrity and non-repudiation on a message. Digital signature can be computed using RSA, DSA and elliptic curve cryptography. It can be simple signature in which hash of the message is encrypted with the private key of the sender. This private key and corresponding public keys are generated by CA, and public key are bind in the digital certificate. Another approach is Identity Based (ID) signature in which private keys are generated by PKG and public key is derived from the user’s identity. This is also known as certificate less communication. In ID based signature, there is no need to transmit public key over unsecure channel. Public keys are efficiently derived from the receiver’s identity information such as name, email address, network address, IP address and now aadhar number. Email address can be forged by unauthorized persons. Today aadhar number is used as unique identity proof that can be used as ID to derive public key of the user. In this paper we have used aadhar number to propose an efficient and secure identity based multiple signature schemes using RSA. Proposed scheme is secure against forgery and public key replacement attack.
Hacker Forum Exploit and Classification for Proactive Cyber Threat Intelligence

The exponential growth in data and technology have brought in prospects for progressively destructive cyber-attacks. Traditional security controls are struggling to match with the intricacy of cybercriminal tools and methods, organizations are now looking for better approaches to strengthen their cyber security capabilities. Cyber Threat Intelligence (CTI) in real-time is one such proactive approach which ensures that deployed appliances, security solutions and strategies are continually evaluated or optimized. Amongst various platforms for threat intelligence, hacker forums deliver affluent metadata, and thousands of Tools, Techniques, and Procedures (TTP). This research paper employs machine learning and deep learning approach using neural networks to automatically classify hacker forum data into predefined categories and develop interactive visualizations that enables CTI practitioners to probe collected data for proactive and opportune CTI. The results from this research shows that among all the models, deep learning model RNN GRU gives the best classification results with 99.025% accuracy and 96.56% precision.
Semi-supervised Lesion Detection with Reliable Label Propagation and Missing Label Mining

Annotations for medical images are very hard to acquire as it requires specific domain knowledge. Therefore, performance of deep learning algorithms on medical image processing is largely hindered by the scarcity of large-scale labeled data. To address this challenge, we propose a semi-supervised learning method for lesion detection from CT images which exploits a key characteristic of the volumetric medical data, i.e. adjacent slices in the axial axis resemble each other, or say they bear some kind of continuity. Specifically, by exploiting such a prior, a semi-supervised scheme is adopted to propagate bounding box annotations to adjacent CT slices to obtain more training data with fewer false positives and more true positives. Furthermore, considering that the NIH DeepLesion dataset has many missing labels, we develop a missing ground truth mining process by considering the continuity (or appearance-consistency) of multi-slice axial CT images. Experimental results on the NIH DeepLesion dataset demonstrate the effectiveness our methods for both semi-supervised label propagation and missing label mining.
EFEKTIFITAS PENGGUNAAN E-LEARNING BERBASIS EDMODO TERHADAP KEMAMPUAN KOMUNIKASI MATEMATIKA

Along with the development of technology both directly and indirectly can change someone in communicating, socializing and learning, this is a challenge for lecturers to be able to use technology optimally and to facilitate interaction with students in teaching and learning both in class and outside the classroom so that indicators in the unit of lecture events and semester learning design can be achieved. Edmodo is a social network platform for lecturers and students to share ideas, files, agenda of activities and assignments. Edmodo is designed to create lecturer and student interactions that emphasize fast communication, polling, assignments, sharing ideas, and much more. The research aims: 1) to find out whether mathematics learning using Edmodo (experimental) is better than learning without using Edmodo (conventional) on mathematical communication; 2) to find out whether students who study using Edmodo are positive towards learning mathematics. Quantitative research with quasi-experiment method, the research design used is nonequivalent control group design: providing two different treatments to two groups, namely the experimental class (Edmodo) and the control class (conventional). Quantitative data collection and analysis techniques using normality test, homogeneity test, two average similarity test, and t test. The results of the study are: 1). Learning using edmodo is better with learning without using edmodo with mathematical communication skills. 2). Students who learn to use edmodo have a positive attitude towards learning mathematics. The implication of the research is for the next researcher, using edmodo media so that maximum control is needed on students. If further research is conducted with different samples, the results shown will also be different. This can be viewed from a variety of different characteristics of students that greatly affect the ability of mathematical communication.
GraphZero: Breaking Symmetry for Efficient Graph Mining.

Graph mining for structural patterns is a fundamental task in many applications. Compilation-based graph mining systems, represented by AutoMine, generate specialized algorithms for the provided patterns and substantially outperform other systems. However, the generated code causes substantial computation redundancy and the compilation process incurs too much overhead to be used online, both due to the inherent symmetry in the structural patterns. In this paper, we propose an optimizing compiler, GraphZero, to completely address these limitations through symmetry breaking based on group theory. GraphZero implements three novel techniques. First, its schedule explorer efficiently prunes the schedule space without missing any high-performance schedule. Second, it automatically generates and enforces a set of restrictions to eliminate computation redundancy. Third, it generalizes orientation, a surprisingly effective optimization that was mainly used for clique patterns, to apply to arbitrary patterns. Evaluated on multiple graph mining applications and complex patterns with 7 real-world graph datasets, GraphZero demonstrates up to 40X performance improvement and up to 197X reduction on schedule generation overhead over AutoMine.
Deep Stacked Bidirectional LSTM Neural Network for Skeleton-Based Action Recognition

Skeleton-based action recognition has made great progress recently. However, many problems still remain unsolved. For example, the representations of skeleton sequences learned by most of the existing methods lack spatial structure information and detailed temporal dynamics features. To this end, we propose a novel Deep Stacked Bidirectional LSTM Network (DSB-LSTM) for human action recognition from skeleton data. Specifically, we first exploit human body geometry to extract the skeletal modulus ratio features (MR) and the skeletal vector angle features (VA) from the skeletal data. Then, the DSB-LSTM is applied to learning both the spatial and temporal representation from MR features and VA features. This network not only leads to more powerful representation but also stronger generalization capability. We perform several experiments on the MSR Action3D dataset, Florence 3D dataset and UTKinect-Action dataset. And the results show that our approach outperforms the compared methods on all datasets, demonstrating the effectiveness of the DSB-LSTM.