Reviewerly/exHarmony huggingface dataset:
Software Implementation of Optimal Pairings on Elliptic Curves with Odd Prime Embedding Degrees

Pairing computations on elliptic curves with odd prime degrees are rarely studied as low efficiency. Recently, Clarisse, Duquesne and Sanders proposed two new curves with odd prime embedding degrees: BW13-P310 and BW19-P286, which are suitable for some special cryptographic schemes. In this paper, we propose efficient methods to compute the optimal ate pairing on this types of curves, instantiated by the BW13-P310 curve. We first extend the technique of lazy reduction into the finite field arithmetic. Then, we present a new method to execute Miller's algorithm. Compared with the standard Miller iteration formulas, the new ones provide a more efficient software implementation of pairing computations. At last, we also give a fast formula to perform the final exponentiation. Our implementation results indicate that it can be computed efficiently, while it is slower than that over the (BLS12-P446) curve at the same security level.
A Study for Low Calculation Cost Side-Channel Resistance Prediction Based on Transfer Impedance of Leakage Path

We investigated the possibility of low-cost prediction of side-channel attack (SCA) vulnerability without the need to observe a large number of leakage traces for application to the side-channel attack countermeasure design for cryptographic devices. Assuming an on-board power analysis attack against the Advanced Encryption Standard (AES), we applied a decoupling capacitor as a countermeasure against SCA on the printed circuit board. The leakage strength after the countermeasure was predicted from the change in the transfer impedance of the leakage path due to the addition of the capacitor. As a result, the prediction agreed with the measured leakage strength for a limited decoupling configuration. This result suggests that it is necessary to improve the method of estimating the effect of the transfer impedance on the leakage strength to predict the SCA resistance of cryptographic devices quantitatively without observing the leakage traces.
Delta-based verification of software product families

The quest for feature- and family-oriented deductive verification of software product lines resulted in several proposals. In this paper we look at delta-oriented modeling of product lines and combine two new ideas: first, we extend Hähnle & Schaefer's delta-oriented version of Liskov's substitution principle for behavioral subtyping to work also for overridden behavior in benign cases. For this to succeed, programs need to be in a certain normal form. The required normal form turns out to be achievable in many cases by a set of program transformations, whose correctness is ensured by the recent technique of abstract execution. This is a generalization of symbolic execution that permits reasoning about abstract code elements. It is needed, because code deltas contain partially unknown code contexts in terms of "original" calls.
Denoised Internal Models: A Brain-inspired Autoencoder Against Adversarial Attacks

Despite its great success, deep learning severely suffers from robustness; that is, deep neural networks are very vulnerable to adversarial attacks, even the simplest ones. Inspired by recent advances in brain science, we propose the Denoised Internal Models (DIM), a novel generative autoencoder-based model to tackle this challenge. Simulating the pipeline in the human brain for visual signal processing, DIM adopts a two-stage approach. In the first stage, DIM uses a denoiser to reduce the noise and the dimensions of inputs, reflecting the information pre-processing in the thalamus. Inspired from the sparse coding of memory-related traces in the primary visual cortex, the second stage produces a set of internal models, one for each category. We evaluate DIM over 42 adversarial attacks, showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness.
Disambiguating Mentions of API Methods in Stack Overflow via Type Scoping

Stack Overflow is one of the most popular venues for developers to find answers to their API-related questions. However, API mentions in informal text content of Stack Overflow are often ambiguous and thus it could be difficult to find the APIs and learn their usages. Disambiguating these API mentions is not trivial, as an API mention can match with names of APIs from different libraries or even the same one. In this paper, we propose an approach called DATYS to disambiguate API mentions in informal text content of Stack Overflow using type scoping. With type scoping, we consider API methods whose type (i.e. class or interface) appear in more parts (i.e., scopes) of a Stack Overflow thread as more likely to be the API method that the mention refers to. We have evaluated our approach on a dataset of 807 API mentions from 380 threads containing discussions of API methods from four popular third-party Java libraries. Our experiment shows that our approach beats the state-of-the-art by 42.86% in terms of F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score.
Age Estimation Using Shortcut Identity Connection of ResNet50 Based on Convolutional Neural Network

Age estimation plays a vital role in real-life applications. Such as age-based facial classification, surveillance, the search for missing people, entertainment like age cameras, and determining how the human face changes with growing age. Current deep learning-based approaches have shown encouraging performance in age estimation. Estimating age through automated analysis with facial images is still a challenging issue. Automatic age estimation is still ongoing research. Most existing algorithms require a large amount of face data. To accurate age estimation from facial images requires a large amount of image data in the training period. The face outlook and the neck changes with age. Thinning skin and increase or loss of muscle tone give the face a different appearance. For that, all existing methods require a large amount of face-aging subjects. This paper proposed an age estimation using shortcut identity connection of ResNet50 architecture. The proposed architecture is 50 layers of Residual Network (ResNet50) based on CNN. The proposed algorithm can solve a few face-aging subject&#x0027;s problems using the shortcut identity connection of residual blocks. The proposed algorithm has used the ImageNet database as the primary face aging database of the proposed gateway. The experimental results of the proposed model are close to the experimental results of other models. The experimental results have shown that using Convolutional Neural Network (CNN) containing ImageNet facial images information with 35 layers of residual network with a Mean Absolute Error (MAE) of 1.38.
Modeling long-term video semantic distribution for temporal action proposal generation

Video temporal segmentation plays a vital role in video analysis since many higher-level computer vision tasks rely on it. Some recent efforts have been dedicated to generating temporal action proposals for long and untrimmed videos, which requires methods to generate accurate boundaries for video semantics. In this paper, we propose a novel and efficient Temporal Distribution Network (TDN), to model the long-term distribution of video semantic units (video dictionary). Firstly, we encode the semantics and context relations of video segments with a boundary-specified video embedding method. Then based on temporal convolutional layers, we design a Temporal Distribution Network (TDN) enumerating all the possible temporal locations in one pass and generating proposals that have high action confidence scores by capturing the long-term distributions of video semantics. We validate our method on temporal action proposal generation tasks and action detection tasks. Experimental results on two benchmark datasets, THUMOS14 and ActivityNet-1.3, show that the proposed method can significantly outperform the state-of-the-art approaches. Our model could obtain high-quality action proposals with a much faster speed.
An efficient algorithm for counting Markov equivalent DAGs

We consider the problem of counting the number of DAGs which are Markov equivalent, i.e., which encode the same conditional independencies between random variables. The problem has been studied, among others, in the context of causal discovery, and it is known that it reduces to counting the number of so-called moral acyclic orientations of certain undirected graphs, notably chordal graphs. Our main empirical contribution is a new algorithm which outperforms previously known exact algorithms for the considered problem by a significant margin. On the theoretical side, we show that our algorithm is guaranteed to run in polynomial time on a broad cubic-time recognisable class of chordal graphs, including interval graphs.
PREDIKSI PRODUKSI SUSU SEGAR DI INDONESIA MENGGUNAKAN ALGORITMA BACKPROPAGATION

Milk is a white liquid produced from female mammals that contain carbohydrates that are useful for humans. Based on data from the Indonesian Statistics Agency, milk productivity in Indonesia from 2012 to 2018 experienced an unstable curve. Therefore this research was conducted to predict and find out the level of development of milk productivity in Indonesia for the following years, so that companies that use milk have a reference to continue to strive to increase milk productivity in Indonesia to remain stable in order to meet the needs of the community and  minimize milk imports. This algorithm used is backpropagation neural network. This algorithm is able to predict good data especially data that is sustainable in a certain period of time. to simplify this research the author uses the Matlab 2011 application. To facilitate writers, authors use 5 architectural model, namely 5-9-1 = 94%, 5-12-1 = 88%, 5-14-1 = 88%, 5-15-1 = 94%, 5-17-1 = 94 %. So we get the best architectural model using the architectural mode 5-15-1 with an accuracy rate of 94% with MSE = 0,000999842.  Finally, this model is good enough to predict fresh milk production by province in Indonesia
An investigation into the deep learning approach in sentimental analysis using graph-based theories

Sentiment analysis is a branch of natural language analytics that aims to correlate what is expressed which comes normally within unstructured format with what is believed and learnt. Several attempts have tried to address this gap (i.e., Naive Bayes, RNN, LSTM, word embedding, etc.), even though the deep learning models achieved high performance, their generative process remains a "black-box" and not fully disclosed due to the high dimensional feature and the non-deterministic weights assignment. Meanwhile, graphs are becoming more popular when modeling complex systems while being traceable and understood. Here, we reveal that a good trade-off transparency and efficiency could be achieved with a Deep Neural Network by exploring the Credit Assignment Paths theory. To this end, we propose a novel algorithm which alleviates the features' extraction mechanism and attributes an importance level of selected neurons by applying a deterministic edge/node embeddings with attention scores on the input unit and backward path respectively. We experiment on the Twitter Health News dataset were the model has been extended to approach different approximations (tweet/aspect and tweets' source levels, frequency, polarity/subjectivity), it was also transparent and traceable. Moreover, results of comparing with four recent models on same data corpus for tweets analysis showed a rapid convergence with an overall accuracy of ≈83% and 94% of correctly identified true positive sentiments. Therefore, weights can be ideally assigned to specific active features by following the proposed method. As opposite to other compared works, the inferred features are conditioned through the users' preferences (i.e., frequency degree) and via the activation's derivatives (i.e., reject feature if not scored). Future direction will address the inductive aspect of graph embeddings to include dynamic graph structures and expand the model resiliency by considering other datasets like SemEval task7, covid-19 tweets, etc.
Modelling argumentation in short text: A case of social media debate

The technological leaps of artificial intelligence (AI) and the rise of machine learning have triggered significant progress in a plethora of natural language processing (NLP) and natural language understanding tasks. One of these tasks is argumentation mining which has received significant interest in recent years and is regarded as a key domain for future decision-making systems, behaviour modelling, and natural language understanding problems. Until recently, natural language modelling tasks, such as computational argumentation schemes, were often tested in controlled environments, such as persuasive essays, reducing unexpected behaviours that could occur in real-life settings, like a public debate on social media. Additionally, the growing demand for enhancing the trust and the explainability of the AI services has dictated the design and adoption of modelling schemes to increase the confidence in the outcomes of the AI solutions. This paper attempts to explore modelling argumentation in short text and proposes a novel framework for argumentation detection under the name Abstract Framework for Argumentation Detection (AFAD). Moreover, different proof-of-concept implementations are provided to examine the applicability of the proposed framework to very short text developing a rule-based mechanism and compare the results with data-driven solutions. Eventually, a combination of the deployed methods is applied increasing the correct predictions in the minority class on an imbalanced dataset. The findings suggest that the modelling process provides solid grounds for technical research while the hybrid solutions have the potential to be applied to a wide range of NLP-related tasks offering a deeper understanding of human language and reasoning.
ThreadRadar: A Thread-Aware Visualization for Debugging Concurrent Java Programs

Due to non-deterministic behavior and thread interleaving of concurrent programs, the debugging of concurrency and performance issues is a rather difficult and often tedious task. In this paper, we present an approach that combines statistical profiling, clustering and visualization to facilitate this task. We implemented our approach in a tool which is integrated as a plugin into a widely used IDE. First, we introduce our approach with details on the profiling and clustering strategy that produce runtime metrics and clusters of threads for source-code artifacts at different levels of abstraction (class and method) and the entire program. Next, we explain the design of our sparkline visualization which represents the clusters in situ, i.e. embedded in the program text next to the related source-code artifact in the source-code editor. More detailed information is available in separate views that also allow the user to interactively configure thread filters. In a demonstration study we illustrate the usefulness of the tool for understanding and fixing performance and concurrency issues. Finally, we report on first formative results from a small-scale user study.
Photonic Quantum Policy Learning in OpenAI Gym

In recent years, near-term noisy intermediate scale quantum (NISQ) computing devices have become available. One of the most promising application areas to leverage such NISQ quantum computer prototypes is quantum machine learning. While quantum neural networks are widely studied for supervised learning, quantum reinforcement learning is still just an emerging field of this area. To solve a classical continuous control problem, we used continuous-variable quantum machine learning. We introduce proximal policy optimization for photonic variational quantum agents and also study the effect of the data re-uploading. We present performance assessment via empirical study using Strawberry Fields, a photonic simulator Fock backend and a hybrid training framework connected to an OpenAI Gym environment and TensorFlow. For the restricted CartPole problem, the two variations of the photonic policy learning achieve comparable performance levels and a faster convergence than the baseline classical neural network of same number of trainable parameters.
SNAPE-FP: SqueezeNet CNN with Accelerated Pooling Layers Extension based on IEEE-754 Floating Point Implementation through SW/HW Partitioning On ZYNQ SoC

It is clearly known that deep learning applications are enormously used in the image classification, object tracking and related image analysis techniques. But deep learning networks usually involve huge number of parameters that need to be extensively processed to produce the classification output, which also takes a considerable time. GPUs are exploited to do such huge parallel computations to be finished within acceptable time. Still GPUs consume huge power, so they are not suitable for embedded solutions, and also they are very expensive. In the current work, complete implementation of floating point based SqueezeNet convolutional neural network (CNN) is done on ZYNQ System-On-Chip (SoC) XC7020 via partitioning the implementation on both the software part (ARM) and the FPGA part (Artix-7), the acceleration is done via parallel implementations of average pool layer on up to 3 channels with speedup = 6.37 for the Max Pool layer accelerated single channel and 13.88 for the Average Pool layer accelerated 3 channels in parallel. The maximum power consumption equals 1.549 watt (only 0.136 watt for the static power consumption) and the remaining is the dynamic power consumption which is greatly less than the GPU power consumption (reaches ~ 60 watt).
Simulation Platform for Flexible Electronics

This paper presents a promising simulation approach for evaluating the performance of arbitrarily deformed flexible electronic components. Arbitrary deformations of electronic components are designed using a computer graphic method for three-dimensional object manipulation. The electromagnetic simulations of these objects are primarily enabled by bespoke tetrahedral discretization that allows for smooth boundaries and thus eliminates discretization noise. The method is used to evaluate the impact of twisting and crumpling on the performance of flexible RF antennas.
Development of Smart Restaurant Application for Dine-In

The Coronavirus disease has dealt the Food and Beverage industry a great blow. Restaurants have to reduce their open hours and customers allowed to follow with the regulations, and it leaves them at a huge financial loss. We conduct this research to find the optimal possible way for restaurant to stay open and still have customer on this pandemic era. We propose that restaurant should make their reservation and booking system online as we have seen that online booking and ordering became popular lately. The purpose of this application is to reduce the contact needed between the customer and the staff to as little as possible, while also avoid creating waiting time for the customer. Our application is consisted of reservation system and ordering system. The reservation system enables customers to book a table online and check in via QR Code. The ordering system enables customers to pick a menu using their Smartphone and confirm their order, as well as paying it directly using electronic money.
Optimizing Deep Neural Network Architectures: an overview

Deep Neural Nets architectures are complex. Training them induces a greedy need for computing power and storage. This complexity is due to the large number of layers and parameters needed to achieve accurate results. From this perspective, several researchers thought of designing less complex models by proposing theoretical limits, constructive and pruning algorithms, Hyper-parameters optimization techniques. However, the most widely used method for designing deep learning models remains the experimental method that requires expertise and a lot of time to tune the model hyperparameters. Several research studies are attempting to automate the entire design process of deep learning models, using optimization algorithms to reduce architectural complexity and maximize accuracy. This paper proposes an overview of the Neural Nets optimization techniques. A comparison and discussion is also given for CNN architectures optimization for CIFAR-10 data-set.
On the (Ir)Replaceability of Global Setups, or How (Not) to Use a Global Ledger

In universally composable (UC) security, a global setup is intended to capture the ideal behavior of a primitive which is accessible by multiple protocols, allowing them to share state. A representative example is the Bitcoin ledger. Indeed, since Bitcoin—and more generally blockchain ledgers—are known to be useful in various scenarios, it has become increasingly popular to capture such ledgers as global setup. Intuitively, one would expect UC to allow us to make security statements about protocols that use such a global setup, e.g., a global ledger, which can then be automatically translated into the setting where the setup is replaced by a protocol implementing it, such as Bitcoin.
A Survey on Security of the Hadoop Framework in the Environment of Bigdata

Abstract The world is becoming increasingly digital at the moment. Every day, a significant amount of data is generated by everyone who uses the internet nowadays. The data are critical for carrying out day-to-day operations, as well as assisting corporate management in achieving their objectives and making the best judgments possible based on the information gathered. BigData is the process of merging many hardware and software solutions to deal with extremely huge amounts of data that surpass storage capability. It’s possible that large amounts of data will be generated. Hadoop systems are used in a variety of areas, including healthcare, finance, and government. insurance, and social media, in order to provide a quick and cost-effective big data solution. The Apache Hadoop is a framework for storing and processing data, managing, and distributing large amounts of information over a large number of server nodes. Here are some solutions that work on top of the Apache Hadoop stack to guarantee data security. To get a complete picture of the problem, we decided to conduct an investigation into existing security solutions for Apache Hadoop security in sensitive data which is stored on a huge data platform employing distributed computing on a cluster of commodity devices. The goal of this paper is to provide knowledge of security and Big Data issues.
Weakly Supervised Learning for Object Localization Based on an Attention Mechanism

Recently, deep learning has been successfully applied to object detection and localization tasks in images. When setting up deep learning frameworks for supervised training with large datasets, strongly labeling the objects facilitates good performance; however, the complexity of the image scene and large size of the dataset make this a laborious task. Hence, it is of paramount importance that the expensive work associated with the tasks involving strong labeling, such as bounding box annotation, is reduced. In this paper, we propose a method to perform object localization tasks without bounding box annotation in the training process by means of employing a two-path activation-map-based classifier framework. In particular, we develop an activation-map-based framework to judicially control the attention map in the perception branch by adding a two-feature extractor so that better attention weights can be distributed to induce improved performance. The experimental results indicate that our method surpasses the performance of the existing deep learning models based on weakly supervised object localization. The experimental results show that the proposed method achieves the best performance, with 75.21% Top-1 classification accuracy and 55.15% Top-1 localization accuracy on the CUB-200-2011 dataset.