Reviewerly/exHarmony huggingface dataset:
FPGA Implementation of Hardware-Oriented Chaotic Boltzmann Machines

Boltzmann machines (BMs) are useful in various applications but are limited by their requirement to generate random numbers. In contrast, chaotic Boltzmann machines (CBMs) are neural networks that imitate the stochastic behavior of BMs with the chaotic dynamics and deterministic behavior, without random numbers. CBMs can potentially require fewer hardware resources than the original algorithms due to the unnecessity of random number generators. In this study, hardware-oriented algorithms and a differential multiply-accumulation operation are proposed to overcome the difficulties of implementing CBMs on field-programmable gate arrays (FPGAs). A hardware-oriented algorithm for CBMs, which includes fixed-point operations and shift operations, is proposed to reduce hardware resource utilization in the implemented circuits. In particular, the differential multiply-accumulate operation allows us to implement the multiply-accumulate operation with block random access memory and digital signal processors to reduce the consumption of lookup tables and flip-flops in FPGAs without losing the calculation speed. Our proposed approach was evaluated in numerical simulations, logical synthesis, and FPGA implementation. The calculation speed of FPGA-implemented CBMs was compared with software-implemented CBMs, which resulted in 1 / 6,500 of calculation time reduction in a 300-neuron CBM. Moreover, 2,048 neurons of CBM were realized by the logical synthesis. Therefore, the proposed hardware implementation of CBMs was shown to be feasible. The proposed CBMs can solve combinatorial optimization problems at a larger scale with fewer resources.
Feature-Driven Super-Resolution for Object Detection

Although some convolutional neural networks (CNNs) based super-resolution (SR) algorithms yield good visual performances on single images recently. Most of them focus on perfect perceptual quality but ignore specific needs of subsequent detection task. This paper proposes a simple but powerful feature-driven super-resolution (FDSR) to improve the detection performance of low-resolution (LR) images. First, the proposed method uses feature-domain prior which extracts from an existing detector backbone to guide the high-resolution (HR) image reconstruction. Then, with the aligned features, FDSR updates SR parameters for better detection performance. Comparing with some state-of-the-art SR algorithms with scale factor 4, FDSR outperforms the detection performance mAP on MS COCO validation and VOC2007 test datasets, and with good generalization to other detection networks.
Neuro-evolutionary models for imbalanced classification problems

Training an Artificial Neural Network (ANN) algorithm is not trivial, which requires optimizing a set of weights and biases that increase dramatically with the increasing capacity of the neural network resulting in such hard optimization problems. Essentially, over recent decades, stochastic search algorithms have shown remarkable abilities for addressing hard optimization problems. On the other hand, pragmatically, abundant real-world problems suffer from the imbalance problem, where the distribution of data varies considerably among classes resulting in more training biases and variances which degrades the performance of the learning algorithm. This paper introduces three stochastic and metaheuristic algorithms for training the Multilayer Perceptron (MLP) neural network to solve the problem of imbalanced classifications. The utilized algorithms are the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and the Salp Swarm Algorithm (SSA). The proposed GWO-MLP, PSO-MLP, and SSA-MLP are trained based on different objective functions; accuracy, f1-score, and g-mean. Whereas, it is evaluated based on 10 benchmark imbalanced datasets. The results show an advantage for f1-score, and g-mean fitness functions over the accuracy when the datasets are imbalanced.
Trans-DF: A Transfer Learning-based end-to-end Deepfake Detector

With the advent of information and communication technologies, there have been breakthrough developments in the field of Artificial Intelligence (AI). Moreover, increasing computation power and decreasing processing times, new applications are being developed at great speeds. One such application is Deepfakes, which tackles the increased manipulated and forged media content. But these fake images and videos hamper the security and privacy of individuals and can have large-scale religious, communal, or political implications that may prove to be catastrophic for a nation. The face swapped content at times can be identified by human observation, but with the use of Generative adversarial networks (GANs), such forged content can be developed with is hard to be identified even by humans. Hence, detecting such videos and images is a challenging task for researchers. Motivated from these gaps, in this paper, we propose a pipeline for detecting and extracting human faces from videos, process them to extract features from them, and then classify them as real or fake. The results of the proposed model achieved an accuracy of 90.2% for classifying fake images from real ones.
A Dual Attention Module for Real-time Facial Expression Recognition

In this paper, a real-time face expression based on a Convolutional Neural Network with a Dual Attention Module is presented for classifying various facial emotions. The system contains two main components. Firstly, local convolutional features of faces are extracted by the VGG13 baseline. Secondly, dual attention masks are automatically employed to enhance the backbone end based on the global probability of features. It represents the position and channel of the feature map. The local features from baseline are combined with the attention to infer the emotional label module. A single network is trained in an end-to-end scheme with five million parameters. Experiments on benchmark datasets show the attention module gives increased accuracy. Besides, this module provides lightweight and runs 60.20 frames per second when working in real-time on CPU devices.
An Analysis of Word2Vec for the Italian Language

Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In this work, analysing the semantic capacity of the Word2Vec algorithm, an embedding for the Italian language is produced. Parameter setting such as the number of epochs, the size of the context window and the number of negatively backpropagated samples is explored.
Generating summaries for methods of event-driven programs: An Android case study

The lack of proper documentation makes program comprehension a cumbersome process for developers. Source code summarization is one of the existing solutions to this problem. Many approaches have been proposed to summarize source code in recent years. A prevalent weakness of these solutions is that they do not pay much attention to interactions among elements of software. An element is simply a callable code snippet such as a method or even a clickable button. As a result, these approaches cannot be applied to event-driven programs, such as Android applications, because they have specific features such as numerous interactions between their elements. To tackle this problem, we propose a novel approach based on deep neural networks and dynamic call graphs to generate summaries for methods of event-driven programs. First, we collect a set of comment/code pairs from Github and train a deep neural network on the set. Afterward, by exploiting a dynamic call graph, the Pagerank algorithm, and the pre-trained deep neural network, we generate summaries. An empirical evaluation with 14 real-world Android applications and 42 participants indicates 32.3% BLEU4 which is a definite improvement compared to the existing state-of-the-art techniques. We also assessed the informativeness and naturalness of our generated summaries from developers' perspectives and showed they are sufficiently understandable and informative.
A Natural Scene Recognition Learning Based on Label Correlation

Because the background information of the multi-label natural scene image is complex and contains many kinds of things at the same time, it is a big challenge to improve recognition accuracy. At present, most methods usually give the same importance to each label for independent prediction, ignoring the correlation between labels. In this paper, an algorithm named Label Correlation based K-Nearest Neighbor (LC-KNN) method is proposed through analyzing and weighting the essential correlation between natural instances in the images. Considering that the label correlation of similar samples is also similar, this method is based on the local weighted method to mark the maximum cross-correlation label with high probability and the minimum cross-correlation label with low probability. It firstly finds out the neighbor samples of each sample to construct a multi-label count vector for the test sample according to the multi-label information of the neighbor samples, and then calculates the weight between the related labels based on naive Bayes model, and finally obtains the statistical correlation between the features and the labels to build classifier, which is more in line with the inherent law of the combination of things in natural scenes. The experimental results on the natural scene dataset show that the LC-KNN algorithm is significantly better than mainstream multi-label learning algorithms such as RELIAB, ML-KNN, Rank-SVM, and BoosTexter in tasks of multi-label natural scene recognition.
2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2-D Deep Learning

Advancements in quantum computing present a security threat to classical cryptography algorithms. Lattice-based key exchange protocols show strong promise due to their resistance to theoretical quantum-cryptanalysis and low implementation overhead. By contrast, their physical implementations have shown vulnerability against side-channel attacks (SCAs) even with a single power measurement. The state-of-the-art SCAs are, however, limited to simple, sequentialized executions of post-quantum key-exchange (PQKE) protocols, leaving the vulnerability of complex, parallelized architectures unknown. This article proposes 2Deep-a deep-learning (DL)-based SCA-targeting parallelized implementations of PQKE protocols, namely, Frodo and NewHope with data augmentation techniques. Specifically, we explore approaches that convert 1-D time-series power measurement data into 2-D images to formulate SCA an image recognition task. The results show our attack's superiority over conventional techniques including horizontal differential power analysis (DPA), template attacks (TAs), and straightforward DL approaches. We demonstrate improvements up to 1.5× to recover a 100% success rate compared to DL with 1-D input data while using fewer data. We furthermore show that machine learning improves the results up to 1.25× compared to TAs. Furthermore, we perform cross-device attacks that obtain profiles from a single device, which has never been explored. Our 2-D approach is especially favored in this setting, improving the success rate of attacking Frodo from 20% to 99% compared to the 1-D approach. Our work thus urges countermeasures even on parallel architectures and single-trace attacks.
Automated construction of clear-sky dictionary from all-sky imager data

All-sky imagers (ASIs) have substantial promise as scalable sensors for short-term solar irradiance forecasting. Many of the current computational techniques that use ASIs for this purpose rely on collections of clear-sky images indexed by time of day, solar angle, or both, called clear-sky dictionaries (CSDs). These CSDs act as baselines against which images can be compared to locate and classify clouds within the image frame. CSDs are often compiled by hand, where individuals visually inspect collections of images one at a time to find clear-sky images. This process is not scalable, and it is prone to error. This paper proposes an automated, nonparametric alternative that uses the principles of digital image processing to find clear-sky images within a set of images taken over several days. We use ground-truth measurements of the clearness index to assess the performance of our method, and we show that the images it selects accurately correspond to clear-sky images. We also compare our proposal, which is nonparametric, with a state-of-the-art parametric method. The numerical results indicate that the performance of the method proposed here is superior.
Nighttime Road Scene Parsing by Unsupervised Domain Adaptation

Due to recent advances in learning-based semantic segmentation, road scene parsing can usually achieve satisfactory results under normal illumination conditions. However, training a robust model for parsing nighttime road scenes is still very challenging, especially when semantic labels of training samples are absent. In this paper, we propose a convolutional neural network (CNN)-based method for parsing nighttime road scenes in an unsupervised manner. The proposed system includes an appearance transferring module and a segmentation module, which are coupled together and learned in an end-to-end fashion. The appearance transferring module aims to transfer unlabeled images acquired during both daytime and nighttime into a shared latent feature space that encodes the image content of both scenes at the semantic level. Then, the segmentation module is used to map the feature to its corresponding semantic labels. To better evaluate the proposed model, we also construct a new semantic segmentation dataset including 1,566 nighttime images. The extensive experimental results on the proposed benchmark illustrate that the proposed model achieves significant improvement compared with the baselines as well as a recently released system.
Blockchain-based Trust Management in Social Internet of Things

Social Internet of Things (social IoT) is a paradigm that integrates the social network concepts with IoT in which objects in the network are able to establish social relationships with each other. In these networks, to preserve privacy and security, the access to IoT resources can be handled based on social trust evaluation of the devices. This paper examines a blockchain-based trust management system for edge-enhanced IoT that uses social information of the system to strengthen the trust evaluation. Moreover, this system leverages information entropy to enhance security. We also developed a proof of concept system to show that the proposed solution can effectively assess trust factors in social IoT.
Utterance-focusing multiway-matching network for dialogue-based multiple-choice machine reading comprehension

Dialogue-based multiple-choice machine reading comprehension (MRC) is one of most difficult and novel tasks because it requires more advanced reading comprehension skills, such as speaker’s intention analysis, non-extractive reasoning, commonsense knowledge. Previous models usually only compute attention scores from the fixed representation of entire dialogue, and also do not fully consider the contribution of dialogue, question, options, and their combinations respectively. In this paper, we introduce Utterance-focusing Multiway-matching Network (UMN), a simple but effective human mimicking model for dialogue-based multiple-choice MRC. First, two utterance-focusing mechanisms called ParaUF and AutoUF are proposed to extract the utterances that are most relevant to the question and option: ParaUF gets the bilinear weighted distance between each utterance of dialogue and question and option during training while AutoUF obtains the scores by the relevance, overlap and coverage (ROC) rules before training process. Second, we adopted the multiway-matching mechanism to capture the relationship among the question, option and selected utterances through calculating the attention weights between the quadruplet of four sequences: utterances, question, option and the concatenation of each two. We evaluate the proposed model on dialogue-based multiple-choice MRC tasks, DREAM, and outperformed recently published methods under the same pre-trained model. A series of detailed analysis is also conducted to interpret the differences of two utterance-focusing mechanisms and the effectiveness of the proposed multiway-matching mechanism.
Boosting Parallel Influence-Maximization Kernels for Undirected Networks With Fusing and Vectorization

Influence maximization (IM) is the problem of finding a seed vertex set which is expected to incur the maximum influence spread on a graph. It has various applications in practice such as devising an effective and efficient approach to disseminate information, news or ad within a social network. The problem is shown to be NP-hard and approximation algorithms with provable quality guarantees exist in the literature. However, these algorithms are computationally expensive even for medium-scaled graphs. Furthermore, graph algorithms usually suffer from spatial and temporal irregularities during memory accesses, and this adds an extra cost on top of the already expensive IM kernels. In this article we leverage fused sampling, memoization, and vectorization to restructure, parallelize and boost their performance on undirected networks. The proposed approach employs a pseudo-random function and performs multiple Monte-Carlo simulations in parallel to exploit the SIMD lanes effectively and efficiently. In addition, it significantly reduces the number of edge traversals, hence the amount of data brought from the memory, which is critical for almost all memory-bound graph kernels. We apply the proposed approach to the traditional MIXGREEDY algorithm and propose INFUSER-MG which is more than 3000χ fasterthan the greedy approaches and can run on large graphs that have been considered as too large in the literature. For instance, the new algorithm runs in 2.09, 0.08, 0.36 seconds on graphs Amazon, NetHEP, NetPhy with 16 threads where the sequential baseline takes 141.3, 259.1 and 1725.2 seconds, respectively. To compare INFUSER-MG with the state-of-the-art approximation algorithms, we conduct a thorough experimental analysis with various influence settings. The results on real-life, undirected networks show that on 16 threads, INFUSER-MG is 2:3χ-173:8χ faster than state-of-the-art while being superior in terms of influence scores, and using a comparable amount of memory.
Efficient Segmentation Pyramid Network

Extensive growth in the field of robotics and autonomous industries, the demand for efficient image segmentation is increasing rapidly. Whilst existing methods have been shown to achieve outstanding results on challenging data sets, they cannot scale the model properly for real-world computational constraints applications due to a fixed large backbone network. We propose a novel architecture for semantic scene segmentation suitable for resource-constrained applications. Specifically, we make use of the global contextual prior by using a pyramid pooling technique on top of the backbone network. We also employ the recently proposed EfficientNet network to make our model efficiently scalable for computational constraints. We show that our newly proposed model - Efficient Segmentation Pyramid Network (ESPNet) - outperforms many existing scene segmentation models and produces 88.5% pixel accuracy on validation and 80.9% on training set of the Cityscapes benchmark.
zfit: Scalable pythonic fitting

Statistical modeling is a key element in many scientific fields and especially in High-Energy Physics (HEP) analysis. The standard framework to perform this task in HEP is the C++ ROOT/RooFit toolkit; with Python bindings that are only loosely integrated into the scientific Python ecosystem. In this paper, zfit, a new alternative to RooFit written in pure Python, is presented. Most of all, zfit provides a well defined high-level API and workflow for advanced model building and fitting, together with an implementation on top of TensorFlow, allowing a transparent usage of CPUs and GPUs. It is designed to be extendable in a very simple fashion, allowing the usage of cutting-edge developments from the scientific Python ecosystem in a transparent way. The main features of zfit are introduced, and its extension to data analysis, especially in the context of HEP experiments, is discussed.
Minority manifold regularization by stacked auto-encoder for imbalanced learning

Imbalanced learning is considered one of the challenging problems in machine learning. This problem arises when a learning algorithm is biased toward the majority class due to the large proportion of the majority class data while detecting the minority class is of greater importance. In the present study, a novel method (MMRAE) is presented for imbalanced learning encompassing feature learning and classification steps. In the feature learning step, meaningful features are extracted from the minority data and their underlying manifold are captured by taking advantage of one-class learning approach through stacking two regularized auto-encoders. The existence of novel and different regularizers in each auto-encoder leads to a new representation with proper data discrimination which improves the between-class and within-class imbalanced problems. Then, in the classification step, the classification between the minority and majority class is performed by constructing a multilayer neural network using features learned throughout pre-training. The proposed method is extensively studied on six artificial and twenty real datasets in order to have a precise evaluation. Based on different criteria such as F-measure, G-mean, and AUC, the results represent considerable performance of the proposed method compared to several other existing methods.
Controlled quantum search on structured databases

This paper shows that a continuous-time quantum walk is able locate a target in a tree structure with a speed comparable to the Grover's algorithm for unstructured databases and with a high success probability.