Reviewerly/exHarmony huggingface dataset:
Mildly Short Vectors in Cyclotomic Ideal Lattices in Quantum Polynomial Time

In this article, we study the geometry of units and ideals of cyclotomic rings and derive an algorithm to find a mildly short vector in any given cyclotomic ideal lattice in quantum polynomial time, under some plausible number-theoretic assumptions. More precisely, given an ideal lattice of the cyclotomic ring of conductor m , the algorithm finds an approximation of the shortest vector by a factor exp (Õ(√ m )). This result exposes an unexpected hardness gap between these structured lattices and general lattices: The best known polynomial time generic lattice algorithms can only reach an approximation factor exp (Õ(m)). Following a recent series of attacks, these results call into question the hardness of various problems over structured lattices, such as Ideal-SVP and Ring-LWE, upon which relies the security of a number of cryptographic schemes. N OTE . This article is an extended version of a conference paper [11]. The results are generalized to arbitrary cyclotomic fields. In particular, we also extend some results of Reference [10] to arbitrary cyclotomic fields. In addition, we prove the numerical stability of the method of Reference [10]. These extended results appeared in the Ph.D. dissertation of the third author [46].
Expandable YOLO: 3D Object Detection from RGB-D Images

This paper aims at constructing a light-weight object detector that inputs a depth and a color image from a stereo camera. Specifically, by extending the network architecture of YOLOv3 to 3D in the middle, it is possible to output in the depth direction. In addition, Intersection over Union (IoU) in 3D space is introduced to confirm the accuracy of region extraction results. In the field of deep learning, object detectors that use distance information as input are actively studied for utilizing automated driving. However, the conventional detector has a large network structure, and the real-time property is impaired. The effectiveness of the detector constructed as described above is verified using datasets. The experiment verified that the proposed model is able to output 3D bounding boxes and detect people whose body is partly hidden. Further, the processing speed of the model reached 44.35 fps.
Deformable Gabor Feature Networks for Biomedical Image Classification

In recent years, deep learning has dominated progress in the field of medical image analysis. We find however, that the ability of current deep learning approaches to represent the complex geometric structures of many medical images is insufficient. One limitation is that deep learning models require a tremendous amount of data, and it is very difficult to obtain a sufficient amount with the necessary detail. A second limitation is that there are underlying features of these medical images that are well established, but the black-box nature of existing convolutional neural networks (CNNs) do not allow us to exploit them. In this paper, we revisit Gabor filters and introduce a deformable Gabor convolution (DGConv) to expand deep networks interpretability and enable complex spatial variations. The features are learned at deformable sampling locations with adaptive Gabor convolutions to improve representitiveness and robustness to complex objects. The DGConv replaces standard convolutional layers and is easily trained end-to-end, resulting in deformable Gabor feature network (DGFN) with few additional parameters and minimal additional training cost. We introduce DGFN for addressing deep multi-instance multi-label classification on the INbreast dataset for mammograms and on the ChestX-ray14 dataset for pulmonary x-ray images.
A novel quantum neural network based on multi-level activation function

Abstract Since the multi-level activation function quantum neural network (QNN) for pattern recognition was firstly proposed by Purushothaman and Karayiannis, more and more researches have been conducted on improving it. However, they all ignore that the QNN only uses multi-level activation function to simulate the concept of quantum superposition rather than really using quantum computing. In this paper, we propose a real QNN model based on multi-layer activation function. In addition, we present algorithms for updating weight parameters and quantum intervals, and also improve the learning algorithm for weight parameters using famous Levenberg–Marquardt algorithm. We also use the QNN for lie detection, and the simulation results of MATLAB prove that the performance of the model based on the corresponding algorithm are very strong.
Solving continuous optimization problems using the tree seed algorithm developed with the roulette wheel strategy

The Tree seed algorithm (TSA) is a metaheuristic algorithm inspired by the relationship between trees and seeds. It has been proposed for very low-dimensional optimization problems and achieved promising results compared to other optimization algorithms. However, it has been determined that the performance of the TSA is lower than other algorithms for high-dimensional problems. This is due to the fact that TSA cannot scan the local optimum and search space effectively. A new TSA based on the roulette wheel strategy (R-TSA) has been proposed in this study to eliminate this disadvantage and solve high-dimensional problems. With this strategy, the trees selected at the seed production phase of TSA were diversified and the locations of the seeds were updated to prevent it from being stuck in local minima, with the aim of scanning the search space more effectively. The R-TSA was applied to high-dimensional (20, 50 and 100) benchmark functions and both convergence and box-plot graphs were obtained by using the results of these functions. Moreover, current algorithms in published literature were applied to these functions and the results obtained were compared with the R-TSA. It was observed from the analysis results that the performance of the R-TSA was higher than that of TSA.
Sentiment Analysis Combination in Terrorist Detection on Twitter: A Brief Survey of Approaches and Techniques

Terrorism is a big concern for many governments and people, especially with using social media such as Twitter that uses new technologies. Terrorism uses many techniques to carry out their actions and plans. Technology can play an important role in providing accurate predictions of terrorist activities. Here, we tried to do so using sentiment analysis for terrorist-related of Twitter because the early detection of terrorist activity is very important to the recent attack and to combat the spread of global terrorist activity. This work studied the techniques of effective analysis of terrorist activity data on Twitter. It is based on 17 articles that used Twitter to study terrorism for different purposes, while highlighting the different techniques used, from this survey one can notice that the machine learning techniques were used the most for sentimental analysis with good accuracy depending on the data used such as AdaBoost, support vector machine, maximum entropy, Naive Bayes, decision tree algorithms. Few number of papers are analyzed tweets in Arabic language as compared to English version because of its complexity parsing beside the complexity in analyzing feelings in Arabic makes tasks more challenging.
Local Augment: Utilizing Local Bias Property of Convolutional Neural Networks for Data Augmentation

Data augmentation is an effective way to increase the diversity of existing training datasets that result in improved generalization ability of convolutional neural networks (CNNs).The augmentation effect is usually global for the existing methods i.e., a single augmentation effect is applied to the whole image, thus limiting the diversity of local characteristics in augmented images.Moreover, the global augmentation effect does not support the most fundamental behavior of CNNs i.e., they focus more on local features (local texture, tiny noise etc.) than global shapes.We refer to this behavior as local bias property.In this paper, we propose a new data augmentation method, called Local Augment (LA), which highly alters the local bias property so that it can generate significantly diverse augmented images and offers the network with a better augmentation effect.First, we select few local patches in an image, then apply different types of augmentation strategies to each local patch.This augmentation process collapses the global structure of the object but creates locally diversified samples, which helps the network to learn the local bias property in a more generalized way.As a result, it increases the generalizability and the prediction accuracy of the network.To verify the effectiveness of the proposed method, we perform comprehensive experiments on image classification with benchmark datasets, where the proposed method outperforms the sate-of-the-art data augmentation techniques on ImageNet and STL10 and shows competitive performance on CIFAR100.
Semantic Information Oriented No-Reference Video Quality Assessment

In this letter, a method called Semantic Information Oriented No-Reference (SIONR) video quality assessment model is developed, which can effectively represent quality degradation of video by taking the variations of semantic information into consideration. Specially, temporal variations of the semantic features between adjacent frames are calculated to consider the inconsistency of the static semantic information. Moreover, low-level features are also applied as a supplementary to take distortions related to local details into consideration. Experimental results demonstrate that our proposed method obtains competitive performance compared with state-of-the-art methods in the two databases. Also, our model achieves good generalization capability. The code is available at: https://github.com/lorenzowu/SIONR.
Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation

Knowledge Distillation is an effective method to transfer the learning across deep neural networks. Typically, the dataset originally used for training the Teacher model is chosen as the "Transfer Set" to conduct the knowledge transfer to the Student. However, this original training data may not always be freely available due to privacy or sensitivity concerns. In such scenarios, existing approaches either iteratively compose a synthetic set representative of the original training dataset, one sample at a time or learn a generative model to compose such a transfer set. However, both these approaches involve complex optimization (GAN training or several backpropagation steps to synthesize one sample) and are often computationally expensive. In this paper, as a simple alternative, we investigate the effectiveness of "arbitrary transfer sets" such as random noise, publicly available synthetic, and natural datasets, all of which are completely unrelated to the original training dataset in terms of their visual or semantic contents. Through extensive experiments on multiple benchmark datasets such as MNIST, FMNIST, CIFAR-10 and CIFAR-100, we discover and validate surprising effectiveness of using arbitrary data to conduct knowledge distillation when this dataset is "target-class balanced". We believe that this important observation can potentially lead to designing base-lines for the data-free knowledge distillation task.
An evolutionary computation‐based privacy‐preserving data mining model under a multithreshold constraint

Abstract Privacy‐preserving data mining (PPDM) is a popular research topic in the data mining field. For individual information protection, it is vital to protect sensitive information during data mining procedures. Furthermore, it is also a serious offense to spill sensitive private knowledge. Recently, many PPDM data mining algorithms have been proposed to conceal sensitive items in a given database to disclose high‐frequency items. These recent methods have already proven to be excellent in protecting confidential information and maintaining the integrity of the input database. All prior techniques, however, ignored a crucial problem in setting minimum support thresholds. If a sensitive itemset includes more items, it will cause it the become more likely to be found. Before performing mining processes, a fixed value of the minimum support threshold will be set. In this paper, a new concept of minimal support for solving this issue is proposed. In compliance with a given threshold function, the proposed approach would set a tighter threshold for an object containing several items. The results of the experiments show the performance of the traditional Greedy PPDM approach, Genetic algorithm (GA)‐based PPDM approaches, and the proposed particle swarm optimization‐based algorithm with the new minimal support function. The results show that the proposed method performs similarly to conventional algorithms and offers higher protection than previous methods.
Concordia: A Streamlined Consensus Protocol for Blockchain Networks

In this paper, we present a novel Byzantine fault-tolerant consensus protocol for sharded blockchain networks that does not rely on expensive leader-driven communication.The proposed protocol selects a single block proposer at a time and uses threshold signatures as a voting mechanism to confirm the validity of the proposed block.By using a gossip-like communication scheme, each node can collect and recover the group signature within O(logN ) steps.With only one block proposer per consensus round, there is no possibility of conflicting blocks and resultant forks.Therefore, our consensus protocol requires only one round of one-way communication to achieve finality for each block.Our protocol guarantees safety and liveness while tolerating up to f faulty participants among 2f + 1 nodes.Our performance study shows that the proposed protocol enables hundreds of nodes to participate in the agreement process, and can finalize large blocks in approximately 10 seconds.
Do not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting

One of the major limitations of deep learning models is that they face catastrophic forgetting in an incremental learning scenario. There have been several approaches proposed to tackle the problem of incremental learning. Most of these methods are based on knowledge distillation and do not adequately utilize the information provided by older task models, such as uncertainty estimation in pre-dictions. The predictive uncertainty provides the distributional information can be applied to mitigate catastrophic forgetting in a deep learning framework. In the proposed work, we consider a Bayesian formulation to obtain the data and model uncertainties. We also incorporate self-attention framework to address the incremental learning problem. We define distillation losses in terms of aleatoric uncertainty and self-attention. In the proposed work, we investigate different ablation analyses on these losses. Furthermore, we are able to obtain better results in terms of accuracy on standard benchmarks.
Appending Adversarial Frames for Universal Video Attack

This paper investigates the problem of generating adversarial examples for video classification. We project all videos onto a semantic space and a perception space, and point out that adversarial attack is to find a counterpart which is close to the target in the perception space but far from the target in the semantic space. Based on this formulation, we notice that conventional attacking methods mostly used Euclidean distance to measure the perception space, but we propose to make full use of the property of videos and assume a modified video with a few consecutive frames replaced by dummy contents (e.g., a black frame with texts of `thank you for watching' on it) to be close to the original video in the perception space though they have a large Euclidean gap. This leads to a new attack approach which only adds perturbations on the newly-added frames. We show its high success rates in attacking six state-of-the-art video classification networks, as well as its universality, i.e., transferring well across videos and models.
Electrogastrography in Autonomous Vehicles—An Objective Method for Assessment of Motion Sickness in Simulated Driving Environments

Autonomous vehicles are expected to take complete control of the driving process, enabling the former drivers to act as passengers only. This could lead to increased sickness as they can be engaged in tasks other than driving. Adopting different sickness mitigation techniques gives us unique types of motion sickness in autonomous vehicles to be studied. In this paper, we report on a study where we explored the possibilities of assessing motion sickness with electrogastrography (EGG), a non-invasive method used to measure the myoelectric activity of the stomach, and its potential usage in autonomous vehicles (AVs). The study was conducted in a high-fidelity driving simulator with a virtual reality (VR) headset. There separate EGG measurements were performed: before, during and after the driving AV simulation video in VR. During the driving, the participants encountered two driving environments: a straight and less dynamic highway road and a highly dynamic and curvy countryside road. The EGG signal was recorded with a proprietary 3-channel recording device and Ag/AgCl cutaneous electrodes. In addition, participants were asked to signalize whenever they felt uncomfortable and nauseated by pressing a special button. After the drive they completed also the Simulator Sickness Questionnaire (SSQ) and reported on their overall subjective perception of sickness symptoms. The EGG results showed a significant increase of the dominant frequency (DF) and the percentage of the high power spectrum density (FSD) as well as a significant decrease of the power spectrum density Crest factor (CF) during the AV simulation. The vast majority of participants reported nausea during more dynamic conditions, accompanied by an increase in the amplitude and the RMS value of EGG. Reported nausea occurred simultaneously with the increase in EGG amplitude. Based on the results, we conclude that EGG could be used for assessment of motion sickness in autonomous vehicles. DF, CF and FSD can be used as overall sickness indicators, while the relative increase in amplitude of EGG signal and duration of that increase can be used as short-term sickness indicators where the driving environment may affect the driver.
Visual-based Autonomous Unmanned Aerial Vehicle for Inspection in Indoor Environments

This paper presents a visual-based autonomous Unmanned Aerial Vehicle (UAV) capable to perform a broad range of navigation tasks in indoor environments. Unlike other related vehicles, our robot does not need an external operator whatsoever or any kind of off-board processing, reducing the overall workload and manual dependency. We evaluated our vehicle in a real indoor environment that simulates an offshore oil platform scenario, demonstrating that it is capable of localize itself, maintaining stable flight, navigating, detecting landing spots, precisely landing on them, and detecting and reading information from the scenario. The methodology presented in this work was used to win the 2019 Brazil Open Flying Robot League during the 2019 Latin America Robotics Competition, among eleven teams and outscoring the second-placing team by almost a factor of two.
Efficient Detection of Shilling’s Attacks in Collaborative Filtering Recommendation Systems Using Deep Learning Models

Recommendation systems, especially collaborative filtering recommenders, are vulnerable to shilling attacks as some profit-driven users may inject fake profiles into the system to alter recommendation outputs. Current shilling attack detection methods are mostly based on feature extraction techniques. The hand-designed features can confine the model to specific domains or datasets while deep learning techniques enable us to derive deeper level features, enhance detection performance, and generalize the solution on various datasets and domains. This paper illustrates the application of two deep learning methods to detect shilling attacks. We conducted experiments on the MovieLens 100K and Netflix Dataset with different levels of attacks and types. Experimental results show that deep learning models can achieve an accuracy of up to 99%.
Facial micro-expressions as a soft biometric for person recognition

Soft biometrics, although not discriminant enough for person recognition provides additional information that aids traditional person recognition. Initially, attempts were made to integrate appearance-based facial soft biometrics, such as facial marks, skin color, and hair color/style, but more recently behavior-based facial soft biometrics, such as head dynamics, visual speech, and facial expressions have also been studied. Facial expressions are further classified as macro and micro-expressions and most of the existing studies using facial expressions as a soft biometric have focused on macro-expressions. Therefore, in this study, we investigate the utility of micro-expressions as a soft biometric for person recognition. The proposed system is based on the fusion of traditional facial features that model the facial appearance with soft biometric features that model the micro-expressions in an image sequence. We tested a texture-based traditional feature extraction technique, two motion-based soft biometric techniques, and several fusion methods at feature, rank, and decision level. The experiments were conducted on three commonly used micro-expression databases and exhibit an improvement of around 5% identification rate when soft biometric traits are fused with traditional face recognition at decision level.
Adding a Verification View for an Autonomous Real-Time System Architecture

Spacecraft software systems continue to increase in complexity and must frequently operate autonomously for extended periods. Thus, the consequences of software flaws are increasing and it is necessary to improve development methods to minimize the risk of latent flaws in the operational software. Traditional software architectures place verification as one element in the development architectural view and treat verification as a later lifecycle activity that is considered complete when the system is certified for flight. The Gateway Vehicle System Manager team, recognizing the importance of verification throughout the system lifecycle, is treating verification as an additional architectural view that receives continuous attention from the requirements analysis phase and continuing for the life of the operational system. The verification view consists of two viewpoints: development and operational. We present background on the verification view and discuss the approaches the Vehicle System Manager team is taking.
Guided parallelized stochastic gradient descent for delay compensation

Stochastic gradient descent (SGD) algorithm and its variations have been effectively used to optimize neural network models. However, with the rapid growth of big data and deep learning, SGD is no longer the most suitable choice due to its natural behavior of sequential optimization of the error function. This has led to the development of parallel SGD algorithms, such as asynchronous SGD (ASGD) and synchronous SGD (SSGD) to train deep neural networks. However, it introduces a high variance due to the delay in parameter (weight) update. We address this delay in our proposed algorithm and try to minimize its impact. We employed guided SGD (gSGD) that encourages consistent examples to steer the convergence by compensating the unpredictable deviation caused by the delay. Its convergence rate is also similar to A/SSGD, however, some additional (parallel) processing is required to compensate for the delay. The experimental results demonstrate that our proposed approach has been able to mitigate the impact of delay for the quality of classification accuracy. The guided approach with SSGD clearly outperforms sequential SGD and even achieves the accuracy close to sequential SGD for some benchmark datasets.