Reviewerly/exHarmony huggingface dataset:
Transciphering, Using FiLIP and TFHE for an Efficient Delegation of Computation

Improved filter permutators are designed to build stream ciphers that can be efficiently evaluated homomorphically. So far the transciphering with such ciphers has been implemented with homomorphic schemes from the second generation. In theory the third generation is more adapted for the particular design of these ciphers. In this article we study how suitable it is in practice. We implement the transciphering of different instances of the stream cipher family FiLIP with homomorphic encryption schemes of the third generation using the TFHE library. We focus on two kinds of filter for FiLIP. First we consider the direct sum of monomials, already evaluated using HElib and we show the improvements on these results. Then we focus on the XOR-threshold filter, we develop strategies to efficiently evaluate any symmetric Boolean function in an homomorphic way, allowing us to give the first timings for such filters. We investigate different approaches for the homomorphic evaluation: using the leveled homomorphic scheme TGSW, an hybrid approach combining TGSW and TLWE schemes, and the gate boostrapping approach. We discuss the costs in time and memory and the impact on delegation of computation of these different approaches, and we perform a comparison with others transciphering schemes.
Synthesizing the Unseen for Zero-Shot Object Detection

The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference. However, since the unseen objects are never visualized during training, the detection model is skewed towards seen content, thereby labeling unseen as background or a seen class. In this work, we propose to synthesize visual features for unseen classes, so that the model learns both seen and unseen objects in the visual domain. Consequently, the major challenge becomes, how to accurately synthesize unseen objects merely using their class semantics? Towards this ambitious goal, we propose a novel generative model that uses class-semantics to not only generate the features but also to discriminatively separate them. Further, using a unified model, we ensure the synthesized features have high diversity that represents the intra-class differences and variable localization precision in the detected bounding boxes. We test our approach on three object detection benchmarks, PASCAL VOC, MSCOCO, and ILSVRC detection, under both conventional and generalized settings, showing impressive gains over the state-of-the-art methods. Our codes are available at https://github.com/nasir6/zero_shot_detection.
On the Hazards of the Technology Age: How Using Emojis Affects Perceptions of Leaders

Work-from-home and remote work arrangements are increasingly common. As a result, much business communication is taking place via computer-mediated means. In two experiments, the effect of emojis in business email is explored: their effect on perceptions of the leader who sends the email, perceptions of the email itself, and the email within the communication context. Study 1 found that emails with emojis led to positive perceptions of a leader’s likability and effectiveness, but only on the part of male recipients; female recipients rated a leader using emojis as less effective, with no difference in likability. In study 2, females perceived emails with emojis as more positive but also less appropriate for the workplace, although those who worked in informal workplaces showed a less pronounced effect of appropriateness. It is clear that leaders should consider both the audience and the workplace communication climate before using emojis in emails.
CPU-Accelerator Co-Scheduling for CNN Acceleration at the Edge

Convolutional neural networks (CNNs) are widely deployed for many artificial intelligence (AI) applications, such as object detection and image classification. Due to the burgeoning revolution in edge AI, CNN hardware accelerators are also being employed in resource-constrained edge devices for achieving better performance and energy efficiency at the edge. Although CNN accelerators enable fast and energy-efficient CNN inference at the edge, the remaining hardware resources on the edge devices except for the CNN accelerator remain idle, which could otherwise be utilized for attaining even better performance and energy efficiency for CNN inferences. In this paper, we propose a CPU-accelerator co-scheduling technique for convolution (CONV) layer operations of CNN inferences in resource-constrained edge devices. Our proposed co-scheduling technique exploits an inherent parallelism in CNN output channels, that is, the operations for generating different output channels in a CONV layer can be executed in parallel. For load balancing between the CPU and the CNN accelerator, we also propose a simple, yet accurate latency model for CONV layer operations in the CPU and the accelerator. Based on the latency estimation of CONV layer operations provided by our proposed model, we distribute the tasks to the CPU and the CNN accelerator in a load-balance manner to minimize the idle period during the CONV layer operations in both the CPU and the CNN accelerator. We implement our proposed hardware/software (HW/SW) co-scheduling technique in various field-programmable gate array system-on-chip (FPGA-SoC) platforms as a proof-of-concept. Experimental results indicate that our proposed co-scheduling technique improves system performance by 1.18×-2.00× with energy reduction of 14.9% - 49.7% as compared to the accelerator-only execution.
The COVID-19 pandemic: Yet another catalyst for governmental mass surveillance?

This commentary addresses the use of surveillance technologies in the context of the Covid-19 pandemic, using examples from the current geopolitical frame, and questioning the possible consequences of data collection for the individual and for society. In this regard, some questions emerge: in the fight against the pandemic, what measures and tools of surveillance are being adopted by the different states? Will the extraordinary measures, that are now being implemented, become permanent? And if so, what will the consequences be for privacy and democracy?
Challenges and Potential Solutions for Designing A Practical Pedestrian Detection Framework for Supporting Autonomous Driving

In recent years, in the face of the increasingly complicated traffic environment caused by the significant increase in the number of motor vehicles, in order to improve road traffic safety, autonomous driving technology has become the focus of research, and various related approaches have been proposed. Among them, the design of practical traffic-related target detection method has received a lot of attention as an indispensable prerequisite for vehicles to independently formulate pedestrian and obstacle collision avoidance strategies. In this article, we will first briefly introduce the development of related detection technologies. Then we will systematically introduce the current development trend of traffic target detection technology, and focus on the technical problems and related technical challenges that have not been solved by the existing methods in practical use. At the end of the article, we will provide some potential solutions to these challenges.
FRNet: Factorized and Regular Blocks Network for Semantic Segmentation in Road Scene

Nowadays, semantic segmentation methods for systems in road scene have a great demand. Most existing methods focus on high accuracy with low inference speed. And some approaches emphasize on speed, significantly sacrificing model accuracy. To make a trade-off between accuracy and inference speed, we propose a real-time network for semantic segmentation titled Factorized and Regular Network (FRNet), which employs an asymmetric encoder-decoder architecture with Factorized and Regular (FR) blocks. Our method achieves 70.4% mIoU on the Cityscapes test set with 1 million parameters at a speed of 127 frames per second (FPS) on a single Titan Xp at a resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$512\times 1024$ </tex-math></inline-formula> . We evaluate FRNet on Cityscapes, Camvid, Kitti, and Gatech datasets to identify that our network stands out from other state-of-the-art networks.
Seismic Data Augmentation Based on Conditional Generative Adversarial Networks

Realistic synthetic data can be useful for data augmentation when training deep learning models to improve seismological detection and classification performance. In recent years, various deep learning techniques have been successfully applied in modern seismology. Due to the performance of deep learning depends on a sufficient volume of data, the data augmentation technique as a data-space solution is widely utilized. In this paper, we propose a Generative Adversarial Networks (GANs) based model that uses conditional knowledge to generate high-quality seismic waveforms. Unlike the existing method of generating samples directly from noise, the proposed method generates synthetic samples based on the statistical characteristics of real seismic waveforms in embedding space. Moreover, a content loss is added to relate high-level features extracted by a pre-trained model to the objective function to enhance the quality of the synthetic data. The classification accuracy is increased from 96.84% to 97.92% after mixing a certain amount of synthetic seismic waveforms, and results of the quality of seismic characteristics derived from the representative experiment show that the proposed model provides an effective structure for generating high-quality synthetic seismic waveforms. Thus, the proposed model is experimentally validated as a promising approach to realistic high-quality seismic waveform data augmentation.
Identification of suitable sites for large‐scale photovoltaic installations through a geographic information system and analytical hierarchy process combination: A case study in Marrakesh‐Safi region, Morocco

Abstract In this paper, an assessment of the large‐scale photovoltaic (PV) potential in the region of Marrakesh‐Safi, Morocco, has been carried out. Considering the significant solar resources that the country possesses, the purpose of this investigation was to highlight suitable sites for a PV power plant. To this end, multicriteria decision making (MCDM) analysis and geographic information system (GIS) tools were combined. Several criteria were taken into account, namely, the constraints related to the system installation and environmental conditions. To the best of our knowledge, no study has considered the wind speed as a criterion for PV site selection. Such a criterion has a cooling effect on the technology itself and also affects the stability of the PV panel mounting system. Results reveal that 74% of the study area was considered suitable to host PV power plants. An analytical hierarchy process (AHP) analysis was conducted to evaluate the adopted criteria and a ranking map was generated for the appropriate surface using GIS tools. Results demonstrate that 24.3% of the suitable area was qualified as highly suitable according to calculations. These findings help stakeholders to select the highest ranked sites, ensuring a high score from a technical and economic point of view.
An improved ShapeShifter method of generating adversarial examples for physical attacks on stop signs against Faster R-CNNs

Vehicles have increasingly deployed object detectors to perceive running conditions, and deep learning networks have been widely adopted by those detectors. Growing neural networks have incurred severe attacks, like adversarial attacks, imposing threats to vehicle safety. Only if adversarial attacks are studied thoroughly can researchers think of better defence measures against them. However, most existing methods of generating an adversarial sample have focused on classification. Plus, stop signs in English have been popular objects to perform adversarial attacks on while whether those in Chinese are likely to be attacked still remains a problem. In this paper, we proposed an improved ShapeShifter method to generate adversarial examples against Faster Region-Convolutional neural networks (Faster R-CNN) object detectors by adding white Gaussian noise to the optimization function of ShapeShifter. Experiments verify that the improved ShapeShifter method can successfully and effectively attack Faster R-CNNs for stop signs both in English and Chinese, which is much better than ShapeShifter under certain circumstances. Plus, it has better robustness and can overcome ShapeShifter's drawback of high requirements on photographic equipment.
Deep Space-Time Video Upsampling Networks

Video super-resolution (VSR) and frame interpolation (FI) are traditional computer vision problems, and the performance have been improving by incorporating deep learning recently. In this paper, we investigate the problem of jointly upsampling videos both in space and time, which is becoming more important with advances in display systems. One solution for this is to run VSR and FI, one by one, independently. This is highly inefficient as heavy deep neural networks (DNN) are involved in each solution. To this end, we propose an end-to-end DNN framework for the space-time video upsampling by efficiently merging VSR and FI into a joint framework. In our framework, a novel weighting scheme is proposed to fuse all input frames effectively without explicit motion compensation for efficient processing of videos. The results show better results both quantitatively and qualitatively, while reducing the computation time ( $$\times $$ 7 faster) and the number of parameters (30%) compared to baselines. Our source code is available at https://github.com/JaeYeonKang/STVUN-Pytorch .
Countermeasure Based on Smart Contracts and AI against DoS/DDoS Attack in 5G Circumstances

The development of 5G has substantially increased the destructiveness of DoS/DDoS attacks because the data processing capability of computers has not been accordingly enhanced, and this contradiction creates a vulnerability for attackers to compromise a server by sending a massive data flow. in practical 5G circumstances, it is difficult to extract distinct features between malicious and benign massive data flows. This amplifies the difficulties of DoS/DDoS detection. Thus, precautions against DoS/DDoS attack in 5G are of great importance. in this article, we present a solution based on smart contracts and machine learning as a countermeasure against DoS/DDoS attacks in the 5G background by hiding a protected server in a blockchain network and flexibly restricting the scale of DoS/DDoS via transaction fees. We also leverage non-repudiation of smart contracts, analyzing users' malicious behavior of communication and executing punishment via smart contracts. Our scheme could effectively mitigate massive DoS/DDoS attacks in advance and dynamically punitively charge DoS/DDoS attacks. Compared to existing DoS/DDoS defense in 4G, our scheme offers numerous benefits, including making benign communication always dominate rational users and countering DoS/DDoS attacks before they are launched. Moreover, compared to common DoS/DDoS detection based on Ai, we consider the source trustworthiness of training samples and take measures to avoid backdoors where model trainers may compromise Ai models to launch DoS/DDoS attacks.
HWOBC-A Handwriting Oracle Bone Character Recognition Database

Abstract The oracle bone character (OBC) from ancient China is the most famous ancient writing systems around the world. Identifying and deciphering OBCs is one of the most important topics in oracle bone study. In research, one of the challenges is that the literature review usually leads to a huge cost of time and manpower. Therefore, the digitazation of OBC literature through the automatic recognition is the inevitable trend of future development. However, the OBCs in the literature are usually writing characters while the database of handwriting OBC has not yet been presented. In this paper, we establish a handwriting oracle bone character database called HWOBC, containing 83,245 character-level samples which are grouped into 3881-character categories. We also present the performance of several baseline DCNN-based methods, in which Melnyk-Net exhibits the best accuracy of 97.64%. It is anticipated that the publication of this database will facilitate the development of OBC research.
Networked Nonlinear Fusion Estimation Under DoS Attacks

This paper is concerned with the distributed fusion estimation problem for nonlinear systems under denial of service (DoS) attacks, where it is not required to know the statistical property of DoS attacks and the bounds of system noises in advance. A compensation model is constructed to defend against DoS attacks in order to reduce performance loss. Following the idea of bounded recursive optimization, two convex optimization problems, which are established in terms of linear matrix inequalities, are designed to obtain the local optimal estimator gains and the optimal weighting matrices by minimizing the upper bounds of estimation errors at each time. Finally, an intelligent vehicle localization system is used to show the validity of the results.
A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip

In recent years, interest in monitoring Physical Activity (PA) has increased due to its positive effect on health. New technological devices have been proposed for this purpose, mainly focused on sports, which include Machine Learning algorithms to identify the type of PA being performed. However, PA monitoring can also provide data useful for assessing the recovery process of people with impaired lower-limbs. In this work, a Machine-Learning based Physical Activity classifier design procedure is proposed, which makes use of the data provided by a Sensorized Tip that can be adapted to different Assistive Devices for Walking (ADW) such as canes or crutches. The procedure is based on three main stages: 1) defining a wide set of potential features to perform the classification; 2) optimizing the number of features by a Random-Forest approach, detecting the most relevant ones to classify five relevant activities (walking at a normal pace, walking fast, standing still, going up stairs and going down stairs); 3) training the ML-based classifiers considering the optimized feature set. A comparative analysis is carried out to evaluate the proposed procedure, using three ML-based classifier (Support Vector Machines, K-Nearest Neighbour and Artificial Neural Networks), demonstrating that the proposed approach can provide very high success rates if proper feature selection is carried out. This work presents four relevant contributions to the PA monitoring area: 1) the approach is focused on people that require ADW, which are not considered in other approaches; 2) an analysis of the features to characterize gait in people that require ADW is carried out; 3) a design procedure to optimize the number of features using a Random-Forest approach is used, avoiding a typical “brute force” procedure; and 4) a comparative analysis is carried out to demonstrate the validity of the approach.
A Novel approach for Gray Scale Image Colorization using Convolutional Neural Networks

Image Colorization is the process of coloring a grayscale image is done by using a black and white known as grayscale image as input and obtaining the output in RGB format simply known as color image is called Image Colorization. The technique of colorizing an image has more impact on different fields such as photography of astronomical objects, visuals of electronic microscope and CCTV surveillance. To build an automated system for color gray scale image use Deep learning algorithm. In this paper the analysis of convolutional neural networks (CNN) is performed. In convolution neural networks, during the training phase of the model the features are extracted by the feature extractor - Inception ResNet V2, a pre-trained model developed and released by Google (trained on huge dataset of nearly 1.3Million images). Now the features extracted by the feature extractor are combined with the output of the encoder at fusion layer. Fusion layer is responsible to train the model with features and decoder helps to do up-sample and gets the required output. The epoch and steps per epoch are compared based on performance measures from the error rate. The epoch with the least error rate is considered as the best one to be used to colorize grayscale image. From this work the best epoch and steps per epoch can be identified. Thus the use of convolutional neural networks to colorize grayscale images shows better performance.
Reinforced Short-Length Hashing

Given that retrieval and storage have compelling efficiency, similarity-preserving hashing has been extensively employed to approximate nearest neighbor search in large-scale image retrieval. Hash codes that are extremely compact not only can further lower the storage cost, but also accelerate the retrieval speed. However, existing methods perform poorly in retrieval based on an extremely short-length hash code, which attributes to the weak ability of classification and poor distribution of hash bit. To tackle this issue, in this study, we propose a novel reinforced short-length hashing (RSLH). In particular, this proposed method applies the mutual reconstruction between the hash representation and semantic label to retain the semantic information. Furthermore, to enhance the accuracy of hash representation, a pairwise similarity matrix is designed to make a balance between accuracy and training expenditure on memory. Besides, we integrate a parameter boosting strategy to strengthen the precision with the consideration of bit balance and uncorrelation constraints. Extensive experiments on three large-scale image benchmarks demonstrate the superior performance of RSLH under various short-length hashing scenarios.
White Blood Cells Classification Using Convolutional Neural Network Hybrid System

Blood and its components play a very important role in human life and it considers the best indicator in determining many biological conditions. Pathologists used optical microscopic blood images to diagnose diseases; some of these diseases are Acquired Immune Deficiency Syndrome (AIDS), Leukemia, after recognizing white blood cells (WBCs) from those images. Recently, a Computer-aided diagnosis system is used for diagnosing blood from its microscopic images. In this research, microscopic WBCs images were classified using a hybrid system where Convolutional Neural Network (CNN) used as features extractor and different machine learning algorithms used as classifiers, then the performances of these classifiers were evaluated to recognize the best of them. These algorithms include Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Random Forest, for training and test parameters we used five features that were extracted from the images. According to results of performance, the RF performed better than the other methods with a testing accuracy reached 98.7%.