Reviewerly/exHarmony huggingface dataset:
Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision

We propose a novel weakly supervised learning segmentation based on several global constraints derived from box annotations. Particularly, we leverage a classical tightness prior to a deep learning setting via imposing a set of constraints on the network outputs. Such a powerful topological prior prevents solutions from excessive shrinking by enforcing any horizontal or vertical line within the bounding box to contain, at least, one pixel of the foreground region. Furthermore, we integrate our deep tightness prior with a global background emptiness constraint, guiding training with information outside the bounding box. We demonstrate experimentally that such a global constraint is much more powerful than standard cross-entropy for the background class. Our optimization problem is challenging as it takes the form of a large set of inequality constraints on the outputs of deep networks. We solve it with sequence of unconstrained losses based on a recent powerful extension of the log-barrier method, which is well-known in the context of interior-point methods. This accommodates standard stochastic gradient descent (SGD) for training deep networks, while avoiding computationally expensive and unstable Lagrangian dual steps and projections. Extensive experiments over two different public data sets and applications (prostate and brain lesions) demonstrate that the synergy between our global tightness and emptiness priors yield very competitive performances, approaching full supervision and outperforming significantly DeepCut. Furthermore, our approach removes the need for computationally expensive proposal generation. Our code is shared anonymously.
Lossy CSI-FiSh: Efficient Signature Scheme with Tight Reduction to Decisional CSIDH-512

Recently, Beullens, Kleinjung, and Vercauteren (Asiacrypt’19) provided the first practical isogeny-based digital signature, obtained from the Fiat-Shamir (FS) paradigm. They worked with the CSIDH-512 parameters and passed through a new record class group computation. However, as with all standard FS signatures, the security proof is highly non-tight and the concrete parameters are set under the heuristic that the only way to attack the scheme is by finding collisions for a hash function. In this paper, we propose an FS-style signature scheme, called Lossy CSI-FiSh, constructed using the CSIDH-512 parameters and with a security proof based on the “Lossy Keys” technique introduced by Kiltz, Lyubashevsky and Schaffner (Eurocrypt’18). Lossy CSI-FiSh is provably secure under the same assumption which underlies the security of the key exchange protocol CSIDH (Castryck et al. (Asiacrypt’18)) and is almost as efficient as CSI-FiSh. For instance, aiming for small signature size, our scheme is expected to take around $$\approx 800$$ ms to sign/verify while producing signatures of size $$\approx 280$$ bytes. This is only twice slower than CSI-FiSh while having similar signature size for the same parameter set. As an additional benefit, our scheme is by construction secure both in the classical and quantum random oracle model.
MII: A Novel Text Classification Model Combining Deep Active Learning with BERT

Active learning has been widely utilized to reduce the labeling cost of supervised learning. By selecting specific instances to train the model, the performance of the model was improved within limited steps. However, rar... | Find, read and cite all the research you need on Tech Science Press
More than plain text: Censorship deletion in the Chinese social media

Abstract Although the Internet allows people to circulate messages using different media, most censorship studies discuss the removal of text content. This article presents a systematic study regarding the censorship of both plain text and multimedia content on the Chinese Internet. By analyzing both censored and surviving posts on the Chinese social media platform Weibo during the 2014 Hong Kong Umbrella Movement, we find that multimedia posts suffered more intensive censorship deletion than plain text posts, with censorship programs being oriented more toward multimedia content like images than the text content of multimedia posts. Our analysis has significant implications for censorship studies, information control, and politics in the “post‐text” era.
Legal Issues Related to Cyber Threat Information Sharing Among Private Entities for Critical Infrastructure Protection

The menace of cyber attacks has become a concern for both the public and private sectors. Several approaches have been proposed to tackle the challenge, but an approach that has received widespread acceptance among cyber security professionals in both public and private sectors is cyber threat information (CTI) sharing. CTI refers to any information that can help an organisation identify, assess, monitor and respond to cyber threats. It includes indicators of compromise; tactics, techniques and procedures used by threat actors; suggested actions to detect, contain, or prevent attacks; and the findings from the analyses of incidents. Sharing CTI has been proposed as an efficient and effective way of improving overall cyber intelligence and defence. However, there are sources of liability that may dissuade private entities from participating in such sharing. The most cited source of liability is privacy and data protection law; although antitrust law, tort of negligence law and intellectual property law are also cited as potential sources of liability. In this study, we review the extent to which the provisions of privacy and data protection law support or refute the sharing of CTI. This will provide guidance and incentives for private entities willing to participate in CTI sharing, especially for critical infrastructure protection.
No-Reference Quality Assessment of In-Capture Distorted Videos

We introduce a no-reference method for the assessment of the quality of videos affected by in-capture distortions due to camera hardware and processing software. The proposed method encodes both quality attributes and semantic content of each video frame by using two Convolutional Neural Networks (CNNs) and then estimates the quality score of the whole video by using a Recurrent Neural Network (RNN), which models the temporal information. The extensive experiments conducted on four benchmark databases (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC) containing in-capture distortions demonstrate the effectiveness of the proposed method and its ability to generalize in cross-database setup.
Using Convolutional Neural Networks Based on a Taguchi Method for Face Gender Recognition

In general, a convolutional neural network (CNN) consists of one or more convolutional layers, pooling layers, and fully connected layers. Most designers adopt a trial-and-error method to select CNN parameters. In this study, an AlexNet network with optimized parameters is proposed for face image recognition. A Taguchi method is used for selecting preliminary factors and experiments are performed through orthogonal table design. The proposed method filters out factors that are significantly affected. Finally, experimental results show that the proposed Taguchi-based AlexNet network obtains 87.056% and 98.72% average accuracy of image gender recognition in the CIA and MORPH databases, respectively. In addition, the average accuracy of the proposed Taguchi-based AlexNet network is 1.576% and 3.47% higher than that of the original AlexNet network in CIA and MORPH databases, respectively.
A Deep Reinforcement Learning Approach for Path Following on a Quadrotor

This paper proposes the Deep Deterministic Policy Grandient (DDPG) reinforcement learning algorithm to solve the path following problem in a quadrotor vehicle. This agent is implemented using a separated control and guidance structure with an autopilot tracking the attitude and velocity commands. The DDPG agent is implemented in python and it is trained and tested in the RotorS-Gazebo environment, a realistic multirotor simulator integrated in ROS. Performance is compared with Adaptive NLGL, a geometric algorithm that implements an equivalent control structure. Results show how the DDPG agent is able to outperform the Adaptive NLGL approach while reducing its complexity.
Keep it Unsupervised: Horizontal Attacks Meet Deep Learning

To mitigate side-channel attacks, real-world implementations of public-key cryptosystems adopt state-of-the-art countermeasures based on randomization of the private or ephemeral keys. Usually, for each private key operation, a “scalar blinding” is performed using 32 or 64 randomly generated bits. Nevertheless, horizontal attacks based on a single trace still pose serious threats to protected ECC or RSA implementations. If the secrets learned through a single-trace attack contain too many wrong (or noisy) bits, the cryptanalysis methods for recovering remaining bits become impractical due to time and computational constraints. This paper proposes a deep learning-based framework to iteratively correct partially correct private keys resulting from a clustering-based horizontal attack. By testing the trained network on scalar multiplication (or exponentiation) traces, we demonstrate that a deep neural network can significantly reduce the number of wrong bits from randomized scalars (or exponents).When a simple horizontal attack can recover around 52% of attacked multiple private key bits, the proposed iterative framework improves the private key accuracy to above 90% on average and to 100% for at least one of the attacked keys. Our attack model remains fully unsupervised and excludes the need to know where the error or noisy bits are located in each separate randomized private key.
Multi-directional beam steering using diffractive neural networks

The modern-day resurgence of machine learning has encouraged researchers to revisit older problem spaces from a new perspective. One promising avenue has been implementing deep neural networks to aid in the simulation of physical systems. In the field of optics, densely connected neural networks able to mimic wave propagation have recently been constructed. These diffractive deep neural networks (D 2 NN) not only offer new insights into wave propagation, but provide a novel tool for investigating and discovering multi-functional diffractive elements. In this paper, we derive an efficient GPU-friendly D 2 NN methodology based on Rayleigh-Sommerfeld diffraction. We then use the implementation to virtually forge cascades of optical phase masks subject to different beam steering conditions. The input and output conditions we use to train each D 2 NN instance is based on commercial electro-optic modulated waveguide systems to encourage experimental follow-on. In total, we analyze the beam steering efficacy of 27 individual D 2 NN instances which explore different permutations of input sources, mask cascades, and output steering targets.
Deductive Proof of Industrial Smart Contracts Using Why3

In this paper, we use a formal tool that performs deductive verification on industrial smart contracts, which are self-executing digital programs. Because smart contracts manipulate cryptocurrency and transaction information, if a bug occurs in such programs, serious consequences can happen, such as a loss of money. This paper aims to show that a language dedicated to deductive verification, called WhyML, can be a suitable language to write correct and proven contracts. We first encode existing contracts, using the Why3 tool, into WhyML program; next, we formulate specifications to be proven as the absence of RunTime Error and functional properties, then we verify the behaviour of the program using the Why3 system. Finally, we compile the WhyML contracts to the Ethereum Virtual Machine (EVM). Moreover, our approach estimates the cost of gas, which is a unit that measures the amount of computational effort during a transaction.
Deep learning-based dynamic object classification using LiDAR point cloud augmented by layer-based accumulation for intelligent vehicles

An intelligent vehicle must identify the exact position and class of the surrounding object in various situations to consider the interaction with them. For this reason, the light detection and range sensor, called LiDAR, is widely used in intelligent vehicles. The LiDAR provides information in the form of a point cloud that can be used to localize and classify the surrounding objects. However, unlike vision-based object detection and classification system, the LiDAR-based recognition system cannot provide sufficient classification performance even with deep learning technologies. The reason is that the LiDAR point cloud does not have enough shape information to classify the dynamic object due to the sparsity of the points. To address this problem, we proposed a framework to enhance the deep learning-based classification performance by augmenting the shape information of the LiDAR point cloud. The augmented shape information not only improves classification performance of the networks, but also allows deep learning networks to train effectively by using artificial data-set which is generated with 3D computer-aided design model without tedious efforts of labeling. In order to enhance this shape information effectively, also, this paper proposes a layer-based accumulation algorithm considering the three degree-of-freedom motion of a dynamic object. In the experimental results, the proposed accumulation method outperformed existing registration-based methods. In real-vehicle data test, moreover, the deep learning networks trained with artificial data showed better performance when the LiDAR point cloud was accumulated.
VerifyMed-A blockchain platform for transparent trust in virtualized healthcare

Patients living in a digitized world can now interact with medical professionals through online services such as chat applications, video conferencing or indirectly through consulting services. These applications need to tackle several fundamental trust issues: 1. Checking and confirming that the person they are interacting with is a real person; 2. Validating that the healthcare professional has competence within the field in question; and 3. Confirming that the healthcare professional has a valid license to practice. In this paper, we present VerifyMed - the first proof-of-concept platform, built on Ethereum, for transparently validating the authorization and competence of medical professionals using blockchain technology. Our platform models trust relationships within the healthcare industry to validate professional clinical authorization. Furthermore, it enables a healthcare professional to build a portfolio of real-life work experience and further validates the competence by storing outcome metrics reported by the patients. The extensive realistic simulations show that with our platform, an average cost for creating a smart contract for a treatment and getting it approved is around 1 USD, and the cost for evaluating a treatment is around 50 cents.
Adversarial Attacks on License Plate Recognition Systems

The license plate recognition system (LPRS) has been widely adopted in daily life due to its efficiency and high accuracy. Deep neural networks are commonly used in the LPRS to improve the recognition accuracy. However, researchers have found that deep neural networks have their own security problems that may lead to unexpected results. Specifically, they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images, resulting in incorrect license plate recognition. There are some classic methods to generate adversarial examples, but they cannot be adopted on LPRS directly. In this paper, we modify some classic methods to generate adversarial examples that could mislead the LPRS. We conduct extensive evaluations on the HyperLPR system and the results show that the system could be easily attacked by such adversarial examples. In addition, we show that the generated images could also attack the black-box systems; we show some examples that the Baidu LPR system also makes incorrect recognitions. We hope this paper could help improve the LPRS by realizing the existence of such adversarial attacks.
Joint Age Estimation and Gender Classification of Asian Faces Using Wide ResNet

Two key facial features, age and gender, have been widely explored. Companies and organizations have investigated in related applications in several fields including insurance, retails, marketing, etc. It would bring tremendous benefit, which allow companies to easily identify their customer demographics. Several approaches have been proposed with remarkable results. However, because of the lack of open and multi-ethnic datasets, most modern age and gender estimating models were trained solely based on white people with Western facial features, and thus fall short with non-Caucasian people. In this paper, we developed an applicable Wide ResNet model to estimate the age and the gender of Asian faces. The model was trained with a newly improved Asian face database. The experiments have shown promising results, as it can match the performance of Microsoft’s how-old API estimator in a specific dataset.
Towards harmonization of microscopy methods for malaria clinical research studies

Microscopy performed on stained films of peripheral blood for detection, identification and quantification of malaria parasites is an essential reference standard for clinical trials of drugs, vaccines and diagnostic tests for malaria. The value of data from such research is greatly enhanced if this reference standard is consistent across time and geography. Adherence to common standards and practices is a prerequisite to achieve this. The rationale for proposed research standards and procedures for the preparation, staining and microscopic examination of blood films for malaria parasites is presented here with the aim of improving the consistency and reliability of malaria microscopy performed in such studies. These standards constitute the core of a quality management system for clinical research studies employing microscopy as a reference standard. They can be used as the basis for the design of training and proficiency testing programmes as well as for procedures and quality assurance of malaria microscopy in clinical research.
OpenGPT-2

When OpenAI released its billion-parameter language model GPT-2, their attempts to withhold the model inspired two researchers to use open research practices to combat the misuse of machine learning.
ALT Submission for OSACT Shared Task on Offensive Language Detection

In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51% on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63% on the test set.
IoT-based model for intelligent innovation practice system in higher education institutions

This article first provides an overview of the Internet of Things technology, mainly analyzing the characteristics of the Internet of Things technology and the impact of core technologies on education reform; secondly, it studies the specific impact of the Internet of Things technology on the education reform of local applied universities, which is mainly divided into three On the one hand, it promotes the construction of smart campus, the second is the realization of personalized learning methods, and the third is the promotion of smart teaching. Subsequently, the intelligent teaching based on the intelligent robot platform was proposed and the teaching demonstration was carried out. This research conducted an empirical study on third-year university students as the objects of teaching implementation. In the preparatory stage of teaching, the micro-curriculum of guided learning is produced, teaching activities are designed, and the study attitude and learner satisfaction questionnaire and interview outline are compiled. After the empirical teaching, analyzing the empirical results and related data, it is found that the application of intelligent robots in science courses can stimulate students’ interest in and enthusiasm in science courses; it can improve students’ creative thinking level and students’ learning satisfaction. Students’ perceived ease of use and perceived usefulness of the intelligent robot platform will affect students’ learning satisfaction, thereby affecting the teaching effect.
CAM: A fine-grained vehicle model recognition method based on visual attention model

Vehicle model recognition (VMR) is a typical fine-grained classification task in computer vision. To improve the representation power of classical CNN networks for this special task, we focus on enhancing the subtle difference of features and their spatial encoding based on the attention mechanism, and then propose a novel architectural unit, which we term the “convolutional attention model” (CAM). It adopts a two-stage attention mechanism for VMR, which includes the global feature map attention (GFMA) algorithm, applied at the lower part of the main network flow to enhance the subtle feature difference from the beginning, and the feature spatial relationship attention (FSRA) algorithm, applied at the higher part to enhance the spatial relationship of features. The experiments are conducted on the benchmark CompCars web-nature and Stanford Car datasets and demonstrate the effectiveness of CAM when integrated with some classical CNN architectures. CAM can improve the top-1 recognition accuracy by an average of 1.15% and top-5 by an average of 0.78%.
WITHDRAWN: Classification of mammography image with CNN-RNN based semantic features and extra tree classifier approach using LSTM

Mammographic density enhances the accuracy of risk models for breast cancer. The use of breast density, however, is restricted by subjective interpretation, limited data, and variability among radiologists. A Deep Learning (DL) model based on mammography would provide a more accurate prediction of mammographic image detection. Developing a deep learning model capable of accurately detecting breast cancer in mammogram testing that used a “end-to-end” learning approach that effectively utilizes training databases either with complete clinical annotation or only the whole image cancer status (label). In this approach, lesion labels are needed just in the preliminary training process, and subsequent stages need only image-level labelling. The Convolutional Neural Network (CNN) itself is an efficient deep learning methodology on images that can deliver more efficient output compared to machine learning approaches. This is because of the capability of CNN to find a strong feature during training the frame/image. In this study, we contrasted the CNN algorithm mostly with a popular simple ANN algorithm. The analysis reveals some enhancement in the use of the CNN Long Short-Term Memory (CNN + LSTM) especially in comparison to multi-layer Perceptron (MLP). In the suggested technique, CNN + LSTM hybridization is done with extra tree ensemble methods. The performance of the proposed algorithm was assessed on the basis of the quality metric recognized as the accuracy and loss rate of the classification.