Reviewerly/exHarmony huggingface dataset:
Video Rain-Streaks Removal by Combining Data-Driven and Feature-Based Models

Video analytics and computer vision applications face challenges when using video sequences with low visibility. The visibility of a video sequence is degraded when the sequence is affected by atmospheric interference like rain. Many approaches have been proposed to remove rain streaks from video sequences. Some approaches are based on physical features, and some are based on data-driven (i.e., deep-learning) models. Although the physical features-based approaches have better rain interpretability, the challenges are extracting the appropriate features and fusing them for meaningful rain removal, as the rain streaks and moving objects have dynamic physical characteristics and are difficult to distinguish. Additionally, the outcome of the data-driven models mostly depends on variations relating to the training dataset. It is difficult to include datasets with all possible variations in model training. This paper addresses both issues and proposes a novel hybrid technique where we extract novel physical features and data-driven features and then combine them to create an effective rain-streak removal strategy. The performance of the proposed algorithm has been tested in comparison to several relevant and contemporary methods using benchmark datasets. The experimental result shows that the proposed method outperforms the other methods in terms of subjective, objective, and object detection comparisons for both synthetic and real rain scenarios by removing rain streaks and retaining the moving objects more effectively.
A data-set and a method for pointing direction estimation from depth images for human-robot interaction and VR applications

3D pointing devices are indispensable in virtual reality (hereafter VR) and human-robot interaction scenarios. Existing devices are cumbersome or non-immersive or have a limited volume of operation. Hand gesture-based interfaces do not suffer from these problems and can be used for 3D pointing purposes. However, there is a lack of robust, accurate hand gesture-based pointing techniques which can be attributed to the non-existence of large and accurate data-set for the same. To overcome this barrier, we propose a data-set consisting of depth images with a large number (107000) of samples collected from 11 subjects, with accurate ground-truth and adequate variation in the orientation and distance of the hand w.r.t. the camera. We propose a 3D convolutional neural network based technique that works on the proposed data-set and achieves an accuracy of 94.49% for an angle error threshold of 10 degrees. The proposed data-set may be used for developing more accurate, robust, less computationally expensive methods.
Automatic Generation of H.264 Parameter Sets to Recover Video File Fragments

We address the problem of decoding video file fragments when the necessary encoding parameters are missing. With this objective, we propose a method that automatically generates H.264 video headers containing these parameters and extracts coded pictures in the partially available compressed video data. To accomplish this, we examined a very large corpus of videos to learn patterns of encoding settings commonly used by encoders and created a parameter dictionary. Further, to facilitate a more efficient search our method identifies characteristics of a coded bitstream to discriminate the entropy coding mode. It also utilizes the application logs created by the decoder to identify correct parameter values. Evaluation of the effectiveness of the proposed method on more than 55K videos with diverse provenance shows that it can generate valid headers on average in 11.3 decoding trials per video. This result represents an improvement by more than a factor of 10 over the conventional approach of video header stitching to recover video file fragments.
Graph-Attention-Based Casual Discovery With Trust Region-Navigated Clipping Policy Optimization

In many domains of empirical sciences, discovering the causal structure within variables remains an indispensable task. Recently, to tackle unoriented edges or latent assumptions violation suffered by conventional methods, researchers formulated a reinforcement learning (RL) procedure for causal discovery and equipped a REINFORCE algorithm to search for the best rewarded directed acyclic graph. The two keys to the overall performance of the procedure are the robustness of RL methods and the efficient encoding of variables. However, on the one hand, REINFORCE is prone to local convergence and unstable performance during training. Neither trust region policy optimization, being computationally expensive, nor proximal policy optimization (PPO), suffering from aggregate constraint deviation, is a decent alternative for combinatory optimization problems with considerable individual subactions. We propose a trust region-navigated clipping policy optimization method for causal discovery that guarantees both better search efficiency and steadiness in policy optimization, in comparison with REINFORCE, PPO, and our prioritized sampling-guided REINFORCE implementation. On the other hand, to boost the efficient encoding of variables, we propose a refined graph attention encoder called SDGAT that can grasp more feature information without priori neighborhood information. With these improvements, the proposed method outperforms the former RL method in both synthetic and benchmark datasets in terms of output results and optimization robustness.
CAA

Temporal action detection aims to locate specific segments of action instances in an untrimmed video. Most existing approaches commonly extract the features of all candidate video segments and then classify them separately. However, they may neglect the underlying relationship among candidates unconsciously. In this paper, we propose a novel model termed Candidate-Aware Aggregation (CAA) to tackle this problem. In CAA, we design the Global Awareness (GA) module to exploit long-range relations among all candidates from a global perspective, which enhances the features of action instances. The GA module is then embedded into a multi-level hierarchical network named FENet, to aggregate local features in adjacent candidates to suppress background noise. As a result, the relationship among candidates is explicitly captured from both local and global perspectives, which ensures more accurate prediction results for the candidates. Extensive experiments conducted on two popular benchmarks ActivityNet-1.3 and THUMOS-14 demonstrate the superiority of CAA comparing to the state-of-the-art methods.
SMCOS: Fast and Parallel Modular Multiplication on ARM NEON Architecture for ECC

Elliptic Curve Cryptography (ECC) is considered a more effective public-key cryptographic algorithm in some scenarios, because it uses shorter key sizes while providing a considerable level of security. Modular multiplication constitutes the “arithmetic foundation” of modern public-key cryptography such as ECC. In this paper, we propose the Cascade Operand Scanning for Specific Modulus (SMCOS) vectorization method to speed up the prime field multiplication of ECC on Single Instruction Multiple Data (SIMD) architecture. Two key features of our design sharply reduce the number of instructions. 1) SMCOS uses operands based on non-redundant representation to perform a “trimmed” Cascade Operand Scanning (COS) multiplication, which minimizes the cost of multiplication and other instructions. 2) One round of fast vector reduction is designed to replace the conventional Montgomery reduction, which consumes less instructions for reducing intermediate results of multiplication. Further more, we offer a general method for pipelining vector instructions on ARM NEON platforms. By this means, the prime field multiplication of ECC using the SMCOS method reaches an ever-fastest execution speed on 32-bit ARM NEON platforms. Detailed benchmark results show that the proposed SMCOS method performs modular multiplication of NIST P192, Secp256k1, and Numsp256d1 within only 205, 310 and 306 clock cycles respectively, which are roughly 32% faster than the Multiplicand Reduction method, and about 47% faster than the Coarsely Integrated Cascade Operand Scanning method.
Embedding Electronic Health Records to Learn BERT-based Models for Diagnostic Decision Support

Accurate diagnosis plays an important role in the clinical decision-making process. To reduce diagnostic errors, many researchers develop models to assist physicians in predicting the most probable diagnoses during a patient’s visit. In this paper, we focus on the development of the models for clinical diagnostic decision support using a real-world Electronic Health Record (EHR) dataset with 592,715 patient-visits. We propose a novel diagnosis prediction framework, which is based on Bidirectional Encoder Representations from Transformers (BERT), for the disease classification based on textual clinical notes and age information from EHR data, which differs from the previously proposed models by the way of the construction of the input representation by four special embeddings and the composition of the classification layer. We conduct the experiments on the task of multi-class classification for 1,987 diagnosis codes. The experimental results demonstrate the improved performance of our models compared to the baselines trained by the advanced text classification methods. We also investigate the influence of different types of clinical text and age information on the model performance.
Euclidean distance stratified random sampling based clustering model for big data mining

Big data mining is related to large-scale data analysis and faces computational cost-related challenges due to the exponential growth of digital technologies. Classical data mining algorithms suffer from computational deficiency, memory utilization, resource optimization, scale-up, and speed-up related challenges in big data mining. Sampling is one of the most effective data reduction techniques that reduces the computational cost, improves scalability and computational speed with high efficiency for any data mining algorithm in single and multiple machine execution environments. This study suggested a Euclidean distance-based stratum method for stratum creation and a stratified random sampling-based big data mining model using the K-Means clustering (SSK-Means) algorithm in a single machine execution environment. The performance of the SSK-Means algorithm has achieved better cluster quality, speed-up, scale-up, and memory utilization against the random sampling-based K-Means and classical K-Means algorithms using silhouette coefficient, Davies Bouldin index, Calinski Harabasz index, execution time, and speedup ratio internal measures.
Federated Route Leak Detection in Inter-domain Routing with Privacy Guarantee

In the inter-domain network, route leaks can disrupt the Internet traffic and cause large outages. The accurate detection of route leaks requires the sharing of AS business relationship information. However, the business relationship information between ASes is confidential. ASes are usually unwilling to reveal this information to the other ASes, especially their competitors. In this paper, we propose a method named FL-RLD to detect route leaks while maintaining the privacy of business relationships between ASes by using a blockchain-based federated learning framework, where ASes can collaboratively train a global detection model without directly disclosing their specific business relationships. To mitigate the lack of ground-truth validation data in route leaks, FL-RLD provides a self-validation scheme by labeling AS triples with local routing policies. We evaluate FL-RLD under a variety of datasets including imbalanced and balanced datasets, and examine different deployment strategies of FL-RLD under different topologies. According to the results, FL-RLD performs better in detecting route leaks than the single AS detection, whether the datasets are balanced or imbalanced. Additionally, the results indicate that selecting ASes with the most peers to first deploy FL-RLD brings more significant benefits in detecting route leaks than selecting ASes with the most providers and customers.
Research on Federated Learning and Its Security Issues for Load Forecasting

The safe and effective management and utilization of the load data of electricity has become one of the important issues for power supply and distribution departments as electricity is an important part of industry 4.0. Accurate forecasting of power load is of great significance for the safety and stability of power grid dispatching and economical operation. However, many of the current power data sets have serious problems of data island; furthermore, the centralized storage of large amounts of data may cause privacy leakage of the original data owners and faces regulations of security supervision. Therefore, federated learning is introduced to address these issues. Nevertheless, this approach is not sufficient to provide adequate data privacy protection. The present research proposes a federated learning model based on improved differential privacy algorithm. The model uses multi-scale Laplacian algorithm to analyze data distribution and generate noises in accordance with data patterns. Moreover, the parameters of the model are protected by attribute-based access control (ABAC). The simulation results show that the model proposed by the present research makes accurate forecasting and the improved differential privacy algorithm has less influence on the model's accuracy; the model also shows a good resistance to attacks, which ensures the security of data while having a high precision.
Autonomous Navigation in Dynamic Environments with Multi-Modal Perception Uncertainties

This paper addresses the safe path planning problem for autonomous mobility with multi-modal perception uncertainties. Specifically, we assume that different sensor inputs lead to different Gaussian process regulated perception uncertainties (named as multi-modal perception uncertainties). We implement a Bayesian inference algorithm, which merges the multi-modal GP-regulated uncertainties into a unified one and translates the unified uncertainty into a dynamic risk map. With the safe path planner taking the risk map as input, we are able to plan a safe path for the autonomous vehicle to follow. Experimental results on an autonomous golf cart testbed validate the applicability and efficiency of the proposed algorithm.
A Quality Assurance Framework for Satellite Earth Observation Missions

Presented is a new quality assurance (QA) framework for Earth Observation missions that has been developed as a joint initiative between ESA and NASA. It aims ensure the rigorous assessment of all aspects of relevant aspects of mission quality, verifying claimed mission performance and, where applicable, reviewing the extent to which the mission follows community best practice in a manner that is “fit for purpose”. The QA framework has potential for more general use in both institutional and commercial Earth Observation – helping mission providers to understand the information their users' needs and empowering users to make informed decisions about which data is fit for their purpose.
A new NFC mobile payment protocol using improved GSM based authentication

Mobile payments have received a lot of attention because they are available at any time and place. One of the various payment methods is Near Field Communication (NFC) mobile payment. As a result of potential problems that may arise using mobile payments such as fraud and information theft, the security of mobile payment protocols is pivotal. One of the critical stages in mobile payment security is the mutual authentication between the user and the Mobile Network Operator (MNO). Global System for Mobile (GSM) proposed a protocol to mutual authentication using symmetric key cryptographic method. In this paper, we have introduced a new protocol for NFC mobile payment, which improves the GSM authentication protocol due to the existence of symmetric cryptographic security threats, and uses an asymmetric encryption method to mutual authentication. Also, this protocol reduces the number of required key-pairs for authentication. Furthermore, our proposed protocol, increases resistance to attacks such as replay attack, DOS, eavesdropping attacks, repudiation, man in the middle attack and de-synchronization. Because mobile devices have limited resources and processing power, our proposed protocol reduces the signalling overhead and processing load of the client-side as much as possible.
Matrix sketching for supervised classification with imbalanced classes

Abstract The presence of imbalanced classes is more and more common in practical applications and it is known to heavily compromise the learning process. In this paper we propose a new method aimed at addressing this issue in binary supervised classification. Re-balancing the class sizes has turned out to be a fruitful strategy to overcome this problem. Our proposal performs re-balancing through matrix sketching. Matrix sketching is a recently developed data compression technique that is characterized by the property of preserving most of the linear information that is present in the data. Such property is guaranteed by the Johnson-Lindenstrauss’ Lemma (1984) and allows to embed an n -dimensional space into a reduced one without distorting, within an $$\epsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϵ</mml:mi> </mml:math> -size interval, the distances between any pair of points. We propose to use matrix sketching as an alternative to the standard re-balancing strategies that are based on random under-sampling the majority class or random over-sampling the minority one. We assess the properties of our method when combined with linear discriminant analysis (LDA), classification trees (C4.5) and Support Vector Machines (SVM) on simulated and real data. Results show that sketching can represent a sound alternative to the most widely used rebalancing methods.
Collective radiation of multi-atom in cavity under the external driving field

There were several works concerning the collective radiation of two atoms in a cavity. According to their respective criteria, they all claim to have reached superradiation in a cavity (see paragraph 3 of the introduction for details). Dicke [1] defined that superradiation occurs when the radiation power of two atoms is greater than twice that of a single atom. We believe that this criterion is still valid when considering the radiation of atoms in the cavity. Here, we reexamine this topic and give a new but more reasonable criterion to test the collective radiation of multi atoms in the cavity. We tend to use the witness parameter WN as the criterion of superradiance. More importantly, as the frequency of the driving field is easy to adjust in the experiment, the radiation power of the atom in the cavity is calculated under the action of the coherent driving field, and the optimal frequency is given, that is, the single atom radiation and multi-atom radiation are enhanced at the same time. With such new criterion, we find it is impossible to get the superradiance in good cavity. On the contrary, superradiance is much easier to be reached in a bad cavity. We take the cases of one atom, two atoms, three atoms and four atoms for example. Anyway, the irradiation of atoms is non-classical whether it is subradiation or superradiation. The radiation power of the system can be significantly increased by simply increasing the number of atoms. These theoretical results can be realized by using the existing technology, which can make people understand the theory of multi-atom superradiance more clearly and stimulate future experiments.
SQSE: A Measure to Assess Sample Quality of Authorial Style as a Cognitive Biometric Trait

Stylistic analysis of text is a widely researched topic in both cognitive biometrics and linguistics. Often referred to as Authorship Attribution (AA), the scope of this problem has expanded from a few hundred authors with similar data characteristics to large-scale corpora having several thousand authors and cross-domain samples. Even though the AA algorithms have evolved to keep up with the requirements of the community, the process for choosing an appropriate text sample with good style characteristics has remained poorly defined. This paper, for the first time, formalizes the sample selection process using a style quality evaluation measure for AA, called Sample Quality for Style Extraction (SQSE). Furthermore, we will demonstrate the utility of the measure on multiple large-scale cross-domain corpora with over 6,500 authors and 250,000 text samples. The SQSE measure, supported by over 200 experiments and 4 million comparisons, exhibits a strong positive correlation with matching performance on a wide variety of AA algorithms resulting in a Pearson correlation coefficient of 0.87, and positively identifies samples of good stylometric quality.
Video-to-Image Casting: A Flatting Method for Video Analysis

Previous mainstream video analysis methods, especially 3D CNNs-based models, mainly aim to transfer frameworks from the image domain to the video domain, and they follow the regime which has been succeeded in image processing, i.e., large-scale benchmarks and deep networks. However, processing videos is still time-consuming due to the increased computational cost. In this paper, we propose to flat the video and construct a Spatio-temporal Image (STI), i.e., squeezing the temporal dimension into a spatial plane. To pursuit the video-level modeling and efficient architecture, we devise a Collective Convolution (CoConv) operation to replace the 2D convolution. With the holistic sampling strategy, this novel operation can extract the video-level spatio-temporal representation. Moreover, we ensure that each CoConv operation has the same number of parameters as the original 2D filter, thus we can utilize a 2D network equipped with CoConv to analyze videos without additional computations. To verify the effectiveness of our method for the general video analysis, we evaluate it on three typical tasks, i.e., supervised action recognition, self-supervised action recognition, and dynamic texture recognition. Extensive experimental results show that our method can achieve comparable or state-of-the-art performances on these benchmarks while using much fewer computations compared with its 3D counterpart.
Smart Technologies for Genre Closeness Evaluation

The concept of ontology is widely used in both Information Technology and Cognitive Linguistic areas. Roughly, in the former, it is used for words or terms arrangement and storing in a form of a network or subject domain, while in the latter, for modeling the system of concepts to recreate the knowledge, which is part of the human cognitive system. Both types represent their key constituents as a system of interrelations. With the development of computational linguistics and smart technologies for natural language processing, borders between these two types of ontologies are becoming increasingly blurred. Cognitive linguists tend to make inferences on human thinking based on computer-aided ontological representations. Current research evaluates the closeness and specificity of genres based on ontologies generated on the texts united by the same Trojan theme (Trojan discourse) but is specific in terms of affiliation to a particular genre. So, our ambitious aim is to determine the role of context in its broad sense and extralinguistic factors in knowledge communication. Drawing from specific research objectives, we use several types of software, namely AntCong for a linguistic corpus building, and TSGraph for ontology generation. The visualization and the statistics on the data are done from a cognitive-linguistic perspective and inferences about the influence of discourse and genre on knowledge discovery.