Reviewerly/exHarmony huggingface dataset:
Data Preprocessing and Quality Evaluation for Building the Power Grid Supervision Knowledge Graph

Abstract In the process of constructing the power grid supervision knowledge graph, it is necessary to sort out and integrate structured and unstructured multi-source data for knowledge extraction and reasoning. In order to solve the quality problems of multi-source data redundancy and errors, this paper proposes a multi-source data quality evaluation system to achieve multi-dimensional quality evaluation of power grid supervision data such as maintenance, defect, measurement, alarm and oil chromatography. Data preprocessing methods are firstly adopted for text and numerical data separately to complete data noise reduction, data filtering, data filling, etc. According to the actual calculation example, the practicability and effectiveness of the data preprocessing method and data quality evaluation system are verified. Finally, the data value of the power grid supervision knowledge graph is significantly improved, which is helpful to comprehensively improve the equipment state perception ability.
Pose-Guided And Style-Transferred Face Reenactment

Unlike most face reenactment that deals with minor pose variation, we propose the Pose-Guided Reenactment (PGR) framework for large-pose face reenactment. The proposed PGR is composed of a landmark detector, a landmark encoder, a face encoder, a landmark decoder, a style encoder, a face generator and two discriminators. The training of these components is divided into two phases. In Phase I, the landmark encoder is trained to encode the landmarks of the reference face to a reference landmark code, and the face encoder is trained to encode the source face to a source face code. The reference landmark code and the source face code are entered to the landmark decoder, which generates a target landmark set. In Phase II, the face generator is trained to generate the target face with the desired identity, pose and expression, given the target landmark set and the source face as input. To handle large pose, we include the large pose data in the training set and propose the pose-guided landmark switch to control the change of the facial landmarks during pose variation. Experiments on MPIE and VoxCeleb1 benchmark databases show that the proposed PGR can effectively reenact the faces with large pose, and delivers a state-of-the-art overall performance compared with other approaches.
Federated Learning in 6G Mobile Wireless Networks

In this chapter, the problem of joint transmission and computation resource allocation for federated learning (FL) over sixth generation (6G) mobile wireless networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model parameters to a base station (BS) which aggregates the local FL models and broadcasts the aggregated FL model back to all the users. Since FL involves learning model exchanges between the users and the BS, both computation and communication latencies are determined by the required learning accuracy level, which affects the convergence rate of the FL algorithm. This joint learning and communication problem is formulated as a delay minimization problem, where it is proved that the objective function is a convex function of the learning accuracy. Then, a bisection search algorithm is proposed to obtain the optimal solution. Simulation results show that the proposed algorithm can reduce delay by up to 27.3% compared to conventional FL methods.
Joint Co-Attention And Co-Reconstruction Representation Learning For One-Shot Object Detection

One-shot object detection aims to detect all candidate instances in a target image whose label class is unavailable in training, and only one labeled query image is given in testing. Nevertheless, insufficient utilization of the only known sample is one significant reason causing the performance degradation of current one-shot object detection models. To tackle the problem, we develop joint co-attention and co-reconstruction (CoAR) representation learning for one-shot object detection. The main contributions are described as follows. First, we propose a high-order feature fusion operation to exploit the deep co-attention of each target-query pair, which aims to enhance the correlation of the same class. Second, we use a low-rank structure to reconstruct the target-query feature in channel level, which aims to remove the irrelevant noise and enhance the latent similarity between the region proposals in target image and the query image. Experiments on both PASCAL VOC and MS COCO datasets demonstrate that our method outperforms previous state-of-the-art algorithms.
Research on Framework of Smart Grid Data Secure Storage from Blockchain Perspective

With the development of technology, the structure of power grid becomes more and more complex, and the amount of data collected is also increasing. In the existing smart power grid, the data collected by sensors need to be uploaded and stored to the trusted central node, but the centralized storage method is easy to cause the malicious attack of the central node, resulting in single point failure, data tampering and other security problems. In order to solve these information security problems, this paper proposes a new data security storage framework based on private blockchain. By using the improved raft algorithm, partial decentralized data storage is used instead of traditional centralized storage. It also introduces in detail the working mechanism of the smart grid data security storage framework, including the process of uploading collected data, data verification, and data block consensus. The security analysis shows the effectiveness of the proposed data storage framework.
An Augmented Reality Application to Teach Human Anatomy to Secondary School Students

Students start learning about human anatomy starting from standard VI (i.e., between ages 10–15 years) and gain a basic idea about all of the important organs and their functions. However, a quick survey reveals that students find it difficult to visualize how human organs look from inside and outside. They also lack an understanding of the basic functions of these organs. Further, as most of the instructional medium is based on a two-dimensional static paper-based medium, it lacks the look and feel of actual organs. To tackle this problem augmented reality (AR) technology was utilized. AR is an interactive experience of a real-world environment that is enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities. AR is an easy and feasible solution through which we can see and understand objects which cannot be seen in the real world. The proposed interactive AR application helps students learn about human anatomy in detail. The application gives the users the option to view six different organs of the human body along with their internal parts in real time. Information about each organ and its classified parts is also provided by the application. The application helps the students to learn and visualize the human organs properly. This paper presents a qualitative analysis study of the application which can help the students as a learning aid.
The Evaluation System of the Practice Base by the Integration of Industry and Education

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
What should we pay attention to when classifying violent videos?

Many works on violent video classification have proposed solutions ranging from local descriptors to deep neural networks. Most approaches use the entire representation of the video as input to extract the appropriate features. However, some scenes may contain noisy and irrelevant parts that confuse the algorithm. We investigated the effectiveness of attention-based models to deal with this problem. We extended the initial implementations to work with multimodal features using the late fusion approach. We performed the experiments on three datasets with different concepts of violence: Hockey Fights, MediaEval 2015, and RWF-2000. We conducted quantitative experiments, analyzing the performance of attention-based models and comparing them with traditional methods, and qualitative, analyzing the relevance scores produced by the attention-based models. Attention-based models surpassed their traditional counterpart for all cases. Also, attention-based models have achieved better results than many more expensive approaches, highlighting the advantage of their use.
Analysis of Evaluation Metrics with the Distance between Positive Pairs and Negative Pairs in Deep Metric Learning

Deep metric learning (DML) acquires embeddings via deep learning, where distances among samples of the same class are shorter than those of different classes. The previous DML studies proposed new metrics to overcome the issues of general metrics, but they have the following two problems; one is that they consider only a small portion of the whole distribution of the data, and the other is that their scores cannot be directly compared among methods when the number of classes is different. To analyze these issues, we consider the histograms of the inner products between arbitrary positive pairs and those of negative pairs. We can evaluate the entire distribution by measuring the distance between the two histograms. By normalizing the histograms by their areas, we can also cancel the effect of the number of classes. In experiments, visualizations of the histograms revealed that the embeddings of the existing DML methods do not generalize well to the validation set. We also confirmed that the evaluation of the distance between the positive and negative histograms is less affected by the variation in the number of classes compared with Recall@1 and MAP@R.
Solving the cold start problem in Trust Management in IoT

Internet of Things has a profound effect on everyday life and critical vertical services including healthcare, factories of the future and intelligent transport systems. The highly distributed nature of such networks and the heterogeneity of the devices, which constitute them, necessitates that their users should be able to trust them at all times. A method to determine the device's service trustworthiness is Trust Management (TM), which assigns scores to devices according to their trustworthiness level, based on evaluations from other entities that interacted with it. Often Internet of Things devices that just joined the network, have not interacted with any other entity of this network before, hence there is no way to determine its trustworthiness. Such an event is referred to as the cold start trust score or initial trust score problem. The majority of the trust management approaches address this problem by setting an arbitrary initial trust score, while others will ignore it. Assigning arbitrary trust scores for devices connected to the network for the first time has the potential to disrupt the operation of the entire system, when a high trust score is assigned to a non-trusted malicious device, or lead to unfair policies, when trusted devices are assumed as potential intruders, which also deteriorates the performance of the system. This paper proposes a mechanism, which combines the blockchain based BARRETT remote attestation protocol with a set of device's properties and communication and operational context parameters, in order to determine accurately and assign the initial trust score to each device. Through a set of extensive simulations over different experimental setups, the proposed scheme is achieving to safely distribute initial trust scores to one thousand devices over less than 6ms, while minimising the risk of computational denial of service attacks due to the inherent characteristics of the BARRETT remote attestation protocol.
Post-Parameter-Selection Maximum-Likelihood Estimation

Practical multi-parameter estimation problems with limited measurements and unidentifiable scenarios usually involve a preliminary data-based selection stage. Recent works have shown that selection-aware estimation methods outperform state-of-the-art estimators in the sense of post-selection bias and post-selection mean-squared-error (PSMSE). In this paper, we discuss non-Bayesian estimation methods where a subset of parameters is selected for estimation from the full unknown parameter vector by a data-based selection-rule. We present four estimators: the maximum likelihood (ML), the coherent ML, the post-selection ML (PSML), and the coherent PSML. Coherent post-selection estimators force the unselected parameters to zero, and thus, can be implemented in practical high-dimensional problems. Additionally, we develop a low-complexity algorithm, the stochastic approximation PSML (SA-PSML) for practical implementation of the coherent PSML estimator. Simulation results show that the SA-PSML algorithm achieves a lower PSMSE than the coherent ML estimator for sparse vector recovery with the orthogonal matching pursuit (OMP) selection rule.
TwinDNN: A Tale of Two Deep Neural Networks

Compression technologies for deep neural networks (DNNs), such as weight quantization, have been widely investigated to reduce the model size so that they can be implemented on hardware with strict resource restrictions. However, one major downside of model compression is accuracy degradation. To deal with this problem effectively, we propose a new compressed network inference scheme with a high accuracy but slower DNN coupled with its highly compressed DNN version that typically delivers much faster inference speed but with a lower accuracy. During the inference, we determine the confidence of the prediction of the compressed DNN, and infer the original neural network for the inputs that are considered not confident by the compressed DNN. The proposed design uses a balanced number of resources available on the hardware and can deliver overall accuracy close to the high accuracy model, but with the inference speed closer to the compressed DNN. We demonstrate our design on two image classification tasks: CIFAR-10 and ImageNet. Our experiments show that our design can recover up to 94% of accuracy drop caused by extreme network compression, with more than 90% speedup compared to just using the original DNN. This is more than 17% extra accuracy recovery and 36% extra speedup compared to the previous work with a similar concept on VGG-16. This is the first work that considers using a highly compressed DNN along with the original DNN in parallel to achieve high accuracy and speed at the same time, while maintaining the resource balance by using two different main computation sources efficiently on an FPGA.
Link quality estimation based on over-sampling and weighted random forest

Aiming at the imbalance problem of wireless link samples, we propose the link quality estimation method which combines the K-means synthetic minority over-sampling technique (K-means SMOTE) and weighted random forest. The method adopts the mean, variance and asymmetry metrics of the physical layer parameters as the link quality parameters. The link quality is measured by link quality level which is determined by the packet receiving rate. K-means is used to cluster link quality samples. SMOTE is employed to synthesize samples for minority link quality samples, so as to make link quality samples of different link quality levels reach balance. Based on the weighted random forest, the link quality estimation model is constructed. In the link quality estimation model, the decision trees with worse classification performance are assigned smaller weight, and the decision trees with better classification performance are assigned bigger weight. The experimental results show that the proposed link quality estimation method has better performance with samples processed by K-means SMOTE. Furthermore, it has better estimation performance than the ones of Naive Bayesian, Logistic Regression and K-nearest Neighbour estimation methods.
Optimizing Packet Scheduling and Path Selection for Anonymous Voice Calls

Onion routing is a promising approach to implement anonymous voice calls. Uniform-sized voice packets are routed via multiple relays and encrypted in layers to avoid a correlation of packet content in different parts in the network. By using pre-built circuits, onion encryption may use efficient symmetric ciphers. However, if packets are forwarded by relays as fast as possible—to minimize end-to-end latency—network flow watermarking may still de-anonymize users. A recently proposed countermeasure synchronizes the start time of many calls and batch processes voice packets with the same sequence number in relays. However, if only a single link with high latency is used, it will also negatively affect latency of all other calls. This article explores the limits of this approach by formulating a mixed integer linear program (MILP) that minimizes latency “bottlenecks” in path selection. Furthermore, we suggest a different scheduling strategy for voice packets, i.e. implementing independent de-jitter buffers for all flows. In this case, a MILP is used to minimize the average latency of selected paths. For comparison, we solve the MILPs using latency and bandwidth datasets obtained from the Tor network. Our results show that batch processing cannot reliably achieve acceptable end-to-end latency (below 400 ms) in such a scenario, where link latencies are too heterogeneous. In contrast, when using de-jitter buffers for packet scheduling, path selection benefits from low latency links without degrading anonymity. Consequently, acceptable end-to-end latency is possible for a large majority of calls.
A Hybrid two-stream approach for Multi-Person Action Recognition in TOP-VIEW 360<sup>°</sup> Videos

Action recognition in top-view 360° videos is an emerging research topic in computer vision. Existing work utilizes a global projection method to transform 360° video frames to panorama frames for further processing. However, this unwrapping suffers from a problem of geometric distortion i.e., people present near the centre in the 360° video frames appear highly stretched and distorted in the corresponding panorama frames (observed in 37.5% of the total panorama frames in 360Action dataset). Thus, recognizing the actions of people who are near the centre becomes difficult, thereby affecting the overall action recognition performance. In this work, we overcome the above challenge by utilizing distortion-free person-centric images of the persons near the centre, extracted directly from the input 360° video frames. We propose a simple yet effective hybrid two-stream architecture consisting of a panorama stream and a person-centric stream where outputs from both streams are combined together to detect the overall actions in a video. We perform experiments to validate the efficacy of the proposed method on the recently introduced 360Action dataset and achieve an overall improvement of 2.3% $\mathrm{m}\mathrm{A}\mathrm{P}$ compared to the state-of-the art method and a maximum improvement of 22.7% AP for pickup action, which happens mostly near the centre.
How we set new world records in breaking Playfair ciphertexts

The Playfair cipher is a well-known manual encryption method developed in the 19th century. Until 2018, known cryptanalysis techniques, with computer assistance, could solve non-keyword-based Playfair ciphertexts if they had at least 60 letters to work with. Shorter ciphertexts were effectively impossible to solve in the absence of a crib. In this article, we show how we introduced several improvements in these cryptanalysis methods, which made it possible to do much better. This resulted in the (unofficial) world record for the shortest Playfair message broken going down from 60 via 50, 40, 32, and 28 to 26 letters. The cryptanalysis techniques used include hill climbing, simulated annealing, tabu search, and plaintext-based dictionary attacks. For readers interested in improving the current record, we also provide unsolved Playfair challenges consisting of 24 and 22 letters.
Unsupervised learning of general-purpose embeddings for code changes

Applying machine learning to tasks that operate with code changes requires their numerical representation. In this work, we propose an approach for obtaining such representations during pre-training and evaluate them on two different downstream tasks — applying changes to code and commit message generation. During pre-training, the model learns to apply the given code change in a correct way. This task requires only code changes themselves, which makes it unsupervised. In the task of applying code changes, our model outperforms baseline models by 5.9 percentage points in accuracy. As for the commit message generation, our model demonstrated the same results as supervised models trained for this specific task, which indicates that it can encode code changes well and can be improved in the future by pre-training on a larger dataset of easily gathered code changes.
Accelerating Spectral Normalization for Enhancing Robustness of Deep Neural Networks

Deep neural networks (DNNs) play an important role in machine learning due to its outstanding performance compared to other alternatives. However, DNNs are usually not suitable for safety-critical applications since DNNs can be easily fooled by well-crafted adversarial examples. To address this issue, spectral normalization (SN) technique was proposed to counter adversarial attacks, which ensures that the trained model has low sensitivity towards the disturbance of input samples. Unfortunately, this strategy requires exact computation of spectral norm, which is computation intensive and impractical for large-scale networks. In this paper, we introduce an acceleration technique for spectral normalization based on Fourier transform and layer separation. The proposed method provides DNNs with promising security protection while maintaining minimized time cost, which turns SN from a theoretically feasible approach to a practically useful framework. Experimental evaluation using autonomous systems demonstrates that our acceleration method is able to significantly improve both time efficiency (up to 60%) and model robustness (61% on average) compared with the state-of-the-art spectral normalization in real-world applications.
Task-Agnostic Continual Learning Using Base-Child Classifiers

Continual learning (CL) aims to learn new tasks by forward transfer of information learnt from previous tasks and without forgetting them. In task incremental CL, task information is vital during both strategy development and inference. Providing such partial knowledge about the test sample demands additional complexity and may become intractable, especially when the sample source is ambiguous. In this work, we design a task-agnostic approach that uses base-child hybrid setup to incrementally learn tasks while mitigating forgetting. Multiple base classifiers guided by reference points learn new tasks and this information is distilled via feature space induced sampling strategy. A central child classifier consolidates information across tasks and infers the task identifier automatically. Experimental results on standard datasets show that the proposed approach outperforms the various state-of-the-art regularization and replay CL algorithms in terms of accuracy, by 50% and 7% with homogeneous and heterogeneous tasks, respectively, in task-agnostic scenarios.