Reviewerly/exHarmony huggingface dataset:
Fairness-Efficiency Tradeoff Allocation with Meta-Types in Cloud Computing

We study the problem of multiple resource allocation in cloud computing systems. Existing fairness-efficiency scheduling procedures can relax fairness constraints by using a knob to improve efficiency. However, these approaches do not take into account users with special needs, i.e., the same resource (meta-type, e.g., CPU) contains different types (e.g., Intel's CPU, AMD's CPU) and the user can only use a specific type of resources (e.g., Intel's CPU). We propose a new allocation mechanism called Fairness-Efficiency Tradeoff Allocation with Meta-Types (FET-MT), which introduces the concept of meta-types. FET-MT not only meets specific requirements proposed by users but also allows users to flexibly balance fairness and efficiency by adjusting the knob values. Finally, we implemented the FET-MT method using GUROBI, and our experiments show that the running time of FET-MT is reduced by approximately a factor of 7 with respect to Maximum Nash Welfare (MNW) and discrete MNW and that FET-MT can still maintain good running efficiency as the number of users increases. The experimental results also show that FET-MT can obtain nearly twice the social welfare of MNW and DRF-MT, and the utilization of meta-types in the system is close to 100%.
Reflective Learning for Online Knowledge Distillation

Great attention has been drawn to knowledge dis-tillation owing to its importance in network deployments in recent years. Conventional knowledge distillation techniques need pre- training of a teacher network and transferring knowledge from the teacher network to a student network. Instead of pre-training the teacher, online knowledge distillation directly utilises collaboration among multiple students to complete the joint training. Online knowledge distillation typically construct a pseudo-teacher with an ensemble of students and uses the pseudo-teacher to distill other students, which may cause severe overfitting due to the potential incorrect predictions. In this work, we propose a reflective learning method for online knowledge distillation to fully exploit the correct knowledge and incorrect information of the training history. To retain the previously produced knowledge, a notebook module and the corresponding update strategy for its records are designed. Furthermore, to keep the reflective intensity on mistakes, an error reflection factor schedule strategy is proposed to adjust the weight on decreasing errors. Comprehensive evaluations are conducted on several public benchmark datasets, including CIFAR-100 and TinyImageNet. The experiment results demonstrate the superi-ority of the proposed approach, whereby DenseNet-40-12 and WRN-20-8 outperform previous methods by 1.79% and 1.35% on CIFAR-100 respectively.
Radar Waveform Recognition Based on Time-frequency Images Under Deteriorating Channel Conditions

In the process of electronic countermeasures, most studies of radar signal recognition focus on Additive White Gaussian Noise (AGWN) channel scenarios, while pay less attention to the influence of channel deterioration on radar signal recognition. Therefore, we propose a low interception rate radar signal recognition method based on time-frequency images under channel impairment. First, we obtain shallow features of LPI radar signals in fading channels by Choi-Williams distribution (CWD) time-frequency transform that converts the signals to time-frequency images. Second, we design a parallel residual network model for feature learning and signal classification to realize the automatic modulation recognition of radar waveforms under channel fading conditions. Simulation results show that the proposed method effectively achieves the recognition of 12 kinds of radar waveforms both in AGWN channels and in multipath channels, which demonstrates the superiority of this model.
OISM: An Occluded Instance Segmentation Method Combining Detection and Tracking

With the vigorous development of mobile internet and artificial intelligence, massive video data are constantly produced. Objects in video are often blurred and varied due to shooting angle, fast motion and partial occlusion, which makes it difficult to segment instances from video data. At present, the video instance segmentation methods show poor performance in the case of occlusion. In this paper, an end-to-end occluded video instance segmentation combining detection and tracking model, called OISM, has been designed to solve this problem to some extent. Briefly, on the time scale, features of the target detected and tracked in other frames are combined and aggregated into the current image features to strengthen the feature response of candidate targets in the current frame. On the image scale, the time feature calibration plug-in is added to the segmentation network to improve the segmentation performance of the occluded instances. Experiments on OVIS and YouTuBe-VIS datasets show the proposed method has better segmentation accuracy than the state of the art.
Kalman Filter Algorithm in the Presence of Multipath

When radar is tracking and measuring a low altitude target, the result of antenna beam pointing and height measurement will seriously deviate from the target truth value due to the influence of multipath effect. For the target data obtained by monopulse or other angular measurement algorithms, after Kalman filtering processing, the measurement results of target height with smaller error and the beam pointing at the next moment of antenna can be obtained. Simulation results of one-dimensional linear array and two-dimensional motion plane show that the algorithm can obtain better data processing results, and can effectively reduce the Angle error even under severe multipath interference.
Image Classification with Frequency Channel Attention under the Few-Shot Condition

Few-shot learning aims to use very few samples for classification. In the existing few-shot learning methods, most of them only use the spatial information of images. In few-shot learning, because of the lack of samples, it is very important to improve the expression of image features. Recent studies have found that the feature representation in frequency domain contains a wealth of information can be used for tasks image classification. Therefore, this paper proposes to use the frequency domain information of images to enhance feature representation to improve the utilization efficiency of a small number of samples. We propose a network called Meta-Baseline-Fca. It takes a simple small-sample learning network Meta-baseline as the baseline, adds a frequency channel attention (Fca) module in the network, and uses the spatial and frequency domain features of the input image to enhance the input image features expression. We conduct comparative experiments in the miniImageNet dataset and tiereimageNet dataset. The results show that under the condition of small samples, the Fca module can significantly improve the classification accuracy. Compared with the Meta-Baseline network, the classification accuracy is improved by 0.92% in the miniImageNet dataset and 0.84% in the tiereimageNet dataset.
Research on Access Control of SWIM Data Sharing Based on Attribute Based Encryption

As a standard implemented by ICAO (International Civil Aviation Organization), SWIM (System Wide Information Management) is a sharing mechanism for global flight operation data, which adopts a service-oriented architecture SOA (Service Oriented Architecture) to carry heterogeneous flight operation data from multiple sources. In order to solve the problems of data security and privacy in flight operation data sharing, this paper proposes an access control architecture for flight operation data sharing based on ABE (Attribute-based Encryption) for SWIM, correlates the refined flight operation data attributes with the private keys of SWIM aviation users, establishes an attribute association matrix, and realizes a fine-grained access policy for flight operation data sharing.
Improved Frequency Estimation Algorithm Based on Local Differential Privacy

Nowadays, people pay more and more attention to personal privacy issues, and how to collect data while protecting personal privacy and data security has become a major challenge. Local differential privacy, as a new privacy protection model, can collect and protect user data without a trusted third party. Among the many local differential privacy algorithms, RAPPOR(Randomized Aggregatable Privacy-Preserving Ordinal Response) is suitable for situations where the privacy budget is small. However, RAPPOR uses Bloom filters to map the data, which can lead to the same encoding result for different data, and the existing solution is to group the data. The paper, we propose the digital RAPPOR algorithm D-RAPPOR (Digitization-RAPPOR) does not require grouping of user data, D-RAPPOR is based on user data on the ordinal number of candidate values The D-RAPPOR algorithm is suitable for data sets with known candidate values. In this paper, the D-RAPPOR algorithm is used for experimental analysis of electricity data, which can be collected safely. The method can obtain the individual frequencies of the customer's electricity data while ensuring the security of the customer's electricity data. The control center can analyze the results of the algorithm to achieve the purpose of regulating electricity. Finally, the algorithm proposed in this paper solves the problem that the original algorithm produces the same encoding, and the results of the D-RAPPOR algorithm are slightly better than the RAPPOR algorithm under the same privacy budget.
Research and Implementation of Asynchronous Transmission and Update Strategy for Federated Learning

In today's era, there is a growing concern about data privacy and security. Data is the foundation of machine learning, and due to the complexity of the reality, data often exists in silos. The great development of computing and storage capacity of edge devices has made it possible to train machine learning models on edge devices with data. Federated learning is a special kind of distributed machine learning in which multiple distributed clients use local data to train a model and upload the resulting model parameters to a central server for aggregation and iteration to finally obtain a global model with good generalization performance. The interaction between the clients and the server is model parameters rather than the data itself, which effectively protects data privacy and security. After research, it is found that Federated Learning is mainly divided into two aggregation models: synchronous aggregation and asynchronous aggregation. In this paper, the classical FedAvg synchronous federated average aggregation algorithm is analyzed. The central server needs to wait for all participating nodes to be trained before aggregating and updating the model, which is inconvenient to achieve global synchronization on resource heterogeneous clients. To improve the utilization of computational resources, FedAsync, an asynchronous federated learning aggregation mechanism with a receivable window based on dual weights, which are data volume size weight and staleness weight, is proposed. And FedAvg and FedAsync are tested and analyzed. Based on this, the impact on federated learning due to asynchronous data arrival rate and limited communication resources is further explored.
Multi-source Data Fusion Based on Wavelet Decomposition and LSTM for Distributed Photovoltaic Power Prediction

Photovoltaic(PV) power generation plays an important role in realizing the sustainable development of energy and accurate photovoltaic power prediction is very important for the stable operation of PV systems. In this paper, a fusion model combining wavelet decomposition technology and Long Short-Term Memory (LSTM) network is proposed to realize short-term photovoltaic power prediction. Firstly, historical power sequence data are processed with wavelet decomposition technology to extract feature data. Secondly, the power feature data combined with meteorological data are inputted into LSTM training model for feature-level fusion. Finally, the trained model is evaluated with the test set and compared with the other two models in different seasons and weather conditions. The results show that the proposed model has better performance than the Back-Propagation Neural Network (BPNN) and LSTM.
Can Pretrained Language Models Reason on Sparse Commonsense Knowledge Graph?

Commonsense knowledge is the knowledge shared by most humans, which is always stored in commonsense knowledge graph (CKG) as triplets. In this paper, we focus on the task of CKG competition, whose target is to predict the tail (head) target given the head (tail) entity and the relation. Most existing works employ the graph-based models, which aggregate information from neighboring entities on CKG. Despite their effectiveness, they still suffer from two main weaknesses. Firstly, the semantic relations between head and tail entities are neglected. Secondly, due to the sparsity of CKG, they rely on the graph densification that it will bring unexpected noises. To solve these problems, we propose a unified framework for COmmonSense knowledge graph completion based on BERT, namely COS-BERT. Firstly, we transfer each triplet into a natural sentence. Then, we fine-tune the pretrained language model using the transformed sentences. Finally, we rank the candidates based on the output representation of sentences. Furthermore, we add a pre-filter to obtain a subset of candidates on the inference stage to save unnecessary computation costs. Comprehensive experiments have demonstrated the superiority of COS-BERT over the state-of-the-arts.
Blockchain Technology Enabled Communication Network for 5G MEC Architecture of Smart Grids

The 5G MEC study for Smart Grids has been popular since the increasing renewable energy sources and the services. However, the openness and distribution characteristics of the network make the 5G MEC architecture face the great challenges in privacy, security, storage, computing, and so on. Blockchain as an emerging technology has many good features, such as trust-free, transparency, pseudonymity, democracy, automation, decentralization, and security. These features of blockchain could be helpful to improve the security, robustness, and reliability of communication network. In this paper, we investigate some of the work that has been done to integrate blockchain and MEC into communication networks and propose a blockchain enabled 5G MEC computing power scheduling framework. We identify the composition and requirments analysis of the distributed computing power scheduling framework and propose a distributed management system based on blockchain within the framework.
Mutually Guided Image Dehazing

This paper presents an efficient regularization scheme for the single image dehazing. The transmission map has been reguarlized to retrieve a dehazed image. Usually, conventional methods try to improve the initial transmission through guided filtering without considering the potential advantage of improving the guidance as well. We have proposed an efficient regularization scheme that jointly optimizes the transmission map and the guidance. Nonconvex energy function is solved by iterative reweighed least squares. As a result, an improved transmission map is obtained that has edges concurrent with the iteratively updated guidance. The regularized transmission map results in better-quality dehazed image which has improved color fidelity and fine details as demonstrated by the experimental results.
Sentiment Analysis on Hydroponic Technology Application for Urban Farming Limitations

The world is undergoing rapid change due to current events. The internet has become a fundamental necessity for everyone, with Web use in virtually every area. With the growth of social networking, people use these platforms to express their views on daily topics. Hydroponics can help urban agriculture operations when residents have limited land. Despite the benefits of a hydroponic technology, it is not widely used, even in urban areas. The researchers used Twitter to analyze hydroponic farmers’ product sentiment. The data was gathered and evaluated using qualitative research methods and sentiment analysis. The tweets were gathered using the Tweepy Module and the keyword hydroponics. The gathered English tweets underwent tokenization, filtering, standardization, and segmentation. Valence Aware Dictionary for Sentiment Reasoning (VADER) was used to categorize the corpus. After scraping 6429 tweets, 5181 cleaned tweets were categorized as positive, negative, or neutral. Researchers used a pie chart and violin plot for output. Five hundred fifty-three tweets are negative, 2922 are positive, and 1706 are neutral. This result showed 56% liked hydroponic technology. 33% of neutrals liked hydroponics, while 11% disliked it. The hydroponic system has several advantages over conventional farming and some disadvantages. It is recommended that modern farmers be given hydroponic technology application awareness programs to maximize their benefits. Additional studies on hydroponic applications in different agricultural settings and crops are also needed to improve the technology’s success rate.
Usability Evaluation of University Websites in Pakistan through User Testing

University websites are important mediums of communication that make important information accessible to the stakeholders of university websites. A good user experience of these websites brings the broader population to these websites. Evaluating the usability of university websites is important because it finds possible usability problems. In this regard, this research discusses the experience of the usability evaluation of three official websites of the universities of Pakistan i.e., Lahore University of Management Sciences (LUMS), University of Karachi (UoK), and Islamia University, Bahawalpur (IUB) by employing user testing method using thinking aloud approach. The findings of the usability tests are summarized using three measures named effectiveness, efficiency, and satisfaction. The results reflect that the LUMS website scores higher in all three discussed measures. UoK and IUB both have the same effectiveness rating. The UoK website is less efficient than the IUB website, though. Finally, in satisfaction scores, users of the UoK website scored higher as compared to the IUB website.
Serverless Video Analysis Pipeline for Autonomous Remote Monitoring System

Cloud computing is the delivery of computing services including servers, storage, databases, networking, software, and analytics over the Internet. The businesses can store the data and run applications in the cloud with a high availability, accessibility from anywhere with a web browser, and on pay-per-use basis. Public cloud platforms make it possible for anyone to deploy end-to-end applications easily and economically. The cloud hosting for business applications provides the additional benefits of security, elasticity, and logging compared to an in-house private cloud.In this paper, we describe the implementation of a serverless remote video monitoring solution on the Amazon Web Services (AWS) cloud. It can provide surveillance of an area against unauthorized access, identification of objects in the scene, and investigating security incidents. This autonomous prototype application provides a frame-by-frame object detection of the live video. A Short Message Service (SMS) notification alert is generated and sent to the third-party on detecting an object of interest. It provides a flexible and economical solution for remote monitoring.
3D Human Reconstruction with Corresponding 3D Texture Model: A Comparison of Salient Approaches

It is the texture for materials development and the technology selected that determines the realism of the finished 3D model. An in-depth literature review is presented on approaches to producing textures for 3D models and reconstructing 3D humans. The process of texture mapping on a 3D model is one of the key steps. Making a model more realistic requires specific properties to be applied to the modeled object. Through reconstruction, insight into qualities of an object could be gained that aren’t apparent from a single perspective like its relative position and volume in relation to others in the scene. Human surface geometry is reconstructed, including invisible areas, and multiple image scales are combined into one 3D space. An extensive review of experimentation on datasets like Market-1501, DeepFashion and SURREAL shows that different analytical approaches work both qualitatively and quantitatively, as measured by evaluation metrics like SSIM and LPIPS. We aimed to estimate the current level of development and potential of these techniques so that there could be further improvements and a wider range of applications for texture generation and 3D human reconstruction.
Subgraph Propagation Network for Few-shot Learning

Few-shot classification is valuable because in some scenarios only a few images are provided. Many graph-network-based few-shot classification approaches obtain better features and smoother decision boundaries by using graph networks. However, using graph networks to propagate information be-tween labels tends to produce over-assimilation of features or introduce too much information of other classes. In this work, we propose a novel approach named subgraph propagation network (SPN) for few-shot classification. It leverages subgraph to improve the propagation of the features and makes it easier for classifiers to classify features without large number of parame-ters. The proposed subgraph propagation is a non-parametric operation which can be inserted after the feature extractor to produce better feature embeddings. Extensive experiments show that subgraph propagation network significantly improves the performance on few-shot classification benchmarks. We also show subgraph propagation improves the results under semi-supervised settings.
Classification and Prediction of Spam Emails Based on AI Enabling Models Using Deep and Machine Learning Techniques

The increasing volume of unwanted/unsolicited bulk emails, also known as “SPAM,” is a devastating issue that provokes a multitude of problems in communication systems. Over the past few years, the work on spam classification has been tremendously enhanced to a greater extent. In this paper, we present an approach that encompasses machine and deep neural networks such as Gaussian Naive Bayes (GNB), Convolution Neural Networks (CNN) network, Long Short Term Memory (LSTM) network, and a customized model developed with the combination of CNN and LSTM to classify and predict the widely used open source spam assassin dataset that contains around 6000 real email samples. The models are trained and tested, and the results are presented in the paper. Overall, CNN-LSTM attained a prediction score of 98.68% on the spam dataset.
Informer-based Anomaly Detection for Internet TV Service in Home Network

With more and more users watch live and on-demand content through set-top boxes, internet TV has become one of China Mobile's main home internet services. Meanwhile, the problems of video freezing and blurred screen have increasingly dominated the user complaints. Because of the large number of network elements involved in Internet TV service, traditional quality inspection methods which mainly consist of manual operation and semiautomatic procedures failed to find abnormalities quickly and accurately. In this paper, we collect set-top box active users and its alarm events through China Mobile's soft probe monitoring system and accurately detect the quality degradation abnormalities in TV service by an Informec-based model, the precision rate of anomaly detection reaches over 80 %, and the accuracy rate reaches over 90 %.