Reviewerly/exHarmony huggingface dataset:
A Look Behind the Curtain: Traffic Classification in an Increasingly Encrypted Web

Traffic classification is essential in network management for operations ranging from capacity planning, performance monitoring, volumetry, and resource provisioning, to anomaly detection and security. Recently, it has become increasingly challenging with the widespread adoption of encryption in the Internet, e.g., as a de-facto in HTTP/2 and QUIC protocols. In the current state of encrypted traffic classification using Deep Learning (DL), we identify fundamental issues in the way it is typically approached. For instance, although complex DL models with millions of parameters are being used, these models implement a relatively simple logic based on certain header fields of the TLS handshake, limiting model robustness to future versions of encrypted protocols. Furthermore, encrypted traffic is often treated as any other raw input for DL, while crucial domain-specific considerations are commonly ignored. In this paper, we design a novel feature engineering approach that generalizes well for encrypted web protocols, and develop a neural network architecture based on Stacked Long Short-Term Memory (LSTM) layers and Convolutional Neural Networks (CNN). We evaluate our approach on a real-world web traffic dataset from a major Internet service provider and Mobile Network Operator. We achieve an accuracy of 95% in service classification with less raw traffic and smaller number of parameters, out-performing a state-of-the-art method by nearly 50% fewer false classifications. We show that our DL model generalizes for different classification objectives and encrypted web protocols. We also evaluate our approach on a public QUIC dataset with finer application-level granularity in labeling, achieving an overall accuracy of 99%.
Hierarchical unsupervised multi-view feature selection

Multi-view feature selection is an important research direction in the field of multi-view learning. To remove the negative effect of irrelevant and redundant information and select the important features from the multi-view data, in this paper, we propose a novel hierarchical unsupervised multi-view feature selection method, which integrates matrix decomposition and hierarchical regularization as a joint model. Specifically, to exploit the consistency information of multiple views for projection matrix, we project the multiple views into a latent basis space based on a matrix decomposition model. The hierarchical regularization containing the [Formula: see text]-norm, dependence and Frobenius-norm is imposed on projection matrix to, respectively, exploit the row-level, dependency-level and view-level feature selection. We also develop an efficient optimization algorithm to optimize our method. Extensive experimental results on six popular multi-view datasets show the effectiveness and superiority of our method by comparing with the state-of-the-art methods.
Oblivious Online Monitoring for Safety LTL Specification via Fully Homomorphic Encryption

Abstract In many Internet of Things (IoT) applications, data sensed by an IoT device are continuously sent to the server and monitored against a specification. Since the data often contain sensitive information, and the monitored specification is usually proprietary, both must be kept private from the other end. We propose a protocol to conduct oblivious online monitoring —online monitoring conducted without revealing the private information of each party to the other—against a safety LTL specification. In our protocol, we first convert a safety LTL formula into a DFA and conduct online monitoring with the DFA. Based on fully homomorphic encryption (FHE) , we propose two online algorithms ( Reverse and Block ) to run a DFA obliviously. We prove the correctness and security of our entire protocol. We also show the scalability of our algorithms theoretically and empirically. Our case study shows that our algorithms are fast enough to monitor blood glucose levels online, demonstrating our protocol’s practical relevance.
Collaborative Filtering-Based Recommendation of Online Social Voting

Abstract: Social vote casting is an rising new characteristic in on line social networks. It poses particular demanding situations and possibilities for advice. In this paper, we increase a fixed of matrix factorization (MF) and nearest-neighbor(NN)-primarily based totally recommendersystems (RSs) that discover consumer social community and institution association facts for social vote casting advice. Through experiments with actual social vote casting lines, we reveal that social community and institution association facts can appreciably enhance the accuracy of popularity-primarily based totally vote casting advice, and social community facts dominates institution association facts in NN- primarily based totally methods. We additionally take a look at that social and institution facts is a good deal greater precious to bloodless customers than to heavy customers. In our experiments, easy metapath primarily based totally NN fashions outperform computation-extensive MF fashions in warm-vote casting advice, even as customers’ pastimes for non-warm votings may be higher mined via way of means of MF fashions. We similarly endorse a hybrid RS, bagging one-of-a-kind unmarried methods to acquire the excellent top-ok hit rate. Keywords: Social vote casting, Social Network, matrix factorization (MF), accuracy, NN fashions etc.
Resilient Multi-agent Reinforcement Learning Using Medoid and Soft-medoid Based Aggregation

A network of reinforcement learning (RL) agents that cooperate with each other by sharing information can improve learning performance of control and coordination tasks when compared to non-cooperative agents. However, networked Multi-agent Reinforcement Learning (MARL) is vulnerable to adversarial agents that can compromise some agents and send malicious information to the network. In this paper, we consider the problem of resilient MARL in the presence of adversarial agents that aim to compromise the learning algorithm. First, the paper presents an attack model which aims to degrade the performance of a target agent by modifying the parameters shared by an attacked agent. In order to improve the resilience, the paper presents aggregation methods using medoid and soft-medoid. Our analysis shows that the medoid-based MARL algorithms converge to an optimal solution given standard assumptions, and improve the overall learning performance and robustness. Simulation results show the effectiveness of the aggregation methods compared with average and median-based aggregation.
Visualizing Competing Items over Time

Choosing between two or more alternatives, ranging from relatively simple alternatives to more complex ones, is a common and important task. Visualization techniques may help users to examine differences between such alternatives. The potential contribution of an adequate visualization may increase as the comparison becomes more complicated, when the compared alternatives include multiple dimensions, and when they are examined over time. We propose a novel visualization technique designed to represent the temporal evolution of multiple dimensions between two competitors at a glance. We exemplify how the visualization supports the comparison of two events according to their changes in eight discrete emotions over time. We report on a study that compared our suggested visualization with two common visualization techniques. User performance and preferences were measured under a formal task taxonomy, using Twitter data of real-world events. The experimental results indicate the effectiveness of our visualization as demonstrated by task completion time and accuracy.
Algorithms and architecture support of degree-based quantization for graph neural networks

Recently, graph neural networks (GNNs) have achieved excellent performance on many graph-related tasks. Existing typical GNNs follow the neighborhood aggregation strategy, which updates nodes’ information by aggregating the feature of neighboring nodes. However, these hybrid execution patterns limit their deployment on resource-limited devices. Quantification is an effective technique for deep neural networks (DNNs) inference acceleration, but few studies have considered exploring suitable quantization algorithms for GNNs. In this paper, we propose a degree-based quantization (DBQ) that can identify sensitive nodes in the graph structure. The protective masks are used to ensure that sensitive nodes perform full-precision operations, and the remaining nodes are quantized. In this way, the effect of dynamically changing the precision is achieved to achieve greater acceleration while retaining better classification accuracy. To support DBQ and convert it into performance improvements, we design a new architecture. Elaborate pipelines and specialized optimizations effectively improve inference speed and accuracy. Compared to state-of-the-art GNN accelerators, DBQ gains on 2.4 × speedups and improves accuracy by 27.7%.
Provide an Improved Model for Detecting Persian SMS Spam by Integrating Deep Learning and Machine Learning Models

Spam is an example of unwanted content sent by unknown users and causing problems for mobile phone users. Disadvantages of spam include the inconvenience to the user, the loss of network traffic, the imposition of a calculation fee, the occupation of the physical space of the mobile phone, the misuse and fraud of the recipient. For this reason, the automatic detection of annoying text messages can be fundamental. Also, recognizing intelligently generated text messages is a challenge. Nevertheless, the current methods in this field face obstacles, such as the lack of appropriate Persian datasets. Experiences have shown that approaches based on deep and combined learning have better results in uncovering the annoying text messages. Accordingly, this study has attempted to provide an efficient method for detecting SMS spam by integrating machine learning classification algorithms and deep learning models. In the proposed method, after performing preprocessing on our collected dataset, two convolutional neural network layers and one LSTM layer and a fully connected layer are applied to extract the features are applied on the data which forms the deep learning part of the proposed method. The Support vector machine then utilizes the extracted information and features to perform the final classification, which is a part of the Machine Learning methods. The results show that the proposed model implements better than other algorithms and 97. 7% accuracy was achieved.
Prediction of Ethereum Blockchain ERC-20 Token Standard Smart Contract Vulnerabilities Using Source Code Metrics

In this study, firstly, a dataset of 10,476 annotated vulnerable ERC-20 standard token smart contracts (belonging to a set of 33 common smart contract vulnerabilities) has been collected from a publicly available repository. Secondly, using the SolMet smart contract metrics measurement tool, the object-oriented software attributes (i.e., metrics) from each smart contract's source code has been extracted. Lastly, using the source code metrics and the vulnerability annotations (i.e., labels) as the input in supervised machine learning (classification) algorithms, the accuracy of each individual algorithm is evaluated against the accuracy of an ensemble classifier (namely voting). The model accuracies demonstrate the feasibility of identifying and prioritising smart contracts for further inspection prior to deployment to the blockchain network. The ensemble classifier performed better (accuracy = 0.79) compared to each classifier when used individually.
Improving Particle Swarm Optimization Analysis Using Differential Models

This paper employs the approach of the differential model to effectively improve the analysis of particle swarm optimization. This research uses a unified model to analyze four typical particle swarm optimization (PSO) algorithms. On this basis, the proposed approach further starts from the conversion between the differential equation model and the difference equation model and proposes a differential evolution PSO model. The simulation results of high-dimensional numerical optimization problems show that the algorithm’s performance can be greatly improved by increasing the step size parameter and using different transformation methods. This analytical method improves the performance of the PSO algorithm, and it is a feasible idea. This paper uses simple analysis to find that many algorithms are improved by using the difference model. Through simple analysis, this paper finds that many AI-related algorithms have been improved by using differential models. The PSO algorithm can be regarded as the social behavior of biological groups such as birds foraging and fish swimming. Therefore, these behaviors described above are an ongoing process and are more suitable for using differential models to improve the analysis of PSO. The simulation results of the experiment show that the differential evolution PSO algorithm based on the Runge–Kutta method can effectively avoid premature results and improve the computational efficiency of the algorithm. This research analyzes the influence of the differential model on the performance of PSO under different differenced conditions. Finally, the analytical results of the differential equation model of this paper also provide a new analytical solution.
Impact and Implications of Big Data Analytics in Cloud Computing Platforms

Abstract: Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Cloud Computing refers to the processing of anything, including Big Data Analytics, on the “cloud”. The “cloud” is just a set of highpowered servers from one of many providers. They can often view and query large data sets much more quickly than a standard computer could. Essentially, “Big Data” refers to the large sets of data collected, while “Cloud Computing” refers to the mechanism that remotely takes this data in and performs any operations specified on that data. Cloud Computing services largely exist because of Big Data. Likewise, the only reason that we collect Big Data is because we have services that are capable of taking it in and deciphering it, often in a matter of seconds. The two are a perfect match, since neither would exist without the other. The combination of both yields beneficial outcome for the organizations. Not to mention, both the technologies are in the stage of evolution but their combination leverages scalable and cost-effective solution in big data analytics. Big data and Cloud computing are perfect combination. Besides that, there are also some real-time challenges to deal with. In this paper, discribes both the aspects. This paper introduces the characteristics, trends and challenges of big data. In addition to that, it investigates the benefits and the risks that may rise out of the integration between big data and cloud computing. KEYWORDS: Cloud Computing, Big Data, Scalable, Analytics.
Improving work detection by segmentation heuristics pre-training on factory operations video

The measurement of work time for individual tasks by using video has made a significant contribution to a framework for productivity improvement such as value stream mapping (VSM). In the past, the work time has been often measured manually, but this process is quite costly and labor-intensive. For these reasons, automation of work analysis at the worksite is needed. There are two main methods for computing spatio-temporal information: by 3D-CNN, and by temporal computation using LSTM after feature extraction in the spatial domain by 2D-CNN. These methods has high computational cost but high model representational power, and the latter has low computational cost but relatively low model representational power. In the manufacturing industry, the use of local computers to make inferences is often required for practicality and confidentiality reasons, necessitating a low computational cost, and so the latter, a lightweight model, needs to have improved performance. Therefore, in this paper, we propose a method that pre-trains the image encoder module of a work detection model using an image segmentation model. This is based on the CNN-LSTM structure, which separates spatial and temporal computation and enables us to include heuristics such as workers' body parts and work tools in the CNN module. Experimental results demonstrate that our pre-training method reduces over-fitting and provides a greater improvement in detection performance than pre-training on ImageNet.
MagicMuseum: Team-based Experiences in Interactive Smart Spaces for Cultural Heritage Education

MagicMuseum is a set of team-based, immersive, full-body activities for Cultural Heritage Education of primary school children. MagicMuseum exploits the interactive and multisensory capability of the Magic Room, an indoor smart space equipped with IoT-enriched components such as floor and wall projections, smart lighting, music and sound, motion and gesture sensors, and smart objects. The paper describes MagicMuseum and briefly reports an exploratory study involving 22 children at a local primary school.
Explainable Object Detection for Uncrewed Aerial Vehicles using KernelSHAP

While the field of object detection has seen remarkable performance gains since the incorporation of Deep Neural Networks (DNNs), a significant drawback in DNN detection algorithms is that they lack transparency, making their behaviour somewhat unpredictable. Without transparency, employing DNNs for on-board object detection on Uncrewed Aerial Vehicles (UAVs) could have massive societal and safety consequences. In this paper, we propose adopting a proven explainer, KernelSHAP, to provide visual explanations for bounding boxes produced by detection algorithms intended for on-board UAVs. Our explainer can identify the important parts of an image that assisted a given detection or contributed to a specific failure mode. We evaluate our explainer&#x2019;s discriminative ability on aerial imagery through a pointing metric and an automatic deletion/insertion metric. We further assess our explainer by intentionally introducing a bias to the dataset for it to detect and using that bias to simulate failure modes that can then be discovered using our explainer.
Fast and accurate variable batch size convolution neural network training on large scale distributed systems

Abstract Large‐scale distributed convolution neural network (CNN) training brings two performance challenges: model performance and system performance. Large batch size usually leads to model test accuracy loss, which counteracts the benefits of parallel SGD. The existing solutions require massive hyperparameter hand‐tuning. To overcome this difficult, we analyze the training process and find that earlier training stages are more sensitive to batch size. Accordingly, we assert that different stages should use different batch size, and propose a variable batch size strategy. In order to remain high test accuracy under larger batch size cases, we design an auto‐tuning engine for automatic parameter tuning in the proposed variable batch size strategy. Furthermore, we develop a dataflow implementation approach to achieve the high‐throughput CNN training on supercomputer system. Our approach has achieved high generalization performance on SOAT CNN networks. For the ShuffleNet, ResNet‐50, and ResNet‐101 training with ImageNet‐1K dataset, we scale the batch size to 120 K without accuracy loss and to 128 K with only a slight loss. And the dataflow implementation approach achieves 93.5% scaling efficiency on 1024 GPUs compared with the state‐of‐the‐art.
Machine Translation of English Language Using the Complexity-Reduced Transformer Model

Previous translation models like statistical machine translation (SMT), rule-based machine translation (RBMT), hybrid machine translation (HMT), and neural machine translation (NMT) have reached their performance bottleneck. The new Transformer-based machine translation model has become the favorite choice for English language translation. For instance, Google’s BERT translation model organizes the Transformer module into bidirectional encoder representations. It is aware of the users’ search intentions as well as the material that the search engine has indexed. It does not need to evaluate previous searches to comprehend what people mean, unlike RankBrain. BERT comprehends words, sentences, and complete information in the same way that we do. It achieves remarkable translation quality improvement over the other state-of-the-art benchmarks. It demonstrates the great potential of the Transformer model. The Transformer-based translation model mainly improves the performance at the cost of growing model sizes and complexity, usually requiring million-scale parameters. It is hard for the traditional computing systems to cope with the growing memory and computation requirements. However, the latest computers can easily run this model without any lag. The biggest challenge of applying the Transformer model is to deploy these models efficiently onto real-time or embedded devices. In this work, we propose a quantization scheme to reduce the parameter and computation complexity. It is of great importance to promote the usage of the Transformer model. Our experiment results show that the original Transformer model in 32 bit floating-point can be quantized to only 8 bits to 12 bits with only negligible translation quality loss. However, due to the perfect transformation of the block part, this quality loss part can easily be managed by the users. Meanwhile, our algorithm achieves <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>2.6</mn> <mo>×</mo> </math> to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>4.0</mn> <mo>×</mo> </math> compression ratio, which is helpful to save the required complexity and energy during the inference phase.
Scale Space Radon Transform for Non Overlapping Thick Ellipses Detection

This paper presents a new elliptical structure detection method, combining the advantages of the multiscale Hessian, and the scale space Radon transform (SSRT) for an ellipse. The advantage of the former is twofold: highlighting the lines defining the ellipses present in the image and reducing the search space for these ellipses in the SSRT space, which will discard the false SSRT maxima. The subsequent application of the SSRT permits, in turn, to alleviate the computation load and to obtain, moreover, a good detection of thick ellipses when they are not threadlike. Experiments carried out on synthetic and real images have shown good detection of thick ellipses, with low computational overhead compared to the Elliptical Radon transform.
EFNN-Gen — a uni-nullneuron-based evolving fuzzy neural network with generalist rules

The evolving fuzzy neural networks have a high degree of interpretability and a high capacity for solving pattern classification problems. However, their accuracy could deteriorate when there are few samples for particular classes available, e.g., when new classes appear in the data stream. One way to improve these models’ performance is to include a priori knowledge in their data-driven architectural structure. This article proposes the new concept of generalist rules based on assessing the specificity of Gaussian functions that make up the neurons of the first layer of the evolving fuzzy neural network (EFNN-Gen). These rules can be seen as general (expert) knowledge about the classification problem. Tests performed with real binary pattern classification bases proved that integrating generalist rules can increase the accuracy of an evolving system and that there is a specific limit on how many rules can be used for this improvement.