Reviewerly/exHarmony huggingface dataset:
AREP: an adaptive, machine learning-based algorithm for real-time anomaly detection on network telemetry data

Abstract Abnormal behaviour detection is an essential task of real-time monitoring to secure the reliable operation of ICT infrastructures. This paper presents AREP, an adaptive, long short-term memory-based machine learning algorithm for real-time anomaly detection on network telemetry data. AREP is an improved version of Alter-Re $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> , the direct predecessor algorithm developed by our research team. AREP introduces automatic tuning of its two key parameters and includes an offset compensation component to increase accuracy. Unfortunately, AREP and its predecessors perform well only on time series showing specific patterns. Thus, we propose also a data type classification method to identify patterns on which AREP performs best. Moreover, we use an extended range of metrics in our performance evaluations, including area under the curve (AUC). AUC computation is based on receiver operating characteristic (ROC) curves. However, generating ROC curves is not straightforward due to the inherent adaptive threshold technique used by AREP and its predecessors, so we had to develop a novel ROC curve generation approach for these algorithms. We show through rigorous experiments that on network time series following specific data patterns AREP overperforms its predecessors and produces similar or even better performance than other state-of-the-art algorithms.
Sign Language Recognition for Needy People Using Machine Learning Model

In this digital era, gesture identification is one of the revolutions to serve specially-abled (deaf and dumb) people, and it is been investigated for several decades. Tactlessly, all research studies have their restrictions and are incompetent to be used commercial. Few studies have been recognized to be effective for gesture identification, but it needs an affluent cost to be marketed. Currently, research scholars have more consideration for developing gesture identification systems that can be used commercially. Scholars do their studies in innumerable ways. It twitches from the data gaining approaches when required. The data gaining approach differs since the cost is required for a decent device, but a low-cost device is needed for the hand gesture identification system to be made saleable. Approaches used in implementing the system are diverse between studies. Separately, approach has its strong point compared to other approach and research still using different approaches in developing their gesture identification. Every approach has setbacks compared to others. This manuscript intends to review the gesture identification system for needy people. Hence, other studies can get more information about the approaches used and develop better applications in the forthcoming.
Flow of quantum correlations in noisy two-mode squeezed microwave states

The authors experimentally investigate the behavior of quantum discord and entanglement in two-mode squeezed states generated by superconducting Josephson parametric amplifiers. They show how correlations flow between the two modes and the noisy environment, and relate their findings to the security of entanglement-based quantum key distribution.
Intelligent Grouping Method of Science and Technology Projects Based on Data Augmentation and SMOTE

The current evaluation of science and technology projects is mainly completed by peer review, and in the process of evaluation, dividing projects into different groups is a crucial step. Project grouping is challenging due to the small amounts of data, sparsity of features, broad range of subject areas, and the seriously uneven distribution of categories. In this paper, we propose an intelligent automatic grouping method for science and technology projects based on keywords. We expanded the small dataset with samples generated by Paraphrasing, Mixup, and the GPT3 model. The text feature extraction techniques TF-IDF, Word2Vec, and TF-IDF weighted Word2Vec were utilized to pre-process the keywords of projects, and SVM and XGBoost as the classifier. Besides, we used SMOTE to process imbalanced data to alleviate model bias toward minority classes. Experiments show that the project grouping accuracy was substantially improved after introducing the data augmentation method and SMOTE. The combination of Paraphrasing, TF-IDF, SVM and SMOTE achieved the best performance, and the F1 score reached 96.78%, which proves the feasibility of the proposed method.
MFRCNN: Marshalled FRCNN with optimized reading order in XY tree for document layout analysis in scientific research articles

Recently computer vision and NLP based techniques have been employed for document layout analysis where different types of elements in the document and their relative position are identified. This process is trickier as there are blocks which are structurally similar but semantically different such as title, text etc. This works attempts to use region-based CNN architecture (F-RCNN) for determining five different sections in the scientific articles. To improve the performance of detection algorithm, reading order is used as an additional feature and this model is known as MF-RCNN. First, an algorithm is formulated to find the reading order in documents which adopts Manhattan-layout using a color-coding scheme. Secondly, this information is fused with the input image without changing its shape. Experimental results show that MF-RCNN which uses the reading order performs better when compared with F-RCNN when tested on Publaynet dataset.
AQuA: A New Image Quality Metric for Optimizing Video Analytics Systems

Millions of cameras at the edge are being deployed to power a variety of different deep learning applications. However, the frames captured by these cameras are not always pristine—they can be distorted due to lighting issues, sensor noise, compression etc. Such distortions not only deteriorate visual quality, they impact the accuracy of deep learning applications that process such video streams. In this work, we introduce AQuA, to protect application accuracy against such distorted frames by scoring the level of distortion in the frames. It takes into account the analytical quality of frames, not the visual quality, by learning a novel metric, classifier opinion score , and uses a lightweight, CNN-based, object-independent feature extractor. AQuA accurately scores distortion levels of frames and generalizes to multiple different deep learning applications. When used for filtering poor-quality frames at edge, it reduces high-confidence errors for analytics applications by 17%. Through filtering, and due to its low overhead (14 ms), AQuA can also reduce computation time and average bandwidth usage by 25%. Finally, we discuss numerous new avenues of optimizations of video analytics pipelines enabled by AQuA.
Enhanced type-2 Wang-Mendel Approach

The Wang-Mendel Approach (WMA) focuses on combining the numerical as well as linguistic information for achieving greater explainability for inference models. The standard WMA models the linguistic information using type-1 (T1) fuzzy sets (FSs), which have a reduced capability to model the semantics of linguistic information. Therefore, we propose a novel Enhanced WMA, which models the linguistic information using the type-2 (T2) FSs. Further, our Enhanced T2 FS-based WMA can be modified to reflect the use of interval type-2 (IT2) FSs, for modelling linguistic uncertainty. IT2 FSs are suitable when better uncertainty handling capabilities are required compared to T1 FSs, however, at a computational cost lesser than the T2 FSs. Performance of Enhanced WMA is demonstrated through a real-world crop-yield prediction problem in smart agriculture and an additional exemplar application on users’ satisfaction ratings. Further, we have compared our approach with the performance obtained from the T1 FS-based WMA and the original estimations given in the original data. We found that our Enhanced WMA provides more precise estimates than the other two with 95% confidence level. To the best of our knowledge, this is the first proposal of a T2 FSs method for enhancing the modeling of linguistic uncertainty in the WMA.
An empirical study on bugs in JavaScript engines

JavaScript is a prototype-based dynamic type scripting language. The correct running of a JavaScript program depends on the correctness of both the program and the JavaScript engine. An in-depth understanding of the characteristics of bugs in JavaScript engines can help detect and fix them. We conduct an empirical study on the bugs in three mainstream JavaScript engines: V8, SpiderMonkey, and Chakra. Such an empirical study involves 19,019 bug reports, 16,437 revisions, 805 test cases, and root causes of randomly selected 540 bugs. (1) The Compiler and the DOM are the most buggy component in V8 and SpiderMonkey, respectively. Most of the source files contain only one bug. (2) The scales of the testing programs that reveal bugs are usually small. Most bug fixes involve only limited modifications since the number of modified source files and lines of code modified are small. (3) Most bugs can be fixed within half a year (80.33% for V8 and 91.9% for SpiderMonkey). Only 4.33% of SpiderMonkey bugs need more than a year to fix. Bugs in SpiderMonkey are usually fixed faster than bugs in V8. (4) High priority tends to be assigned to Infrastructure bugs in V8 and Release Automation bugs in SpiderMonkey. The duration of bugs is not strictly correlated with their priorities. (5) Semantic bugs are the most common root causes of bugs. And among semantic bugs, the processing bugs, missing features bugs and function call bugs are more than others. This study deepens our understanding of bugs in JavaScript engines, and empirical results could indicate some potential problems during the detecting and fixing of bugs in JavaScript engines, assist developers of JavaScript engines in improving their development quality, assist maintainers in detecting and fixing bugs more effectively, and suggest users of JavaScript evade potential risks.
Mediated authenticated differential phase shift quantum key distribution

This paper proposes the first mediated authenticated differential phase shift quantum key distribution (DPS-QKD) protocol. An untrusted third party is introduced to reduce the burden of the sender and help two participants to share a session key. Besides, a pre-shared secret key between two participants is used for mutual authentication and key distribution without assuming the existence of an ideal authenticated classical channel needed in existing DPS-QKD schemes. The security analysis shows that our modified scheme is resistant to the Trojan-horse attack, and the unconditional security proof is presented.
Doubled coupling for image emotion distribution learning

Image emotion prediction has a great impact on wide applications, such as social network analysis, advertising, and human–computer interaction. Recently, image emotion distribution learning (IEDL) has attracted increasing attention as it holds the potential to tackle the challenging emotion ambiguity problem for image emotion prediction. Existing efforts focus more on the emotion distribution learning with the assumption of independently identically distribution. However, we observe that the connections between objects in an image (e.g., butterfly and flower) and the connections between different images (e.g., the images taken in the same place), commonly exist in real-world datasets. Coupling information has been proved greatly helpful for many tasks, and also is crucial for image emotion analysis. Such observations motivate us to explore the above two coupling relations for better IEDL. With this in mind, we propose DoubledIEDL, a novel IEDL approach that consists of two sub-modules for object and image coupling learning, respectively. Specifically, our IEDL relies on a unified framework equipped with densely connected graph convolutional networks (DCGCN) for both coupling learning. The learning of our proposed framework has two stages: static stage and dynamic stage. In the first stage, a static graph is constructed to extract the shallow coupling information with DCGCN. Then, in the second stage, the deep coupling information is further mined via DCGCN on dynamically updated graphs in an iterative manner. The sub-modules for object and image coupling learning share this framework, but differ in the static graph constructing strategy. Extensive experiments on the two public benchmarks, FlickrLDL and TwitterLDL, demonstrate the effectiveness of the proposed DoubledIEDL, yielding significant improvement against previous state-of-the-art models. On FlickrLDL, CoupledIEDL achieves 0.8596 in Cosine and 0.4356 in Kullback–Leibler Divergence (K–L). On TwitterLDL, CoupledIEDL achieves 0.8717 in Cosine and 0.4705 in K–L.
Density Awareness and Neighborhood Attention for LiDAR-Based 3D Object Detection

Light detection and ranging (LiDAR) is widely used in the automotive industry as it can provide point cloud information containing precise distances. Three-dimensional (3D) object detection based on LiDAR point clouds is significant for environment perception tasks. However, feature learning for point clouds remains challenging. This paper proposes a two-stage voxel-based LiDAR 3D object detector, referred to as density-aware and neighborhood attention (DenNet), that focuses on the neighborhood information of objects. DenNet mainly integrates two modules: voxel density-aware (VDA) and neighborhood attention (NA). VDA introduces density information of the point cloud. Here, point cloud density information was added as voxel features in the voxel-based framework to alleviate the information loss during voxelization. Additionally, to extract neighbor information, the characteristics of 3D objects were analyzed for traffic scenes. The NA mechanism was adopted, which localizes the receptive field for each query to its nearest neighboring points. DenNet yielded competitive results, as compared with state-of-the-art methods, for the KITTI and One Million Scenes (ONCE) datasets; notably, it afforded an improvement of 3.96% relative to the baseline mean average precision on the more challenging ONCE dataset.
Automated Context-Aware Vulnerability Risk Management for Patch Prioritization

The information-security landscape continuously evolves by discovering new vulnerabilities daily and sophisticated exploit tools. Vulnerability risk management (VRM) is the most crucial cyber defense to eliminate attack surfaces in IT environments. VRM is a cyclical practice of identifying, classifying, evaluating, and remediating vulnerabilities. The evaluation stage of VRM is neither automated nor cost-effective, as it demands great manual administrative efforts to prioritize the patch. Therefore, there is an urgent need to improve the VRM procedure by automating the entire VRM cycle in the context of a given organization. The authors propose automated context-aware VRM (ACVRM), to address the above challenges. This study defines the criteria to consider in the evaluation stage of ACVRM to prioritize the patching. Moreover, patch prioritization is customized in an organization’s context by allowing the organization to select the vulnerability management mode and weigh the selected criteria. Specifically, this study considers four vulnerability evaluation cases: (i) evaluation criteria are weighted homogeneously; (ii) attack complexity and availability are not considered important criteria; (iii) the security score is the only important criteria considered; and (iv) criteria are weighted based on the organization’s risk appetite. The result verifies the proposed solution’s efficiency compared with the Rudder vulnerability management tool (CVE-plugin). While Rudder produces a ranking independent from the scenario, ACVRM can sort vulnerabilities according to the organization’s criteria and context. Moreover, while Rudder randomly sorts vulnerabilities with the same patch score, ACVRM sorts them according to their age, giving a higher security score to older publicly known vulnerabilities.
A Simulation Study of Risk-Aware Path Planning in Mitigating the Third-Party Risk of a Commercial UAS Operation in an Urban Area

UAS-based commercial services such as urban parcel delivery are expected to grow in the upcoming years and may lead to a large volume of UAS operations in urban areas. These flights may pose safety risks to persons and property on the ground, which are referred to as third-party risks. Path-planning methods have been developed to generate a nominal flight path for each UAS flight that poses relative low third-party risks by passing over less risky areas, e.g., areas with low-density unsheltered populations. However, it is not clear if risk minimization per flight works well in a commercial UAS operation that involves a large number of annual flights in an urban area. Recently, it has been shown that when using shortest flight path planning, a UAS-based parcel delivery service in an urban area can lead to society-critical third-party risk levels. The aim of this paper is to evaluate the mitigating effect of state-of-the-art risk-aware path planning on these society-critical third-party risk levels. To accomplish this, a third-party risk simulation using the shortest paths is extended with a state-of-the-art risk-aware path-planning method, and the societal effects on third-party risk levels have been assessed and compared to those obtained using shortest paths. The results show that state-of-the-art risk-aware path planning can reduce the total number of fatalities in an area, but at the cost of a critical increase in safety risks for persons living in areas that are favored by a state-of-the-art risk-aware path-planning method.
Investigating Prompt Learning for Chinese Few-Shot Text Classification with Pre-Trained Language Models

Text classification aims to assign predefined labels to unlabeled sentences, which tend to struggle in real-world applications when only a few annotated samples are available. Previous works generally focus on using the paradigm of meta-learning to overcome the classification difficulties brought by insufficient data, where a set of auxiliary tasks is given. Accordingly, prompt-based approaches are proposed to deal with the low-resource issue. However, existing prompt-based methods mainly focus on English tasks, which generally apply English pretrained language models that can not directly adapt to Chinese tasks due to structural and grammatical differences. Thus, we propose a prompt-based Chinese text classification framework that uses generated natural language sequences as hints, which can alleviate the classification bottleneck well in low-resource scenarios. In detail, we first design a prompt-based fine-tuning together with a novel pipeline for automating prompt generation in Chinese. Then, we propose a refined strategy for dynamically and selectively incorporating demonstrations into each context. We present a systematic evaluation for analyzing few-shot performance on a wide range of Chinese text classification tasks. Our approach makes few assumptions about task resources and expertise and therefore constitutes a powerful, task-independent approach for few-shot learning.
Efficient Heuristic Replication Techniques for High Data Availability in Cloud

Most social networks allow connections amongst many people based on shared interests.Social networks have to offer shared data like videos, photos with minimum latency to the group, which could be challenging as the storage cost has to be minimized and hence entire data replication is not a solution.The replication of data across a network of read-intensive can potentially lead to increased savings in cost and energy and reduce the end-user's response time.Though simple and adaptive replication strategies exist, the solution is non-deterministic; the replicas of the data need to be optimized to the data usability, performance, and stability of the application systems.To resolve the non-deterministic issue of replication, metaheuristics are applied.In this work, Harmony Search and Tabu Search algorithms are used optimizing the replication process.A novel Harmony-Tabu search is proposed for effective placement and replication of data.Experiments on large datasets show the effectiveness of the proposed technique.It is seen that the bandwidth saving for proposed harmony-Tabu replication performs better in the range of 3.57% to 18.18% for varying number of cloud datacenters when compared to simple replication, Tabu replication and Harmony replication algorithm.
SECDA-TFLite: A toolkit for efficient development of FPGA-based DNN accelerators for edge inference

In this paper we propose SECDA-TFLite, a new open source toolkit for developing DNN hardware accelerators, integrated within the TFLite framework. The toolkit leverages the principles of SECDA, a hardware/software co-design methodology, to reduce the design time of optimized DNN inference accelerators on edge devices with FPGAs. With SECDA-TFLite, we reduce the initial setup costs associated with integrating a new accelerator design within a target DNN framework, allowing developers to focus on the design. SECDA-TFLite also includes modules for cost-effective SystemC simulation, profiling, and AXI-based data communication. As a case study, we use SECDA-TFLite to develop and evaluate three accelerator designs across seven common CNN models and two BERT-based models against an ARM A9 CPU-only baseline, achieving an average performance speedup across models of up to 3.4× for the CNN models and of up to 2.5× for the BERT-based models. Our code is available at https://github.com/gicLAB/SECDA-TFLite . • We propose SECDA-TFLite, a toolkit for efficient development and integration of hardware accelerators for DNN inference on edge devices based on TFLite. • We port two previously hardware accelerators for quantized CNN inference with TFLite. • We develop a new hardware accelerator for quantized BERT inference with TFLIte. • We evaluate the CNN accelerators on five popular CNN models achieving up to 2.5x speedup. • We evaluate the BERT accelerator on MobileBert and Albert models achieving up to 2.9x speedup.
Interpreting the antecedents of a predicted output by capturing the interdependencies among the system features and their evolution over time

Decision support systems (DSS) assist in a wide array of decision-making tasks in different domains. However, one of their common drawbacks is that their working is back box in nature. This means that while they recommend a decision, they cannot explain the 'why' behind reaching that decision. In prescriptive tasks such as risk management, this does not assist the risk manager in identifying the contributing features leading to the occurrence of a risk output against which corrective action/s needs to be taken. This limitation has sparked interest in explainability, where glass-box methods interpret the contributing features leading to the recommended decision. Approaches which do that however do not model how the contributing features' evolve over a period of time, till the predicted time period, to determine the output class before interpreting the reason for the decision output. To address these gaps, in this work we propose An Automated Interpretable Artificial Intelligence framework for Proactive Risk Management (AIAI-PRM). AIAI-PRM augments Local Interpretation-Driven Abstract Bayesian Network (LINDA-BN) with Knowledge Graph to determine the inter dependencies among the features, model how they evolve over a period of time and interpret the contributing features leading to the recommended output. In the domain of risk management, we show how this knowledge can be used by the risk manager to determine those key features against which risk management strategies need to be developed. Finally, we compare the AIAI-PRM's output with that of the most commonly used XAI approaches, namely LIME and SHAP, to prove its superiority.