Reviewerly/exHarmony huggingface dataset:
TBRm: A Time Representation Method for Industrial Knowledge Graph

With the development of the artificial intelligence industry, Knowledge Graph (KG), as a concise and intuitive data presentation form, has received extensive attention and research from both academia and industry in recent years. At the same time, developments in the Internet of Things (IoT) have empowered modern industries to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). Using knowledge graphs (KG) to process data from the Industrial Internet of Things (IIoT) is a research field worthy of attention, but most of the researched knowledge graph technologies are mainly concentrated in the field of static knowledge graphs, which are composed of triples. In fact, many graphs also contain some dynamic information, such as time changes at points and time changes at edges; such knowledge graphs are called Temporal Knowledge Graphs (TKGs). We consider the temporal knowledge graph based on the projection and change of space. In order to combine the temporal information, we propose a new representation of the temporal knowledge graph, namely TBRm, which increases the temporal dimension of the translational distance model and utilizes relational predicates in time add representation in time dimension. We evaluate the proposed method on knowledge graph completion tasks using four benchmark datasets. Experiments demonstrate the effectiveness of TBRm representation in the temporal dimension. At the same time, it is also practiced on a network security data set of the Industrial Internet of Things. The practical results prove that the TBRm method can achieve good performance in terms of the degree of harm to IIoT network security.
A Deep Facial BRDF Estimation Method Based on Image Translation

The reconstruction of photorealistic 3D face geometry, textures and reflectance (BRDF) is one of the most popular fields in computer vision, graphics and machine learning. However, the acquisition of facial reflectance remains a challenge. In this article, we propose a method for estimating the facial reflection properties of a single portrait image based on image translation. From a RGB face image, we obtain the BRDF with a large amount of detail. To achieve it, we perform a reverse engineer, which renders face images with the obtained texture map to form training data pairs based on the Blinn-Phong illumination model. We also apply random rotate-and-crop and sliding-window-crop to augment the data and optimize the network weights by minimizing the generated adversarial loss and reconstruction loss. As demonstrated in a chain of quantitative and qualitative experiments, our method achieves superior performance compared to the state-of-the-art methods.
Improvement of Vulnerable Code Dataset Based on Program Equivalence Transformation

Code vulnerability dataset plays an important role in the development and evaluation of vulnerability detection tools. Aiming at the problem that programs in existing vulnerability datasets usually have simple structures and small scales, which is not satisfying for testing, we proposed a generator of complex code vulnerability dataset based on program equivalence transformation in this paper. It improves the complexity of program structure and code size while preserving the labels of the original case. Based on the PHP vulnerability test suite in Software Assurance Reference Dataset (SARD), a large number of complex cases were constructed and tested using two open-source PHP vulnerability detection tools RIPS and WAP. Experimental results show that the cyclomatic complexity and code size of the generated cases increase by about five times after transformation. The detection accuracy of the tools on the generated dataset decreases significantly compared with the results on the original dataset. The false positive rate increases by about 30%, and the false negative rate increases by about 10%.
Text Independent Writer Identification Based on Pre-training Model and Feature Fusion

Handwriting is one of the natural biological characteristics of human beings. People have a high degree of acceptance of identity recognition technology based on handwriting. It has a wide application prospect in the fields of finance, government affairs, justice and so on. In this paper, firstly, the original document image is segmented into smaller regional samples by using the sliding window method. Secondly, multiple pre-training models are used to extract the sample features, and multiple features of the samples are fused. Finally, the euclidean distance is used to express the degree of difference between samples. Experiments show that this method has a high recognition rate and has a certain application value.
Global Mean Square Error Separation Loss

Class imbalance problem greatly affects the accuracy of deep-learning-based object detectors. To weaken its influence, this paper proposes a global mean square error separation loss (GMSES Loss). GMSES Loss assigns a factor to each example, and then enhances the learning of hard-to-learn examples by reducing the factors of easy-to-learn examples. Meanwhile, the global mean square error separation method is introduced to enhance the learning of foreground classes. To verify the effectiveness of our algorithm, experiments are conducted with YOLOv4 and RetinaNet as the baselines, respectively. The results have shown that our loss function can improve the performance of one-stage object detectors without adding any extra hyperparameters.
A Novel Privacy-Preserving Computing System Based on VAE Federated Meta-Learning

In recent years, the release of data value under privacy-preserving has always been the research focus. As an essential data protection technology, encrypted computing provides a good solution. Based on this, a federated learning model is first constructed to meet the needs of participants using local data for joint modeling. Secondly, the meta-learning method accelerates the federated learning model’s training effect and improves the accuracy of model detection. Finally, the variational auto-encoder is used to optimize the federated learning model, which speeds up the training of the federated learning model and improves the system’s overall security. Experimental results show that the proposed system can provide secure computing conditions for each data provider and better performance.
Sustainable Technique to Reduce Sea Pollution During Boring Operation

Rapid growth of construction projects in sea increases the water pollution in all over the world. It will be mandatory to adopt innovative and sustainable techniques during construction activity. In offshore and marine piling work, the polymers and bentonite are using during boring operation to prevent cave in condition. Bentonite is the highly colloidal suspensions which have excellent suspensive waterproofing and lubricating characteristics. Moreover, polymers are very long molecules mainly made up of plastics, rubbers, thermoplastic elastomers, adhesives, foams, paints, and other sealants. After usage bentonite (VINIMUD P40) muds, before disposal can be treated with oxidizing agents in order to be sure that any trace of active material has been eliminated. The most popular products used for treatment of the vinyl polymer muds, are sodium hypochlorite (bleaching agent) and oxygen peroxide. Disposal of bentonite/polymers after using it into the sea, pollutant the sea environment, which may be harmful for living organisms inside sea. Hence, it is essential to reused and disposed it ecofriendly. A new methodology was adopted to fulfill this requirement. In this approach, cutting in casing/liner in the form of window are used to collect bentonite/polymers in tank. Also, after storage, bentonite/polymers will be reused for next operation. Moreover, mud wastage coming from borehole will also be disposed at suitable dumping places.
A fair-multicluster approach to clustering of categorical data

Abstract In the last few years, the need of preventing classification biases due to race, gender, social status, etc. has increased the interest in designing fair clustering algorithms. The main idea is to ensure that the output of a cluster algorithm is not biased towards or against specific subgroups of the population. There is a growing specialized literature on this topic, dealing with the problem of clustering numerical data bases. Nevertheless, to our knowledge, there are no previous papers devoted to the problem of fair clustering of pure categorical attributes. In this paper, we show that the Multicluster methodology proposed by Santos and Heras (Interdiscip J Inf Knowl Manag 15:227–246, 2020. https://doi.org/10.28945/4643 ) for clustering categorical data, can be modified in order to increase the fairness of the clusters. Of course, there is a trade-off between fairness and efficiency, so that an increase in the fairness objective usually leads to a loss of classification efficiency. Yet it is possible to reach a reasonable compromise between these goals, since the methodology proposed by Santos and Heras (2020) can be easily adapted in order to get homogeneous and fair clusters.
Measuring Bias and Fairness in Multiclass Classification

Algorithmic bias is of increasing concern, both to the research community, and society at large. Bias in AI is more abstract and unintuitive than traditional forms of discrimination and can be more difficult to detect and mitigate. A clear gap exists in the current literature on evaluating the relative bias in the performance of multi-class classifiers. In this work, we propose two simple yet effective metrics, Combined Error Variance (CEV) and Symmetric Distance Error (SDE), to quantitatively evaluate the class-wise bias of two models in comparison to one another. By evaluating the performance of these new metrics and by demonstrating their practical application, we show that they can be used to measure fairness as well as bias. These demonstrations show that our metrics can address specific needs for measuring bias in multi-class classification. Demonstration code is available at https://github.com/gentry-atkinson/CEV_SDE_demo.git.
A Distributed Architecture for Cooperative Deep Learning System in Intelligent Vehicle Systems

The recent proposed deep learning system is a promising technology to extract essential information from vision based high dimension sensors. The current intelligent vehicle systems heavily rely on vision sensors to be aware of the environment. Even though a single vehicle may have several vision sensors installed, view and range restrictions may result in information loss, leading to incorrect autopilot system decisions. To ease this limitation, a cooperative deep learning architecture for intelligent vehicle systems has been proposed in this paper. In this architecture, the observed raw data from vehicles will be processed by a partitioned deep learning system with convolution layers deployed in geographically distributed computation units. With the proposed cooperative deep learning architecture, numerous observations of a single object from various views can be utilized to increase detection accuracy. As to be expected, almost all of the currently available intelligent vehicle systems have been designed in the context of a single entity deep learning system. This means that in order to deploy a cooperative deep learning system, which has been discussed in this paper, various aspects such as computation, communication, and storage may need to be modified. A simple but representative experiment has been deployed to demonstrate the feasibility of the proposed system.
SCAIL: Encrypted Deduplication With Segment Chunks and Index Locality

Encrypted deduplicated storage systems face multiple challenges: long-term backup workloads with metadata storage approaching the size of the deduplicated data itself and the disk bottleneck problem caused by fingerprint indexes that will not fit in memory. Resource contention issues may arise when backup loads at peak-time force deduplication of many concurrent backup streams. We propose a system that performs both client and server deduplication. The client forms segments from groups of chunks, enabling coarse-grained client-side deduplication. Deduplicating segment-sized chunks reduces server memory requirements, metadata upload and storage volume but can cause the upload of some previously uploaded data chunks. For longer-term workloads, this redundant upload volume can be offset by the reduced metadata upload volume. On the server, chunk-level deduplication is performed by merge-sorting the fingerprints of multiple clients in a single pass. The server-side deduplication produces high levels of duplicate elimination, and we use index locality to reduce resource contention between concurrent backup streams and significantly reduce disk I/O for indexing. Experiments with two real-world backup datasets using an 8KiB chunk size and 512KiB-4MiB segment sizes reduced server memory requirements by up to 81.6% and metadata storage volume by up to 97.2%. Client to server upload volume ranged from a reduction of 23.9% to an increase of 13.3%. The use of index locality deduplication reduced index I/Os for 2MiB segments to less than 350 per backup generation.
Accelerating the Energy Efficient Design of Traditional Data Centers Through Modeling

Power Management Strategies and the impact of carbon dioxide emission from the data centers across the globe have drawn significant attention worldwide. The rapid growth of energy consumed in data centers has lead to 1) huge costs 2) depletion of non-renewable resources such as coal and petroleum, and 3) emission of greenhouse gases like CO2 in the atmosphere. These greenhouse gas significantly contributes to the climate change of the earth. To tackle this challenge, our research deals with modeling the energy resources of data centers, thereby offering insights to reduce global carbon footprint and energy cost. In our model, we prioritize green energy consumption by eliminating the brown energy resources. In this process, we devise an algorithm that can determine the amount of CO2 emission in the atmosphere per hour by different energy resources. We create an energy model for data centers by incorporating the support vector regression algorithm. Our model is adroit at projecting energy consumed in data centers powered by green energy. Our experimental results confirm that our model consistently delivers high prediction accuracy in terms of energy usage in data centers. The model is expected to facilitate data analytic venues to optimize energy efficiency and sustainability for the development of future data centers.
Design of a Live Networking Device Update Mechanism For Cloud Computing Systems

With the rapid deployment of cloud services and the transformation of traditional on-prem services, e.g., infrastructure, platform, and security, to cloud platforms, the demand for High Availability for cloud services is essential. The growth of compute services through cloud platforms can no longer be met via standard resource provisioning techniques, which includes resources such as compute devices, network devices, etc. Device virtualization is key for cloud resource allocation. Even with the continued adoption of workload containerization, the requirement of domain isolation and the flexibility of service provisioning, containerization within a Virtual Machine is the preferred cloud deployment model. In this paper, we present the design of a live network device update mechanism, e.g., driver and firmware, through a host managed device emulation interface for services deployed on a cloud compute platform. This design does not require scheduled shutdowns of the cloud server, nor does it require hardware redundancy for a system administrator to perform driver and/or firmware maintenance updates. The innovative design presented in this paper enables dynamic resource sharing based upon the service requirements, e.g., failover switching time and network throughput, between the target devices to be updated along with other network devices either in service or on standby. A live networking device mechanism reduces compute service interruption and provides a transparent network function transition between the target interfaces and the failover interfaces.
Advanced Driver Assistance System for Autonomous Vehicles Using Deep Neural Network

Connected vehicle technology has been on demand in recent years for improving the efficiency and safety of vehicles. Advancements in connectivity and computation will enable vehicles to be used as development platforms which can generate big data and perform feed forward computations, thereby enhancing the growth of transportation and economy. In this paper we propose an Internet of Things (IoT) enabled system that is capable of real time tracking of various parameters of a vehicle which includes vehicle's speed, RPM (Revolutions Per Minute), battery level and fuel level that can help in tracking of vehicle's health. An on-board camera has also been included in the proposed system that captures multimedia in real time and analyses the object detected. This camera based Advanced Driver Assistance System (ADAS) can help in avoiding accidents up to a greater extent. ADAS in this work uses DetectNet Deep Neural Network (DNN) for detecting the objects. We implement and analyze the performance of an ADAS system on an Nvidia Jetson Nano Developer Kit and the results are reported.
Increasing Robustness against Adversarial Attacks through Ensemble of Approximate Multipliers

Over the past few years, deep neural networks (DNNs) have been used to solve a wide range of real-life problems. However, DNNs are vulnerable to adversarial attacks where carefully crafted input perturbations can mislead a well-trained DNN to produce false results. As DNNs are being deployed into security-sensitive applications such as autonomous driving, adversarial attacks may lead to catastrophic consequences. In this work, we propose ensemble of approximate multipliers (EAM) where DNNs with different approximate multipliers are used as a new approach to boost robustness against adversarial attacks. A DNN equipped with an approximate multiplier is an effective method to enhance resiliency of DNNs. However, the degree of robustness in a DNN varies with the type of approximate multiplier. Depending on the level of approximation, the accuracy of the DNN varies for different adversarial attacks. We exploit this variability and propose mixing DNNs with different types of approximate multipliers. Our proposed technique does not require changing the architecture of a model nor memory hierarchy. We only use additional approximate units within a multiplier. We evaluate EAM across different DNNs and under a variety of adversarial attacks. Our evaluations reveal that EAM increases robustness by a large margin compared to an exact model while maintaining accuracy on benign inputs.
Deep Vision-Data Mining To Find Insights and Visualization in Code Repositories

Deep Vision is a code mining system for analyzing and visualizing a repository's codebase so that its users may obtain a sense of the repository's insights. This system will examine codebases and support as many languages as feasible. This system visualizes the file structure, vocabulary and length change rates, comprehensibility and defect rates, etc. It is vital to have a comprehensive grasp of the codebase to manage the program's complexity by calculating multiple factors and presenting them in a descriptive and engaging dashboard to enhance the quality of the software process and the project's controllability. Improved code visualization may help improve code understandability while lowering development costs. In addition, our visualization regions and methodologies are one-of-a-kinds. To get rapid and reliable results, we will create new machine learning models and algorithms for analysis and new categories of a code repository. Our dataset for this research will be GitHub open-source code repositories.
Facial Expression Recognition with Geometric Scattering on 3D Point Clouds

As one of the pioneering data representations, the point cloud has shown its straightforward capacity to depict fine geometry in many applications, including computer graphics, molecular structurology, modern sensing signal processing, and more. However, unlike computer graphs obtained with auxiliary regularization techniques or from syntheses, raw sensor/scanner (metric) data often contain natural random noise caused by multiple extrinsic factors, especially in the case of high-speed imaging scenarios. On the other hand, grid-like imaging techniques (e.g., RGB images or video frames) tend to entangle interesting aspects with environmental variations such as pose/illuminations with Euclidean sampling/processing pipelines. As one such typical problem, 3D Facial Expression Recognition (3D FER) has been developed into a new stage, with remaining difficulties involving the implementation of efficient feature abstraction methods for high dimensional observations and of stabilizing methods to obtain adequate robustness in cases of random exterior variations. In this paper, a localized and smoothed overlapping kernel is proposed to extract discriminative inherent geometric features. By association between the induced deformation stability and certain types of exterior perturbations through manifold scattering transform, we provide a novel framework that directly consumes point cloud coordinates for FER while requiring no predefined meshes or other features/signals. As a result, our compact framework achieves 78.33% accuracy on the Bosphorus dataset for expression recognition challenge and 77.55% on 3D-BUFE.
Knowledge Sharing for Pulmonary Nodule Detection in Medical Cyber-Physical Systems

With the rapid development of edge intelligence (EI) and machine learning (ML), the applications of Cyber-Physical Systems (CPS) have been discovered in all aspects of the life world. As one of its most essential branches, Medical CPS (MCPS) determines human health and medical treatment in the Internet of Everything (IOE) era. Knowledge sharing is the critical point of MCPS and has also been humanity's best dream through the ages. This paper explores a novel knowledge-sharing model in MCPS and takes a pulmonary nodule detection task as a significant case for building an Unet-based mask generator. A Classification-guided Module (CGM)-based discriminator with knowledge from EMRs is set against a generator to offer a promising result for each mask from the inexperienced participant of federated ML. After an iterative communication between the federated server and its clients for knowledge sharing, the segmented sub-image owns a coincident attribute distribution with that of the EMRs from the experts. Besides, the adversarial network augment the data to normalize the data distribution for all the clients as a remission for none independent identically distributed (non-IID) data problem. We implement a detection framework on the simulated EI environment following an existing adaptive synchronization strategy based on data sharing and median loss function. On 1304 scans of the merged dataset, our proposed framework can help boost the detection performance for most of the existing methods of pulmonary nodule detection.