Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of different additive manufacturing technologies for MIDs in the context of smart sensor systems for retrofit applications

Industry 4.0 describes a concept where machines and systems are smart and interconnected. Production processes can be monitored, optimized and the failure of individual components can be predicted. While it may be possible for big companies to plan a new, smart and interconnected production facility on the “greenfield”, this is hardly realistic for small and medium-sized enterprises. Due to the long deployment duration of production facilities, alternative approaches must be identified to upgrade the existing machines and systems with sensor technology, software and IT connectivity. This process is called retrofitting. Nevertheless, no universal solution exists on the market that meets the requirements for all use-cases. Moreover, small and medium sized enterprises in general do not have the abilities and the knowhow to develop and manufacture these complex and tailored solutions in small lot sizes, which is an essential requirement for retrofitting. Standard shaped sensors cannot be used due to the individual installation situation in the machines. 3D Mechatronic Integrated Devices (MID), a generic term for distinctive spatial integration technologies breaks through the known limitations of printed circuit boards and enables the development of new highly innovative products. The most common form to produce MIDs are injection molding based. While this production technology is state of the art for high quantities, it is too expensive, time-consuming and complex for smaller batch sizes. Additive manufacturing technologies (AM) provide an attractive approach to this problem. AM allows a faster reaction to product changes, is more cost efficient in small quantities and allows a higher degree of freedom in product design. The combination of MIDs and AM not only fulfils the requirements regarding costs and flexibility but also enables the creation of new, highly individual and electrically functionalized products in general. In the context of this paper, three additive technologies are evaluated with respect to their applicability against the background of different retrofitting applications. A focus lies on the creation of 3D shaped circuit carriers. Based on different use cases in the context of retrofitting applications the benefits as well as the restrictions are pointed out and their utilization is evaluated.
Unified Attribute-Based Encryption Scheme for Industrial Internet of Things

The Internet of Things (IoT) provides significant benefits for industry due to connect the devices together through the internet. Attribute-Based Encryption (ABE) is a technique can enforce an access control over data to guarantee the data security. In this paper, we propose an ABE scheme for data in industrial IoT. The scheme achieves both security and high performance. When there is a shared subpolicy among the access policies of a sensor, the scheme optimizes the encryption of the messages. Through analysis and simulation, we show that our solution is security and efficient.
A Practical Method to Cover Evenly a Dynamic Region With a Swarm

Many applications require exploring or monitoring a region. This can be achieved by a sensor network, a large team of robots which can cover only a very small fraction each. When the region is convex, small, and static, it suffices to deploy the robots as a Centroidal Voronoi Tessellation (CVT). Instead, we consider that the area to cover is wide, not necessarily convex, and complex. Then, a smaller simple region is maneuvered and deformed to rake the full area. A few waypoints describing the region along time are provided to the robots. The goal is that the robots coordinate to dynamically deploy over this region evenly, near a CVT. Unfortunately, the distributed CVT computation algorithm converges too slowly for such exploration method to be practical. In this work, CVT computation is complemented with feedback and feedforward based control techniques, and dynamic consensus, to adjust the robot speeds so that they coordinate to cover the dynamic region. We demonstrate in simulation that the proposed method succeeds to achieve the goal of tracking the region, with the robots evenly deployed, while keeping the connectivity and avoiding collisions. We also compare the performance of the proposed method versus other alternatives.
Load Balance Awared Data Sharing Systems In Heterogeneous Edge Environment

Edge computing has become the de facto method for delay-sensitive applications, in which the computation and storage resources are placed at the edge of network. The main responsibility of edge computing is to carry data from the Cloud downlinks and terminal uplinks, and organize these data well on the edge side. This is the basis for subsequent analysis and processing of data. Therefore, this brings about a question as to how those data should be organized on the edge and how to store and retrieve them. In response to this demand, some methods have been proposed to solve the related problems of how to build data storage and retrieval services on the edge side. Those methods propose three different solutions: structured, unstructured, and hybrid schemes. However, the data storage and retrieval services for the heterogeneous edge environment is still lack of research. It is still not considered an important design test load balancing when the data is stored on the edge side. In this paper, we design and implement w-strategy, a load balance approach that implements the appropriate load balance among the heterogeneous edge nodes by using the weighted Voronoi diagram. Our solution utilizes the software defined networking paradigm to support a virtual-space based distributed hash tables (DHTs) to distribute data. Evaluation results show that wstrategy achieves better load balancing among the heterogeneous edge nodes compared to the existing methods, GRED and Chord. And, the w-strategy improves the average underutilization of the resources by 20%.
WANHEDA: A Machine Learning Based DDoS Detection System

In today's world computer communication is used almost everywhere and majority of them are connected to the world's largest network, the Internet. There is danger in using internet due to numerous cyber-attacks which are designed to attack Confidentiality, Integrity and Availability of systems connected to the internet. One of the most prominent threats to computer networking is Distributed Denial of Service (DDoS) Attack. They are designed to attack availability of the systems. Many users and ISPs are targeted and affected regularly by these attacks. Even though new protection technologies are continuously proposed, this immense threat continues to grow rapidly. Most of the DDoS attacks are undetectable because they act as legitimate traffic. This situation can be partially overcome by using Intrusion Detection Systems (IDSs). There are advanced attacks where there is no proper documented way to detect. In this paper authors present a Machine Learning (ML) based DDoS detection mechanism with improved accuracy and low false positive rates. The proposed approach gives inductions based on signatures previously extracted from samples of network traffic. Authors perform the experiments using four distinct benchmark datasets, four machine learning algorithms to address four of the most harmful DDoS attack vectors. Authors achieved maximum accuracy and compared the results with other applicable machine learning algorithms.
Design of Convolution Operation Accelerator based on FPGA

Because of the high complexity and long time of CPU in convolution operation, the power consumption of GPU is high and it can not be deployed to small hardware devices. Field Programmable Logic Gate Array (FPGA) has parallelism and fast speed , Low power consumption and etc. Therefore, this paper proposes an improved RLeNet model, which optimizes model parameters and used a parallel technology to accelerate CNN hardware. The improved model parameter data volume is reduced to 3.2% of the original. Under the MNIST data set, the accuracy of the prediction result is 98.5%. Finally, on the Xilinx Nexys 4 DDR: Artix-7 development board, the prediction process of the convolutional neural network for handwriting recognition is implemented. When the system predicts with a 200MHz clock, the time to predict a picture is 31. 8μs.
Knowledge Graph Completeness: A Systematic Literature Review

The quality of a Knowledge Graph (also known as Linked Data) is an important aspect to indicate its fitness for use in an application. Several quality dimensions are identified, such as accuracy, completeness, timeliness, provenance, and accessibility, which are used to assess the quality. While many prior studies offer a landscape view of data quality dimensions, here we focus on presenting a systematic literature review for assessing the completeness of Knowledge Graph. We gather existing approaches from the literature and analyze them qualitatively and quantitatively. In particular, we unify and formalize commonly used terminologies across 56 articles related to the completeness dimension of data quality and provide a comprehensive list of methodologies and metrics used to evaluate the different types of completeness. We identify seven types of completeness, including three types that were not previously identified in previous surveys. We also analyze nine different tools capable of assessing Knowledge Graph completeness. The aim of this Systematic Literature Review is to provide researchers and data curators a comprehensive and deeper understanding of existing works on completeness and its properties, thereby encouraging further experimentation and development of new approaches focused on completeness as a data quality dimension of Knowledge Graph.
A Review of Text-Based Recommendation Systems

Many websites over the Internet are producing a variety of textual data; such as news, research articles, ebooks, personal blogs, and user reviews. In these websites, the textual data is so large that the process of finding pertinent information by a user often becomes cumbersome. To overcome this issue, “Text-based Recommendation Systems (RS)” are being developed. They are the systems with the capability to find the relevant information in a minimal time using text as the primary feature. There exist several techniques to build and evaluate such systems. And though a good number of surveys compile the general attributes of recommendation systems, there is still a lack of comprehensive literature review about the text-based recommendation systems. In this paper, we present a review of the latest studies on text-based RS. We have conducted this survey by collecting literature from preeminent digital repositories, that was published during the period 2010-2020. This survey mainly covers the four major aspects of the textual based recommendation systems used in the reviewed literature. The aspects are datasets, feature extraction techniques, computational approaches, and evaluation metrics. As benchmark datasets carry a vital role in any research, publicly available datasets are extensively reviewed in this paper. Moreover, for text-based RS many proprietary datasets are also used, which are not available in the public. But we have consolidated all the attributes of these publically available and proprietary datasets to familiarize these attributes to new researchers. Furthermore, the feature extraction methods from the text are briefed and their usage in the construction of text-based RS are discussed. Later, various computational approaches that use these features are also discussed. To evaluate these systems, some evaluation metrics are adopted. We have presented an overview of these evaluation metrics and diagramed them according to their popularity. The survey concludes that Word Embedding is the widely used feature selection technique in the latest research. The survey also deduces that hybridization of text features with other features enhance the recommendation accuracy. The study highlights the fact that most of the work is on English textual data, and News recommendation is the most popular domain.
LTG-LSM: The Optimal Structure in LSM-tree Combined with Reading Hotness

A growing number of KV storage systems have adopted the Log-Structured-Merge-tree (LSM-tree) due to its excellent write performance. However, the high write amplification in the LSM-tree has always been a difficult problem to solve. The reason is that the design of traditional LSM-tree under-utilizes the data distribution of query, and the design space does not take into account the read and write performance concurrently. As a result, we may sacrifice one to improve another performance. When advancing the writing performance of the LSM-tree, we can only conservatively select the design pattern in the design space to reduce the impact on reading throughputs, resulting in limited improvement. Aiming at the shortcomings of existing methods, a new LSM-tree structure (Leveling-Tiering-Grouped-LSM-tree, LTG-LSM) is proposed by us that combined with reading hotness. The LTG-LSM structure maintains hotness prediction models at each level of the LSM-tree. The structure of the newly generated disk components is determined by the predicted hotness. Finally, a specific compaction algorithm is carried out to handle the compaction between the different structural components and processing workflow hotness changes. Experiments show that the scheme proposed by this paper significantly reduces the write amplification (up to about 71%) of the original LSM-tree with almost no sacrificing reading performance and improves the write throughputs (up to about 24%) in workflows with different configurations.
Malware Classification Through Attention Residual Network based Visualization

With the exponential growth of malware variants nowadays, malware detection poses to be an active field of research related to computer security. Traditional methods of malware detection and classification such as static program analysis and dynamic execution analysis, usually combined with machine learning, are restricted due to difficulties of reverse-engineering the program executables, real-time execution trace collection, and manual construction of effective feature sets. Malware classification based on representation of the binary executables as images, followed by advanced machine learning techniques such as deep learning, has been explored to overcome these shortcomings. In this work, we propose a malware classification technique based on malware visualization using an Attention Residual Network (a specialized convolutional neural network), with RGB and grayscale image representations of the malware program binaries. Experimental results for two common malware datasets establish the effectiveness of the proposed neural network in malware classification, even when trained with imbalanced datasets.
Study on Technologies of Overhead Line Recognition and Obstacle Distance Measurement by Patrol Robots Based on Deep Learning

At present, the power inspection work is mainly carried out manually, and there are problems such as low work efficiency, heavy workload, and insecurity. Based on this, people have studied a large number of inspection robot methods based on computer vision to achieve inspection work. The more important part is line identification and obstacle distance measurement. The current research on wire recognition problems mainly have problems such as insufficient research, high application conditions, and low robustness. Therefore, this paper designs a deep learning-based route, obstacle detection, and ranging model, which can be well applied to a variety of Complex environment; by constructing the overhead line data set, performing pixel-level edge labeling, processing the labeling area through Gaussian heat mapping, and sending it to the neural network for pixel classification, you can get a good line recognition effect, and then you can identify obstacles. The position and obstacle type are convenient for the robot to cross obstacles. Experiments show that the method proposed in this paper is highly robust and has extremely high value in the application scenarios of inspection robots.
Lightweight Mobile Devices Indoor Location Based on Image Database

Among the numerous indoor localization technologies, image-based solution has great advantages on convenient access from smartphone and its infrastructure-less deployment. However, the image-based localization also suffers from two key disadvantages, which hinders the universal application. Firstly, it requires a large amount of computing and storage resources, which is difficult to achieve for the mobile device, while cloud-based scheme incurs unacceptable delay. Secondly, although this solution doesn't require infrastructure, it still suffers from labor intensive image-database construction and updates. To overcome these limitations, we propose an image-based indoor localization method featured with realtime localization and labor-less image database update. This method mainly innovates in two aspects. First, we propose a mobile device compatible image database compressing framework, which enable realtime and accurate on-device image searching even in a large scenario. Our localization method achieves resource efficiency (in terms of storage and processing) by only keeping image feature vectors, and employing the efficient k-mean tree to search for the best matched image. Secondly, to achieve labor-less image database updating, we mainly add high-quality and informative query image into the database. These query image could compensate the missing information or changed scenario in a up-to-date manner. We conduct real experiments in Android Platform to verify the feasibility and performance of the localization method. Experiment results show that our method has good accuracy (90% location errors are within 1.5m) and high real-time performance (average location delay is less than 0.5s).
Snapshot Compressive Imaging: Theory, Algorithms, and Applications

Capturing high-dimensional (HD) data is a long-term challenge in signal processing and related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (≥3D) data in a snapshot measurement. Via novel optical designs, the 2D detector samples the HD data in a compressive manner; following this, algorithms are employed to reconstruct the desired HD data cube. SCI has been used in hyperspectral imaging, video, holography, tomography, focal depth imaging, polarization imaging, microscopy, and so on. Although the hardware has been investigated for more than a decade, the theoretical guarantees have only recently been derived. Inspired by deep learning, various deep neural networks have also been developed to reconstruct the HD data cube in spectral SCI and video SCI. This article reviews recent advances in SCI hardware, theory, and algorithms, including both optimizationbased and deep learning-based algorithms. Diverse applications and the outlook for SCI are also discussed.
Solvothermal synthesis of carbon nitrogen nanotubes and nanofibers

Carbon nitrogen nanotubes and nanofibers with controlled nitrogen concentration have been prepared by reaction of cyanuric chloride and hexachlorobenzene (HCB) with sodium metal at 250 °C in cyclohexane. Electron microscopy and spectroscopic analysis were used to characterize the products. The total yields of the tubes and fibers decrease as the ratio of cyanuric chloride to HCB increases, and nitrogen content in the products could be controlled by the ratio. The nanostructures depended strongly on the nitrogen content. Lower nitrogen content was favorable for producing linear products.
Automation of Noise Detection Using Internet of Things

With a significant increase in population in modern cities, the noise pollution also increases at an unprecedented rate. This system presents a low cost flexible and reliable automation system with personal alert messages using Arduino along with internet connectivity and remote control over devices by using a smart phone application. The proposed system uses Internet of Things [IoT] model to detect human voices present in an enclosed area. The user can also receive the alert messages with the help of a smart phone. This research work presents the automation of noise detection system using Arduino Nano, battery, LCD panel and buttons. C programming language has been used to program the functions of this application. This application can be implemented by using a tool called Pick2kit and PROTEUS. The proposed noise detector system can be used in library, office and classroom environments to identify the noisy people so that a necessary action can be taken against them.
DGA Domain Detection using Deep Learning

Domain generation algorithms (DGAs) are used by attackers to generate a large number of pseudo-random domain names to connect to malicious command and control servers $(C\&Cs)$. These domain names are used to evade domain based security detection and mitigation controls. Reverse engineering of malware samples to discover the DGA algorithm and seed to generate the list of domains is one of the techniques used to detect DGA domains. These domains are subsequently preregistered and sinkholed, or published on security device blacklists to mitigate malicious activity. This technique is time-consuming and can be easily circumvented by attackers and malware authors. Statistical analysis is also used to identify DGA domains over a time window, however many of these techniques need contextual information which is not easily or feasibly obtained. Existing studies have also demonstrated the use of traditional machine learning techniques to detect DGA domains. Our goal was to detect DGA domains on a per domain basis using the domain name only, with no additional information. This paper presents a DGA classifier that leverages a recurrent neural network (RNN) based architecture for the detection of DGA domains without the need for contextual information or manually created features. We compared the performance of different RNN based architectures by evaluating them against a dataset of 2 million plus domain names. The results indicated little difference in performance metrics among the RNN architectures.
IoT Based Sign Language Recognition System

Sign language is the key communication medium, which deaf and mute people use in their day - to-day life. Talking to disabled people will cause a difficult situation since a non-mute person cannot understand their hand gestures and in many instances, mute people are hearing impaired. Same as Sinhala, Tamil, English, or any other language, sign language also tend to have differences according to the region. This paper is an attempt to assist deaf and mute people to develop an effective communication mechanism with non-mute people. The end product of this project is a combination of a mobile application that can translate the sign language into digital voice and loT-enabled, light-weighted wearable glove, which capable of recognizing twenty-six English alphabet, digits, and words. Better user experience provides with voice-to-text feature in mobile application to reduce the communication gap within mute and non-mute communities. Research findings and results from the current system visualize the output of the product can be optimized up to 25 % –35 % with an enhanced pattern recognition mechanism.