Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of sewage and design of sewagetreatment plant

The National Institute of Technology Rourkela is one of the most important educational institutes in the state of Orissa with a large number of people residing in its campus consisting of a number of laboratories of various departments, residential units, academic blocks and number of hostels. A study on domestic waste water characterization has been performed followed by the design of sewage treatment plant. The present study involves the analysis of pH value, total solids, total suspended solids,hardness, acidity, alkalinity, chloride, chlorine, BOD, DO and heavy metals such as Iron, Copper, Zinc, Magnesium, Nickel, Chromium, Lead, Calcium, Aluminium, Silicon, Potassium.A sewage treatment plant is quite necessary to receive the domestic and commercial waste and removes the materials which pose harm for general public. Its objective is to produce an environmentally-safe fluid waste stream (or treated effluent) and a solid waste (or treated sludge) suitable for disposal or reuse (usually as farm fertilizer). The samplings of the domestic waste from hostels have been done in different times of the day to have an average data of the measured parameters. The average values of pH, Turbidity, Acidity, Chloride,Residual Chlorine, Hardness, Total Solid, BOD, DO, Alkalinity, Total Iron Content, Zinc Content, Potassium, Copper, Magnesium, Nickel,Chromium, Lead, Calcium, Aluminum and Silicon are found out.A sewage treatment plant has been designed with the treatment units, a bar screen of dimension 1.7m, an aeration tank of dimension 4.5 x 4.5 x 3.7 m3,a collection pit of diameter 4m and depth 5 m.
Deposition of long-range transported particulate matter on the needle surfaces of Japanese cypress (Chamaecyparis obtusa) grown in Nagasaki located in the western region of Japan

To characterize the deposition of long-range transported particulate matter (PM) on the foliar surface of Japanese forest trees, we periodically collected the PM deposited on the needle surface of mature Japanese cypress (Chamaecyparis obtusa) grown in the mountainous area of Nagasaki located in the western region of Japan from 24 April to 30 October, 2017. Metal element compositions and concentration ratios along with the ratios of Pb isotopes in the PM were analyzed. The total amount of metal elements (Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, As and Pb) on the needle surfaces was relatively low during summer and autumn but was high during the spring when there was high atmospheric concentration of PM with diameter less than 2.5 μm due to influence of outflow from Asian continent. The seasonal variations in the amounts of less-abundant metal elements (Al, V, Ni, Cu, Zn, As and Pb) exhibited similar trends. The Pb amount varied with Zn amount at a constant ratio of 0.4, which was close to the Pb /Zn ratio of PM in China. Most of the Pb isotope ratios (Pb / Pb and Pb / Pb) in the PM were close to those observed in the Chinese coal. Therefore, Pb on the needle could have originated from the coal combustion in China. The enrichment factor (EF) of Pb ranged from 650 to 2270, and was significantly correlated with the Pb / Pb ratios, suggesting that components having EFs of greater than 650 could have originated from anthropogenic source. The amount of Pb significantly correlated with that of Ni, Cu, Zn, and As, which showed EFs more than 650. These results indicate that the long-range transported PM, including Pb, Ni, Cu, Zn, and As, originated from the anthropogenic sources in China, and deposited on the needle of C. obtusa grown in Nagasaki.
The data ambition matrix

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Social, individual and technological issues for groupware calendar systems

Designing and deploying groupware is difficult. Groupwareevaluation and design are often approached from a singleperspective, with a technologically-, individually-, orsocially-centered focus. A study of Groupware Calendar Systems(GCSs) highlights the need for a synthesis of these multipleperspectives to fully understand the adoption challenges thesesystems face. First, GCSs often replace existing calendarartifacts, which can impact users calendaring habits and in turninfluence technology adoption decisions. Second, electroniccalendars have the potential to easily share contextualizedinformation publicly over the computer network, creatingopportunities for peer judgment about time allocation and raisingconcerns about privacy regulation. However, this situation may alsosupport coordination by allowing others to make useful inferencesabout ones schedule. Third, the technology and the socialenvironment are in a reciprocal, co-evolutionary relationship: theuse context is affected by the constraints and affordances of thetechnology, and the technology also co-adapts to the environment inimportant ways. Finally, GCSs, despite being below the horizon ofeveryday notice, can affect the nature of temporal coordinationbeyond the expected meeting scheduling practice.
This paper is included in the Proceedings of the 31st USENIX Security Symposium

In recent years, microarchitectural attacks have been demonstrated to be a powerful attack class. However, as our empirical analysis shows, there are numerous implementation challenges that hinder discovery and subsequent mitigation of these vulnerabilities. In this paper, we examine the attack development process, the features and usability of existing tools, and the real-world challenges faced by practitioners. We propose a novel approach to microarchitectural attack development, based on rapid prototyping , and present two open-source software frameworks, libtea and SCFirefox , that improve upon state-of-the-art tooling to facilitate rapid prototyping of attacks. libtea demonstrates that native code attacks can be abstracted sufﬁciently to permit cross-platform implementations while retaining ﬁne-grained control of microarchitectural behavior. We evaluate its effectiveness by developing proof-of-concept Foreshadow and LVI attacks. Our LVI prototype runs on x86-64 and ARMv8-A, and is the ﬁrst public demonstration of LVI on ARM. SCFirefox is the ﬁrst tool for browser-based microarchitectural attack development, providing the functionality of libtea in JavaScript. This functionality can then be used to iteratively port a prototype to unmodiﬁed browsers. We demonstrate this process by prototyping the ﬁrst browser-based ZombieLoad attack and deriving a vanilla JavaScript and WebAssembly PoC running in an unmodiﬁed recent version of Firefox. We discuss how libtea and SCFire-fox contribute to the security landscape by providing attack researchers and defenders with frameworks to prototype attacks and assess their feasibility.
Solar UV dosimetry

[Abstract]: UV sensitive polymer dosimeters are important tools in research to optimize UV exposures to humans and to reduce the impacts of UV on agricultural production. The erythemal exposures to humans and the effectiveness of UV protective strategies have been measured with polysulphone dosimeters. Polysulphone dosimeters have been employed with appropriate calibration to measure the previtamin D3 effective UV and UVB exposures to plant leaves. A dosimeter based on polyphenylene oxide has been found to have a dynamic range that is approximately four times longer than that of polysulphone and a dosimeter that is sensitive to the visible waveband has been developed to measure photosynthetically active radiation.
STAN: A Spatio-Temporal Attention Network for Traffic Forecasting

Traffic prediction plays a vital role in intelligent transportation systems, such as flow control and route planning. As a classical spatial-temporal prediction task, timely accurate traffic prediction is challenging due to complicated spatial-temporal correlation in traffic data. Traffic data contains short-term and long-term temporal sequential correlations, as well as local and global spatial correlations. In addition, traffic patterns at different time steps and the distribution of traffic data at various nodes may differ, so models need to have the ability to capture these temporal and spatial heterogeneities. To solve these issues, we propose a novel traffic prediction model, called Spatio-Temporal Attention Network (STAN). It models short-term and long-term temporal correlations using Gate Recurrent Unit (GRU) and Heterogeneity-Aware Temporal Self-Attention (HA-TSA) mechanism, respectively. It further models local and global spatial correlations using Graph Attention Network (GAT) and Heterogeneity-Aware Spatial Self-Attention (HA-SSA) mechanism, respectively. Based on traditional self-attention mechanism, HA-TSA and HA-SSA further learn different parameters for different nodes and different time steps to capture temporal and spatial heterogeneities. Besides, in order to extract the periodicity of traffic data, time stamps are concatenated to the original traffic features as auxiliary information. STAN also adds a centrality encoding based on node’s degree to measure the importance of a node in the traffic network, thus helping to discover similar traffic patterns between nodes. Experimental results on two benchmark traffic datasets show that compared with the state-of-the-art baseline method, Mean Absolute Errors (MAEs) of STAN decrease by 7.48% and 7.77%, and Root Mean Squared Errors (RMSEs) of STAN decrease by 1.89% and 5.19%, respectively.
Identifying Influential Communicators based on Correlation and Minimum Distance in Social Media Data Streams

Twitter has become a crucial social networking platform for users to receive and exchange information about current events. Identifying the most influential users in social networks is pivotal for the efficient propagation of information. However, traditional influencer identification models often fall short of assessing users' influence comprehensively and neglect overlapping influence, which greatly restrains their efficiency and accuracy in identifying key users. To address such issues, we propose a method for identifying key users based on correlation and minimum distance (CM-UDM). This method can evaluate users' influence based on their individual, global, local, and propagation path characteristics in social networks. Additionally, we propose a key user decision graph in order to address the problem of overlapping users' influence. We have conducted experiments using one classical dataset and three real Twitter datasets, where the performance of our proposed method in identifying highly influential users is demonstrated.
Writer Identification from Nordic Historical Manuscripts using Transformer Networks

Handwriting has been used as a form of authentication for the last 1000 years. Forensic analysis of handwriting using computers has been in practice since the 1970s. With the evolution of deep-learning techniques over the last decade, such automated forensic analysis of handwritten text has become dependent on deep-learning techniques. In this paper, we investigate the prowess of transformer networks in the context of identifying the writer of a handwritten sample. We here propose a deep feature embedding-based transformer network, WiT, for writer identification. Experiments were conducted on a historical Nordic manuscript dataset comprising 9253 handwritten samples scribbled by 50 writers for the very first time, and encouraging results were obtained. Rigorous experiments were also conducted to check the noise / damage resiliency of WiT, and the outcomes were quite promising.
Conditional GAN-Based Gradient Recovery Attack for Differentially Private Deep Image Processing

It has been demonstrated that gradients of deep learning models, especially image processing models, can be exploited to memorize sensitive information from training data, rendering them vulnerable to gradient inversion attacks. Differential privacy, a provably secure privacy-preserving technique, has been widely adopted to protect gradients of deep learning models. In this paper, we propose a gradient recovery attack against differential privacy-preserving models. Adversaries can gather a small set of differential privacy perturbed and original gradients and leverage a cGAN network to train a gradient recovery model, which can recover the perturbed gradient to an approximately original state. Subsequently, the recovered gradient can be targeted by gradient inversion attacks, leading to further privacy leakage from the training data. Our experiments demonstrate that the proposed gradient recovery attack achieves promising results when using commonly used differential privacy budgets (from 1 to 7). When the privacy budget is 7, the recovered gradient and the original gradient have a similarity of 94-99% in gradient inversion attack.
Development of an Augmented Reality User Interface for Collaborative Robotics in Quality Inspection for Manufacturing

This paper presents, implements and evaluates an augmented reality user interface for collaborative robots (cobots) in a quality inspection environment. The aim is to enable a worker to understand the movement of a cobot through an innovative projection design and to ease the teaching of new movement points into the robotic system. A virtual camera captures the position of the cobot and additional information in the RViz simulation, and a projector projects the actual and future positions of the cobot as well as visual cues and buttons on the ground of the shared workspace. Laser scanners are utilized to detect feet interacting with the visual buttons. To evaluate the effectiveness of the augmented reality user interface, a survey with people working alongside the cobot equipped with the visualization-system is conducted. The survey assesses the perceived usefulness, ease of understanding, and overall satisfaction with the user interface. The result of the survey indicates that the user interface significantly improves the comprehension of cobots actions. Participants working with this visualization system report enhanced situational awareness, reduced ambiguity, and increased efficiency. The qualitative feedback highlights the effectiveness of the visual cues in conveying information and the ease of integration into existing production processes.
A Representation Learning Method of Knowledge Graph Integrating Relationship Path, Text Description and Entity Hierarchy Type Information

Knowledge representation learning aims to encode the semantic information of objects into compact lowdimensional vectors. Many methods only focus on the structural information of the triple itself. Other valid information in the triple cannot be effectively utilized, leading to the problem of incomplete semantic representation of entities and relations. To address the above problems, this paper proposes a method for learning knowledge graph representations. The proposed method not only considers the structural details of the triplet but also integrates additional relevant information, such as relational path, text description, and entity hierarchical type. The proposed method effectively utilizes these multiple sources of information to improve the expressiveness and generalization of knowledge representation. The experimental results on the FB15K, WN18, and FB15K-237 datasets demonstrate the effectiveness of the proposed method in link prediction and triplet classification tasks. This demonstrates the importance of fusing multiple useful information to improve the expressiveness and generalization of knowledge representation.
Machine Vision-Based Recognition for fruit cracking in cherry

In order to realize the intelligent real-time and accurate identification of cherry fruit cracking, a set of end-to-end data remote acquisition device was designed with normal and cracked cherry fruit as the research object. Aiming at the problem of unbalanced number of cherry cracked fruit and normal fruit samples, a sample intelligent transplantation data enhancement algorithm is proposed. At the same time, the YOLOX-S model is improved and designed. CBAM-MASFF-YOLOX increases the output of the feature map in the backbone feature extraction network, and introduces the CBAM attention mechanism to ensure that the high-level semantic information is not lost and the feature extraction is strengthened. In the feature pyramid module, after feature extraction of the newly transmitted feature map, it is introduced into the MASFF multilevel adaptive feature fusion module together with the original three feature maps. Finally, the binary cross entropy in the loss function is replaced by VariFocalLoss, and IoU Loss is replaced by α-IoU Loss to improve the training weight and robustness of the model for cherry samples. The results of the model test show that the average accuracy of the whole category of cherry recognition reaches 94.18 %, and the detection time is 34.29 ms / sheet. Compared with the original YOLOX-S, the average accuracy of normal and cracked cherry fruit was increased by 4.07 % and 7.47 %, respectively, and the mAP was increased by 5.77 %. The method proposed in this study has higher accuracy while satisfying the application of detection time.
Real-Time Vision-Based Violent Actions Detection Through CCTV Cameras With Pose Estimation

In large structures under video surveillance, or when a place is crowded, a CCTV operator cannot monitor hundreds of people on different video streams. This paper presents a proof of concept for a real-time vision-based system for detecting violent actions through CCTV cameras with pose estimation. The proposed system uses a combination of computer vision techniques, including pose estimation, object tracking, and a deep learning algorithm based on time-series features, to accurately identify violent actions in real-time. Our features are based on a fixed-size rolling window that computes the position of each person’s limb along with their velocity. The proposed pipeline achieves a high accuracy rate of 92% with an overall latency of around 0.07 seconds per frame using a RTX 3060 Mobile GPU, making it a powerful tool for enhancing public safety and security. This system can be deployed in a wide range of scenarios, including public places, transportation hubs, and other critical infrastructure, to provide real-time alerts and facilitate rapid response in case of violent incidents.
Human-centric Artificial Intelligence enabled Digital Images and Videos Forensic Triage

Digital forensics and incident response (DFIR) involve huge volume of data collected from digital systems that requires investigators to quickly sift through and prioritise relevant evidence. The forensics investigator team faces many challenges when analysing the processes to keep them on target and improve them. A lack of a systematic approach to data analysis can lead to slower decision-making. This work aims to enhance the effectiveness and efficiency in forensics analysis using human-centric artificial intelligence (HAI) enabled data triage. Specifically, an image and video triage was proposed that can significantly speed up the investigation and identify highly related evidence items from huge volume of images and videos.
Ant Colony Algorithm Path Planning Based on Genetic Algorithm Fusion in 2D Grid Maps

In order to address the slow speed of traditional ant colony algorithms in searching for the optimal path in two-dimensional environments and the tendency to fall into local optima, this paper proposes an ant colony algorithm path planning that integrates genetic algorithms. The traditional ant colony algorithm has slow search speed and is prone to falling into local optima mainly due to the uncertainty of the initial search direction of the ant colony, as well as the difficulty in finding the global optimal solution due to the low initial pheromone concentration. Therefore, this article introduces the genetic algorithm in intelligent algorithms to improve the ant colony algorithm. By setting the parameters of the genetic algorithm and its fast search speed, it enhances the initial pheromone concentration of the ant colony algorithm, and makes a judgment on whether the path searched by the ant colony algorithm is optimal. This can lead to the result of shortening the optimal path with smaller iterations. Finally, this article conducted a simulation experiment comparison on a grid map under the same obstacle, using traditional ant colony algorithm and ant colony algorithm fused with genetic algorithm. The same starting point and endpoint were set, and the parameter values of the ant colony algorithm were fixed to meet the experimental comparison requirements. The Experiments show that the proposed algorithm has a shortest path length of 28.1253, which is 3.86% shorter than the traditional algorithm. The number of iterations is also reduced by 32.76% on the basis of the traditional algorithm, which proves that the proposed method has a good improvement effect on the traditional ant colony path planning in two-dimensional environment.
A Review on Vibration Causes, Effects and Control in Machine Tools

Machine tool vibration is an acute issue that can have a negative impact on the efficiency, precision, and dependability of machining operations. Vibrations impact the machining operation's background noise, tool life, and the standard of the manufactured art. Thus, these bothersome vibrations have to be subdued while machining. The review abridges the effects of vibration (poor surface finish, increased tool wear and reduced machining accuracy etc.), with a focus on active, passive and semi active vibration which are the techniques to control vibration characteristics and the recent developments on technology involving vibration. Series of operations are reviewed with different approaches to control vibration in their machine tools. This paper offers a thorough grasp of machine tool vibration phenomena, their impact on machining efficiency, and methods for assessment and mitigation. Challenges have been assessed with preferable solutions provided by researchers for future related studies.
Rotation-Lossless Non-Local Attention and Fine-Grained Feature Enhancement Based Fine-Grained Oriented Object Detection in Remote Sensing Images

Context and fine-grained features are crucial for fine-grained oriented object detection in remote sensing images. The commonly used non-local attention like self-attention fails in oriented scenes. The similarities of similar objects are low caused by orientation. And fine-grained object detection performance of the existing detectors needs further improvement. In this paper, a novel Rotation-lossless Attention and Fine-grained feature Enhancement based detection network (RAFE) is proposed. Specially, Rotation Lossless Attention (RLA) is designed for suppressing the potential influence of rotation on similarity measurement of similar objects and capturing accurate context information. Then a fine-grained feature enhancement module is proposed to enhance fine-grained features, including De-redundant Feature Fusion (DFF) and Channel Feature Enhancement (CFE). DFF is developed to separate de-redundant fine-grained and context features from dense scale features based on a difference metric function and fuse them. CFE further enhances fine-grained features through the similarity between feature channels. Extensive experiments performed on large-scale remote sensing fine-grained and oriented object detection dataset, FAIR1M, demonstrate that the proposed RAFE achieves competitive performance compared with state-of-the-art methods. The overall accuracy is 42.7% and 5.5% higher than the baseline method, Gliding vertex.