Reviewerly/exHarmony huggingface dataset:
Automated Roadmap Graph Creation and MAPF Benchmarking for Large AGV Fleets

In this paper we present a concept and first evaluation of an automated topology creation and two different Multi-Agent Path Finding (MAPF) approaches for large robot fleets with the focus on logistic applications. Based on a map or a CAD file and the robot size an algorithm for creating a so called Roadmap Graph will be introduced. The algorithm is able to utilize as much of the available space as possible and can also guarantee that neither edges nor vertices overlap each other which may lead to collisions of the robots. In order to analyse the quality of the Roadmap Graph, an analysis of different graph variants using measures from graph theory is presented. Additionally, an evaluation of routing performance using one of the MAPF algorithms has been conducted on these graphs. Finally, a brief evaluation and comparison of the two MAPF algorithms on exemplaric gridmap graphs show the generic advantages and disadvantage of both algorithms.
Unranked Fuzzy Logic and Reasoning

One of the main tools in knowledge representation is ontology, which is a collection of logic-based formal language sentences. These sentences are used by automated reasoning programs to extract new knowledge and answer to the given questions. Although ontology languages are standardized by W3C, there are still many problems remaining. One of the most important problems is related to ontologies, where information is vague and incomplete. Such kind of ontologies have applications in many different fields, such as medicine, biology, e-commerce and the like. They require concepts from both, fuzzy and unranked theories. This is a follow-up paper of a one by the authors, where an unranked fuzzy logic was introduced. Here we develop a tableau method for reasoning over such logic. The unranked fuzzy logic is an extension of many-valued logics with sequence variables and flexible-arity function and predicate symbols. The unranked fuzzy tableau calculus corresponds to Hájek’s witnessed fuzzy logics and is therefore complete.
Spatio-Temporal Context Based Adaptive Camcorder Recording Watermarking

Video watermarking technology has attracted increasing attention in the past few years, and a great deal of traditional and deep learning-based methods have been proposed. However, these existing methods usually suffer from the following two challenges: First, most algorithms cannot resist camcorder recording attack, which limits their practical application. Second, watermark embedding may cause substantial degradation of video quality. Through analyzing the unique distortions presented in the camcorder recording process, including geometric distortion, temporal sampling distortion, sensor distortion and processing distortion, this paper proposes a novel spatio-temporal context based adaptive camcorder recording watermarking scheme STACR. In STACR, considering the geometric distortion and video visual quality, we embed the watermark by constructing a spatio-temporal histogram and incorporate a content features based adaptive locating algorithm to select embedding blocks and embedding strengths. As for the temporal sampling attack, we put forward a watermark correlation-based synchronization algorithm and combine it with cross-validation. Moreover, to resist the sensor distortion, we design a local matching-based algorithm to improve the extraction accuracy. In addition, grouped and repeated embedding strategies are combined to cope with the processing distortion. Experimental results compared with the state-of-the-art show that the proposed scheme achieves high video quality and is robust to geometric attacks, compression, scaling, transcoding, recoding, frame rate changes and especially for camcorder recording.
Video Denoising using Temporal Coherency of Video Frames and Sparse Representation

Sparse representation based on dictionary learning has been widely used in many applications over the past decade. In this article, a new method is proposed for removing noise from video images using sparse representation and a trained dictionary. To enhance the noise removal capability, the proposed method is combined with a block matching algorithm to take the advantage of the temporal dependency of video images and increase the quality of the output images. The simulations performed on different test data show the appropriate response of the proposed algorithm in terms of video image output quality.
RAE-Net: a deep learning system for staging of estrous cycle

Histopathological examination of animal tissue by pathologists forms a crucial part of preclinical drug development. Machine learning techniques have been utilized to develop increasingly reliable and accurate analytical solutions in tissue imaging in recent years. Accurate assessment of the estrous cycle in rats and mice is important in evaluating pathogenesis and identifying potential test article related toxicity in toxicologic studies. In this paper, we present a Deep-Learning based framework for the classification of different stages of the Estrous cycle using whole slide images (WSI) of Hematoxylin & Eosin (H&E) stained sections from the Wistar rat vagina. We present an encoder-decoder convolution neural network, RAE-Net, based on three key criteria: <strong>R</strong>esidual blocks for the decoder, <strong>A</strong>ttention gate in the skip connection, and <strong>E</strong>fficientNetB4 for the encoder backbone. We show that the architecture achieves significant performance improvement over state-of-the-art segmentation architecture. The proposed estrous staging system could advance the pathology workflow in female preclinical reproductive toxicology studies.
CrowdDeep: deep-learning from the crowd for nuclei segmentation

In recent years, deep convolutional neural networks (CNNs) have shown tremendous success in solving many biomedical tasks. However, the development of deep convolutional networks requires access to large quantities of high-quality annotated images for training and evaluation. As image annotation is a tedious task for biomedical experts, recruiting non-expert crowd workers is an economical and efficient way to provide a rich dataset of annotated images. We present an approach to improve the accuracy of segmenting nuclei in Hematoxylin and Eosin (H&E) slides by hiring crowd workers. We first present a crowdsourcing framework that enables fast and efficient acquisition of nuclei-segmented masks from the crowd by providing manual and semi-automatic annotation methods. Then, we present <i>CrowdDeep</i>, a novel technique to improve the accuracy of deep learning models trained on expert annotation by efficiently hiring crowd-annotated data. CrowdDeep consists of two sub-networks: Crowd-Subnet, and Expert-Subnet. The Crowd-Subnet is trained on the crowd-annotated images to extract crowd-related features from the crowd-annotated masks, while the Expert-Subnet is trained on the expert-driven annotations to extract expert-related features from the expert-annotated masks. Then, it calculates the final segmentation mask from the generated segmentation masks by two sub-networks. The results show that CrowdDeep outperforms a CNN model trained on solely expert-derived annotations in terms of F1-Score, IOU, and Pixel Accuracy. This approach is multi-organ and generalizes across different organs, staining, and disease states and is easily expandable by crowdsourcing images with an assortment of nuclei shapes and sizes from any desirable body tissue.
Depth estimation from a single CD-SEM image using domain adaptation with multimodal data

There is a growing need for accurate depth measurements of on-chip structures, fueled by the ongoing size reduction of integrated circuits. However, current metrology methods do not offer a satisfactory solution. As Critical Dimension Scanning Electron Microscopes (CD-SEMs) are already being used for fast and local 2D imaging, it would be beneficial to leverage the 3D information hidden in these images. In this paper, we present a method that can predict depth maps from top-down CD-SEM images. We demonstrate that the proposed neural network architecture, together with a tailored training procedure, leads to accurate depth predictions on synthetic and real experimental data. Our training procedure includes a domain adaptation step, which utilizes data from a different modality (scatterometry), in the absence of ground truth data in the experimental CD-SEM domain. The mean relative error of the proposed method is smaller than 6.2% on a contact-hole dataset of synthetic CD-SEM images with realistic noise levels. Furthermore, we show that the method performs well in terms of important semiconductor metrics. To the extent of our knowledge, we are the first to achieve accurate depth estimation results on experimental data, by combining data from the aforementioned modalities. We achieve a mean relative error smaller than 1%.
Behavior-Based Ethical Understanding in Chinese Social News

Ethical understanding aims at morally analyzing and discriminating ethical scenarios described in natural language. By classifying behaviors that occur in ethical scenarios as ethical or unethical, ethical understanding empowers artificial intelligence systems to understand human values so as to discern right from wrong morally. However, most existing ethical understanding methods lack fine-grained analysis and cannot handle the problem that an ethical scenario may contain multiple behaviors with multiple polarities. In this paper, we introduce a novel natural language processing task, behavior-based ethical understanding (BEU), for mining the purpose relation(s) and ethical polarity of a specific behavior from the social news. It contains three subtasks: behavior term extraction (BTE) to extracts behavior terms, purpose relation inference (PRI) to identifies purposive relations among behaviors, and polarity discrimination (PD) to predicts the ethical polarities of behaviors, respectively. To perform this task, we constructed a Chinese BEU dataset, named FG-ETHICS. Besides, we propose a three-stage framework, BEU-BERT, based on the pre-trained language model BERT and deliberately designed downstream models for three subtasks. Experimental results show that the proposed framework achieves the best performance from the BTE and PD tasks, and achieves a promising performance of 75% on the PRI task.
RVprio: A tool for prioritizing runtime verification violations

Summary Runtime verification (RV) helps to find software bugs by monitoring formally specified properties during testing. A key problem in using RV during testing is how to reduce the manual inspection effort for checking whether property violations are true bugs. To date, there was no automated approach for determining the likelihood that property violations were true bugs to reduce tedious and time‐consuming manual inspection. We present RVprio , the first automated approach for prioritizing RV violations in order of likelihood of being true bugs. RVprio uses machine learning classifiers to prioritize violations. For training, we used a labelled dataset of 1170 violations from 110 projects. On that dataset, (1) RVprio reached 90% of the effectiveness of a theoretically optimal prioritizer that ranks all true bugs at the top of the ranked list, and (2) 88.1% of true bugs were in the top 25% of RVprio ‐ranked violations; 32.7% of true bugs were in the top 10%. RVprio was also effective when we applied it to new unlabelled violations, from which we found previously unknown bugs—54 bugs in 8 open‐source projects. Our dataset is publicly available online.
Neural Network Aided Kalman Filter to Maximize Accuracy

With the growing need for data, and ever-growing demand for prediction and error-correction, Kalman Filters are undoubtedly at the forefronts of real-time estimation. While these filters are designed to achieve convergence shortly after getting exposed to the data, the filters might not be able to maximize all the data it can extract from the system. In order to extract most of the information that is otherwise unusable by the Kalman Factor, an initial assumption of a pre-trained Machine Learning model that correlates a feedable parameter with the unusable data is made. The feedable parameter is then given to the Kalman Filter along with the other standard parameters which boosts the accuracy by adding another dimension to the filter.
IoT based Face Mask Detection System

The COVID-19 virus was first reported in December 2019 in China. Since then, it has created havoc on global level by causing health and financial crisis. The novel corona virus is continuously evolving into new variants of virus and still spreading in every part of the world. The virus is spread mostly by the droplets transferring from the infected person to an uninfected person. The only direct method of preventing such transfer of droplets can be done by washing of hands, avoiding contact with nostrils, eyes and mouth. Only complete vaccination of citizens can help in containment of virus till then use of face mask and social distancing has been made mandatory by authorities. This project proposes a contactless face mask detection model based on machine learning and image processing technology. It will follow the protocols of social distancing. This project will be helpful in accurately identifying whether a person is wearing a mask properly or not. This model can be easily installed in the entrance of ATMs, malls, educational institutions, offices and in the entrance of public offices. Being a contact less model it will effectively reduce the need of having a manual way of detection and will reduce the spreading of disease to further people. It is highly effective and cheap model as all the tools required for this project are free of cost and easily available online. Proposed model can be easily used on low end computers like Raspberry Pi. The model will encourage people to continuously wear mask in correct manner.
Nanovised Control Flow Attestation

This paper presents an improvement of control flow attestation (C-FLAT) for Linux. C-FLAT is a control attestation system for embedded devices. It was implemented as a software executing in ARM’s TrustZone on bare-metal devices. We extend the design and implementation of C-FLAT through the use of a type 2 Nanovisor in the Linux operating system. We call our improved system “C-FLAT Linux”. Compared to the original C-FLAT, C-FLAT Linux reduces processing overheads and is able to detect the SlowLoris attack. We describe the architecture of C-FLAT Linux and provide extensive measurements of its performance in benchmarks and real-world scenarios. In addition, we demonstrate the detection of the SlowLoris attack on the Apache web server.
Exploring the factors influencing mobile technology integration in higher education

This study proposed and tested an empirical model to examine the relationships between the factors influencing mobile technology integration in higher education from the students’ and faculty points of view. A sequential mixed method was used to meet the aim of the study. The findings of the qualitative phase were used to develop the quantitative instrument. The participants in the qualitative data collection were 39 students and faculty members. In addition, the participants were 300 graduate students from two large universities in northern Palestine. The findings of the study revealed that the proposed model helps in explaining factors affecting students’ mobile technology integration and exploring relationships between the factors. Furthermore, students’ attitudes and beliefs, the quality of service, patronisation, the cost of service and the instructors had a significant impact on students’ mobile technology integration in higher education.
Laser sampling: Implications for matrix effect in the analysis of laser ablation inductively coupled plasma mass spectrometry

To investigate the matrix effect in the analysis of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), a 193-nm ArF ns-LA-ICP-MS is used to quantitatively analyze 46 trace elements in United States Geological Survey (USGS) samples (BCR-2G, BHVO-2G, BIR-1G, and GSD-1G). The relative deviations of most trace elements are within 5% for the matrix-matched calibration (external standard sample GSE-1G). For the non-matrix-matched calibration (external standard sample NIST610), the relative deviations of most volatile elements are within 10%, while the relative deviations of most refractory elements are between −10% and −30%, indicating that the contents of refractory elements are underestimated. Contrary to NIST 610, the deposited ablation particles at the bottom of ablation craters, the ablation depth under different laser fluence, and the characteristics (shock wave (SW) radius, pressure, and temperature) of ablation plumes, for USGS samples are more similar to each other. The laser sampling from different matrix samples will directly determine the final analysis accuracy of LA-ICP-MS.
An Implementation of Image Secret Sharing Scheme Based on Matrix Operations

The image secret sharing scheme shares a secret image as multiple shadows. The secret image can be recovered from shadow images that meet a threshold number. However, traditional image secret sharing schemes generally reuse the Lagrange’s interpolation in the recovery stage to obtain the polynomial in the sharing stage. Since the coefficients of the polynomial are the pixel values of the secret image, it is able to recover the secret image. This paper presents an implementation of the image secret sharing scheme based on matrix operations. Different from the traditional image secret sharing scheme, this paper does not use the method of Lagrange’s interpolation in the recovery stage, but first identifies the participants as elements to generate a matrix and calculates its inverse matrix. By repeating the matrix multiplication, the polynomial coefficients of the sharing stage are quickly derived, and then the secret image is recovered. By theoretical analysis and the experimental results, the implementation of secret image sharing based on matrix operation is higher than Lagrange’s interpolation in terms of efficiency.
Scriptable and composable SNARKs in the trusted hardware model1

Non-interactive zero-knowledge proof or argument (NIZK) systems are widely used in many security sensitive applications to enhance computation integrity, privacy and scalability. In such systems, a prover wants to convince one or more verifiers that the result of a public function is correctly computed without revealing the (potential) private input, such as the witness. In this work, we introduce a new notion, called scriptable SNARK, where the prover and verifier(s) can specify the function (or language instance) to be proven via a script. We formalize this notion in UC framework and provide a generic trusted hardware based solution. We then instantiate our solution in both SGX and Trustzone with Lua script engine. The system can be easily used by typical programmers without any cryptographic background. The benchmark result shows that our solution is better than all the known SNARK proof systems w.r.t. prover’s running time (1000 times faster), verifier’s running time, and the proof size. In addition, we also give a lightweight scriptable SNARK protocol for hardware with limited state, e.g., Θ ( λ ) bits. Finally, we show how the proposed scriptable SNARK can be readily deployed to solve many well-known problems in the blockchain context, e.g. verifier’s dilemma, fast joining for new players, etc.
SDPTool : A tool for creating datasets and software defect predictions

One of the most difficult and time-consuming aspects of machine learning is gathering high-quality data that can be used to train the algorithm. Historical data is a gold mine for predicting the future with a high level of confidence and accuracy in a certain field. Data is typically available in its raw form, which is in most circumstances unsuitable for machine learning applications. The work presented here introduces a tool that will considerably assist in the generation of a high-quality dataset, starting with raw data collection and progressing on to data pre-processing and validation, and finally to prediction using the selected machine learning algorithm. So far, we have built capability in the SDPTool to collect data dynamically from a GitHub repository. Data pre-processing, various defect predictions, and other machine learning settings are all in the pipeline. Once the data is ready to use, SDPTool will assist in selecting the appropriate machine learning algorithm and performs the desired prediction in the associated field. The entire process will be orchestrated in a user-friendly Java Swing application, from data collection to prediction.
Protecting Online Media Information Using Customized Positioning Based Proposal

Persevering privacy in social media is a way of establishing a secure collaboration between the eCommerce network and the social media site. It is very easy to retrieve users&#x0027; personal information from the social media network through e-commerce which can lead to the forgery of user data. The customized proposal is difficult to assist the users to find appropriate information. It regularly depends on an outsized assortment of client information, particularly clients&#x0027; online action via web-based media, to mine client inclination. Delivering client action information makes clients powerless against deduction assaults, as private information can frequently be construed from the clients&#x0027; movement information. Targeting beating the test, we propose a technique were adaptable and ceaseless security-saving information distributing to ensure clients against deduction assaults while empowering customized positioning-based suggestions. It gives persistent security of client indicated private information against induction assaults by muddling both the verifiable and streaming client action information before delivering them, while as yet protecting the value of the data by efficiently limiting the pairwise ranking loss incurred from data obfuscation. Our methodology accomplishes better security assurance in all the positioning-based proposals.
EW-KNN: evaluating information technology courses in high school with a non-parametric cognitive diagnosis method

With the continuous development of education, personalized learning has attracted great attention. How to evaluate students' learning effects has become increasingly important. In information technology courses, the traditional academic evaluation focuses on the student's learning outcomes, such as "scores" or "right/wrong," which seldom reflects the development of students' cognitive level and lacks effective diagnostic information. This article proposes a non-parametric multi-level scoring cognitive diagnosis method based on the KNN and the characteristics of information technology courses named the EW-KNN (E-weight K-Nearest Neighbor). Compared with the KNN, the EW-KNN improved two key points. One is that it takes the number of IRP (Ideal Response Pattern) as the K value to adapt to different types of tests. The other is that the nearest neighbor distance is introduced to solve the problem of misjudgment of the categories. The Monte Carlo simulation method is used to test its performance. The results indicate that the EW-KNN has a higher accuracy rate and is suitable for information technology courses. Furthermore, the method is applied in information technology course to make a cognitive diagnosis of 120 students of high school in Shanghai. Results demonstrate that the EW-KNN can accurately diagnose each student's cognition levels and knowledge structure accurately.