Reviewerly/exHarmony huggingface dataset:
A Dynamic-Attention-Based Heuristic Fuzzy Expert System for the Tuning of Microwave Cavity Filters

The tuning of microwave cavity filters (MCFs) is mostly conducted by experienced operators, and thus, is time and resource intensive. The automatic tuning method has received lots of research attention. However, the experience of tuning experts is not described systematically, and the existing methods are not adequate for MCFs with individual differences. Considering that fuzzy expert systems are superior in flexibility and interpretability, a dynamic-attention-based heuristic fuzzy expert system is proposed in this article to achieve automatic tuning with high applicability, accuracy, and efficiency. First, the dynamic tuning process is divided into several stages, and the evaluation function of filtering performance is designed for each stage. Second, an improved expert system with dynamic attention is constructed to facilitate the comprehensive evaluation of filtering performance and to balance tuning efficiency and accuracy. Third, a heuristic fuzzy logic system is presented to obtain appropriate tuning values, with low design complexity of fuzzy rules and high utilization of tuning process information. Finally, the effectiveness, applicability, and practicality of the proposed method are demonstrated by both simulations and experiments.
PAS: A new powerful and simple quantum computing simulator

Abstract In recent years, many researchers have been using CPU for quantum computing simulation. However, in reality, the simulation efficiency of the large‐scale simulator is low on a single node. Therefore, striving to improve the simulator efficiency on a single node has become a serious challenge that many researchers need to solve. After many experiments, we found that much computational redundancy and frequent memory access are important factors that hinder the efficient operation of the CPU. This paper proposes a new powerful and simple quantum computing simulator: PAS (power and simple). Compared with existing simulators, PAS introduces four novel optimization methods: efficient hybrid vectorization, fast bitwise operation, memory access filtering, and quantum tracking. In the experiment, we tested the QFT (quantum Fourier transform) and RQC (random quantum circuits) of 21 to 30 qubits and selected the state‐of‐the‐art simulator QuEST (quantum exact simulation toolkit) as the benchmark. After experiments, we have concluded that PAS compared with QuEST can achieve a mean speedup of (QFT), (RQC) (up to , ) on the Intel Xeon E5‐2670 v3 CPU.
Automated Reasoning for Probabilistic Sequential Programs with Theorem Proving

Semantics for nondeterministic probabilistic sequential programs has been well studied in the past decades. In a variety of semantic models, how nondeterministic choice interacts with probabilistic choice is the most significant difference. In He, Morgan, and McIver’s relational model, probabilistic choice refines nondeterministic choice. This model is general because of its predicative-style semantics in Hoare and He’s Unifying Theories of Programming, and suitable for automated reasoning because of its algebraic feature. Previously, we gave probabilistic semantics to the RoboChart notation based on this model, and also formalised the proof that the semantic embedding is a homomorphism, and revealed interesting details. In this paper, we present our mechanisation of the proof in Isabelle/UTP enabling automated reasoning for probabilistic sequential programs including a subset of the RoboChart language. With mechanisation, we even reveal more interesting questions, hidden in the original model. We demonstrate several examples, including an example to illustrate the interaction between nondeterministic choice and probabilistic choice, and a RoboChart model for randomisation based on binary probabilistic choice.
UAC: An Uncertainty-Aware Face Clustering Algorithm

We investigate ways to leverage uncertainty in face images to improve the quality of the face clusters. We observe that popular clustering algorithms do not produce better quality clusters when clustering probabilistic face representations that implicitly model uncertainty – these algorithms predict up to 9.6X more clusters than the ground truth for the IJB-A benchmark. We empirically analyze the causes for this unexpected behavior and identify excessive false-positives and false-negatives (when comparing face-pairs) as the main reasons for poor quality clustering. Based on this insight, we propose an uncertainty-aware clustering algorithm, UAC, which explicitly leverages uncertainty information during clustering to decide when a pair of faces are similar or when a predicted cluster should be discarded. UAC considers (a) uncertainty of faces in face-pairs, (b) bins face-pairs into different categories based on an uncertainty threshold, (c) intelligently varies the similarity threshold during clustering to reduce false-negatives and false-positives, and (d) discards predicted clusters that exhibit a high measure of uncertainty. Extensive experimental results on several popular benchmarks and comparisons with state-of-the-art clustering methods show that UAC produces significantly better clusters by leveraging uncertainty in face images – predicted number of clusters is up to 0.18X more of the ground truth for the IJB-A benchmark.
Leukemia Sub-Type Classification by Using Machine Learning Techniques on Gene Expression

Early diagnosis and correct treatment are very important in leukemia. The diagnosis of cancer according to the sub-type can be made through gene expression. Leukemia is divided into two sub-types, Acute Lymphoblastic Leukemia (ALL) and Acute Myeloblastic Leukemia (AML). After diagnosis, correct drug treatment can cure this disease. Machine learning can be a good approach to identify leukemia sub-type, in which time and accurate diagnosis are important. In this study, it has been proposed to use machine learning to make a leukemia sub-type detection based on gene expression. K-nearest neighbor, Linear Discriminant, Support Vector Machine, and Ensemble classifiers machine learning techniques have been utilized. Obtained results are presented comparatively.Simsek, Ebru Badem, Hasan Okumus, Ibrahim Taner
Deep Learning based Brake Light Detection for Two Wheelers

_ The detection of brake light plays an important role in applications such as autonomous driving, to avoid rear-end collision. In this work, we propose a frame work to identify the location of two-wheelers and their brake lights in traffic visual data using a deep learning-based approach. The system uses the Yolo object detection model for multi-class detection and localization. The experimental study was conducted on a new dataset of two-wheeler vehicles in traffic.
Category boundary re-decision by component labels to improve generation of class activation map

Class Activation Maps (CAMs) visualize the pixels within an image that contribute to classify the image to a certain category, and can be used to localize the object regions from images that benefit to many tasks such as image segmentation and object detection. However, the object regions highlighted by the existing methods are usually small and local. We believe such drawback lies in the image-level labels, because the the image-level label prefers the network to capture the common regions intra-class and the discriminative region inter-class. Due to the variations of objects intra-class and the common region widely shared inter class, the regions based on image-level labels are usually small and local. Based on such observation, we propose a new strategy to use a set of lower-level labels called component labels to replace the image-level labels. The advantage is that the component regions have small feature variation intra-class and are non-overlapping inter-class, which leads to better CAMs generation through their simple combination. Furthermore, since the component labels are also shared by unknown classes, the proposed CAMs generation method can be easily extended to unknown classes, which facilitate the improvement of tasks related to new class processing, such as few-shot segmentation and detection. Specifically, the component labels are set based on the WordNet hierarchy firstly, which can also provides the relationships of classes. Besides, graph convolution networks (GCNs) are used as the classifiers, which can exactly describe not only the component, but also their structural relationships. Based on the component features, a feature fusion module is also designed to merge local component features into the global feature. Better CAM is finally obtained. The experiment section shows the effectiveness of our component labels in terms of better subjective and objective results compared with the existing CAM generation methods. Furthermore, we also show good generalization of our component label on unknown classes.
A Legal-Relationship Establishment in Smart Contracts: Ontological Semantics for Programming-Language Development

Machine-readable smart contracts (SC) on blockchains promise drastic enhancements in collaboration efficiency and effectiveness in that cost- and time reductions can be achieved while the quality of services increases. We address existing shortcomings of SCs that are in tendency incomplete for legal recognition especially to smart-contract-enabled funding rounds, not collaborative business-process reflective and are also not aware of their own processing state to justify the claim of smartness. When conflicts occur, tracing the past performance of conventional contract (CC) execution is very slow and expensive while in addition, CCs are challenging to enforce. On the one hand, the legal status of SCs based funding rounds is currently not clarified and the question arises if SCs comprise the necessary legal- concepts and properties. Current SC solutions do not suffice in those regards. To fill this gap, we develop the smart-legal-contract (SCL) ontology to define the legal- and collaborative business concepts and properties in the SCs. Formal methods, such as Colored Petri Nets (CPNs), are suitable to design, develop and analyze processing state of SCs in order to trace the performance of contractual-rights and obligations. In this work, SCL ontology is formalized using Colored Petri Nets resulting in a verifiable CPN model. Furthermore, we conduct a state-space analysis on the resulting CPN model and derive specific model properties. A running case from the automotive supply chain domain demonstrates the utility and validity of our approach.
R3V: Robust Round Robin VDF-based Consensus

Proof of Stake (PoS) based consensus provides a better mechanism than Proof of Work (PoW) consensus for extending the blockchain without significant energy waste. Most of the PoS consensus protocols derive or use some randomness to elect a leader candidate. This makes the consensus weaker and attracts more attackers to mount different attacks, e.g., long-range attacks and block withholding attacks. In PoS consensus, having more stakes gives more chances to be a leader among participating stakeholders. Therefore, most PoS protocols do not provide better fairness for the stakeholders participating in the consensus protocol. Moreover, these protocols suffer from high communication complexity for selecting a leader candidate in each consensus round. In this work, we propose a novel consensus protocol “R3V” that selects a set of leader candidates in a round-robin manner according to age. Finally, these leader candidates compete to be the block leader by solving a Verifiable Delay Function (VDF) based puzzle. We propose different methods to generate verifiable identities for the stakeholders. The identities are enrolled in the blockchain, which provides the age norm needed for the consensus. Compared with the other PoS consensus protocols, our protocol shows better resilience against most of the common attacks on PoS protocols. Additionally, it proclaims low energy consumption, less communication complexity, and better fairness.
Efficient Uncertainty Estimation for Monocular 3D Object Detection in Autonomous Driving

Monocular 3D object detection is an ill-posed problem due to the loss of geometry information in 2D images. To improve the reliability of monocular vision system, we propose to replace the deterministic estimation with a probabilistic prediction by computing both aleatoric and epistemic uncertainty in 3D detection task. We further design a continuous focal loss that can improve location accuracy and when combining with aleatoric uncertainty estimation, also reduce the effect of noise data during training. Our method is capable of estimating uncertainty in a single forward pass while achieving nearly real-time performance with 0.1s per image on a modern platform. Additionally, to make fair comparison between point and probabilistic prediction, we present a novel probabilistic localization evaluation metric which computes the average error over the predicted location distribution. Our experiments indicate that we achieve competitive result on the official KITTI 3D detection leaderboard, among other deterministic methods. Furthermore, our method gains more accurate localization accuracy under different localization evaluation metrics.
Innovative Wallet Using Trusted On-Line Keystore

This demonstration presents a prototype wallet, secured by an innovative on-line keystore, based on TLS1.3 server running in secure element. The main idea is to use via internet, keystore dedicated to blockchain, thanks to command shell, running over TLS1.3 server embedded in secure element. TLS1.3 exchanges are secured thanks to 32 bytes pre-shared-key (PSK), which acts as a strong PIN for secure element. A cheap keystore board is detailed, based on Wi-Fi SoC and Javacard 3.04. On the wallet side, a TLS 1.3 client uses a dedicated smartcard that protects PSK from eavesdropping.
An Evaluation of Different Methods for 3D-Driver-Body-Pose Estimation

Driver monitoring systems are increasingly introduced in modern commercial vehicles. Their importance will rise with automated vehicles, requiring the driver to pay attention or to take over in a timely manner. With the success of deep learning methods for human body pose estimation, these systems are also more and more employed in research projects for driver monitoring. However, their accuracy for driver body pose estimation is not yet evaluated thoroughly. We therefore annotate a part of the Drive&Act dataset [1] and evaluate both 2D- and 3D-body-pose performance based on triangulation and depth images. To this end we also introduce a deep learning based post processing step for depth image based 3D-pose-estimation that can be applied without much cost to the result of any 2D-pose detector, lifting the pose prediction to 3D. Our evaluation gives an overview of the performance of current state of the art methods and shows that our depth post processing method can close the gap to triangulation based methods using complex camera setups.
Research on Named Entity Recognition Based on Bert-BiGRU-CRF model in Spacecraft Field

With the gradual advancement of human exploration of space, textual information related to spacecraft has grown exponentially. It's necessary to achieve the automatic acquisition of relevant information, explore the information extraction of the spacecraft domain knowledge map, and solve the problem lacking existing labeled data sets. The book “Encyclopedia of World Spacecraft” is selected as the data source to build a spacecraft-related data set and the entities in the field are divided into spacecraft, research countries, research institutions and platforms by the spacecraft text and experts. Based on the TFT model composed of text representation, feature extraction and sequence labeling, set up multiple sets of comparative experiments to find a model with the best recognition effect. Comparative experiments show that the Bert-BiGRU-CRF model combined with transfer learning can fully learn the characteristic content of the spacecraft field and has a better recognition effect than other models in terms of accuracy, recall and F1 value.
Autoencoder Based Inter-Vehicle Generalization for In-Cabin Occupant Classification

Common domain shift problem formulations consider the integration of multiple source domains, or the target domain during training. Regarding the generalization of machine learning models between different car interiors, we formulate the criterion of training in a single vehicle: without access to the target distribution of the vehicle the model would be deployed to, neither with access to multiple vehicles during training. We performed an investigation on the SVIRO dataset for occupant classification on the rear bench and propose an autoencoder based approach to improve the transferability. The autoencoder is on par with commonly used classification models when trained from scratch and sometimes out-performs models pre-trained on a large amount of data. Moreover, the autoencoder can transform images from unknown vehicles into the vehicle it was trained on. These results are corroborated by an evaluation on real infrared images from two vehicle interiors.
Automating the Bypass of Image-based CAPTCHA and Assessing Security

CAPTCHA also known as Completely Automated Public Turing test to tell Computers and Humans Apart is implemented on many websites all round the world to control and limit the entry of bot users in order to tackle issues of unnecessarily generated network traffic resulting in Denial of Service (DoS). A lot of work has been done in breaking text-based CAPTCHA but there is very little research on bypassing an image CAPTCHA. The conventional method used for recognizing image CAPTCHA has been proven to give inefficient results and hence through our paper we have assessed the security of the image-based CAPTCHA by using more efficient techniques and identified the vulnerabilities in it. With the rise of Deep Learning and Computer Vision, we can now bypass image-based CAPTCHAs easily. There have been models developed to test the robustness of text-based CAPTCHA with satisfactory results but not much has been dwelled upon the image section. In this paper, we have built a Convolutional Neural Network (CNN) model to bypass the reCaptcha v2 and this is automated with the help of Selenium. The Selenium tool is used to extract the images and pass them to the Deep Learning model. The output generated from the model is used to crack the image CAPTCHA and eventually bypass it. The accuracy of the proposed multi-class CNN model came out to be 92.98 % and hence we were able to bypass the reCaptcha v2. This paper also suggests methods to enhance the security of reCaptcha v2.
Hardware implementation of the upper confidence-bound algorithm for reinforcement learning

The upper confidence-bound algorithm has been identified as a popular and useful approach in reinforcement learning, suitable for solving diverse modern-day problems. In this paper, we have developed efficient, multiple-clock-cycle hardware for this algorithm to ensure its practical application in real-time. The real-life situation that belongs to a class of problems commonly known as multi-armed bandit problems has been observed. The developed design is tested and verified by a field-programmable gate array circuit design. The obtained results have the degree of accuracy of the ones achieved in software simulation, which proofs the robustness of the developed solution. In terms of execution time, the proposed hardware implementation significantly outperforms the software simulation. Finally, the calculation complexity of the implementation does not depend on the number of observed iterations, which guarantees the effective implementation of the developed design. All implementation details have been provided.
Towards More Generalizable One-shot Visual Imitation Learning

A general-purpose robot should be able to master a wide range of tasks and quickly learn a novel one by leveraging past experiences. One-shot imitation learning (OSIL) approaches this goal by training an agent with (pairs of) expert demonstrations, such that at test time, it can directly execute a new task from just one demonstration. However, so far this framework has been limited to training on many variations of one task, and testing on other unseen but similar variations of the same task. In this work, we push for a higher level of generalization ability by investigating a more ambitious multi-task setup. We introduce a diverse suite of vision-based robot manipulation tasks, consisting of 7 tasks, a total of 61 variations, and a continuum of instances within each variation. For consistency and comparison purposes, we first train and evaluate single-task agents (as done in prior few-shot imitation work). We then study the multi-task setting, where multi-task training is followed by (i) one-shot imitation on variations within the training tasks, (ii) one-shot imitation on new tasks, and (iii) fine-tuning on new tasks. Prior state-of-the-art, while performing well within some single tasks, struggles in these harder multi-task settings. To address these limitations, we propose MOSAIC (Multi-task One-Shot Imitation with self-Attention and Contrastive learning), which integrates a self-attention model architecture and a temporal contrastive module to enable better task disambiguation and more robust representation learning. Our experiments show that MOSAIC outperforms prior state of the art in learning efficiency, final performance, and learns a multi-task policy with promising generalization ability via fine-tuning on novel tasks.
Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

Presently, suspect prediction of crime scenes can be considered as a classification task, which predicts the suspects based on the time, space, and type of crime. Performing digital forensic investigation in a big data environment poses several challenges to the investigational officer. Besides, the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime scenes. The sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program (composite sketches) based on the verbal explanation given by the eyewitness or victim. Since this suspect identification process is slow and difficult, it is required to design a technique for a quick and automated facial sketch generation. Machine Learning (ML) and deep learning (DL) models find it useful to automatically support the decision of forensics experts. The challenge is the incorporation of the domain expert knowledge with DL models for developing efficient techniques to make better decisions. In this view, this study develops a new artificial intelligence (AI) based DL model with face sketch synthesis (FSS) for suspect identification (DLFSS-SI) in a big data environment. The proposed method performs preprocessing at the primary stage to improvise the image quality. In addition, the proposed model uses a DL based MobileNet (MN) model for feature extractor, and the hyper parameters of the MobileNet are tuned by quasi oppositional firefly optimization (QOFFO) algorithm. The proposed model automatically draws the sketches of the input facial images. Moreover, a qualitative similarity assessment takes place with the sketch drawn by a professional artist by the eyewitness. If there is a higher resemblance between the two sketches, the suspect will be determined. To validate the effective performance of the DLFSS-SI method, a detailed qualitative and quantitative examination takes place. The experimental outcome stated that the DLFSS-SI model has outperformed the compared methods in terms of mean square error (MSE), peak signal to noise ratio (PSNR), average actuary, and average computation time.