Reviewerly/exHarmony huggingface dataset:
Convergence analysis for sigma-pi-sigma neural network based on some relaxed conditions

This work proves the deterministic convergence of the Sigma-Pi-Sigma neural network based on the batch gradient learning algorithm under certain relaxed conditions. We establish strong and weak convergence results and prove that the error function decreases monotonically and tends to zero. The boundedness of weights is also proved simply and efficiently. In contrast to the usual requirements for the boundedness of weights, this work shows that such weight boundedness is no more one of the necessary conditions for ensuring convergence. In addition, we also show that the requirements on the learning rate and the stationary point set of the error function can be relaxed. Finally, the effectiveness of the proposed algorithm is validated by numerical experiments, followed by brief conclusions.
Quantum information scrambling: from holography to quantum simulators

In this review, we present the ongoing developments in bridging the gap between holography and experiments. To this end, we discuss information scrambling and models of quantum teleportation via Gao-Jafferis-Wall wormhole teleportation. We review the essential basics and summarize some of the recent works that have so far been obtained in quantum simulators towards a goal of realizing analogous models of holography in a lab.
Research on College English Teaching System Based on Computer Big Data

Abstract With the development and maturity of computer and multimedia technology, computer-aided teaching represents advanced teaching ideas and teaching methods. Computer-aided learning systems can dynamically generate personalized teaching content and strategies through intelligent system inferences. The diagnosis mechanism is used to judge the learning level of students and provide a favorable basis for teachers to adjust teaching content. Based on the advancement of the current English teaching reform, the use of computer aid has become a trend in English teaching. It analyzes the current design requirements of computer aided English teaching systems and designs a teaching system based on C++ and Windows. The results prove that based on C++ and Windows, the design and realization of computer-assisted English teaching cannot increase the actual teaching efficiency by 20%, and can also play the value of computer-assisted English teaching, improve the level of English teaching by 12.0%. The conclusion shows that in the design of computer-assisted English teaching system, the design system based on C++ and Windows can effectively improve the quality of computer-assisted English teaching and have a positive impact..
YOLOv5_CSL_F: YOLOv5’s Loss Improvement and Attention Mechanism Application for Remote Sensing Image Object Detection

With the continuous improvement of the resolution of satellite remote sensing images and aerial remote sensing images, more and more useful data and information are obtained from remote sensing images. At the same time, compared with ordinary images, remote sensing images have the characteristics of variable directions, unbalanced categories, complex backgrounds, and difficult detection of small objects. All of these make remote sensing image object detection very challenging. In this paper, based on the deep learning framework and the YOLOv5 object detection algorithm, according to the characteristics of remote sensing images, adopting Circular Smooth Label (CSL) [1] to calculate the loss of the rotating object detection bounding box and introducing the FcaNet [2] attention mechanism to design new feature fusion modules, we propose the remote sensing image object detection algorithm YOLOv5_CSL_F. We tested the algorithm model on the DOTA dataset. Compared with the detection performance of the original YOLOv5 algorithm, our algorithm improves the detection accuracy by 0.6%.
Efficient Combinatorial Optimization for Word-Level Adversarial Textual Attack

Over the past few years, various word-level textual attack approaches have been proposed to reveal the vulnerability of deep neural networks used in natural language processing. Typically, these approaches involve an important optimization step to determine which substitute to be used for each word in the original input. However, current research on this step is still rather limited, from the perspectives of both problem-understanding and problem-solving. In this paper, we address these issues by uncovering the theoretical properties of the problem and proposing an efficient local search algorithm (LS) to solve it. We establish the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> provable approximation guarantee on solving the problem in general cases. Extensive experiments involving 5 NLP tasks, 8 datasets and 26 NLP models show that LS can largely reduce the number of queries usually by an order of magnitude to achieve high attack success rates. Further experiments show that the adversarial examples crafted by LS usually have higher quality, exhibit better transferability, and can bring more robustness improvement to victim models by adversarial training.
Optimization-Based Maneuver Planning for a Tractor-Trailer Vehicle in a Curvy Tunnel: A Weak Reliance on Sampling and Search

This study is focused on the maneuver planning problem for a tractor-trailer vehicle in a curvy and tiny tunnel. Due to the curse of dimensionality, the prevalent sampling-and- search-based planners used to handle a rigid-body vehicle well become less efficient when the trailer number grows or when the tunnel narrows. This fact also has impacts on an optimization-based planner if it counts on a sampling-and-search-based initial guess to warm-start. We propose an optimization-based maneuver planner that weakly relies on the sampling and search, hoping to get rid of the curse of dimensionality and thus find optima rapidly. The proposed planner comprises three stages: stage 1 identifies the homotopy class via A* search in a 2D grid map; stage 2 recovers the kinematic feasibility with softened intermediate problems iteratively solved; stage 3 finds an optimum that strictly satisfies the nominal collision-avoidance constraints. Optimization-based planners are commonly known to run slowly, but this work shows that they have obvious advantages over the prevalent sampling-and-search-based planners when the solution space dimension is high and/or the constraints are harsh.
Fusion-Based Correlation Learning Model for Cross-Modal Remote Sensing Image Retrieval

With the increasing of cross-modal data, cross-modal retrieval has attracted more attention in remote sensing (RS), since it provides a more flexible and convenient way to obtain interesting information than traditional retrieval. However, existing methods cannot fully exploit the semantic information, which only focuses on the semantic consistency, and ignore the information complementarity between different modalities. In this letter, to bridge the modality gap, we propose a novel fusion-based correlation learning model (FCLM) for image-text retrieval in RS. Specifically, a cross-modal-fusion network is designed to capture the intermodality complementary information and fused feature. The fused knowledge is furtherly transferred to supervise the learning of modality-specific network by knowledge distillation, which is helpful in improving the discriminative ability of feature representation and enhancing the intermodality semantic consistency to solve the heterogeneity gap problem. Finally, extensive experiments have been conducted on a public dataset and experimental results have shown that the FCLM method is effective in performing cross-modal retrieval and outperforms several baseline methods.
Incorporating FAT and privacy aware AI modeling approaches into business decision making frameworks

We present a formal approach to build and evaluate AI systems that include principles of Fairness, Accountability and Transparency (FAT), which are extremely important in various domains where AI models are used, yet their utilization in business settings is scant. We develop and instantiate a FAT-based framework with a privacy-constrained dataset and build a model to demonstrate the balance among these 3 dimensions. These principles are gaining prominence with higher awareness of privacy and fairness in business and society. Our results indicate that FAT can co-exist in a well-designed system. Our contribution lies in presenting and evaluating a functional, FAT-based machine learning model in an affinity prediction scenario. Contrary to common belief, we show that explainable AI/ML systems need not have a major negative impact on predictive performance. Our approach is applicable in a variety of fields such as insurance, health diagnostics, government funds allocation and other business settings. Our work has broad policy implications as well, by making AI and AI-based decisions more ethical, less controversial, and hence, trustworthy. Our work contributes to emerging AI policy perspectives worldwide.
A core ontology for modeling life cycle sustainability assessment on the Semantic Web

Abstract The use of Semantic Web and linked data increases the possibility of data accessibility, interpretability, and interoperability. It supports cross‐domain data and knowledge sharing and avoids the creation of research data silos. Widely adopted in several research domains, the use of the Semantic Web has been relatively limited with respect to sustainability assessments. A primary barrier is that the framework of the principles and technologies required to link and query data from the Semantic Web is often beyond the scope of industrial ecologists. Linking of a dataset to Semantic Web requires the development of a semantically linked core ontology in addition to the use of existing ontologies. Ontologies provide logical meaning to the data and the possibility to develop machine‐readable data format. To enable and support the uptake of semantic ontologies, we present a core ontology developed specifically to capture the data relevant for life cycle sustainability assessment. We further demonstrate the utility of the ontology by using it to integrate data relevant to sustainability assessments, such as EXIOBASE and the Yale Stocks and Flow Database to the Semantic Web. These datasets can be accessed by the machine‐readable endpoint using SPARQL, a semantic query language. The present work provides the foundation necessary to enhance the use of Semantic Web with respect to sustainability assessments. Finally, we provide our perspective on the challenges toward the adoption of Semantic Web technologies and technical solutions that can address these challenges.
First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland)

Recent advances in search and rescue methods include the use of unmanned aerial vehicles (UAVs), to carry out aerial monitoring of terrains to spot lost individuals. To date, such searches have been conducted by human observers who view UAV-acquired videos or images. Alternatively, lost persons may be detected by automated algorithms. Although some algorithms are implemented in software to support search and rescue activities, no successful rescue case using automated human detectors has been reported on thus far in the scientific literature. This paper presents a report from a search and rescue mission carried out by Bieszczady Mountain Rescue Service near the village of Cergowa in SE Poland, where a 65-year-old man was rescued after being detected via use of SARUAV software. This software uses convolutional neural networks to automatically locate people in close-range nadir aerial images. The missing man, who suffered from Alzheimer’s disease (as well as a stroke the previous day) spent more than 24 h in open terrain. SARUAV software was allocated to support the search, and its task was to process 782 nadir and near-nadir JPG images collected during four photogrammetric flights. After 4 h 31 min of the analysis, the system successfully detected the missing person and provided his coordinates (uploading 121 photos from a flight over a lost person; image processing and verification of hits lasted 5 min 48 s). The presented case study proves that the use of an UAV assisted by SARUAV software may quicken the search mission.
Specific Radar Recognition Based on Characteristics of Emitted Radio Waveforms Using Convolutional Neural Networks

With the increasing complexity of the electromagnetic environment and continuous development of radar technology we can expect a large number of modern radars using agile waveforms to appear on the battlefield in the near future. Effectively identifying these radar signals in electronic warfare systems only by relying on traditional recognition models poses a serious challenge. In response to the above problem, this paper proposes a recognition method of emitted radar signals with agile waveforms based on the convolutional neural network (CNN). These signals are measured in the electronic recognition receivers and processed into digital data, after which they undergo recognition. The implementation of this system is presented in a simulation environment with the help of a signal generator that has the ability to make changes in signal signatures earlier recognized and written in the emitter database. This article contains a description of the software's components, learning subsystem and signal generator. The problem of teaching neural networks with the use of the graphics processing units and the way of choosing the learning coefficients are also outlined. The correctness of the CNN operation was tested using a simulation environment that verified the operation's effectiveness in a noisy environment and in conditions where many radar signals that interfere with each other are present. The effectiveness results of the applied solutions and the possibilities of developing the method of learning and processing algorithms are presented by means of tables and appropriate figures. The experimental results demonstrate that the proposed method can effectively solve the problem of recognizing raw radar signals with agile time waveforms, and achieve correct probability of recognition at the level of 92-99%.
Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty

Uncertain working conditions will lead to abnormal energy consumption and energy loss of high energy consumption machines. Therefore, it is necessary to detect abnormal energy consumption data, which is designed to optimize energy efficiency. In this study, two unsupervised anomaly detection methods are proposed, suitable for dynamic real–time abnormal energy consumption detection. As for point anomaly, a Rain Flow–based Connectivity Outlier Factor algorithm is proposed, which is a connectivity–based detection method. While, as for collective anomaly, a Rain Flow–based Mean Nearest Neighbor Distance Anomaly Factor algorithm is proposed, which is a distance–based detection method. The proposed two algorithms adopt the methods of time sorting and piecewise cubic Hermite interpolating polynomial, respectively. The aim is to accurately locate anomalies in the time dimension. Then, abnormal energy consumption is automatically found by setting a dynamic real–time threshold. To verify the superiority and the effectiveness of the proposed method, the actual energy consumption data from aluminum extrusion enterprises are used for simulation experiments. The results demonstrate that the proposed method can detect anomalies quickly and accurately. Finally, this study presents an energy efficiency optimization framework based on anomaly detection. Our tests in energy management systems show that the proposed framework can optimize energy efficiency and reduce energy waste. This provides an effective solution to clean production and low carbon manufacturing of high energy consumption machines.
Fifty years of P vs. NP and the possibility of the impossible

Advances in algorithms, machine learning, and hardware can help tackle many NP-hard problems once thought impossible.
Semi-device-independent certification of multiple unsharpness parameters through sequential measurements

Based on a sequential communication game, semi-device-independent certification of an unsharp instrument has recently been demonstrated [K. Mohan, A. Tavakoli, and N. Brunner, New J. Phys. 21, 083034 (2019)]. In this paper, we provide semi-device-independent self-testing protocols in the prepare-measure scenario to certify multiple unsharpness parameters along with the states and the measurement settings. This is achieved through the sequential quantum advantage shared by multiple independent observers in a suitable communication game known as the parity-oblivious random-access code. We demonstrate that in the three-bit parity-oblivious random-access code at most three independent observers can sequentially share quantum advantage. The optimal pair (triple) of quantum advantages enables us to uniquely certify the qubit states, the measurement settings, and the unsharpness parameter(s). The practical implementation of a given protocol involves inevitable losses. In a suboptimal scenario, we derive a certified interval within which a specific unsharpness parameter has to be confined. We extend our treatment to the four-bit case and show that at most two observers can share quantum advantage for the qubit system. Further, we provide an outline to argue that four sequential observers can share the quantum advantage for the two-qubit system, thereby enabling the certification of three unsharpness parameters.
Evaluating Human versus Machine Learning Performance in a LegalTech Problem

Many machine learning-based document processing applications have been published in recent years. Applying these methodologies can reduce the cost of labor-intensive tasks and induce changes in the company’s structure. The artificial intelligence-based application can replace the application of trainees and free up the time of experts, which can increase innovation inside the company by letting them be involved in tasks with greater added value. However, the development cost of these methodologies can be high, and usually, it is not a straightforward task. This paper presents a survey result, where a machine learning-based legal text labeler competed with multiple people with different legal domain knowledge. The machine learning-based application used binary SVM-based classifiers to resolve the multi-label classification problem. The used methods were encapsulated and deployed as a digital twin into a production environment. The results show that machine learning algorithms can be effectively utilized for monotonous but domain knowledge- and attention-demanding tasks. The results also suggest that embracing the machine learning-based solution can increase discoverability and enrich the value of data. The test confirmed that the accuracy of a machine learning-based system matches up with the long-term accuracy of legal experts, which makes it applicable to automatize the working process.
Flimma: a federated and privacy-aware tool for differential gene expression analysis

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.
Adjusted stochastic gradient descent for latent factor analysis

A high-dimensional and incomplete (HDI) matrix is a common form of big data in most industrial applications. Stochastic gradient descent (SGD) algorithm optimized latent factor analysis (LFA) model is often adopted in learning the abundant knowledge in HDI matrix. Despite its computational tractability and scalability, when solving a bilinear problem such as LFA, the regular SGD algorithm tends to be stuck in a local optimum. To address this issue, the paper innovatively proposes an Adjusted Stochastic Gradient Descent (ASGD) for Latent Factor Analysis, where the adjustment mechanism is implemented by considering the bi-polar gradient directions during optimization, such mechanism is theoretically proved for its efficiency in overstepping local saddle points and avoiding premature convergence. Also, the hyper-parameters of the model are implemented in a self-adaptive manner using the particle swarm optimization (PSO) algorithm, for higher practicality. Experimental results show that the proposed model outperforms other state-of-the-art approaches on six different HDI matrices from industrial applications, especially in prediction accuracy for missing data.
MACFI: A multi-authority access control scheme with efficient ciphertext and secret key size for fog-enhanced IoT

With the rapid development of Internet of things (IoT), the number of connected devices and the data generated by them are increasing dramatically. This led to the development of paradigm fog computing, which facilitates data analysis and processing at the edge. Along with fog, cloud co-exists to provide massive storage, processing resources, etc. However, data storage and computation at multiple levels raise the risk of data security. Ciphertext-policy attribute-based encryption (CP-ABE) is a well-known cryptographic technique for providing fine-grained access control. Unfortunately, the existing multi-authority CP-ABE schemes are incompatible with resource-limited IoT devices as the ciphertext and secret key size grow linearly with the number of attributes. Therefore, we propose a multi-authority CP-ABE scheme named MACFI, where the ciphertext and secret key size are efficient. The size of the secret key held by the user is constant irrespective of the number of attributes. And, the ciphertext size increases linearly with the number of authorities rather than the number of attributes. Further, expensive decryption operations are outsourced to fog, which reduces the computation overhead of data users. Additionally, the overall encryption and decryption time are highly efficient. According to security and performance analysis, MACFI is secure and suitable for IoT applications.