Reviewerly/exHarmony huggingface dataset:
A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English

Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to.Compared with the general coreference resolution task, the main challenge of PCR is the coreference relation prediction rather than the mention detection.As one important natural language understanding (NLU) component, pronoun resolution is crucial for many downstream tasks and still challenging for existing models, which motivates us to survey existing approaches and think about how to do better.In this survey, we first introduce representative datasets and models for the ordinary pronoun coreference resolution task.Then we focus on recent progress on hard pronoun coreference resolution problems (e.g., Winograd Schema Challenge) to analyze how well current models can understand commonsense.We conduct extensive experiments to show that even though current models are achieving good performance on the standard evaluation set, they are still not ready to be used in real applications (e.g., all SOTA models struggle on correctly resolving pronouns to infrequent objects).All experiment codes will be available upon acceptance.
Construction and Research of Safety Management System for Machine Patrol Operation

Aiming at the flight safety problems existing in UAV power patrol inspection, this study proposes a UAV patrol inspection safety management system (SMS) scheme, which takes the safety target level specified in soar as the target management, based on safety management, modern control theory, risk management, and UAV operation regulations, combined with the actual needs of machine patrol inspection of China Southern Power Grid and the requirements of local government. The UAV SMS scheme is developed from four parts: safety policy, risk management, safety guarantee, and safety promotion, and the application analysis is carried out in combination with the Kobe accident case in 2020. The experimental results demonstrate that the implementation of SMS scheme can effectively reduce the accident risk level during UAV power patrol inspection.
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training

Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique that approximately solves a robust optimization problem to minimize the worst-case loss and is widely regarded as the most effective defense against such attacks. Due to the high computation time for generating strong adversarial examples in the AT process, single-step approaches have been proposed to reduce training time. However, these methods suffer from catastrophic overfitting where adversarial accuracy drops during training, and although improvements have been proposed, they increase training time and robustness is far from that of multi-step AT. We develop a theoretical frameworkfor adversarial training with FW optimization (FW-AT) that reveals a geometric connection between the loss landscape and the distortion of l-inf FW attacks (the attack's l–2 norm). Specifically, we analytically show that high distortion of FW attacks is equivalent to small gradient variation along the attack path. It is then experimentally demonstrated on various deep neural network architectures that l-inf attacks against robust models achieve near maximal l-2 distortion, while standard networks have lower distortion. Furthermore, it is experimentally shown that catastrophic overfitting is strongly correlated with low distortion of FW attacks. This mathematical transparency differentiates FW from the more popular Projected Gradient Descent (PGD) optimization. To demonstrate the utility of our theoretical framework we develop FW-AT-Adapt, a novel adversarial training algorithm which uses a simple distortion measure to adapt the number of attack steps during training to increase efficiency without compromising robustness. FW-AT-Adapt provides training time on par with single-step fast AT methods and improves closing the gap between fast AT methods and multi-step PGD-AT with minimal loss in adversarial accuracy in white-box and black-box settings.
Multivariate FMRI Signatures of Learning in a Hebb Repetition Paradigm With Tone Sequences

Important information from the environment often arrives to the brain in temporally extended sequences. Language, music, actions, and complex events generally unfold over time. When such informational sequences exceed the limited capacity of working memory, the human brain relies on its ability to accumulate information in long-term memory over several encounters with a complex stimulus. A longstanding question in psychology and neuroscience is whether the neural structures associated with working memory storage—often viewed as capacity limited and temporary—have any builtin ability to store information across longer temporal delays. According to the classic Hebbian dual memory theory, temporally local “activity traces” underlie immediate perception and working memory, whereas “structural traces” undergird long-term learning. Here we examine whether brain structures known to be involved in working maintenance of auditory sequences, such as area Spt, also show evidence of memory persistence across trials. We used representational similarity analysis (RSA) and the Hebb repetition paradigm with supracapacity tonal sequences to test whether repeated sequences have distinguishable multivoxel activity patterns in the auditory-motor networks of the brain. We found that, indeed, area Spt and other nodes of the auditory dorsal stream show multivoxel patterns for tone sequences that become gradually more distinct with repetition during working memory for supracapacity tone-sequences. The findings suggest that the structures are important for working memory are not “blank slates,” wiped clean from moment to moment, but rather encode information in a way can lead to cross-trial persistence.
The Principle of Maximal Simplicity for Modular Inorganic Crystal Structures

Modularity is an important construction principle of many inorganic crystal structures that has been used for the analysis of structural relations, classification, structure description and structure prediction. The principle of maximal simplicity for modular inorganic crystal structures can be formulated as follows: in a modular series of inorganic crystal structures, the most common and abundant in nature and experiments are those arrangements that possess maximal simplicity and minimal structural information. The latter can be quantitatively estimated using information-based structural complexity parameters. The principle is applied for the modular series based upon 0D (lovozerite family), 1D (biopyriboles) and 2D (spinelloids and kurchatovite family) modules. This principle is empirical and is valid for those cases only, where there are no factors that may lead to the destabilization of simplest structural arrangements. The physical basis of the principle is in the relations between structural complexity and configurational entropy sensu stricto (which should be distinguished from the entropy of mixing). It can also be seen as an analogy of the principle of least action in physics.
Adaptive quantum state tomography via weak value

Quantum state tomography strategy via weak value is valid for arbitrary dimensional systems, and can be easily realized. However, the efficiency of quantum state tomography strategy via weak value is lower than that of the conventional quantum state tomography (QST), especially for high-dimensional systems. To improve the efficiency of the scheme, we propose a two-step adaptive quantum state tomography via weak value for pure states (AQSTW). In AQSTW, by using appropriate measurement strength, the efficiency of AQSTW is significantly larger than that of the conventional QST, especially for high-dimensional systems. And AQSTW can be easily implemented. These results demonstrate AQSTW may be useful in the measurements of the unknown quantum states.
Power log partition collection and storage system based on alliance blockchain

The power system log is used to record the operating data of power equipment, and it has a pivotal position in the management of power equipment. Green development is an inevitable requirement for building a modern economic system and an important part of the high-quality development of power companies. Aiming at the problems of the authenticity, validity, and security of the information in the power system, as well as the requirements of decarbonization and emission reduction, a power system log partition collection and storage system based on alliance blockchain technology is proposed. The system uses blockchain technology's distributed storage, decentralization, data resistance to tampering, consensus authentication, and traceability characteristics to store power system data, avoiding artificial attacks and tampering of information, and ensuring the authenticity of information, Traceability and safety, provide effective guarantee for later use.
Design a Framework for Content Based Image Retrieval Using Hybrid Features Analysis

In recent years, scholars have found content-based image recovery to be a particularly interesting and exciting field. This field develops quick image recovery algorithms that are very similar to pictures from various data sources. There is currently a lot of space and sources for data storage. Finally, once data sources are obtained, there is just competition for the exact and best result. The result is filtered using the heuristic correlation based on the above features of the photographs. The overall matching scores are calculated by adding all these individual feature ratings. The recommended method will be used to retrieve all images containing the content of the query image. The scores will be used to rate the match. The results of the combined method simulation demonstrate that the strategy is successful. This proposed model will improve the accuracy of search results. This research model, like the other metadata models on the web, is interactive and familiar for searching pictures from huge databases and data sources. This model also has a number of handy features built in to improve precision and efficiency. Although there are a few little mysteries in this model, they must be answered subsequently.
Partition Coverage Planning Method Based on Adjustable Rectangular Decomposition for Airport Pavement

The complete coverage detection of airport pavement is one of the important tasks to ensure the safety of civil aviation. Due to the large coverage area of the airport and the rigid requirements for ultra-high security, humans and robots are required to interact when performing specific coverage detection tasks. Therefore, it is a challenge to divide the large airport environment into suitable working areas. In this paper, we propose a partition coverage planning method based on the adjustable rectangular decomposition environment. First, we use rectangles to decompose the environment into the initial working areas without obstacles, where the size of the rectangle can be adjusted according to the task requirements. Then we design the rules for optimizing the working areas so that the optimized working areas are more reasonable and can be directly used for coverage planning by the line scan method. Finally, simulation experiments show the effectiveness of the method.
Vision-Based Communication System for Patients with Amyotrophic Lateral Sclerosis

Patients with amyotrophic lateral sclerosis (ALS) require an external communication device. The system takes on different characteristics depending on the stage of the disease. This research provides a vision-based communication system for end-stage patients who can communicate with their eyes. Hardware and software were used to create the communication system. A headset with a wearable glass, sensitive camera, and near-infrared light was used to implement eye-tracking technology. The commands were displayed on a computer screen. The software for the system was created with the eye-tracking and calibration process in consideration. Furthermore, smart software for the user interface was created, which included six critical needs to be reported. The system has been tested and has yielded convenient results. In this way, patients with ALS can have a dependable, safe, non-invasive, and low-cost communication device to help them with their daily activities.
An Optimization Method for Video Upsampling and Downsampling Using Interpolation-Dependent Image Downsampling

In this paper, an optimization method is proposed by analyzing interpolation-dependent image downsampling (IDID) and bicubic interpolation algorithms to improve the quality of video upsampling and downsampling. The bicubic algorithm is used as a reference to find the optimal downsampled image by setting its mapping relationship to geometric center alignment and improving bicubic for the interpolation kernel coefficients. The Peak Signal to Noise Ratio(PSNR) of the optimized algorithm is improved by 0.64 dB and 0.14 dB on average over bicubic and IDID_cubic, respectively. To verify the effectiveness of this scheme, the upsampling and downsampling effects of bicubic and bicubic of interpolation-dependent image downsampling (IDID_bicubic) in video sequences are compared and analyzed. The results show that the optimized algorithm improves the video image quality, which is important for the application to improve the overall quality of video codec.
Ensemble online weighted sequential extreme learning machine for class imbalanced data streams

Class imbalanced data streams often have unbalanced sample distribution and a certain type of sample size is too small, which will lead to over fitting phenomenon due to insufficient sample learning, and most current classifiers have problems such as model instability. Therefore, choosing the online sequential extreme learning machine (OSELM) as the basic theoretical model, and combining the AdaBoost ensemble learning ideas and cost-sensitive strategies, an ensemble online weighted sequential extreme learning machine algorithm (ABC-OSELM) is proposed. Firstly, in order to solve the problem that the minority classes are easily misclassified due to class imbalance, the OSELM algorithm based on cost-sensitive learning (C-OSELM) is proposed, which improves the misclassification by assigning different penalty parameters to various samples, it can effectively alleviate the phenomenon of excessive deviation of the decision-making surface. On this basis, in order to further improve the classification accuracy and stability of the algorithm, combining C-OSELM with ensemble learning ideas, an ensemble C-OSELM algorithm based on AdaBoost (ABC-OSELM) is proposed. By adopting a homogeneous integration strategy, iteratively adjust the weight of the base classifier to generate a more stable strong classifier. Finally, the effectiveness and feasibility of the ABC-OSELM algorithm are verified through 15 class II imbalanced datasets.
Badminton Path Tracking Algorithm Based on Computer Vision and Ball Speed Analysis

With the development of artificial intelligence and the rapid development of the computer industry, the practicability of computer vision programs is gradually improved. In this paper, the badminton path tracking algorithm based on computer vision analyzes the badminton trajectory and speed. This paper is aimed at analyzing the image processing technology and path tracking algorithm by using computer vision to obtain relevant data and then exploring the factors of badminton path and ball speed transformation, which provides reference significance for badminton players in future training. The path tracking algorithm is used to predict the rotation angle, the ball speed, and the athlete’s body information during the badminton movement through sensors, and the position information of the moving target is captured based on the visual field tracking and target dynamic tracking. Combined with specific badminton players, we first analyze the angle of each limb and the speed of the racket in the process of movement and record the data. Determine different positioning points for different actions, such as pushing the ball, picking the ball, hooking the ball, and rubbing the hair. In this process, we aim at the connection between the highest point and the lowest point of the badminton trajectory and the ball speed. This process fully combines the theoretical knowledge of the path tracking algorithm. The experimental results show that different service skills have different effects on the trajectory and speed of badminton. In the test of relevant data by using the push and receive skills, the lowest point of the ball served by player A in the first three times is higher than that by player B. The most significant difference between the lowest points of the five times is the second time, with a difference of 0.2 m, and the third time, with a minimum difference of 0.03 m.
Covert Sequential Hypothesis Testing

We consider the problem of covert sequential testing, in which a legitimate party attempts to run a sequential test while escaping detection from an adversary. Specifically, the legitimate party’s decisions should meet prescribed risk constraints and, simultaneously, the adversary’s observations induced by the test should remain indistinguishable from the observations obtained in the absence of a test. Our main result is the characterization of the risk exponent ${\gamma}_{{\theta}}$, which captures the asymptotic exponential decrease of the risk with the square-root of the averaged stopping time in the limit of low risk. An example is provided to illustrate how the covertness constraint influences the design of the sequential test.
Quantifying fermionic nonlinearity of quantum circuits

Variational quantum algorithms (VQAs) have been proposed as one of the most promising approaches to demonstrate quantum advantage on noisy intermediate-scale quantum (NISQ) devices. However, it has been unclear whether VQAs can maintain quantum advantage under the intrinsic noise of the NISQ devices, which deteriorates the quantumness. Here we propose a measure, called fermionic nonlinearity, to quantify the classical simulatability of quantum circuits designed for simulating fermionic Hamiltonians. Specifically, we construct a Monte Carlo type classical algorithm based on the classical simulatability of fermionic linear optics, whose sampling overhead is characterized by the fermionic nonlinearity. As a demonstration of these techniques, we calculate the upper bound of the fermionic nonlinearity of a rotation gate generated by four fermionic modes under the dephasing noise. Moreover, we estimate the sampling costs of the unitary coupled cluster singles and doubles quantum circuits for hydrogen chains subject to the dephasing noise. We find that, depending on the error probability and atomic spacing, there are regions where the fermionic nonlinearity becomes very small or unity, and hence the circuits are classically simulatable. We believe that our method and results help to design quantum circuits for fermionic systems with potential quantum advantages.
Development of Falling Notification System for Elderly Using MPU6050 Sensor and Short Message Service

Falls in the elderly are quite common because their motor skills have begun to decline.The elderly who live alone are often late in getting first aid after a fall, as a result of which intensive care is needed.This paper describes the design of falling detection and notification system for the elderly in the form of short messages sent to mobile phones and stored in the system database.The proposed prototype is designed using the MPU6050 sensor to detect falling movements and a continuous notification system in the form of three different messages immediately sent after falling.The tools used to test the research results are the confusion matrix method.The experimental results of this study show that the recall value is 90%, the precision is approximately 82%, the specificity is about 94%, and the accuracy equals 93% respectively.
LCB: Light Cipher Block An Ultrafast Lightweight Block Cipher For Resource Constrained IOT Security Applications

In this fast-paced technological world, the Internet of Things is a ground breaking technology which finds an immense role in the present electronic world which includes different embedded sensors, devices and most other things which are connected to the Internet.The IoT devices are designed in a way that it helps to collect various forms of data from varied sources and transmit them in digitalized form.In modern era of IoT technology data security is a trending issue which greatly affects the confidentiality of important information.Keeping the issue in mind a novel light encryption strategy known as LCB is designed for IoT devices for optimal security.LCB exploits the benefits of Feistel structure and the architectural benefits of substitution permutation network both to give more security.Moreover, this newly designed technique is tested on (Virtex-7) XC7VX330T FPGA board and it takes much little area of 224 GE (Gate Equivalent) and is extremely fast with very less combinational path delay of 0.877 ns.An in-depth screening confirms the proposed work to promise more security to counter cryptographic attacks.Lastly the Avalanche Effect (AE) of LCB showed as 63.125% and 63.875% when key and plaintext (PT) are taken into consideration respectively.
The Analysis about Compressed Sensing Reconstruction Algorithm Based on Machine Learning Applied in Interference Multispectral Images

Interferometric multispectral images contain rich information, so they are widely used in aviation, military, and environmental monitoring. However, the abundant information also leads to the disadvantages that longer time and more physical resources are needed in signal compression and reconstruction. In order to make up for the shortcomings of traditional compression and reconstruction algorithms, the stacked convolution denoising autoencoder (SCDA) reconstruction algorithm for interference multispectral images is proposed in this paper. And, the experimental code based on the TensorFlow system is built to reconstruct these images. The results show that, compared with D-AMP and ReconNet algorithms, the SCDA algorithm has the advantages of higher reconstruction accuracy and lower time complexity and space complexity. Therefore, the SCDA algorithm proposed in this paper can be applied to interference multispectral images.
Adoption of IoT Technology among Elderly NCD Patients in Malaysia: A Conceptual Study Based on the Theory of Planned Behaviour

Non-Communicable diseases (NCDs) are rising as the prime cause of death worldwide as well as in Malaysia. This is the result of evolutions occurring in social and economic factors in health. Some of these factors include a proliferation of business and promotion, improved style of living, change in socio-demographics, enhanced financial affordability, effortless travel and mobility, financial evolution, and import-export of unhealthy goods, causing high-risk behavioural deviations and spike metabolic hazards. Elderly people have a high chance of having NCDs because of poor sickness opposition, the impact of progressing lifestyle, terrible eating routine, and reduced amount of physical activities. At the end of the year 2020, individuals matured by 60 years of age and above in Malaysia are anticipated to be 9.7% of the entire populace. With the arrival of numerous wearable gadgets and smart devices, the IoT (Internet of Things) gadgets are transforming the conventional medical management system into a further customised and smart one. Therefore, the objective of this study is to identify the factors that influence the actual behaviour towards IoT adoption based on the underlying concept of the “theory of planned behaviour”. This study is expected to provide meaningful outcomes in creating awareness of the use of IoT among elderly NCD patients in Malaysia and understanding their behavioural intentions and actual behaviour that can help to maximise the utilisation of the available healthcare resources.