Reviewerly/exHarmony huggingface dataset:
A method of generating Key.Net sub-pixel key-points by local gradient fitting

In this paper, we propose a new approach for sub-pixel registration based on Key.Net combined with a local gradient fitting method. Considering Key.Net of pixel-level accuracy cannot meet the need of high-precision image matching, we innovatively calculate the gradient of the response values of key-point and its neighbors in the response map obtained from the Key.Net, which effectively avoids errors caused by factors such as illumination and shadow for gray value calculation by traditional gradient methods. We first apply the Key.Net detector to extract multi-scale key-points from input images, then build response a map using response values of all pixels on each scale plane and calculate the sub-pixel offset of a pixel-level key-point based on the local gradient relations of the key-point and its neighbors in the response map. Finally, we get the sub-pixel coordinates of the key-point. The experimental results show that the proposed method successfully matched some low-quality images and detected large numbers of correct matching points. The localization accuracy of key-points was improved to 0.1 pixels, while the excellent feature detection ability of Key.Net was retained. When testing using the HPatches sequences dataset, our method achieved the highest matching score for view-point, which is 15.5% higher than SIFT, and 0.6% higher than Key. Net-HardNet after increasing the number of scale layers.
GomokuPro: An Implementation of Enhanced Machine Learning Algorithm Utilizing Convolutional Neural Network in Gomoku Strategy and Predictions Model

In this paper, the researcher proposes a machine learning Gomoku model, GomokuPro, which is based on CNN (Convolutional Neural Network) as well as Monte Carlo tree search. The purpose of this research aggregation is to solve the previous problem of artificial intelligence algorithms taking too long to compute in board games. The researchers turned a more multidimensional fractional model into an adaptive model based on a single machine learning strategy previously developed by the researchers and capable of making judgments and predictions based on the following human algorithms. Due to the limitations of the hardware used for testing, the researchers found that the results were not applicable to some specific cases. Although this algorithm significantly reduces the computational effort, it still does not provide robust performance in some cases. The computational effort of convolutional neural networks is also influenced by the type of data. However, most of the time, machine learning models are still able to make master predictions.
Research on the Application of Target Detection Algorithm in the Field of Unmanned Driving

Based on the rapid development of deep learning and the application of high-performance embedded devices, the fusion application of target detection algorithms based on deep convolutional neural networks in the field of unmanned driving is described. Through the research of two types of target detection algorithms based on candidate regions and regression, an example of the application process of YOLO (You Only Look Once) real-time target detection series algorithms for target detection on traffic roads is proposed. Vehicles can have better road sign recognition capabilities, providing the basis for vehicle unmanned behavior decision-making.
Low Light Enhancer: A Low Light Image Enhancement Model Based on U-Net using Smartphone

In this paper, we propose an improved way low light image-enhancing model for Smartphone devices by training a U-Net based fully convolutional neural network (CNN) with raw sensor images. We collected a new dataset captured via a Smartphone camera which is a set of low light images, corresponding long exposure images are captured for ground truth. Our primary goal is to train our model with different Smartphone camera devices having a Bayer filter with different image sensors. The second goal is to get noise-free images with minimum color distortion.
Clustering and Evolutionary System Analysis of Data Mining Algorithms in the Field of Football

Football is a competitive sport in China. It plays an important role in modern sports. Its development level directly affects people's health. In recent years, with the advent and popularization of economic globalization and the information age, athletes pay more attention to their own achievements. Therefore, it is particularly necessary to analyze and study the competition rules. Firstly, this paper introduces clustering mining and expounds the development of data mining. Then, the application of clustering mining technology is studied, the system structure of information clustering mining in the field of football is designed, and the relevant information of football players in a football club is extracted for testing. Finally, the test results show that the system can mine specific information from a large number of messy information data, and can effectively analyze the data value and present it to football trainers.
Improvement of English Teaching Process Management Based on Intelligent Data Sampling

In order to improve the management effect of English teaching process, this paper combines intelligent data to use technology to improve the management of English teaching process, improve the effect of English teaching, and construct an intelligent English teaching process management system. Moreover, this paper considers the interpolation problem of time series data in the metric space defined by dynamic time warping, and proposes an oversampling method for unbalanced time series data. In addition, this paper chooses to classify the Gaussian process model that is sensitive to unbalanced time series data to test the effect of the model. The experimental research results show that the English teaching process management system based on intelligent data sampling proposed in this paper can play an important role in English teaching management and can effectively improve the efficiency of English teaching.
Linear Coded Federated Learning under Multiple Stragglers over Heterogeneous Clients

Recently, federated learning (FL) becomes a emerging research area, and the combination of edge computing and FL is one of the important research contents. However, there are many kinds of edge devices in heterogeneous federated learning, such as personal computers, embedded devices, and the resource-limited devices will reduce the efficiency of FL. In this paper, we propose an efficient linear coded federated learning under multiple stragglers (LCFLMS) to (1) accelerate the training speed and improve the efficiency of heterogeneous FL under multiple stragglers and (2) provide the certain level of privacy protection. We design a client-based multiple stragglers task outsourcing (C-MSTO) algorithm and a server-based multiple stragglers task outsourcing (S-MSTO) algorithm to meet the model calculation acceleration in general environment under multiple stragglers. In the process of outsourcing, the raw data are protected by using linear coding computing (LCC) scheme. Finally, the experimental results demonstrate that LCFLMS reduces the training time by 90.22% when the performance difference between clients in FL system is large.
Improved particle filter algorithm based on piezoelectric Lamb wave monitoring characteristics

The prediction of fatigue crack growth by particle filter algorithm is to establish the state space-model of particle filter by using pairs formula combined with state noise model, to establish the observation space model of particle filter by using Lamb and its damage factor monitoring crack growth method combined with observation noise model, and to predict the crack growth by using the state-space model at the same time. In the process of monitoring fatigue crack growth by Lamb wave, the longer the crack is, the greater the uncertainty of the damage factor is. Based on the above characteristics, an observation space model with an observation noise model continuously modified with crack length is established. The hole edge crack propagation experiments are carried out to predict the crack propagation of particle filter algorithm and the improved particle filter algorithm. The experimental results show that the improved particle filter algorithm is not easy to diverge in the later stage of crack propagation, that is, after 25,000 cycles in this experiment. After 27650 cycles, the crack propagation prediction error is only 0.7mm.
A Profiling and Query Platform for Research Management Based on Knowledge Graph

Researching is a process whereby a large amount of new and unstructured knowledge is created and accumulated. In this context, the capture of complex knowledge about detailed work and decision-making issues throughout a research project is very challenging for modern researchers. Knowledge graph technology can help machines better understand complicated relationships between entities, has great potential for helping researchers with organizing and automating such kinds of repetitive works, and even uncovering and providing new insights into related topics.This paper introduces a way to construct a research management platform by providing a profiling and query system visualized as a knowledge graph. With the scope of this platform being restricted to the research field, typical ontologies are proposed on different levels. For better and more meaningful visualization, specific modifications and improvements to the traditional knowledge graph structure are discussed. A prototype system that is under construction is then described based on the above work, with the extensive applications discussed.
Understanding News Background Information Seeking from Readers’ Perspective

Background information is very important for news reading and understanding, especially with today’s rapid new media development for news production and consumption. When researchers actively develop tools to automatically provide back-ground information for people, we still have little understanding of how people, from readers’ perspective, engage with news and what background information is needed. To address such a gap, we conducted a study with a news reading platform, observed participants’ news reading behaviors using the platform and interviewed them over the process. Our study highlighted that news reading is a learning activity between leisure and study, and their background information searching behavior is part of Gestalt knowing – to make their existing knowledge more complete, by deepening their prior understandings, broadening their horizons, filling the gaps, clearing their confusion, as well as tracking the progress of an event as a whole, so a more holistic understanding could be achieved. We end by discussing implications of our findings.
Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes

Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment regime (DTR) with reinforcement learning (RL) on electronic health records (EHR) has attracted interest from both the healthcare industry and machine learning research community. However, most learned DTR policies might be biased due to the existence of confounders. Although some treatment actions non-survivors received may be helpful, if confounders cause the mortality, the training of RL models guided by long-term outcomes (e.g., 90-day mortality) would punish those treatment actions causing the learned DTR policies to be suboptimal. In this study, we develop a new deconfounding actor-critic network (DAC) to learn optimal DTR policies for patients. To alleviate confounding issues, we incorporate a patient resampling module and a confounding balance module into our actor-critic framework. To avoid punishing the effective treatment actions non-survivors received, we design a short-term reward to capture patients' immediate health state changes. Combining short-term with long-term rewards could further improve the model performance. Moreover, we introduce a policy adaptation method to successfully transfer the learned model to new-source small-scale datasets. The experimental results on one semi-synthetic and two different real-world datasets show the proposed model outperforms the state-of-the-art models. The proposed model provides individualized treatment decisions for mechanical ventilation that could improve patient outcomes.
Efficient approximate search for sets of lineage vectors

One can approximate the lineage of a Database (DB) tuple using a small set of low dimensional vectors. To identify actual lineage tuples using these vector sets, given a set of vectors (of the target tuple), one needs to locate "close" sets of vectors associated with the lineage tuples. We first consider a similarity measure between two sets A and B of vectors, that balances the average and maximum cosine distance between pairs of vectors, one from set A and one from set B. The proposed similarity measure is intuitive and permutation invariant. To practically realize this measure, we need an approximate search algorithm that given a set of vectors A and sets of vectors B1, ..., Bn, the algorithm quickly locates the k-closest sets Bi1 ..., Bik that maximize the similarity measure. For the case where all sets are singleton sets, essentially each is a single vector, there are known efficient approximate search algorithms, e.g., approximated versions of tree search algorithms, locality-sensitive hashing (LSH), vector quantization (VQ) and proximity graph algorithms. We utilize the mathematical properties of the cosine distance measure to transform the set-set search problem into a vector-vector search problem. However, this abovementioned transformation cannot handle the Euclidean-based version of the similarity measure. For this version, we devise a more elaborate transformation. For this latter transformation, we present algorithms for the general case, with sets of differing cardinalities. The underlying idea in both of these transformations is encoding a set of vectors A via |A| "long" independent representative vectors. Then, we are able to transform the set-set search problem into the well-studied approximate (ordinary) vector search problem. For both cosine-based and Euclidean-based similarity measures, the proposed approximate search achieves significant performance gains over an optimized, exact search on vector sets.
Theory as a source of software and system requirements

Abstract Today, when undertaking requirements elicitation, engineers attend to the needs and wants of the user groups considered relevant for the software system. However, answers to some relevant questions (e.g., how to improve adoption of the intended system) cannot always be addressed through direct need and want elicitation. Using an example of energy demand-response systems, this paper demonstrates that use of grounded theory analysis can help address such questions. The theories emerging from such analysis produce a set of additional requirements which cannot be directly elicited from individuals/groups, and would otherwise be missed. Thus, we demonstrate that the theories generated through grounded theory analysis can serve as additional valuable sources of requirements for software and its wider system. This paper extends our previous work by demonstrating how several theories can be constructed and utilised for a single system analysis.
Hearing Check Failed

Sound waves from speech can potentially induce vibrations, proportional to the speech signal, on nearby objects. Each of these objects introduces the risk for a malicious attacker to exploit the induced vibrations to eavesdrop on the speech. Such an eavesdropping attack is critical when we consider the potential for induced vibrations in standard magnetic hard disk drives (HDDs). As an instance of this threat, prior research has demonstrated that speech in certain scenarios can induce vibrations on the read/write head of an HDD in order to eavesdrop on the speech (Kwong et al.; Oakland'19). In this paper, we revisit this line of research and aim to provide a closer investigation into whether HDDs can in fact be used as a source for eavesdropping on speech vibrations. As a foundation for our study, we utilize an effective, and robust methodology using laser vibrometry to measure the subtle speech vibrations induced on the read/write head. The prior study tested only a single HDD and only machine-rendered speech in a single setting with very loud speech. Our work broadens the scope of this research in many significant ways. First, we test multiple popular HDDs of different models and sizes to evaluate the generalizability of the overall threat. Second, we evaluate the threat from live human speech spoken near an HDD, expanding the scope of the attack to include most real-world speech settings involving normal human conversations. Third, we define machine-rendered speech scenarios to explore different propagation media and degrees of speech loudness. Our findings are two-fold. First, we observed that live human speech traveling through the air is not generally strong enough to impact HDDs such that intelligible speech information is leaked. Second, most tested HDDs did not seem capable of eavesdropping on machine-rendered speech unless the speech is loud enough, or the HDD shares a surface or is in direct contact with the speaker device. This implies HDDs cannot eavesdrop live human speech.
AtM-DNN: A Multimodal Attention Fusion Network with Auxiliary Function for Sentiment Classification

Multimodal sentiment classification is an important research attracting many scientists’ attention in natural language processing. In most multimodal sentiment research, each modal of the dataset is labeled with a unified label. However, this unified label of multimodal data may limit the model to obtain the different information between multimodal in the training process. To address the above issues, this paper proposes AtM-DNN, a model based on multimodal attention fusion network in collaboration with auxiliary function for multimodal sentiment classification. Different single-modal encoders are used to map the original data of different modal into vectors of different information dimensions. Attention mechanism is introduced to control the information weight of each modal in the interaction, so as to remove the redundancy of information. Besides, each modal is introduced into the loss function as an auxiliary function to assist the calculation of the final loss function. Experiments on CH-SIMS dataset show that the attention mechanism of each modal can exactly improve the accuracy and F1 of sentiment classification of each single-modal, and also can improve the accuracy and F1 of multimodal sentiment classification. Although the collaboration of auxiliary function can improve the effect of multimodal sentiment classification, it is likely to reduce the effect of each single-modal. And these models are better than the single-modal sentiment classification model.
Key Frame Extraction Based on Sports Statistical Characteristics

In order to enable users to quickly obtain the most informative frame in the video, we proposed a sports video summarization technology that comprehensively applies the global optical flow and local optical flow characteristic. On the premise of capturing visual differences, we consider the simple semantic features of sports video. In this method, shot segmentation is done based on color features and some denoise process is added. Then we used spline curve to obtain the global optical flow and local optical flow of each frame, and obtain the camera motion function. The significant difference of camera motion function in one shot between frames is used to extract shot key frames. Then, we introduce local optical flow to solve the problem of redundant frames.
A Speech Recognition Model Building Method Combined Dynamic Convolution and Multi-Head Self-Attention Mechanism

The Conformer enhanced Transformer by using convolution serial connected to the multi-head self-attention (MHSA). The method strengthened the local attention calculation and obtained a better effect in auto speech recognition. This paper proposes a hybrid attention mechanism which combines the dynamic convolution CNNs and multi-head self-attention. This study focuses on generating local attention by embedding DY-CNNs in MHSA, followed by parallel computation of the globe and local attention inside the attention layer. Finally, concatenate the result of global and local attention to the output. In the experiments, we use the Aishell-1 (178 hours) Chinese database for training. In the testing folder dev/test, 4.5%/4.8% CER was obtained. The proposed method shows better performance in computation speed and the number of experimental parameters. The results are extremely close to the best result (4.4%/4.7%) of the Conformer.
Automatic construction of real‐world‐based typing‐error test dataset

In this study, we aim to automatically construct a test dataset for testing the performance of spelling error correction systems. The Google Web 1T corpus, which includes data on 10 quadrillion phrases, is used for this purpose. Therefore, error words used in the test dataset use error words generated by real web users. There are seven types of error words. In order to obtain the error word, a word set that appears simultaneously with the surrounding context (3-g range) of the location of the error word generation is searched. In this calculation, we exclude error words with wide edit distances that cause the resolution of original words to become exceedingly difficult. In order to select the final error word from the word set, a word with a high value is selected by calculating the context probability using 3-g. In the experiment, the performance was measured for two systems (grammarly, MS Word) in service and the recently announced spelling error correction system (Neuspell). The highest performance was the F1 score of 56%, which shows the overall performance, indicating the need for research on spelling errors.
Use of Framework Synthesis to Identify the Factors Considered for Five Popular Prioritisation Approaches

Software requirement prioritisation is an important task that ultimately determines whether the software is successful and achieves customer satisfaction. Startups use agile methodologies to develop software, as it adapts to requirement changes well and delivers software quickly in short increments, called sprints. However, there is little research about the practices of agile requirement re-prioritisation, the activity to reprioritise requirements at the start of each sprint. This research contributes to this gap by identifying the factors considered for five popular prioritisation approaches and compares them to the agile requirement re-prioritisation process. The results show that the approaches studied do not address all factors of the agile requirement re-prioritisation process. The planning game covers five of the factors whereas analytical hierarchy process covers three of the factors. This may influence the choice of approach used for agile requirement re-prioritisation. This study contributes important insights for requirement prioritisation literature and practice.