Reviewerly/exHarmony huggingface dataset:
Research on combat simulation agent modelling methods combined with reinforcement learning

Agent-based combat simulation is an important research method in the field of military science and system simulation. Behaviour decision model plays the key role in the design of combat simulation agents. The behaviour tree (BT) designed by nonplayer characters (NPCs) in the game provides an efficient and concise method for the construction of combat simulation agents and has been widely used. Because the rationality of BT construction directly affects the rationality of agent decision logic, designing a reasonable BT has become a crucial step. The design of the operational agent BT not only relies on rich tactical experience but also needs to repeatedly adjust and optimize the BT according to the operational deduction and simulation results. To avoid unreasonable BT design caused by lack of experience and eliminate the process of repeated debugging, a modelling method of a combat simulation agent that combines reinforcement learning and the BT method was proposed. This method not only makes the design process of BT more automatic but also simplifies the experience requirements of the combat simulation agent designers. Experiments show that RL-BT effectively integrates the reinforcement learning method and can endow combat simulation agents with battlefield learning ability while making independent decisions. The agent based on RL-BT for decision modelling can continuously adjust and optimize the decision process through experience accumulation, and its performance in combat simulation is significantly better than that of the agent using the original BT.
Low Power Enhanced Trivium Implementation Using Parallel-Pipeline Technique

The power depletion is major fundamental needs in number of applications such as Internet of Things. The data transfer is major critical undertakings in IoT where more power is absorbed while carrying out cryptography. Lightweight cryptography and many encoding algorithms have been presented under this for software and hardware executions. Trivium is synchronous stream cipher in Profile II of ECRYPT comes under hardware implementation. In Low Power applications, trivium is widely used. Applied a new technique called Parallel-Pipeline technique where the pipeline technique is added to existing parallelization technique to analyze the dynamic power usage of Trivium. The Parallel-pipeline method increases the throughput of the design which is efficient and also the power has been reduced considerably while comparing to the parallelization technique which is implemented in FPGA using Vivado design suite 2016.4. The Parallel-Pipeline technique is also used with Enhanced Trivium to analyze the Power and Resource consumption in FPGA.
A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning

Subword tokenization is a commonly used input pre-processing step in most recent NLP models.However, it limits the models' ability to leverage end-to-end task learning.Its frequency-based vocabulary creation compromises tokenization in low-resource languages, leading models to produce suboptimal representations.Additionally, the dependency on a fixed vocabulary limits the subword models' adaptability across languages and domains.In this work, we propose a vocabulary-free neural tokenizer by distilling segmentation information from heuristicbased subword tokenization.We pre-train our character-based tokenizer by processing unique words from multilingual corpus, thereby extensively increasing word diversity across languages.Unlike the predefined and fixed vocabularies in subword methods, our tokenizer allows end-to-end task learning, resulting in optimal task-specific tokenization.The experimental results show that replacing the subword tokenizer with our neural tokenizer consistently improves performance on multilingual (NLI) and code-switching (sentiment analysis) tasks, with larger gains in low-resource languages.Additionally, our neural tokenizer exhibits a robust performance on downstream tasks when adversarial noise is present (typos and misspelling), further increasing the initial improvements over statistical subword tokenizers.
Sentiment analysis of Twitter data during Farmers' Protest in India through Machine Learning

One of the most essential analysis methods is sentiment analysis using machine learning and tweets as data. The difficulty of performing sentiment analysis of important events was addressed in this work. The farmers&#x0027; protest, a popular topic on social media for months, was the centre of this article. We can examine the depth of this protest and analyze the data based on various people&#x0027;s viewpoints utilizing data acquired in the form of tweets connected to farmer protests using Twitter API. We investigated various strategies for performing sentiment analysis on datasets we produced by gathering and cleaning data from Twitter using popular protest hashtags. We focused on Naive Bayesian network classifiers, Support Vector Machines, and Logistic Regression for machine learning. When working with vast amounts of text data, we can conclude that Naive Bayes is more efficient in Sentiment Analysis than Support Vector Machine and Logistic Regression.
Evolving Dynamic Bayesian Networks by an Analytical Threshold for Dealing with Data Imputation in Time Series Dataset

Datasets with multiple variables are useful to identify trends promptly that can be used to support planning and decision making. However, it is quite common for these datasets to miss observations, in turn influencing the quality of the predictions. This is a challenging problem that is attracting increasing research. Although several imputation methods exist, most imputing methodologies are centred around a specific application, and these approaches fall short of desired goals when applied to time series datasets. In this sense, this paper describes a method for using evolving Dynamic Bayesian Networks by an analytical threshold for dealing with data imputation in time series datasets. Our method smoothly converges to a robust data imputation model and constantly adapts to the arrival of new data, obtaining more reliable network models. We conducted tests using real and simulated datasets. To insert missing values, we consider missing completely at random (MCAR) and missing not at random (MNAR). For each dataset, 10%, 20%, 30% and 40% missing rate was used. For each missing data rate, we inserted the missing values in 40% of the total intervals of each dataset. We benchmarked the results against widely used imputation methods on time series datasets. There is a significant improvement with reductions of values observed for imputation performance metrics NRMSE, MAE and MedAE compared with other existing methods.
Transforming RDM Conversations Into Collaborations

This chapter explores the development of university-wide research data management conversations and collaborations involving key stakeholders at a Research 1 (R1) higher education institution in the southeastern United States of America. Research data management conversations led to collaborations of several key stakeholders across campus (i.e., university, office of research, research compliance office, information technology [IT], researchers, academic units, library) resulting in the development of an inaugural Supporting Data Management at University of Florida (UF) Proposal to the Office of Research draft document in 2019. Funding agencies, research foundations, and research associations increasingly request plans outlining how scientific data from funded research will be annotated, managed, shared, and stored. The National Institutes of Health (NIH) new Policy on Data Management and Sharing becomes effective January 25, 2023. The authors employed a participatory action research method to explore evolving aspects of the data science ecosystem, including research data management.
Hiding Sensitive Information in Surveillance Video without Affecting Nefarious Activity Detection

Protection of private and sensitive information is the most alarming issue for security providers in surveillance videos. So to provide privacy as well as to enhance secrecy in surveillance video without affecting its efficiency in detection of violent activities is a challenging task. Here a steganography based algorithm has been proposed which hides private information inside the surveillance video without affecting its accuracy in criminal activity detection. Preprocessing of the surveillance video has been performed using Tunable Q-factor Wavelet Transform (TQWT), secret data has been hidden using Discrete Wavelet Transform (DWT) and after adding payload to the surveillance video, detection of criminal activities has been conducted with maintaining same accuracy as original surveillance video. UCF-crime dataset has been used to validate the proposed framework. Feature extraction is performed and after feature selection it has been trained to Temporal Convolutional Network (TCN) for detection. Performance measure has been compared to the state-of-the-art methods which shows that application of steganography does not affect the detection rate while preserving the perceptual quality of the surveillance video.
Extended clustering algorithm based on cluster shape boundary

Based on the shape characteristics of the sample distribution in the clustering problem, this paper proposes an extended clustering algorithm based on cluster shape boundary (ECBSB). The algorithm automatically determines the number of clusters and classification discrimination boundaries by finding the boundary closures of the clusters from a global perspective of the sample distribution. Since ECBSB is insensitive to local features of the sample distribution, it can accurately identify clusters on complex shape and uneven density distribution. ECBSB first detects the shape boundary points of the cluster in the sample set with edge noise points eliminated, and then generates boundary closures around the cluster based on the boundary points. Finally, the cluster labels of the boundary are propagated to the entire sample set by a nearest neighbor search. The proposed method is evaluated on multiple benchmark datasets. Exhaustive experimental results show that the proposed method achieves highly accurate and robust clustering results, and is superior to the classical clustering baselines on most of the test data.
Logodetect: One-shot detection of logos in image and video data

logodetect allows the detection of logos in images and videos after being trained with just one example of a logo.One-shot learning systems are not only great because they require little data, but also because they practically remove the need for specialists whenever the user wants to extend or re-purpose their functionality.This means that the benefits are manifold: the user requires little effort to collect and label training data, there is practically no time or economic costs for the training procedure, and it also provides a strategic benefit for business as they become self-sufficient for a large part of the system maintenance.logodetect comes with pretrained models, data and an interface that allows users to detect logos in images and videos with a single command.
Digital Archives and Data Science

This chapter describes work by the UCSF Industry Documents Library to develop resources, programs, and initiatives to support data science work with a diverse audience in the fields of health sciences, history of medicine, public health policy, and tobacco control. The Industry Documents Library (IDL) is a digital archive of over 15 million documents created by industries impacting public health, hosted by the University of California, San Francisco (UCSF) Library. The chapter describes the public health impact of industry documents research, highlights several examples of computational projects conducted by IDL scholars, outlines the IDL's developing plans for using data science techniques to assist with large-scale digital collection appraisal and metadata enhancement, and discusses how the IDL is expanding its collaborations with the UCSF Library's Data Science Initiative and Archives and Special Collections departments to further develop impactful data science programs across the university.
A Construction Method of Electric Power Professional Domain Corpus Based on Multi-model Collaboration

This paper proposes a method for constructing a corpus in the electric power field based on multi-method collaboration. Aiming at avoid the disadvantage of the excessive small granularity of the words segmentation results by Jieba word segmentation method which cause the incorrectly split of the words, TF-IDF method is used to extract keywords from the Jieba word segmentation results. An improved information entropy word combination method and TextRank method are applied to make word associations from the Jieba word segmentation results to form new phrases. For the information entropy word segmentation method uses strict phrases forming rule, which may cause the number of words formed decrease, all the word segmentation results of the above methods are collected to establish a relatively complete set of candidate words. Then, an improved word2vec clustering algorithm is presented to cluster electric power professional words and remove non-electric power words. Through the above-multi-method collaborative algorithm, a more comprehensive electric power professional field corpus is finally established. Compared with the Jieba word segmentation method, information entropy word combination algorithm (IEWCA), information entropy word segmentation algorithm (IEWSA), the experimental results prove that the electric power professional field corpus constructed by the presented method in this paper is more accurate and with richer vocabulary.
Performance Analysis of Semantic Segmentation using Optimized CNN based SegNet

Segmentation of different objects from a live video is a highly challenging task. Semantic segmentation aims at culminating the proficiency of deep learning algorithms in segmenting different objects from every frame of a video, making image segmentation a computer vision task. Semantic segmentation, in essence maps every pixel in an image to a label and this requires the label for an image to be an image. SegNet, a deep learning architecture which performs semantic segmentation has two major networks: encoder and decoder networks. The encoder of the SegNet performs feature engineering task and is basically a convolutional neural network (CNN). Contrarily, the decoder network performs deconvolution operation with memory enabled max pooling operations to reconstruct the image from the feature maps. The final pixel classification layer results in dense prediction by assigning a label to every pixel in the image and performs segmentation. The SegNet is implemented using the popular optimized CNNs namely AlexNet, VGG – 16 and ResNet – 18 as the encoder architecture and their segmentation performance on Camvid dataset is compared. The results demonstrate that the ResNet – 18 comparatively performs well with a better segmentation accuracy and faster convergence rate.
Multi-Layer Contextual Passage Term Embedding for Ad-Hoc Retrieval

Nowadays, pre-trained language models such as Bidirectional Encoder Representations from Transformer (BERT) are becoming a basic building block in Information Retrieval tasks. Nevertheless, there are several limitations when applying BERT to the query-document matching task: (1) relevance assessments are applicable at the document-level, and the tokens of documents often exceed the maximum input length of BERT; (2) applying BERT to long documents leads to a great consumption of memory usage and run time, owing to the computational cost of the interactions between tokens. This paper explores a novel multi-layer contextual passage architecture that leverage text summarization extraction to generate passage-level evidence for the pre-selected document passage thus brought new possibilities for the long document relevance task. Experiments were conducted on two standard ad-hoc retrieval collections from the Text Retrieval Conference (TREC) 2004 Robust Track (Robust04) and ClueWeb09 with two different characteristics individually. Experimental results show that our approach can significantly outperform the strong baselines and even compared with the same BERT-based models, the precision of our methods as well as state-of-the-art neural ranking models.
CNN based Static Hand Gesture Recognition using RGB-D Data

Hand gesture recognition refers to identification of various hand postures which interprets the signs of non verbal communication. It finds various applications like Sign Language Recognition (SLR), Human Computer Interaction (HCI) for robotics control, 3D modeling etc., Efficiently recognizing the hand gestures in various complex background scenarios is still a challenging problem. This issue can be effectively addressed by using depth data as a additional cue along with RGB image. Depth refers to the distance between camera sensor and image scene, hence depth cues can be used in suppressing the complex backgrounds which are far away from the hand region. Depth can also be effectively used to handle the illumination issues. In this paper, we propose a two stage approach where first stage involves k-means algorithm based depth clustering and removal of the background region. In the later stage, the foreground filtered depth map is fused with RGB and the resultant RGB-D data is used for gesture recognition using Convolutional Neural Network (CNN) classification model. Experiments are conducted on OUHANDS datasets and the results are compared with well known existing methods. Experimental result shows that accuracy of 87.57 % can be achieved on OUHANDS test dataset using the proposed method.
Competence-Based Skill Functions and Minimal Sets of Skills

As we know, there is some relationship, such as precedence relation, among skills. Each precedence relation induces a competence structure. Thus, we study competence-based skill functions, which rely on competence structures and go from somethings observable to somethings invisible. Conversely, competence-based problem functions go from somethings invisible to somethings observable. In fact, these two dual types of functions based on competence structures are symmetry. Remarkably, there are two kinds of special competence-based skill functions: one is disjunctive, while the other is conjunctive. The former delineates knowledge spaces, which are symmetrical to simple closure spaces delineated by the latter. Based on these facts, we shows some theoretical results on competence-based skill functions, then design the corresponding algorithms for delineating knowledge structures. Sometimes for competence-based skill functions, some skills are maybe reducible. Thus, we discuss what kind of skills are reducible and obtain sufficient and some necessary conditions for skills being reducible for competence-based skill functions. Based on this, we design algorithms to reduce reducible skills and get minimal sets of skills. By comparison, for competence-based skill functions, we can find minimal sets of skills with the smallest cardinality whenever sets of skills are finite. For each algorithm, we take a corresponding example to illustrate the detailed procedure.
Gesture Position Detection Based on Generative Adversarial Networks

In order to improve the real-time and accuracy of gesture position information acquisition in gesture interaction, this paper proposes a gesture position detection method based on generative adversarial networks. Firstly, this paper collects gesture images from two perspectives by binocular camera, and then uses semi-supervised generative adversarial network to segment gesture images. This method overcomes the problem that traditional semantic segmentation ignores the correlation between pixels, and obtains better gesture segmentation images. Secondly, fingertip detection and three-dimensional reconstruction of fingertips are performed on gesture semantic segmentation images. Finally, the method is verified by experiments, and the gesture segmentation results and fingertip detection results are evaluated by using mIoU value and fingertip detection accuracy. The experimental results show that the mIoU value of gesture segmentation can reach 94.8, and the accuracy of fingertip detection can reach 96.5.
COVID-19 and the fear of other unknowns: challenges and lessons learned from a digital contact tracing activity in the Rohingya camps in Cox’s Bazar, Bangladesh

Contact tracing can play an important role in controlling infectious disease outbreaks such as the COVID-19 pandemic. Containing the spread of COVID-19 is crucial in humanitarian settings such as in the Rohingya camps in Cox’s Bazar, Bangladesh. This manuscript describes the COVID-19 contact tracing activities undertaken by a group of researchers and implementers in Cox’s Bazar, Bangladesh. The paper details the design and development of the Commcare ‘Contact tracing and case monitoring app’, subsequent implementation of the contact tracing activity, challenges faced during the implementation process, and the strategies adopted by the research team to overcome these challenges. The research team leveraged the suite of template applications for COVID-19 response developed by Dimagi in response to the COVID-19 pandemic. Research partners organized a series of brainstorming meetings and workshops with relevant stakeholders to finalize the ‘COVID- 19 contact tracing and case monitoring app’ for final implementation. This app was implemented in 10 Rohingya camps from Ukhiya and Teknaf sub-districts of Cox’s Bazar for 4.5 months from 1st January 2021 to 15th May 2021. Due to a restriction on internet availability in the Rohingya camps by the government of the host country, the research team had to adopt a manual approach to implement the contact tracing activity. During these 4.5 months, 249,452 individuals from 10 Rohingya camps were screened for COVID-19 case registration. Of all the screened individuals, 431 were identified as COVID suspected cases, and 77 were identified as confirmed cases. The research team experienced several implementation challenges such as inexperience of contact tracers with the nature of the work, convincing the community to register in a digital system, obtaining information around COVID-19 symptoms, and many cultural, linguistic, gender, and other social barriers. The team adopted challenge-specific mitigation strategies for the effective implementation of the activity. The modalities of operation adopted by the team engaged with this present intervention to overcome the difficulties experienced in its conduction can hopefully provide some guidance to future parties attempting to conduct similar activities in complex humanitarian settings.