Reviewerly/exHarmony huggingface dataset:
Generative linguistics and neural networks at 60: Foundation, friction, and fusion

The birthdate of both generative linguistics and neural networks can be taken as 1957, the year of the publication of foundational work by both Noam Chomsky and Frank Rosenblatt. This article traces the development of these two approaches to cognitive science, from their largely autonomous early development in the first thirty years, through their collision in the 1980s around the past-tense debate (Rumelhart & McClelland 1986, Pinker & Prince 1988) and their integration in much subsequent work up to the present. Although this integration has produced a considerable body of results, the continued general gulf between these two lines of research is likely impeding progress in both: on learning in generative linguistics, and on the representation of language in neural modeling. The article concludes with a brief argument that generative linguistics is unlikely to fulfill its promise of accounting for language learning if it continues to maintain its distance from neural and statistical approaches to learning.
An Augmented Reality-Based Simulation Guide for Apparel Assembly

Augmented Reality (AR)-based technologies have revolutionized prototyping and manufacturing operations in many industries despite limited applications in the context of apparel. Currently, digital technologies are demanded in the process of apparel ‘design to manufacturing’ to improve efficiency while maintaining the product defects to a minimum level. This study proposes an AR-based tool to educate sample machinists to assemble even a complicated new product to meet the exact design specifications. Such guiding device overcomes product defects which occurs due to misinterpretations of design details. This study was carried out in two phases. During the first phase, qualitative research methods were utilized to analyze the impact of new product failures which occur due to misinterpretations of prototyping instructions. Also, this phase was utilized to analyze their perceptions of AR-based guiding tool, along with the suggestions to develop such system to support product assembly. The second phase proposes relevant guide to develop the new AR tool and presents the acceptance of such tool from the focus group consists of potential users. Future work will include creating relevant contents based on the proposed screen images. Then it will be useful tool to train sample developers to produce the prototypes right first time.
Human Face Recognition using LBPH

During the beginning of seventieth centuries, human facial recognition has become one among the researched areas in the area of finger print scanning and computer vision. Identifying a person with an image has been popularized through the mass media. The recent technologies are totally focusing on developing the smart systems that will recognize the faces for biometric purposes. In this context automatic face recognition is applied for security purposes to find the criminal, attendance system, scientific laboratories etc. This research paper presents the frame work for real time face detection. However, it is less robust to finger print or retina scanning. This paper describes about the face detection and recognition. These technologies are available in the Open-Computer-Vision (OpenCV) library and methodology to implement them using Python in image processing and machine learning. For face detection, Haar-Cascades algorithms were used and for face recognition the algorithm like Eigen faces, and Local binary pattern histograms were used.
A BLOCKCHAIN-BASED APPROACH TO ENABLE REMOTE SENSING TRUSTED DATA

Abstract. Remote sensing considerably benefits from the fusion of open data from different sources, including far-range sensors mounted on satellites and short-range sensors on drones or Internet of Things devices. Open data is an emerging philosophy attracting an increasing number of data owners willing to share. However, most of the data owners are unknown and thus, untrustable, which makes shared data likely unreliable and possibly compromising associated outcomes. Currently, there exist tools that distribute open data, acting as intermediaries connecting data owners and users. However, these tools are managed by central authorities that set rules for data ownership, access, and integrity, limiting data owners and users. Therefore, a need emerges for a decentralized system to share and retrieve data without intermediaries limiting participants. Here, we propose a blockchain-based system to share and retrieve data without the need for a central authority. The proposed architecture (i) allows sharing data, (ii) maintains the data history (origin and updates), and (iii) allows retrieving and evaluating the data adding trustworthiness. To this end, the blockchain network enables the direct connection of data owners and users. Furthermore, blockchain automatically interacts with participants and keeps a transparent record of their actions. Hence, blockchain provides a decentralized database that enables trust among the participants without a central authority. We analyzed the potentials and critical issues of the architecture in a remote sensing use case of precision farming. The analysis shows that participants benefit from the properties of the blockchain in providing trusted data for remote sensing applications.
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task

In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages.We combine two existing objective functions to make images and captions close in a joint embedding space while adapting the alignment of word embeddings between existing languages in our model.We show that our approach enables better generalization, achieving state-of-the-art performance in text-to-image and image-to-text retrieval task, and caption-caption similarity task.Two multimodal multilingual datasets are used for evaluation: Multi30k with German and English captions and Microsoft-COCO with English and Japanese captions.
Medical image segmentation based on fuzzy 2-partition Kapur entropy using fast recursive algorithm

Segmentation is a foremost part of computer-aided diagnosis (CAD) and medical image analysis. This paper provides an automatic threshold selection approach for the segmentation of medical images based on fuzzy 2-partition using Kapur entropy. The fuzzy 2-partition Kapur entropy approach converts the image into fuzzy 2-partition by applying two parameterised fuzzy membership functions. The optimal threshold is attained by searching for an ideal combination of the parameter for the fuzzy membership functions so that the fuzzy 2-partition Kapur entropy is maximised. The complexity of searching the optimal combination of parameters is reduced by integrating the concept of fast recursive algorithm and Kapur entropy. The proposed approach performance is measured using several medical images and it is found that results are considerable encouraging. The proposed approach could be put up as a component of a CAD system for early detection of diseases like cancer.
An adaptive technique for cost reduction in cloud data centre environment

The developing interest for utilising the Cloud Computing (CC) has expanded the energy consumption of data centres which has become a critical issue. High energy consumption not only is translated to high operational cost but also reduces the profit margin for the cloud providers and leads to high carbon emissions, which are not environmentally friendly. Therefore, there is a need for energy-saving solutions to minimise the negative impact of CC. This research proposes an Energy Saving Load Balancing (ESLB) technique that plans to make energy saving in the cloud server while keeping up the Service Level Agreement (SLA), which includes the Quality of Service (QoS) between the cloud service provider and cloud customers. This proposed technique aims to enhance the performance, resources' utilisation and reduce the both of energy consumption and carbon dioxide in order to mitigate the negative impact of CC on the environment.
Hand Gesture Control Robot

In this paper we have designed a basic robotic chassis which can be easily controlled with the help of accelerometer instead of using button control. Here the most significant device is accelerometer. The accelerometer is the 3 axis estimation gadget with +-3g range. This gadget is made by utilizing polysilicon surface sensor and signal controlling circuit to quantify acceleration. The outcome of the accelerometer is analog in nature and corresponding to the acceleration. This gadget measures the static acceleration of gravity when we tilt it. And gives an outcome in type of movement or vibration. The hand position is sensed and the coordinates generated is considered as the parameter and if neccessary conditions are met, the statement prescribed in the arduino code is executed and the direction of the robot chassis is changed accordingly. So that it can perform the task such as forward moving, backward moving, turning left, turning right and stop. In many cases, the robot devices are some tough and complex while we control it with the help of buttons and switches. The major fields that prefers hand gesture robots are Defence field, industrial robotics, vehicle part assemble in civil side, medical field for surgery. In these fields if we are using remote controls and the button, the task will become complex. Sometimes the operator may get confuse in the switch control and button control, so a new concept is used to manage the gadget with the motion of the hand and at the same time it will manage the motion of the device.
Psychometric Properties of a Questionnaire to Assess Perceptions of Corporal Expression in Future Spanish Teachers

Corporal expression is a content that is forgotten by most educators but has been proved to bring numerous benefits to students of all ages. Teacher perceptions and beliefs play a fundamental role in the teaching-learning process, influencing students to a great extent. This study aims to present the factor structure and reliability of a questionnaire for the assessment of teachers’ perceptions about corporal expression. The sample consisted of 212 Spanish prospective teachers who completed the questionnaire to assess their readiness and appreciation about corporal expression. Exploratory and confirmatory factor analyses, as well as reliability testing, were carried out. The results showed a factor structure with 3 dimensions (pleasure, preference, and evaluation of corporal expression) composed of 23 items with good and excellent goodness-of-fit values and high reliability (Cronbach’s alpha = 0.71–0.93). Thus, the questionnaire can be considered a quick and easy-to-apply tool to analyze prospective teacher’s perceptions about their preparation to address their students’ corporal expression, allowing stakeholders to take actions to promote it.
Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

Abstract The miniature sensor devices and power-efficient Body Area Networks (BANs) for Human Activity Recognition (HAR) have gained increasing interest in different fields, including Daily Life Assistants (DLAs), medical treatment, sports analysis, etc. The HAR systems normally collect data with wearable sensors and implement the computational tasks with a host machine, where real-time transmission and processing of sensor data raise a challenge for both the network and the host machine. This investigation focuses on the hardware/software co-design for optimized sensing and computing of wearable HAR sensor networks. The contributions include (1) design of a miniature wearable sensor node integrating a Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS IMU) with a Bluetooth Low Energy (BLE) in-built Micro-Control Unit (MCU) for unobtrusive wearable sensing; (2) task-centric optimization of the computation by shifting data pre-processing and feature extraction to sensor nodes for in-situ computing, which reduces data transmission and relieves the load of the host machine; (3) optimization and evaluation of classification algorithms Particle Swarm Optimization-based Support Vector Machine (PSO-SVM) and Cross Validation-based K-Nearest Neighbors (CV-KNN) for HAR with the presented techniques. Finally, experimental studies were conducted with two sensor nodes worn on the wrist and elbow to verify the effectiveness of the recognition of 10 virtual handwriting activities, where 10 recruited participants each repeated an activity 5 times. The results demonstrate that the proposed system can implement HAR tasks effectively with an accuracy of 99.20 %.
Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer

The High Throughput Truthing project aims to develop a dataset for validating artificial intelligence and machine learning models (AI/ML) fit for regulatory purposes. The context of this AI/ML validation dataset is the reporting of stromal tumor-infiltrating lymphocytes (sTILs) density evaluations in hematoxylin and eosin-stained invasive breast cancer biopsy specimens. After completing the pilot study, we found notable variability in the sTILs estimates as well as inconsistencies and gaps in the provided training to pathologists. Using the pilot study data and an expert panel, we created custom training materials to improve pathologist annotation quality for the pivotal study. We categorized regions of interest (ROIs) based on their mean sTILs density and selected ROIs with the highest and lowest sTILs variability. In a series of eight one-hour sessions, the expert panel reviewed each ROI and provided verbal density estimates and comments on features that confounded the sTILs evaluation. We aggregated and shaped the comments to identify pitfalls and instructions to improve our training materials. From these selected ROIs, we created a training set and proficiency test set to improve pathologist training with the goal to improve data collection for the pivotal study. We are not exploring AI/ML performance in this paper. Instead, we are creating materials that will train crowd-sourced pathologists to be the reference standard in a pivotal study to create an AI/ML model validation dataset. The issues discussed here are also important for clinicians to understand about the evaluation of sTILs in clinical practice and can provide insight to developers of AI/ML models.
Modeling Medical Guidelines by Prova and SHACL Accessing FHIR/RDF. Use Case: The Medical ABCDE Approach

Decision-making based on so-called medical guidelines supported by semantic AI solutions is an essential and significant task for medical personnel in both a pre-clinical setting and an inner-clinical environment. Semantic representations of medical guidelines and Fast Healthcare Interoperability Resources (FHIR) using Semantic Web technologies, i.e., Resource Description Framework (RDF), rules (RuleML and Prova), and Shape Constraint Language (SHACL), provide a semantic knowledge base for the decision-making process and ease technical implementation and automation tasks. Current medical decision support systems lack Semantic Web integration using FHIR-RDF representations as a data source. In this paper, we implement a particular medical guideline using two different approaches: Prova [8] and SHACL [13]. We generate a series of raw FHIR-data for a selected guideline, the ABCDE approach, and compare the implemented two programs' (Prova and SHACL) results. Both approaches deliver the same results in terms of content. Both may be used within a distributed medical environment depending on the need of organizations.
Learning Coated Adversarial Camouflages for Object Detectors

An adversary can fool deep neural network object detectors by generating adversarial noises. Most of the existing works focus on learning local visible noises in an adversarial "patch" fashion. However, the 2D patch attached to a 3D object tends to suffer from an inevitable reduction in attack performance as the viewpoint changes. To remedy this issue, this work proposes the Coated Adversarial Camouflage (CAC) to attack the detectors in arbitrary viewpoints. Unlike the patch trained in the 2D space, our camouflage generated by a conceptually different training framework consists of 3D rendering and dense proposals attack. Specifically, we make the camouflage perform 3D spatial transformations according to the pose changes of the object. Based on the multi-view rendering results, the top-n proposals of the region proposal network are fixed, and all the classifications in the fixed dense proposals are attacked simultaneously to output errors. In addition, we build a virtual 3D scene to fairly and reproducibly evaluate different attacks. Extensive experiments demonstrate the superiority of CAC over the existing attacks, and it shows impressive performance both in the virtual scene and the real world. This poses a potential threat to the security-critical computer vision systems.
I Need a CAVAA: How Conversational Agent Voting Advice Applications (CAVAAs) Affect Users' Political Knowledge and Tool Experience

In election times, millions of voters consult Voting Advice Applications (VAAs) to learn more about political parties and their standpoints. While VAAs have been shown to enhance political knowledge and increase electoral turnout, research also demonstrates that voters frequently experience comprehension problems when responding to the political attitude statements in a VAA. We describe two studies in which we test a new type of VAA, called Conversational Agent VAA (CAVAA), in which users can easily access relevant information about the political issues in the VAA statements by asking questions to a chatbot. Study 1 reports about an online experiment (N = 229) with a 2 (Type: traditional VAA/CAVAA) x 2 (Political sophistication: low/high) design. Results show that CAVAA users report higher perceived political knowledge scores and also answer more factual knowledge questions correctly than users of a regular VAA. Also, participants' CAVAA experience was evaluated better. In Study 2 (N = 180), we compared three CAVAA designs (a structured design with buttons, a non-structured design with an open text field, and a semi-structured design with both buttons and an open text field), again for higher and lower politically sophisticated users. While the three designs score equally high on factual and perceived knowledge indicators, the experience of the structured CAVAA was evaluated more positively than the non-structured version. To explore the possible cause for these results, we conducted an additional qualitative content analysis on 90 chatbot-conversations (30 per chatbot version). This analysis shows that users more frequently access additional information in a structured design than in a non-structured design, whereas the number of break-offs is the same. This suggests that the structured design delivers the best experience, because it provides the best trigger to ask questions to the chatbot.
An Anti-UAV Long-Term Tracking Method with Hybrid Attention Mechanism and Hierarchical Discriminator

To prevent unmanned aerial vehicles (UAVs) from threatening public security, anti-UAV object tracking has become a critical issue in industrial and military applications. However, tracking UAV objects stably is still a challenging issue because the scenarios are complicated and the targets are generally small. In this article, a novel long-term tracking architecture composed of a Siamese network and re-detection (SiamAD) is proposed to efficiently locate UAV targets in diverse surroundings. Specifically, a new hybrid attention mechanism module is exploited to conduct more discriminative feature representation and is incorporated into a Siamese network. At the same time, the attention-based Siamese network fuses multilevel features for accurately tracking the target. We further introduce a hierarchical discriminator for checking the reliability of targeting, and a discriminator-based redetection network is utilized for correcting tracking failures. To effectively catch up with the appearance changes of UAVs, a template updating strategy is developed in long-term tracking tasks. Our model surpasses many state-of-the-art models on the anti-UAV benchmark. In particular, the proposed method can achieve 13.7% and 16.5% improvements in success rate and precision rate, respectively, compared with the strong baseline SiamRPN++.
A Discriminative-Based Geometric Deep Learning Model for Cross Domain Recommender Systems

Recommender systems (RS) have been widely deployed in many real-world applications, but usually suffer from the long-standing user/item cold-start problem. As a promising approach, cross-domain recommendation (CDR), which has attracted a surge of interest, aims to transfer the user preferences observed in the source domain to make recommendations in the target domain. Traditional machine learning and deep learning methods are not designed to learn from complex data representations such as graphs, manifolds and 3D objects. However, current trends in data generation include these complex data representations. In addition, existing research works do not consider the complex dimensions and the locality structure of items, which however, contain more discriminative information essential for improving the performance accuracy of the recommender system. Furthermore, similar outcomes between test samples and their neighboring training data restrained in the kernel space are not fully realized from the recommended objects belonging to the same object category to capture the embedded discriminative information effectively. These challenges leave the problem of sparsity and the cold-start of items/users unsolved and hence impede the performance of the cross-domain recommender system, causing it to suggest less relevant and undistinguished items to the user. To handle these challenges, we propose a novel deep learning (DL) method, Discriminative Geometric Deep Learning (D-GDL) for cross-domain recommender systems. In the proposed D-GDL, a discriminative function based on sparse local sensitivity is introduced into the structure of the DL network. In the D-GDL, a local representation learning (i.e., a local sensitivity-based deep convolutional belief network) is introduced into the structure of the DL network to effectively capture the local geometric and visual information from the structure of the recommended 3D objects. A kernel-based method (i.e., a local sensitivity deep belief network) is also incorporated into the structure of the DL framework to map the complex structure of recommended objects into high dimensional feature space and achieve an effective recognition result. An improved kernel density estimator is created to serve as a weighing function in building a high dimensional feature space, which makes it more resistant to geometric noise and computation performance. The experiment results show that the proposed D-GDL significantly outperforms the state-of-the-art methods in both sparse and dense settings for cross-domain recommendation tasks.