Reviewerly/exHarmony huggingface dataset:
Time expression as update operations: Normalizing time expressions via a distantly supervised neural semantic parser

Existing approaches to normalize time expressions highly rely on expert-engineered rules or grammars, which are labor-intensive and difficult to scale to different scenarios. In this article, we formulate a novel idea to model the semantics of time expression as update operations. We use the update semantics-based representation to model the normalization task as predicting an operation sequence from a given natural language sequence, which is compatible with the popular Seq2Seq scheme in deep learning. We propose a distantly supervised neural parsing approach DNPTime for parsing the time expressions. DNPTime parses recognized expressions into executable operations to obtain normalized values. Specifically, DNPTime uses the intermediate results from the automatic rule generation approach ARTime to construct distant supervision samples for fine-tuning T5 models. Experimental results show that DNPTime gives the best average performances on different benchmarks with fewer human interventions.
Dissemination of knowledge potential in the e-learning process

The key terms in the process of knowledge management and knowledge potential are analyzed. Groups of internal and external factors affecting knowledge potential are indicated. The factors of influence on the choice of electronic educational resources are highlighted. The interaction of participants in the educational process is depicted schematically, particularly in communities of the electronic educational environment. The list of probabilistic selection rules for choosing a source of knowledge and learning is given. The model of dynamics of dissemination of knowledge potential, taking into account the flow of knowledge from source to agent, is indicated. Modeling is described in the form of a generalized diffusion model of processes of redistribution of knowledge potential during e-learning, taking into account the replenishment of the source of knowledge. The influence of electronic educational resources on the replenishment of the teacher's knowledge, which transfers knowledge to students within a certain community, is given. The general structure of the process of formation of knowledge potential during e-learning, indicating sources of knowledge, factors of influence on participants of the educational process is shown, the processes of replenishment, transfer, and redistribution of knowledge are indicated.
Doctor’s voice assistant application prototype

This research examines the main approaches to visualization and modeling of key processes in the creation and testing of software in the domain of telemedicine, namely, such an application as a voice assistant to a doctor. Emphasis is placed on the use of UML diagrams at various stages of the software development life cycle, including requirements analysis, design, description of basic functionality, documentation, testing, etc. With the help of modeling, the answer not only to the questions “what does the system do” and “what does the system consist of” was demonstrated, but also to “how and how efficiently the system works” – in this case, a prototype of a doctor’s voice assistant application. Approaches to using effective frontend technology for the application are analyzed.
Information technology for gender recognition by voice

Gender recognition from voice is a challenging problem in speech processing. This task involves extracting meaningful features from speech signals and classifying them into male or female categories. In this article, was implemented a gender recognition system using Python programming. I first recorded voice samples from both male and female speakers and extracted Mel-frequency cepstral coefficients (MFCC) as features. Then trained, a Support VectorMachine (SVM) classifier was on these features and evaluated its performance using accuracy, precision, recall, and F1-score metrics. These experiments demonstrated that proposed system should achieve high accuracy on the test set and will accurately predict the gender of a speaker based on their voice. I also explored using pre-trained models to reduce the need for large amounts of training data and found that they can provide good performance while requiring less computation. This study highlights the potential of using machine learning techniques for gender recognition from voice and can be extended to other speech processing applications.
Characterization and Analytical Evaluation Based on the 3- Layer Approach of Smart Buildings

This article is concerned with characterizing and analytical evaluation of the intelligence of buildings. After a brief reminder of the definition of the concept, the authors explain how necessary it is to evaluate the intelligence of a building based on it functions, in order to define an intelligence profile. The suggested evaluation method aims at qualifying three levels (layers) of intelligence from passive behavior (communication layer) to representative layer (physical) and predictive behavior (scheduling layer). These levels should be interpreted through 3 dimensions: time, space and energy. The analytical evaluation used SPSS from the intrinsic data of the building extracted by ArcGIS software in order to make buildings intelligent based on dynamic scheduling or optimization algorithms. We illustrate the approach by applying approach to the technological Hall of the University of Tiaret (THUT).
Multi-hop Information-based Graph Convolutional Network for Clustering

Abstract Clustering is an essential and demanding undertaking in data analysis. The combination of traditional neural networks and graph convolutional networks (GCNs) has been extensively discussed in clustering tasks, in which the deep clustering methods learn useful content information and the graph convolutional networks mine the structured neighboring information in the graph data. However, the existing works equally consider the importance of different features to clustering and only focus on the nearest neighboring information in the structured features, ignoring the features of the long distant neighbors in the multi-hop information, resulting in inferior performance. We propose a novel multi-hop information-based graph convolutional network (MIGCN) for clustering to overcome these disadvantages. Specifically, we fuse content features and structured features with adaptive weights, which are dynamically adjusted according to the training results. Meanwhile, we utilize a multi-hop information module to extract structured features. Moreover, we design a multi-supervision mechanism and guide the training and updating of the whole model. Our method achieves better results than previous deep clustering approaches because it takes into account the diverse features embedded in the neural network and a dynamic fusion strategy for clustering. Our model has undergone rigorous testing on widely-accepted standard datasets, employing both objective and subjective evaluation measures. The results consistently demonstrate that our model consistently outperforms state-of-the-art techniques, validating its superior performance.
The Case of Unsustainable CPU Affinity

CPU affinity reduces data copies and improves data locality and has become a prevalent technique for high-performance programs in datacenters. This paper explores the tension between CPU affinity and sustainability. In particular, affinity settings can lead to significant uneven aging of cores on a CPU. We observe that infrastructure threads, used in a wide spectrum of network, storage, and virtualization sub-systems, exercise their affinitized cores up to 23× more when compared to typical μs-scale application threads. In addition, we observe that the affinitized infrastructure threads generate regional heat hot spots and preclude CPUs from being used with the expected lifetime. Finally, we discuss design options to tackle the unbalanced core-aging problem to improve the overall sustainability of CPUs and call for more attention to sustainability-aware affinity and mitigation of such problems.
Enhance Prototypical Network with Hybrid Loss for Few-Shot Text Classification

Abstract Text classification is the most basic task of natural language processing. The continuous improvement of deep learning has driven the ongoing success of the natural language process. However, the current deep-learning text classification tasks rely heavily on a vast number of annotated data. Therefore, the value of few-shot learning is reflected. In the classification task of few-shot text classification, each category provides only a few samples. Due to the diversity of text and the similarity among samples in different categories, the predictions are biased. We propose an enhanced prototype network with hybrid loss to address the existing issues in few-shot text classification. We designed instance-level attention and hybrid loss for the model to improve the prototypes’ feature representation capabilities. Experimental results show that the model proposed in this paper performs best under several sets of few-shot scenarios.
Unsupervised Anomaly Detection of Subway Vibration Signal Based on Ultra-weak FBG Sensing Array Combined with RCMDE and Local Density

Abstract The subway vibration signal can reflect the health status of the passing train and tunnel structure. Identifying the abnormal vibration signal is a prerequisite for safe subway operation. In view of the difficulties in collecting anomalous samples in actual engineering and extracting effective features of high-dimensional time series, an unsupervised anomaly detection approach on the ground of RCMDE and local density has been put forward to identify vibration signals captured by the ultra-weak FBG sensing array cable. Two experiments demonstrated that compared with other methods, RCMDE could better evaluate the signal and extract features more precisely and that the abnormal vibration signals of passing trains consistent with the facts were accurately detected by the proposed method while other methods had errors.
Few-shot Object Detection Based on Sample Balance Correction

Abstract We propose a few-shot object detection algorithm based on sample balance correction, which introduces categorical sample balance correction and positioning sample balance correction on the basis of the Faster R-CNN network to improve the performance of few-shot detection, and categorical sample balance correction increases the passing probability of the new type pre-selection box by changing the selection rules of the pre-selection box. And the similarity calculation branch is added to the detection head to help improve the classification ability, and the positioning sample balance correction solves the sample balance in the training process and the balance problem of correcting difficult samples by improving the positioning loss function. Experiments on the PASCAL VOC dataset show that the proposed algorithm improves the detection effect when the sample size is small, up to 3.2 percentage points higher than the comparison algorithm, and has good robustness and generalization ability.
A differential miniaturized thickness measurement calibration system based on laser triangulation sensors

Abstract Steel plate thickness inspection is important in high-precision steel processing and manufacturing. Traditional inspection methods use manual contact measurement, which has low inspection efficiency and large human error, so we proposed a differential non-contact thickness measurement system based on laser triangulation sensors. Firstly, we designed a differential measurement structure by placing the laser triangulation sensors on the top and bottom of the steel plate to be measured, so that the data from the top and bottom sensors can be sampled and the steel plate thickness can be calculated simultaneously. Secondly, based on the principle of error averaging effect, we proposed a multi-point measurement averaging method to take multiple points on the surface of the steel plate and calculate the average value as the thickness of the steel plate. Further, to improve the measurement system’s long-term stability, the temperature drift is corrected by the polynomial fitting method. Finally, the experiment proves that the repeatability accuracy of our proposed measurement system can reach ±0.5 um and the measurement drift is better than ±0.1% of the steel plate thickness.
Research on Risk Assessment Model for Social High-Risk Individuals Based on Graph Attention Network

To better carry out early warning and control work for high-risk individuals in society, this paper proposes a risk assessment model based on graph attention networks. The model analyzes relevant background and relationship information of these individuals and constructs a knowledge graph accordingly. An improved graph attention mechanism is introduced to establish the risk assessment model. Real police character data was used to train and test the model, and experimental results indicated a prediction accuracy of 89.4%, with both accuracy and recall rates around 90%. This model can provide decision-making basis and technical support for early warning of public security personnel by identifying potential risks of high-risk individuals.
NLP Research Based on Transformer Model

Natural language processing technology is an important research area in artificial intelligence which occupies a pivotal position in deep learning. This paper describes in detail the research of NLP based on Transformer structure, thus showing its ultra-high performance and development prospects. Therefore, this article provides a detailed description of the research on NLP based on the Transformer structure, in order to demonstrate its ultra-high performance and development prospects.
A Novel Approach to Extract Digital Signatures from Handwritten Character Images

Manuscript character recognition is undoubtedly an interesting and practical problem. While the current state of the art solutions are very effective, they mostly rely on Artificial Neural Network based models for performing characters recognition. Such models typically require significant amount of training data in order to produce high accuracy results. We are proposing a method for extracting the most significant features of character images and creating a low dimensional space numeric signature for each character image shape. Such signature can be used to measure similarities between character images, and subsequently perform clustering, classification or searching for character images. Our methodology is suitable to manuscript character images, for which, typically, there are no pre-trained advanced models that can be used to perform clustering or classification tasks. It can be used as a pre-classification step to perform automatic labeling of training data.
Feature selection using genetic algorithms for improving accuracy in image classification tasks

Feature selection can be an effective tool for increasing the robustness and predictive accuracy of classifiers, especially in the presence of noisy features or when their dimensionality is high. Genetic algorithms (GA) lend themselves well for optimizing the search for the best subset of features. This paper present how GA can be integrated in the training of neural networks (NNs) as a feature selection step to increase the model performance. The reported experiments cover the effect such a technique can have when confronted with various sizes for the trained NN in the context of both harder and easier datasets. Moreover, the experimental setups make use of feature selection both as a traditional pre-processing step, before training the NN, as well as an intermediary processing layer between the features extractor part of a convolutional neural network (CNN), used in conjunction with more conventional statistical features, and the classification head. Although CNNs are known to inherently model the selection of features, meaning that the impact of a GA as a feature selector after the CNN backbone could be inhibited, marginal improvements in the final performance still show meaningful insight into the working of such a classifier, in the context of managing relevant features.
Virtual Reality System using Explainable AI for Identification of Specific Expert Refinery Inspection Skills

In this study, we propose a virtual reality system for identifying expert-specific skills in a visual inspection task in a refinery by using an eXplainable Artificial Intelligence (XAI) technique. Most previous studies have applied statistical analysis such as t-tests to the mean value of the experimental data, and there is a consequent lack of specificity in the results (i.e., when and where expert skill appears within a long inspection duration). It is thus difficult to provide feedback based on the most important part of the collected experts’ data to the novices. To address this issue, we introduce a Convolutional Neural Network (CNN) with Class Activation Map (CAM) technique, an XAI method, to analyze the experimental data of experienced and novice field operators, and identify the most significant contributors for classifying expert and novice behavior for 120 seconds inspections. The resulting model can classify field operators as expert or novice with an accuracy of 99.1% on average, and visualize the classification criteria as a heat map for each experimental trial. Based on those results, we propose a virtual reality training system for learning expert inspection skills by referencing the CNN results. The contribution of our study is the proposition of a new analytical framework, as well as a training system beyond the limitations of conventional statistical analysis.
An AI Based Enhanced and Customized Doodler-Bot with Vector Mutation And Clustering

Currently high usage of smart phone is observed in millennials for learning, playing games or drawing. Drawing being quite popular among all age groups of people, still the most generic problem faced is the fear to draw neatly and correctly. We have tried to make drawing experience more innovative and effective by proposing a unique AI based Doodler-Bot which will help people of all age groups to improve their drawing skills. There exists some way through which the user's sketch can be improved by replacing it with the professionally drawn sketch stored in database, but by doing this the user's originality of sketch is lost. Our proposed work will help user in drawing better sketches by generating new and unique sketches which are better version of user's sketch. Whenever user draws a sketch, the application would first predict the class of sketch and then the generator model will be used to generate new sketches depending on user's sketch and display all similar suggestions to user as output. The results are improvised in comparison to existing Sketch RNN model. First, by modifying the model architecture i.e. by mutating the latent space of user sketch with similar latent space of sketches available in cluster data. Secondly, the model is also trained on improvised dataset (Quick draw dataset and Newly generated dataset). New dataset is generated by converting the SVG format images to the stroke format as required by model for training. The proposed AI based Doodler-Bot will intelligently guide and improve the drawing skills of the person.
Handling Delayed Labeling of EEG Data Stream Using Semi-Supervised Label Propagation

Research interest in data stream classification is increasing through the development of adaptive machine learning techniques. These techniques involve continuously adjusting the classification model in response to changes in the data distribution. Most of these techniques assume instance labeling for the classes to perform the model adapting process, and this assumption is rare with actual data. This work proposes using a semi-supervised label propagation technique to infer many delayed labels (considered missing values) from limited known values in a data stream. The work's implementation included using two imbalanced EEG datasets, CUB-MIT Scalp and Siena Scalp datasets, to evaluate the proposed method with various values for missing ratios. The results showed the proposed method's ability to recover all the negative class values in both datasets with a missing percentage reaching 70%. Due to the rare positive class, the recovery of its value decreased with more than 30% missing ratio.
A Supervisory Learning Control Framework for Autonomous &amp; Real-time Task Planning for an Underactuated Cooperative Robotic task

We introduce a framework for cooperative manipulation, applied on an underactuated manipulation problem. Two stationary robotic manipulators are required to cooperate in order to reposition an object within their shared work space. Control of multi-agent systems for manipulation tasks cannot rely on individual control strategies with little to no communication between the agents that serve the common objective through swarming. Instead a coordination strategy is required that queries subtasks to the individual agents. We formulate the problem in a Task And Motion Planning (TAMP) setting, while considering a decomposition strategy that allows us to treat the task and motion planning problems separately. We solve the supervisory planning problem offline using deep Reinforcement Learning techniques resulting into a supervisory policy capable of coordinating the two manipulators into a successful execution of the pick-and-place task. Additionally, a benefit of solving the task planning problem offline is the possibility of real-time (re)planning, demonstrating robustness in the event of subtask execution failure or on-the-fly task changes. The framework achieved zero-shot deployment on the real setup with a success rate that is higher than 90%.
Virtual Reality (VR) - The Effectiveness in Interior Architecture Technical Drafting

Abstract Technical drafting knew as drafting in detailed drawing. It is essential for communicating ideas in the industry by using familiar symbols, units of measurement, notation systems, visual styles, and page layouts using Virtual Reality (VR). Interior Architecture students spend most of their time creating those physical models or digital models instead of creating and developing ideas. However, lack of research and knowledge on technical drawing is one of the reasons that hard to understand and explore for the beginner level student. This research aims to develop a new interactive virtual reality application that can be used in beginner-level technical drafting. Therefore, this project uses the ADDIE model to develop the application and collect data at the early and final stages. This study concludes that virtual reality applications for technical drafting can guide and become a source of information for students understanding technical drafting and developing space planning in interior architecture.
Wind vector estimation considering difference of propeller model characteristics for fully actuated drone

Recently force control for drones is receiving interest. One of the challenges to conduct force control for drones is the separation of wind disturbance from total disturbance. This paper focuses on the method which can apply fully actuated drones whose propellers are mounted in different directions. The wind disturbance estimation is conducted based on blade element theory. Our last proposed method has not considered the difference between the propellers’ characteristics which can cause the estimation error. In this paper, we propose a new estimation method to improve the estimation accuracy compared with our conventional wind vector estimation method. The proposed method considers the model of each propeller and solves the non-linear equation using an optimization method. The proposed method is compared with the conventional method and its effectiveness is confirmed by simulations and bench experiments.