Reviewerly/exHarmony huggingface dataset:
High-Performance Computational Early Warning Analysis of Agricultural Economy Relying on Binary Fuzzy Cluster Analysis Algorithm

In this paper, a binary fuzzy cluster analysis algorithm is used for an in-depth study and analysis of high-performance computational early warning in the agricultural economy. The definition of interval type-two fuzzy set and its operation are summarized. Considering the uncertain information that will be encountered in the evaluation process, this paper constructs the evaluation model of rural informatization construction performance based on interval type-two fuzzy numbers, which improves the accuracy of the evaluation results. The fuzzy clustering centres are modified according to the nature of the fuzzy clustering centre matrix, and the optimal fuzzy clustering centres and optimal fuzzy clustering division matrix with consistent order are solved. Using the level eigenvalues to find out in the abundant water period, there are 4 monitoring sections of water quality evaluation results for still clean and 5 monitoring sections of water quality evaluation results for slight pollution. In the flat-water period, there are 2 monitoring sections of the water quality evaluation results that are still clean and 7 monitoring sections of the water quality evaluation results are slightly polluted. In the dry water period, the water quality evaluation results of the 9 monitoring sections are slightly polluted. The results and the use of integrated pollutant index evaluation method results are consistent, indicating that the use of fuzzy clustering model for water quality evaluation is practical and effective. Its functional structure includes management business, innovation business, service business, production business, and operation business functions, in addition to the design of the collection and storage scheme based on Hadoop data warehouse, to achieve accurate matching of information of science and technology agricultural services in the whole agricultural industry chain. Finally, the implementation and maintenance of the cloud-based science and technology agricultural service information system are deployed, and the operational effects of its information system-related functions are demonstrated using prototype design. This paper constructs a comprehensive information system for science and technology agricultural services based on cloud technology that integrates management, innovation, service, production, and operation, which meets not only the needs of traditional science and technology agricultural service information systems, such as expert consultation and training demonstration, but also other needs spawned by the development of agricultural modernization.
“I Don’t Think So”: Summarizing Policy Disagreements for Agent Comparison

With Artificial Intelligence on the rise, human interaction with autonomous agents becomes more frequent. Effective human-agent collaboration requires users to understand the agent's behavior, as failing to do so may cause reduced productivity, misuse or frustration. Agent strategy summarization methods are used to describe the strategy of an agent to users through demonstrations. A summary's objective is to maximize the user's understanding of the agent's aptitude by showcasing its behaviour in a selected set of world states. While shown to be useful, we show that current methods are limited when tasked with comparing between agents, as each summary is independently generated for a specific agent. In this paper, we propose a novel method for generating dependent and contrastive summaries that emphasize the differences between agent policies by identifying states in which the agents disagree on the best course of action. We conducted user studies to assess the usefulness of disagreement-based summaries for identifying superior agents and conveying agent differences. Results show disagreement-based summaries lead to improved user performance compared to summaries generated using HIGHLIGHTS, a strategy summarization algorithm which generates summaries for each agent independently.
Cognitive Emotional Interaction Model of Robot Based on Reinforcement Learning

In order to enhance the cognitive emotional computing ability of robot, a cognitive emotional interaction model of robot based on reinforcement learning is proposed, which combines immediate feedback and long-term trend according to PAD(Pleasure-Arousal-Dominance) emotional space. Firstly, according to the psychology theory of interpersonal communication, the human emotion generation process is simulated to generate human-like emotions, and the three influencing factors of similarity, positivity and empathy are extracted. Secondly, the relationship between the response emotion+ state and the contexted long-term emotion state is established by using the global co-ordination feature of reinforcement learning, so as to model the robot emotion generation process. Then, three factors are incorporated into the model reward mechanism for the evaluate of the interactive emotion state, to update the model and get the optimal emotional strategy. Finally, the optimal emotional state corresponding to the obtained optimal emotional strategy is used to update the robot's emotional state transition probability, and based on the sentiment values of the six basic emotional states in space, them are mapped to continuous emotional space to get the optimal response emotional value of the robot. Subjective and objective comparison experiments show that the model in this paper can effectively increase the delicateness, continuity, positivity and empathy of the robot's emotional expression, and can effectively reduce the robot's dependence on external emotional stimuli, further improving the harmonious and friendly human-computer interaction.
Self-Trust And Academic Motivation Of Students In Virtual Educational Space

The paper presents the analysis of a theoretical and empirical study of self-trust and academic motivation of students in electronic educational communication.The situation of forced distance learning and pedagogical and social interaction in a pandemic has revealed some socio-psychological characteristics in the network communication of students.In new virtual educational space, new forms of cognitive, educational and social activity of students started to form, which quickly adapted to the prevailing conditions.The involvement of students in educational activities is determined by the level of self-trust, trust in teachers, close people and friends, educational institution and the world.The purpose of this research was to study the characteristics of self-trust and academic motivation of students.The survey method and mathematical methods of data processing were used in this research.The results of empirical research showed that, in the context of electronic communication among students, self-trust dominates over trust in other people and the world.In the structure of academic motivation of students, cognitive motivation prevails over achievement motivation, introjected and external motivation.Students are focused on new knowledge and gaining new experience and they strive to get satisfaction from educational activity.Self-trust is associated with academic motivation.Student trust in them and in other participants in the interaction contributes to the development of cognitive motivation and achievement motivation.Self-trust of students is associated with a sense of duty to oneself, family and friends.
Multi-stream mixed graph convolutional networks for skeleton-based action recognition

The skeleton-based action recognition task has recently drawn much attention in computer vision, and graph convolutional networks (GCNs) have shown great advantages in this task. However, in shallow layers of GCNs, the sharing of convolution kernels in regular graph convolution (RGC) may result in the underutilization of some convolution kernels due to the particularity of the skeleton data. To capture richer locally discriminative action features, we propose the exclusive kernel graph convolution (EKGC) for the shallow layers of GCNs, which assigns the vertexes exclusive convolution kernels for graph convolution. At the same time, following the mixed graph convolution strategy, RGC is retained in the deep layers of the network, and the mixed graph convolutional network (MGCN) is proposed to utilize EKGC and RGC reasonably in all layers of the network. In addition, as a new form of skeleton information in GCN-based methods, angle information is explored in our work. Compared with joint data and bone data, angle data have better invariance against rotation and scaling, and they expand the relationships between skeletons and enrich the data representation of action. Finally, our final model, the multi-stream MGCN, is validated on two challenging datasets, achieving the state-of-the-art performance on the NTU-RGBD dataset and making competitive performance on a kinetics-skeleton dataset.
Multimedia English Learning Materials for Deaf or Hard of Hearing (DHH) Children

In supporting learning for Deaf or Hard of Hearing/DHH children, it is not easy finding learning sources that are accessible online, especially in English. Furthermore, during the COVID-19 pandemic, when learning was required to be carried out online, there were no learning applications that included English sign language that could be used by teachers and students as learning media. Thus, this study aims to analyze the need for developing multimedia-based English learning materials for Deaf or Hard of Hearing (DHH) children. This qualitative study involved DHH students and instructors in Special School (SLB), Bali. The need analysis was conducted to identify the ongoing situation of DHH children in the context of developing English multimedia learning materials. Interview and observation techniques applying two types of instruments in the form of an interview guide and field notes were used to collect the information towards the English learning needs for DHH children. The results of the study identified several needs that underlie the development of English multimedia learning materials, namely inadequate English instructors, text-based teaching materials, and the less than optimal teaching and learning process during the COVID-19 pandemic. Therefore, the development of multimedia-based English learning materials is needed.
Does local pruning offer task-specific models to learn effectively?

The need to deploy large-scale pre-trained models on edge devices under limited computational resources has led to substantial research to compress these large models.However, less attention has been given to compress the task-specific models.In this work, we investigate the different methods of unstructured pruning on task-specific models for Aspect-based Sentiment Analysis (ABSA) tasks.Specifically, we analyze differences in the learning dynamics of pruned models by using the standard pruning techniques to achieve high-performing sparse networks.We develop a hypothesis to demonstrate the effectiveness of local pruning over global pruning considering a simple CNN model.Later, we utilize the hypothesis to demonstrate the efficacy of the pruned state-of-the-art model compared to the over-parameterized state-of-the-art model under two settings, the first considering the baselines for the same task used for generating the hypothesis, i.e., aspect extraction and the second considering a different task, i.e., sentiment analysis.We also provide discussion related to the generalization of the pruning hypothesis.
Weight hierarchies of a family of linear codes associated with degenerate quadratic forms

We restrict a degenerate quadratic form $f$ over a finite field of odd characteristic to subspaces. Thus, a quotient space related to $f$ is introduced. Then we get a non-degenerate quadratic form induced by $f$ over the quotient space. Some related results on the subspaces and quotient space are obtained. Based on this, we solve the weight hierarchies of a family of linear codes related to $f.$
Driving-Pattern Identification and Event Detection Based on an Unsupervised Learning Framework: Case of a Motorcycle-Riding Simulator

Analysis of human driving behavior aims to inspect drivers’ behavior in the real-world and in a virtual environment. The study of driving behaviors can be conducted in naturalistic situations or controlled experiments. Analyzing driving behaviors based on the data collected in naturalistic driving experiments or controlled experiments in the real-world or in a virtual environment is beneficial to fill in many of the knowledge gaps about driving behaviors and risk factors. The amount of data collected during complex experiments with many laps and many drivers tested under different experimental conditions and with different instructions can be huge. Analyzing such data can thus be considered challenging and time-consuming if done manually because it requires calling on experts in traffic psychology to inspect and understand various specific situations at a macroscopic scale involving different riders and at a microscopic scale for a particular rider on a specific lap. Also, it can be challenging in an unsupervised context to detect and match the same patterns in different laps to study similar patterns and spot important and risky events. This paper proposes a multi-step framework for analyzing driving behavior on both the macroscopic and microscopic scales. The core step of this framework is based on unsupervised machine learning algorithms applied to driving-pattern identification and the detection of critical driving events using anomaly-detection algorithms. The detected events are interpreted and described by computing their feature importance using graphs centrality measures. This provides new insight into driving behavior by identifying the motives behind the driver’s actions. The present experimental study, based on a dataset collected from the Honda Riding Trainer (HRT) simulator was conducted in the context of the European project SimuSafe and demonstrates the effectiveness of the proposed methodology. These results argue in favor of the development of such methodologies in driving-behavior studies.
A Demonstration of ScriptEase Ambient and PC-Interactive Behavior Generation for Computer Role-Playing Games

ScriptEase is a visual tool that enables game designers to create complex interactive stories for computer role-playing games, without programming. In particular, ScriptEase automatically generates the scripting code for ambient and PC-interactive non-player character (NPC) behaviors from a set of behavior patterns. Without ScriptEase, a game designer would have to write scripting code manually to specify NPC behaviors. This demonstration describes the steps of generating complex and non-repetitive ambient and PC-interactive behavior scripts using generative behavior patterns with ScriptEase. We show how ambient behavior patterns are used to re-generate and improve the behaviors of all ambient NPCs in the Prelude module of the BioWare Corporation's Neverwinter Nights official campaign. We also demonstrate PC-interactive behaviors for a guard NPC in a custom Neverwinter Nights game module. With ScriptEase behavior patterns, game designers can easily and quickly populate a story with an engaging group of NPCs.
DEVELOPMENT OF IOT ENABLED GLOBAL TRACKING SYSTEM AND MOBILE APPLICATION FOR PEOPLE WITH ALZHEIMER’S DISEASE

Abstract. Alzheimer’s is a degenerative disease meaning that it gets worse with time. Memory loss, speaking problems, wandering, and getting lost are some of the signs of the disease. The risk of wandering results in high demand for extensive monitoring solutions for the patients suffering from the disease. Tracking solutions are crucial, especially for family members and caregivers, so researchers develop new wearable tracking devices to overcome missing patients. GPS technology can provide location data with high accuracy, but it is not sufficient to use only by itself. Thus, a more extensive solution should be provided. In this paper, a mobile wearable tracking device that can provide data to the mobile application through internet has been developed for patient tracking purposes.
EFFICIENT LINE DETECTION METHOD BASED ON 2D CONVOLUTION FILTER

The article proposes an efficient line detection method using a 2D convolution filter. The proposed method was compared with the Hough transform, the most popular method of straight lines detection. The developed method is suitable for local detection of straight lines with a slope from -45˚ to 45˚. Also, it can be used for curve detection which shape is approximated with the short straight sections. The new method is characterized by a constant computational cost regardless of the number of set pixels. The convolution is performed using the logical conjunction and sum operations. Moreover, design of the developed filter and the method of filtration allows for parallelization. Due to constant computation cost, the new method is suitable for implementation in the hardware structure of real-time image processing systems.
Teacher education reform in Thailand: A semiotic analysis of pre-service English teachers’ perceptions

The teacher education program in Thailand has undergone various reforms, with the latest being the shortening of the teacher education curriculum from five years to four. To date, very few studies have accounted for this reform. To address this gap, our study sought to analyse pre-service English teachers’ perceptions towards the reform. This was done through a semiotic analysis of visuals contributed by the pre-service teachers. There were a total of 43 visual contributions, which were analysed through Labbo’s typology of symbolisms and attributes discerned from the visuals and their corresponding captions. We were able to categorize the visuals to three types of symbolisms, which were transformative, typographic, and the third type being a combination of the first and second. The most common attribute was positive affect (emotion), with a few visuals rationalizing or questioning the reform. The analysis revealed that the pre-service teachers did not really understand the nature of the reform, despite their positive perception towards change.
COPIM statement on the corporate acquisition of OA infrastructure

A statement by the COPIM project addressing our stance towards the corporate acquisition of OA infrastructure, and the structures and safeguards we are putting in place to ensure that our infrastructure will remain community-owned and governed.
Designing a Novel Efficient Substitution-Box by Using a Flower Pollination Algorithm and Chaos System

Substitution box (S-box) is a crucial component of symmetric cryptosystems and is mostly used in modern cryptographic ciphers to provide strong data sanctuary.The cryptographic strength of an S-box used in a cipher is directly proportional to the data security provided by the cipher.Thus, this study used a novel approach by combining the hybrid chaos (integrated three functions of chaos maps) to generate pseudo-random number generators and metaheuristic techniques based on the flower pollination algorithm (FPA) to develop an acceptable configuration (8 × 8) S-box.Chaotic maps enhance the initial population of FPA to be an appropriate starting point for the FPA.The objective function of the FPA was the nonlinearity score of the S-box.The FPA is used to balance exploration and exploitation operations.The suggested S-box was compared with several references and passed all the security requirements of S-box, including bijection, strict avalanche criteria (SAC), output bits independence criteria (BIC), nonlinearity, histogram, entropy, correlation coefficients, unified average changing intensity (UACI) and number of pixel change rates (NPCR), in addition to the algebraic cryptanalysis test.The proposed approach had an asymptotic computational complexity of O(n2).The nonlinearity property was 107.25.It is considered an effective and acceptable result.Whereas SAC and BIC were 0.498 and 104.7857, respectively.Simultaneously, all results of UACI and NPCR were close to the predicted normal levels 33.2255 and 99.5693, respectively.While the encryption histogram is more uniform than the simple histogram.Finally, the entropy and correlation coefficients scores result of various images were close to eight and zero, respectively.Therefore, the proposed S-box is proven to be strong and resistant to cryptography attacks.
Efficient Vehicle Counting Based On Time-Spatial Images By Neural Networks

A highly efficient vehicle counting approach based on timespatial images with deep learning is proposed in this paper. Most vehicle counting solutions are based on frame-by frame object detection and tracking to calculate the number of cars that cross a counting line. However, these approaches incur a great deal of redundancy because they track vehicles in a large area though it matters only when vehicles cross the counting line. In this work, we use time-spatial images to focus only on the information happening along the counting lines, instead of whole images, to reduce redundancy. Due to the nature of time-spatial images, vehicle counting can be achieved by object detection in such images without frame-by-frame tracking. We propose Foreground Favorable Model to conquer occlusion, congestion, and lighting change problems and Cross-Image Object Linking to conquer the distortion problem of nearly static vehicles. We also present an automatic time-spatial image dataset generation flow and the first time-spatial image dataset, called DRIVE-TSI, for vehicle counting tasks. Our vehicle counting accuracy beats state-of-the-art solutions in accuracy and is proved to be much more efficient because it only focuses on a small number of pixels. Our model achieves a 97.95% counting accuracy at 2.91 ms per frame in day time urban scenarios.
Receiver selection for multi-target tracking in multi-static Doppler radar systems

Abstract This paper presents a novel receiver selection method for multi-target tracking in multi-static Doppler radar systems. The assumption is that in the surveillance volume of interest, a single transmitter with a known frequency is active and several spatially distributed radar receivers collect and report Doppler-only measurements. The Doppler measurements are not only affected by the additive noise but also contaminated by false and missed detections. In this paper, multi-target tracking is obtained by modeling the multi-target state as a labeled multi-Bernoulli random finite set and receiver selection is implemented during tracking. Receiver selection is solved under the partially observed Markov decision framework, and the variance of the cardinality estimate is used as the selection criterion. To increase the diversity of the selected sensors and overcome the low observability of the Doppler measurement, the receivers selected at previous time steps are taken into account by adding a window. Simulation studies demonstrate the tracking performance of the proposed method with different window lengths. The results show that the observability of the target state is a crucial factor in determining the performance of receiver selection. The proposed method with a suitable window length can effectively improve the tracking accuracy.
Fusing RGB and depth with Self-attention for Unseen Object Segmentation

We present a Synthetic RGB-D Fusion Mask R-CNN (SF Mask R-CNN) for unseen object instance segmentation. Our key idea is to fuse RGB and depth with a learnable spatial attention estimator, named Self-Attention-based Confidence map Estimator (SACE), in four scales upon a category-agnostic instance segmentation model. We pre-trained this SF Mask R-CNN on a large synthetic dataset and evaluated it on a public dataset, WISDOM, after fine-tuning on only a small number of real-world datasets. Our experiments showed the state-of-the-art performance of SACE in unseen object segmentation. Also, we compared the feature maps varying the input modality and fusion method and showed that SACE could be helpful to learn distinctive object-related features. The codes, dataset, and models are available at https://github.com/gist-ailab/SF-Mask-RCNN