Reviewerly/exHarmony huggingface dataset:
Automatic Agricultural Robot — Agrobot

Agriculture is a time consuming as well as labour demanding sector nowadays. Traditional methods of agriculture will lead to huge losses to farmers both in productivity as well as financially. This paper focused on the development of a novel model named Automatic Agricultural Robot - Agrobot which can execute the operations like digging the soil, planting seeds, and watering with more precision and accuracy with the help of robotics and automation. This will be a solution to the time constraints and labour issues and it helps to achieve a higher rate of production. Agrobot is a four-wheeled robot integrated with Arduino mega 2560. The ultrasonic sensor interfaced to Arduino senses the edge of the field and helps in the automatic movement of the robot. The device can be operated by providing input through an android application. The values like distance between the seeding, direction to which the robot turns at first, the number of seeds to be planted in one hole, etc. are given as the initial inputs to make the robot starts functioning. Agrobot will also help to reduce the usage of petroleum products in the agriculture sector and thereby reducing environmental pollution.
Fabsim-X: A Simulation Framework for the Analysis of Large-Scale Topologies and Congestion Control Protocols in Data Center Networks

The explosive growth in cloud-computing and overall data center system growth has created an unprecedented demand on system architects and designers to continuously develop more complex system networks to effectively satisfy the insatiable appetite to process, move, and store large amounts of data. Nonlinear system behavior caused by emerging workloads and use-cases, varying end-to-end congestion protocols, and heterogeneity in the various compute and storage capabilities of custom designed accelerators further compounds the design problem. Modern simulation methodologies lack a cohesive and efficient framework to address the interoperability of the intersecting layers at scale. In this paper, we present a simulation framework for evaluating congestion control protocols. Furthermore, we present a set of optimizations that enable analysis for longer simulated times and at network scales up to 128K nodes, which is vital for proper analysis of workloads that require long run times (e.g., AI training) or workloads that are known to have scaling issues (e.g., RDMA). Specifically, we evaluate congestion control performance at various scales, study the implications of topology scaling on congestion, and the performance impact of simultaneous heterogeneous protocols.
Profiling Balinese Dances with Silhouette Sequence Pattern Analysis

The Balinese dances are a very valuable asset for the tourism industry in Bali. The first objective of this study is to produce the first and standardized dataset of Balinese dance videos for the research of sequence pattern analysis of the digital image frames contained therein. The second objective is to implement and to evaluate the performance of our proposed scheme of feature extraction methods and Bag of Visual Movement on the pattern analysis. The third objective is to detect and to extract the patterns of Balinese dance silhouettes with quantitative analysis data in the field of computer vision. Results show that the Bag of Visual Movement generates visually differentiable clusters for image frames and our proposed method with HoG feature extraction and Bag of Visual Movement are visually able to detect patterns. The 13 pattern measures proposed are able to build profile of different Balinese dances from different maestros.
Applying Hidden Markov Model for Dynamic Game Balancing

In Artificial Intelligence (AI) field, Machine Learning (ML) techniques present an interesting approach for games, where it allows some sort of adaptation along the game session. This adaptation can make games more attractive, avoiding that Non-Player-Characters (NPC) present too easy or hard patterns during the game. In both cases, the player may be frustrated due to undesired experience. Although ML techniques are appealing to be used in games, some games characteristics are hard to model. Besides, there are techniques that require a wide variety of observations, which implies two hard barriers for game application: the first is the power processing to compute a huge amount of data in games, considering the real-time characteristic of this kind of application. The second threat is related to the vast majority of games' attributes that must be described in the model. This work proposes a novel approach using ML technique based on Hidden Markov Model (HMM) for game balancing process. HMM is a powerful technique which can be used to learn patterns based on a strong co-relational between an observation and an unknown variable (the hidden part). Our proposed approach learns the player's pattern based on temporal frame observation by co-relating his/her actions (movements) with game events (NPC destruction). The temporal frame observation approach allows the game to learn about player's pattern even if a different person plays it. After the learning process, the following step is to use the knowledge pattern to adapt the game according to the current player, which normally involves making the game harder for a certain period of time. During this time, another pattern may arise, subjected to be learned. In order to validate the presented approach, a Space Invaders clone has been built, allowing to observe that 54 % of participants had more fun while playing it with ML activated in relation to a base version that did not take into account dynamic difficult balancing.
Dog and Cat Classification with Deep Residual Network

I With the development of artificial intelligence, the deep neural network(DNN) has achieved excellent results in image processing domain such as image classification[1] and objct detection[2]. The convolution neural networks(CNN) [3] is a Representative algorithm of DNN which have the representation learning ability. According to its convolutional structure, input information is extracted with translation invariance. Based on the widely used CNN, there are many efficient models. For image classification there are Lenet-5[4], VGG[5], Resnet[6] and so on. For object detection, the yolo series[7] is well-known. Also few well known datasets are proposed to measure their performance such as ImageNet and Cifar-10[8]. These data sets are dedicated to the classification of multiple objects in natural scenes. Nowadays, pets play an increasingly important role in our life, so we built a cat and dog dataset, each of which categories with 12500 samples which is larger then 1260 in Imagenet. For our dataset, we trained an image classification model. We focus on the performance of distinguish dog and cat In different scenes, lighting and noise. Our method achieved an accuracy of 92.7 percent and remained robust under adversarial attack.
Model and Simulation of Maximum Entropy Phrase Reordering of English Text in Language Learning Machine

This paper proposes a feature extraction algorithm based on the maximum entropy phrase reordering model in statistical machine translation in language learning machines. The algorithm can extract more accurate phrase reordering information, especially the feature information of reversed phrases, which solves the problem of imbalance of feature data during maximum entropy training in the original algorithm, and improves the accuracy of phrase reordering in translation. In the experiment, they were combined with linguistic features such as parts of speech, words, and syntactic features extracted by using the syntax analyzer, and the maximum entropy classifier was used to predict translation errors, and the experimental verification was performed on the Chinese-English translation data set and compared. The experimental results show that different word posterior probabilities have a significant impact on the classification error rate, and the combination of linguistic features based on the word posterior probability can significantly reduce the classification error rate and improve the translation error prediction performance.
A summary of the research on the foundation and application of blockchain technology

Abstract Blockchain is an emerging distributed database technology. It has the characteristics of decentralization, non-tampering, traceability and final consistency. Blockchain can solve data management problems in untrusted environments. Based on the research and analysis of the blockchain system, this article expounds the application of the blockchain system in the distributed database environment. First, this article introduces the concept of the blockchain system and the classification of the blockchain system from multiple aspects. Then it introduces the data storage technology and data encryption technology adopted by the blockchain system in detail. Finally, the application prospects of the blockchain system in today’s society are introduced.
Image Classification Performance Evaluation for 3D Model Reconstruction

3D reconstruction of 2D images is a classical problem in computer vision. Conventional methods have been proposed using multiple image registration, intrinsic and extrinsic camera parameter estimation, and optimization methods. Recently, the availability of a 3D dataset publicly shared has encouraged a deep-learning-based method for single-view reconstruction. One approach was by employing direct image encoding to the 3D point decoding approach as in PointNet and AtlasNet. Some other research directions attempted to retrieve 3D data using deep-learning-based methods, which employed a convolutional neural network (CNN). However, the use of CNN in image classification specific for the 3D reconstruction task still needs to be investigated because, usually, CNN was used as an image encoder in an auto-encoder setting instead of a classification module in a point generation network. Moreover, there is a lack of reports on the performance evaluation of deep-learning-based method on images rendered from 3D data, such as ShapeNet rendering images. In this paper, we implemented several deep-learning models to decode the ShapeNet rendering images that contain 13 model categories to examine the various hyper-parameters' impacts on each 3D model category. Our experiments showed that the hyper-parameters of the learning rate and epochs set to either 0.001 or 0.0001 and 60-80 epochs significantly outperformed other settings. Moreover, we observed that regardless of network configuration, some categories (plane, watercraft, car) performed better throughout the study. Therefore, a 3D reconstruction based on image classification can be designed based on the best performing categories.
Measurement of Information Security Awareness Level: A Case Study of Mobile Banking (M-Banking) Users

Public awareness of the importance of information security is a major factor at a time of high public interest in Mobile Banking (M-Banking) application. This is one of the triggers for fraud in cyberspace. Therefore, this study was conducted to measure information security awareness for Mobile Banking (M-Banking) application users using the Knowledge-Attitude-Behavior (KAB) model. The assessment of information security awareness level was calculated using the Analytic Hierarchy Process (AHP) method. Data were obtained through a questionnaire that was given to 210 respondents who used the Mobile Banking (M-Banking) application in Indonesia. The results showed that the security awareness level of Mobile Banking (M-Banking) application users was at the “good” level with a value of 83.32%. However, there is one focus area, namely “use of social media” which showed “average” and unsatisfactory result in all three dimensions, both in the dimensions of knowledge, attitude and behavior, so it is necessary to take certain actions and strategies in order to improve information security awareness of Mobile Banking (M-Banking) application users.
AFT-Net: Active Fusion-Transduction for Multi-stream Medical Image Segmentation

As an important building block in automatic medical applications, image segmentation has made a great progress due to the data-driving mechanism of deep architecture. Recently, numerous methods have been proposed to boost the segmentation performance based on U-shape network. However, they often built feature encoders with only one data routine, which have limited the representation ability of the networks. Although some methods applied multiple learning paths to fix this problem, the deep supervision techniques are required to monitor the training status at individual path, which may bring extra burden to practical usage of the algorithm. Additionally, under these frameworks, the semantic gap between different paths may interfere with model's learning performance, and the potential transduction ability of skip connections still needs further investigation. To address these issues, we introduce a novel medical image segmentation framework, namely AFT-Net, in which an attention-based data fusion model is proposed to effectively cooperate with multi-stream encoder. By progressively accumulating the features from different paths, our method can establish meaningful connections between structural and semantic features, while keeping an integral and flexible layout without deeply customized supervisions. Extensive experiments on two medical image data sets demonstrate that our method is able to acquire image features with both diversity and quality, thereby outperforms current state-of-the-art segmentation methods.
The integrated sigma-max system and its application in target recognition

Randomness and fuzziness are two fundamental kinds of uncertain phenomena, which can be handled respectively by probability theory (sigma system) and possibility theory (max system). For many practical problems of information processing, random uncertainty may often co-exist with fuzzy uncertainty, which reminds us to look for a novel mechanism called sigma-max hybrid uncertainty inference. This mechanism can cope with randomness and fuzziness jointly and achieve a direct fusion of heterogeneous information modeled by probability or possibility. Specifically, we are interested in two typical forms of uncertainty inference, i.e., uncertainty update equation combining heterogeneous information as well as composition rule of heterogeneous relations. Such an objective is achieved by the adoption of joint description of a random variable and a fuzzy variable, i.e., hybrid distribution of probability and possibility, which lays the foundation for connecting the two uncertainty systems of probability and possibility, and results in the integrated sigma-max system. The hybrid distribution, other than joint probability (or possibility) distribution, has a couple of special normalization requirements that are order-dependent with respective to “addition” operation over random variable and “max’ operation over fuzzy variable. The derived sigma-max inference is applied to target recognition, an example of which is simulated to demonstrate the merit of the proposed method over the classic Bayesian classifier when randomness and fuzziness are both involved.
A pixel pair–based encoding pattern for stereo matching via an adaptively weighted cost

Stereo matching, which is a key problem in computer vision, faces the challenge of radiometric distortions. Most of the existing stereo matching methods are based on simple matching cost algorithms and appear the problem of mismatch under radiometric distortions. It is necessary to improve the robustness and accuracy of matching cost algorithms. A novel encoding pattern is proposed for stereo matching. In the proposed encoding pattern, each of the matching windows in the grey image and gradient images is divided into several isoline-like sets with different radii. Then, pixel pairs are defined in the isoline-like sets. An encoding function is used to decide the relative order between the two pixels in each pixel pair. To apply the pattern for matching cost computation and enhance the matching accuracy, an adaptively weighted cost is designed that is related to the isoline-like sets. Experiments are conducted on the Middlebury and KITTI data sets to show the validity of the proposed method under severe radiometric distortions. Also, the comparisons with some widely used methods are made in the experiments to illustrate the advantage of the proposed method.
Customized Diagnostic Tool for The Security Maturity Level of The Enterprise Information Based on ISO/IEC 27001

This paper presents the design of a customized digital tool based on the ISO/IEC 27001 to determine the level of maturity of security in information resources in companies. Due to the detailed process established by ISO/IEC 27001, the technological proposal shown in this paper is based on a prioritization process of the security aspects according to the size and line-business of the particular company that wishes to implement the mentioned international standard. After the development of the diagnostic tool, the Beta test was carried out in a company as Early-Adopter in order to improve the complete diagnostic tool.
Open-World Relationship Prediction

Knowledge Graph Completion (KGC) aims at completing the missing information in Knowledge Graphs (KGs) that provide a structured representation of knowledge. Most existing methods focus on completing existing entities and relationships that have not been discovered, while neglecting the importance of discovering new knowledge outsides KGs, especially the relationships, to the KGC. In this paper, we work further and try to detect relationship between two entities no matter they are inside or outside KGs. To complete this task, we propose a novel and unified model named Structured Attention Graph Neural Network (SAGNN) which can mine the new relationships outside KGs easily without relying on much external resource. By mining the latent association features from structural information such as in-degree, out-degree and co-occurrence frequency, the model enables specifying different weights to different nodes in a neighborhood even if the enter node lacks embedding representation, and generates the embedding with high-level semantic features eventually. Experiments on multiple datasets show that SAGNN performs well on the open-world relationship prediction task and even outperforms existing KGC models on the triple classification task in which entities outside KGs are involved.
A Novel Parsing-based Approach for Verification of Hierarchical Plans

Hierarchical Task Networks were proposed as a method to describe plans by decomposition of tasks to subtasks until primitive tasks, actions, are obtained. Valid plans - sequences of actions - must adhere both to causal dependencies between the actions and to the structure given by the decomposition of the goal task. Plan verification aims at finding if a given plan is valid, that is, if it is causally consistent and it can be obtained by decomposition of some task. The paper describes a novel parsing-based approach for hierarchical plan verification that is orders of magnitude faster than existing methods.
Towards Finite-Time Consensus with Graph Convolutional Neural Networks

This work proposes a learning framework for distributed finite-time consensus with graph convolutional neural networks (GCNNs). Consensus is a central problem in distributed and adaptive optimisation, signal processing, and control. We leverage the link between finite-time consensus and graph filters, and between graph filters and GCNNs to study the potential of a readily distributed architecture for reaching consensus. We have found GCNNs outperform classical graph filters for distributed consensus and generalize better to unseen topologies such as distributed networks affected by link losses.
Nonparametric Analysis of Tracking Data in the Context of COVID-19 Pandemic

Methods of statistical pattern recognition are powerful tools for analysis of statistical data on COVID-19 pandemic. In this chapter, we offer a new effective online algorithm for detection of change-points in tracking data (movement data, health rate data etc.). We developed a non-parametric test for evaluation of the statistical hypothesis that data in two adjacent time intervals have the same distribution. In the context of the mobile phone tracking this means that the coordinates of the tracked object does not deviated from the base point significantly. For estimation of the health rate it means the absence of significant deviations from the norm. The significance level for the test is less than 0.05. The test permits ties in samples. Also, we show that results of comparison of the test with well-known Kolmogorov–Smirnov test. These results show that the proposed test is more robust, sensitive and accurate than the alternative ones. In addition, the new method does not require high computational capacity.
Trust But Verify: A Framework for the Trustworthiness of Distributed Systems

Many real-time process-control and industrial control systems, such as Supervisory Control and Data Acquisition (SCADA), use a distributed software architecture and rely on trusted message exchanges among software components. This article presents the Trust but Verify (TBV) middleware that promotes the idea that software components should not blindly trust each other. The TBV intercepts messages between a sender and a receiver to verify the consistency of the messages against rules associated with message types; this verification considers the system state. Based on the verification, a message is either delivered to the recipient or blocked. Even when components are mutually authenticated, it is possible that their counterparts are faulty or acting maliciously, persuading the receiver to take harmful actions. The contributions of this article are: (1) The design of the TBV middleware. (2) A proof-of-concept implementation of the TBV on a cyberphysical system—a water treatment facility. (3) An experimental validation of the TBV through several attack scenarios that allow compromised or faulty components to randomly send erroneous messages. These experiments measure the TBV’s detection rate as well as its overhead. (4) An evaluation of the TBV overhead and performance degradation.
Research on an improved algorithm for image dehazing in underground coal mine

Abstract Coal mine images have low illuminance, large dust and high noise intensity, etc. It brings great obstacle to extract effective image information. This paper proposes an image haze removal algorithm for coal mine underground based on dark channel prior. First, histogram equalization method is introduced to enhance the underground image, obtain the dark channel image from the thumbnail of the image, and then restore the pixel value of the image through bilinear interpolation, for shortening the acquisition time of the dark channel image and increasing the speed of the algorithm. Results demonstrate this method greatly reduces the amount of calculation and improves the processing speed of the haze removal algorithm while ensuring the haze removal effect.
Routing and Key Assignment for Secure Multicast Services in Quantum Satellite Networks

A routing and key assignment algorithm is first proposed for secure multicast services in Low-Earth-orbit(LEO) quantum satellite networks, which is evaluated in terms of secure probability compared with the benchmark algorithm.