Reviewerly/exHarmony huggingface dataset:
Genetic Programming-Based Inverse Kinematics for Robotic Manipulators

In this paper, we introduce an inverse kinematics model for a robotic manipulator using Genetic Programming (GP). The underlying problem requires learning of multiple joint parameters of the manipulator to reach a desired position in the Cartesian space. We present a new approach to identify a closed-form solution for the Inverse Kinematics (IK) problem, namely IK-CCGP. The novelty of IK-CCGP is the cooperative coevolutionary learning strategy. Unlike other GP approaches, IK-CCGP is not limited to a certain angle combination to reach a given pose and is designed to achieve more flexibility in the learning process. Moreover, it can operate both as single- and multi-objective variants. In this paper, we investigate whether the inclusion of further objectives, i.e. correlation and the consistency of a solution with physical laws, contributes to the search process. Our experiments show that the combination of the two objectives, error and correlation, performs very well for the given problem and IK-CCGP performs the best on a kinematic unit of two joints. While our approach cannot attain the same accuracy as Artificial Neural Networks, it overcomes the explainability gap of IK models developed using ANNs.
Label Dependency Classifier using Multi Feature Graph Convolution Networks for Automatic Image Annotation

Large scale image repository superintendence and retrieval of intelligent images is crucial issue due to abundant growth of images on digital platform. Automatic Image Annotation (AIA) certainly helps in this regards with huge influential development in machine learning technique. However gradual evolution methods developed by many researchers still demand the manageability and efficiency in large image dataset. High level semantic concept in correlation with low level image feature can be useful for huge dataset image retrieval. Additionally traditional approach of assigning labels manually for growing image dataset is practically not suitable as it is time-intensive and costly. Manual assignment of labels may lead to Error-prone and difficult to get quality labels. Only limited number training images with appropriate labels are available. Description associated with images on digital platform can help up to some extent to uplift the performance of AIA. We propose multiple label identification framework for AIA. In this framework we have used the joint optimization of multi label graph, Modality feature correlation and latent semantics. Semi supervised machine learning approach is used for Image annotation as image description associated with image can suggest good labels and results can be improved. Capacious experimentation is done on NUS-WIDE and MIR Flickr dataset. Results indicate the effective improvement in AIA .
Mining Frequent Serial Positioning Episode Rules with Forward and Backward Search Technique from Event Sequences

Abstract A large event sequence can generate episode rules that are patterns which help to identify the possible dependencies existing among event types. Frequent episodes occurring in a simple sequence of events are commonly used for mining the episodes from a sequential database. Mining serial positioning episode rules (MSPER) using a fixed-gap episode occurrence suffers from unsatisfied scalability with complex sequences to test whether an episode occurs in a sequence. Large number of redundant nodes was generated in the MSPER-trie-based data structure. In this paper, forward and backward search algorithm (FBSA) is proposed here to detect minimal occurrences of frequent peak episodes. An extensive correlation of parameter settings and the generating procedure of fixed-gap episodes are carried out. To generate a fixed-gap episode and estimate the variance that decides the parameter selection in event sequences, Spearman’s correlation coefficient is used for verifying the sequence of occurrences of the episodes. MFSPER with FBSA is developed to eliminate the frequent sequence scans and redundant event sets. The MFSPER–FBSA stores the minimal occurrences of frequent peak episodes from the event sequences. The experimental evaluation on benchmark datasets shows that the proposed technique outperforms the existing methods with respect to memory, execution time, recall and precision.
A Multirobot Person Search System for Finding Multiple Dynamic Users in Human-Centered Environments

Multirobot coordination for finding multiple users in an environment can be used in numerous robotic applications, including search and rescue, surveillance/monitoring, and activities of daily living assistance. Existing approaches have limited coordination between robots when generating team plans or do not consider user location probability within these plans. This results in long searches and robots potentially revisiting the same locations in succession. In this article, we present a novel multirobot person search system to generate search plans for multirobot teams to find multiple dynamic users before a deadline. Our approach is unique in that it simultaneously considers the search actions of all robots and user location probabilities when generating team plans, where user location probabilities are represented as conditional spatial-temporal probability density functions. We model this multirobot person search problem as a two-stage optimization problem to maximize the expected number of users found before the deadline. Stage 1 solves the action selection problem to determine a set of team actions, and the second stage solves the action allocation problem to distribute these actions amongst the robots. Namely, in stage 1, a novel conditional multiperiod multiknapsack problem is modeled as a min-flow graph solved sequentially by the Bellman-Ford shortest path algorithm. Stage 2 is a variant of the min-max multitraveling salesperson problem which models the environment topology as a search region network and search times selected by the previous stage. This stage is solved by a novel fuzzy clustering method. Numerous experiments comparing our proposed method to other existing approaches with varying environment sizes, search durations, and the number of users showed that our approach was able to find more target users before a defined deadline.
Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block

At present, underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system.However, the processed underwater terrain images have inconspicuous and few feature points.In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed, we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block.First, the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information.The accelerated-KAZE (A-KAZE) algorithm is used to combine the feature block information to match the reference image and the target image.Then, the random sample consensus (RANSAC) is applied to optimize the matching results.Finally, image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion.The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information, which not only solves the problem of stitching failure of underwater terrain images due to unobvious features, and further reduces the sensitivity to noise, but also effectively reduces the iterative calculation in the feature point matching process of the traditional method, and improves the stitching speed.Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.
Efficient deniable authentication and its application in location-based services

Deniable authentication (DA) assures message authentication between sets of participants. The actual participants can be certain about the messages’ authenticity but cannot prove it to any third party after the event. In applications such as location-based service (LBS) systems, DA is inevitably relevant to assure the coercion-free state of its usage and to protect user privacy. To protect user privacy in LBS systems, we present two heterogeneous deniable authentication (HDA) schemes called HDA-I and HDA-II scheme. The HDA-I scheme permits communication sessions from certificateless cryptography (CLC) to identity-based cryptography (IBC) setting, whiles the HDA-II scheme runs in a vice versa mode. Our schemes admit security proof in random oracle model (ROM) under computational Diffie–Hellman (CDH) and bilinear Diffie–Hellman (BDH) assumptions. Furthermore, both HDA-I and HDA-II provide batch verification, that can accelerate the verification of authenticators. Lastly, via comprehensive performance evaluation, our schemes show up efficient against other recent related schemes.
D-H Parameter Method-based Wearable Motion Tracking

Abstract Motion capture is a key technology for robots to accurately understand pedestrian intentions in the scene of human-machine integration. Due to the limited spatial distance and easy obstruction by obstacles, traditional optical motion capture systems often lose detection targets. This paper proposes a wearable motion tracking method based on D-H parameter method. By binding multiple wireless inertial sensor units composed of accelerometers, magnetic flux sensors and gyroscopes to various moving parts of the user’s body, accurate and robust tracking of moving targets is achieved. This method uses the known pose information of the root node to find the pose state of each level in the reference coordinate system, and establishes the human body joint rotation model and the bone position state model. The results show that the motion tracking method proposed in this paper reduces 9 degrees of freedom compared with the traditional forward kinematics method, and the algorithm efficiency is increased by about 20%, which can accurately obtain the posture characteristics of the human body. It can be seen that the D-H parameter method is reasonable for the wearable human body motion tracking.
Program Synthesis with Genetic Programming: The Influence of Batch Sizes

Genetic programming is a method to generate computer programs automatically for a given set of input/output examples that define the user's intent. In real-world software development this method could also be used, as a programmer could first define the input/output examples for a certain problem and then let genetic programming generate the functional source code. However, a prerequisite for using genetic programming as support system in real-world software development is a high performance and generalizability of the generated programs. For some program synthesis benchmark problems, however, the generalizability to previously unseen test cases is low especially when lexicase is used as parent selection method. Therefore, we combine in this paper lexicase selection with small batches of training cases and study the influence of different batch sizes on the program synthesis performance and the generalizability of programs generated with genetic programming. For evaluation, we use three common program synthesis benchmark problems. We find that the selection pressure can be reduced even when small batch sizes are used. Moreover, we find that, compared to standard lexicase selection, the obtained success rates on the test set are similar or even better when combining lexicase with small batches. Furthermore, also the generalizability of the found solutions can often be improved.
Towards Achieving the 2030 Vision, the Case study of Automating the Food Production Services during the Hajj Season and Quality Control Using the Blockchain Technology

Improving the services provided for the pilgrims is one of the Saudi Arabian government priorities, as well as for each person who works in the agencies of serving them. Food providing is considered one of the main services for the pilgrims. The current food service providers are 3-4 food plants that do not cover the whole number of the needed meals, kitchens, and the local market. In the current situation, the meals are provided under no specific standards or regulations, and the quality assurance process needs to work with several providers. In this paper, we propose a solution for this problem by suggesting the automation of the whole food providing services by establishing several centralized and specialized food plants that work under a single authority, distributed widely to cover a large area of the pilgrims' locations. The plants will be responsible for the food service starting from providing the raw materials up to the delivery of the meals to the performers on time without any compromise in its quality. This is done by applying blockchain technology as a proposed method for quality control on the supply chain to record every transaction and verify the origin of every material used to produce the meals, as well as trace back the source of any problem to its origin. This will ensure the availability of the total number of required meals in time, the meals to be produced under specific standards and specifications, and will digitize the quality assurance process and make it more centralized. The result of simulating the proposed "to-be" business model by using the Bizagi platform showed that the proposed model outperformed the current "as-is" model by saving 54.46% of the needed total needed time for food providing service. It also showed promising results regarding quality when applying blockchain technology.
A Walking Speed Measurement System Using PIR Sensors for Early Detection of Pre-Frailty

In aging society, for elderly people to maintain the quality of life and lead a healthy and independent life, it is necessary to detect frailty between the state of health and the state of needed long-term care at an early stage. The purpose of this study is to construct a system that detects pre-frailty, which is the pre-stage of frailty, and notifies the watcher. Focusing on the decrease in walking speed of older adults in pre-frailty, we researched on measuring walking speed using a non-wearable pyroelectric infrared (PIR) sensor that the measurement could be performed with an average error of 3.5% compared to the manual measurement. By installing the system indoors, elderly people can continuously their walking speed just by living daily lives. By detecting pre-frailty at an early stage and notifying the watcher, it is possible to recover to a healthy state by appropriate intervention such as exercise and dietary improvement.
An Advance Cryptosystem Using Extended Polybius Square with Qwerty Pattern

In this modern epoch, securing the data by using encryption and decryption techniques is a common practice becoming a necessity significantly. While information travel from sender to receiver end data is passed through several vulnerable layers; therefore securing data plays an integral role in communication. Cryptography plays an important role to secure message on both wireless and wired mediums and provides amazing solutions and provides the essential protections techniques against the unwanted attacks. Polybius Square is built on substitution cipher technique of 5X5 matrix representation of letters. In this paper an extended 8X8 Polybius square has been proposed which includes alphabets, digits and special symbols encountered, and its row and column intersection is further encrypted by using Hexadecimal form for more security with columnar transposition technique.
Image Aesthetic Computer Assessment Model Based on Score Distribution

The quantification of image aesthetic quality has always been a hot topic in the field of computer vision. Aesthetic quality assessment is a very challenging task because of its subjectivity. This paper regarded image aesthetics assessment as an aesthetic score distribution task by fine-tuning the traditional convolutional neural network VGG16. We replaced the fully connected layer with a convolutional layer and added an adaptive spatial pooling layer at the end of the network to circumvent the traditional network&#x0027;s limitations of the input size. In addition, we proposed the cross-entropy loss function for sample reliability. On the aesthetic dataset AVA, our improved model achieved a better performance.
AI+5G Empowers the Construction of a Smart Learning Space for Private Higher Vocational Education in the Guangdong-Hong Kong-Macao Greater Bay Area

The integration of new-generation information technologies such as AI and 5G into education provide strong technical support for private colleges and universities to implement teaching reforms and achieve the goals of smart education. The new generation of information technologies such as AI and 5G play an important role in empowering the construction of smart learning spaces, and play a very important role in the development of private higher education in the Guangdong-Hong Kong-Macao Greater Bay Area. Therefore, the construction of AI+5G empowered smart learning space should be based on the learning concept of the smart era, with smart education as the goal, and the teaching effectiveness of smart learning space to achieve effective improvement of teaching quality. To promote the agglomeration and development of private higher education in the Guangdong-Hong Kong-Macao Greater Bay Area.
Research on Prediction of Link Embedding in Maritime Knowledge Graph

The knowledge graph is essentially a multi-relational network, which uses a structured method to store the knowledge system of the corresponding relationships between entities in the real world. It is a structured representation of the real world and brings new technical means to the knowledge representation of the maritime world. However, the data is often incomplete, missing many nodes or links, because they may be only a small part of all based on credible facts. We can solve this problem by predicting missing links. Recent studies on multi-hop KGQA try to use relevant external text to deal with KG sparsity, this method still has relatively large limitations. In another study, a KG embedding method has been proposed to reduce KG sparsity by performing missing link prediction.
Evaluation of the Control Interface of Multiple Cooperative UAVs Based on Extracted Eye Movement Patterns

Controlling multiple cooperative UAVs is still challenging for a human operator, especially when the operator has to allocate the tasks dynamically and switch the interface view as needed. In this paper, we evaluate the control interface of UAVs from the perspective of human factors. First, eye movement patterns are extracted from eye-tracking data. Some certain patterns are found to be associated with emergent events such as handling messages from other UAVs. Then, we analyze the focus areas of the operator with two different control modes of the wingman UAVs, i.e., a following mode and an autonomous mode. Besides, the impact of two target shape designs is also compared, a circular and square, which the operator needs the UAVs to through. In addition, we obtain the distribution characteristics of the operator’s operation frequency in the two control modes. Finally, we demonstrate the effectiveness of the interactive and non-interactive reminding messages sent by the monitoring system or from other UAVs.
Machine Learning Meets Data Modification

We explore how data modification can enhance privacy by examining the connection between data modification and machine learning. Specifically, machine learning “meets” data modification in two ways. First, data modification can protect the data that is used to train machine learning models focusing it on the intended use and inhibiting unwanted inference. Second, machine learning can provide new ways of creating modified data. In this chapter, we discuss data modification approaches, applied during data pre-processing, that are suited for online data sharing scenarios. Specifically, we define two scenarios “User data sharing” and “Data set sharing” and describe the threat models associated with each scenario and related privacy threats. We then survey the landscape of privacy-enhancing data modification techniques that can be used to counter these threats. The picture that emerges is that data modification approaches hold promise to enhance privacy, and can be used alongside of conventional cryptographic approaches. We close with an outlook on future directions focusing on new types of data, the relationship among privacy, and the importance of taking an interdisciplinary approach to data modification for privacy enhancement.
Research on the Evaluation Algorithm of College English Teaching Ability Based on Fuzzy Comprehensive Evaluation

College English classroom teaching evaluation is an important basis to reflect the quality of classroom teaching, which has some uncertainties. However, the traditional college English classroom teaching evaluation is plagued by the randomness of the evaluation process and the fuzziness of the evaluation criteria. This paper analyzes the problems existing in the current college English teaching quality evaluation system and the evaluation principles that should be followed, and constructs a college English teaching quality evaluation system with learning as the center and students as the evaluation subject. On this basis, the interval-valued fuzzy set can effectively reduce the loss of fuzzy information in the process of information processing, and introduces the evaluation method of college English classroom teaching quality based on interval-valued fuzzy comprehensive evaluation. Compared with the general fuzzy comprehensive evaluation method, the interval value fuzzy comprehensive evaluation method can more accurately and comprehensively evaluate the quality of college English classroom teaching. The test of specific English classroom examples proves that the method is objective, effective, scientific and feasible.
An investigation of the relationship between numerical precision and performance of Q-learning for hardware implementation

Reinforcement learning is promising as a machine learning paradigm in edge computing. However, its high computational cost poses a challenge when implementing in devices with limited circuit resources and power consumption. In this study, we investigated the relationship between the bit-length of floating-point operations and the learning performance of the reinforcement learning algorithm. In the case of the FrozenLake maze problem, we found that the learning performance of 8-bit floating-point arithmetic decreased, while that of 16-bit floating-point arithmetic was comparable to that of 64-bit CPU arithmetic. Our results provide a practical guideline for designing a dedicated reinforcement learning hardware with minimum circuit resources and power consumption.
Ensuring Data Integrity Using Digital Signature in an IoT Environment

The Internet of Things (IoT) devices produce large amounts of data which increase their susceptibility to cyber attacks. Data Integrity attack is a serious limitation to the evolution of IoT as it leads to bad financial impact or even human fatality. In this work, we will focus on ensuring exchanged data integrity using digital signature for public key cryptography. Elliptical Curve Digital Signature Algorithm (ECDSA) is gaining more attention than RSA in IoT for its same level of security with smaller keys. ECDSA is applied on our predefined Software Defined Architecture (SDN).