Reviewerly/exHarmony huggingface dataset:
CATE: Cloud-Aided Trustworthiness Evaluation Scheme for Incompletely Predictable Vehicular Ad Hoc Networks

Incompletely predictable vehicular ad hoc networks (IPNs) are presented for vehicular networks with vehicles moving in particular ranges. A group of vehicles in the same region are required to encrypt and sign the uploaded region information in IPNs. So far, research has not been able to provide an appropriate trustworthiness evaluation system ensuring security and fairness in IPNs. In this paper, we first establish an evaluation system for vehicles in the network for secure and fair trustworthiness evaluation. Various vehicle attributes are utilized to evaluate the trustworthiness level of a vehicle. The novel trustworthiness evaluation can provide a more comprehensive and real-time update of vehicle status. Second, based on the system, we propose a cloud-aided trustworthiness evaluation (CATE) scheme. According to the evaluation results provided by the system, the CATE scheme also guarantees lightweight trustworthiness level confirmation with the help of session key generation. Besides, the information upload security is ensured with group signature and encryption. The security proof indicates that the proposed CATE scheme is secure for vehicles in IPNs to interact with trustworthiness. According to quantitative comparative analysis and experimental simulation based on paring-based cryptography (PBC) library, the designed scheme has been proved to be efficient.
Super-Low Resolution Face Recognition using Integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN)

Several deep image-based models which depend on deep learning have shown great success in the recorded computational and reconstruction efficiencies, especially for single high-resolution images. In the past, the use of super-resolution was commonly characterized by interference, and hence, the need for a model with higher performance. This study proposed a method for low to super-resolution face recognition, called efficient sub-pixel convolution neural network. This is a convolutional neural network which is usually employed at the time of image pre-processing to increase the chances of recognizing images with low resolution. The proposed Efficient Sub-Pixel Convolutional Neural Network is used for the conversion of low-resolution images into a high-resolution format for onward recognition. This conversion is based on the features extracted from the image. Using several evaluation tools, the proposed Efficient Sub-Pixel Convolutional Neural Network recorded a higher performance in terms of image resolution when compared to the performance of the benchmarked traditional methods. The evaluations were carried out on a Yale face database and ORL dataset faces. For Yale and ORL datasets, the obtained accuracy of the proposed method was 95.3% and 93.5%, respectively, which were higher than those of the other related methods.
Large space dimension Reinforcement Learning for Robot Position/Force Discrete Control

In this work a large space dimension reinforcement learning (RL) approximation is developed for a Discrete Impedance Position/Force control of robot manipulators that interacts with an unknown environment model. The Q-value function is designed in the sense of optimal control theory. The approximator is based on normalized radial basis functions (NRBFs), and are built using the K-means clustering algorithm which generates a family of approximators for the Q-value function. The RL algorithms learn on-line the optimal impedance model which is equivalent to the desired force without any prior knowledge of the environment dynamics; this feeds a force controller and its output feeds the position controller. Real time experiments are shown using a 2 degree of freedom (DOF) pan and tilt robot and a 6-DOF force/torque (F/T) sensor.
Accuracy Level of Backpropagation Algorithm to Predict Livestock Population of Simalungun Regency in Indonesia

Abstract The development of livestock agribusiness includes all activities that begin with the procurement and regulation of production and marketing suggestions. With the many types of livestock found in Simalungun Regency, Indonesia should be able to increase the potential for livestock agribusiness development. In this study, the authors will analyze the best architecture that can be used to predict the number of livestock populations according to the type of livestock in Simalungun District Indonesia so that certain parties can make improvements to the development of livestock agribusiness in Simalungun District Indonesia. In this study, there are five 5 architectural models namely, 3-5-1 architecture, 3-6-1, 3-7-1, 3-8-1, and 3-9-1. Of the five architectural models, the best architectural model is 3-7-1 with 75% accuracy and 1693 epoch. While the error rate is 0.001-0.01. It is expected that this architectural model can help academics in the process of predicting the number of livestock populations in Simalungun Regency in the coming year.
An empirical study on bug propagation through code cloning

Code clones are identical or nearly similar code fragments in a code-base. According to the existing studies, code clones are directly related to bugs. Code cloning, creating code clones, is suspected to propagate temporarily hidden bugs from one code fragment to another. However, there is no study on the intensity of bug-propagation through code cloning. In this paper, we define two clone evolutionary patterns that reasonably indicate bug propagation through code cloning. By analyzing software evolution history, we identify those code clones that evolved following the bug propagation patterns. According to our study on thousands of commits of seven subject systems, overall 18.42% of the clone fragments that experience bug-fixes contain propagated bugs. Type-3 clones are primarily involved with bug-propagation. Bug propagation is more likely to occur in the clone fragments that are created in the same commit rather than in different commits. Moreover, code clones residing in the same file have a higher possibility of containing propagated bugs compared to those residing in different files. Severe bugs can sometimes get propagated through code cloning. Automatic support for immediately identifying occurrences of bug-propagation can be beneficial for software maintenance. Our findings are important for prioritizing code clones for management.
A hybrid egocentric video summarization method to improve the healthcare for Alzheimer patients

Alzheimer patients face difficulty to remember the identity of persons and performing daily life activities. This paper presents a hybrid method to generate the egocentric video summary of important people, objects and medicines to facilitate the Alzheimer patients to recall their deserted memories. Lifelogging video data analysis is used to recall the human memory; however, the massive amount of lifelogging data makes it a challenging task to select the most relevant content to educate the Alzheimer’s patient. To address the challenges associated with massive lifelogging content, static video summarization approach is applied to select the key-frames that are more relevant in the context of recalling the deserted memories of the Alzheimer patients. This paper consists of three main modules that are face, object, and medicine recognition. Histogram of oriented gradient features are used to train the multi-class SVM for face recognition. SURF descriptors are employed to extract the features from the input video frames that are then used to find the corresponding points between the objects in the input video and the reference objects stored in the database. Morphological operators are applied followed by the optical character recognition to recognize and tag the medicines for Alzheimer patients. The performance of the proposed system is evaluated on 18 real-world homemade videos. Experimental results signify the effectiveness of the proposed system in terms of providing the most relevant content to enhance the memory of Alzheimer patients.
Parallel Image Completion with Edge and Color Map

Over the last few years, image completion has made significant progress due to the generative adversarial networks (GANs) that are able to synthesize photorealistic contents. However, one of the main obstacles faced by many existing methods is that they often create blurry textures or distorted structures that are inconsistent with surrounding regions. The main reason is the ineffectiveness of disentangling style latent space implicitly from images. To address this problem, we develop a novel image completion framework called PIC-EC: parallel image completion networks with edge and color maps, which explicitly provides image edge and color information as the prior knowledge for image completion. The PIC-EC framework consists of the parallel edge and color generators followed by an image completion network. Specifically, the parallel paths generate edge and color maps for the missing region at the same time, and then the image completion network fills the missing region with fine details using the generated edge and color information as the priors. The proposed method was evaluated over CelebA-HQ and Paris StreetView datasets. Experimental results demonstrate that PIC-EC achieves superior performance on challenging cases with complex compositions and outperforms existing methods on evaluations of realism and accuracy, both quantitatively and qualitatively.
Using electrochromic displays to display ambient information and notifications

Ambient Displays are a promising means to reduce notification overload and work towards the vision of Calm Computing. In this paper, we present electrochromic displays as a novel class of displays to convey information. electrochromic displays are non-light-emitting, flexible, free-form, transparent, energy-efficient, easily integrated, and slow-switching, making them ideal candidates for information that changes over time and does not require immediate user attention. We describe the key features of electrochromic displays as well as application areas and provide an outlook into future developments of the technology.
Sentiment Analysis In Twitter Using Lexicon Based and Polarity Multiplication

Twitter is one type of social media that is often used. Users use Twitter to convey their tweet to the general public. The number of Twitter users has reached 330 million people worldwide. Besides that, in Twitter there are tweets that can be sentiments. Sentiment itself can be defined as policy, opinion, logic, or mud, etc. Therefore, sentiment analysis is determining the polarity or type of opinion in a predetermined text or subject. NLP (Natural Language Processing) technique is used to support the beginning of a text. The technique used in an analysis is tokenization sentiment, elimination of stop words, and stemming. This study focuses on developing sentiment analysis using lexicons and multiplication polarity. Accuracy results are still smaller than using machine learning. Therefore, this lexicon needs to be improved in terms of its semantics.
An Extended Multilayer Perceptron Model Using Reduced Geometric Algebra

An extended model of multilayer perceptron (MLP) based on reduced geometric algebra (RGA), namely RGA-MLP, is proposed for multi-dimensional signal processing. The RGA-MLP model treats multi-dimensional signals as multivectors in RGA space and all neuronal parameters such as inputs, connection weights, activation function and outputs, and also operators are encoded by RGA. The RGA-based back propagation (BP) algorithm is also provided. Thanks to the commutative property of RGA, multi-dimensional signals can be processed in a holistic manner which avoids losing relationship of multiple dimensions. The experiments demonstrate that the RGA-MLP model outperforms the traditional real-valued MLP model and quaternion based MLP model (QMLP) with faster convergence rate, higher classification accuracy and Lower computational complexity.
Visual Attention in Multi-Label Image Classification

One of the most significant challenges in multi-label image classification is the learning of representative features that capture the rich semantic information in a cluttered scene. As an information bottleneck, the visual attention mechanism allows humans to selectively process the most important visual input, enabling rapid and accurate scene understanding. In this work, we study the correlation between visual attention and multi-label image classification, and exploit an extra attention pathway for improving multi-label image classification performance. Specifically, we propose a dual-stream neural network that consists of two sub-networks: one is a conventional classification model and the other is a saliency prediction model trained with human fixations. Features computed with the two sub-networks are trained separately and then fine-tuned jointly using a multiple cross entropy loss. Experimental results show that the additional saliency sub-network improves multi-label image classification performance on the MS COCO dataset. The improvement is consistent across various levels of scene clutterness.
GP-ELM-RNN: Garson-pruned extreme learning machine based replicator neural network for anomaly detection

Replicator Neural Network (RNN) is a popular algorithm for anomaly detection, but finding optimal number of hidden layers and then finding optimal number of neurons in each hidden layer is quite a challenging and time-consuming task. Extreme Learning Machines (ELM) are neural networks with single-hidden layer but the learning algorithm is different and faster than back-propagation. ELM-based RNNs solve our problem of determining the number of hidden layers and the learning algorithm is also faster than gradient-descent based RNN. The problem of identifying the optimal number of neurons in the hidden layer can be solved by Garson algorithm. In this work, the author propose an optimal Replicator Neural Network which is optimized using ELM learning and Garson algorithm for anomaly detection. The experimental results show that the proposed method is fast as well as highly accurate.
The Multi-objective Dynamic Traveling Salesman Problem: Last Mile Delivery with Unmanned Aerial Vehicles Assistance

In this paper, we present an approach to optimizing the last-mile delivery route of a truck using coordination with unmanned aerial vehicles (UAVs). First, a traveling salesman problem is formulated to determine the truck's route. Then, a scheduling problem is formulated to determined the routes for the UAVs. A genetic algorithm is used to solve these problems, and simulated results are presented.
Iranian female students’ perceptions of the impact of mobile-assisted instruction on their English speaking skill

The study aimed at investigating the impacts of mobile-assisted instruction on improving Iranian female EFL learners' speaking skill and exploring their perceptions of the experience. Random sampling was applied to select 90 female students at the Zand Higher Education Institute in Shiraz, Iran. Their ages ranged from 18 to 24. They were randomly assigned to one control and two experimental groups. The control group was subjected to traditional instruction and the experimental ones were subjected to mobile-assisted instruction on their course-related contents through Voice Thread and Twitter as their out-of-class activities for three months. The needed data were collected using speaking papers of the Preliminary English Test and an interview. A paired samples t-test and one-way ANOVA were used to compare the performances of the participants in the speaking pre- and post-tests. Findings revealed mobile-assisted instruction played a prominent role in improving learners' speaking skill. The interview results showed the majority of the participants (71.25%) had positive attitudes toward mobile learning. This study provided experimental evidence that both Voice Thread and Twitter could be used as educational tools to help the students of English as a foreign language to improve their speaking skill.
Multi-agent reinforcement learning with approximate model learning for competitive games

We propose a method for learning multi-agent policies to compete against multiple opponents. The method consists of recurrent neural network-based actor-critic networks and deterministic policy gradients that promote cooperation between agents by communication. The learning process does not require access to opponents' parameters or observations because the agents are trained separately from the opponents. The actor networks enable the agents to communicate using forward and backward paths while the critic network helps to train the actors by delivering them gradient signals based on their contribution to the global reward. Moreover, to address nonstationarity due to the evolving of other agents, we propose approximate model learning using auxiliary prediction networks for modeling the state transitions, reward function, and opponent behavior. In the test phase, we use competitive multi-agent environments to demonstrate by comparison the usefulness and superiority of the proposed method in terms of learning efficiency and goal achievements. The comparison results show that the proposed method outperforms the alternatives.
Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages

Polysynthetic languages pose a challenge for morphological analysis due to the rootmorpheme complexity and to the word class "squish".In addition, many of these polysynthetic languages are low-resource.We propose unsupervised approaches for morphological segmentation of low-resource polysynthetic languages based on Adaptor Grammars (AG) (Eskander et al., 2016).We experiment with four languages from the Uto-Aztecan family.Our AG-based approaches outperform other unsupervised approaches and show promise when compared to supervised methods, outperforming them on two of the four languages.
Integrations between Autonomous Systems and Modern Computing Techniques: A Mini Review

The emulation of human behavior for autonomous problem solving has been an interdisciplinary field of research. Generally, classical control systems are used for static environments, where external disturbances and changes in internal parameters can be fully modulated before or neglected during operation. However, classical control systems are inadequate at addressing environmental uncertainty. By contrast, autonomous systems, which were first studied in the field of control systems, can be applied in an unknown environment. This paper summarizes the state of the art autonomous systems by first discussing the definition, modeling, and system structure of autonomous systems and then providing a perspective on how autonomous systems can be integrated with advanced resources (e.g., the Internet of Things, big data, Over-the-Air, and federated learning). Finally, what comes after reaching full autonomy is briefly discussed.
On the Interpretation of Recurrent Neural Networks as Finite State Machines

The behavior of simple recurrent neural networks trained on regular languages is analyzed in terms of accuracy and interpretability. We use controlled amounts of noise and L1 regularization to obtain stable and accurate responses that are at the same time highly interpretable, and introduce a shocking mechanism that reactivates silent neurons when learning stops due to an excessive regularization. Proper parameter tuning allows the networks to develop a strong generalization capacity, and at the same time provides solutions that may be interpreted as finite automata. Experiments carried out with different regular languages show that, in all cases, the trained networks display activation patterns that automatically cluster into a set of discrete states without any need to explicitly perform quantization. Analysis of the transitions between states in response to the input symbols reveals that the networks are in fact implementing a finite state machine that in all cases matches the regular expressions used to generate the training data.
Toward collisions produced in requirements rankings: A qualitative approach and experimental study

Requirements prioritization is an important issue that determines the way requirements are selected and processed in software projects. There already exist specific methods to classify and prioritize requirements, most of them based on quantitative measures. However, most of existing approaches do not consider collisions, which are an important concern in large-scale requirements sets and, more specifically, in agile development processes where requirements have to be uniquely selected for each software increment. In this paper, we propose QMPSR (Qualitative Method for Prioritizing Software Requirements), an approach that features the prioritization of requirements by considering qualitative elements that are related to the project's priorities. Our approach highlights a prioritization method that has proven to reduce collisions in software requirements rankings. Furthermore, QMPSR improves accuracy in classification when facing large-scale requirements sets, featuring no scalability problems as the number of requirements increases. We formally introduce QMPSR and then define prioritization effort and collision metrics to carry out comprehensive experiments involving different sets of requirements, comparing our approach with well-known existing prioritization methods. The experiments have provided satisfactory results, overcoming existing approaches and ensuring scalability.
Autonomous Driving of Six-Wheeled Dump Truck with a Retrofitted Robot

In Japan, expectations for the automation of construction machines are increasing to solve the labor shortage in the construction industry. In this research, a robotization method by retrofitting a robot to conventional construction machines is introduced to lower the introduction barrier for regional construction companies. The target machine is a six-wheeled dump truck. With a retrofitted internal sensor unit and derived kinematics of six-wheeled articulated dump truck, a conventional Global Navigation Satellite System (GNSS)-based path tracking method was implemented on it. In addition, to ensure safety during operation, an emergency stop function was installed on the dump truck with three-dimensional Light Detection and Ranging (3D LiDAR). Initial experiments of forward and backward path tracking with an actual dump truck confirmed the validity of the method, and the maximum tracking error was 1 m. Further, in an emergency stop experiment, the dump truck detected the obstacle and stopped immediately after obstacle detection within the emergency-stop region, i.e., 25 m x 3 m in front of the dump truck. Based on the initial experiments, the authors concluded that even the retrofitted conventional dump truck could perform basic functions for autonomous driving, such as path tracking and emergency stop.