Reviewerly/exHarmony huggingface dataset:
Which Channel to Ask My Question?: Personalized Customer Service Request Stream Routing Using Deep Reinforcement Learning

Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers' questions, for example, Chatbot and Hotline. On one hand, each channel has limited capacity to respond to customers' requests; on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules that merely consider the tradeoffs between the resources and customers' satisfaction. To achieve the optimal tradeoff between the resources and customers' satisfaction, we propose a new framework based on deep reinforcement learning that directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning-based routing method-double dueling deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning-based framework is superior to the existing production system. Moreover, we also show that our proposed PER-DoDDQN is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the tradeoff, where both channel resources and customers' satisfaction are optimal.
Implementation of voyage data recording device using a digital forensics-based hash algorithm

&lt;p&gt;Identifying the causes of marine accidents is difficult because of problems in scene preservation, reenactment, and procuring of witnesses. Thanks to new regulations, larger vessels are now required to carry voyage data recorders (VDRs) and automatic identification systems (AISs). However, the content of these devices, which is created, stored, and managed digitally, has security vulnerabilities such as the potential for data modification. Therefore, when managing digital records it is important to guarantee reliability. To this end, we suggest a digital forensics-based digital records migration method using a hash algorithm to guarantee the integrity and authenticity of digital records.&lt;/p&gt;
Fisher-Bures Adversary Graph Convolutional Networks.

In a graph convolutional network, we assume that the graph $G$ is generated wrt some observation noise. During learning, we make small random perturbations $\Delta{}G$ of the graph and try to improve generalization. Based on quantum information geometry, $\Delta{}G$ can be characterized by the eigendecomposition of the graph Laplacian matrix. We try to minimize the loss wrt the perturbed $G+\Delta{G}$ while making $\Delta{G}$ to be effective in terms of the Fisher information of the neural network. Our proposed model can consistently improve graph convolutional networks on semi-supervised node classification tasks with reasonable computational overhead. We present three different geometries on the manifold of graphs: the intrinsic geometry measures the information theoretic dynamics of a graph; the extrinsic geometry characterizes how such dynamics can affect externally a graph neural network; the embedding geometry is for measuring node embeddings. These new analytical tools are useful in developing a good understanding of graph neural networks and fostering new techniques.
Top-N Recommendation Based on Mutual Trust and Influence

To improve recommendation quality, the existing trust-based recommendation methods often directly use the binary trust relationship of social networks, and rarely consider the difference and potential influence of trust strength among users. To make up for the gap, this paper puts forward a hybrid top-N recommendation algorithm that combines mutual trust and influence. Firstly, a new trust measurement method was developed based on dynamic weight, considering the difference of trust strength between users. Secondly, a new mutual influence measurement model was designed based on trust relationship, in light of the social network topology. Finally, two hybrid recommendation algorithms, denoted as FSTA(Factored Similarity model with Trust Approach) and FSTI(Factored similarity models with trust and influence), were presented to solve the data sparsity and binarity. The two algorithms integrate user similarity, item similarity, mutual trust and mutual influence. Our approach was compared with several other recommendation algorithms on three standard datasets: FilmTrust, Epinions and Ciao. The experimental results proved the high efficiency of our approach.
Three-dimensional holographic hiding and display using layer-oriented theorem

Image hiding is an important technology of secure communications, which has a broad application in military field. In this paper, we present a three-dimensional (3-D) holographic hiding and display technology using layer-oriented theorem. As the secret image, the hologram is generated using layer-oriented theorem. Angular spectra from each layer are synthesized based on the fast Fourier transform without paraxial approximation. And the 3-D image, which is coded as a hologram based on layer-oriented theorem, is transformed into a quick response (QR) code and embedded into the singular value matrix of the carrier image. The simulation and optical experimental results show the effectiveness and display application with our proposed technology.
Do You Like Sclera? Sclera-region Detection and Colorization for Anime Character Line Drawings

Many studies have been conducted on automated line drawing colorization.
Human activity recognition by using convolutional neural network

&lt;p&gt;In recent years, many researchers have studied the HAR (Human Activity Recognition) system. HAR using smart home sensor is based on computing in smart environment, and intelligent surveillance system conducts intensive research on peripheral support life. The previous system studied in some of the activities is a fixed motion and the methodology is less accurate. In this paper, vision-based studies using thermal imaging cameras improve the accuracy of motion recognition in intelligent surveillance systems. We use one of the deep learning architectures widely used in image recognition systems called Convolutional Neural Networks (CNN). Therefore, we use CNN and thermal cameras to provide accuracy and many features through the proposed method.&lt;/p&gt;
Boosting for Comparison-Based Learning

We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form ``object A is closer to object B than to object C.'' In this paper we introduce TripletBoost, a new method that can learn a classifier just from such triplet comparisons. The main idea is to aggregate the triplets information into weak classifiers, which can subsequently be boosted to a strong classifier. Our method has two main advantages: (i) it is applicable to data from any metric space, and (ii) it can deal with large scale problems using only passively obtained and noisy triplets. We derive theoretical generalization guarantees and a lower bound on the number of necessary triplets, and we empirically show that our method is both competitive with state of the art approaches and resistant to noise.
A Comparative Study of Various Intelligent Optimization Algorithms Based on Path Planning and Neural Controller for Mobile Robot

In this paper, a cognitive system based on a nonlinear neural controller and intelligent algorithm that will guide an autonomous mobile robot during continuous path-tracking and navigate over solid obstacles with avoidance was proposed. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithms are used to finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths and finding the reference path equation of the optimal path. As well as, PSO algorithm is used to find and tune on-line the neural control gains values of the nonlinear neural controller to obtain the best torques actions of the wheels for the mining autonomous mobile robot. Simulation results by matlab showed that the proposed cognitive system is more accurate in terms of planning reference path to avoid obstacles and online finding and tuning parameters of the controller which generated smoothness control action without saturation state for tracking the reference path equation as well as minimize the mobile robot tracking pose error to zero value.&#x0D;
Automatic Successive Reinforcement Learning with Multiple Auxiliary Rewards

Reinforcement learning has played an important role in decision making related applications, e.g., robotics motion, self-driving, recommendation, etc. The reward function, as a crucial component, affects the efficiency and effectiveness of reinforcement learning to a large extent. In this paper, we focus on the investigation of reinforcement learning with more than one auxiliary reward. It is found that different auxiliary rewards can boost up the learning rate and effectiveness in different stages, and consequently we propose the Automatic Successive Reinforcement Learning (ASR) for auxiliary rewards grading selection for efficient reinforcement learning by stages. Experiments and simulations have shown the superiority of our proposed ASR on a range of environments, including OpenAI classical control domains and video games; Freeway and Catcher.
Visual tracking based on robust appearance model

This paper aims at solving the problem of target drifting or losing due to the dramatic changes in the appearance of the target caused by the presence of severe target occlusion, background clutter, and illumination variations in video sequence. Firstly, we obtain a discriminative appearance description of the target by calculating the global and local Fisher vectors of the target area, and subsequently we utilize a semi-supervised linear kernel classifier to calculate the confidence of each candidate for well distinguish the foreground target from the background region. Meanwhile, as the change in the appearance of the object has caused serious pollution and interfered with the original information of the target area, we obtain the similarity between the sub-patches of the target template set and all the candidates, according to the pollution degree of patches of each candidate. Finally, we retrieve the best candidate according to the production of the weight, confidence and the similarity. The extensive experiment results show that our algorithm can effectively cope with the pollution of the target area and the drastic change of the target appearance caused by the severe occlusion and illumination changes in complex scenes. This also demonstrates that our algorithm has considerably accuracy and robustness in visual tracking.
An ASP Approach to Generate Minimal Countermodels in Intuitionistic Propositional Logic

Intuitionistic Propositional Logic is complete w.r.t. Kripke semantics: if a formula is not intuitionistically valid, then there exists a finite Kripke model falsifying it. The problem of obtaining concise models has been scarcely investigated in the literature. We present a procedure to generate minimal models in the number of worlds relying on Answer Set Programming (ASP).
Reversible Circuit Synthesis Using Evolutionary Algorithms

With the unprecedented growth in VLSI technology in recent years, managing power dissipation has become a challenging task for many researchers. In this aspect, reversible logic emerges as one of the basis of future lossless computing system that promises zero energy dissipation, meanwhile classical physics cannot survive due to constant scaling of transistors and the exponential growth of transistor density in integrated circuits. It has applications in various domain such as low power VLSI, fault-tolerant designs, quantum computing, nanotechnology, DNA computing, optical computing, cryptography, and informatics. There are many existing works for the synthesis of reversible logic circuits; some are exact methods while others based on heuristic approaches. In this survey, we review a range of evolutionary computation approaches to the problem of optimal synthesis of reversible Logic—GA (Genetic Algorithm) based, PSO (Particle Swarm Optimization) based, ACO (Ant Colony Optimization)-based circuits where aim is to obtain a near-optimal solution by efficiently exploring the entire search space. This study provides an algorithmic review with comparative study on metaheuristic-based reversible logic synthesis methods proposed in existing literatures. Comparison of experimental results based on large number of benchmark circuits conform that evolutionary algorithms-based technique enables optimal or near-optimal solutions with lesser synthesis time.
The magnetic response of hydrothermal alteration in iron-oxide basement complex, NW Nigeria

Aeromagnetic data was processed in order to delineate structural trends which may be indicative of potential gold mineral traps in the Watari hill gold prospect located between longitude 5°8''and 5°16'', and latitude 11°24'' and 11°30'' in Garin Hawal district, northwest Nigeria. Landsat-8 OLI data was processed to produce colour composite, band ratio and minimum noise fraction images that mapped the hydrothermal alteration zone in the study area. The multispectral images which were displayed in Red-Green-Blue colours were subsequently pan-sharpened to display a greater image resolution of the area. The application of enhancement algorithms such as an analytic signal to the aeromagnetic data over the alteration zone helped in mapping the mafic metavolcanics, felsic metasediments and intruding granitoid complexes. Further reductions using the second vertical derivative and tilt angle derivative filters aided in delineating fracture/faults and the contact zones of the formations, especially the metasediments-metavolcanic boundary that delineates the shear zone. This study showed that the low-angle geologic structure between the metasediments and metavolcanics where hydrothermal alteration is present is likely to accommodate veins of gold mineralization in the study area.
Autonomous Indoor Mobile Robot Exploration Based on Wavefront Algorithm

Autonomous’ exploration is an important part of mobile robots. In an unknown environment, if a mobile robot wants to complete a task, the robot must be able to explore the environment. This paper proposes a new exploration strategy based on the wavefront algorithm. The wavefront algorithm is used to find the closest frontier point in very short time for the mobile robot. After determining the next frontier point, the mobile robot moves to the frontier point according to the path planned by the wavefront algorithm, which is the shortest. The exploration task is completed when there are no frontier points in the map. Finally, the exploration strategy is tested using the Robot Operating System (ROS). Simulation experiments show that the exploration based on the wavefront algorithm can find the frontier points rapidly and ensure the integrity of the exploration environment.
Assistive and Monitoring Multifunctional Smart Crutch for Elderly

Most of the areas in the world are turning into aging societies since 1999 with the largest elderly population today. Due to this huge population of elderly people, their health care problems have become a significant focus of the study. With aging, these senior citizens are most likely to be mobility-impaired and vulnerable to accidents such as falling down. Besides that, they are at high risk of age associated disease like tachycardia, dementia, loss of physical control, and social challenges like solitary. Continuous monitoring and assistive living environment have become crucial to overcome such hazards and run their activities of daily life smoothly. Towards this end, a smart crutch capable of monitoring several conditions of users, especially old people, is designed and developed. The functions it can perform include the detection of the falling condition, monitoring of the heart rate, and tracking of the user's location. A gyroscope is utilized to detect whether the motion for fall detection; a heartbeat sensor to monitor the heart rate; a GPS navigation system to locate the elderly quickly and send information of the location to the website or mobile terminals. In addition, the crutch also has a flashlight which can adjust the intensity of the light according to the intensity of the surrounding light. This paper illustrates the design, system integration, and the working principle of the proposed smart crutch.
Construction of building fire information monitoring model based on adaptive clustering scheduling

In order to solve the problem of unsatisfactory monitoring information transmission and large time overhead during conventional building fire monitoring, an optimisation method of building fire information monitoring based on adaptive clustering scheduling is proposed. In this method, a channel model for building fire information monitoring is constructed through the bi-directional link transmission control method, and then node deployment for building fire information monitoring is optimised through the shortest path optimisation method. The deployment of the largest coverage of fire information monitoring sensor nodes is designed through the self-adaptive rotation scheduling, and balance control of the output link layer of internet of things is performed through the adaptive clustering scheduling method to improve the accurate forwarding and real-time transmission capabilities of internet of things for fire detection information, and then a building fire information monitoring model is constructed. Experimental results show that the proposed method can effectively improve the success rate of fire information monitoring packet forwarding with an average increase of 24.7%, which greatly improves the monitoring information transmission efficiency, and it reduces the time overhead of fire information monitoring by 160s. The proposed method meets the actual needs and ensures the effectiveness of fire monitoring.
Regarding Jump Point Search and Subgoal Graphs

In this paper, we define Jump Point Graphs (JP), a preprocessing-based path-planning technique similar to Subgoal Graphs (SG). JP allows for the first time the combination of Jump Point Search style pruning in the context of abstraction-based speedup techniques, such as Contraction Hierarchies. We compare JP with SG and its variants and report new state-of-the-art results for grid-based pathfinding.
A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors

Clustering is widely used in data analysis, and density-based methods are developed rapidly in the recent 10 years. Although the state-of-art density peak clustering algorithms are efficient and can detect arbitrary shape clusters, they are nonsphere type of centroid-based methods essentially. In this paper, a novel local density hierarchical clustering algorithm based on reverse nearest neighbors, RNN-LDH, is proposed. By constructing and using a reverse nearest neighbor graph, the extended core regions are found out as initial clusters. Then, a new local density metric is defined to calculate the density of each object; meanwhile, the density hierarchical relationships among the objects are built according to their densities and neighbor relations. Finally, each unclustered object is classified to one of the initial clusters or noise. Results of experiments on synthetic and real data sets show that RNN-LDH outperforms the current clustering methods based on density peak or reverse nearest neighbors.
A tourist walk approach for internal and external outlier detection

Outlier detection is a fundamental task for knowledge discovery in data mining, especially in the Big Data era. It aims to detect data items that deviate from the general pattern of a given data set. In this paper, we present a new outlier detection technique using tourist walks starting from each data sample and varying the memory size. Specifically, a data sample gets a higher outlier score if it participates in few tourist walk attractors, while it gets a low score if it participates in a large number of attractors. Experimental results on artificial and real data sets show good performance of the proposed method. In comparison to classical outlier detection methods, the proposed one shows the following salient features: (1) It finds out outliers by identifying the structure of the input data set instead of considering only physical features, such as distance, similarity or density. (2) It can detect not only external outliers as classical methods do, but also internal outliers staying among various normal data groups. (3) By varying the memory size, the tourist walks can characterize both local and global structures of the data set. (4) A parallel implementation is quite convenient due to the nature of large amount of independent walking of the algorithm. (5) The proposed method is a deterministic technique. Therefore, only one run is sufficient, in contrast to stochastic techniques, which require many runs. Moreover, in this work, we find, for the first time, that tourist walks can generate complex attractors in various crossing shapes. Such complex attractors reveal data structures in more details. Consequently, it can improve the outlier detection performance.
Attention Guided Imitation Learning and Reinforcement Learning

&#x0D; &#x0D; &#x0D; We propose a framework that uses learned human visual attention model to guide the learning process of an imitation learning or reinforcement learning agent. We have collected high-quality human action and eye-tracking data while playing Atari games in a carefully controlled experimental setting. We have shown that incorporating a learned human gaze model into deep imitation learning yields promising results.&#x0D; &#x0D; &#x0D;
When the Power of the Crowd Meets the Intelligence of the Middleware

The data gluttony of AI is well known: Data fuels the artificial intelligence. Technologies that help to gather the needed data are then essential, among which the IoT. However, the deployment of IoT solutions raises significant challenges, especially regarding the resource and financial costs at stake. It is our view that mobile crowdsensing, aka phone sensing, has a major role to play because it potentially contributes massive data at a relatively low cost. Still, crowdsensing is useless, and even harmful, if the contributed data are not properly analyzed. This paper surveys our work on the development of systems facing this challenge, which also illustrates the virtuous circles of AI. We specifically focus on how intelligent crowdsensing middleware leverages on-device machine learning to enhance the reported physical observations. Keywords: Crowdsensing, Middleware, Online learning.