Reviewerly/exHarmony huggingface dataset:
Improving Collaborative Filtering with Social Influence over Heterogeneous Information Networks

The advent of social networks and activity networks affords us an opportunity of utilizing explicit social information and activity information to improve the quality of recommendation in the presence of data sparsity. In this article, we present a social-influence-based collaborative filtering (SICF) framework over heterogeneous information networks with three unique features. First, we integrate different types of entities, links, attributes, and activities from rating networks, social networks, and activity networks into a unified social-influence-based collaborative filtering model through the intra-network and inter-network social influence. Second, we propose three social-influence propagation models to capture three kinds of information propagation within heterogeneous information networks: user-based influence propagation on user rating networks, item-based influence propagation on user-rating activity networks, and term-based influence propagation on user-review activity networks, respectively. We compute three kinds of social-influence-based user similarity scores based on three social-influence propagation models, respectively. Third, a unified social-influence-based CF prediction model is proposed to infer rating tastes by incorporating three kinds of social-influence-based similarity measures with different weighting factors. We design a weight-learning algorithm, SICF, to refine the prediction result by quantifying the contribution of each kind of information propagation to make a good balance between prediction accuracy and data sparsity. Extensive evaluation on real datasets demonstrates that SICF outperforms existing representative collaborative filtering methods.
Two Agent-Oriented Programming Approaches Checked Against a Coordination Problem

This paper discusses two approaches to agent-oriented programming and compares them from a practical point of view. The first approach is exemplified by Jadescript, which is an agent-oriented programming language that has been recently proposed to simplify the adoption of JADE. The second approach is exemplified by Jason, which is currently one of the most popular agent-oriented programming languages. Jason can be configured to use JADE to support the distribution of agents, which ensures that the discussed comparison between the two approaches can also take into account the performance of implemented multi-agent systems. In order to devise a quantitative comparison, the two considered languages are used to solve the same coordination problem, and obtained implementations are compared to discuss advantages and drawbacks of the two approaches.
A multi-modal approach to cognitive training and assistance in minimally invasive surgery

Minimally-invasive surgery (MIS) offers many benefits to patients, but is considerably more difficult to learn and perform than is open surgery. One main reason for the observed difficulty is attributable to the visuo-spatial challenges that arise in MIS, taxing the surgeons’ cognitive skills. In this contribution, we present a new approach that combines training and assistance as well as the visual and the auditory modality to help surgeons to overcome these challenges. To achieve this, our approach assumes two main components: An adaptive, individualized training component as well as a component that conveys spatial information through sound. The training component (a) specifically targets the visuo-spatial processes crucial for successful MIS performance and (b) trains surgeons in the use of the sound component. The second component is an auditory display based on a psychoacoustic sonification, which reduces and avoids some of the commonly experienced MIS challenges. Implementations of both components are described and their integration is discussed. Our approach and both of its components go beyond the current state of the art in important ways. The training component has been explicitly designed to target MIS-specific visuo-spatial skills and to allow for adaptive testing, promoting individualized learning. The auditory display is conveying spatial information in 3-D space. Our approach is the first that encompasses both training for improved mastery and reduction of cognitive challenges in MIS. This promises better tailoring of surgical skills and assistance to the needs and the capabilities of the surgeons and, thus, ultimately, increased patient safety and health.
The technological advancements that enabled the age of big data in the environmental sciences: A history and future directions

Advancements in environmental sensors and laboratory instrumentation are inherently linked with Big Data in environmental chemistry. Technological advancements in instrumentation have greatly increased the throughput and precision of measuring chemical, biological, and physical variability and have also resulted in the generation of vast quantities of digital data that can be used to monitor the fate of chemicals in the environment. However, many challenges to accessing and analyzing these data are proving persistent. This review provides a brief overview of the technological advancements that enabled the age of Big Data in the environmental chemistry, the current status of data integration in the environmental sciences, and areas of opportunity for more efficient data integration and comprehensive environmental study, particularly accessibility and use by a multidisciplinary audience.
Identifying Cybersickness When Wearing a Head-Mounted Display through Heart Rate Variability Data

Abstract Cybersickness is one of the common problems related to Virtual Reality (VR). The symptoms are classified into three categories: nausea, oculomotor, and disorientation. The purpose of this study is to identify cybersickness symptoms based on simulator sickness questionnaire (SSQ) and physiological signal using heart rate variability (HRV) when wearing a head-mounted display. Fifteen participants performed a 15-min driving simulation task using head-mounted display. From the SSQ results, the most dominant category of cybersickness symptoms induced by participants are nausea and disorientation. The results showed a correlation between the nausea score and the high frequency (HF)/component of HRV (r=0.618, p=0.032) as well as the ratio of low and high frequency component (LF/HF) (r=-0.718, p=0.009). These findings confirm that the occurrence of nausea when wearing a head-mounted display is influenced by the activation of sympathetic nervous system.
Polo: Receiver-Driven Congestion Control for Low Latency over Commodity Network Fabric

Recently, numerous novel transport protocols are proposed for the low latency of applications deployed in data center networks, e.g., web search and retail recommendation system. The state-of-art receiver-driven protocols, e.g., Homa and NDP, show the superior performance for achieving the lowest possible latency. However, Homa assumes that the core layer in data center network has no congestion, which limits its application for the existing over-subscribed networks. NDP requires to modify the switch hardware since it trims packets to headers when the packets cause the switch buffer to overflow, resulting in high deployment cost. In this paper, we present Polo to realize low latency for flows over commodity network fabric relying on Explicit Congestion Notification (ECN) and priority queues. According to packets with ECN marking, the Polo receiver obtains the congestion information such that it dynamically adjusts the number of data packets in network for maintaining the small switch queue. The adjustment is carried out periodically. The time interval is determined by keeping the extra high priority packet always in flight nor by a fine-grained timer. Further, Polo designs the packet recovery mechanisms to retransmit the lost packets as soon as possible. Simulation experiment results show that Polo outperforms the state-of-art receiver-driven protocols in a wide range of scenarios including incast.
Instant Tracking-Based Approach for Interior Décor Planning with Markerless AR

There is an increase in the demand for the interior décor planning industry. Although initially used for extravagant events, the facilities have become gradually availed by everyone. The applications of Augmented Reality could potentially transform this industry by making it more convenient for consumers to plan their home and office interiors in their own time on their phone or system. This application would help in designing and planning the floor space virtually with a range of options available for selection. The implemented system works by using not just the images captured by the camera on the device, rather by using the orientation of the camera with respect to the ground plane that has to be detected. The Maxst SDK performs instant tracking to find the relative distance between the camera and the plane by focusing on the ground plane. This gives a more realistic view for the consumer to judge their fit efficiently with the dimensions of items, and save the manual work of moving furniture. The implemented system is evaluated for its deployment time for various models and performance under different distances.
Reinforcement learning for quadrupedal locomotion with design of continual–hierarchical curriculum

End-to-end reinforcement learning is a promising approach to enable robots to acquire complicated skills. However, this requires numerous samples to be implemented successfully. The issue is that it is often difficult to collect the sufficient number of samples. To accelerate learning in the field of robotics, knowledge gathered from robotics engineering and previously learned tasks must be fully exploited. Specifically, we propose using a sample-efficient curriculum to establish quadrupedal robot control in which the walking and turning tasks are divided into two hierarchical layers, and a robot learns them incrementally from lower to upper layers. To develop such a curriculum, two core components are designed. First the fractal design of neural networks in reservoir computing is aimed at allocating the tasks to be learned to respective modules in fractal networks. This allows mitigating the problem of catastrophic forgetting in neural networks and achieves the capability of continuous learning. The second task includes hierarchical task decomposition according to robotics knowledge for controlling legged robots. Owing to the combination of these two components, the proposed curriculum enables a robot to tune the lower layer even when the upper layer is optimized. As a result of implementing the proposed design, we confirm that a quadrupedal robot in a dynamical simulator succeeds in learning skills hierarchically according to the given curriculum, starting from moving legs and finally, walking/turning, unlike the considered conventional curriculums that are unable to achieve such results.
Introducing Time-Delays to Analyze Driver Reaction Times When Using a Powered Wheelchair

This paper investigates the introduction of time-delays into wheelchair driving. Two dissimilar ways in which wheelchair drivers interact are compared. Users were observed as they drove their wheelchairs with and without time-delays. Tests took place with a computer system and sensors which provided assistance and then without any assistance provided. As delays became longer then drivers found it more difficult to drive. If the wheelchair moved through a more complicated environment or if the time-delay was made longer, then driving was better if the computer and sensors assisted. Time delays were introduced between the motor controller and the wheelchair joystick. With shorter time-delays or in simpler environments then less assistance was needed from the computer system and sensors. In more complicated environments or if time-delays were longer, then more assistance was needed. That suggest varying sensor support could be helpful depending on the complexity of the environment or the difficulties being experienced by the drivers.
Depth-guided saliency detection via boundary information

Existing efforts of saliency detection have achieved excellent performance in RGB images, thus to sufficiently exploit existing RGB saliency models and further do some extensions on them, we can transfer existing RGB saliency models to the similar research field, i.e. RGBD saliency detection, by introducing depth cues. Here, we construct a novel RGBD saliency model upon an existing RGB saliency model. To be specific, firstly, our model deploys a depth-guided module to guide the deep features extraction, where the multi-level deep depth features obtained from depth branch are embedded into the backbone network and integrate with multi-level deep RGB features. Secondly, to further promote the performance of our model, we devise a boundary constraint module to elevate the detection accuracy, where the boundary information is compounded by the low-level deep RGB and depth features. Comprehensive experiments are performed on five public RGBD saliency detection datasets, and the experimental results clearly demonstrate the effectiveness and superiority of our model when compared with the state-of-the-art RGBD saliency models.
Modelling Multi-Agent Epistemic Planning in ASP

Abstract Designing agents that reason and act upon the world has always been one of the main objectives of the Artificial Intelligence community. While for planning in “simple” domains the agents can solely rely on facts about the world, in several contexts, e.g. , economy, security, justice and politics, the mere knowledge of the world could be insufficient to reach a desired goal. In these scenarios, epistemic reasoning, i.e. , reasoning about agents’ beliefs about themselves and about other agents’ beliefs, is essential to design winning strategies. This paper addresses the problem of reasoning in multi-agent epistemic settings exploiting declarative programming techniques. In particular, the paper presents an actual implementation of a multi-shot Answer Set Programming -based planner that can reason in multi-agent epistemic settings, called PLATO (e P istemic mu L ti-agent A nswer se T programming s O lver). The ASP paradigm enables a concise and elegant design of the planner, w.r.t. other imperative implementations, facilitating the development of formal verification of correctness. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature.
Mathematical Model of Asynchronous Parallel Evolutionary Algorithm to Analyze Influence of Evaluation Time Bias

This paper proposes a mathematical model to analyze the behavior of an asynchronous parallel evolutionary algorithm (APEA) and demonstrates its validity through the comparison of the theoretical result with the simulation one. The proposed model considers the transition of the probability density of solutions in the population and the slave nodes in the parallel computing environment during the optimization process of an APEA. This paper demonstrates the convergence state of the population and the slave nodes on the flat fitness landscape by using the proposed model. From the result of the comparison between the theoretical result acquired by the proposed model and the simulation one, the validity of the proposed model is confirmed. Additionally, it is revealed that an APEA is necessarily biased toward the search area having a shorter evaluation time regardless of the fitness landscape. This paper finally shows a possible attempt to avoid the evaluation time bias on APEAs and demonstrates its effectiveness in the simulation experiment.
Joint patch and instance discrimination learning for unsupervised person re-identification

The unsupervised person re-identification (re-ID) has become increasingly significant in the community because it is more scalable than the supervised method when dealing with the large-scale person re-ID. However, it is difficult to learn discriminative enough features from across-camera images without labelling information. To address this problem, we propose a joint patch and instance discrimination learning (JPIL) framework for the unsupervised person re-ID. The JPIL framework exploits a patch feature extraction model to generate patch-wise features for each input image. Then the patch discrimination learning (PDL) loss is designed to guide the model to mine the patch-wise discriminative information from unlabelled person image patches. On the other hand, we introduce the instance discrimination learning (IDL) loss to provide instance-wise supervision. The IDL loss aims to pull features of the same instance under different transformations closer and push features belonging to different instances away. Finally, we combine the PDL and IDL loss to apply the joint training. Extensive experiments on Market-1501 and DukeMTMC-reID datasets demonstrate the effectiveness of the proposed method for unsupervised person re-ID.
Telemedicine System Design using Blockchain in Bangladesh

Data security is an important issue in healthcare system in Bangladesh, especially in a rural and remote area where proper medical care doesn39;t reach in time, and the infrastructures of the medical system in those areas are impracticable. Providing proper medical care to the citizens of rural and remote regions with secure information is a challenge in Bangladesh. Telemedicine systems have been introduced for serving rural people with proper medical care, but in those systems, patients39; data is insecure. To address this issue, this paper proposed a model of telemedicine system using blockchain technology for medical care of rural people and the security of personal data of patients. Implementation of this model will help those people with proper and reliable medical care with the security of private information and may also bring more medical benefits for the citizens of Bangladesh economically, socially, and technologically.
Big Data Solutions for Controversies in Breast Cancer Treatment

The digital world of data is expanding with an annual growth rate of 40%, and health care is among the fastest growing sector of the digital world with an annual growth rate of 48%. Rapid growth in technology has augmented data generation; for example, electronic health records produce huge amounts of patient-level data, whereas national registries capture information on numerous factors affecting health care delivery and patient outcomes. This big data can be utilized to improve health care outcomes. This review discusses relevant applications in breast cancer treatment.
Comparison of Speeded-Up Robust Feature (SURF) and Oriented FAST and Rotated BRIEF (ORB) Methods in Identifying Museum Objects Using Low Light Intensity Images

Abstract Museum is a place of education and learning in the field of culture and history for all levels of society. As one of the first and largest museums in Lampung, Museum Lampung presents a variety of collections that are conditional on cultural values and are very useful if they can be identified through digital media. Speeded-Up Robust Feature (SURF) and Oriented FAST and Rotated BRIEF (ORB) methods are two examples of feature extraction methods that are relatively robust for object recognition in images by finding key points. Digital media determined by the value of key points in this study are images that are classified as having low intensity with an intensity value of &lt;2250 Lux. This study compares the two methods using the digital object media of museum objects. The image of the museum object is given treatment in terms of rotation, scaling, and cropping to test the durability of the image matching process. In terms of feature matching time, the best time is achieved by SURF with 0.16 seconds in testing of 1/3 image region. Meanwhile, the highest matching percentage was also obtained by SURF method from rotational distortion at an angle of 90 degrees which is 76.79% instead of to 63.79% of the percentage obtained by ORB.
Abstractive Summarization Model with a Feature-Enhanced Seq2Seq Structure

Abstractive text summarization task is mainly through deep learning method to summarize one or more documents to produce a concise summary that can express the main meaning of the document. Most methods are mainly based on the traditional Seq2Seq structure, but the traditional Seq2Seq structure has limited ability to capture and store long-term features and global features, resulting in a lack of information in the generated summary. In our paper, we put forward a new abstractive summarization model based on feature-enhanced Seq2Seq structure for single document summarization task. This model utilizes two types of feature capture networks to improve the encoder and decoder in traditional Seq2Seq structure, to enhance the model's ability to capture and store long-term features and global features, so that the generated summary more informative and more fluency. Finally, we verified the model we proposed on the CNN/DailyMail dataset. Experimental results demonstrate that the model proposed in this paper is more effective than the baseline model, and has improved by 5.6%, 5.3%, 6.2% on the three metrics R-1, R-2, and R-L.
Rough sets and social ski-driver optimization for drug toxicity analysis

Background and objectives: Toxicity testing is an important step for developing new drugs, and animals are widely used in this step by exposing them to the toxicants. Zebrafishes are widely used for measuring and detecting the toxicity. However, measuring and testing toxicity manually is not feasible due to the large number of embryos. This work presents an automated model to investigate the toxicity of two toxicants (3, 4-Dichloroaniline (34DCA) and p-Tert-Butylphenol (PTBP)). Methods: The proposed model consists of two steps. In the first step, a set of features is extracted from microscopic images of zebrafish embryos using the Segmentation-Based Fractal Texture Analysis (SFTA) technique. Secondly, a novel rough set-based model using Social ski-driver (SSD) is used to find a global minimal subset of features that preserves important information of the original features. In the third step, the AdaBoost classifier is used to classify an unknown sample to alive or coagulant after exposing the embryo to a toxic compound. Results: For detecting the toxicity, the proposed model is compared with (i) three deterministic rough set reduction algorithms and (ii) the PSO-based algorithm. The classification performance rate of our model was ranged from 97.1% to 99.5% and it outperformed the other algorithms. Conclusions: The results of our experiments proved that the proposed drug toxicity model is efficient for rough set-based feature selection and it obtains a high classification performance.
C-Space tunnel discovery for puzzle path planning

Rigid body disentanglement puzzles are challenging for both humans and motion planning algorithms because their solutions involve tricky twisting and sliding moves that correspond to navigating through narrow tunnels in the puzzle's configuration space (C-space). We propose a tunnel-discovery and planning strategy for solving these puzzles. First, we locate important features on the pieces using geometric heuristics and machine learning, and then match pairs of these features to discover collision free states in the puzzle's C-space that lie within the narrow tunnels. Second, we propose a Rapidly-exploring Dense Tree (RDT) motion planner variant that builds tunnel escape roadmaps and then connects these roadmaps into a solution path connecting start and goal states. We evaluate our approach on a variety of challenging disentanglement puzzles and provide extensive baseline comparisons with other motion planning techniques.