Reviewerly/exHarmony huggingface dataset:
Personalized Image Recommendation with Photo Importance and user-item Interactive Attention

Human encounter a variety of images when browsing some websites, such as Flickr, Pinterest and Instagram. How to incorporate user and item attributes to give users a personalized recommendation is challenging. Aiming at this problem, we propose a new model based on Bayesian Personalized Ranking while combining photo importance and user-item interactive attention. Specifically, we define photo importance according to the average user 'favor' information, which will be further added to Bayesian Personalized Ranking model as a weighting factor. In addition, considering that the interaction between users and items is mutual, that is, one user may likes a series of images and one image may also be liked by a series of users, So we introduce attention mechanism(i.e., user-item interactive attention) to indicate users' different preference on their interested images, and different preference of different users on the same image they like. Finally, we construct a new user representation and item representation which contains much richer user-item interactive information. Experimental results on real-world datasets demonstrate the superiority of our proposed model.
Statistical Processing of Stopwords on SNS

For the purpose of text classification or information retrieval, we apply preprocessing to these texts such as stemming and stopwords removal. Almost all the techniques could be useful only to well-formed text information like textbooks and news articles, but is not true to social network services (SNS) or any other texts in internet world. In this investigation, we propose how to extract stopwords in context of social network services. To do that, first we discuss what stopwords mean, how different from conventional ones, and we propose statistical filters TFIG and TFCHI, to identify. We examine categorical estimation to extract characteristic values putting our attention on Kullback Leibler Divergence (KLD) over temporal sequences on SNS data. Moreover we apply several preprocessing to manage unknown words and to improve morphological analysis.
QAECN: Dynamically Tuning ECN Threshold with Micro-burst in Multi-queue Data Centers

Packet loss is a common problem in data center networks. The factors causing packet loss are various. Among them, micro-burst is the most important reason. Some previous works have studied the causes and influence o f micro-burst in single queue data center. However, through simulations and experiments, we find that micro-burst could bring m ore serious performance degradation in multi-queue data centers. The micro-burst traffic could cause E CN marking ratio rising from 4% to 22%, and cause throughput loss by up to 40%. Through observing queue length, we find that the standard E CN, which adopts immutable threshold, is not suitable for micro-burst traffic because micro-burst could trigger spurious congestion signals frequently, especially in DCTCP. In this paper, we not only show how much influence the micro-burst brings, but also propose Queue-length Aware ECN (QA-ECN) scheme to mitigate micro-burst. Finally, the simulations and experiments show that QAECN could reduce ECN marking ratio to 2.5%. In addition, the throughput and flow completion time could be improved by up to 22.9% and 34.1%, respectively.
Online Multi-Agent Based Cooperative Exploration and Coverage in Complex Environment

In this article, an online collaborative exploration and coverage method is proposed for the unknown complex environment with multiple agents. The exploration and coverage is based on Boustrophedon motion, while the detection conditions for backtracking points have been modified based on mission requirements, the battery charge level of each agent is considered to reduce agent loss, and collision free paths are generated. The proposed method is evaluated in simulation, where complex environment with multiple branches is explored by multiple agents.
Inverse Reinforcement Learning of Interaction Dynamics from Demonstrations

This paper presents a framework to learn the reward function underlying high-level sequential tasks from demonstrations. The purpose of reward learning, in the context of learning from demonstration (LfD), is to generate policies that mimic the demonstrator's policies, thereby enabling imitation learning. We focus on a human-robot interaction (HRI) domain where the goal is to learn and model structured interactions between a human and a robot. Such interactions can be modeled as a partially observable Markov decision process (POMDP) where the partial observability is caused by uncertainties associated with the ways humans respond to different stimuli. The key challenge in finding a good policy in such a POMDP is determining the reward function that was observed by the demonstrator. Existing inverse reinforcement learning (IRL) methods for POMDPs are computationally very expensive and the problem is not well understood. In comparison, IRL algorithms for Markov decision process (MDP) are well defined and computationally efficient. We propose an approach of reward function learning for high-level sequential tasks from human demonstrations where the core idea is to reduce the underlying POMDP to an MDP and apply any efficient MDPIRL algorithm. Our extensive experiments suggest that the reward function learned this way generates POMDP policies that mimic the policies of the demonstrator well.
Study on the Interactive Mode of Eye Control Mode in Human–Computer Interface

The ergonomic experiment of human-computer interaction mode of eye movement control is carried out for efficient and precise interaction requirements of the human-machine interface. Based on eye movement behavior, operational performance and subjective feeling data, the advantages and disadvantages of eye movement control behaviors such as eye gaze and blink in human-machine interface design are studied. The results show that blink is relatively quicker to pick up the target, and the advantage of eye gaze is that the target control is more targeted.
Improved Coverage Path Planning Using a Virtual Sensor Footprint: a Case Study on Demining

Coverage performance in a coverage path planning problem depends both on the path created and on the footprint of the sensor used. The footprint can be increased either by increasing the size of the sensor, or by mounting the sensor on a robotic arm to allow scanning over larger areas as the platform moves, effectively creating a virtual sensor with a larger footprint than the physical sensor's. However, the virtual footprint comes at a cost requiring formulating an optimization problem for the area of interest. In this work, three common strategies to use a metal detector on a platform are discussed, their time and energy performances are formulated and the corresponding optima are found.
Performance Analysis of Whale Optimization Algorithm

Through the research and analysis of a relatively novel natural heuristic, meta-heuristic swarm intelligence optimization algorithm, this swarm intelligence algorithm is defined as a whale optimization algorithm. The algorithm builds a mathematical model by simulating a social behavior of humpback whales. This optimization algorithm was inspired by the bubble-like net hunting phenomenon that humpback whales prey on. By analyzing the four benchmark optimization problems with or without offset and rotation, the convergence performance of the whale optimization algorithm and the ability to solve the optimization problem are proved. The performance of the whale optimization algorithm is based on the computer simulation technology. Through the convergence curve obtained from the experiment, we can see that the whale optimization algorithm performs best for the five benchmark optimization problems without rotation.
Applying the QuARS Tool to Detect Variability

In this demo paper we present how to use the QuARS tool to extract variability information from requirements documents. The main functionality of QuARS is to detect ambiguity in Natural Language (NL) requirement documents.
Variability-Aware Semantic Slicing Using Code Property Graphs

A common problem in program analysis is to identify semantically related statements in programs, for example, which statements change the value of a variable, or implement a specific feature or functionality. This is a very challenging task for large programs and gets even more complicated in the presence of variability implementations like #ifdef-annotations. Program slicing is a technique that can be used to aid developers with this challenge. But while slicing is a well-established technique for individual programs, there has been so far only little work on program slicing of product lines.
Early warning of nursing risk based on patient electronic medical record information

In order to study the nursing risk warning of patients’ electronic medical record (EMR) information, data of 200 cases of patient from Eastern Medical District of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital is selected. The Logistic regression model and nursing risk warning information system are used to calculate the death probability of the recovered patients 24 h before discharge and 24 h before the death of the patients. The predictive ability of the system is verified, and incidence of infusion, blood transfusion, surgical patient handover, blood sample collection nursing risk, and corresponding indirect care hours before and after the use of nursing risk warning are compared. The probability of death of the recovered patients within 24 h before discharge is less than 0.5, and the death probability of the dead patients before the death of 24 h is greater than 0.5. After the nursing risk warning is used, the infusion, surgical patient handover, and incidence of blood sample collection nursing risk are significantly lower than those before using the nursing risk warning and the difference is statistically significant. The indirect nursing hours of infusion, blood transfusion, surgical patient handover, and blood sample collection are significantly lower than those before using the nursing risk warning, and the difference is statistically significant. Therefore, the predictive ability of the patient's EMR information risk warning system has important reference value in clinical nursing, which can significantly reduce the incidence of infusion, surgical patient handover, and blood sample collection and nursing risk, reduce the infusion, blood transfusion, surgical patient handover, and indirect nursing hours for collecting blood samples, and improve the quality and efficiency of hospital diagnosis and treatment.
RDFPartSuite: Bridging Physical and Logical RDF Partitioning

The Resource Description Framework (RDF) has become a very popular graph-based standard initially designed to represent information on the Web. Its flexibility motivated the use of this standard in other domains and today RDF datasets are big sources of information. In this line, the research on scalable distributed and parallel RDF processing systems has gained momentum. Most of these systems apply partitioning algorithms that use the triple, the finest logical data structure in RDF, as a distribution unit. This merely physical strategy implies losing the graph structure of the model causing performance degradation. We believe that gathering the triples storing the same logical entities first contributes not only to avoid scanning irrelevant data but also to create RDF partitions with an actual logical meaning. Besides, this logical representation allows defining partitions with a declarative language leaving aside implementation details. In this study, we give the formal definition and detail the algorithms to gather the logical entities, which we name graph fragments ( $$\mathcal {G}f$$ ), used as distribution units for RDF datasets. The logical entities proposed, harmonize with the notion of partitions by instances (horizontal) and by attributes (vertical) in the relational model. We propose allocation strategies for these fragments, considering the case when replication is available and in which both fragments by instances and by attributes are considered. We also discuss how to incorporate our declarative partitioning definition language to the existing state of the art systems. Our experiments in synthetic and real datasets show that graph fragments avert data skewness. In addition, we show that this type of data organization exhibits quantitative promise in certain types of queries. All of the above techniques are integrated into the same framework that we called RDFPartSuite.
Controlling Software Evolution Process Using Code Smell Visualization

Software change is inevitable, evolution becomes a part of software lifetime, and software release becomes more frequent. Hence there is a need for the project manager to inspect and control the process during software development and evolution. In the evolutionary stage, developers will face problems related to program code, one of that is identification of code smells. This problem could negatively affect maintainability in evolution, a developer needs more time and money. Visualization techniques turn data into a visual form so that it can provide information that is easier to understand. In software evolution, visualization mostly is used to view structure code. Previous research on evolution visualization limited to visualize the addition of code, the last update, release history, and information of developer that made the last change in program. However, this visualization is not enough to support understanding for the developers. We propose a visualization method for identifying code smell of the evolution software on java programming, so the developers can more easily understand the code that will be evolved. By knowing where the smell of code in the program, programmers can immediately do refactoring, the time and costs needed will also be low. Visualization of code smell is something new in the domain of software evolution. Finally, this design created to build tools in detecting code smells for software evolution process control.
Differentially Private Non-parametric Machine Learning as a Service

Machine learning algorithms create models from training data for the purpose of estimation, prediction and classification. While releasing parametric machine learning models requires the release of the parameters of the model, releasing non-parametric machine learning models requires the release of the training dataset along with the parameters. The release of the training dataset creates a risk of breach of privacy. An alternative to the release of the training dataset is the presentation of the non-parametric model as a service. Still, the non-parametric model as a service may leak information about the training dataset. We study how to provide differential privacy guarantees for non-parametric models as a service. We show how to apply the perturbation to the model functions of histogram, kernel density estimator, kernel SVM and Gaussian process regression in order to provide $$(\epsilon , \delta )$$ -differential privacy. We empirically evaluate the trade-off between the privacy guarantee and the error incurred for each of these non-parametric machine learning algorithms on benchmarks and real-world datasets. Our contribution is twofold. We show that functional perturbation is not only pragmatic for releasing machine learning models as a service but also yields higher effectiveness than output perturbation mechanisms for specified privacy parameters. We show a practical step to perturbate the model functions of histogram, kernel SVM, Gaussian process regression along with kernel density estimator and perform evaluation on a real-world dataset as well as a selection of benchmarks.
Contact-Event-Triggered Mode Estimation for Dynamic Rigid Body Impedance-Controlled Capture

This paper presents a contact-event-triggered filter using only a force-torque sensor with impedance control for non-cooperative, rotating, heavy object capture. Contact events are modeled for prediction, and detected to trigger the particle filter's updating process. By combining these features, a computationally efficient, contact-event-triggered filter is proposed. For our purpose of capture using impedance control, expected contact events, collisions and sliding are defined for prediction and detection. This novel method is implemented in an air bearing robotic system, and has demonstrated its superiority with the highest success rate (100%) for sliding contact mode cases, whereas the previous method could only yield a success rate of 87.9%. The computation resource is demonstrated to be limited, with a computation time of 4.2 milliseconds on average and 8.3 milliseconds at worst.
Kawaii in Tweets: What Emotions Does the Word Describe in Social Media?

The Japanese word kawaii is a particular word that represents a kind of emotion. It is similar to “pretty” or “cute” in English. However, there is some difference between kawaii and these English words. This study reveals how the word kawaii is used in message exchanges over social networking services (SNS) and in what context it is used. The experiment compared two sets of tweets: one set with the word kawaii and the other without it. Also, it compared several sets of words within five predetermined emotional categories. The experiment revealed that there was a marked difference between the two groups, especially in the context of expressing the emotions of “enjoy, ” “happy,” and “like.”
Pushpak

Effective utilization of screen reading software requires a user to remember various keystrokes/gestures. For beginners, this requirement increases the learning curve. This is even more challenging on touch screen devices due to limited possible gestures and lack of semantic intuition. In this project, we have implemented a voice command-based eBook navigator to address the challenge of the heavy learning curve, as voice command provides flexibility through the intent understanding module.
Scanclave: Verifying Application Runtime Integrity in Untrusted Environments

Data hosted in a cloud environment can be subject to attacks from a higher privileged adversary, such as a malicious or compromised cloud provider. To provide confidentiality and integrity even in the presence of such an adversary, a number of Trusted Execution Environments (TEEs) have been developed. A TEE aims to protect data and code within its environment against high privileged adversaries, such as a malicious operating system or hypervisor. While mechanisms exist to attest a TEE's integrity at load time, there are no mechanisms to attest its integrity at runtime. Work also exists that discusses mechanisms to verify the runtime integrity of programs and system components. However, those verification mechanisms are themselves not protected against attacks from a high privileged adversary. It is therefore desirable to combine the protection mechanisms of TEEs with the ability of application runtime integrity verification. In this paper, I present Scanclave, a lightweight design which achieves three design goals: Trustworthiness of the verifier, a minimal trusted software stack and the possibility to access an application's memory from a TEE. Having achieved these goals, I am able to verify the runtime integrity of applications even in the presence of a high privileged adversary. I refrain from discussing which properties define the runtime integrity of an application, as different applications will require different verification methods. Instead, I show how Scanclave enables a remote verifier to determine the runtime integrity of an application. Afterwards, I perform a security analysis for the different steps of my design. Additionally, I discuss different enclave implementations that might be used for the implementation of Scanclave.
Updating A Secret Key for MAC Implemented on CAN Using Broadcast Encryption Scheme

In recent years, CAN is spread widely as the in-vehicle network. However, CAN protocol does not guarantee the security for traffics against falsification and spoofing. Therefore, the MAC is required for CAN so as to communicate exactly. MAC is the countermeasure for falsification and spoofing using the secret key. The problem is a method of delivering the secret key securely. This paper proposes a method to update the secret key using broadcast encryption over a non-supersingular elliptic curve. The method is implemented on Raspberry Pi and experimentally find out to be able to update the secret key efficiently.
BaLuna: Floating Balloon Screen Manipulated Using Ultrasound

In this paper, we present BaLuna, a prototype of an externally actuated midair display for indoor use in a room-scale workspace. This system is the first battery-less midair display with a one-meter-cubic workspace. The system projects an image onto a balloon screen whose position is controlled by airborne ultrasound phased array (AUPA) devices. Users can naturally manipulate the screen position by dragging and dropping the screen directly with their hands. We adapted feedback-based acoustic manipulation technology that enables sparsely distributed AUPA devices to control the screen position. This is combined with a depth-image-based tracking and a three-dimensionally calibrated projector.
Semantic Oppositeness Embedding Using an Autoencoder-Based Learning Model

Semantic oppositeness is the natural counterpart of the much popular natural language processing concept, semantic similarity. Much like how semantic similarity is a measure of the degree to which two concepts are similar, semantic oppositeness yields the degree to which two concepts would oppose each other. This complementary nature has resulted in most applications and studies incorrectly assuming semantic oppositeness to be the inverse of semantic similarity. In other trivializations, "semantic oppositeness" is used interchangeably with "antonymy", which is as inaccurate as replacing semantic similarity with simple synonymy. These erroneous assumptions and over-simplifications exist due, mainly, to either lack of information, or the computational complexity of calculation of semantic oppositeness. The objective of this research is to prove that it is possible to extend the idea of word vector embedding to incorporate semantic oppositeness, so that an effective mapping of semantic oppositeness can be obtained in a given vector space. In the experiments we present in this paper, we show that our proposed method achieves a training accuracy of 97.91% and a test accuracy of 97.82%, proving the applicability of this method even in potentially highly sensitive applications and dispelling doubts of over-fitting. Further, this work also introduces a novel, unanchored vector embedding method and a novel, inductive transfer learning process.
Data Exchange Ontology for Interoperability Facilitation Within Industrial Automation Domain

The current gradual digitization emphasizes the needs for easy, faultless, and flexible data exchange. However, the main obstacle is not an exchange of messages but resides in sharing a mutual understanding of the message meaning. Thus, Semantic Web technologies are exploited for facilitation of the data exchange in the approach presented in this paper. Furthermore, the presented solution is based on the modeling of system interfaces and messages instead of designing and implementing clumsy shared not a versatile data model. The proposed solution also benefits from the exploitation of SPARQL and SWRL during the integration of a new component into the system.