Reviewerly/exHarmony huggingface dataset:
Towards Integrative Personal Character Modeling Using Multi-strategy Fusion Across Scenarios and Periods

The individual-like intelligent artifact is a special humanoid, which resembles a real human being in terms of appearance, behavior, and characteristics. Such intelligent artifacts can help achieving numerous fantastic applications, including better-personalized services and digital immortality. Currently, limited progress has been made in recognizing personal inner characteristics. As a comprehensive description of an individuals' characteristics, the personal character model (PCM) is the key to individual-like intelligent artifacts. Based on personality and differential psychology, the proposed PCM consists of personal characteristics in affect, behavior and cognition (ABC), personality (P), and their relations (R). In this paper, three properties of PCM in terms of scenario and period are summarized. More specifically, the original PCM couldn't be calculated accurately and comprehensively given only one single scenario and a certain temporal period. An integrative personal character modeling is proposed on the basis of multistrategy fusion, which features accurate strategy fusion and comprehensive strategy fusion. These two fusion strategies are used to further improve the results of personal characteristics computed from various scenarios and different periods of time, so as to obtain a more accurate and more comprehensive PCM. Furthermore, the experimental categories and evaluation methods in personal character modeling are discussed and clarified in the current and future study.
Cultural Environment in Coastal Islamic Boarding School Regarding the Form of Politeness to Kyai

The study examines santri speech in a coastal Islamic boarding school which shows politeness in respecting the Kyai . This study aims to describe marks of politeness as a form of respect for Kyai and classify politeness factors in santri speech in the coastal boarding school. This study used a qualitative approach with analysis of pragmatic politeness. The study finds that maxim of wisdom and Javanese proverb are dominating the santri speech. Honorific is the most dominant word chosen to address Kyai . The determinant factors found are linguistic and nonlinguistic factors. The linguistic factor for respecting the Kyai is dominated by Javanese diction and euphemism. The nonlinguistic factor is influenced by the mother tongue of speakers as native speakers. Most of the speakers are Javanese, so santri speech is also in Javanese. The speakers’ loyalty as native speakers is also very high. The linguistic behavior of the santri greatly reflected respect for Kyai . The respect for Kyai can be seen in the diction, language loyalty, and linguistic behavior of the santri who are domiciled in Islamic boarding school environment. This politeness builds the strength of characteristics in the coastal Islamic boarding school.
A Combined Reward-Penalty Loss Function based Extreme Learning Machine for binary classification

In this paper, we have introduced a novel combined reward cum penalty loss function to measure the empirical risk in Extreme Learning Machine. The proposed Reward cum Penalty loss function based Extreme Learning Machine (RP-ELM) penalizes those data points which do not lie on the desired location and assigns reward for those data points which lie on the desired location. Unlike OP-ELM, the RP-ELM can utilize the full information contained in the training set which makes it to own better generalization ability.
The framework of collision detection for rigid bodies in the virtual environment

Abstract Collision detection includes the detection of a collision between the object in the virtual environment especially to stop the moving object through each other. However, the collision detection refers to the detection of the intersection between two or more objects. There are two basic types of collision detection namely rigid bodies that refer to the hard object and constant distance between vertices, and the deformable object that refers to soft bodies like cloth may collide with themselves. Usually, the main problem of collision detection for rigid bodies that often studies involves the fast, effective and efficiency of the collision response. In this paper, the hierarchical representative bounding volume introduced to develop the new framework of collision detection and analyse which technique performance is fast and effective for collision detection for rigid bodies in the virtual environment. The bounding volume (BV) technique such as Axis-Aligned Bounding Box (AABB), Oriented Bounding Box (OBB), Sphere and Discrete Oriented Polytopes (k-DOPs) will be applied to construct the hierarchical representative bounding volume.
Scalable Receptive Field GAN: An End-to-End Adversarial Learning Framework for Crowd Counting

Crowd counting is challenging for unrestricted open outdoor and diverse scenes. To address large variety of perspective, density distribution and clutter problems, a novel end-to-end deep generative adversarial framework with scalable receptive field (SRFGAN) is proposed for obtaining high quality density estimation in this paper. Specifically, our generator adopts an encoder-decoder network with residual blocks to achieve multi-scale features due to scalable receptive fields which adapts to different scale crowd distribution. We also explore a spatial global pooling layer to acquire image-level prior representation which helps to tackle severe perspective distortion and background clutter. Besides, feature matching loss and adversarial loss are combined via a joint training scheme, which helps to improve the quality of generated density map. Experiment results on ShanghaiTech and UCF_CC_50 datasets illustrate the superior effectiveness.
Moral hazard in games of miners

Cryptocurrency mining, which has attracted substantial attention recently is a game where miners cost mining power and compete for an amount of digital token. In this paper, we revisit this game under new settings that miners' maximal mining powers, i.e. capacities, are disparate. We show it has a unique pure Nash-equilibrium and derive the closed-form. In the PNE, miners fall into two types: half-hearted and all-out, the former of which owns more capacity than the latter. An all-out miner mines with her full capacity while a half-hearted miner does not. Compared with the desired equilibrium that every miner does her best, our result reveals that the disparity of capacity leads to less total mining power, compromising the security of cryptocurrency systems.
Capturing Frame-Like Object Descriptors in Human Augmented Mapping

The model of an environment plays a crucial role in autonomous mobile robots, by providing them with the necessary task-relevant information. As robots become more intelligent, they need a richer and more expressive environment model. This model is a map that contains a structured description of the environment that can be used as the robot’s knowledge for several tasks, such as planning and reasoning. In this work, we propose a framework that allows to capture important environment descriptors, such as functionality and ownership of the robot’s surrounding objects, through verbal interaction. Specifically, we propose a corpus of verbal descriptions annotated with frame-like structures. We use the proposed dataset to train two multi-task neural architectures. We compare the two architectures through an experimental evaluation, discussing the design choices. Finally, we describe the creation of a simple interactive interface with our system, implemented through the trained model. The novelties of this work are: (i) the definition of a new problem, i.e., addressing different object descriptors, that plays a crucial role for the robot’s tasks accomplishment; (ii) a specialized corpus to support the creation of rich Semantic Maps; (iii) the design of different neural architectures, and their experimental evaluation over the proposed dataset; (iv) a simple interface for the actual usage of the proposed resources.
Campus bullying detection based on motion recognition and speech emotion recognition

In many areas, school bullying incidents often occur. Such incidents not only damage the learning environment of the school but also cause great harm to the students who are bullied. The occurrence of the campus bullying incident is generally hidden and not easy to be discovered by teachers or parents in time. Aiming at the problem that the campus bullying incident needs to be discovered in time, a campus bullying detection method based on motion recognition and speech emotion recognition is proposed. The electronic device such as smart phone and smart watch worn by people are used to collect the data of human motion and voice in real time. Thus, it is possible to detect in time whether the wearer is being bullied. In this paper, six characteristics of human motion data and MFCC features of speech data are extracted. Then the PCA algorithm is used to reduce the dimension of the obtained feature matrix. Finally, the KNN algorithm is used to form the motion and speech recognition network. After cross-validation, the average recognition rate of the bullying movement and speech emotion of the system in this paper are 77.8% and 81.4%, respectively. The experimental results show that the campus bullying detection method based on KNN algorithm has obtained a good recognition rate.
AnatomyMR: A Multi-User Mixed Reality Platform for Medical Education

Abstract Mixed Reality (MR) has opened up new means of imparting education, especially in the medical field. Students could view the whole human anatomy and it’s components at multiple levels of scale to learn their location and naming nomenclature, and also interact with them separately to familiarize themselves with their functions. In this paper we propose a platform for Medical education in MR which creates shared experiences, providing support for teaching within classrooms. The application would also work as a standalone experience, augmenting important information over the anatomy model for the student’s reference when a shared experience is not in session, thereby facilitating self-study. The platform would be built in such a way that the instructors would also have the option to add 3D models and relevant content to the existing courseware and have it rendered in the mixed reality space.
Machine-Learning-Generated Synthetic Data for Regulation Best Interest Compliance

Big Data hype has not missed investment management, the reality is that price data from U.S. financial markets are not really Big Data. Price data is Small Data. The fact that sellers and advisors in financial markets use Small Data to generate and test investment strategies creates two major problems. First, the economic mechanisms that generate prices (and therefore returns) may change through time, so that historical data from an earlier time may tell us little or nothing about future prices and returns. Second, even if data-generating-mechanisms are somewhat stable through time, inferences about the profitability of investment strategies may be sensitive to a handful of outliers in the data that get picked up again and again in different strategies mined from the same Small Data set. In this article, we present an answer to the financial Small Data problem: using machine-learning methods to generate “synthetic” financial data. The essential part of our approach to developing synthetic data is the use of machine learning methods to generate data that might have been generated by financial markets but was not. Synthetic price and return data have numerous uses, including testing new investment strategies and helping investors plan for retirement and other personal investment goals with more realistic future return scenarios. In this article, we focus on a particularly important use of synthetic data: meeting legal and regulatory requirements such as best interest and fiduciary requirements.
Sharing Of Public Harassment Experiences on Social Media in Bangladesh

This study explores the notions of sharing public harassment experiences on social media and the associated construction of online social support and justice, based on an anonymous survey (n=340) and semi-structured interviews (n=10) in Bangladesh. Our initial findings show the wider prevalence of public harassment against women in Bangladesh and indicate the existence of implicit societal biases and socio-technical infrastructural weaknesses that manipulate the sharing experiences of harassment on social media as well as limit the expected social support and justice online. These findings advocate for better design policies on safer social media participation, especially for women and call for more attention on fixing the existing technical infrastructures that address justice online.
Dealing with Comprehension and Bugs in Native and Cross-Platform Apps: A Controlled Experiment

In this paper, we present the results of a controlled experiment aimed to investigate whether there is a difference when comprehending apps implemented with either cross-platform (Ionic-Cordova-Angular) and native (Android) technologies. We divided participants into two groups. The participants in each group were asked to comprehend the source code of either the app implemented using Ionic-Cordova-Angular technology or its Android version. We also asked the participants to identify and fix faults in the source code. The goal was to verify if the technology might play a role in the execution of these two kinds of tasks. We also investigated the affective reactions of participants and the difficulty they perceived when accomplishing the tasks mentioned before. The most important take-away result is: there is not a statistically significant difference in the comprehension and in the identification and fixing of bugs when dealing with either native or cross-platform apps.
Rewritable Data Embedding in JPEG XT Image using Coefficient Count Across Layers

A JPEG XT format-compliant data embedding method is put forward by exploiting the natural relationship of coefficient count in the base and residual layers. Specifically, the number of coefficients for each MCU in the base layer is often larger than that of the respective MCU in the residual layer. This happens when the quality factor of the base layer is ≥ 75 and the quality factor of the residual layer is lower than that of the base layer. The proposed method then swaps the MCUs within these two layers to embed data. In case when a MCU is indistinguishable, a marker is injected to explicitly mark this bad MCU. When only AC coefficients are swapped, the output image quality can be maintained with some expansion of bit stream size. On the other hand, when both DC and AC coefficients are swapped, the proposed method is able to generate a fully distorted output image, hence masking the perceptual semantic of the image. Experiments are carried out to evaluate the performance of the proposed data embedding method.
Comparison of Image Formats to Apply Image Steganography in Smartphones: A Preliminary Study

The growing popularity of smartphones has, in turn, increased the dependency on them. People share more and more data using smartphones. Its excess usage has intensified the concern to secure the data stored in the smartphones. Image steganography is becoming popular to hide the sensitive data stored in them. Images are present in various formats. This research paper is threefold. Firstly, it compares various image formats and secondly, it studies the pros and cons of using image steganography on these image formats conforming to smartphones. Thirdly, it analyzes the issues related to jpeg steganography and compares various popular jpeg steganography algorithms and suggests how jpeg images can be used effectively for image steganography.
Real-time Edge Repartitioning for Dynamic Graph

To improve the performance of large graph computing, graph partitioning has become a mandatory step in distributed graph computing frameworks. Some existing frameworks partition edges of an input graph in a streaming way. As the scale of real-world graphs grows dynamically, they need to limit the increasing communication cost and time cost in graph computing by reducing vertex replicas(each vertex can be replicated to multiple partitions). In this paper, we propose a real-time edge repartitioning algorithm for dynamic graph, which reduces the vertex replicas by reassigning edges near the new edge. We find that some edges are migrated just after being assigned, which leads to unnecessary migrations. To reduce migration cost, according to the replicas distribution of neighbors of two vertices connected by the new edge, we assign the new edge to the partition where it is most likely to be located after repartitioning. Our evaluation shows that it improves the performance of graph computing by only a small amount of migration.
Automatic Registration of Multi-Projector Based on Coded Structured Light

Multi-projector display systems are widely used in virtual reality, flight simulators, and other entertainment systems. Geometric distortion and color inconsistency are two key problems to be solved. In this paper a geometric correction principle is theoretically demonstrated and a consistency principle of geometric correction is first proposed. A new method of automatic registration of a multi-projector on a curved screen is put forward. Two pairs of binocular-cameras are used to reconstruct the curved screen. To capture feature points of the curved screen precisely, a group of red-blue coded structured light images is designed to be projected onto the screen. Geometric homography between each projector and the curved screen is calculated to gain a pre-warp template. Work which can gain a seamless display is illustrated by a six-projector system on the curved screen.
A Framework for Multi-Variant Execution Environment

Abstract Software diversity has been proven to be an effective approach to enhance system security. To make the best of the advantage brought by software diversity, a multi-variant execution environment is needed. However, although some MVEEs have been proposed, most of them are either too simple or only focusing one aspect which limits their widely adoption in industry. In this paper we propose a framework for multi-variant execution environment to enhance the security of software systems. The framework addresses different aspects when implementing a MVEE and can help to make the best of software diversity to enhance the system’s security.
Towards a Robotic Personal Trainer for the Elderly

The use of robots in the environment of the elderly has grown significantly in recent years. The idea is to try to increase the comfort and well-being of older people through the employment of some kind of automated processes that simplify daily work. In this paper we present a prototype of a personal robotic trainer which, together with a non-invasive sensor, allows caregivers to monitor certain physical activities in order to improve their performance. In addition, the proposed system also takes into account how the person feels during the performance of the physical exercises and thus, determine more precisely if the exercise is appropriate or not for a specific person.
Hierarchical Graph Neural Networks for Personalized Recommendations with User-Session Context

With the swift development of Internet and artificial intelligence, recommender systems have become more and more important as useful information has a risk of submerging in huge amounts of data or smart services always provide a behavior predictor to meet diversified user’s needs. User-session based recommendations are commonly applied in many modern online platforms. Graph Neural Networks (GNNs) have been shown to have a strong ability to address the problem of session-based recommendation with accurate item embedding. However, there are a lot of application scenarios that have already provided user profiles. We propose a model based on Hierarchical GNNs for personalized recommendations, which evolves both item information in sessions and user profiles. The experiments on two industry datasets show the superiority of our model over the state-of-the-art methods.
An Improved Algorithm Based on Exemplar Based Image Inpainting

Image restoration technology is an important image processing technology. In image restoration, it not only can reconstruct the loss part of impaired image caused by external factors, but also can remove unwanted parts from the specified image. Criminisi et al proposed an algorithm for image restoration processing, namely Exemplar Based Image Inpainting (EBII). Using the provided template window, this algorithm can find the best matching image data in the original image for filling after calculating the filling priority of the contour region of the image to be repaired. This approach can process various kinds of images effectively. However, in the treatment of some details in an image, the EBII algorithm needs to be improved. For instance, we discovered that the calculations of the priority and image matching would be influenced by the size of the template window, which was fixed in the EBII algorithm. Therefore, in this paper we present an improved EBII algorithm: a collective of template windows with various sizes will be used in image inpainting; in the repair process, image restoration is performed by judging the optimal template size in the collection of template windows. Our results show that the ameliorated EBII algorithm can significantly improve image quality owing to the delicate treatment of the details.
An Evaluative Framework for the Most Suitable Theory of Mobile Learning

The focus of ICT in education has shifted from e-learning to mobile learning as mobile devices offer many technological and pedagogical affordances coupled with their affordability. It is imperative to say their potential can be leveraged efficiently in teaching-learning communities in developing countries in general and South-Asian countries in particular. To ensure its successful implementation a theory of mobile learning is required. This chapter draws an evaluative framework of existing theories of learning and proposes the most suitable theory of learning which can be leveraged to promote mobile learning to promote access of education in the developing nations.
Content Based Image Retrieval Using Machine Learning Based Algorithm

In research field, CBIR (Content Based Image retrieval) has played a vital role. This paper deals with the realization of different approaches used in image retrieval based on content. It gives a general idea of the currently accessible literature on content based image retrieval. In CBIR, a query image is searched from larger database and an exact match image is retrieved using efficient machine learning algorithms. Different algorithms i.e. Bacteria Foraging optimization algorithm, Swarm optimization algorithm, Convoltional neural network, Firefly network, Deep Belief Network, Support vector machine and Genetic algorithm are reviewed and their performance parameters are compared.
AUDITORY THINKING IN THE STRUCTURE OF ARTISTIC COGNITION

The author examines auditory thinking as a means of rational cognition of irrational phenomena, a means of modelling the acoustic worldview. The study focuses on social determination of auditory thinking, which is a means of non-verbal communication. Non-verbalized meaning presented in an auditory form is the most efficient means to influence human mentality, mass consciousness, to establish models of social behaviour. Auditory thinking reconstructs and transforms unconscious thoughts, emotions to clear, subjectified acoustic models. Audition not only represents objective reality but transforms it, promotes the formation and manifestation of new ideals.