Reviewerly/exHarmony huggingface dataset:
Kernelized Constrained Gaussian Fields and Harmonic Functions for Semi-supervised Learning

Graph-based semi-supervised learning (SSL) methods are effective on many application domains. Despite such an effectiveness, many of these methods are transductive in nature, being uncapable to provide generalization for the entire sample space. In this paper, we generalize three existing graph-based transductive methods through kernel expansions on reproducing kernel Hilbert spaces. In addition, our methods can easily generate an inductive model in a parameter-free way, given a graph Laplacian constructed from both labeled and unlabeled examples and a label matrix. Through experiments on benchmark data sets, we show that the proposed methods are effective on inductive SSL tasks in comparison to manifold regularization methods.
End-to-End Phoneme Recognition using Models from Semantic Image Segmentation

We train fully convolutional neural networks with no recurrent layers for the end-to-end phoneme recognition task, using the Connectionist Temporal Classification (CTC) loss function. The adopted network, U-Net, was introduced initially for semantic image segmentation tasks, and is often applied to segmenting features in medical imaging and remote sensing. The similarities between CTC-based automatic speech recognition and semantic segmentation problems are discussed. We extend the encoder-decoder architecture of U-Net and show it is capable of good performance in the acoustic modelling of a speech recognition system. We investigate the importance of the concatenation step in the design of U-net, and report results using the core test set of the TIMIT corpus.
DGViewer: A Hybrid Approach towards Visualisation of Dynamic Networks

Dynamic networks are networks in which nodes and edges come and go at different times. The dynamic nature of this type of networks has imposed a challenge in network visualisation. Many techniques have been proposed to visualise dynamic networks in an effort of not only capturing the dynamic features of networks over the time but also laying out the network in a way that is fast and makes the visualisation aesthetically pleasing. However, empirical research has shown that the current techniques have limitations in one way or another. In the paper, we present an interactive visualisation called DGViewer which gives users control and flexibility allowing them to decide what, when and how to view visualisations to improve the analysis and comprehension of dynamic networks. A preliminary usability study indicates that users are positive with this user-centred visualisation for better user experience and task performance.
Deep Learning Assisted Resource Partitioning for Improving Performance on Commodity Servers

In this paper, we introduce a deep reinforcement learning (DRL) framework for solving the problem of partitioning LLC and memory bandwidth coordinately in an end-to-end manner. To this end, we formulate the problem as a markov decision process and utilize DRL algorithm to derive the optimal partition. To avoid the extensive cost of training the policy on physical server, we present a model-based solution, where a reward prediction model is leveraged to train the partitioning policy offline. To construct a precise reward prediction model, we introduce a novel representation for the partitioning scheme, where graph convolutional networks (GCN) is employed to represent the LLC partition as a bipartite graph so that those heterogeneous but identical partitions could result in the same representations and thus eases the prediction task.
Applying Reinforcement Learning in Context Limited Environments

Reinforcement Learning has seen some interesting development over the last years, which made it very attractive to use on recommendation scenarios. In this work, we have extended the previously developed pervasive system, which is aware of the conversational context to suggest documents potentially useful to the users, with the ability to use users' click data as a way to perform better suggestions over time, through a Reinforcement Learning approach. Furthermore, to assure the real significance of these types of approaches in conversational environments, we also conducted a case study regarding the accuracy of feedback on context limited conversational systems.
Mobile Learning with Augmented Reality: A Case Study of Acupuncture Points

Augmented reality (AR) has been widely applied to educational practices in various disciplines. This chapter presents a case study of its use in nursing education for learning of acupuncture points. Acupuncture plays an important role in Traditional Chinese Medicine (TCM), and nursing students need to learn the locations and functions about acupuncture points (acupoints). Learning acupoints is challenging due to the difficulty in locating the acupoints and memorising the functions of each acupoint. In this chapter, a mobile application is introduced for learning acupoints in an effective way. The application contains three main parts. The first part is the AR system which shows the 3D hand model with acupoints and their details using the AR technology. The second part is the Quiz system which tests a user’s knowledge on acupoints. The last part is the acupoint application system which allows users to apply the acupoints in daily life. With the use of AR technology, the mobile application provides a new paradigm of interactions that can improve the learning efficiency of acupoints.
Complementing Behavioural Modeling with Cognitive Modeling for Better Recommendations

Recommender systems are systems that help users in decision-making situations where there is an abundance of choices. We can find them in our everyday lives, for example in online shops. State-of-the-art research in recommender systems has shown the benefits of behavioural modeling. Behavioural modeling means that we use past ratings, purchases, clicks etc. to model the user preferences. However, behavioural modeling is not able to capture certain aspects of the user preferences. In this talk I will show how the usage of complementary research in cognitive models, such as personality and emotions, can benefit recommender systems.
Performance Evaluation of VLSI Implemented WSN Algorithms

The VLSI implemented security and routing algorithms with various performance issues for WSNs have been discussed. FPGA/VHDL and ASIC implementation of security algorithms for WSNs has been explored with RTL level/block level design. This work has been covered with large range of symmetric and asymmetric security algorithms and proposes classification of these existing security algorithms. The contribution of the proposed work would be to develop ultra-low power and area-efficient WSN security algorithms.
Dynamic Co-located VM Detection and Membership Update for Residency Aware Inter-VM Communication in Virtualized Clouds

Inter-VM traffics are witnessed as one of the dominating costs for massive parallel computation jobs in virtualized clouds nowadays. One way to improve the efficiency is to exploit shared memory facilities provided by the hypervisor or the host domain to bypass traditional network path for co-located VMs. Thus it is important to be capable of determining whether two VMs are co-located on the same physical node or not. Existing approaches employ either static or dynamic method to maintain co-located VM membership. Since static methods are not adaptive to support runtime detection of VM existence changes, polling based dynamic methods are adopted, with which the response time to membership change is hold up by pre-configured polling cycle. Moreover, the overhead for the membership change and the scalability over the number of co-located VMs are not considered. In this paper, we propose CoKeeper, a dynamic event driven co-located VM detection and membership update approach for residency aware inter-VM communication. CoKeeper responses faster than polling based methods since the membership updates are immediately visible after the events of VM existence changes. Experimental results show that the response time of CoKeeper is more than 10 times lower than that of polling based method and more stable. It achieves lower CPU and network overhead as well as co-located VM scalability. With real network intensive application workloads, we validate that CoKeeper can ensure user level and Linux kernel transparency even in cases of dynamic VM existence changes.
Shared Images in A Connected-Vehicle Network Registration System Using Harris-Stephens Approach

Connected vehicles technology allows to share information between road elements, like vehicles, pedestrians and infrastructure. The main goal of the connected vehicles technology is to improve road safety and achieve autonomous driving. Many kinds of information can be shared over connected vehicles, like navigation information, road conditions and weather conditions. Videos and images are also important information to be shared over the connected vehicles network due to the rich information they carry to achieve autonomous driving, especially when they are processed by machine learning algorithms to detect and classify road objects. One of the issues in dealing with the images shared over connected vehicles is the image registration. Because the cameras used in vehicles have different specifications, like resolution, field of view and alignment. To analyze these images by computer vision algorithms, images have to be registered and aligned together. This paper proposes a solution to register the shared images that are collected by front vehicle cameras. Harris-Stephens approach for corner detection is used for feature extraction. The nearest neighbor distance ratio does the feature matching between the target image and the reference image. The algorithm calculates transformation parameters and applies them on target images to register them to a reference image. The paper evaluates the proposed system results using vehicle front camera collected dataset.
Extending Hashing Towards Fast Re-Identification

Searching accuracy and efficiency are two challenges in person and vehicle Re-identification (Re-ID), which one focuses on robust representations learning which usually generating high-dimensional features while the other has not been fully explored. Hashing is a suitable solution to make REID efficient. However, directly extending the existing hashing methods to fast Re-ID faces two challenges: one is the non-overlap between training and testing set which need more discriminative hash codes, the other is the large identities in Re-ID tasks which will lead to slow convergence and hard optimization. In this work, we propose an attention pooling operator to exploit both local and global visual attributes which can break limited discriminative power in hash methods. To further make training procedure converge faster and optimize the network more easily, we substitute non-differentiable l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularization with smooth l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularization. In experiments, our work outperforms state-of-the-art hashing and quantization methods on both person and vehicle Re-ID datasets. Besides, the results can serve as a strong baseline in the field of deep hashing for fast Re-ID. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Code is available at https://github.com/cynthia951031/Hashing ReID.
The Analysis of Value Shop as a Basic Design of Mobile Applications in Cirebon and Surrounding Areas

The improvement of prosperity in city can be achieved, among others, by increasing tourism activities. The city and regency of Cirebon were selected in this study to see the potential and the obstacles that occur to make these areas as tourist destination. Kertajati international airport is available, dozens of tourist destinations both for enjoying culinary, historical, religious sites, supporting adequate intercity and local transportation, and so on. The main problem for Cirebon for tourists who will or are visiting the city is the specific and clear information about the destination. The opportunity and obstacles to find the best solution are evaluated through the Value Shop system. The need for a mobile application framework that is well developed and backed with comprehensive data is proposed. The design of the application framework uses the OOAD process. It is hoped that this application framework will make it easy for prospective travelers to feel comfortable toward what they can get from the city and regency of Cirebon. In addition, the Population and the government of Cirebon city and regency will be able to boost their wealth.
Factors Affecting the Cost to Accuracy Balance for Real-Time Video-Based Action Recognition

For successful real-time action recognition in videos, the compromise between computational cost and accuracy must be carefully considered. To explore this balance, we focus on the popular Bag-of-Words (BoW) framework. Although computationally efficient, the BoW has weak classification power. Thus, many variants have been developed. These variants aim to increase classification power whilst maintaining computational efficiency; achieving the ideal cost-to-accuracy balance. Four factors affecting the computational cost vs accuracy balance were identified: ‘Sampling’ strategy, ‘Optical Flow’ algorithm, ‘Saliency’ of extracted features and overall algorithm ‘Flexibility’. The practical effects of these factors were experimentally evaluated using the Dense Trajectories feature framework and the KTH and HMDB51 (a reduced version) datasets. The ‘Saliency’ of extracted information is found to be the most vital factor - spending computational resources to process large amounts of non-salient information can decrease accuracy.
Correcting Low-Illumination Images Using Multi-Scale Fusion in a Pyramidal Framework

In this work, the authors have proposed a method for improving the visual quality of color images suffering from low illumination. In this pyramid based multiscale technique, the input image is decomposed into four different levels of resolutions. Starting from the coarsest resolution, each image is converted to HSV space, and the illumination is computed from the V component employing multiscale Gaussian function. At this stage, Weber-Fechner law is used to construct two enhanced versions of the illumination component corresponding to two different values of a parameter. These two images are fused using PCA to construct a new V image, which is subsequently super-sampled to its next higher level of resolution. The V images of the next higher level of resolution are subjected to the same treatment until we reach the base level of the pyramid. Finally, all the V component images at the base level are fused employing PCA to construct the final resultant V component, which in turn is combined with the H and S components to construct the final result. The method has been implemented and tested on a set of real 2D color images, and the results are found satisfactory. The experimental results have been compared with those of other methods based on some objective measures.
Factors associated with direct use of laptop on the lap among male university undergraduates in Southwestern Nigeria

Laptop use comes with potential reproductive health risks for men. This study assessed the knowledge of reproductive health hazards and factors associated with direct use of laptop on the lap among male undergraduates of the University of Ibadan, Nigeria. A cross-sectional study conducted among 444 male undergraduates using multi-stage sampling and data collected through a validated questionnaire. Mean age was 21.0 years ± 2.6. Knowledge and perception of reproductive health hazards associated with direct laptop use were fairly good but practice was high. The knowledge of reproductive health hazards associated with direct laptop use on the lap was not associated with use (p = 0.08) and factors influencing use include; influence by colleagues (67.6%), non-availability of laptop cooling pads (42.6%), high cost of cooling pads (39.9%), perception of convenience (41.7%). Interventions on laptop ergonomics awareness and provision of affordable laptop ergonomic products for university students are recommended.
Minimum square distance thresholding method applying gray-gradient co-occurrence matrix

In image thresholding segmentation, gray level of pixels is the basic element to describe images. Besides, the gradient information of pixels is also a key feature to represent image space distribution. Therefore, the co-occurrence probability of gray and gradient of pixels is an effective information to describe image. In this paper, gray-gradient asymmetrical co-occurrence matrix is constructed, uniformity probability of image region is produced, and a minimum square distance criterion function based on gray-gradient co-occurrence matrix is proposed to measure the deviation between original and binary images. Comparing with gray-gray asymmetrical co-occurrence matrix and relative entropy-based symmetrical co-occurrence matrix method, the proposed method can obtain more complete segmentation results, especially for small-size object extraction. The peak signal to noise ratio probability also shows the better segmentation performance of our proposed method.
Modeling legal conflict resolution based on dynamic logic

Abstract Conflicts between legal norms are common in reality. In many legislations, legal conflicts between norms are resolved by applying ordered principles. This work presents a formalization of the conflict resolution mechanism and introduces action legal logic ($\textsf{ALL}$) to reason about the normative consequences of possibly conflicting legal systems. The semantics of $\textsf{ALL}$ is explicitly based on legal systems consisting of norms and ordered principles. Legal systems specify the legal status of transitions in transition systems and the language of $\textsf{ALL}$ describes the legal status of paths in transition systems. The formalization is used to study abstract revisions of legal systems. The expressivity of $\textsf{ALL}$ is studied and its completeness is proved.
Label Prediction Framework For Semi-Supervised Cross-Modal Retrieval

Cross-modal data matching refers to retrieval of data from one modality, when given a query from another modality. In general, supervised algorithms achieve better retrieval performance compared to their unsupervised counterpart, as they can learn better representative features by leveraging the available label information. However, this comes at the cost of requiring huge amount of labeled examples, which may not always be available. In this work, we propose a novel framework in a semi-supervised cross-modal retrieval setting, which can predict the labels of the unlabeled data using complementary information from different modalities. The proposed framework can be used as an add-on with any baseline cross-modal algorithm to give significant performance improvement, even in case of limited labeled data. Extensive evaluation using several baseline algorithms across three different datasets show the effectiveness of our label prediction framework.
Happiness and the Arts

In this chapter, we provide conceptual perspectives by examining various pathways that lead one to experience higher levels of happiness. We begin by connecting the arts to positive psychology within the context of Dr. Mihaly Csikzentmihalyi’s (1997) eight stages of the flow theory. Examples are provided to show how the arts activate each stage, opening the doors for one to experience flow. Next, we connect the arts to the happiness constructs of perceived control, perceived progress, connectedness, and a larger vision, providing further conceptualization on the arts delivering happiness. We then apply the popular art form of storytelling for three main purposes: (1) telling stories is a universal practice found in various art genres; (2) it triggers flow; and (3) it connects with the happiness constructs. We then provide a rational argument on why businesses must leverage the arts within the workplace to deliver results such as developing creative cultures.
Feature Comparison Based Channel Attention For Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) remains challenging because a majority of samples have large intra-class variations and small inter-class variations. However, samples belonging to one category are essentially identical in some discriminative visual patterns. Intuitively, we want models to reinforce the relationship between these discriminative visual patterns and image-level labels. In this paper, we propose a feature comparison based channel attention (FCCA) to achieve this intuition. In FCCA, the feature comparison mechanism is designed to recognize discriminative visual patterns. The weights assignment scheme guarantees that feature channels related to discriminative visual patterns have larger weights. The state-of-the-art performance has been achieved on two public FGVC datasets. Extensive experiments further prove the effectiveness of our method.
Numerical Analysis Investigation of Acoustic Shadow Moiré Interference

Acoustic shadow moiré has unique properties to be used for many potential applications in medical diagnostics, manufacturing, and material characterization. In this paper, numerical analysis, using Comsol, is used to investigate the principles of acoustic moiré interference phenomenon. The study confirmed that the expected fringe images of shadow interference can be created at Talbot distances. The study confirms the experimental results reported by this group [1] and proves without any doubt that acoustic moiré interference phenomenon exists.
DECiSION: Data-drivEn Customer Service InnovatiON

The paper presents DECiSION, an innovative framework in the field of Information Seeking Support Systems, able to retrieve all the data involved in a decision-making process, and to process, categorize and make them available in a useful form for the ultimate purpose of the user request. The platform is equipped with natural language understanding capabilities, allowing the interpretation of user requests and the identification of information sources from which to independently retrieve the information needed for the sensemaking task. The project foresees the implementation of a chatbot, which acts as a virtual assistant, and a conversational recommender system, able to dialogue with the user to discover their preferences and orient their answers in a personalized way. The goal is therefore to create an intelligent system to answer autonomously and comprehensively questions posed in natural language about a specific reference domain, to support the decision-making process. The paper describes the general architecture of the framework and then focuses on the key component that automatically translate the natural language user query into a machine-readable query for the service repository.