Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Multipath BGP

Multipath BGP (M-BGP) allows a BGP router to install multiple `equally-good' paths, via parallel inter-domain border links, to a destination prefix. M-BGP differs from the multipath routing techniques in many ways, e.g. M-BGP is only implemented at border routers of Autonomous Systems (ASes); and while it shares traffic to different IP addresses in a destination prefix via different border links, any traffic to a given destination IP always follows the same border link. Recently we studied Looking Glass data and reported the wide deployment of M-BGP in the Internet; in particular, Hurricane Electric (AS6939) has implemented over 1,000 cases of M-BGP to hundreds of its peering ASes. In this paper, we analyzed the performance of M-BGP. We used RIPE Atlas to send traceroute probes to a series of destination prefixes through Hurricane Electric's border routers implemented with M-BGP. We examined the distribution of Round Trip Time to each probed IP address in a destination prefix and their variation during the measurement. We observed that the deployment of M-BGP can guarantee stable routing between ASes and enhance a network's resilience to traffic changes. Our work provides insights into the unique characteristics of M-BGP as an effective technique for load balancing.
Smartphone as a Paired Game Input Device: An Application on HoloLens Head Mounted Augmented Reality System

From social media filters to medical applications, AR (augmented reality) is a rapidly growing technology. On the other hand, AR gaming is a popular application of this field of study. Microsoft’s see-through augmented reality headset HoloLens is one of the first and powerful headsets in the market today but it has limited interaction methods especially for gaming. This study deals with enhancing gaming experience in augmented reality devices by creating new interaction methods through a smartphone used as a game-controller an input device. We developed the Remote Input SDK (software development kit) to allow users to share input data between Unity applications to use a smartphone as remote controller while playing a game through HoloLens. This study explains the design and implementation of our SDK, which is available as an open-source component for Unity game development platform.
Path Planning under Risk and Uncertainty of the Environment

This manuscript addresses the path planning problem under risk and uncertainty to reduce the chances of undesirable damage or even loss of the autonomous vehicle. Here, it is assumed that the configuration space is partially known and that a set of points is provided to pursue the exploration of the uncertain region. Furthermore, this work considers a set of alternative risk scenarios are available for the uncertain regions of the vehicle workspace. A novel solution framework incorporating stochastic and deterministic optimisation techniques is proposed as a method of handling the tradeoff between exploration and exploitation in the chosen path. The decisions on whether to explore the uncertain region or exploit the known information are made based on a risk/reward function, that is iteratively re-evaluated when new information becomes available under the proposed algorithm. The feasibility of the algorithm is proven and results characterising the cost of exploring the unknown environment are presented here.
NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs

Deep Neural Networks (DNNs) may be partitioned across the edge and the cloud to improve the performance efficiency of inference. DNN partitions are determined based on operational conditions such as network speed. When operational conditions change DNNs will need to be repartitioned to maintain the overall performance. However, repartitioning using existing approaches, such as Pause and Resume, will incur a service downtime on the edge. This paper presents the NEUKONFIG framework that identifies the service downtime incurred when repartitioning DNNs and proposes approaches for reducing edge service downtime. The proposed approaches are based on ‘Dynamic Switching’ in which, when the network speed changes and given an existing edge-cloud pipeline, a new edge-cloud pipeline is initialised with new DNN partitions. Incoming inference requests are switched to the new pipeline for processing data. Experimental studies are carried out on a lab-based testbed to demonstrate that Dynamic Switching reduces the downtime by at least an order of magnitude when compared to a baseline using Pause and Resume that has a downtime of 6 seconds. A trade-off in the edge service downtime and memory required is noted. The Dynamic Switching approach that requires the same amount of memory as the baseline reduces the edge service downtime to 0.6 seconds and to less than 1 millisecond in the best case when twice the amount of memory as the baseline is available.
Infinite-Dimensional Programmable Quantum Processors

Programmable quantum processors for continuous-variable systems are defined, and investigated in view of their memory requirements, linking universal processors to their discrete counterparts, and deriving upper and lower bounds on the programmability of Gaussian transformations of Bosonic modes.
A Decentralized Token-Based Negotiation Approach for Multi-Agent Path Finding

This paper introduces a negotiation approach to solve the Multi-Agent Path Finding problem. The approach aims to achieve a good trade-off between the privacy of the agents and the effectiveness of solutions. Accordingly, a token-based bilateral negotiation protocol and a compatible negotiation strategy are presented. The proposed approach is evaluated in a variety of scenarios by comparing it with state-of-the-art centralized approaches such as Conflict Based Search and its variant. The experimental results showed that the proposed approach can find conflict-free path solutions with a higher success rate, especially when the search space is large and high-density compared to centralized approaches while the gap between path cost differences is reasonably low. The proposed approach enables agents to have their autonomy; thus, it is convenient for MAPF problems involving self-interested agents.
Topic Shift Detection for Mixed Initiative Response

Topic diversion occurs frequently with engaging open-domain dialogue systems like virtual assistants. The balance between staying on topic and rectifying the topic drift is important for a good collaborative system. In this paper, we present a model which uses a fine-tuned XLNet-base to classify the utterances pertaining to the major topic of conversation and those which are not, with a precision of 84%. We propose a preliminary study, classifying utterances into major, minor and off-topics, which further extends into a system initiative for diversion rectification. A case study was conducted where a system initiative is emulated as a response to the user going off-topic, mimicking a common occurrence of mixed initiative present in natural human-human conversation. This task of classifying utterances into those which belong to the major theme or not, would also help us in identification of relevant sentences for tasks like dialogue summarization and information extraction from conversations.
Generalized Reversible Data Hiding with Content-Adaptive Operation and Fast Histogram Shifting Optimization

Reversible data hiding (RDH) has become a hot spot in recent years as it allows both the secret data and the raw host to be perfectly reconstructed, which is quite desirable in sensitive applications requiring no degradation of the host. A lot of RDH algorithms have been designed by a sophisticated empirical way. It is not easy to extend them to a general case, which, to a certain extent, may have limited their wide-range applicability. Therefore, it motivates us to revisit the conventional RDH algorithms and present a general framework of RDH in this paper. The proposed framework divides the system design of RDH at the data hider side into four important parts, i.e., binary-map generation, content prediction, content selection, and data embedding, so that the data hider can easily design and implement, as well as improve, an RDH system. For each part, we introduce content-adaptive techniques that can benefit the subsequent data-embedding procedure. We also analyze the relationships between these four parts and present different perspectives. In addition, we introduce a fast histogram shifting optimization (FastHiSO) algorithm for data embedding to keep the payload-distortion performance sufficient while reducing the computational complexity. Two RDH algorithms are presented to show the efficiency and applicability of the proposed framework. It is expected that the proposed framework can benefit the design of an RDH system, and the introduced techniques can be incorporated into the design of advanced RDH algorithms.
A Generative Bayesian Graph Attention Network for Semi-Supervised Classification on Scarce Data

This research focuses on semi-supervised classification tasks, specifically for graph-structured data under data-scarce situations. It is known that the performance of conventional supervised graph convolutional models is mediocre at classification tasks, when only a small fraction of the labeled nodes are given. Additionally, most existing graph neural network models often ignore the noise in graph generation and consider all the relations between objects as genuine ground-truth. Hence, the missing edges may not be considered, while other spurious edges are included. Addressing those challenges, we propose a Bayesian Graph Attention model which utilizes a generative model to randomly generate the observed graph. The method infers the joint posterior distribution of node labels and graph structure, by combining the Mixed-Membership Stochastic Block Model with the Graph Attention Model. We adopt a variety of approximation methods to estimate the Bayesian posterior distribution of the missing labels. The proposed method is comprehensively evaluated on three graph-based deep learning benchmark data sets. The experimental results demonstrate a competitive performance of our proposed model BGAT against the current state of the art models when there are few labels available (the highest improvement is 5%), for semi-supervised node classification tasks.
Application of Deep Reinforcement Learning to Decision-Making System based on Consciousness

We have interested in a conscious decision-making system in an environment where multiple types of rewards and penalties exist. We know a method using a basis function and a method using a penalty avoidance list for this problem. Though the method using the penalty avoidance list is considered promising compared to the former, it has the problem that when all actions are registered in the avoidance list, the actions are selected at random. We propose a method for selecting actions using deep reinforcement learning in order to avoid such random selection as much as possible in this paper. The effectiveness of the proposed method is confirmed by numerical experiments.
Application of Blockchain in Libraries and Information Centers

This chapter explored the concept and application of blockchain technology in libraries and information centers. Blockchain is one of the emerging technologies thriving in the fourth industrial revolution. It is the application of cryptography for creating a time-stamped, immutable, and dynamic database, distributed across nodes in a network. Although its emergence began with cryptocurrencies, advancement in this technology has given birth to a fourth generation of blockchain with industrial disruptive capabilities, cutting across various fields including library and information science. Accordingly, the application of blockchain in libraries and information centers was thoroughly examined. Specifically, the chapter underscored the application of blockchain in circulation services, collection development, storage and archiving of records, research data management, cataloging and classification, indexing and abstracting, digital first right (DFR), etc. Lastly, the merits and demerits of blockchain in libraries and information centers were furnished accordingly.
Virtual Control Interface: A System for Exploring AR and IoT Multimodal Interactions Within a Simulated Virtual Environment

This paper discusses the development of Virtual Control Interface (VCI), a Virtual Reality (VR) system for simulating and exploring multimodal context-sensitive interactions between Augmented Reality (AR) glasses and Internet of Things (IoT) devices. Current trends in controlling IoT devices focus on using existing control interfaces such as smartphones. Parallel technological trends indicate that AR glasses are likely to become the next consumer terminal. Therefore, there is a need to explore a new paradigm for IoT control interfaces that leverages the benefits of AR. Towards developing future-proof AR-IoT interfaces, we observed the HCI community’s challenges in using existing hardware to explore the full potential and the limitations of future AR-IoT interfaces. That became the foundation for developing VCI. This paper provides an overview of the advantages and shortcomings of developing such interfaces on smartphones, state-of-the-art AR/Mixed Reality (MR) glasses, and our proposed VR simulated AR environment. This overview outlines how our proposed system could be a superior choice for researchers to investigate, challenge assumptions, prototype, develop, and experimentally test AR-IoT interactions and interfaces with currently available technologies. Furthermore, we present the three pillars we followed to guide the design and development of VCI to make it resemble an actual AR-IoT framework as much as possible while abstracting parts that have little to no effect on AR-IoT HCI research. These pillars are: design transferable architecture, use viable multimodal interactions and explore user interfaces in real-life scenarios. These three principles informed the VCI’s system architecture built upon three main subsystems: handling multi-modal user input, IoT device emulation and connectivity, and managing the AR user interface (UI). Finally, we provide a heuristic evaluation and future work for VCI.
Quantifying emotionally grounded discursive knowledge with cognitive-affective maps

Abstract Belief systems matter for all kinds of human social interaction. People have individual cognitions and feelings concerning processes in their environment, which is why they may evaluate them differently. Belief systems can be visualized with cognitive-affective maps (CAMs; as reported by Thagard (in: McGregor (ed) EMPATHICA: A computer support system with visual representations for cognitive-affective mapping, AAAI Press, CA, 2010)). However, it is unclear whether CAMs can be constructed in an intersubjective way by different researchers attempting to map the beliefs of a third party based on qualitative text data. To scrutinize this question, we combined qualitative strategies and quantitative methods of text and network analysis in a case study examining belief networks about participation. Our data set consists of 10 sets of two empirical CAMs: the first CAM was created based on participants’ freely associating concepts related to participation in education (N = 10), the second one was created based on given text data which the participants represented as a CAM following a standardized instruction manual (N = 10). Both CAM-types were compared along three dimensions of similarity (network similarity, concept association similarity, affective similarity). On all dimensions of similarity, there was substantially higher intersubjective agreement in the text-based CAMs than in the free CAMs, supporting the viability of cognitive affective mapping as an intersubjective research method for studying the emotional coherence of belief systems and discursive knowledge. In addition, this study highlights the potential for identifying group-level differences based on how participants associate concepts.
DivNet: Efficient Convolutional Neural Network via Multilevel Hierarchical Architecture Design

Designing small and efficient mobile neural networks is difficult because the challenge is to determine the architecture that achieves the best performance under a given limited computational scenario. Previous lightweight neural networks rely on a cell module that is repeated in all stacked layers across the network. These approaches do not permit layer diversity, which is critical for achieving strong performance. This paper presents an experimental study to develop an efficient mobile network using a hierarchical architecture. Our proposed mobile network, called Diversity Network (DivNet), has been shown to perform better than the basic architecture generally employed by the best high-efficiency models-with simply stacked layers-, regarding complexity cost and performance. A set of architectural design decisions are described that reduce the proposed model size while yielding a significant performance improvement. Our experiments on image classification show that compared to, respectively, MobileNetV2, SqueezeNet, and ShuffleNetV2, our proposal DivNet can improve accuracy by 2.09%, 0.76%, and 0.66% on the CIFAR100 dataset, and by 0.05%, 4.96%, and 1.13% on the CIFAR10 dataset. On more complex datasets, e.g., ImageNet, our proposal DivNet achieves 70.65% Top-1 accuracy and 90.23% Top-5 accuracy, still better than other small models like MobilNet, SqueezeNet, ShuffleNet.
SADet: Learning An Efficient and Accurate Pedestrian Detector

Although the anchor-based detectors have taken a big step forward in pedestrian detection, the overall performance of algorithm still needs further improvement for practical applications, e.g., a good trade-off between the accuracy and efficiency. To this end, this paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector, forming a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection, which includes three main improvements. Firstly, we optimize the sample generation process by assigning soft labels to the outlier samples to generate semi-positive samples with continuous tag value between 0 and 1. Secondly, a novel Center-IoU loss is applied as a new regression loss for bounding box regression, which not only retains the good characteristics of IoU loss, but also solves some defects of it. Thirdly, we also design Cosine-NMS for the post-processing of predicted bounding boxes, and further propose adaptive anchor matching to enable the model to adaptively match the anchor boxes to full or visible bounding boxes according to the degree of occlusion. Though structurally simple, it presents state-of-the-art result and real-time speed of 20 FPS for VGA-resolution images (640×480) tested on one GeForce GTX 1080Ti GPU on challenging pedestrian detection benchmarks, i.e., CityPersons, Caltech, and human detection benchmark CrowdHuman, leading to a new attractive pedestrian detector.
Prevent Overfitting Problem in Machine Learning: A Case Focus on Linear Regression and Logistics Regression

Supervised machine learning algorithms often suffer with overfitting during training steps which prevent it to perfectly generalizing the models. Overfitting is modelling concept in which machine learning algorithm models training data too well but not able to repeat the same accuracy on the testing data set. In this paper, we focus on regularization, which can help models to avoid overfitting problem with special focus on supervised learning algorithm, i.e. linear regression, logistic regression and neural network. Proposed regularization strategy guaranteed models performance and generalized for test data set by proper selection of features, and identifying less and more important features for data modelling purpose.
Analysis of Crystals-Dilithium for BlockChain Security

The emergence of quantum computers has threatened all the areas of internet security and privacy by providing an efficient solution to the underlying mathematical assumptions of the present cryptographic paradigms. The security of current internet applications utilizes this inefficiency in solving the above mathematical problems as a tool for security. One such prominent area where these security paradigms are at utmost core are Block-chains. They build a tamperproof network among trustless peers using these mathematical problems. Quantum attacks to applications utilizing blockchains pose a threat to a market worth 700 million USD. Therefore studies towards quantum secure blockchain are of utmost priority. Crystals Dilithium seems to be promising candidate for post-quantum blockchain security. This article reviews work done on this frontier and conclude Crystals-Dilithium relevance for quantum secure blockchains.
Introduction to the Special Issue on: Grey Literature and Multivocal Literature Reviews (MLRs) in software engineering

In parallel to academic (peer-reviewed) literature (e.g., journal and conference papers), an enormous extent of grey literature (GL) has accumulated since the inception of software engineering (SE). GL is often defined as “literature that is not formally published in sources such as books or journal articles”, e.g., in the form of trade magazines, online blog-posts, technical reports, and online videos such as tutorial and presentation videos. GL is typically produced by SE practitioners. We have observed that researchers are increasingly using and benefitting from the knowledge available within GL. Related to the notion of GL is the notion of Multivocal Literature Reviews (MLRs) in SE, i.e., a MLR is a form of a Systematic Literature Review (SLR) which includes knowledge and/or evidence from the GL in addition to the peer-reviewed literature. MLRs are useful for both researchers and practitioners because they provide summaries of both the state-of-the-art and -practice in a given area. MLRs are popular in other fields and have started to appear in SE community. It is timely then for a Special Issue (SI) focusing on GL and MLRs in SE. From the pool of 13 submitted papers, and after following a rigorous peer review process, seven papers were accepted for this SI. In this introduction we provide a brief overview of GL and MLRs in SE, and then a brief summary of the seven papers published in this SI.
K-Means Algorithm with Rapidminer in Clustering School Participation Rate in Indonesia

School participation rate is a measurement of the absorptive capacity of educational institutions towards school-age population. School participation rate is a basic indicator used to assess the access of the population to educational facilities, especially for the school-aged population. The higher the School participation rate, the greater the number of people who have the opportunity to education. However, the increase in School participation rate cannot always be interpreted as increasing the equalization of community opportunities for education. Therefore, this paper employs K-Means algorithm which is implemented in data clustering. We cluster the school participation rate of provinces in Indonesia, where the data are obtained from Central Bureau of Statistics. The data used is from 2003–2016 which consists of 34 provinces. The criteria used are School Participation Numbers, which are grouped into four categories. Data will be processed in three clusters i.e. high, medium and low clusters. Centroid data for high cluster are 99.49, 95.74, 77.33 and medium cluster are 44.81, 95.25, 85.88, 59.62 and 16.18, meanwhile for low cluster are 14.06, 13.38, 10.65 and 2.64. We found that the assessment of School participation rate as follow: The highest cluster is achieved by DI Yogyakarta Province; 32 provinces are in the medium cluster and one province in the low cluster i.e. North Kalimantan. These results can be an input to the government in increasing the number of School participation rate as an effort to educate more the school-age population.