Reviewerly/exHarmony huggingface dataset:
Collision Avoidance of High-Speed Obstacles for Mobile Robots via Maximum-Speed Aware Velocity Obstacle Method

It is challenging for a mobile robot to avoid moving obstacles in dynamic environments.Traditional velocity obstacle methods do not fully consider the obstacles moving with the speeds larger than the maximum speed of the robot.In this article, a new obstacle avoidance method, named the maximum-speed aware velocity obstacle (MVO) algorithm, is proposed for a mobile robot to avoid one or multiple high-speed obstacles.The proposed algorithm expands the velocity obstacle region into two parts, where one of the parts foresees collisions beyond the time horizon to ensure the feasible solutions of the current and the next control step.In practical applications, the perception capability of the robot is generally limited, and a non-holonomic robot can't move into any direction due to its kinematic constraints.In this article, the limited sensing field of view and non-holonomic kinematic constraints of the mobile robot are incorporated into the proposed MVO method.Moreover, continuity, safety, and computational complexity of the MVO approach are analyzed and presented.Extensive simulations and physical experiments are conducted to verify the efficacy of the MVO method, where a quadrotor and a differential-drive robot are used to perform dynamic obstacle avoidance.
Smart Contracts on the Move

Blockchain systems have received much attention and promise to revolutionize many services. Yet, despite their popularity, current blockchain systems exist in isolation, that is, they cannot share information. While interoperability is crucial for blockchain to reach widespread adoption, it is difficult to achieve due to differences among existing blockchain technologies. This paper presents a technique to allow blockchain interoperability. The core idea is to provide a primitive operation to developers so that contracts and objects can switch from one blockchain to another, without breaking consistency and violating key blockchain properties. To validate our ideas, we implemented our protocol in two popular blockchain clients that use the Ethereum virtual machine. We discuss how to build applications using the proposed protocol and show examples of applications based on real use cases that can move across blockchains. To analyze the system performance we use a real trace from one of the most popular Ethereum applications and replay it in a multi-blockchain environment.
Multi-Step Solar Irradiance Forecasting and Domain Adaptation of Deep Neural Networks

The problem of forecasting hourly solar irradiance over a multi-step horizon is dealt with by using three kinds of predictor structures. Two approaches are introduced: Multi-Model (MM) and Multi-Output (MO). Model parameters are identified for two kinds of neural networks, namely the traditional feed-forward (FF) and a class of recurrent networks, those with long short-term memory (LSTM) hidden neurons, which is relatively new for solar radiation forecasting. The performances of the considered approaches are rigorously assessed by appropriate indices and compared with standard benchmarks: the clear sky irradiance and two persistent predictors. Experimental results on a relatively long time series of global solar irradiance show that all the networks architectures perform in a similar way, guaranteeing a slower decrease of forecasting ability on horizons up to several hours, in comparison to the benchmark predictors. The domain adaptation of the neural predictors is investigated evaluating their accuracy on other irradiance time series, with different geographical conditions. The performances of FF and LSTM models are still good and similar between them, suggesting the possibility of adopting a unique predictor at the regional level. Some conceptual and computational differences between the network architectures are also discussed.
An Emotional Roller Coaster: Electrophysiological Evidence of Emotional Engagement during a Roller-Coaster Ride with Virtual Reality Add-On

Emotions are crucial ingredients of meaningful and memorable tourism experiences. Research methods borrowed from experimental psychology are prime candidates for quantifying emotions while experiences are unfolding. The present article empirically evaluates the methodological feasibility and usefulness of ambulatory recordings of skin conductance responses (SCRs) during a tourism experience. We recorded SCRs in participants while they experienced a roller-coaster ride with or without a virtual reality (VR) headset. Ride elements were identified that related to physical aspects (such as accelerations and braking), to events in the VR environment, and to the physical theming of the roller coaster. VR rides were evaluated more positively than normal rides. SCR time series were meaningfully related to the different ride elements. SCR signals did not significantly predict overall evaluations of the ride. We conclude that psychophysiological measurements are a new avenue for understanding how hospitality, tourism and leisure experiences dynamically develop over time.
Transfer Learning: Survey and Classification

A key notion in numerous data mining and machine learning (ML) algorithms says that the training data and testing data are essentially in the similar feature space and also have the alike probability distribution function (PDF). Though, in several real-life applications, this theory might not retain true. There are issues where training data is costly or tough to gather. Thus, there is a necessity to build high-performance classifiers, trained using more commonly found data from distinct domains. This methodology is stated as transfer learning (TL). TL is usually beneficial when enough data is not available in the target domain but the large dataset is available in source domain. This survey paper explains transfer learning along with its categorization and provides examples and perspective related to transfer learning. Negative transfer learning is also discussed in detail along with its effects on the accomplishment of learning in target domain.
O3BNN-R: An Out-of-Order Architecture for High-Performance and Regularized BNN Inference

Binarized Neural Networks (BNN), which significantly reduce computational complexity and memory demand, have shown potential in cost- and power-restricted domains, such as IoT and smart edge-devices, where reaching certain accuracy bars is sufficient and real-time is highly desired. In this article, we demonstrate that the highly-condensed BNN model can be shrunk significantly by dynamically pruning irregular redundant edges. Based on two new observations on BNN-specific properties, an out-of-order (OoO) architecture, O3BNN-R, which can curtail edge evaluation in cases where the binary output of a neuron can be determined early at runtime during inference, is proposed. Similar to instruction level parallelism (ILP), fine-grained, irregular, and runtime pruning opportunities are traditionally presumed to be difficult to exploit. To further enhance the pruning opportunities, we conduct an algorithm/architecture co-design approach where we augment the loss function during the training stage with specialized regularization terms favoring edge pruning. We evaluate our design on an embedded FPGA using networks that include VGG-16, AlexNet for ImageNet, and a VGG-like network for Cifar-10. Results show that O3BNN-R without regularization can prune, on average, 30 percent of the operations, without any accuracy loss, bringing 2.2× inference-speedup, and on average 34× energy-efficiency improvement over state-of-the-art BNN implementations on FPGA/GPU/CPU. With regularization at training, the performance is further improved, on average, by 15 percent.
The socio-technical assemblages of blockchain system: how blockchains are framed and how the framing reflects societal contexts

Purpose With the conceptualization of the blockchain as a socio-technical assemblage, this study aims to critically examine the blockchain initiatives in Korea in terms of the opportunities, risks and challenges embedded in their development. Design/methodology/approach This paper analyzes blockchain design and development from socioecological views: social, technological and cultural phenomena that represent the strategic interaction among people, technology and society. The qualitative data were collected from a variety of sources and diverse means. Findings The results imply that blockchain needs a close socio-technical examination to avoid simplistic assumptions of its promises and pitfalls. The development of blockchains in Korea will need to consider a range of socio-technical issues to facilitate the best outcomes for blockchain in society. Research limitations/implications Despite proactive drives, new risks, such as security, privacy and transparency, emerge as critical concerns of the social implications of the blockchain and their impact on the new digital environment. Questions are raised as to how to govern blockchains, and how to address the unexpected outcomes that such a policy choice may have on society and industry. Originality/value The sociopolitical implications of Korean blockchains are examined to identify key concerns and issues as the country progresses rapidly toward a blockchain-driven society.
SDCF: A Software-Defined Cyber Foraging Framework for Cloudlet Environment

The cloudlets can be deployed over mobile devices or even fixed state powerful servers that can provide services to its users in physical proximity. Executing workloads on cloudlets involves challenges centering on limited computing resources. Executing Virtual Machine (VM) based workloads for cloudlets does not scale due to the high computational demands of a VM. Another approach is to execute container-based workloads on cloudlets. However, container-based methods suffer from the cold-start problem, making it unfit for mobile edge computing scenarios. In this work, we introduce executing serverless functions on Web-assembly as workloads for both mobile and fixed state cloudlets. To execute the serverless workload on mobile cloudlets, we built a lightweight Web-assembly runtime. The orchestration of workloads and management of cloudlets or serverless runtime is done by introducing software-defined Cyber Foraging (SDCF) framework, which is a hybrid controller including a control plane for local networks and cloudlets. The SDCF framework integrates the management of cloudlets by utilizing the control plane traffic of the underlying network and thus avoids the extra overhead of cloudlet control plane traffic management. We evaluate SDCF using three use cases: (1) Price aware resource allocation (2) Energy aware resource scheduling for mobile cloudlets (3) Mobility pattern aware resource scheduling in mobile cloudlets. Through the virtualization of cloudlet resources, SDCF preserves minimal maintenance property by providing a centralized approach for configuring and management of cloudlets.
A Cloud-Optimized Transport Protocol for Elastic and Scalable HPC

Amazon Web Services (AWS) took a fresh look at the network to provide consistently low latency required for supercomputing applications, while keeping the benefits of public cloud: scalability, elastic on-demand capacity, cost effectiveness, and fast adoption of newer CPUs and GPUs. We built a new network transport protocol, scalable reliable datagram (SRD), designed to utilize modern commodity multitenant datacenter networks (with a large number of network paths) while overcoming their limitations (load imbalance and inconsistent latency when unrelated flows collide). Instead of preserving packets order, SRD sends the packets over as many network paths as possible, while avoiding overloaded paths. To minimize jitter and to ensure the fastest response to network congestion fluctuations, SRD is implemented in the AWS custom Nitro networking card. SRD is used by HPC/ML frameworks on EC2 hosts via AWS elastic fabric adapter kernel-bypass interface.
Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods

The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector’s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, and the autoregressive linear models, from the Box and Jenkins methodology. The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters.
Optimal Path Planning and Speed Control Integration Strategy for UGVs in Static and Dynamic Environments

In this paper, we present an optimal path planning and speed control strategy for motion planning of unmanned ground vehicles to follow the desired path in various driving environments. The novelty of this work is that it integrates the longitudinal speed and lateral motion control by maximizing the longitudinal progression and minimizing the lateral path tracking errors and formulates a standard quadratic optimization problem. Firstly, a dynamic programming approach is introduced to convexify the corridor constraints in path pre-selection and replanned for obstacle avoidance. The idea is to find a convex feasible set for the optimization problem using the convex constraints. And then a standard quadratic convex problem is formulated to optimize the longitudinal motion and steering angle control in prediction and control horizon. The longitudinal progression and lateral tracking errors are decomposed in orthogonal curvilinear coordinates. The centerline as well as road borders are also described by using cubic spline curves. Some challenge scenarios including static and moving participating obstacle vehicles are tested and validated for the proposed motion planning strategy. The feasibility and performance of the proposed strategy are demonstrated by numerical experiments in terms of the stability and maneuverability of unmanned ground vehicles in different driving conditions.
Discriminative and informative joint distribution adaptation for unsupervised domain adaptation

Domain adaptation learning is proposed as an effective technology for leveraging rich supervision knowledge from the related domain(s) to learn a reliable classifier for a new domain. One popular kind of domain adaptation methods is based on feature representation. However, such methods fail to consider the within-class and between-class relations after obtaining the new representation. In addition, they do not consider the negative effects of features that might be redundant or irrelevant to the final classification. To this end, a novel domain-invariant feature learning method based on the maximum margin criterion and sparsity technique for unsupervised domain adaptation is proposed in this paper, referred to as discriminative and informative joint distribution adaptation (DIJDA). Specifically, DIJDA adopts the maximum margin criterion in the adaptation process such that the transformed samples are near to those in the same class but segregated from those in different classes. As a result, the discriminative knowledge referred from source labels can be transferred to target domain effectively. Moreover, DIJDA imposes a row-sparsity constraint on the transformation matrix, which enforces rows of the matrix corresponding to inessential feature attributes to be all zero. Therefore, the most informative feature attributes can be extracted. Compared with several state-of-the-art methods, DIJDA substantially improves the classification results on five widely used benchmark datasets, which demonstrates the effectiveness of the proposed method.
Tracing Cryptocurrency Scams: Clustering Replicated Advance-Fee and Phishing Websites

Over the past few years, there has been a growth in activity, public knowledge, and awareness of cryptocurrencies and related blockchain technology. As the industry has grown, there has also been an increase in scams looking to steal unsuspecting individuals' cryptocurrency. Many of the scams operate on visually similar but seemingly unconnected websites, advertised by malicious social media accounts, which either attempt an advance-fee scam or operate as phishing websites. This paper analyses public online and blockchain-based data to provide a deeper understanding of these cryptocurrency scams. The clustering technique DBSCAN is applied to the content of scam websites to discover a typology of advance-fee and phishing scams. It is found that the same entities are running multiple instances of similar scams, revealed by their online infrastructure and blockchain activity. The entities also manufacture public blockchain activity to create the appearance that their scams are genuine. Through source and destination of funds analysis, it is observed that victims usually send funds from fiat-accepting exchanges. The entities running these scams cash-out or launder their proceeds using a variety of avenues including exchanges, gambling sites, and mixers.
Anthropomorphism and object attachment

Anthropomorphism refers to seeing non-human objects as humans. Recent research suggests that anthropomorphizing objects could influence people's psychological and emotional bond with the objects. Anthropomorphism imbues non-human objects with human-like characteristics, alters people's relationship with the objects, and shift people's emotional and cognitive responses towards the objects. Based on the role played by the primary caregivers in shaping children's attachment to them, this article offers a resource-based analysis on how anthropomorphism provides resources to address people's needs in three domains: a sense of comfort and pleasantness, self-identity (i.e. individual self, relational self, collective self), and self-efficacy. We conclude with a discussion of anthropomorphism and object attachment as well as future research opportunities.
Effective Activation Functions for Homomorphic Evaluation of Deep Neural Networks

CryptoNets and subsequent work have demonstrated the capability of homomorphic encryption (HE) in the applications of private artificial intelligence (AI). In convolutional neural networks (CNNs), many computations are linear functions such as the convolution layer which can be homomorphically evaluated. However, there are layers such as the activation layer which is comprised of non-linear functions that cannot be homomorphically evaluated. One of the most commonly used methods is approximating these non-linear functions using low-degree polynomials. However, using the approximated polynomials as activation functions introduces errors which could have a significant impact on accuracy in classification tasks. In this paper, we present a systematic method to construct HE-friendly activation functions for CNNs. We first determine what properties in a good activation function contribute to performance by analyzing commonly used functions such as Rectified Linear Units (ReLU) and Sigmoid. Then, we compare polynomial approximation methods and search for an optimal range of approximation for the polynomial activation. We also propose a novel weighted polynomial approximation method tailored to the output distribution of a batch normalization layer. Finally, we demonstrate the effectiveness of our method using several datasets such as MNIST, FMNIST, CIFAR-10.
A novel technique to prevent SQL injection and cross-site scripting attacks using Knuth-Morris-Pratt string match algorithm

Abstract Structured Query Language (SQL) injection and cross-site scripting remain a major threat to data-driven web applications. Instances where hackers obtain unrestricted access to back-end database of web applications so as to steal, edit, and destroy confidential data are increasing. Therefore, measures must be put in place to curtail the growing threats of SQL injection and XSS attacks. This study presents a technique for detecting and preventing these threats using Knuth-Morris-Pratt (KMP) string matching algorithm. The algorithm was used to match user’s input string with the stored pattern of the injection string in order to detect any malicious code. The implementation was carried out using PHP scripting language and Apache XAMPP Server. The security level of the technique was measured using different test cases of SQL injection, cross-site scripting (XSS), and encoded injection attacks. Results obtained revealed that the proposed technique was able to successfully detect and prevent the attacks, log the attack entry in the database, block the system using its mac address, and also generate a warning message. Therefore, the proposed technique proved to be more effective in detecting and preventing SQL injection and XSS attacks
Combo-Attention Network for Baidu Video Advertising

With the progress of communication technology and the popularity of the smart phone, videos grow to be the largest medium. Since videos can grab a customer's attention quickly and leave a big impression, video ads can gain more trust than traditional ads. Thus advertisers start to pour more resources into making creative video ads to built the connections with potential customers. Baidu, as the leading search engine company in China, receives billions of search queries per day. In this paper, we introduce a technique used in Baidu video advertising for feeding relevant video ads according to the user's query. Note that, retrieving relevant videos using the text query is a cross-modal problem. Due to the modal gap, the text-to-video search is more challenging than well exploited text-to-text search and image-to-image search. To tackle this challenge, we propose a Combo-Attention Network (CAN) and launch it in Baidu video advertising. In the proposed CAN model, we represent a video as a set of bounding boxes features and represent a sentence as a set of words features, and formulate the sentence-to-video search as a set-to-set matching problem. The proposed CAN is built upon the proposed combo-attention module, which exploits cross-modal attentions besides self attentions to effectively capture the relevance between words and bounding boxes. To testify the effectiveness of the proposed CAN offline, we built a Daily700K dataset collected from HaoKan APP. The systematic experiments on Daily700K as well as a public dataset, VATEX, demonstrate the effectiveness of our CAN. After launching the proposed CAN in Baidu's dynamic video advertising (DVA), we achieve a $5.47%$ increase in Conversion Rate (CVR) and a $11.69%$ increase in advertisement impression rate.