Reviewerly/exHarmony huggingface dataset:
Digital forensics and evolving cyber law: case of BIMSTEC countries

Purpose The purpose of this paper is to discuss the role of digital forensics in an evolving environment of cyber laws giving attention to Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) countries, comprising Bangladesh, India, Myanmar, Sri Lanka, Thailand, Nepal and Bhutan, in a dynamic global context. Design/methodology/approach This study uses a case study approach to discuss the digital forensics and cyber laws of BIMSTEC countries. The objective of the study was expected to be achieved by referring to decided cases in different jurisdictions. Cyber laws of BIMSTEC countries were studied for the purpose of this study. Findings The analysis revealed that BIMSTEC countries are required to amend legislation to support the growth of information technology. Most of the legislation are 10-15 years old and have not been amended to resolve issues on cyber jurisdictions. Research limitations/implications This study was limited to the members of the BIMSTEC. Originality/value This paper is an original work done by the authors who have discussed the issues of conducting investigations with respect to digital crimes in a rapidly changing environment of information technology and deficient legal frameworks.
Asynchronous classification-based optimization

Asynchronous parallelization is an effective way to accelerate optimization. While asynchronous parallelization can destroy the sequential structure of optimization algorithms, it has been found counter-intuitively that some optimization algorithms are proven to preserve their performance under asynchronous parallelization, including the stochastic gradient descent for first-order optimization of differentiable functions and Pareto optimization for zeroth-order optimization in binary space. Following this direction, in this paper, we show that the classification-based optimization, which is a recently developed framework for zeroth-order optimization in continuous space, can also enjoy the asynchronous parallelization. We implement ASRacos, an asynchronous version of a classification-based optimization algorithm SRacos, to accelerate the optimization through asynchronous parallelization. We theoretically provide the query complexity of ASRacos and further show that on certain conditions, ASRacos can achieve a better performance than SRacos even if using the same number of evaluations. Experiments on synthetic functions and controlling tasks in OpenAI Gym demonstrate that ASRacos can achieve almost linear speedup while preserving good solution quality.
From Lab to Field: Demonstrating Mixed Reality Prototypes for Augmented Sports Experiences

Traditional sports events related data have no direct spatial relationship to what spectators see when attending a live sports event. The idea of our work is to address this gap and ultimately to provide spectators insights of a sports game by embedding sports statistics into their field of view of the game using mobile Augmented Reality.
ODIN: Optimal Discovery of High-value INformation Using Model-based Deep Reinforcement Learning

We consider the problem of active feature selection where we dynamically choose the set of features that acquires the highest predictive performance relative to a task. We propose a modelbased deep reinforcement learning framework for Optimal Discovery of high-value INformation (ODIN) in which the agent either chooses to ask for a new feature or to stop and predict. Utilizing the ability of the partial variational autoencoder (Ma et al., 2018) the framework models the conditional distribution of the features allowing for data efficiency. We introduce a novel cost function that is sensitive to both cost and order of feature acquisition. ODIN handles missing data naturally and ensures the globally optimal solution for most efficient feature acquisition while preserving data efficiency. We show improved performance on both synthetic and real-life datasets.
Reliable Design Method for Service Function Chaining

SFC (Service Function Chaining) is one of the new service construction technologies in virtual network, and is a technology to construct a “service chain,” which is an idea to select and to connect VNF (Virtualized Network Function) - a network function separated from proprietary network equipment and virtualized to run on open architecture servers - in series to offer as single service. It is important to ensure high reliability of the service (service chain). In general, reliability improves by increasing redundancy of service components, such as network routes, network equipment, and applications, and for service chain, it is considered to adopt a method such as duplicating of service chains. However, when a VNF shared by multiple service chains fails, many users are affected because all service chains sharing the malfunctioning VNF are switched to a spare. This article proposes a design method to increase the reliability of service chain while suppressing the consumption of resources, by weighting VNF (component of the service chain) to choose important VNFs to be duplicated, according to the number of accommodated users and other indexes.
Fingerspelled Alphabet Sign Recognition in Upper-Body Videos

Fingerspelling is a crucial part of sign-based communication, however its recognition remains a challenging and mostly overlooked computer vision problem. To address it, this paper presents a system that recognizes the 24 static fingerspelled alphabet signs of the American Sign Language. The system consists of two algorithmic stages, comprising an efficient preprocessing phase that generates candidate hand-region proposals, followed by their deep-learning based classification. Specifically, the first stage exploits own earlier work on hand detection and segmentation in videos that also contain the signer's face, allowing face detection to drive skin-tone based hand segmentation, with motion further utilized to localize hands, extending it with a peak detection module that yields proposal regions likely to contain the signs of interest. These regions are then classified by a variant of a convolutional neural network that extends traditional convolutions to quadratic operations on the inputs, being, to our knowledge, the first application of such architecture to this task. Both system stages are evaluated on three well-known fingerspelling corpora, significantly outperforming a number of alternative approaches under both multi-signer and signer-independent experimental frameworks.
An Implementation of Shared Symmetric Key Generation Extracted from Received Signal Strength in Vehicle to Infrastructure Communication

In this paper, we propose a key generation extracted from received signal strength (RSS) for securing vehicle-to-infrastructure (V2I) communication using Raspberry Pi embedded system equipped with 5.8 GHz radio frequency of wireless interface as well as CEN-DSRC standard for V2I. To achieve an equality of shared symmetric key between vehicle and road side unit (RSU), the probed RSS values are correlated, quantized, reconciled, and amplified. The obtained shared symmetric key is used for securing the exchange information between vehicle and RSU using symmetric encryption. We varied the speed of vehicle such that ping time interval can be varied as well on the condition of traffic both quiet and crowded. The correlation of bits key stream between vehicle and RSU is achieved by Kalman Filter over various speed of vehicle either on the condition of quiet or crowded traffic. Meanwhile, multibit quantization contributes to reduce key disagreement rate (KDR) and the remaining mismatching bits key stream are corrected by BCH error code correction, hence the key generation rate (KGR) can be increased and the shared symmetric key is able to be obtained successfully. The experimental results showed that the practicality of our proposed system whereas the total average time to generate shared symmetric key is about 11 seconds.
Automatic Multi-Camera Extrinsic Parameter Calibration Based on Pedestrian Torsors †

Extrinsic camera calibration is essential for any computer vision task in a camera network. Typically, researchers place a calibration object in the scene to calibrate all the cameras in a camera network. However, when installing cameras in the field, this approach can be costly and impractical, especially when recalibration is needed. This paper proposes a novel, accurate and fully automatic extrinsic calibration framework for camera networks with partially overlapping views. The proposed method considers the pedestrians in the observed scene as the calibration objects and analyzes the pedestrian tracks to obtain extrinsic parameters. Compared to the state of the art, the new method is fully automatic and robust in various environments. Our method detect human poses in the camera images and then models walking persons as vertical sticks. We apply a brute-force method to determines the correspondence between persons in multiple camera images. This information along with 3D estimated locations of the top and the bottom of the pedestrians are then used to compute the extrinsic calibration matrices. We also propose a novel method to calibrate the camera network by only using the top and centerline of the person when the bottom of the person is not available in heavily occluded scenes. We verified the robustness of the method in different camera setups and for both single and multiple walking people. The results show that the triangulation error of a few centimeters can be obtained. Typically, it requires less than one minute of observing the walking people to reach this accuracy in controlled environments. It also just takes a few minutes to collect enough data for the calibration in uncontrolled environments. Our proposed method can perform well in various situations such as multi-person, occlusions, or even at real intersections on the street.
Correlative Analysis and Impact of Intelligent Virtual Assistants on Machine Learning

Intelligent Virtual Assistants are very important part of today’s life if we have smartphones. Whether, to call a number, message anyone, checking weather or traffic status, it can just happen in a few seconds by giving verbal commands. Artificial Intelligence is obviously making strides in this field as new assistants are being integrated into many applications in automotive, medicine, security, heavy and robotics industry. This paper discusses about how these intelligent assistants work, which AI techniques are used, their applications and their inexpediencies and how they can be improved upon.
Online Anomaly Detection of Time Series at Scale

Cyber breaches can result in disruption to business operations, reputation damage as well as directly affecting the financial stability of the targeted corporations, with potential impacts on future profits and stock values. Automatic network-stream monitoring becomes necessary for cyber situation awareness, and time-series anomaly detection plays an important role in network stream monitoring. This study surveyed recent research on time-series analysis methods in respect of parametric and non-parametric techniques, and popular machine learning platforms for data analysis on streaming data on both single server and cloud computing environments. We believe it provides a good reference for researchers in both academia and industry to select suitable (time series) data analysis techniques, and computing platforms, dependent on the data scale and real-time requirements.
A Low-cost Enterprise Application Integration Architecture for Large-scale Environment

Enterprise Application Integration (EAI) plays an important role in the enterprise ecosystem. The systems in the enterprise usually communicate with each other through EAI. The way reduces the complexity of system integration because EAI provides uniform interface and central endpoint to the systems. Because EAI stays in the core of the systems, it must be stable enough and can be extensible easily to serve a large amount of requests every day, especially for IoT today. Moreover, we also need a low-cost architecture for supporting the operation of the company, especially for large-scale environment. In order to achieve these goals, we built an flexible EAI based on several open sources to support the services in the telecom, which faces ever-changing requirements today, with several features. First, it can provide SLA based on the importance of the service. Second, the request can be redirected to different backend processes at runtime without interrupting service operation. It supports quick switch between different implementations and the feature increases the availability of the ecosystem. Third, the capacity of the service can be extended easily at every level when the service becomes more important based on a low-cost architecture. Finally, it also can add new machine to the system when the capacity of the whole system is not enough.
Sensor Information Processing for Wearable IoT Devices

Sensing technology is one of the core enablers of IoT and the improvement in sensing technology has lead to the proliferation of small form-factor, cost-effective and accurate sensors for wide variety of wearable applications. With wearable devices receiving widespread acceptance, their requirements are becoming more demanding, with the focus shifting from simple monitoring to context aware intelligent devices. This chapter presents a comprehensive description of the technical opportunities and challenges in the design of sensor information processing systems for wearables. A systematic survey of the state of the art architectures for sensor fusion for different application classes of wearable’s is presented. A discussion on design considerations for architecting sensor processing systems, including hardware, networking protocols, and algorithms at the edge, cloud level is provided. The chapter is concluded with a discussion on innovation directions in smart sensing and information processing in wearable devices.
AppMine

Web applications in widespread use have always been the target of large-scale attacks, leading to massive disruption of services and financial loss, as in the Equifax data breach. It has become common practice to deploy web applications in containers like Docker for better portability and ease of deployment. We design a system called AppMine for lightweight monitoring of web applications running in Docker containers and detection of unknown web vulnerabilities. AppMine is an unsupervised learning system, trained only on legitimate workloads of web applications, to detect anomalies based on either traditional models (PCA and one-class SVM), or more advanced neural-network architectures (LSTM). In our evaluation, we demonstrate that the neural network model outperforms more traditional methods on a range of web applications and recreated exploits. For instance, AppMine achieves average AUC scores as high as 0.97 for the Apache Struts application (with the CVE-2017-5638 exploit used in the Equifax breach), while the AUC scores for PCA and one-class SVM are 0.81 and 0.83, respectively.
Fused Spectral Features in Kernel Weighted Collaborative Representation for Gender Classification Using Ocular Images

Ocular images have been used to supplement and complement the face-based biometrics. Ocular images are further investigated for identifying the gender of a person such that the soft label can be used to boost biometric performance of the system. Although there are number of works in visible spectrum and Near-Infrared spectrum for gender classification using ocular images, there are limited works in spectral imaging which explore ocular images for gender classification. Considering the advantages of spectral imaging, we explore the problem of gender identification using ocular images obtained using spectral imaging. To this end, we have employed a recent database of 104 unique ocular instances across 2 different sessions and 5 different attempts in each session with a spectral imaging camera capable of capturing 8 different images corresponding to different bands. Further, we present a new framework of using fused feature descriptors in kernalized space to fully leverage the number of spectral images for robust gender classification. With the set of experiments, we obtain an average classification accuracy of \(81\%\) with the proposed approach of using fused GIST features along with the weighted kernel representation of features in collaborative space.
Can You Easily Perceive the Local Environment? A User Interface with One Stitched Live Video for Mobile Robotic Telepresence Systems

Many existing mobile robotic telepresence systems have equipped with two cameras, one is a forward-facing camera for video communication, and the other is a downward-facing camera for robot navigation. However, the two live videos from these two cameras would cause some confusion which makes it difficult for a remote operator to perceive the local environment. In this paper, we propose to use a user interface with one stitched live video instead of two live videos for mobile robotic telepresence systems. We used a video stitching algorithm to stitch the two live videos into one live video through which a remote operator can well perceive the local environment. We conducted a user study to investigate the difference between one stitched live video and two separate live videos in the user interface. The results show that the user interface with one stitched live video improves task efficiency, the number of errors, and remote operators’ feelings of presence, and enables remote operators to concentrate on the work they are doing.
The Geological Survey of Finland strengthening its role as a key player in mineral raw materials innovation ecosystems

Abstract The Geological Survey of Finland (GTK) has over 130 years of history in mapping and studying mineral resources and their sustainable use. This has resulted in a globally top-ranking geodatabase and profound knowledge of Finnish geology and mineral resources, and has had a crucial impact on the continuously developing mining and exploration business in Finland. The basic mandate of the GTK has remained the same, but the strategic focus and mode of operation have changed considerably to meet new demands. Today, the GTK plays a vital role in providing geoscientific expertise and specialist services for a wide range of stakeholders and commercial clients in government, the business sector, academia and the wider community, in Finland and internationally. The GTK is actively building new ways to co-operate with universities, research organizations and companies to support future development and to expand its own expertise. This is further supported by the proactive use of cutting-edge technologies, such as the geomaterials research infrastructure, which allows studies from the nanoscale up to kilotons for diverse applications of mineral materials. The GTK plans to further strengthen its role as a key player in the minerals sector innovation ecosystems with a focus on primary minerals, the circular economy, digital solutions and water issues, which are expected to be essential factors for sustainable development through the 2020s and beyond. The GTK's main challenge is to ensure the continuous enhancement and renewal of expertise, to adapt and respond to future opportunities.
Deep Domain Adaptation for Regression

Developing machine learning algorithms under the constraint of limited labeled data has attracted significant attention in the research community in recent years. Domain adaptation or transfer learning algorithms alleviate this challenge by transferring relevant knowledge from a source domain to induce a model for a related target domain, where labeled data are scarce. Further, deep learning algorithms are instrumental in learning informative feature representations from a given dataset and have replaced the need for hand-crafted features. In this chapter, we propose a novel framework, DeepDAR, for domain adaptation for regression applications, using deep convolutional neural networks (CNNs). We formulate a loss function relevant to the research task and exploit the gradient descent algorithm to optimize the loss and train the deep CNN. To the best of our knowledge, domain adaptation for regression applications using deep neural networks has not been explored in the literature. Our extensive empirical studies on two popular regression applications (age estimation and head pose estimation from images) depict the merit of our framework over competing baselines.
User Requirements for the Design of Smart Homes: Dimensions and Goals

The ‘Smart Home’ is a strongly technology-driven field. While user-centered requirements have been reported for specific features, a considerable gap persists for design based on an everyday home context and the social and emotional nature of the home. To address this, we identify specific leverage points and functionalities for energy-efficiency and smart control in a domestic context. A three-step investigation of user requirements, employing cultural probing, participatory design fiction and focus groups allowed us to progress from the home context “as-is” towards a blending of requirements with technological solutions. Our results highlight the home as a complex construct imbued with organically grown routines and individual needs, values and emotions. Based on empirical, real-user data we present features and system expectations that address this multifaceted overall picture. This paper advises the design process of future smart home solutions in two facets: First, we point out several design dimensions, namely time, space, relations, individual factors, and values that allow design for a heterogeneity of users and situations. Second, we derive specific design goals to highlight directions of smart home system design: design for control, low effort, integration, evolvability, identity, sociability, and benefits.
Space Vector Generation for 3D Scenes from Text Descriptions

3D scene generation using voice and text is convenient and low cost of learning. In the previous text-to-3D scene system, the user must use a fixed format for text input. In this paper, we present a newly system that generates space vector for 3D scenes from text natural language input. It is intended to benefit 3D scene producer and applications by generates space vector from text descriptions. This system consists of three parts: text processing, computational spatial relationships, and spatial vector generation. It computes the space vector by processing the text and then using the neural network to find the spatial relationships in the statement. The space vector is used to represent the spatial relationship between different objects in the text. This system eliminates the need for the user to control 3D scenes in fixed format text from the text to the 3D scene. The system is mainly used to process Chinese text, which can be used as a reference when dealing with other languages.