Reviewerly/exHarmony huggingface dataset:
Distributed Autonomous Energy Organizations: Next-Generation Blockchain Applications for Energy Infrastructure

Abstract Blockchain technology combines cryptography and distributed computing to provide a multiparty consensus algorithm to securely exchange value. Combining the disintermediation benefits of blockchain with the intelligence of smart contracts can help automate energy exchanges and give impetus to more distributed autonomous energy organizations (DAEO). This chapter combines applied blockchain research by the author at Pacific Northwest National Laboratory with a new theoretical approach that explores how blockchain technology could help enable more distributed, autonomous, and secure energy organizations. DAEO may also help simplify and improve the efficiency of energy utilities by securely linking producers with consumers and creating prosumers with increased flexibility and control of how they generate, consume, and exchange energy.
High-Accuracy Calibration of a Visual Motion Measurement System for Planar 3-DOF Robots Using Gaussian Process

Visual motion measurement systems (VMMSs) are widely used in robotics for pose estimation. A common characteristic of VMMSs is that the measurement repeatability is high, but the absolute measurement accuracy is relatively low, once a commercial lens with a large field of view is used. To improve the measurement accuracy of the VMMS for pose tracking of planar 3-degree-of-freedom (3-DOF) robots, a coarse-to-fine calibration method is presented in this paper. In this paper, after the VMMS is constructed, a degenerated perspective-n-point (DPnP) algorithm for pose estimation of 3-DOF robots is introduced. Then, an analytical calibration technique is implemented to obtain the camera intrinsic parameters required by the DPnP algorithm. Subsequently, a fine calibration step based on Gaussian process is developed to compensate the estimated pose of the DPnP algorithm. To investigate the effectiveness and performance of the proposed method, a series of the simulations and experiments are carried out on a planar 3-DOF robot. The results demonstrate that the proposed calibration method is quite robust and can improve the measurement accuracy up to 90.47%. Specifically, in a measurement field of view of 200 mm × 200 mm, the absolute errors of the 3-DOF pose obtained from the VMMS are reduced to below 0.03 mm and 0.016°.
NumPyCNNAndroid: A Library for Straightforward Implementation of Convolutional Neural Networks for Android Devices

A new open source library called NumPyCNNAndroid is proposed that minimizes the overhead of building and running convolutional neural networks on Android devices. The library is written in Python 3. It uses Kivy for building the application interface and Numerical Python for building the network itself. The library supports the most common layers. Compared to the widely known deep learning libraries, NumPyCNNAndroid avoids the extra overhead of making the network suitable for running on mobile devices. The experimental results validate the correctness of the library implementation by comparing results from both the proposed library and TensorFlow based on mean absolute error.
Adversarial Reinforcement Learning under Partial Observability in Software-Defined Networking.

Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised setting. Accordingly focus has remained with computer vision, and full observability. This paper focuses on reinforcement learning in the context of autonomous defence in Software-Defined Networking (SDN). We demonstrate that causative attacks---attacks that target the training process---can poison RL agents even if the attacker only has partial observability of the environment. In addition, we propose an inversion defence method that aims to apply the opposite perturbation to that which an attacker might use to generate their adversarial samples. Our experimental results illustrate that the countermeasure can effectively reduce the impact of the causative attack, while not significantly affecting the training process in non-attack scenarios.
ES-COINA: A novel energy scalable quality-aware color interpolation architecture

Energy-efficiency is the critical requirement for the battery operated portable devices exhibiting compute-intensive image/video processing applications. The color interpolation is the most energy consuming operation used within these devices to reconstruct the full resolution color image. In this paper, three approximate color interpolation architectures are proposed by exploiting the relative significance of the computations. These architectures provide high energy efficiency at the cost of small degradation in quality. Further, novel energy scalable color interpolation algorithm and its architecture (ES-COINA) are proposed that provide improved quality-energy trade-off. The efficacy of the proposed architectures is evaluated using various quality metrics by simulating them with different benchmark images from Kodak dataset. The proposed architectures are implemented on Artix-7 FPGA board and compared over the existing. Further, the proposed and existing architectures are implemented on Cadence RTL Compiler and synthesized with 180nm technology file. The simulation results show that the proposed approximate architectures provide 88%, 71.3%, and 46% reduced energy consumption over the best-known color interpolation architecture with a little degradation in quality. Further, the proposed ES-COINA reduces the energy consumption by 21% which can be extended to 92.6% when switched from high quality to high energy-efficient mode of operation.
Automated Negotiation Framework Based on Intelligent Agents for Cloud Computing

Cloud Computing is, by nature, multi-tenant, complex, large-scale, and heterogeneous distributed systems. Thus respectively, its processes and strategies need to be automated and integrated. one of the essential processes in the Cloud computing system is negotiating the service level agreement which always has to be elastic and flexible in handling and translating the user services' requirements. This paper aims at presenting an automated SLA negotiation framework based on autonomous and flexible agents and multi agent systems based on the autonomic computing features as suitable tools for self-detection of failures and self-monitoring for the Cloud operations and services.
CoalaViz: Supporting Traceability of Adaptation Decisions in Pervasive Communication Systems

Today's pervasive communication systems are highly configurable to adapt themselves dynamically to continuously changing contexts of the system such as varying workloads and user preferences. For a particular context, usually numerous valid system configurations exist, and each configuration may perform differently in terms of nonfunctional properties like energy consumption or task throughput. For tackling these challenges, in previous work, we introduced Coala, a model-based adaptation approach to derive optimal system configurations considering multiple performance goals. In this paper, we present CoalaViz, a novel tool for visualizing the self-adaptive behavior of pervasive communication systems. With CoalaViz, we provide a tool for making adaptation decisions in self-adaptive pervasive communication systems traceable while being applicable for a wide range of use cases. CoalaViz (i) visualizes the system performance over time, (ii) visualizes the system state as context feature model and graph-based network view, (iii) allows the user to change priorities of performance goals interactively, and (iv) provides a modular, extensible design. We demonstrate the applicability of CoalaViz using three pervasive system use cases.
An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec

Adaptive multi-rate wideband (AMR-WB) speech codecs have been widely used for high speech quality in modern mobile communication systems, e.g., handheld mobile devices. Nevertheless, a major handicap is that a remarkable computational load is required in the vector quantization (VQ) of immittance spectral frequency (ISF) coefficients of an AMR-WB coding. In view of this, a two-stage search algorithm is presented in this paper as an efficient way to reduce the computational complexity of ISF quantization in AMR-WB coding. At stage 1, an input vector is assigned to a search subspace in an efficient manner using the binary search space-structured VQ (BSS-VQ) algorithm, and a codebook search is performed over the subspace at stage 2 using the iterative triangular inequality elimination (ITIE) approach. Through the use of the codeword rejection mechanisms equipped in both stages, the computational load can be remarkably reduced. As compared with the original version of the BSS-VQ algorithm, the upgraded version provides a computational load reduction of up to 51%. Furthermore, this work is expected to satisfy the energy saving requirement when implemented on an AMR-WB codec of mobile devices.
Improving the proof of “Privacy-preserving attribute-keyword based data publish-subscribe service on cloud platforms”

Most recently, Kan Yang et al. proposed an attribute-keyword based encryption scheme for data publish-subscribe service(AKPS), which is highly useful for cloud storage scenario. Unfortunately, we discover that there is a flaw in the security proof of indistinguishability of the tag and trapdoor against chosen keyword attack under the Bilinear Diffie-Hellman (BDH) assumption. As the security proof is a key component for a cryptographic scheme, based on the Decisional Diffie-Hellman (DDH) assumption, we improve the security proof method and give a new security proof of the AKPS scheme for indistinguishability of the tag and trapdoor in our proposal, which is more rigorous than the original one. Furthermore, we also demonstrate that the AKPS scheme is secure against data Replayable Chosen Ciphertext Attack (RCCA).
An Effective Identification Technology for Online News Comment Spammers in Internet Media

With the development of mobile Internet, it is changing the way we communicate with others.Internet media have gradually become the main mobile crowdsourcing applications for information dissemination and user communication, including online news and social networks.However, the potential business opportunities have stimulated the emergence of a large number of spammers, who release false speech, advertisements, pornographic contents, and phishing websites on the media to gain commercial benefits, which seriously affects the experience of normal users.Therefore, in order to reduce the harm of false information, the research on the identification technology of spammers has been carried out extensively.However, the traditional technologies of identifying spammers involve high data costs and poor effects, and most of them are concentrated in the field of social networks, while less research is carried out in the field of online news.In this paper, we propose an effective technology of identifying online news comment spammers based on the label propagation algorithm (LPA), making full use of the user comment behaviors and contents.First of all, we collect a large amount of news and comments from NetEase News and label some users in the data as spammers or normal users manually to construct a labeled dataset.Then, a set of behavioral and semantic features are extracted and quantified from the user comment behaviors and comment contents by statistical analysis.Next, we propose the identification technology based on the LPA.Finally, the set of feature values is input into the proposed technology in different combinations, and experiments and evaluations are carried out to determine the most effective combination of features and improve the technology.The results show that the technology proposed in this paper involves a lower data cost but a better identification effect than some traditional technologies based on the supervised classifier.
Sensing and knowing noises: an acoustemology of the chainsaw

Within sensory anthropology, scholars have for some time now developed ways to think with acoustic phenomena and to interpret the multiple meanings of sounds and soundscapes. Yet actual practices of listening and experiences of listening subjects feature rather less in that field. Drawing on a case study of chainsaw use in tree felling, this article presents listening as a mode of acoustic knowing that is both aesthetic and epistemological. This is achieved by combining a consideration of listening as a skilled practice with a problematisation of the notion of ‘noise’. Whereas noise is commonly conceived of as unwanted, chaotic and meaningless sound, skilled chainsaw use shows how a particular practice re‐evaluates what is defined as noise and even takes it as an entry to acoustic knowing. Through a careful description and analysis of the process of tree felling, this article traces how skilfully mediated listening with the chainsaw develops from a felt, embodied sense of a sound world that is still indeterminate and ambiguous to recognisable ‘objects’ of clearly identifiable sounds. It is argued that through such a broader conceptualisation of listening as a form of sensing, we can more deeply investigate the sonic orders of sociocultural practices.
VizTract: Visualization of Complex Social Networks for Easy User Perception

Social networking platforms connect people from all around the world. Because of their user-friendliness and easy accessibility, their traffic is increasing drastically. Such active participation has caught the attention of many research groups that are focusing on understanding human behavior to study the dynamics of these social networks. Oftentimes, perceiving these networks is hard, mainly due to either the large size of the data involved or the ineffective use of visualization strategies. This work introduces VizTract to ease the visual perception of complex social networks. VizTract is a two-level graph abstraction visualization tool that is designed to visualize both hierarchical and adjacency information in a tree structure. We use the Facebook dataset from the Social Network Analysis Project from Stanford University. On this data, social groups are referred as circles, social network users as nodes, and interactions as edges between the nodes. Our approach is to present a visual overview that represents the interactions between circles, then let the user navigate this overview and select the nodes in the circles to obtain more information on demand. VizTract aim to reduce visual clutter without any loss of information during visualization. VizTract enhances the visual perception of complex social networks to help better understand the dynamics of the network structure. VizTract within a single frame not only reduces the complexity but also avoids redundancy of the nodes and the rendering time. The visualization techniques used in VizTract are the force-directed layout, circle packing, cluster dendrogram, and hierarchical edge bundling. Furthermore, to enhance the visual information perception, VizTract provides interaction techniques such as selection, path highlight, mouse-hover, and bundling strength. This method helps social network researchers to display large networks in a visually effective way that is conducive to ease interpretation and analysis. We conduct a study to evaluate the user experience of the system and then collect information about their perception via a survey. The goal of the study is to know how humans can interpret the network when visualized using different visualization methods. Our results indicate that users heavily prefer those visualization techniques that aggregate the information and the connectivity within a given space, such as hierarchical edge bundling.
Effect of outside view on attentiveness in using see-through type augmented reality device

When a viewer watches a virtual image through a see-through augmented reality (AR) device and an outside view simultaneously, the viewer’s attentiveness is affected. In this study, the authors examined the relationship between the viewer’s attentiveness and viewing distance of the outside view. The virtual image in the see-through AR device was at a fixed distance of 2.5 m, while a TV at distances of 0.33, 1, 2.5 and 5 m was considered as the outside view. In this experiment, thirty participants viewed a movie on an AR device and ten target images of pictures and words were sequentially displayed on the TV by overlapping them onto the virtual image. The participants were then asked to identify these target images from examples of sixty images. A correct answer (CA) ratio was used to measure the participant’s attentiveness. Results demonstrated that CA ratios at distances of 0.33 and 1 m was better than CA ratios at distances of 2.5 and 5 m. Further, the CA ratio of target images of pictures was better than that of words. When the virtual and target images were located at similar distances, these images were sharply focused on the retina. However, when the distances of these images were different, the participant’s eyes focused on one image causing a defocus on the other. A better CA ratio at different distances implied that the difference of sharpness in overlapped virtual and target images helped the participants in identifying the virtual or target image rather than two overlapped images of similar sharpness. Thus, sharply focused virtual and target images at the same distance did not guarantee better identification of two distinct images.
PAON usability testing using system usability scale

One of the important variables in developing software is users' satisfaction of the software. Hence, there is a need to measure users' satisfaction of the software's that have been developed. The measurement of the users' reactions covers some aspects such as users' satisfaction, ease of use, efficiency, and whether the systems that have been developed can meet the users' needs. This measurement is often called usability testing. It can be done using various instruments that have been developed by experts and communities in the field of computer. One of them is System Usability Scale (SUS). This article discusses an online peer assessment using usability testing. The instrument used was the usability scale system that was developed by John Brooke in 1986. The measurement using SUS yielded a mean score of 80.00. This mean score shows that the online peer assessment can be accepted and it can be categorized as grade B with Good rating. Hence, the online peer assessment is a feasible system.
Sigma protocol for faster proof of simultaneous homomorphism relations

The -protocols for homomorphism relations are one of the cryptographic protocols which are used to prove knowledge of homomorphism relations. The Schnorr protocol is one of the most famous -protocols used for proving knowledge of discrete logarithm (DL) relation in which the verifier essentially performs one double-exponentiation (i.e. a group computation of the form ax by). A direct application of the Schnorr protocol for proving simultaneous knowledge of n DLs with a common base leads to a -protocol in which the verifier performs n double-exponentiations. In this study, the authors propose another -protocol for homomorphism relations. The proposed -protocol has fast verification when is used to prove the simultaneous homomorphism relations with a common homomorphism. Also, when the DL instantiation (DL-instantiation) of the proposed -protocol is used to prove simultaneous knowledge of n DLs with a common base, it leads to a -protocol in which the verifier performs n +1 single-exponentiations.
Securely Solving Linear Algebraic Equations in a Distributed Framework Enhanced With Communication-Efficient Algorithms

Solving linear algebraic equations (a.k.a., an LAE problem) distributedly in a network with multiple agents has wide applications in distributed control, estimation, and signal processing. A consensus-based distributed computing framework is studied in this paper. Specifically, each agent knows only a subproblem of the LAE, i.e., a subset of all equations, and then all agents apply a consensus-based algorithm to update their estimates of the correct solution of the LAE problem iteratively. Under certain conditions, it has been shown that all the estimates converge to the exact solution exponentially fast. However, such a distributed paradigm is vulnerable to malicious behaviors in an adversarial environment. In this paper, we indicate a number of security threats in this process, and thus develop robust computing solutions against those attacks. With particular attention to low storage overhead, we develop a new alternating projection method to enhance the consensus algorithm. Furthermore, we design an innovative misbehavior detection mechanism by exploiting the homomorphic signature technique. Our method can detect misbehaving agents without the common, yet sometimes infeasible, assumption of local good majority. Another significant contribution presented in this paper is an original component dropping approach for mitigating the communication overhead during the consensus process. From both theoretical and engineering perspectives, we study how the consensus can still be reached when a big portion of elements in exchanged message vectors are dropped. In our preliminary numerical results, roughly 80% data transmission can be reduced per epoch at the expense of 35% extra epochs to reach the consensus, implying 73% reduction in terms of the communication overhead.
Entrepreneurial Competency Development of the Engineering Students at the Research University

State-of-the-art science and industry changed professional activities of an engineer, e.g. he or she has to conduct entrepreneurial activity. One of the trends in university development is tending to University 3.0 (Education, Science, and Entrepreneurship). It determines inescapable changes in a future engineer education. Demand in such educational programs is obvious; they would combine technical aspect of the idea with its commercial implementation. Synergetic effect appears only when engineer’s talent meets manager’s talent. That’s why development of entrepreneurial competencies of the students is set to become one of the learning objectives in engineering university. The paper describes system of the entrepreneurial competencies development in the context of a research university.
Using transfer learning for a deep learning model observer

Recent developments in technology assessment and optimization methodology have seen an expansion in the use of Virtual Clinical Trials (VCT) as an alternative to conventional clinical trials. However, the ultimate value gained from VCTs relies on the speed and quality of results generated from the VCT pipeline. In many cases the end-point human observer represents a bottle-neck due to resource and time limitations. This motivates the development of a machine-based observer for key task-based assessment studies. Previous work using Deep Learning for detection and observer studies has shown significant promise, but requires large amounts of data for training. We therefore have built a model observer based on the VGG19 neural network architecture combined with transfer learning to successfully train a TLMO (Transfer Learning Model Observer) that can detect both screen-detected malignancies and simulated lesions in images of 303 x 303 pixels. Our results demonstrate a strong response for the detection of simulated lesions, 4mm in diameter, using the OPTIMAM VCT Toolbox, achieving a sensitivity of 0.78 and a specificity of 0.92. The model has also been tested using well-defined and ill-defined screen-detected masses where it achieved a sensitivity of 0.85 and a specificity of 0.83.