Reviewerly/exHarmony huggingface dataset:
A New Protocol Based on Blockchain Technology for Transparent Operation of Corporate Social Responsibility

This chapter proposes a protocol based on blockchain technology applied to corporate social responsibility (CSR). The first part discusses the characteristics associated with CSR actions and the main difficulties its development faces, such as transparency, security, fault tolerance, among others. Subsequently, the authors describe the characteristics and concepts related to blockchain-based developments to later describe our framework for the control and development of CSR actions based on blockchain. Herein, the authors also describe how to publicly and privately identify the participating elements of CSR and the operations and resources necessary for the implementation and operation of the proposed protocol.
Optimization Control Strategy of Electricity Information Acquisition System Based on Edge-Cloud Computing Collaboration

With the rigorous development of the power industry and information technology, various types of information need to be collected by the electricity information acquisition system, indeed leading to huge data transmission, storage, and processing. This leads to the requirements of high-end technology for the electricity information acquisition system. Simultaneously, the requisite for power supply quality, service demand and power marketing smooth management increases. In order to meet the growing challenges, this paper presents a novel architecture of the customer electricity information acquisition system based on Edge-Cloud computing collaboration technology. This architecture is dedicated to solve the problems of long-distance communication, a large amount of data transmission through the channel, heavy data processing and storage burden of the main data center. Further, this architecture will assist to reduce the burden of information transmission through the channel, fully ensuring the timeliness of information processing and control instructions, and realizing local information processing. To achieve the concentrator information processing priority and make the concentrator highly effective information analysis and processing, this paper uses an analytical hierarchy process (AHP).
User Interface for an Immersive Virtual Reality Greenhouse for Training Precision Agriculture

In the past 50 years, farm producers have increased use of electronic control systems for controlled environment agriculture in greenhouses and animal production. These systems use environmental sensors to automate environmental management of greenhouses and can lead to significantly larger yields. However, mismanagement of a CEA system can also lead to loss of entire crops. Due to the sensitivity of crops in CEA and potential losses, students are rarely allowed to work directly with CEA systems. In order to increase opportunities for students to interact with CEA systems, an immersive virtual greenhouse simulation was created. Implementing realistic interfaces for physical tasks and for the CEA system provides students with 'hands-on' training in the virtual greenhouse. However, there are challenges that limit implementation of physical actions and realistic interfaces in virtual reality. We consider interfaces for three types of interactions: system interactions, physical greenhouse interactions, and interactions with the CEA system. Potential interface designs are presented with a discussion of the benefits and costs associated with each design.
Emulation of up-conversion based quantum key distribution scheme using active pump-controlled basis selection and adaptive thresholding

We emulate a method for quantum key distribution (QKD) that employs up-conversion detection. The scheme uses differential quadrature phase shift modulation and actively chooses the measurement basis by applying a dynamic phase-shift on the pump of the up-conversion interaction. The QKD rate of the quantum-hacking resistant system over an emulated 7.5 km marine-like turbulent channel is evaluated including the impact of thresholding the received data so as to ignore signals that are below a tolerable signal-to-noise ratio. The emulated system is well suited to operate over marine free space optical links coupled to single mode fiber.
Flexible Generative Adversarial Networks with Non-parametric Activation Functions

Generative adversarial networks (GANs) have become widespread models for complex density estimation tasks such as image generation or image-to-image synthesis. At the same time, training of GANs can suffer from several problems, either of stability or convergence, sometimes hindering their effective deployment. In this paper we investigate whether we can improve GAN training by endowing the neural network models with more flexible activation functions compared to the commonly used rectified linear unit (or its variants). In particular, we evaluate training a deep convolutional GAN wherein all hidden activation functions are replaced with a version of the kernel activation function (KAF), a recently proposed technique for learning non-parametric nonlinearities during the optimization process. On a thorough empirical evaluation on multiple image generation benchmarks, we show that the resulting architectures learn to generate visually pleasing images in a fraction of the number of the epochs, eventually converging to a better solution, even when we equalize (or even lower) the number of free parameters. Overall, this points to the importance of investigating better and more flexible architectures in the context of GANs.
Security Issues &amp; Seclusion in Bitcoin System

In the dawn of crypto-currencies the most talked currency is Bitcoin. Bitcoin is widely flourished digital currency and an exchange trading commodity implementing peer-to-peer payment network. No central athourity exists in Bitcoin. The users in network or pool of bitcoin need not to use real names, rather they use pseudo names for managing and verifying transactions. Due to the use of pseudo names bitcoin is apprehended to provide anonymity. However, the most transparent payment network is what bitcoin is. Here all the transactions are publicly open. To furnish wholeness and put a stop to double-spending, Blockchain is used, which actually works as a ledger for management of Bitcoins. Blockchain can be misused to monitor flow of bitcoins among multiple transactions. When data from external sources is amalgamated with insinuation acquired from the Blockchain, it may result to reveal user's identity and profile. In this way the activity of user may be traced to an extent to fraud that user. Along with the popularity of Bitcoins the number of adversarial attacks has also gain pace. All these activities are meant to exploit anonymity and privacy in Bitcoin. These acivities result in loss of bitcoins and unlawful profit to attackers. Here in this paper we tried to present analysis of major attacks such as malicious attack, greater than 52% attacks and block withholding attack. Also this paper aims to present analysis and improvements in Bitcoin's anonymity and privacy.
Interval Type-2 Fuzzy Dynamic Parameter Adaptation in Bee Colony Optimization for Autonomous Mobile Robot Navigation

This paper describes a comparative study of Interval Type-2 Membership Functions applied for dynamic parameter adaptation in Bee Colony Optimization Algorithm for the controlling of the trajectory in an autonomous mobile robot. The main goal of the work is comparing the impact of the results when different Interval Type-2 Membership Functions are used in this study case to adapt some parameters of bee colony optimization (BCO) algorithm. Two trajectories are optimized to test the methods. Perturbation is added in the model, and a comparative study is performed.
BlockAM: An Adaptive Middleware for Intelligent Data Storage Selection for Internet of Things

Current Internet of Things (IoT) infrastructures, with its massive data requirements, rely on cloud storage: however, usage of a single cloud storage can place limitations on the IoT applications in terms of service requirements (performance, availability, security etc.). Multi-cloud storage architecture has been emerged as a promising infrastructure to solve this problem, but this approach has limited impact due to the lack of differentiation between competing cloud solutions. Multiple decentralized storage solutions (e.g., based on blockchains) are entering the market with distinct characteristics in terms of architecture, performance, security and availability and at a lower price compared to cloud storage. In this work, we introduce BlockAM: an adaptive middleware for the intelligent selection of storage technology for IoT applications, which jointly considers the cloud, multi-cloud and decentralized storage technologies to store large-scale IoT data. We model the cost-minimization storage selection problem and propose two heuristic algorithms: Dynamic Programming (DP) based algorithm and Greedy Style (GS) algorithm, for optimizing the choice of data storage based on IoT application's service requirements. We also employ blockchain to store IoT data on-chain in order to provide data integrity, auditability and accountability to the middleware architecture. Comparisons among the heuristic algorithms are conducted through extensive experiments, which demonstrates that DP heuristic and GS heuristic achieve up to 92% and 80% accuracy respectively. Moreover, the price associated with a specific IoT application data storage decrease by up to 31.2% by employing our middleware solution.
RPP Algorithm: A Method for Discovering Interesting Rare Itemsets

The importance of rare itemset mining stems from its ability to discover unseen knowledge from datasets in real-life domains, such as identifying network failures, or suspicious behavior. There are significant efforts proposed to extract rare itemsets. The RP-growth algorithm outperforms previous methods proposed for generating rare itemsets. However, the performance of the RP-growth degrades on sparse datasets, and it is costly in terms of time and memory consumption. Hence, in this paper, we propose the RPP algorithm to extract rare itemsets. The advantage of the RPP algorithm is that it avoids time for generating useless candidate itemsets by omitting conditional trees as RP-growth does. Furthermore, our RPP algorithm uses a novel data structure, RN-list, for creating rare itemsets. To evaluate the performance of the proposed method, we conduct extensive experiments on sparse and dense datasets. The results show that the RPP algorithm is around an order of magnitude better than the RP-growth algorithm.
Negative Effects Associated with HMDs in Augmented and Virtual Reality

Head mounted displays (HMD) are becoming ubiquitous. Simulator sickness has been an issue since the first simulators and HMDs were created. As computational power and display capabilities increase, so does their utilization in technologies such as HMDs. However, this does not mean that the issues that once plagued these systems are now obsolete. In fact, evidence suggests that these issues have become more prevalent. Whether the system is Augmented Reality (AR), Virtual Reality (VR), or Mixed Reality (MR) the issues associated with simulator sickness or cybersickness have become more widespread. The reasons are uncertain, but probably multiple. One possible reason is the concept of vection, which is the illusion of movement to the participant where there is none physically. Vection plays a vital role in immersion and presence, however; it is also integral in simulator sickness. Another potential reason is the availability of HMDs. Traditionally a tool used in military training or laboratory settings, HMDs have now become a consumer item. This work reviews the current state of HMD issues such as simulator sickness or cybersickness. It reviews the similarities and differences of the sickness states that are commonly found with HMDs. Also, terms such as presence and immersion are delineated so they are used appropriately. The current theories on simulator sickness and cybersickness are reviewed. Further, the measurement and mitigation strategies currently being employed to reduce sickness are reviewed. Lastly, suggestions for more accurate measurement are recommended.
BNN Pruning: Pruning Binary Neural Network Guided by Weight Flipping Frequency

A binary neural network (BNN) is a compact form of neural network. Both the weights and activations in BNNs can be binary values, which leads to a significant reduction in both parameter size and computational complexity compared to their full-precision counterparts. Such reductions can directly translate into reduced memory footprint and computation cost in hardware, making BNNs highly suitable for a wide range of hardware accelerators. However, it is unclear whether and how a BNN can be further pruned for ultimate compactness. As both 0s and 1s are non-trivial in BNNs, it is not proper to adopt any existing pruning method of full-precision networks that interprets 0s as trivial. In this paper, we present a pruning method tailored to BNNs and illustrate that BNNs can be further pruned by using weight flipping frequency as an indicator of sensitivity to accuracy. The experiments performed on the binary versions of a 9-layer Network-in-Network (NIN) and the AlexNet with the CIFAR-10 dataset show that the proposed BNN-pruning method can achieve 20-40% reduction in binary operations with 0.5-1.0% accuracy drop, which leads to a 15-40% runtime speedup on a TitanX GPU.
The automated design of local optimizers for memetic algorithms employing supportive coevolution

One promising method of improving Evolutionary Algorithm (EA) performance is to improve its fine tuning capabilities by using an additional local optimization operator in the evolutionary cycle. This customization on the traditional EA is typically called a Memetic Algorithm (MA). Adding the appropriate local optimization algorithm can increase performance, while a poor choice can decrease performance. Thus, some knowledge is required to select the correct algorithm. In many cases the optimal algorithm selection is not known and it may not be static, but could change during evolution.
DID: Disentangling-Imprinting-Distilling for Continuous Low-Shot Detection

Practical applications often face a challenging continuous low-shot detection scenario, where a target detection task only has a few annotated training images, and a number of such new tasks come in sequence. To address this challenge, we propose a generic detection scheme via Disentangling-Imprinting-Distilling (DID). DID can leverage delicate transfer insights into the main development flow of deep learning, i.e., architecture design (Disentangling), model initialization (Imprinting), and training methodology (Distilling). This allows DID to be a simple but effective solution for continuous low-shot detection. In addition, DID can integrate the supervision from different detection tasks into a progressive learning procedure. As a result, one can efficiently adapt the previous detector for a new low-shot task, while maintaining the learned detection knowledge in the history. Finally, we evaluate our DID on a number of challenging settings in continuous/incremental low-shot detection. All the results demonstrate that our DID outperforms the recent state-of-the-art approaches. The code and models are available at https://github.com/chenxy99/DID.
Non-Destructive Identification of Fibre Orientation in Multi-Ply Biaxial Laminates Using Contact Temperature Sensors

Fibre orientation within composite structures dictates the material properties of the laminate once cured. The ability to accurately and automatically assess fibre orientation of composite parts is a significant enabler in the goal to optimise the established processes within aftermarket aerospace industries. Incorrect ply lay-up results in a structure with undesirable material properties and as such, has the potential to fail under safe working loads. Since it is necessary to assure structural integrity during re-manufacture and repair assessment, the paper demonstrates a novel method of readily and non-destructively determining fibre orientation throughout multi-ply Biaxial woven composite laminates using point temperature contact sensors and data analysis techniques. Once cured, only the outermost laminates are visible to assess orientation. The inspection method is conducted visually, with reference guides to allow for rapid adoption with minimum training, as well as harnessing established temperature sensors within the Maintenance Repair and Overhaul (MRO) environment. The system is amenable to integration within existing repair/re-manufacture processes without significant impact to process flow. The method is able to identify noisy samples with an accuracy, precision and recall of 0.9, and for synthetically created samples of double the cure ply thickness, a precision of 0.75, a recall of 0.7 and an accuracy of 0.87.
Learner Modeling in the Context of Caring Assessments

Learner models maintain representations of students’ cognitive, metacognitive, affective, personality, social and perceptual skills. This information can be used to adapt the adaptive instructional system’s interactions with the student. Our work on caring assessments has provided us with an opportunity to explore learner modelling issues applied to assessment. This paper elaborates on issues such as the nature of the learner model, types of student emotions in assessment and opportunities for adaptations, and the role of individual differences in student characteristics that could inform an expanded learner model to support fine-tuned adjustments to assessment tasks. Other issues discussed include using cognitive and affective information to implement adaptations, as well as implications for reporting systems and open learner models, supporting student access to these systems, and data privacy and data security challenges.
Emotional Valence Coded in the Phonemic Content – Statistical Evidence Based on Corpus Analysis

Abstract This study investigates the relationship between the phonemic content of texts in English and the emotional valence they inspire. The sublexical content is presented in terms of biphones composed by one vowel and one consonant. The statistical analysis of a vast corpus of emotionally evaluated sentences reveals a strong correlation between this sublexical presentation and the evaluations of valence provided by the readers. An initial test performed with other valence-rated prose texts makes believing that the feature observed within the corpus can be useful for the emotion classification of texts.
Hierarchical Gaussian Process Models for Improved Metamodeling

Simulations are often used for the design of complex systems as they allow one to explore the design space without the need to build several prototypes. Over the years, the simulation accuracy, as well as the associated computational cost, has increased significantly, limiting the overall number of simulations during the design process. Therefore, metamodeling aims to approximate the simulation response with a cheap to evaluate mathematical approximation, learned from a limited set of simulator evaluations. Kernel-based methods using stationary kernels are nowadays widely used. In many problems, the smoothness of the function varies in space, which we call nonstationary behavior [20]. However, using stationary kernels for nonstationary responses can be inappropriate and result in poor models when combined with sequential design. We present the application of two recent techniques: Deep Gaussian Processes and Gaussian Processes with nonstationary kernel, which are better able to cope with these difficulties. We evaluate the method for nonstationary regression on a series of real-world problems, showing that these recent approaches outperform the standard Gaussian Processes with stationary kernels. Results show that these techniques are suitable for the simulation community, and we outline the variational inference method for the Gaussian Process with nonstationary kernel.
STGL: Spatial-Temporal Graph Representation and Learning for Visual Tracking

Tracking-by-detection framework has been normally adopted in visual tracking methods. It aims to localize the visual target object with a bounding box. However, the bounding box is usually difficult to describe the target object accurately and thus easily introduces noisy background information, which usually degrades the final tracking results. Recently, weighted patch representation of the object has been shown very effectively for suppressing the undesirable background information and thus can obviously improve the tracking results. In this paper, we propose a novel Spatial-Temporal Graph representation and Learning (STGL) model to generate a kind of robust target representation for visual tracking problem. The main aspect of STGL is that it aims to exploit both spatial (within each frame) and temporal (between consecutive frames) structure of patches simultaneously in a unified graph representation and semi-supervised learning model. Comparing with existing works, STGL naturally exploits the learned representation of object in previous frame and thus can obtain the representation of object in current frame more accurately and robustly. A new ADMM algorithm is derived to solve the proposed STGL model. Based on the proposed object representation, we then adapt the structured SVM by introducing scale estimation to achieve object tracking. Extensive experiments show that our method outperforms the state-of-the-art patch based tracking methods on two standard benchmark datasets.
A Comparative Study of Hand-Gesture Recognition Devices for Games

Gesture recognition devices provide a new means for natural human-computer interaction. However, when selecting these devices to be used in games, designers might find it challenging to decide which gesture recognition device will work best. In the present research, we compare three vision-based, hand-gesture devices: Leap Motion, Microsoft's Kinect, and Intel's RealSense. The comparison provides game designers with an understanding of the main factors to consider when selecting these devices and how to design games that use them. We developed a simple hand-gesture-based game to evaluate performance, cognitive demand, comfort, and player experience of using these gesture devices. We found that participants preferred and performed much better using Leap Motion and Kinect compared to using RealSense. Leap Motion also outperformed or was equivalent to Kinect. These findings were supported by players' accounts of their experiences using these gesture devices. Based on these findings, we discuss how such devices can be used by game designers and provide them with a set of design cautions that provide insights into the design of gesture-based games.
Fall Detection under Privacy Protection Using Multi-layer Compressed Sensing

As the phenomenon of empty nest elderly becomes increasingly obvious, how to protect the health of the elderly living alone has attracted researchers' attention. Among all the researches, fall as the most common accident of the elderly, its corresponding vision-based detection system design is the most important. However, directly using image-clear traditional cameras to monitor the daily lives of the elderly at home will bring the risk of privacy disclosure. Therefore, this paper proposes a fall detection system under privacy protection. Firstly, multi-layer compressed sensing (CS) model is introduced to process the video frames, so that the video can reach visual shielding effect. Then, for the compressed video, we improve the local binary pattern on three orthogonal planes (LBP-TOP) feature to represent the object behavior effectively. Finally, the fall detection problem is transformed into a behavioral binary classification problem. The experimental results on two public datasets show that the specificity, sensitivity and accuracy of the algorithm proposed in this paper have maintained at a good level.