Reviewerly/exHarmony huggingface dataset:
Design and Evaluation of Three Interaction Models for Manipulating Internet of Things (IoT) Devices in Virtual Reality

More and more things are getting connected to the internet, including lights, speakers, and refrigerators. These connected things are an example of what a smart home system that is part of the Internet of Things (IoT) can incorporate. IoT enables advanced services by interconnecting physical and virtual things. But, building interactive prototypes for smart home systems can be difficult and costly, since it involves a number of different devices and systems with varying technological readiness level. Virtual reality (VR) is a technology that can create computer-generated environments and has been used as a design tool in many different domains, such as architecture, city planning, and industrial design. However, the focus has traditionally been on visualizing design proposals rather than letting the intended users directly interact with them. Recently, we have seen an intensified development of VR headsets such as HTC Vive and Oculus Rift. These headsets come with relatively well-developed hand controllers, which can be used to interact with the virtual environment. This opens up opportunities to develop and evaluate interactive virtual smart home systems. This paper presents three interaction models developed and evaluated using the new generation of VR technology. The interaction models were then compared in a user study with 18 participants. Some statistically significant differences and subjective preferences could be observed in the quantitative and qualitative data respectively. The main contribution of this paper is to elucidate knowledge about using VR as a prototyping tool to explore IoT interaction. Moreover, this study implies that you can collect and analyze data for statistical analysis using VR.
Broad Attribute Prediction Model With Enhanced Attribute and Feature

For the zero-shot image classification without intersection between training and testing sets, the high-quality representation of image attributes and features plays a key role to improve the classification performance. In order to overcome the limitations related to insufficient attribute and feature expression in zero-shot image classification, we propose a broad attribute prediction model with enhanced attribute and feature (EAF-BAP) based on broad learning and elastic net constraint. Firstly, the EAF-BAP enhances pre-defined attributes by elastic net constraint to obtain hybrid attributes, which effectively improves the finiteness of semantic attributes. Secondly, the enhanced features are constructed by broad learning to increase the discrimination ability of features in different classes. Meanwhile, the broad learning is employed to train multiple attribute classifiers synchronously, which is more efficient compared to traditional support vector machines. Finally, the similarity between predicted attributes and hybrid attributes in testing classes is calculated by Manhattan distance, which is further used to implement image classification. Experiments on both AwA and Shoes datasets show that the proposed EAF-BAP model is capable of improving the accuracy of zero-shot image classification efficiently.
Outlier-Suppressed Triplet Loss with Adaptive Class-Aware Margins for Facial Expression Recognition

Triplet loss has been proposed to increase the inter-class distance and decrease the intra-class distance for various tasks of image recognition. However, for facial expression recognition (FER) problem, the fixed margin parameter does not fit the diversity of scales between different expressions. Meanwhile, the strategy of selecting the hardest triplets can introduce noisy guidance information since various persons may present significantly different expressions. In this work, we propose a new triplet loss based on class-aware margins and outlier-suppressed triplet for FER, where each pair of expressions, e.g. 'happy' and 'fear', is assigned with an adaptive margin parameter and the abnormal hard triplets are discarded according to the feature distance distribution. Experimental results of the proposed triplet loss on the FER2013 and CK+ expression databases show that the proposed network achieves much better accuracy than the original triplet loss and the network without using the proposed strategies, and competitive performance compared with the state-of-the-art algorithms.
Empowering Owners with Control in Digital Data Markets

We propose an approach for allowing data owners to trade their data in digital data market scenarios, while keeping control over them. Our solution is based on a combination of selective encryption and smart contracts deployed on a blockchain, and ensures that only authorized users who paid an agreed amount can access a data item. We propose a safe interaction protocol for regulating the interplay between a data owner and subjects wishing to purchase (a subset of) her data, and an audit process for counteracting possible misbehaviors by any of the interacting parties. Our solution aims to make a step towards the realization of data market platforms where owners can benefit from trading their data while maintaining control.
DCT Based Information-Preserving Pooling for Deep Neural Networks

Pooling is used in most of deep convolutional neural networks as a feature downsampling method, in order to reduce computation complexity and increase the receptive field size. Since the traditional max/average pooling layers tend to cause severe information loss, spectral pooling with discrete Fourier transform (DFT) is considered as an advisable alternative. In this paper, we propose a novel 2D-discrete cosine transform (2D-DCT) based pooling method for deep neural networks. Due to the energy compaction property, DCT pooling preserves considerably more information than DFT. Moreover, inspired by the separability of DCT, we precompute the transform matrices and embed them into linear layers for parallelization and acceleration on GPUs. Experimental results indicate that the proposed DCT based pooling layer outperforms previous pooling methods on multiple image classification datasets with negligible extra time consumption.
Hybrid cycle reservoir with jumps for multivariate time series prediction: industrial application in oil drilling process

Industrial oil drilling processes usually produce high-dimensional multivariate time series data, in which the significant data changes associated with key variables possibly indicate potential drilling accidents. Therefore, it is of great significance to establish a multivariate time series prediction model for the safety of drilling operations. Recently, echo state networks (ESNs) have been widely used in multitime series predictions. However, traditional ESNs use randomly generated sparse network structures and single neuron models, which makes it difficult to achieve a satisfactory performance for complex multivariate time series predictions. In response to this problem, this study proposes a novel hybrid cycle reservoir with jumps (HCRJ) which combines the hybrid wavelet neuron model with the cycle reservoir with jumps (CRJ) structure. In the face of high-dimensional data sets generated by oil drilling, this paper selects a principal component analysis algorithm combined with certain process knowledge to find key variables before related variables are specified by means of Gray correlation analysis methods. The HCRJ networks are used to realize temporal predictions based on data of related variables. To confirm the validity of the model, the HCRJ networks are applied to an oil drilling process and compared to traditional ESNs and CRJ methods. The results show that the HCRJ networks enrich the dynamic characteristics of networks without increasing the complexity of the reserve pool, which helps improve the prediction accuracy of the oil drilling sequence and reduce the occurrence of drilling accidents.
Defining and Delimitating Distributed Ledger Technology: Results of a Structured Literature Analysis

There are currently different views in the scientific community on how the distributed ledger technology (DLT) relates to the blockchain technology. Some view them as synonyms, whereas others view DLT as the umbrella term for all blockchain related technologies. This paper approaches this topic by deriving definitions and core characteristics of DLT and blockchain technology. By using these definitions and an additional market research, a categorization for DLT and blockchain can be created. This categorization will operate as an additional component for IT-architects to decide on appropriate DLT solutions for their specific distributed ledger use cases.
Finnish Enterprise Agile Transformations: A Survey Study

Modern large software-intensive development organizations are nowadays more and more often believed to transform their structures and operations towards large-scale agility in search for higher performances. Based on a survey conducted in Finland in 2018, in this paper we explore the current state of the affairs with respect to how extensively organizations are actually transforming themselves, in what ways this takes place in practice and for what goals. Most of the respondents were in large organizations. The results show that the majority of the surveyed respondents indicated that their organizations have conducted agile transformations or are currently doing so. Different strategies and tactics have been used in the transformations, but markedly the respondents reported most that the company has had external consultants (subcontracting) to assist in the change. The most important goals aimed to be achieved with agile means were productivity and quality (operative) and responsiveness to customer/market changes (new features). Notably only very few respondents reported their organizations to be currently non-agile (do not use at all agile methods in software development).
Joint Regression Modeling and Sparse Spatial Refinement for High-Quality Reconstruction of Distorted Color Images

High quality algorithms are demanded to reconstruct distorted color images in a variety of applications. For example, distortions can result during transmission over lossy channels in image coding or in multi-view imaging scenarios. In general, not all color channels are equally affected and the losses distribute differently in-between channels. However, state-ofthe-art methods process color channels independently and do not take the cross color information into account. Thus, a novel and powerful reconstruction algorithm is formulated in this contribution that exploits color as well as spatial information. Therefore, an initial model is estimated for the distorted area using a reference channel. Then, its quality is estimated and a spatial weighting model is set-up. Afterwards, the initial inter channel prediction is refined by generating a sparse model that takes the spatial correlations into account, as well. Consequently, the proposed method achieves an outstanding quality compared to state-of-the-art methods.
MessageFusion: On-path Message Coalescing for Energy Efficient and Scalable Graph Analytics

The natural ability of graphs to capture complex relationships within a large amount of data makes graph-based algorithms critical kernels for a wide range of data-analytics applications. Recent processing-in-memory solutions based on a network of 3D-stacked memory, such as Hybrid Memory Cubes (HMCs), have been shown to be a good fit to run graph-based algorithms. However, the communication bandwidth of the network limits their energy and performance efficiencies. In this work, we propose MessageFusion, a domain-specific architecture that greatly reduces network traffic by computing many vertex-updates at the source node, as well as in the network. To this end, we observe that, for many algorithms, vertex-updates need not be atomic, but can be decomposed and computed in a distributed manner. MessageFusion leverages a novel edge-reordering mechanism to boost the number of partial update operations that can be completed before reaching their destination. In addition, to counteract the power overhead introduced by Message-Fusion's edge-reordering mechanism, our solution employs module-level utilization-based, power-gating techniques. Our experimental evaluation shows that MessageFusion achieves a 3× energy savings over a highly-optimized processing-in-memory solution, while also improving performance by 2.1×, on average.
Wavelet Based Digital Image Watermarking Algorithm Using Fractal Images

With increasing development of information technology, electronic publishing is becoming more popular, so need for copyright protection has been increased. Digital watermarking is used to protect digital data from being altered. This paper presents an algorithm to embed fractal images in wavelet domain using LWT (Lifting Wavelet Transform). Fractal image is generated from Quadtree decomposition and converted into binary image. This binary image is used as watermark. Cover image is converted into RGB components and binary watermark is inserted into mid frequency bands of cover image to protect information from attackers. Watermark is implanted in the blue component of RGB image. The PSNR, SSIM, and NC values obtained in this research show that the method is imperceptible and robust against signal distortions such as contrast enhancement, gamma correction, image sharpening, Gaussian filter, etc.
Higher Education Teaching and Learning With Augmented Reality

This chapter is based on a review of the literature, initial lab examinations, and experiences teaching university undergraduate pre-service teachers and master degree students in Instructional Design and Technology. The authors analyzed the literature, benefits, drawbacks, experiences, and educational implications of integrating augmented reality in higher education to prepare students for eventual workplace success. Using augmented reality, three-dimensional interactive digital imaging provides an immersive, engaging learning environment to interact with content in new ways not previously possible. The 3D models can impart significant content information by viewing digital objects from any angle, sometimes peeling back the layers, all in real time. In addition, they consider the educational implications for integrating and evaluating augmented reality.
Rhythm dungeon

Rhythm Dungeon is a rhythm game which leverages the blockchain as a shared open database. During the gaming session, the player explores a roguelike dungeon by inputting specific sequences in time to music rhythm. By integrating smart contract to the game program, the enemies through the venture are generated from other games which share the identical blockchain. On the other hand, the player may upload their characters at the end of their journey, so that their own character may appear in other games and make an influence. Rhythm Dungeon is designed and implemented to show the potential of decentralized gaming experience, which utilizes the blockchain to provide asynchronous interactions among massive players.
An Autonomous Docking Mechanism for Vertical Lift Unmanned Aircraft

Abstract Unmanned aerial vehicles are increasingly being tasked to connect to payload objects or docking stations for the purposes of package transport or recharging. However, autonomous docking creates challenges in that the air vehicle must precisely position itself with respect to the dock, oftentimes in the presence of uncertain winds and measurement errors. This paper describes an autonomous docking mechanism comprising a static ring and actuated legs, coupled with an infrared tracking device for closed-loop docking maneuvers. The dock’s unique mechanical design enables precise passive positioning such that the air vehicle slides into a precise location and orientation in the dock from a wide range of entry conditions. This leads to successful docking in the presence of winds and sensor measurement errors. A closed-loop infrared tracking system is also described in which the vehicle tracks an infrared beacon located on the dock during the descent to landing. A detailed analysis is presented describing the interaction dynamics between the aircraft and the dock, and system parameters are optimized through the use of trade studies and Monte Carlo analysis with a three degree-of-freedom simulation model. Experimental results are presented demonstrating successful docking maneuvers of an autonomous air vehicle in both indoor and outdoor environments. These repeatable docking experiments verify the robustness and practical utility of the dock design for a variety of emerging applications.
Justification of STL-10 dataset using a competent CNN model trained on CIFAR-10

Deep Learning is a subfield of machine learning concerned through algorithms stimulated by the edifice and purpose of the brain called ANN (artificial neural networks). A convolutional neural network (CNN) is a class of deep neural networks, utmost generally applied to examining painterly images. It uses a distinction of multilayer perceptrons intended to necessitate nominal preprocessing. The CIFAR-10 dataset contains 60000 32×32 color images distributed in 10 classes, having 6000 images in each class and it is recurrently used for training, testing and validation of CNN. In this article, we have designed a well-organized model with several convolutions a hidden layers. 90.91% training accuracy and 83.69 validation accuracy are obtained. Further, it is validated on STL 10 data set which is inspired by CIFAR 10 data set and significant testing accuracy has been obtained. This article will help the researchers who are working on CNN deep models.
A Face Detection Framework Based on Deep Cascaded Full Convolutional Neural Networks

In practical applications, the collected face images are often affected by the surrounding environment, resulting in multiple expressions, multiple poses, occlusion, light intensity, and complex background issues in face detection. Hence this paper presents a novel face detection framework based on deep cascaded full convolutional neural networks (CNNs) to solve the mentioned issues in face detection. This frame work supports face detection, and positioning of face key points at the same time by using its 3-order cascaded CNN architecture. The 3-order cascaded architecture is a combination of three phases of network layers; phasel (I-Net, Initial Network), phase2 (A-Net, Advanced Network), and phase3 (U-Net, Ultimate Network). In the CNN design, a depthwise separable convolution is used instead of the traditional convolution, and also a convolutional layer with stride of 2 is used instead of the pooling layer. A bottleneck structure (1x1 convolution kernel structure) is used for convolution layer, and the network uses a convolutional layer instead of the fully connected layer (FC). The carried-out experiments proved that the presented framework is an effective one for face detection applications.
String similarity algorithms for a ticket classification system

Fuzzy string matching allows for close, but not exactly, matching strings to be compared and extracted from bodies of text. As such, they are useful in systems which automatically extract and process documents. We summarise and compare various existing algorithms for achieving string similarity measures: Longest Common Subsequence (LCS), Dice coefficient, Cosine Similarity, Levenshtein distance and Damerau distance. Based on previously classified customer support enquiries (tickets), we considered the effectiveness of different algorithms and configurations to automatically identify keywords of interest (such as error phrases, product names and warning messages) in instances where such key phrases are misspelled, copied incorrectly or are otherwise differently formed. An optimal algorithm selection is made based on novel studies of the aforementioned similarity measures on text strings tokenised into characters. Such analysis also allowed for an optimum similarity threshold to be identified for various categories of enquiries, to reduce mismatched strings whilst allowing optimal coverage of the correctly matched key phrases. This led to a 15% improvement in the ratio of false positives to true positive classifications over the existing approach used by a customer support system.
Application of the Modified Imputation Method to Missing Data to Increase Classification Performance

Incomplete data or missing data diminishes the effectivity of statistical results, and may cause bias estimates, which in turn leads to unsound judgment. Inefficiency and impediments in data treatment analysis, which are among the predicaments linked with missing values, may affect the supervised learning process and reduce the classification accuracy and performance of the prediction model in a data mining task. This study applied the modified imputation method-which was previously tested with well-known imputation algorithms-to renowned classification techniques namely Naive Bayes, One-R, k-Nearest Neighbor (kNN), C4.5, and Support Vector Machine (SVM) using open data sets from the UCI Repository. The level of performance in terms of precision, accuracy, and Receiver Operating Characteristics (ROC) using Weka tool, before and after imputation was examined. This study manifests that there was an improvement in the classification performance upon the application of the modified imputation method on datasets during preprocessing, compared to that of datasets with missing values.
Differential Neural Network Identification for Homogeneous Dynamical Systems

In this paper, a non parametric identifier for homogeneous nonlinear systems affine in the input is proposed. The identification algorithm is based on the neural networks using sigmoidal activation functions. The learning algorithm is derived by means of Lyapunov function method and homogeneity theory. A numerical example demonstrates the performance of the proposed identifier.
An Optical Flow based Moving Objects Detection Algorithm for the UAV

Study on moving objects detection from the UAV's camera has been increasingly emphasized with wide application of the UAV. It is a challenging problem to detect moving objects from moving background due to the motion of the camera. This paper proposes a novel moving objects detection algorithm aimed at the complex changed background in image sequences captured by the UAV's camera. The algorithm distinguishes objects from background by the inconsistency of optical flow, which adopts remapping error of points through homography transformation to extract motion regions firstly. Furthermore, a cluster and convex hull based foreground refinement strategy is proposed to ensure the integrity of detected objects. To deal with large area noise, a false foreground discriminant criterion based on spatiotemporal consistency is designed in this paper. In addition, a frame skipping strategy is proposed to adjust the detection interval based on optical flow vector size for accelerating our algorithm. Extensive experiments show our algorithm achieves outstanding detection performance on VIVID benchmarking dataset, including 5 challenging image sequences recorded in UAV's cameras.