Reviewerly/exHarmony huggingface dataset:
Clustering and Recognition of Spatiotemporal Features Through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks

Encoder-decoder recurrent neural network models (RNN Seq2Seq) have achieved great success in ubiquitous areas of computation and applications. It was shown to be successful in modeling data with both temporal and spatial dependencies for translation or prediction tasks. In this study, we propose an embedding approach to visualize and interpret the representation of data by these models. Furthermore, we show that the embedding is an effective method for unsupervised learning and can be utilized to estimate the optimality of model training. In particular, we demonstrate that embedding space projections of the decoder states of RNN Seq2Seq model trained on sequences prediction are organized in clusters capturing similarities and differences in the dynamics of these sequences. Such performance corresponds to an unsupervised clustering of any spatio-temporal features and can be employed for time-dependent problems such as temporal segmentation, clustering of dynamic activity, self-supervised classification, action recognition, failure prediction, etc. We test and demonstrate the application of the embedding methodology to time-sequences of 3D human body poses. We show that the methodology provides a high-quality unsupervised categorization of movements.
Multi-label feature ranking with ensemble methods

In this paper, we propose three ensemble-based feature ranking scores for multi-label classification (MLC), which is a generalisation of multi-class classification where the classes are not mutually exclusive. Each of the scores (Symbolic, Genie3 and Random forest) can be computed from three different ensembles of predictive clustering trees: Bagging, Random forest and Extra trees. We extensively evaluate the proposed scores on 24 benchmark MLC problems, using 15 standard MLC evaluation measures. We determine the ranking quality saturation points in terms of the ensemble sizes, for each ranking-ensemble pair, and show that quality rankings can be computed really efficiently (typically 10 or 50 trees suffice). We also show that the proposed feature rankings are relevant and determine the most appropriate ensemble method for every feature ranking score. We empirically prove that the proposed feature ranking scores outperform current state-of-the-art methods in the quality of the rankings (for the majority of the evaluation measures), and in time efficiency. Finally, we determine the best performing feature ranking scores. Taking into account the quality of the rankings first and—in the case of ties—time efficiency, we identify the Genie3 feature ranking score as the optimal one.
Efficient solution of the SVD recommendation model with implicit feedback

Collaborative filtering, an important part of the recommendation system, has become a mainstream recommendation method. In collaborative filtering methods based on potential factors, SVD recommendation models are often used to analyze user preferences. With the recent research of SVD recommendation models, some SVD recommendation models with implicit feedback, such as SVD+ and TrustSVD, have been successively proposed. These types of models can more effectively mine useful information from limited data sources and achieve better results than traditional SVD recommendation model, thereby garnering widespread attention. However, most existing papers focus on model design and the lack of efficient algorithms for SVD recommendation models with implicit feedback. Therefore, this paper first studies the general gradient solution framework of the SVD recommendation model. Then, it considers the SVD+ recommendation model as a breakthrough and designs an efficient solution algorithm, namely, BCDSVD+, based on the block coordinate descent method. Furthermore, we solve the two key problems of capacity matrix inversion and sparse data optimization processing. Experiments show that the proposed BCDSVD+ algorithm yields better solution efficiency and convergence ability than the traditional parallel gradient descent method.
EXPLORING PRETRAINED MODELS FOR JOINT MORPHO-SYNTACTIC PARSING OF RUSSIAN

In this paper, we build a joint morpho-syntactic parser for Russian. We describe a method to train a joint model which is significantly faster and as accurate as a traditional pipeline of models. We explore various ways to encode the word-level information and how they can affect the parser’s performance. To this end, we utilize learned from scratch character-level word embeddings and grammeme embeddings that have shown state-of-theart results for similar tasks for Russian in the past. We compare them with the pretrained contextualized word embeddings, such as ELMo and BERT, known to lead to the breakthrough in miscellaneous tasks in English. As a result, we prove that their usage can significantly improve parsing quality.
Recognition of Vehicles, Pedestrians and Traffic Signs Using Convolutional Neural Networks

As the present and future of the robotic world and automation, autonomous vehicles and Advanced Driver Assistance Systems (ADAS) that work in conjunction with autonomous vehicles are important technologies that can benefit drivers through current driving environments. Some elementary factors of these autonomous cars are recognizing surroundings, barriers, pedestrians, traffic signs and other vehicles. In this study, as one of the functions of an autonomous car, the operation of peripheral object recognition is carried out through the use of deep learning which has been mentioned with great accuracy and speed these years in the field of solving problems in machine learning. Signs and objects in various environments, different viewing angles and dimensions can be recognized through the video images taken from the vehicle. Application of object recognition is achieved through the use of 517 images of 10 objects consisting of pedestrians, cars, bicycles and 7 traffic signs, and of convolutional neural networks models including SSD Inception V2, Faster R-CNN Inception V2, Faster R-CNN Resnet 50 and Faster R-CNN Resnet 101, which are known as the basis of deep learning. The models previously trained on the COCO data set are retrained and evaluated on the new data set with the transfer learning method. The new data set is formed by part of the image from the GRAZ-01 and GRAZ-02 data sets and part of the image from the mobile phone camera. As a result of performance analyzes, Faster R-CNN Resnet 101 model is found to be successful in object detection on both images and videos with 85.1% accuracy.
Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering Algorithm Using Particle Swarm Optimization for Medical Image Segmentation

This paper is concerned with Magnetic Resonance (MR) brain image segmentation using Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) clustering algorithm. However this algorithm is sensitive to the random initialization of the clusters' centers and moreover its optimal solution can be trapped into a local rather than a global solution. To overcome these drawbacks, this paper proposes the Particle Swarm Optimization (PSO) strategy to compute the clusters' centroids instead of using directly the derived analytic expression of the centroids given by the ARKFCM algorithm. Experimental results, carried out on MR brain images from the BrainWeb database, show that the revisited ARKFCM algorithm improves the performance of its original version.
Blockchain Valuation: Internet of Value, Digital Networks and Smart Transactions

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Blockchain Applications, Opportunities, Challenges and Risks: A Survey

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Fruit Fly Optimization Algorithm Based on Single-Gene Mutation for High-Dimensional Unconstrained Optimization Problems

The fruit fly optimization (FFO) algorithm is a new swarm intelligence optimization algorithm. In this study, an adaptive FFO algorithm based on single-gene mutation, named AFFOSM, is designed to aim at inefficiency under all-gene mutation mode when solving the high-dimensional optimization problems. The use of a few adaptive strategies is core to the AFFOSM algorithm, including any given population size, mutation modes chosen by a predefined probability, and variation extents changed with the optimization progress. At first, an offspring individual is reproduced from historical best fruit fly individual, namely, elite reproduction mechanism. And then either uniform mutation or Gauss mutation happens by a predefined probability in a randomly selected gene. Variation extent is dynamically changed with the optimization progress. The simulation results show that AFFOSM algorithm has a better accuracy of convergence and capability of global search than the ESSMER algorithm and several improved versions of the FFO algorithm.
Insecure Until Proven Updated: Analyzing AMD SEV's Remote Attestation

Cloud computing is one of the most prominent technologies to host Internet services that unfortunately leads to an increased risk of data theft. Customers of cloud services have to trust the cloud providers, as they control the building blocks that form the cloud. This includes the hypervisor enabling the sharing of a single hardware platform among multiple tenants. Executing in a higher-privileged CPU mode, the hypervisor has direct access to the memory of virtual machines. While data at rest can be protected using well-known disk encryption methods, data residing in main memory is still threatened by a potentially malicious cloud provider. AMD Secure Encrypted Virtualization (SEV) claims a new level of protection in such cloud scenarios. AMD SEV encrypts the main memory of virtual machines with VM-specific keys, thereby denying the higher-privileged hypervisor access to a guest's memory. To enable the cloud customer to verify the correct deployment of his virtual machine, SEV additionally introduces a remote attestation protocol. This protocol is a crucial component of the SEV technology that can prove that SEV protection is in place and that the virtual machine was not subject to manipulation. This paper analyzes the firmware components that implement the SEV remote attestation protocol on the current AMD Epyc Naples CPU series. We demonstrate that it is possible to extract critical CPU-specific keys that are fundamental for the security of the remote attestation protocol. Building on the extracted keys, we propose attacks that allow a malicious cloud provider a complete circumvention of the SEV protection mechanisms. Although the underlying firmware issues were already fixed by AMD, we show that the current series of AMD Epyc CPUs, i.e., the Naples series, does not prevent the installation of previous firmware versions. We show that the severity of our proposed attacks is very high as no purely software-based mitigations are possible. This effectively renders the SEV technology on current AMD Epyc CPUs useless when confronted with an untrusted cloud provider. To overcome these issues, we also propose robust changes to the SEV design that allow future generations of the SEV technology to mitigate the proposed attacks.
Blockchain Reference System Architecture Description for the ISA95 Compliant Traditional and Smart Manufacturing Systems

The next-generation technologies enabled by the industry 4.0 revolution put immense pressure on traditional ISA95 compliant manufacturing systems to evolve into smart manufacturing systems. Unfortunately, the transformation of old to new manufacturing technologies is a slow process. Therefore, the manufacturing industry is currently in a situation that the legacy and modern manufacturing systems share the same factory environment. This heterogeneous ecosystem leads to challenges in systems scalability, interoperability, information security, and data quality domains. Our former research effort concluded that blockchain technology has promising features to address these challenges. Moreover, our systematic assessment revealed that most of the ISA95 enterprise functions are suitable for applying blockchain technology. However, no blockchain reference architecture explicitly focuses on the ISA95 compliant traditional and smart manufacturing systems available in the literature. This research aims to fill the gap by first methodically specifying the design requirements and then meticulously elaborating on how the reference architecture components fulfill the design requirements.
Peripheral Dimming: A New Low-Power Technology for OLED Display Based on Gaze Tracking

In this paper, a new low-power technology based on gaze tracking, called peripheral dimming, is proposed for organic light-emitting diode (OLED) displays. The goal of the proposed method is to save power without degrading the perceptual image quality. In the proposed method, the peripheral vision area on the screen is gradually darkened depending on the distance from the gaze point. In this work, quantitative conditions for preventing degradation of the perceived image quality are studied through a psychophysical experiment by using three video clips. We suggest a lightness reduction ratio (LRR) that determines the amount of reduced luminance per viewing angle based on the lightness. Four conditions of the LRR from 0.1 to 1.0%/degree are applied to each clip. The experiment is designed based on a two-alternative forced choice: a test clip with the proposed method is compared to the original one, and subjects are forced to choose the brighter clip between the two clips shown in random order. In this way, the threshold of the LRR from which people begin to notice a difference between the test and original clips is obtained. The experimental results demonstrate that the proposed method saves the power of OLED displays up to 34.4% while keeping the image quality high in terms of both subjective and objective quality (the mean structural similarity index is higher than 0.94). Therefore, the proposed method will help to enable low-power operation of OLED displays used for head-mounted display devices while maintaining the quality of experience.
Spectral Alteration Zonation Based on Close Range HySpex-320 m Imaging Spectroscopy: A Case Study in the Gongchangling High-Grade Iron Ore Deposit, Liaoning Province, NE China

Research on wall rock alteration is of great importance to the understanding and exploration of ore deposits. The microscopic changes of the same mineral in different alteration zones can provide information about the migration and enrichment of ore-forming elements. In this paper, a typical profile of a high-grade iron ore body in Gongchangling iron deposit was investigated and sampled. The samples were checked by polarized microscopy, and alterations zonation were delineated according to the hydrothermal mineral assemblages and paragenesis. Moreover, hyperspectral images of wall rocks from each alteration zone were obtained by Norsk Elektro Optikk (NEO) HySpex-320 m imaging system. A customer Interactive Data Language (IDL) software package was used to process the images, and spectral features were extracted from the selected samples. The results indicate that spectral characteristics manifest obviously regular variations; i.e., from proximal to distal for the high-grade iron ore body, the wavelengths at ca. 1200 nm of chlorite and garnet, which accounts for most of the hydrothermal alteration minerals, become longer, and the absorption depths gradually smaller. The spectral features at 1200 nm of chlorite and garnet are always caused by the crystal field effect of Fe2+; therefore, the wavelength variations indicate the increase of Fe2+ and a reduced environment, which can provide more detailed information about the metallogeny and water–rock interaction. Since the hyperspectral features of the altered rocks can disclose unique mineralogical and structural information, the conventional classification of alteration zonation should be combined with the spectral feature, i.e., spectral alteration zonation, which is of great help to the understanding of the forming conditions of wall rock alteration and also the high-grade iron ore bodies.
Implementing Mobile Learning Within Personal Learning Environments: A Study of Two Online Courses

This article presents a case-study of two distance learning courses, in order to address the question of universal adoption of mobile devices and applications by students, and the impact of these devices in personal learning environments (PLEs). First, a critical discussion of the value of these concepts in the current technological context was carried out, followed by an analysis of their impact on educational use, based on data collected in online courses on physics and statistics at Universidade Aberta, the Portuguese Open University. The results indicated that all students have adopted mobile learning, and the make-up of an individual’s PLE depends more on the learning resources available rather than on gender or age. These findings can help provide more efficient ways to implement learning by connecting current social needs to learners’ mobile PLEs, particularly when flexibility of time and space are of utmost importance. Further studies at the Portuguese Open University will address a larger and more balanced sample of students across more course units.
RobotSculptor: Artist-Directed Robotic Sculpting of Clay

We present an interactive design system that allows users to create sculpting styles and fabricate clay models using a standard 6-axis robot arm. Given a general mesh as input, the user iteratively selects sub-areas of the mesh through decomposition and embeds the design expression into an initial set of toolpaths by modifying key parameters that affect the visual appearance of the sculpted surface finish. These parameters were identified and extracted through a series of design experiments, using a customized loop tool to cut the water-based clay material. The initialized toolpaths are fed into the optimization component of our system afterwards for optimal path planning, aiming to find the robotic sculpting motions that match the target surface, maintaining the design expression, and resolving collisions and reachability issues. We demonstrate the versatility of our approach by designing and fabricating different sculpting styles over a wide range of clay models.
Trajectory/Path-Following Controller Based on Nonlinear Jerk-Level Error Dynamics

This study proposes a novel, nonlinear trajectory/path-following controller based on jerk-level error dynamics. Therefore, at first the nonlinear acceleration-based kinematic equations of motion of a dynamic system are differentiated with respect to time to obtain a representation connecting the translation jerk with the (specific) force derivative. Furthermore, the path deviation, i.e., the difference between the planned and the actual path, is formulated as nonlinear error dynamics based on the jerk error. Combining the derived equations of motion with the nonlinear error dynamics as well as employing nonlinear dynamic inversion, a control law can be derived that provides force derivative commands, which may be commanded to an inner loop for trajectory control. This command ensures an increased smoothness and faster reaction time compared to traditional approaches based on a force directly. Furthermore, the nonlinear parts in the error dynamic are feedforward components that improve the general performance due to their physical connection with the real dynamics. The validity and performance of the proposed trajectory/path-following controller are shown in an aircraft-related application example.
Prioritization in visual attention does not work the way you think it does.

A common assumption in attention theories is that attention prioritizes search items based on their similarity to the target. Here, we tested this assumption and found it wanting. Observers searched through displays containing candidates (distractors that cannot be confidently differentiated from the target by peripheral vision) and lures (distractors that can be). Candidates had high or low similarity to the target. Search displays were either candidate-homogeneous (all items of same similarity) or candidate-heterogeneous (equal numbers of each similarity). Response times to candidate-heterogeneous displays were equivalent to the average of high- and low-similarity displays, suggesting that attention was allocated randomly, rather than toward the high-similarity candidates first. Lures added a response time cost that was independent of the candidates, suggesting they were rejected prior to candidates being inspected. These results suggest a reverse prioritization process: Distributed attention discards least target-similar items first, while focused spatial attention is randomly directed to target-similar items. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Correction to: Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations

In the originally published version of chapter 43, the second affiliation stated a wrong city and country. This has been corrected.
Designing and Evaluating Multimodal Interactions for Facilitating Visual Analysis With Dashboards

Exploring and analyzing data using visualizations is at the heart of many decision-making tasks. Typically, people perform visual data analysis using mouse and touch interactions. While such interactions are often easy to use, they can be inadequate for users to express complex information and may require many steps to complete a task. Recently natural language interaction has emerged as a promising technique for supporting exploration with visualization, as the user can express a complex analytical question more easily. In this paper, we investigate how to synergistically combine language and mouse-based direct manipulations so that the weakness of one modality can be complemented by the other. To this end, we have developed a novel system, named Multimodal Interactions System for Visual Analysis ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIVA</i> ), that allows user to provide input using both natural language (e.g., through speech) and direct manipulation (e.g., through mouse or touch) and presents the answer accordingly. To answer the current question in the context of past interactions, the system incorporates previous utterances and direct manipulations made by the user within a finite-state model. The uniqueness of our approach is that unlike most previous approaches which typically support multimodal interactions with a single visualization, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIVA</i> enables multimodal interactions with multiple coordinated visualizations of a dashboard that visually summarizes a dataset. We tested MIVA’s applicability on several dashboards including a COVID-19 dashboard that visualizes coronavirus cases around the globe. We further empirically evaluated our system through a user study with twenty participants. The results of our study revealed that MIVA system enhances the flow of visual analysis by enabling fluid, iterative exploration and refinement of data in a dashboard with multiple-coordinated views.