Reviewerly/exHarmony huggingface dataset:
A taxonomic overview and pilot study for evaluation of Augmented Reality based posture matching technique using Technology Acceptance Model

Motor skill training, posture matching and Augmented Reality (AR) based learning technology are considered to be the cornerstones of dance learning paradigm. In this paper, an AR based posture matching technique is presented based upon skeletal mapping and movement matching where each posture is modelled by a sequence of pivotal movements and continuous data frames. An extension of the previously published work by the authors, this paper aims to provide a taxonomic overview of dance learning/ training technologies with respect to existing learning theories. Furthermore, the proposed system is evaluated using the Technology Acceptance Model (TAM) and a pilot study is carried out to assess the acceptability of the system. The results of the pilot study are also utilized for identifying flaws and to calculate the sample size for further evaluation using a larger group of subjects. This research also aims at providing a taxonomical knowledge and categorization of the proposed Augmented Reality Dance Training System (ARDTS) in the state-of-art hierarchical structure of learning theories.
WeaGAN:Generative Adversarial Network for Weather Translation of Image among Multi-domain

Weather translation of image refers to the task of changing the weather of an input image to desired weather while preserving the structure of the image's content, which belongs to a task of image-to-image translation. Recent works have made great process in image-to-image translation between two domains and some works have even achieved multi-domain translation within a single model. However, existing works have limited robustness in handling weather translation among multi-domain, since bad weather produces a loud noise and it is challenging to process scene images without fixed pattern in a unified model. In this paper, we propose WeaGAN based on encoder-decoder architecture and generative adversarial training process to translate the weather of image among multi-domain. In particular, We employ SE block in generator and combine adversarial loss, classification loss and content loss for visually detailed and realistic result. Experience in qualitative and quantitative aspect on synthetic dataset and real dataset show the effectiveness and competitiveness of our method compared with state-of-the-art works.
Robotic Scene Understanding by Using a Dictionary

Scene understanding is a fundamental task for intelligent robots, especially in human-robot interaction. It is challenging due to the complexity of the human environment. In this paper, the proposed method integrates semantic analysis with object detection that enables robots to perceive scenes and deeply understands working environments. The model can extract deterministic entities of objects by analyzing their dictionary definitions. Therefore, robots can understand a scene at the object-level. These deterministic entities include the category, function, property, and composition of objects, and they can be used to generate feedback on how much robots can understand a scene by describing it in natural language. The feedback on how well robots understand the working space is an essential aspect to eliminate confusion during human-robot interactions. The experiment part of this paper discussed the applicability of the proposed method on robots.
Motion Planning for Manipulators in Unknown Environments with Contact Sensing Uncertainty

Localization error, sensor noise, and occlusions can lead to an imperfect model of the environment, which can result in collisions between a robot arm and unobserved obstacles when manipulating. The robot must navigate around these obstructions despite not knowing their shape or location. Without tactile sensors, the robot only observes that a contact occurred somewhere on its surface, a measurement containing very little information. We present the Collision Hypothesis Sets representation for computing a belief of occupancy from these observations, and we introduce a planning and control architecture that uses this representation to navigate through unknown environments. Despite the dearth of information, we demonstrate through experiments that our algorithms can navigate around unseen obstacles and into narrow passages. We test in multiple environments in simulation and on a physical robot arm both with and without the aid of a 2.5D depth sensor. Compared to a baseline representation Collision Hypothesis Sets produce an approximately 1.5-3x speed-up and improve the success rate from 40–60% to 100% in tested scenarios with narrow passages.
INSTalytics

We present the design, implementation, and evaluation of INSTalytics , a co-designed stack of a cluster file system and the compute layer, for efficient big-data analytics in large-scale data centers. INSTalytics amplifies the well-known benefits of data partitioning in analytics systems; instead of traditional partitioning on one dimension, INSTalytics enables data to be simultaneously partitioned on four different dimensions at the same storage cost, enabling a larger fraction of queries to benefit from partition filtering and joins without network shuffle. To achieve this, INSTalytics uses compute-awareness to customize the three-way replication that the cluster file system employs for availability. A new heterogeneous replication layout enables INSTalytics to preserve the same recovery cost and availability as traditional replication. INSTalytics also uses compute-awareness to expose a new sliced-read API that improves performance of joins by enabling multiple compute nodes to read slices of a data block efficiently via co-ordinated request scheduling and selective caching at the storage nodes. We have built a prototype implementation of INSTalytics in a production analytics stack, and we show that recovery performance and availability is similar to physical replication, while providing significant improvements in query performance, suggesting a new approach to designing cloud-scale big-data analytics systems.
An infinite family of m-ovoids of Q(4,q)

In this paper, we construct an infinite family of q−12-ovoids of the generalized quadrangle Q(4,q), for q≡1(mod4) and q>5. Together with [3] and [11], this establishes the existence of q−12-ovoids in Q(4,q) for each odd prime power q.
Diagnosis of nutrient composition in fruit crops: Major developments

Early methods of tissue diagnosis opposed the concept of critical nutrient concentrations supported by the laws of the minimum and the optimum to the more complex one of nutrient balances. But this proved to be a futile debate. Dual interactions and individual nutrient concentrations were later integrated into the Diagnosis and Recommendation Integrated System (DRIS). Methods of compositional data analysis (CoDa) corrected biases in DRIS on strong mathematical basis using centered (clr) and isomeric (ilr) log ratios. Log ratios were designed to adjust each nutrient to the level of others within the closed space or subspace of compositional data. Nutrient imbalance was assessed using a multivariate distance between given composition or ionome and a reference composition. In the near future, machine learning techniques and ionomics will allow predicting crop yield and quality using many more metadata collected in large data sets. Lines of research are proposed to improve models under international collaboration.
Disrupting Business as Usual

This article analyzes the research on cryptocurrency and blockchain technology and the alternate banking system, led by giants like Bitcoin and Ethereum, and its potential utility for the millions of migrants living in working around the globe, who send nearly half a trillion dollars through various formal and informal channels to family and friends in their home countries. While research on migrant remittances is well established and has been examined from many different lenses, including economic, political, financial, sociological and entrepreneurial, there are few studies to date that examine how the growing crypto-blockchain channel can impact remittance flows, as a lower cost alternative to MoneyGram and Western Union, which have high transaction costs, and also considering that many migrants do not have formal bank accounts, eliminating formal bank transfers, and also that many migrants hold a vulnerable legal status, and may avoid formal money transfer channels. This article is exploratory in nature and identifies the work that has been done to date on this topic, and identifies potential future research in the area.
Entities with Quantities

Quantities are more than numeric values. They represent measures for entities, expressed in numbers with associated units. Search queries often include quantities, such as athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. Processing such queries requires understanding the quantities, where capturing the surrounding context is an essential part of it. Although modern search engines or QA systems handle entity-centric queries well, they consider numbers and units as simple keywords, and therefore fail to understand the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.) As a result, they cannot generate the correct candidate answers. In this work, we demonstrate a prototype QA system, called Qsearch, that can handle advanced queries with quantity constraints using the common cues present in both query and the text sources.
FPGA-Based Optimized Convolutional Neural Network Framework for Handwritten Digit Recognition

The size of ConNN (Convolutional neural network) tends to increase every year. Thus, it consumes plenty of power to be able to run it. To reduce the power consumption, choosing the right hardware platform is important. Field programmable gate area (FPGA) is also known as low power platform for low level digital design. Therefore, this paper proposes an implementation of ConNN using FPGA in order to reduce the power consumption. We describe the simple ConNN which is optimized ConNN framework compatible with FPGA. Moreover, we implement the handwritten digit recognition to evaluate the performance of our framework.
Protection and Hiding Algorithm of QR Code Based on Multi-channel Visual Masking

Quick Response (QR) Code is extensively used due to its advantages in fast readability as well as large capacity. However, when using QR codes, users may suffer from loss of private information because of peeping and scanning by unauthorized attackers. In this paper, we propose a novel algorithm to hide and protect the QR code based on multichannel visual masking. With our algorithm, the appearance of the QR code is dramatically changed while it maintains the original secret information. Unauthorized users can not extract any information from the protected QR code with the standard QR code reader. For authorized users, we design a truth table based decoder that works with the standard QR code reader. Extensive experiments are performed to evaluate the robustness and effectiveness of our method. The codes of this paper are published at https://github.com/JiaheZhang/Protection- QR-Code-MVM.
Hybrid Digital Image Correlation

Digital image correlation (DIC) is a commonly used full-field technique for measuring surface deformations in experimental solid mechanics. It directly provides full-field displacements by comparing digital images of the speckle pattern subsets painted on a structure's surface in the undeformed and deformed states, respectively. Although DIC measures displacements in solid structures, it is often necessary to determine the individual stress components for proper fatigue and failure analysis. Hybrid digital image correlation (hybrid-DIC) is an experimental-analytical approach where theoretical formulations are combined with DIC displacement data for the identification of the full-field stress components. The present paper demonstrates the ability in providing reliable full-field stresses in numerous two-dimensional (2D) plane stress engineering situations involving circular and irregular geometric discontinuities in isotropic or orthotropic composite materials. Hybrid-DIC's main advantages are (1) it does not require the differentiation of measured displacements, and (2) it provides stresses at the edges of discontinuities, often the location of maximum stress magnitudes.
A multi-feature image retrieval scheme for pulmonary nodule diagnosis

Deep analysis of radiographic images can quantify the extent of intra-tumoral heterogeneity for personalized medicine.In this paper, we propose a novel content-based multi-feature image retrieval (CBMFIR) scheme to discriminate pulmonary nodules benign or malignant. Two types of features are applied to represent the pulmonary nodules. With each type of features, a single-feature distance metric model is proposed to measure the similarity of pulmonary nodules. And then, multiple single-feature distance metric models learned from different types of features are combined to a multi-feature distance metric model. Finally, the learned multi-feature distance metric is used to construct a content-based image retrieval (CBIR) scheme to assist the doctors in diagnosis of pulmonary nodules. The classification accuracy and retrieval accuracy are used to evaluate the performance of the scheme.The classification accuracy is 0.955 ± 0.010, and the retrieval accuracies outperform the comparison methods.The proposed CBMFIR scheme is effective in diagnosis of pulmonary nodules. Our method can better integrate multiple types of features from pulmonary nodules.
Adalward: a deep-learning framework for multi-class malicious webpage detection

A global village is what the father of digital media and communications, Marshall McLuhan had dreamt of in the late 1970s. In June 2017, reaching over 51.7% of the global population, the Internet has made it a reality. In past couple of decades, with upsurge of wireless communication technologies, the Internet has spread its web to connect all corners of the world. Its undeniable merits aside, interconnectivity on such a massive scale ushered in a whole new era of rampant malfeasance, characterized by an ever-increasing rate of cyber-crimes. Intrinsically, cyber-security researchers around the globe have been trying to develop several effective mechanisms to deal with the threats posed by cybercriminals. In this paper, we are presenting Adalward – a five-layer deep-learning framework, which has the potential for overcoming most of the challenges faced by such existing systems. Unique framework of Adalward allows it to utilize both static and dynamic web features for making accurate classification decisions with unmatched efficiency. Adalward was trained on one million labelled URLs obtained from numerous trustworthy sources. By the end of its training phase, Adalward achieved an overall detection accuracy of 99.76%, with a negligible false-positive rate of 0.10% and a nominal false-negative rate of 0.14%.
FlexGraph: Flexible partitioning and storage for scalable graph mining

How can we analyze large graphs such as the Web, and social networks with hundreds of billions of vertices and edges? Although many graph mining systems have been proposed to perform various graph mining algorithms on such large graphs, they have difficulties in processing Web-scale graphs due to massive communication and I/O costs caused by communication between workers, and reading subgraphs repeatedly. In this paper, we propose FlexGraph, a scalable distributed graph mining method reducing the costs by exploiting properties of real-world graphs. FlexGraph significantly decreases the communication cost, which is the main bottleneck of distributed systems, by exploiting different edge placement policies based on types of vertices. Furthermore, we propose a flexible storage format to reduce I/O costs when reading input graph repeatedly. Experiments show that FlexGraph succeeds in processing up to 64× larger graphs than existing distributed memory-based graph mining methods, and consistently outperforms previous disk-based graph mining methods.
On the List Decodability of Rank Metric Codes

Let k, n, m ∈ ℤ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> be integers such that k ≤ n ≤ m, let G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n,k</sub> ∈ F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> (q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) be a Delsarte-Gabidulin code. Recently, Wachter-Zeh proved that codes belonging to this family cannot be efficiently list decoded for any radius τ, providing τ is large enough. This achievement essentially relies on proving a lower bound for the list size of some specific words in F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> (q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ). Some years later, Raviv and Wachter-Zeh improved this bound in a special case, i.e. when n|m. As a consequence, they were able to detect infinite families of Delsarte-Gabidulin codes that cannot be efficiently list decoded at all. In this article we determine similar lower bounds for Maximum Rank Distance codes belonging to a wider class of examples, containing Generalized Gabidulin codes, Generalized Twisted Gabidulin codes, and examples recently described by Trombetti and Zhou. By exploiting arguments such as those used by Raviv and Wachter-Zeh, when n|m, we also show infinite families of generalized Gabidulin codes that cannot be list decoded efficiently at any radius greater than or equal to ⌊d-1/2⌋ + 1, where d is its minimum distance. Nonetheless, in all the examples belonging to above mentioned class, we detect infinite families that cannot be list decoded efficiently at any radius greater than or equal to ⌊d-1/2⌋ + 2, where d is its minimum distance. In particular, this leads to show infinite families of Gabidulin codes, with underlying parameters not already covered by the result of Raviv and Wachter-Zeh, having this decodability defect. Finally, relying on the properties of a set of subspace trinomials recently presented by McGuire and Mueller, we are able to prove our main result, that is any rank-metric code of F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> (q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) of order q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">kn</sup> with n dividing m, such that 4n - 3 is a square in ℤ and containing G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n,2</sub> , is not efficiently list decodable at some values of the radius τ.
Word embedding and cognitive linguistic models in text classification tasks

The paper considers two linguistic models, analyzed the possibility of their use for the text data classification as well as their associations in the integrated texts presentation. A cognitive approach for the text classification issues is presented. An algorithm to identify the words basic level using WordNet is considered. A model for text classification based on the pre-trained word embeddings is presented. The model consists of three layers: embedding layer Long-Short Term Memory (LSTM) layer, and softmax layer. The model was trained and evaluated on the 20 Newsgroups dataset. The classification quality was assessed by F- measure, precision and recall. The obtained results analysis is carried out. Both described models show good results, low scores for some texts are explained. The advantages and limitations of the linguistic models are shown. In future works the authors are going to combine proposed models and modify them. Thus, for model based on word embedding there are pretty vast opportunities for extension: from experimenting with different word embeddings and various distance metrics to more complicated architecture of layers and even promising state of the art artificial neural network models, activation functions and their modifications. In addition, there is research area of proper ensemble strategy selection.
Secure firmware update in embedded systems

Great numbers of embedded devices are performing safety critical operations, which means it is very important to keep them operating without interference. Update is the weak point that could be exploited by potential attackers to gain access to the system, sabotage it or to simply steal someone else's intellectual property. This paper presents an implementation of secure update process for embedded systems which prevents man-in-the-middle attacks. By using a combination of hash functions, symmetric and asymmetric encryption algorithms it demonstrates how to achieve integrity, authenticity and confidentiality of the update package that is sent to the target hardware. It covers implementation starting from key exchange, next explaining update package encryption process and then decryption on the target hardware. It does not go into a detail about specific encryption algorithms that could be used. It presents a generalized model for secure update that could be adjusted to specific needs.
A Rademacher Complexity Based Method for Controlling Power and Confidence Level in Adaptive Statistical Analysis

While standard statistical inference techniques and machine learning generalization bounds assume that tests are run on data selected independently of the hypotheses, practical data analysis and machine learning are usually iterative and adaptive processes where the same holdout data is often used for testing a sequence of hypotheses (or models), which may each depend on the outcome of the previous tests on the same data. In this work, we present RADABOUND a rigorous, efficient and practical procedure for controlling the generalization error when using a holdout sample for multiple adaptive testing. Our solution is based on a new application of the Rademacher Complexity generalization bounds, adapted to dependent tests. We demonstrate the statistical power and practicality of our method through extensive simulations and comparisons to alternative approaches. In particular, we show that our rigorous solution is a substantially more powerful and efficient than the differential privacy based approach proposed in Dwork et al. [1]-[3].
Mercury: A Vision-Based Framework for Driver Monitoring

In this paper, we propose a complete framework, namely Mercury, that combines Computer Vision and Deep Learning algorithms to continuously monitor the driver during the driving activity. The proposed solution complies to the requirements imposed by the challenging automotive context: the light invariance, in order to have a system able to work regardless of the time of day and the weather conditions. Therefore, infrared-based images, i.e. depth maps (in which each pixel corresponds to the distance between the sensor and that point in the scene), have been exploited in conjunction with traditional intensity images. Second, the non-invasivity of the system is required, since driver’s movements must not be impeded during the driving activity: in this context, the use of cameras and vision-based algorithms is one of the best solutions. Finally, real-time performance is needed since a monitoring system must immediately react as soon as a situation of potential danger is detected.