Reviewerly/exHarmony huggingface dataset:
A new representation of polygonal curves based on piecewise-linear functions with potential application in shape identification

In this paper, a new representation of polygonal curves is proposed. The motivation behind this proposal is to find a descriptive model with potential application in shape identification. In particular, our work addresses the problem of identifying the shape of a given polygonal curve from a set of different ones. In accordance with the proposed representation, a curve is originally described as a series of consecutive points whose connection by straight lines sketches its characteristic shape. In order to define a proper identification scheme, each series of points is mapped to a one-dimensional piecewise-linear function that assigns to each point its corresponding angle. Depending on the case under study, this assignation is performed by following two possible alternatives: (i) in the case where the shape to be identified exactly corresponds with one of the previously stored ones, a sorted sequence beginning from the minimum value of angle and ending with the maximum one is considered, and (ii) in the case where the shape to be identified is similar to one of the previously stored ones, the assignation is performed by following the sequence of points as they appear in the polygonal curve (without sorting). Under this scheme of representation, by a cyclic comparative process between the computed functions, after several steps it is decided whether the graph of any input piecewise-linear function matches the polygonal curve to be recognized within a certain tolerance. In particular, in the case of the identification of two equal shapes, this proposal exploits the well-known principle of similarity geometry, which allows a polygon to be recognized independently of scale, translation and rotation. In order to validate this representation, a comparative analysis between two different shape identification methods (Fourier descriptors and canonical representation) and the piecewise-linear proposal is performed.
Fuzzy logic-based disparity selection using multiple data costs for stereo correspondence

Stereo matching algorithms are capable of generating depth maps from two images of the same scene taken simultaneously from two different viewpoints. Traditionally, a single cost function is used to calculate the disparity between corresponding pixels in the left and right images. In the present research, we have considered a combination of simple data costs. A new method to combine multiple data costs is presented and a fuzzy-based disparity selection method is proposed. Experiments with different combinations of parameters are conducted and compared through the Middlebury and Kitti Stereo Vision Benchmark.
Supervised Learning Enhanced by an Entangled Sensor Network.

Many existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedup awaits the development of quantum random access memories (qRAMs) and fault-tolerant quantum computing. There, however, also exist a multitude of SL tasks whose data are acquired by sensors, e.g., pattern classification based on data produced by imaging sensors. Solving such SL tasks naturally requires an integrated approach harnessing tools from both quantum sensing and quantum computing. We introduce supervised learning assisted by an entangled sensor network (SLAEN) as a means to carry out SL tasks at the physical layer. The entanglement shared by the sensors in SLAEN boosts the performance of extracting global features of the object under investigation. We leverage SLAEN to construct an entanglement-assisted support-vector machine for data classification and entanglement-assisted principal component analyzer for data compression. In both schemes, variational circuits are employed to seek the optimum entangled probe states and measurement settings to maximize the entanglement-enabled {enhancement}. We observe that SLAEN enjoys an appreciable entanglement-enabled performance gain, even in the presence of loss, over conventional strategies in which classical data are acquired by separable sensors and subsequently processed by classical SL algorithms. SLAEN is realizable with available technology, opening a viable route toward building NISQ devices that offer unmatched performance beyond what the optimum classical device is able to afford.
Semi-supervised Semantic Matching

Convolutional neural networks (CNNs) have been successfully applied to solve the problem of correspondence estimation between semantically related images. Due to non-availability of large training datasets, existing methods resort to self-supervised or unsupervised training paradigm. In this paper we propose a semi-supervised learning framework that imposes cyclic consistency constraint on unlabeled image pairs. Together with the supervised loss the proposed model achieves state-of-the-art on a benchmark semantic matching dataset.
Cross-Language Comparability and Its Applications for MT

The concept of comparability, or linguistic relatedness, or closeness between textual units or corpora has many possible applications in computational linguistics. Consequently, the task of measuring comparability has increasingly become a core technological challenge in the field, and needs to be developed and evaluated systematically. Many practical applications require corpora with controlled levels of comparability, which are established by comparability metrics. From this perspective, it is important to understand the linguistic and technological mechanisms and implications of comparability and develop a systematic methodology for developing, evaluating and using comparability metrics. This chapter presents our approach to developing and using such metrics for machine translation (MT), especially for under-resourced languages. We address three core areas: (1) systematic meta-evaluation (or calibration) of the metrics on the basis of parallel corpora; (2) the development of feature-selection techniques for the metrics on the basis of aligned comparable texts, such as Wikipedia articles and (3) applying the developed metrics for the tasks of MT for under-resourced languages and measuring their effectiveness for corpora with unknown degrees of comparability. This has led to redefining the vague linguistic concept of comparability in terms of task-specific performance of the tools, which extract phrase-level translation equivalents from comparable texts.
Informing the Digital Archive with Altmetrics

Altmetrics can be used to understand impact beyond citations, particularly for digitized collections. As cultural institutions look to pursue more active engagement with communities of practice, altmetrics help archivists understand the conversations happening in real time that will allow them to provide access to the most relevant materials. Through the use of case studies, we aim to demonstrate how applying altmetrics while considering the curation of digital collections can allow archivists to stay engaged with target communities outside traditional channels, demonstrating both the applicability of altmetrics to legacy scholarly work and the value of digitization as an access method.
Real-time Detection of Violent Behaviors with a Motion Descriptor

A real-time violent behavior detection algorithm based on a new descriptor is proposed. This descriptor reflects a common observation that the changes in both the magnitude and direction of movement in violent images are more abrupt than non-violent ones. During several frames, descriptor feature vectors consisting of descriptor values are generated, and they are inputs to the Support Vector Machine (SVM) classifier for discriminating violent actions from non-violent actions. Comparison experiments among the Motion Binary Pattern (MBP), the Violent Flow (ViF) and the proposed algorithm were conducted with three different types of datasets. In all datasets, the proposed algorithm was above 80% in the F-measure and outperformed the other methods in every case.
Is the term ‘serials’ relevant any longer? Some thoughts on the matter…

Key points The term 'serial' is becoming obsolete largely because what was once a dominant paper-based medium for communicating has been replaced by a complex ecosystem of online information sources, social media, digital subscription packages, databases, and emerging new formats. While the term serial, associated with publishing in neat instalments, may be fading, the notion of serializing remains very hot. For many users, the 'serial' -or what is left of it -persists only as a Cheshire Cat smile, a mystifying element that must be included as part of the student's properly structured citation. Although seriality is still a valid concept, in the technical environment, there are hints that 'serial' might be replaced by new phrases that include the term 'diachronic'.
Hardware-efficient color correlation–adaptive demosaicing with multifiltering

Color demosaicing is a key image processing step aiming to reconstruct the missing pixels from a recorded raw image that has a color filter array (CFA) pattern. The color correlation–based guided filters, such as minimized-Laplacian residual interpolation (MLRI), are known as the state-of-the-art demosaicing techniques. However, in the conventional guided filter-based techniques, the artifacts are generated in areas with low color correlation. Furthermore, a large number of line memories are required in the hardware implementation of a conventional guided filter-based technique because of the large effective field size. To overcome these two problems, we propose a color correlation–adaptive demosaicing algorithm that selectively applies a specific intracolor demosaicing to regions with low color correlation. We also propose an algorithm structure that reduces the effective field size in the vertical direction to reduce the number of line memories, while maintaining the image quality performance during the hardware implementation. The experimental results show that the proposed scheme can reduce the line memory to one-third while showing marginal performance degradation compared to the state-of-the-art MLRI weighted framework in terms of the color peak signal-to-noise ratio for the IMAX datasets.
The Doctrine of Reality: A New Paradigm of Science

The doctrine of reality and a new paradigm of science are proposed.The unity of formal logic and rational dialectics is the correct methodological basis for the solution of the problem of reality.The main result is the following system of conceptions: (1) reality represents the unity of opposites: the controlling (governing) aspect and the controllable aspect.The controlling (governing) aspect is God, and the controllable aspect is the Universe; (2) the principle of existence and of uniqueness of God reads as follows: the scientific object "Absolute, Creator, and Governor of essence (information) and of material manifestation of essence" exists.This scientific object is the unique and correct theoretical model (identifier) of the religious object "God (Creator and Governor of the World)"; (3) the Universe represents the informational-material system: the unity of essence (information) and of material manifestation of essence.The manifestation of information is matter.The material structure of the Universe represents the set of states of matter: the physical vacuum, the system block, the ether, and the discrete objects; (4) God created the system block, the ether and the objects, entering information into the physical vacuum.God governs the Universe by means of information; (5) the correct science of the 21st century should research the fundamental relation between the controlling (governing) information and the material manifestation of the controlling (governing) information in the Universe.
Semi-supervised Blockmodelling with Pairwise Guidance

Blockmodelling is an important technique for detecting underlying patterns in graphs. Existing blockmodelling algorithms are unsupervised and cannot take advantage of the existing information that might be available about objects that are known to be similar. This background information can help finding complex patterns, such as hierarchical or ring blockmodel structures, which are difficult for traditional blockmodelling algorithms to detect. In this paper, we propose a new semi-supervised framework for blockmodelling, which allows background information to be incorporated in the form of pairwise membership information. Our proposed framework is based on the use of Lagrange multipliers and can be incorporated into existing iterative blockmodelling algorithms, enabling them to find complex blockmodel patterns in graphs. We demonstrate the utility of our framework for discovering complex patterns, via experiments over a range of synthetic and real data sets. Code related to this paper is available at: https://people.eng.unimelb.edu.au/mganji/ .
Machine to Machine Metamorphosis to the IOT

The Internet of Things (IoT) is going to be the next group of Internet revolution linking more and more devices on Internet network or encouraging domain for next-generation communication systems. The data transferred by the machines or set device over the internet should be safely secured, and the data should be transmitted only to the intended system, then delivering the data to any another users or devices. The Machine-To-Machines and Internet of Things (IoT) communications are used in the wide-areas of applications such as transportation, smart grids, interactive education, e-healthcare, home area networks, agriculture sector. This paper will focus the Machine-To-Machines and Internet of Things (IoT) and the subset of Industrial Internet of Things (IoT) ecosystem and also focuses on vulnerabilities and security prone issues, these paper will provide insight guidance and reference for future research works.
Linked Data: Storing, Querying, and Reasoning. SherifSakr, MarcinWylot, RaghavaMutharaju, DanhLe Phuoc, and IriniFundulaki. Cham, Switzerland: Springer International Publishing, 2018. 223 pp. $129.00 (hardcover). (ISBN: 9783319735146)

Journal of the Association for Information Science and TechnologyVolume 70, Issue 8 p. 905-907 BOOK REVIEW Linked Data: Storing, Querying, and Reasoning. Sherif Sakr, Marcin Wylot, Raghava Mutharaju, Danh Le Phuoc, and Irini Fundulaki. Cham, Switzerland: Springer International Publishing, 2018. 223 pp. $129.00 (hardcover). (ISBN: 9783319735146) Dean Allemang, Corresponding Author Dean Allemang dallemang@workingontologist.com Principal Consultant, Working Ontologist LLC, Columbus, OH 43215E-mail: dallemang@workingontologist.comSearch for more papers by this author Dean Allemang, Corresponding Author Dean Allemang dallemang@workingontologist.com Principal Consultant, Working Ontologist LLC, Columbus, OH 43215E-mail: dallemang@workingontologist.comSearch for more papers by this author First published: 25 January 2019 https://doi.org/10.1002/asi.24140Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume70, Issue8August 2019Pages 905-907 RelatedInformation
Agent-Oriented Engineering for Cyber-Physical Systems

Cyber-physical systems are the most relevant mainstream in the contemporary automation of industry, smart cities, and ubiquitous systems. Although most of its components and their behaviors are software-based elements, there are no suitable approaches for specifying, designing, testing and delivering cyber physical systems under a software engineering point of view, i.e. having a systematic, disciplined and measurable approach to its development. This paper describes the suitability of agent oriented software engineering for confronting the development of cyber-physical systems. The contribution is focused on two labels, first, we show a set of requirements for an cyber-physical engineering methodology, that were gathered by means of a literature review, and, second, we present a match between agent-oriented principles and the collected requirements. To illustrate some examples we use Tropos, one of the most disseminated agent-oriented methodologies.
Hunting for life in the machine

Get it wrong and living with smart machines will be hell. Designer Yamanaka Shunji tells Simon Ings why art, not engineering, is key
PuppetGAN: Transferring Disentangled Properties from Synthetic to Real Images.

In this work we propose a model that can manipulate individual visual attributes of objects in a real scene using examples of how respective attribute manipulations affect the output of a simulation. As an example, we train our model to manipulate the expression of a human face using nonphotorealistic 3D renders of a face with varied expression. Our model manages to preserve all other visual attributes of a real face, such as head orientation, even though this and other attributes are not labeled in either real or domain. Since our model learns to manipulate a specific property in isolation using only synthetic demonstrations of such manipulations without explicitly provided labels, it can be applied to shape, texture, lighting, and other properties that are difficult to measure or represent as real-valued vectors. We measure the degree to which our model preserves other attributes of a real image when a single specific attribute is manipulated. We use digit datasets to analyze how discrepancy in attribute distributions affects the performance of our model, and demonstrate results in a far more difficult setting: learning to manipulate real human faces using nonphotorealistic 3D renders.
Investigating Heavy Metal Pollution of a Residential Area in Nsukka, Enugu State of Nigeria and Associated Risks

The soils of residential areas close to high traffic roads are susceptible to heavy metal contamination as a result of urban or sub-urban activities.In this study, the heavy metal contamination of a soil area in Nsukka of Enugu state was investigated with a view to ascertaining the geochemical baseline and hitherto contamination.The heavy metals determined were Cu, Pb, Zn, Cr and Cd which were measured by means of Atomic Absorption Spectrometry.The extent of pollution of the soil environment was evaluated using, contamination factor (CF), geochemical accumulation index I geo , and pollution load index (PLI) whereas the ecological risks was evaluated using potential ecological risk index (PERI).Obtained results show that the mean concentration values of the heavy metals were 18.5, 23.4,57.5, 78.2 and 3.1 mg/Kg for Pb, Cu, Cr, Zn Cd respectively.The soil is contaminated with Cd as CF and I geo are both higher than unity.However, the PLI of 0.33 for the entire soil area indicates insignificant pollution.The PERI value of 98.4 which is less than 110 also indicates insignificant ecological risks.The soil area therefore can be used for the intended project of rearing household animals but the Cd level would need to be constantly monitored.
An extensible framework for ECG anomaly detection in wireless body sensor monitoring systems

Automatic anomaly detection from the sensor streams of the Electrocardiography requires online techniques of data stream processing and analysis. The significant increase in the availability of the Electrocardiography collected from wireless sensor networks (WSNs) has attracted heaps of research interests in identifying the anomalies in an Electrocardiography. However, most existing models focus on a particular type of anomaly, like arrhythmia, and they are not dynamically extensible for the identification of an unknown type of anomaly. This work proposes an extensible method named shapelet-base (SH-BASE) to solve this problem. Essentially, SH-BASE is a knowledge-base of shapelets, in which the shapelets are classified into different groups (we call them branches), and each branch stores the most distinctive shapelets of a particular type of anomaly. Experimental results show that the SH-BASE can achieve a very competitive performance in anomaly detection for Electrocardiography sensor streams by comparing with the state-of-the-art models.
Immersive Telepresence Systems and Technologies

Modern immersive telepresence systems enable people at different locations to meet in virtual environments using realistic three-dimensional representations of their bodies. For the realization of such a three-dimensional version of a video conferencing system, each user is continuously recorded in 3D. These 3D recordings are exchanged over the network between remote sites. At each site, the remote recordings of the users, referred to as 3D video avatars, are seamlessly integrated into a shared virtual scenery and displayed in stereoscopic 3D for each user from his or her perspective.This thesis reports on algorithmic and technical contributions to modern immersive telepresence systems and presents the design, implementation and evaluation of the first immersive group-to-group telepresence system in which each user is represented as realistic life-size 3D video avatar. The system enabled two remote user groups to meet and collaborate in a consistent shared virtual environment. The system relied on novel methods for the precise calibration and registration of color- and depth- sensors (RGBD) into the coordinate system of the application as well as an advanced distributed processing pipeline that reconstructs realistic 3D video avatars in real-time. During the course of this thesis, the calibration of 3D capturing systems was greatly improved. While the first development focused on precisely calibrating individual RGBD-sensors, the second stage presents a new method for calibrating and registering multiple color and depth sensors at a very high precision throughout a large 3D capturing volume. This method was further refined by a novel automatic optimization process that significantly speeds up the manual operation and yields similarly high accuracy. A core benefit of the new calibration method is its high runtime efficiency by directly mapping from raw depth sensor measurements into an application coordinate system and to the coordinates of its associated color sensor. As a result, the calibration method is an efficient solution in terms of precision and applicability in virtual reality and immersive telepresence applications. In addition to the core contributions, the results of two case studies which address 3D reconstruction and data streaming lead to the final conclusion of this thesis and to directions of future work in the rapidly advancing field of immersive telepresence research.
Huber collaborative representation for robust multiclass classification

Recent advances have shown a great potential of collaborative representation (CR) for multiclass classification. However, conventional CR-based classification methods adopt the mean square error (MSE) criterion as the cost function, which is sensitive to gross corruption and outliers. To address this limitation, inspired by the success of robust statistics, we develop a Huber collaborative representation-based classification (HCRC) method for robust multiclass classification. Concretely, we cast the classification problem as a Huber collaborative representation problem with the Huber estimator. Our another contribution is to design an efficient half-quadratic (HQ) algorithm with guaranteed convergence to solve the proposed model efficiently. Furthermore, we also give a theoretical analysis of the classification performance of HCRC. Experiments on real-world datasets corroborate that HCRC is an effective and robust algorithm for multiclass classification tasks.