Reviewerly/exHarmony huggingface dataset:
Normalized Residue Analysis for Deep Learning Based Probabilistic Forecasting of Photovoltaic Generations

In this study, probabilistic forecasting schemes of day-ahead photovoltaic (PV) generations are investigated with the auto-regressive recurrent neural network model named DeepAR, and are evaluated based on the normalized residues. For PV generations, probabilistic outcomes should be helpful for efficient grid managements to account uncertainties such as sudden changes in the local weather. The tightness of the prediction interval for local PV generations is investigated with DeepAR models with varying input data like the local weather forecasts of the day and historical records of the PV generations. For performance measure, normalized residue with the mean and standard deviation of the predicted traces is compared to the standard normal distribution. For evaluation, local PV generation data captured at Hadong, Korea is tested by the DeepAR models with optional input of local weather forecasts data. The evaluation results of the PV generation tests show that the local weather data provides extra tightness of the prediction interval with the normalized residues close to the standard normal distribution.
The Placement of Extra-Clausal Constituents in Functional Discourse Grammar

Although Functional Discourse Grammar offers a detailed set of placement rules for the linear ordering of elements within the clause, phrase and word, little attention has been paid to the placement of extra-clausal constituents (EECs).Thus, so far there has been no systematic attempt to link the rhetorical or discourse-pragmatic functions of EECs to the position they take vis-à-vis (elements of) their host; nor has there been any attempt to integrate any observations about the position of ECCs into the general placement rules proposed in FDG.Using authentic data from various corpora of English, this paper shows that FDG can capture the (interaction between the) various interpersonal, contextual and processing factors that determine the linear placement of EECs.In addition it is argued that the placement of EECs can be integrated into the placement rules of FDG, provided that a third extra-clausal position (interpolated) is distinguished for ECCs interrupting the host.
Decentralized control of a group of quadrocopters using the molecular dynamics method

The development of artificial intelligence systems based on various principles, including anthropomorphic and nature-like systems, as well as progress in the construction of quadrocopters for various purposes, made relevant the practical application of these tools for the effective monitoring of underlying surfaces by groups of such devices. The solution of this problem is associated with the effective control of them in conditions of passive and active interference that impedes the fulfillment of missions, as well as with the problem of reconfiguring their construction in case of fail. The model obtained in the work and the calculations made it possible to conclude that the use of the following approach in the future will allow the creation of a self-government system by an independent group of quadrocopters, capable of performing various missions without control from the Earth under conditions of active and passive interference, as well as with permanent failure of quadrocopters.
Multi-Attention-Based Capsule Network for Uyghur Personal Pronouns Resolution

Anaphora resolution of Uyghur is a challenging task because of complex language structure and limited corpus. We propose a multi-attention based capsule network model for Uyghur personal pronouns resolution, which can obtain the multi-layer and implicit semantic information effectively. Independently recurrent neural network (IndRNN) is applied in this model to achieve the interdependent features with long distance. Moreover, the capsule network can extract richer textual information to improve expression ability. Compared with the single attention-based model which combines Long Short-Term Memory (LSTM), the multi-attention based capsule network can capture multi-layer semantic information through a multi-attention mechanism without using any external parsing results. Experimental results on Uyghur dataset show that our approach surpasses the state-of-the-art models and gets the highest F-score of 83.85%. Meanwhile, our experimental results demonstrate the proposed method can effectively improve the performance of Uyghur personal pronouns resolution.
Coupled cascade regression from real and synthesized faces for simultaneous landmark detection and head pose estimation

The existing approaches usually perform facial landmark detection and head pose estimation independently and sequentially, ignoring their coupled relations. We introduce a unified framework, named coupled cascade regression (CCR), for simultaneous facial landmark detection and head pose estimation. Based on the cascade regression framework, we propose to learn two separate regressors to update the landmark locations and three-dimensional (3D) face model parameters at each cascade level. To capture the coupled relations of the landmark locations and head pose, we further apply the 3D face projection model to refine the prediction results in each cascade iteration and make them consistent. CCR can leverage both the learning methods and the projection model to simultaneously perform facial landmark detection and pose estimation to enhance the performances of both tasks. We also propose to learn the cascade regressors from the combination of real and synthesized face images to solve the problem of limited variations in head pose for training. Experimental results on Helen, labeled face parts in the wild, 300-W, and Boston University datasets show that our proposed CCR method outperforms other conventional methods both for landmark detection and head pose estimation.
A gesture recognition system for the Colombian sign language based on convolutional neural networks

Sign languages (or signed languages) are languages that use visual techniques, primarily with the hands, to transmit information and enable communication with deaf-mutes people. This language is traditionally only learned by people with this limitation, which is why communication between deaf and non-deaf people is difficult. To solve this problem we propose an autonomous model based on convolutional networks to translate the Colombian Sign Language (CSL) into normal Spanish text. The scheme uses characteristic images of each static sign of the language within a base of 24000 images (1000 images per category, with 24 categories) to train a deep convolutional network of the NASNet type (Neural Architecture Search Network). The images in each category were taken from different people with positional variations to cover any angle of view. The performance evaluation showed that the system is capable of recognizing all 24 signs used with an 88% recognition rate.
Tree-Miner: Mining Sequential Patterns from SP-Tree

Data mining is used to extract actionable knowledge from huge amount of raw data. In numerous real life applications, data are stored in sequential form, hence mining sequential patterns has been one of the most popular fields in data mining. Due to its various applications, across the past decades, a significant number of literature have addressed this problem and provided elegant solutions. In this paper we propose a novel tree data structure, SP-Tree, to store the sequence database in a new and efficient manner. Additionally, we propose a new mining algorithm Tree-miner to mine sequential patterns from SP-Tree. To further enhance the performance of our algorithm, we incorporate multiple pruning techniques and optimizations. As our SP-Tree stores the complete database, it can also be used for incremental and dynamic databases, tree-structure is particularly advantageous for interactive mining. We demonstrate how our SP-Tree based Tree-miner algorithm significantly outperforms all of the existing state-of-the-art algorithms, across 6 real life datasets. We conclude by discussing the possible extensions of our approach to other related fields of sequential data.
Fake Face Detection via Adaptive Residuals Extraction Network

With the proliferation of face image manipulation (FIM) techniques such as Face2Face and Deepfake, more fake face images are spreading over the internet, which brings serious challenges to public confidence. Face image forgery detection has made considerable progresses in exposing specific FIM, but it is still in scarcity of a robust fake face detector to expose face image forgeries under complex scenarios. Due to the relatively fixed structure, convolutional neural network (CNN) tends to learn image content representations. However, CNN should learn subtle tampering artifacts for image forensics tasks. We propose an adaptive residuals extraction network (AREN), which serves as pre-processing to suppress image content and highlight tampering artifacts. AREN exploits an adaptive convolution layer to predict image residuals, which are reused in subsequent layers to maximize manipulation artifacts by updating weights during the back-propagation pass. A fake face detector, namely ARENnet, is constructed by integrating AREN with CNN. Experimental results prove that the proposed AREN achieves desirable pre-processing. When detecting fake face images generated by various FIM techniques, ARENnet achieves an average accuracy up to 98.52%, which outperforms the state-of-the-art works. When detecting face images with unknown post-processing operations, the detector also achieves an average accuracy of 95.17%.
Toward Practical and Accurate Touch-Based Image Guidance for Robotic Partial Nephrectomy

Partial nephrectomy involves removing a tumor while sparing surrounding healthy kidney tissue. Compared to total kidney removal, partial nephrectomy improves outcomes for patients but is underutilized because it is challenging to accomplish minimally invasively, requiring accurate spatial awareness of unseen subsurface anatomy. Image guidance can enhance spatial awareness by displaying a 3D model of anatomical relationships derived from medical imaging information. It has been qualitatively suggested that the da Vinci robot is well suited to facilitate image guidance through touch-based registration. In this paper we validate and advance this concept toward real-world use in several important ways. First, we contribute the first quantitative accuracy evaluation of touch-based registration with the da Vinci. Next, we demonstrate real-time, touch-based registration and display of medical images for the first time. Lastly, we perform the first experiments validating use of touch-based image guidance to improve a surgeon's ability to localize subsurface anatomical features in a geometrically realistic phantom.
Survey on Visualization and Visual Analytics pipeline-based models: Conceptual aspects, comparative studies and challenges

In the last decade, many studies have focused on visualization. The main key to make it practical for research and engineering applications is the suitable definition of a pipeline-based visualization model. It provides effective abstractions for designing visualization tools. In this article, we present a comprehensive survey on pipeline-based models for Information Visualization. The basic principles of visualization models and processes are reviewed in literature as proposed in the original versions and their different revisions. Their significant extensions are grouped into two main categories: Information Visualization process and Visual Analytics process. We propose three summaries: The first one focuses on the conceptual aspects; the second one compares existing studies, and the third one discusses the challenges and opportunities for future research in the field of visualization.
An Improved Method of Refining Relative Orientation in Global Structure from Motion with a Focus on Repetitive Structure and Very Short Baselines

Recently, global structure from motion has successfully gained many followers, mainly because of its computational speed. Most of these global methods take the parameters of relative orientation (ROs ) as input and then perform averaging operations. Therefore, eliminating incorrect ROs is of great significance for improving the robustness of global structure from motion. In this article, we propose a method to eliminate wrong ROs which have resulted from repetitive structure and very short baselines. We present two corresponding criteria that indicate the quality of ROs. Repetitive structure is detected based on counts of conjugate points of the various image pairs, while very short baselines are found by inspecting the intersection angles of corresponding image rays. By analyzing these two criteria, we detect and eliminate incorrect ROs. As correct ROs of image pairs with a longer baseline nearly parallel to both viewing directions can be valuable, a method to identify and keep these ROs is also part of our approach. We demonstrate the new method on various data sets, including public benchmarks as well as close-range images and images from unmanned aerial vehicles, by inserting our refined ROs into a global structure-from-motion pipeline. The experiments show that compared to other methods, we can generate the best results.
Functional Path Optimisation for Exploration in Continuous Occupancy Maps

Autonomous exploration is a complex task where the robot moves through an unknown environment with the goal of mapping it. The desired output of such a process is a sequence of paths that efficiently and safely minimise the uncertainty of the resulting map. However, optimising over the entire space of possible paths is computationally intractable. Therefore, most exploration methods relax the general problem by optimising a simpler one, for example finding the single next best view. In this work, we formulate exploration as a variational problem which allows us to directly optimise in the space of trajectories using functional gradient methods, searching for the Next Best Path (NBP). We take advantage of the recently introduced Hilbert maps to devise an information-based functional that can be computed in closed-form. The resulting trajectories are continuous and maximise safety as well as mutual information. In experiments we verify the ability of the proposed method to find smooth and safe paths and compare these results with other exploration methods.
An Interference-Aware Application Classifier Based on Machine Learning to Improve Scheduling in Clouds

To maximize resource utilization and system throughput in cloud platforms, hardware resources are often shared across multiple virtualized services or applications. In such a consolidated scenario, performance of applications running concurrently in the same physical host can be negatively affected due to interference caused by resource contention. This should be taken into account for efficient scheduling of such applications and performance prediction at user level. Nevertheless, resource scheduling in cloud computing is usually based solely on resource capacity, implemented by heuristics such as bin-packing. Our previous work has introduced an interference-aware scheduling model for web-applications considering their resource utilization profile, and to classify applications we applied fixed interference intervals based on common utilization patters. Although this resulted in placements with better overall results, we observed that some applications with more dynamic workload patterns were wrongly classified with intervals. In this paper, we propose an alternative to the use of intervals and present an interference-aware application classifier for cloud-based applications that deals better with dynamic workloads. Our classifier defines automatically interference levels ranges combining two well-known machine learning techniques: Support Vector Machines and K-Means. Preliminary experiments evaluated the applied machine learning techniques in three quality metrics: Accuracy, F1-Score and Rand Index, observing rates over 80%. The proposed solution creates a workload-aware fine-grained classification that was compared with previous work over different workload scenarios. The results demonstrate that our classification approach improves the placement efficiency by 23% on average.
Generalized State-Dependent Exploration for Deep Reinforcement Learning in Robotics

Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL -- often very successful in simulation -- leads to jerky motion patterns on real robots. Consequences of the resulting shaky behavior are poor exploration, or even damage to the robot. We address these issues by adapting state-dependent exploration (SDE) to current Deep RL algorithms. To enable this adaptation, we propose three extensions to the original SDE, which leads to a new exploration method generalized state-dependent exploration (gSDE). We evaluate gSDE both in simulation, on PyBullet continuous control tasks, and directly on a tendon-driven elastic robot. gSDE yields competitive results in simulation but outperforms the unstructured exploration on the real robot.
An open source medical passport based on an Android mobile application and near-field communication

The open source medical passport presented in this study consists of a near-field communication (NFC) tag and an Android application. The app works with structured and normalized data and allows the user to write the tag with relevant medical information, such as health conditions, allergies, recent diseases, blood type, health insurance, medication, as well as other important information including: emergency contact, birth year, nationality, among others. The tag can be read using the app or with another NFC app. The information recorded in the tag can be reliably translated to the reader’s language due to information normalization. The reader of the tag will be a health professional, who will make use of the information recorded by the patient in order to provide a better medical care or act more adequately in emergency situations. The prototype presented includes an offline operation mode, especially envisioned for primary care in remote locations. The whole project has been developed with the support of the UBORA e-infrastructure, a sort of universal “Wikipedia” for open source medical devices, which guides designers through the whole development cycle. All the development information of the medical passport project is made available through such online infrastructure.
Student learning motivation on Actionbound application implementation in construction and building utilities subject

Abstract The research aimed to determine the students’ learning motivations on Actionbound application implementation as learning media in construction and building utilities subject. This research was grounded by monotonous and boring learning media, so the researcher tried to innovate the learning media used Actionbound application to make students more interested to take class. This research was conducted by 30 second grade students of modelling design and building information program in SMKN 2 Garut, with Research and Development as research method. The data collection was carried out by media assessment sheets, learning motivations questionnaires, and student response questionnaires. Media assessed by expert judgement was considered very suitable to use. The students’ media responses considered by media content, media language, strategy effect, and media visual had very good responses. The self-efficacy, active learning strategies, learning value, performance goal, achievement goal, learning environment stimulation, as learning motivation aspects showed a “high” category value. Therefore, the developed Actionbound application is suitable for use as learning media in construction and building utilities subject in SMKN 2 Garut.
The Ecosystem of Cryptocurrency as an Object of Civil Rights in BRICS Countries

The article presents a comparative legal analysis of the modern legal regulation of the multidimensionality of digital electronic currency in BRICS countries. It assesses the possibility of civil circulation of a digital property right as an economic and legal segment without clear legal regulation. It analyzes the judicial practice related to confidentiality, acquisition, and trading of virtual currency. The article justifies the ability to integrate a single digital currency – CRYPTOBRICS, a single equivalent for all payments in the form of cryptocurrency within the framework of BRICS for settlements and increase in the trade exchange volume on these international platforms. This will provide for the legalization and consolidation of the legal framework of cryptocurrency within the context of objects of civil rights, allowing BRICS members to become regulatory leaders in the field of digital assets. We formulated a proposal to create an international agreement defining the parameters of the digital currency issue based on blockchain technology for interstate transactions, which allows the BRICS counties to establish the next stage of their mutual integration for the free trade zone and the customs union. Unifying the civil circulation of cryptocurrency and using the platform of modern non-monetary digital circulation as our foundation, we concluded that BTC can be classified as a type of digital property right. The article justified the theoretical definition of digital property right in the form of cryptocurrency as a resource stored in a device or electronic system which allows the end user to complete transactions using virtual currency and denominated in another payment unit, as opposed to currencies issued by sovereign states. We suggested that insurance companies be insured against all possible risks associated with cryptocurrency circulation and cybersecurity as a civil measure to protect the order of intangible digital codes – cryptocurrencies.
COVID19: THE BENEFITS OF INFORMATIOM TECHNOLOGY (IT) FUNCTIONS IN INDUSTRIAL REVOLUTION 4.0 IN THE TEACHING AND FACILITATION PROCESS

The landscape of teaching has changed from face to face methods to online due to the Coronavirus Disease 19 (COVID-19) outbreak. This gives a new challenge for every teachers and students to adapt it. On the other hand, the industrial phase of Revolution 4.0 was first fully implemented in other sectors in terms of Information Technology (IT) usage and technology tools. Therefore, in this concept paper by using qualitative methods of library research will highlight the issue of COVID19 on the world especially in the education sector, the concept of Industrial revolution 4.0 (IR 4.0), relationship between IT and IR 4.0, and the teaching method revolution.
On the Signal-to-Noise Ratio for Boolean Functions

The notion of the signal-to-noise ratio (SNR), proposed by Guilley, et al. in 2004, is a property that attempts to characterize the resilience of (n,m)-functions F=(f1,...,fm) (cryptographic S-boxes) against differential power analysis. But how to study the signal-to-noise ratio for a Boolean function still appears to be an important direction. In this paper, we give a tight upper and tight lower bounds on SNR for any (balanced) Boolean function. We also deduce some tight upper bounds on SNR for balanced Boolean function satisfying propagation criterion. Moreover, we obtain a SNR relationship between an n-variable Boolean function and two (n-1)-variable decomposition functions. Meanwhile, we give SNR(f⊞g) and SNR(f⊡g) for any balanced Boolean functions f,g. Finally, we give a lower bound on SNR(F), which determined by SNR(fi) (1≤i≤m), for (n,m)-function F=(f1,f2,…,fm).