Reviewerly/exHarmony huggingface dataset:
Object detection for autonomous search and rescue quadrotor application

Abstract Indonesia is located in the ring of fire zone. It causes a high risk of disaster threats. Volcanoes and earthquakes are unpredictable sources of natural disaster in a massive size, which many times caused an inaccessible area. We need to prepare how to overcome this situation. Quadrotor is an aerial vehicle that can access the disaster location easily and passing through many obstacles. Its autonomous ability can increase searching efficiency. Object detection will be a primary parameter which responsible to move the quadrotor. In this paper, we proposed to use cascaded Canny Edge with SURF clarified with SIFT to get more robust detection in real-time application. Our method is using image detection in guiding the quadrotor movements. First, we detect the object by cascaded Canny Edge with SURF and ORB, both of them checks whether the target is true or not. When both methods approved that target have been detected, then the process uses only the cascaded Canny Edge with SURF methods to locate the victims and to decrease the image processing time. Output of this image detection method applies into our quadrotor control movements, by using simple Proportional Integral Derivative (PID) control. This visual feedback control guides the quadrotor flies to the target and note the victim position and reported in its log. This paper will also provide data comparison between our method with the only SURF and the only ORB method to do the same task.
Ammonium abundance and short-wave infrared absorption spectra of altered rocks

Rhyolitic rocks hosting the El Zapote epithermal Ag deposit are pervasively altered and contain ammonium ranging from 290 to 1750 ppm. High ammonium values are found in samples containing abundant fine-grained illite. This fine-grained illite shows overall low ratios of K/(Al + Fe), likely due to ammonium substituting K + . Samples containing high ammonium, greater than 1000 ppm in the proximity of Ag-bearing veins, show distinct absorption features of ammonium in short-wave infrared (SWIR) absorption spectra. Samples containing ammonium ranging from 500 to 1000 ppm show mixed absorption signatures; some show prominent absorption features related to ammonium, whereas others have no recognizable features. There is no discernible absorption feature related to ammonium for samples containing less than 500 ppm NH 4 . The data suggest that SWIR spectroscopy is useful in locating the proximal areas to mineralization, but the extent of ammonium alteration is much larger than that identified by SWIR spectroscopy. Nitrogen isotope compositions of ammonium are similar to those of sedimentary rocks, suggesting that the source of ammonium in altered rhyolite is sedimentary basement rocks in the area. Supplementary material : bulk rock composition is available at https://doi.org/10.6084/m9.figshare.c.5015663
Singular Value Decomposition (SVD) based Image Tamper Detection Scheme

Image authentication techniques are basically used to check whether the received document is accurate or actual as it was transmitted by the source node or not. Image authentication ensures the integrity of the digital images and identify the ownership of the copyright of the digital images. Singular Value Decomposition (SVD) is method based on spatial domain which is used to extract important features from an image. SVD function decomposes an image into three matrices (U, S, V), the S matrix is a diagonal matrix constitutes singular values. These values are important features of that particular image. The quick response code features are utilized to create QR code from the extracted values. The evaluations produced represents that this designed method is better in producing authenticated image as compared to existing schemes.
Accelerated Message Passing for Entropy-Regularized MAP Inference

Maximum a posteriori (MAP) inference in discrete-valued Markov random fields is a fundamental problem in machine learning that involves identifying the most likely configuration of random variables given a distribution. Due to the difficulty of this combinatorial problem, linear programming (LP) relaxations are commonly used to derive specialized message passing algorithms that are often interpreted as coordinate descent on the dual LP. To achieve more desirable computational properties, a number of methods regularize the LP with an entropy term, leading to a class of smooth message passing algorithms with convergence guarantees. In this paper, we present randomized methods for accelerating these algorithms by leveraging techniques that underlie classical accelerated gradient methods. The proposed algorithms incorporate the familiar steps of standard smooth message passing algorithms, which can be viewed as coordinate minimization steps. We show that these accelerated variants achieve faster rates for finding $\epsilon$-optimal points of the unregularized problem, and, when the LP is tight, we prove that the proposed algorithms recover the true MAP solution in fewer iterations than standard message passing algorithms.
Team and Interactive Methods of Teaching as Means to Form Secondary School Pupils’ Financial Competence

The research objective includes theoretical and experimental justification of a model to form financial competence using active teaching methods. The paper provides theoretical foundations of an integration model, the efficiency of which was justified by statistical data. It is proved that team and interactive methods raise efficiency of teaching if used together with the moderation technology. Scientific originality of the study includes developing an efficient model to form financial competence under the conditions of the class-and-lesson system; the proposed educational module can be used within the framework of the social science course. The research findings are as follows: the publication fills the existing gaps in studies on the use of team and interactive methods to form secondary school pupils’ financial competence; the authors develop an efficient and up-to-date competence formation model, which includes the moderation technology and the Singaporean methodology and is based on team and interactive methods of teaching. The proposed model is especially relevant in the light of upcoming modernization of educational content stipulated by the necessity to introduce larger pieces of financial information.
Beginning the Operationalization Process: First Step, Descriptions – From Dimensions to Objects

This chapter begins the operationalization process. Through the perception and comparison of “differences” and the identification of similarities – understood as differences that do not present notable differences – the cognitive mind distinguishes the differences arising from different sensors and “constructs” observational (cognitive) dimensions (Sect. 2.2), i.e., dimensional scales (or cognitive variables) through which it can “perceive objects” from the constructivist perspective; operationally, this is equivalent to the “construction” of “technical descriptions of objects” (Sect. 2.4). This is the first step in cognition: a certain number of differences at various levels, even recognized as belonging to different dimensions – which the mind, through memory, recognizes as being associated in some way with or referring to the same source – represent the technical description of an “object”, which is mental content that is not yet translated into a language. Technical descriptions allow the cognitive mind to compare objects (Sect. 2.6) and find further differences or similarities that enable the objects to be classified (Sect. 2.7).
Selecting Auxiliary Data Using Knowledge Graphs for Image Classification with Limited Labels

In this paper, we propose a learning algorithm for training deep neural networks when there is not sufficient labeled data. To improve the generalization capabilities of the deep model, we adopt a learning scheme to train two related tasks simultaneously. One is the original task (target), and the other is an auxiliary task (source). In order to create a related auxiliary task, we leverage an available knowledge graph to query for semantically related concepts that are grounded in labeled images; hence we call our method KGAuxLearn. We jointly train the target and source tasks in a multi-task architecture. We evaluate our method on two fine-grained visual categorization benchmarks: Oxford Flowers 102 and CUB-200-2011. Our experiments demonstrate that the error rate reduced by at least 2.1% over finetuning for both datasets. We also improve the error rate by 1.36% and 2.93% over using randomly selected concepts as an auxiliary task for Oxford Flowers 102 and CUB-2002011, respectively. In addition, comparing our method with auxiliary data selection methods that do not use a knowledge graph, the error rate improves by 0.69% and 2.57% on Oxford Flowers 102 and CUB-200-2011, respectively.
The Where of Mineral Names: Nigerite (Now Ferronigerite-2N1S) Egbe District, Kogi State, Nigeria

Nigerite, an oxide of aluminum, iron, zinc, and tin, was first described from the Egbe district (formerly Kabba Province) in Nigeria (fig. 2) (Jacobson and Webb 1947; Bannister, Hey, and Stadler 19...
A simple motion deblurring method by two continuous images

Restoring blurred images to clear images is a challenging problem. Most previous methods only analyzed the single image. However, for motion blurring, this method missed the key trajectory description process. Based on the essence of motion blur, our research combined the continuous frame image information in video to estimate the motion track, the SIFT algorithm was proposed for the first time to match the feature points of the target in the continuous frame image. The accurate motion blur angle and motion blur distance were obtained by combining the matching information to obtain an accurate PSF. Finally, the Wiener filtering algorithm combined with PSF was used for deblurring. Experiments showed that the proposed method can improve the image sharpness after restoration compared with the previous algorithms.
Interval-parametric synthesis of a robust controller on a base of characteristic polynomial with desired stability in a sector

Abstract In the paper a characteristic polynomial of a linear control system which coefficients include interval parameters of a control object and adjustable parameters of a controller is considered. In order to find polytope of controller parameters providing desired sector stability of a polynomial, values of polynomial coefficients limits are found on a base of desired oscillability degree of a system. On a base of these calculations a controller synthesis method is developed. Application example of the method proposed is also provided.
Intelligent Scene Caching to Improve Accuracy for Energy-Constrained Embedded Vision

We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.
Gibbardian Collapse and Trivalent Conditionals

In conditional logics the law of Import-Export states the equivalence between right-nested sentences of the form “if A, then if B, then C” and their simplification “if A and B, then C”. While Import-Export appears as a plausible principle of conditional reasoning, Allan Gibbard proved that under certain assumptions, a conditional operator cannot satisfy this principle without collapsing to the material conditional of two-valued logic. Gibbard’s result is often taken to pose a dilemma for a semantic account of indicative conditionals: either give up Import-Export, or other plausible properties such as supraclassicality or the logical implication from the indicative to the material conditional. This chapter examines how this dilemma may be averted in trivalent logics based on Reichenbach’s and de Finetti’s idea that a conditional with a false antecedent is undefined.
FoNet: A Memory-efficient Fourier-based Orthogonal Network for Object Recognition

The memory consumption of most Convolutional Neural Network (CNN) architectures grows rapidly with the increasing depth of the network, which is a major constraint for efficient network training and inference on modern GPUs with limited memory. Several studies show that the feature maps (as generated after the convolutional layers) are the big bottleneck in this memory problem. Often, these feature maps mimic natural photographs in the sense that their energy is concentrated in the spectral domain. In this paper, we propose a Fourier-based Orthogonal Network (FoNet) that incorporates orthogonal representations and performs both the convolution and the activation operations in the spectral domain to achieve memory reduction. The performance of our FoNet is evaluated on four standard object recognition benchmarks (i.e., MNIST, CIFAR10, SVHN, and ImageNet), and compared with four state-of-the-art implementations (i.e., LeNet, AlexNet, VGG, and DenseNet). Encouragingly, FoNet is able to reduce memory consumption by about 60% without significant loss of performance for all tested network architectures.
Coding mechanism and soft systems technique applied to integrate the fuzzy based Decision Support System with HRM factors in the SMEs of Bangladesh

In this article, the objective is to integrate fuzzy based decision support system (DSS) with human resource management framework (HRM) of the manufacturing SMEs in Bangladesh, where various decision-making styles regarding HRM factors are exhibited. In the decision support system, few decision-making styles are selected and sorted using fuzzy inference mechanism. Fuzzy based decision-support system is integrated with HRM framework by applying coding mechanism and soft systems technique. This fuzzy approach allows the researchers to examine the relations between decision-making styles and human resource management factors when there is uncertainty, vagueness, multiple elements of problems and solutions. Human resource management has various factors incorporating with hiring employees, career development and management, employees' performance management, handling management styles, building relationship between the managers and the employees. Well, decision support system is involved in each of this process. At present, in the competitive world using fuzzy logic approach in decision support system in human resources management has achieved particular stance and integrated decision support system in HRM assists the managers in making decisions meaningfully. The purpose is to provide results to the manufacturing SMEs that can assist the managers to exercise the most appropriate decision making style and take such a decision that is beneficial for all in the SMEs.
High-speed time measurement system based on binary optical representation

Photo-mechanics is an important experiment means to study the structural mechanics behaviour. At present, joint high-speed observation system has become an important method with multiple high-speed cameras, but unknown startup delays among cameras will decrease the system's accuracy. To address the problem caused by the asynchronization of multi-camera, a high-speed time measurement system is proposed to locate camera capturing time. Binary optical representation of time is adopted to design and implement the system. In order to reduce error in the system operating, three theorems are presented and proved to illustrate the essence of binary code. Based on that, an optimized algorithm is developed to transform optical representation to time information, and then the errors are evaluated mathematically. The experimental results show that the proposed system successfully locates time at microsecond level with a shutter time of 9.615 us and the optimized approach reduces the maximum error and standard deviation of error by 722 and 52 times respectively. The systematic method proposed in this paper contributes to practical applications efficiently and accurately.
Schedule Control System for Wearable Medicine Box Using Bluetooth Low Energy

The development of technology can facilitate human work in many fields, one of that is health, its use has many benefits, for example in drug administration. This applies to people who do not obey the rules of taking medication that have been given by doctors or other medical personnel, other cases patients often experience forgetfulness to take the medicine even though treatment is important for the recovery of his illness and some cases of people who forget to bring the medicine while being traveling or going to work, in the above case an electronic device is needed which helps in reminding to take drugs which are connected to an Android-based smartphone using Bluetooth Low Energy. Therefore, researchers conducted research by implementing Bluetooth Low Energy in real life. In designing this system using Bluetooth Low Energy is used to control the medicine box that is connected to the user's cell phone or patient. Bluetooth Low Energy contained in the ESP32 microcontroller to connect the smartphone with a medicine box, LED and Buzzer as a marker and alarm when taking medication. Infrared sensors to detect the presence of drugs and become sensors to ensure the drug is taken at a predetermined time.
Sparse CNN Architecture Search (Scas)

Advent of deep neural networks has revolutionized Computer Vision. However, designing of such models with high accuracy and low computation requirements is a difficult task and needs extensive human expertise. Recent advances in Neural Architecture Search use various methods like Deep Reinforcement Learning, Evolutionary methods, Gradient Descent, HyperNetworks etc. to automatically generate neural networks with high level of accuracy. However, large size of such generated models limit their practical use. Recent findings about lottery ticket hypothesis suggest the existence of sparse subnetworks (winning tickets) which can reach the accuracy comparable to that of original dense network. In this paper, we present a method for leveraging redundancies inherent to deep Convolutional Neural Networks (CNN) to guide the generation of sparse CNN models (to find the architectures with winning tickets) without significant loss in accuracy. We evaluate our proposed method with different NAS methods on CFAR-10,CIFAR-100 and MNIST datasets. Our results show a reduction ranging from $2\times \mathrm{to} 12\times$ in terms of model size and $2\times \mathrm{to}19\times$ in terms of number of MAC operations with less than 1% drop in accuracy.
A Hybrid Encryption Scheme for Securing Images in the Cloud

With the introduction of Cloud computing, a new era of computing has begun. Cloud has the ability to provide flexible, cost effective pay-as-you-go service. In the modern day computing, outsourcing of data/multimedia into the cloud has become an effective trend as cloud provides storage as a service, platform/software as a service, infrastructure as a service etc. Seamless exchange of data /multimedia is made possible ensuring the data available anytime, anywhere. Even though cloud based services offer many advantages, data owners are still hesitant to keep their data with the third party. Confidentiality, Integrity, Privacy and Non-repudiation are the major concerns of the outsourced data. To secure the data exchange between users and the cloud, many traditional security approaches are proposed. In this paper, a hybrid encryption technique to secure the images is proposed. The scheme uses Elliptic Curve Cryptography to generate the secret key, which in turn used for DES and AES algorithms.
SERVICE-ORIENTED TECHNOLOGIES IN MUNICIPAL LIBRARIES OF THE NOVOSIBIRSK REGION

The report presents an overview of information technologies used by municipal libraries of the Novosibirsk region. It highlights the fact that digital technologies in all libraries, regardless of their level, assure better quality of library service, make possible remote access to holdings and attract the growing number of users.
Robust Task Learning Based on Nonlinear Regression With Mixtures of Student-<i>t</i> Distributions

We propose a robust task learning method based on nonlinear regression model with mixtures of t-distributions. The model can adaptively reduce the effects of complex noises and accurately learn the nonlinear structure of targets. By introducing latent variables, the model is expressed into a hierarchical structure, which helps explain the advantage of flexibility compared to the traditional Gaussian based learning model. We develop a two-stage efficient estimation procedure to obtain penalized likelihood estimator of the parameters combined an expectation-maximization algorithm with Lagrange multiplier method. The learning performances of the model are investigated through experiments on both synthetic and real data sets.
Development of Line Locator Software Component for Mobile Operating Systems

The paper describes the architecture of a cross-platform software component of a line locator. The architecture is based upon sharing of the software code depending on the target platform, on the software code, which can run on any platform without introducing any changes. The ability to run on different platforms is achieved by making use of such toolkits as Kivy and such tools designed to build package software as CMake and Buildozer, which make it possible to port the software component of the line locator on Android mobile operating system ensuring its safe operation as well as to port it on Windows and Linux desktop operating systems. The suggested method could be used to port it on other mobile devices based on iOS and Windows 10 operating systems.
Multitask Attentive Network For Text Effects Quality Assessment

Along with the fast development of image style transfer, large amounts of style transfer algorithms were proposed. However, not enough attention has been paid to assess the quality of stylized images, which is of great value in allowing users to efficiently search for high quality images as well as guiding the designing of style transfer algorithms. In this paper, we focus on artistic text stylization and build a novel deep neural network equipped with multitask learning and attention mechanism for text effects quality assessment. We first select stylized images from TE141K [1] dataset and then collect the corresponding visual scores from users. Then through multitask learning, the network learns to extract features related to both style and content information. Furthermore, we employ an attention module to simulate the process of human high-level visual judgement. Experimental results demonstrate the superiority of our network in achieving a high judgement accuracy over the state-of-the-art methods. Our project website is available at https://ykq98.github.io/projects/TEA/.