Reviewerly/exHarmony huggingface dataset:
Self-Supervised Task Learning For Robotic Underfloor Insulation

Effective under-floor insulation (UFI) of residential buildings to reduce their energy consumption and CO2 emissions is a substantive challenge in retrofitting existing homes. Traditional UFI installation techniques require suspended floors to be taken up, damaging and disrupting the living space for several days. To deliver the low-disruption insulation of suspended floors, a robot has been developed for accessing and spraying thermal insulation to the underside of a suspended floor. Q-Bot has been installing UFI with a fleet of robots in a largely teleoperated mode for several years. In this mode, the operator is in complete control of all actions the robot takes, which is a significant cognitive burden. This paper addresses Q-Bot's steps toward automating the UFI installation process, reducing the operator's cognitive load and eventually freeing the operator to perform other tasks. Years of recorded experience are leveraged to train a simplified U-Net model in a self-supervised fashion, enabling robots to decide where to apply the insulation foam next. Results obtained from the on-site collected data show that the weighted symmetric cross-entropy loss function yields better spray-region prediction results than the base loss, Cross-Entropy. Our method can adapt to various operator preferences, generalise to novel building crawl spaces, and improve with more data.
Recognition of colonial and multicellular microplankton organisms in surface waters using computer vision

When assessing the quality of surface water by biological indicators, there is a problem of measuring the concentration of microplankton by individual classification groups, and this involves the recognition of microorganisms. A method is proposed for increasing the accuracy of an information-measuring system that measures the concentration of microplankton in a running stream. The article considers the problem of accuracy increasing in recognition and counting of colonial and multicellular microplankton organisms in determining the biological indicators of water quality in natural reservoirs using computer vision. In the paper was presented a method has been developed to improve the recognition accuracy of colonial and multicellular organisms of microplankton, which is notable for low computational complexity and allows increasing the accuracy of measuring the concentrations of various groups of microplankton. Experimental results confirmed that the use of the developed modified algorithm allows to increase the accuracy of recognition of colonial algae Pediastrum duplex and multicellular Ulothrix and reduce the number of false-positive recognition of them as several unicellular organisms.
Topological Groups and Topological Vector Spaces

This chapter studies certain topological-algebraic structures such as topological groups and topological vector spaces. The book Basic Topology, Volume 1 of the present series of books studies the general properties of topological spaces and their continuous maps. But this chapter studies the topological spaces with other structures (algebraic) compatible with the given topological structures. For example, the circle group $$ S^1$$ in the complex plane $$ \textbf{C},$$ the 3-spheres $$ S^3$$ (group of unit quaternions), the general linear group $$ GL ( n. \textbf{R})$$ , $$ GL ( n. \textbf{C})$$ , etc., admit a natural group structure under usual multiplication such that their usual topological and algebraic group structures are compatible in the sense that the corresponding group operations are continuous. It asserts that the concept of a topological group is precisely that concept in which the algebraic and topological structures are united and interrelated. This phenomenon leads to the concept of topological groups.
A TARGET TRACKING METHOD BASED ON MODIFIED IMM/MHT ALGORITHM

Aiming at the tracking problem of multiple maneuvering targets in clutter environment, the application of radar resource management in IMMMHT algorithm is proposed. In the IMMMHT algorithm, the normalized intensity function of the track is established, and the maximum differential entropy of the intensity function is solved by using the covariance of the tracking outputs to obtain the optimal solution of the time-on-target combination, which is used to control the time resources of the next radar update period. Simulation results show that this method can effectively deal with the tracking problem of multiple maneuvering targets in clutter environment, and its performance is better than IMMMHT algorithm.
Exploring Abnormal Behavior in Swarm: Identify User Using Adversarial Examples

With appearing of adversarial examples which are able to fool deep neural networks, some defense methods are developed to recognize adversarial images. This paper focuses on a new kind of detection on adversarial examples, which aims to find the abnormal user who utilized adversarial examples among a number of normal users. Due to the utilization of adversarial images, the abnormal user is significantly deviated from the majority normal users. Based on this, we propose a straightforward method to extract abnormity information, and design a pooled detection scheme to identify the abnormal user by hierarchical clustering. Experimental results show that our scheme is able to identify the utilization of popular adversarial example methods, and achieve low computational complexity.
Perception-Based Pseudo-Motion Response for 360-Degree Video Streaming

Streaming high-quality 360-degree video over constrained networks with low latency is very challenging due to high bandwidth requirement. Tile-based viewport adaptive streaming that proactively delivers predicted visible fields with higher quality is bandwidth-friendly, but limited prediction accuracy of head movement results in degraded viewport quality. In this letter, we propose a perception-based pseudo-motion response strategy to mitigate the damage to viewport quality, benefiting from human perception thresholds for head rotation losses and gains in virtual environment. It employs imperceptible virtual rotation losses when the imminent physical viewports may exceed the high-quality region, and immediate losses compensation once the prediction performs well. Experiments results show that our proposed strategy achieves an additional average 1.43% coding gain compared to traditional tile-based video streaming. Most notably, the proposed method is compatible with any tile-based video streaming.
A Tighter Converse for the Locally Differentially Private Discrete Distribution Estimation Under the One-bit Communication Constraint

We consider a discrete distribution estimation problem under the local differential privacy and the one-bit communication constraints. A fundamental privacy-utility tradeoff in this problem is formulated as the minimax squared loss. We show a tighter lower bound on the minimax squared loss, which has exactly the same form with the upper bound by the recursive Hadamard response by Chen et al. up to a constant factor of 4 for arbitrary LDP constraint and arbitrary finite data space. To derive the lower bound, we modify the van Trees inequality to involve a symmetrized Fisher information, which is invariant under the choice of the coordinate system on the probability simplex. We further characterize the maximum of the symmetrized Fisher information by considering the joint effect of the privacy and the communication constraints.
Learning Functions and Classes Using Rules

In the current work, a novel method is presented for generating rules for data classification as well as for regression problems. The proposed method generates simple rules in a high-level programming language with the help of grammatical evolution. The method does not depend on any prior knowledge of the dataset; the memory it requires for its execution is constant regardless of the objective problem, and it can be used to detect any hidden dependencies between the features of the input problem as well. The proposed method was tested on a extensive range of problems from the relevant literature, and comparative results against other machine learning techniques are presented in this manuscript.
Automatic dottization of Arabic text (Rasms) using deep recurrent neural networks

Arabic letters in their early stages were only shapes (Rasm) without dots. Dots were added later to ease reading and reduce ambiguity. Thereafter, diacritics were introduced for phonetic guidance, mainly for nonnative speakers. Many studies have been conducted to automatically diacritize Arabic texts using machine learning techniques. However, to the best of our knowledge, automatically adding dots to Arabic Rasms has not been reported in the literature. In this work, we present the automatic addition of dots to Arabic Rasms using deep recurrent neural networks. Different design choices were explored, including the use of character sequences and word sequences as tokens. The presented techniques were evaluated on four diverse publicly available datasets. Character-level models with stacked BiGRU architecture outperformed all the other architectures with character error rates ranging from 2.0% to 5.5% and dottization error rates ranging from 4.2% to 11.0% on independent test sets.
Automaton for Hamiltonian, tournament and their complements

The concept of folding (Automata or Graphs) has been studied by several researches over the past years. Parallel computing folded cubic graphs have been employed as a potential network topology as an alternative to the hypercubic. The study of graph folding has applications in various emerging research area like chemical structure, protein structure and biological structure. Motivated by these observations, an idea of folding a special Hamiltonian circuit graph to a cycle triple is initiated. An infinite graph G is constructed and the iterative level construction leads to a Hamiltonian circuit graph. Further the folding process to be performed must ensure in the end that the iterative folding provides the final folded as a cycle triple pseudosymmetric graph. And also a special kind of Tournament is considered and output table of directed edge labeling for the same are discussed. Both Hamiltonian Circuit and Tournament Graph have been constructed by the Finite State Machine. The main intention is to study the generalization of automaton for Hamiltonian circuit.
$\mathbb{F}_q$-Zeros of Sparse Trivariate Polynomials and Toric 3-Fold Codes

For a given lattice polytope $P\subset\mathbb{R}^3$, consider the space $\mathcal{L}_P$ of trivariate polynomials over a finite field $\mathbb{F}_q$, whose Newton polytopes are contained in $P$. We give an upper bound for the maximum number of $\mathbb{F}_q$-zeros of polynomials in $\mathcal{L}_P$ in terms of the Minkowski length of $P$ and $q$, the size of the field. Consequently, this produces lower bounds for the minimum distance of toric codes defined by evaluating elements of $\mathcal{L}_P$ at the points of the algebraic torus ${(\mathbb{F}_q^*)}^3$. Our approach is based on understanding factorizations of polynomials in $\mathcal{L}_P$ with the largest possible number of nonunit factors. The related combinatorial result that we obtain is a description of Minkowski sums of lattice polytopes contained in $P$ with the largest possible number of nontrivial summands.
Multi-Step Gold Refinement and Collection Using Bi-Minerals in the Laozuoshan Gold Deposit, NE China

Gold and Bi-bearing parageneses are pivotal to understanding gold concentration and deposition processes. The large-scale Laozuoshan gold deposit is located in the Jiamusi Block, northeastern China, and has experienced complex mineralization processes with abundant gold and Bi-bearing minerals. However, the relationship between Bi-minerals and gold is unclear, preventing our understanding of the gold enrichment and precipitation mechanism in the Laozuoshan gold deposit. Optical microscope and SEM results show three stages of gold mineralization: pyrrhotite (Po-1) + arsenopyrite (Apy-1) + Bi-bearing minerals (Bis-1) + Au-1; arsenopyrite (Apy-2) + chlorite + Bi-bearing minerals (Bis-2) + Au-2; and arsenopyrite (Apy-3) + graphite + Bi-bearing minerals (Bis-3) + Au-3. The abundant amount of gold (Au-1~Au-3) is associated with Bi-bearing minerals (Bis-1~Bis-3), which coexist as inclusions and fill in fractures in these minerals. The mineral assemblages of arsenopyrite, Bi-minerals, and gold exhibit a clear As-Bi-Au mineralogy in the ores, and the ternary diagram of the chemical compositions of the Bi-minerals shows that Bi-minerals all fall in reducing regions, indicating that Bi-minerals are precipitated under reducing conditions. The gold compositions demonstrate a positive correlation (R2 = 0.58) between Au and Bi. Consequently, we propose that the gold experienced the ore-forming fluids concentration and further Bi-melts scavenging for the Laozuoshan gold deposit mineralization. The Bi collector model is essential in interpreting the high-grade gold in the Laozuoshan gold deposit, indicating that the geochemical anomalies observed with bismuth may be a critical potential exploration target for the high-grade gold deposits in the Jiamusi Block.
An intelligent ubiquitous compression technique for DNA sequencing using Hadoop

To solve the problem of reducing the amount of data storage in the practical application of massive biomedical data and efficiently using existing storage devices and bandwidth resources to store shared data. The proposed model includes both compression modes: the first is a single sequence compression mode designed for the characteristics of a large number of repeated substrings in DNA sequences; the second is a reference-based multi-sequence compression mode designed for the very similar characteristics of DNA sequences of different individuals of the same species. Both types of compression use the Lempel–Ziv–Welch (LZ) compression method, which is quite comparable to one another, to examine the sequence, as well as to study and classify the repetitive data that exists between a single sequence and numerous sequences in the sequence set. The proposed method aims to solve the problem of high pressure caused by single-point processing of large sequence files, effectively reduces redundant information by using the local correlation of data, and effectively uses the computing resources of a cloud platform that is used for biological information processing to support the efficient storage, transmission, and sharing of data.
Engineering Predictable Water Supply: The Humans Behind the Tech

Abstract Although it was reported in 2012 that 89% of the world’s population had access to piped water, it is estimated that at least one billion people receive this water for fewer than 24 h per day. Intermittency places a variety of burdens upon households, including inadequate quantities of supply at the household level, unpredictability of water utilities in making water available, and a disproportionate time burden on poorer households. For many intermittent water systems, the availability of water is controlled by valvemen who turn access on/off to various portions of their service area. Using this information, NextDrop sends notifications via mobile phones to customers as to when water is likely to be available. Although a pilot of NextDrop was successfully implemented in Hubli-Dharwad in India, NextDrop faced significant challenges when expanding to Bangalore. This case study investigates how a breakdown in the information pipeline, as well as corresponding human factors, prevented adoption of NextDrop in Bangalore. Specifically, randomized controlled trials found that valvemen sent reports of their activities to NextDrop only 70% of the time. Even when NextDrop passed messages onto customers, only 38% of customers reported receiving notifications, primarily because either the household “waiters” for water, usually women, did not have daytime access to the mobile phone registered with NextDrop or the notifications are buried under the many other solicitations and informational messages regularly received via SMS. Valvemen were further studied through observation and semi-structured interviews to understand their incentives for complying with NextDrop.
Digital Video Steganography: An Overview

The recent evolution of digital communication demands securing the integrity of data transfer. Under such necessity, the data hiding practice, steganography allures the attention of security and forensic departments. Steganography is broadly classified into two types depending on the linguistic and technical features. Based on the domain in which the steganography is applied, the technical steganography is categorized into the following types: digital media, network, hardware, circuitry, and genome. Concerning the cover medium, digital media steganography is further classified into four types: image, audio, video, and documents. By widespread usage of video-based applications, video-specific techniques have recently got more emphasis. This rapid growth motivated the authors to provide a comprehensive literature review of this subject. This paper mainly talks about video steganography techniques in the recent decade. In addition, quality metrics used in the performance evaluation process are also discussed. This review is mainly focused on providing an overall guide for new researchers approaching the field of steganography.
Computer Vision Human Activity Recognition Using Cumulative Difference Energy Representation-Based Features and Employing Machine Learning Techniques

Vision-based human action and motion recognition system are very demanding assignments for many computer processor request comprises criminal investigations, public video surveillance system, and applications of sports. For lengthy videos capture, it is very hard to identify within a video frame for a particular human action. This paper presents an efficient technique to human action detection, feature extraction, and recognition. In the first phase, in order to identify the moving object, the cumulative difference scheme is applied. The next phase, in aim feature tracking based on the proposed cumulative difference energy representation (CDER) technique is used. In our proposed research, we have evaluated different machine learning techniques methods, namely K-nearest neighbors, support vector machine, random forest, naïve Bayes, and decision tree. Experimental results were conducted on the publically available human action KTH datasets considering six activities, viz. (hand clapping, walking, hand waving boxing jogging, and running). The research results express that proposed CDER technique outperforms the majority of previous schemes, accomplishing accuracy of 98.67% for SVM classifier.
A Survey on Advancements of Real-Time Analytics Architecture Components

With the evolving technological landscape, Real-Time Analytics has become a de-facto practice and area of attraction for various industrial applications. Real-Time Analytics has proven to be impactful in plenty of critical problem statements, and a wide range of use cases have been raised in the real-time world. Working with streaming data includes various steps and aspects of software development and engineering starting from data collection to movement to processing and building actionable insights. Every such component of streaming analytics architecture needs to be designed and developed keeping in mind that the materialization of insights generated with this kind of dataset needs to be nearly real time. Many industrial organizations and large-scale enterprises have grown by adopting and investing heavily into building their systems using event-based streaming architectures. In this article, we intend to provide insights on primarily three aspects of architectural components in the context of Real-Time Analytics as well as advancements in the field. These aspects include recent research and development with regards to Real-Time Analytics Architecture and its use cases, industrial applications, development of tools and technologies. We also attempt to expose some open challenges and research issues that need further attention from researchers and industrial experts.
The characteristics of higher-order derivative coherent states (DCS)

The concept of a near superposition [Othman A, Yevick D. Int. J. Theor. Phys. 2018; 57:2293–2308] has recently been introduced as the superposition of two semi-identical states and has, in particular, been realized with coherent states. This paper provides a higher-order generalization of this procedure and, in particular, examines the ‘derivative limit’ for which the phases of all the lower-order states are identical and equal to a particular constant. In the derivative limit, the near coherent state superposition is transformed after successive derivative operations into a high-order Derivative Coherent State (DCS). The DCS is accordingly generated by constructing and subsequently displacing a certain stationary seed state formed by superimposing only even or odd Fock states. The mathematical and statistical properties of the DCS display several behaviours and non-classical features that are pronounced for high-order states. These non-classical properties include the Q-Mandel, higher-order antibunching, linear squeezing, and amplitude squared squeezing.
Differentiated approach to assessing students' academic achievements

The article highlights a brief analysis of trends in education, a review of Kazakhstani and foreign teachers’ studies in the field of applying differentiated approach in teaching and students’ academic achievement assessing system in recent years. The effectiveness of applying differentiated approach in the context of the current criteria assessment system is shown. The importance of the teacher's role in the application of differentiation in assessment is noted. The results of surveying secondary schools teachers of mathematics are presented. "Pearson’s chi-square criterion" method was used to analyze the results of the survey. Conclusions are drawn based on the results of the study.
Segmentation and counting of multiple myeloma cells using IEMD based deep neural network

In biomedical image analysis, segmentation of cell nuclei from microscopic images is a highly challenging research problem. In the computer-assisted health care system, the segmented microscopic cells have been used by many biological researchers for the early prediction of various diseases. Multiple myeloma is one type of disease which is also term as a plasma cell cancer. The segmentation of the nucleus and cell is a very critical step for multiple myeloma detection. Here, In this work, we have designed two modules. One is for recognizing the nucleus of myeloma cells with a deep IEMD neural network, and the other is for differentiating the cell i.e cytoplasm. The different IMFs provides detailed frequency component of an image which are used for feature extraction. This will significantly improves the performance. We proposed a new counting algorithm for counting the myeloma-affected plasma cells in this paper. An algorithm for counting overgrowth plasma cells within the myeloid tissue has been developed using the Python TensorFlow framework. Experimental outcomes on SegPC datasets substantiate that, the proposed deep learning approach outperforms other competitive methods in myeloma recognition and detection. The result of this research indicates that, the proposed image segmentation mechanism can recognize multiple myeloma with superiority. Early detection of multiple myeloma at the initial stage increases the chances to cure patients.
Iontronics: A path towards biomimetic information processing

Summarizing the accomplishments thus far in iontronics, an emerging field based on sophisticated control of ions as signal carriers