Reviewerly/exHarmony huggingface dataset:
Conjugate priors for Bayesian object tracking

Object tracking refers to the problem of using noisy sensor measurements to determine the location and characteristics of objects of interest in clutter. Nowadays, object tracking has found applications in numerous research venues as well as application areas, including air traffic control, maritime navigation, remote sensing, intelligent video surveillance, and more recently environmental perception, which is a key enabling technology in autonomous vehicles. This thesis studies conjugate priors for Bayesian object tracking with focus on multi-object tracking (MOT) based on sets of trajectories. Finite Set Statistics provides an elegant Bayesian formulation of MOT in terms of the theory of random finite sets (RFSs). Conjugate priors are also of great interest as they provide families of distributions that are suitable to work with when seeking accurate approximations to the true posterior distributions. Many RFS-based MOT approaches are only concerned with multi-object filtering without attempting to estimate object trajectories. An appealing approach to building tracks is by computing the multi-object densities on sets of trajectories. This leads to the development of trajectory filters, e.g., filters based on Poisson multi-Bernoulli mixture (PMBM) conjugate priors. In this thesis, [Paper A] and [Paper B] consider the problem of point object tracking where an object generates at most one measurement per scan. In [Paper A], it is shown that the trajectory MBM filter is the solution to the MOT problem for standard point object models with multi-Bernoulli birth. In addition, the multi-scan implementations of trajectory PMBM and MBM filters are presented. In [Paper B], a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories from a sequence of multi-object filtering densities and the multi-object dynamic model, is presented. [Paper C] and [Paper D] consider the problem of ex- tended object tracking where an object may generate multiple measurements per scan. In [Paper C], the extended object PMBM filter for sets of objects is generalized to sets of trajectories. In [Paper D], a learning-based extended ob- ject tracking algorithm using a hierarchical truncated Gaussian measurement model tailored for automotive radar measurements is presented.
Heuristic methods that use random numbers

In my previously published book, Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition (2018), I reviewed a collection of heuristic techniques that provide data analysts with an easy way to approach a wide range of tough computational problems. These methods can be implemented in a few lines of code, using virtually any programming language on just about any computer. It sounds too good to be true, but such quick, first-pass analyses can always be validated later by more rigorous and formal analytic methods. These heuristic methods fall under a variety of nearly synonymous names: resampling, bootstrapping, and Monte Carlo processes. They all use readily available random number generators (the computer equivalent to throwing dice), and they all provide intuitively simple alternatives to the techniques taught in college mathematics and statistics courses. Examples will cover methods for hypothesis testing, discerning subpopulations within your data sets, demonstrating why chronic diseases occur in disproportionately high numbers among the elderly population, simulating the growth of tumor cell populations, determining power and sample size, and predicting runs of diagnostic errors. In this chapter, discussions of heuristic methods will focus on problems drawn from the biomedical sciences that can be performed with a few lines of code that any motivated reader can understand.
LIBRARY INFORMATION SERVICES FOR THE MEDICAL COMMUNITY: FOREIGN EXPERIENCE

The authors study the experience of foreign libraries (especially of USA and Canada) in rendering information services to the professional medical community. Publications on this item from 1980-es up to present times are the material for the analysis. As an example of successful interaction between foreign libraries and physicians, the following models are listed as “clinical librarianship”, ”embedded librarianship”, research information management.
Easily solving dynamic programming problems in Haskell by memoization of hylomorphisms

Summary Dynamic programming is a well‐known algorithmic technique that solves problems by a combination of dividing a problem into subproblems and using memoization to avoid an exponential growth of the costs. We show how to implement dynamic programming in Haskell using a variation of hylomorphisms that includes memoization. Our implementation uses polymorphism so the same function can return the best score or the solution to the problem based on the type of the returned value.
A Polynomial Size Model with Implicit SWAP Gate Counting for Exact Qubit Reordering

Due to the physics behind quantum computing, quantum circuit designers must adhere to the constraints posed by the limited interaction distance of qubits. Existing circuits need therefore to be modified via the insertion of SWAP gates, which alter the qubit order by interchanging the location of two qubits’ quantum states. We consider the Nearest Neighbor Compliance problem on a linear array, where the number of required SWAP gates is to be minimized. We introduce an Integer Linear Programming model of the problem of which the size scales polynomially in the number of qubits and gates. Furthermore, we solve 131 benchmark instances to optimality using the commercial solver CPLEX. The benchmark instances are substantially larger in comparison to those evaluated with exact methods before. The largest circuits contain up to 18 qubits or over 100 quantum gates. This formulation also seems to be suitable for developing heuristic methods since (near) optimal solutions are discovered quickly in the search process.
How to tame your chatbot

The best way to stop a bad chatbot spreading misinformation may be a good chatbot providing facts. But how can we deploy these bots on short notice without compromising quality and user privacy?
FORMATION OF FUTURE TEACHER'S READINESS TO RESEARCH ACTIVITY AS A PEDAGOGICAL PROBLEM

The article deals with theoretical aspects of formation of readiness of the future teacher for pedagogical research activity. To our mind research skills mean analytical-synthetic, informational, creative and prognostic skills, which integrate with the focus on research activities and personal qualities of the individual, provide its effective search-and-transformative activity in educational process.
INFORMATION RESOURCES OF THE ACADEMIC LIBRARY (ON THE EXAMPLE OF LIBRARIES OF SCIENTIFIC ORGANIZATION OF THE SIBERIAN BRANCH OF THE RUSSIAN ACADEMY OF SCIENCES)

The results of examining sites of scientific organizations of the Siberian Branch of the Russian Academy of Sciences are given. Particular attention is paid at siting information about the library and its activities, providing search capabilities in electronic catalogs and databases of libraries, informing about remote resources.
Investigation and Analysis of the Development of Three-Player Basketball in Universities

This paper takes the students of colleges and universities in Heilongjiang Province as the research object, and investigates and studies the three-player basketball game through the methods of literature review, interview and questionnaire methods. The results show that the three-player basketball can promote the unity and cooperation of the students in colleges and universities, cultivate the students' comprehensive practical ability in sports and the spirit of innovation. The development of three-player basketball is important and necessary in colleges and universities.
Blockchain Risk and Uncertainty in Automated Applications

Blockchain could be called a string of blocks that acts like a ledger that is also distributed. Members in a defined P2P network are given access to the blockchain and can create new blocks. When the data is stored in a blockchain, changing it becomes virtually impossible. The data stored within blocks is timestamped to avoid tampering. Blockchain has applications in numerous fields like IoT, digital currency, financial services, reputation systems, smart contracts, security services, etc. If any virtual or real asset transaction is happening online, blockchain technology can be easily applied to optimize and secure the transaction better. Blockchain-based applications bring controversies, and yet many exceptional and diverse use-cases have been found for blockchain in both financial and non-financial sectors. Although it holds immense promise, it doesn't come without risks and uncertainties. This chapter elucidates the growing risks and uncertainties which accompany the use of blockchain in automated systems.
What Is an Intelligent System?

Dive into the foundations of intelligent systems, machine learning, and control with this hands-on, project-based introductory textbook. Precise, clear introductions to core topics in fuzzy logic, neural networks, optimization, deep learning, and machine learning, avoid the use of complex mathematical proofs, and are supported by over 70 examples. Modular chapters built around a consistent learning framework enable tailored course offerings to suit different learning paths. Over 180 open-ended review questions support self-review and class discussion, over 120 end-of-chapter problems cement student understanding, and over 20 hands-on Arduino assignments connect theory to practice, supported by downloadable Matlab and Simulink code. Comprehensive appendices review the fundamentals of modern control, and contain practical information on implementing hands-on assignments using Matlab, Simulink, and Arduino. Accompanied by solutions for instructors, this is the ideal guide for senior undergraduate and graduate engineering students, and professional engineers, looking for an engaging and practical introduction to the field.
Learning company embeddings from annual reports for fine-grained industry characterization

Organizingcompaniesbyindustrysegment(e.g.artiﬁcial intelligence, healthcare or ﬁntech) is useful foranalyzingstockmarketperformanceandfordesigning theme base investment funds, among others. Current practice is to manually assign companies to sectors or industries from a small predeﬁned list, which has two key limitations. First, due to the manual effort involved, this strategy is only feasible for relatively mainstream industry segments, and can thus not easily be used for niche or emerging topics. Second, the use of hard label assignments ignores the fact that different companies will be more or less exposed to a particular segment. To address these limitations, we propose to learn vector representations of companies based ontheirannualreports. Thekeychallengeistodistill the relevant information from these reports for characterizing their industries, since annual reports also contain a lot of information which is not relevant for our purpose. To this end, we introduce a multi-task learning strategy, which is based on ﬁne-tuning the BERT language model on (i) existingsectorlabelsand(ii)stockmarketperformance. Experiments in both English and Japanese demonstrate the usefulness of this strategy.
Evaluation of scientific and technological achievements in colleges based on machine learning and image feature retrieval

At present, experts and scholars have conducted more research on the ability of colleges and universities to transform scientific and technological achievements. However, they pay more attention to the holistic research on the transformation of scientific and technological achievements in colleges and universities across the country, while rarely divide the research objects in detail. In order to improve the evaluation effect of scientific and technological achievements in colleges and universities, this paper builds a university science and technology achievement evaluation system based on machine learning and image feature retrieval on the basis of analyzing the needs of high-tech achievement evaluation. The system has certain flexibility. Moreover, this study selects the appropriate network architecture based on the actual data and mission objectives of the high-tech achievement evaluation. In addition, this paper proposes a FT-GRU model of a gated recurrent unit network incorporating N nearest neighbor text, and a more stable model structure is obtained through system optimization. Finally, this study designs experiments to verify the performance of the model. The research results show that the university science and technology achievement evaluation system based on machine learning and image feature retrieval constructed in this study meets the expected goals and has certain practical significance.
ECC based Encryption and Decryption using Cloud

The Elliptic curve cryptography is a form of cryptography[6], which uses public key that is computationally harder to implement yet faster, and provides better security when compared to the widely used RSA or AES cryptography. What makes the ECC encryption algorithm better is its smaller key sizes which save up on computing time. The formula for finding the keys is complex. That’s why breaking the encryption is much more difficult.In our paper, through the research on the basic working of the elliptic curve encryption algorithm, we have come up with an optimized method from the perspective of data protection in cloud and it has been designed using the elliptic curve encryption algorithm with data protection in cloud technology to ensure that the system is running safely and efficiently [2], where the data is encrypted when sent and stored in the cloud and can be downloaded and decrypted in a similar way.Indexterms:Cloud, Elliptic Curve Cryptography, ECC Encryption , Decryption[5]
OSED: Object-specific edge detection

Object-specific edge detection (OSED) aims to detect object edges in an image along with classify the edge into object or non-object. It prunes edges which are not belonging to the object class for following processing, such as, feature matching for object detection, localization and three-dimensional reconstruction. In this paper, an OSED method that combines region proposal detectors with deep supervision nets to identify object-specific edges is proposed. It minimizes errors of object proposal by learning from hidden layers. Additionally, it combines features from different scales to detect object edges. In order to evaluate the performance of the OSED, we present two datasets which are captured in real scenes. The OSED method demonstrates a high accuracy of 90% and a high speed of 0.5 s for an image whose size is 512 × 448 pixels on the proposed datasets.
Survey of 2D &3D Image Steganography Techniques for Data Security System

Image steganography refers to hiding information i.e. text, images or audio files in another image or video files. The current project aims to use steganography for an image with another image using spatial domain technique. This hidden information can be retrieved only through proper decoding technique. This examination displays an outline of different three-dimensional (3D) picture steganography methods from overview perspective. This paper exhibit scientific categorization of 3D picture steganography systems and distinguish the ongoing advances in this field. Steganalysis and assaults on 3D picture steganography calculations have likewise been examined. 3D picture steganography strategies in all the three spaces: geometrical, topological and portrayal areas have been contemplated and thought about among each other on different parameters, for example, inserting limit, reversibility and reaction towards assaults. A few difficulties which restrain the advancement of 3D steganography calculations have been recognized. This investigation finishes up with some valuable discoveries at last.
Consistency, Acyclicity, and Positive Semirings

In several different settings, one comes across situations in which the objects of study are locally consistent but globally inconsistent. Earlier work about probability distributions by Vorob’ev (1962) and about database relations by Beeri et al. (1983) produced characterizations of when local consistency always implies global consistency. Towards a common generalization of these results, we consider K-relations, that is, relations over a set of attributes such that each tuple in the relation is associated with an element from an arbitrary, but fixed, positive semiring K. We introduce the notions of projection of a K-relation, consistency of two K-relations up to normalization, and global consistency of a collection of K-relations; these notions are natural extensions of the corresponding notions about probability distributions and database relations. We then show that a collection of sets of attributes has the property that every pairwise consistent collection of K-relations over those attributes is globally consistent if and only if the sets of attributes form an acyclic hypergraph. This generalizes the aforementioned results by Vorob’ev and by Beeri et al., and demonstrates that K-relations over positive semirings constitute a natural framework for the study of the interplay between local and global consistency. In the course of the proof, we introduce a notion of join of two K-relations and argue that it is the “right” generalization of the join of two database relations. Furthermore, to show that non-acyclic hypergraphs yield pairwise consistent K-relations that are globally inconsistent, we generalize a construction by Tseitin (1968) in his study of hard-to-prove tautologies in propositional logic.
An Aerodynamic Extension for Motion Planning with Dynamics Awareness in Aerial Long-Reach Manipulators

This paper presents a novel method for motion planning of aerial long-reach manipulators that considers the aerodynamic effects generated by close surfaces in the trajectory generation process. The aerial manipulation system consists of a multirotor equipped with a robotic long-reach arm that enables multidirectional inspection and also increases considerably the safety distance between the rotors and the inspected elements. Since these systems operate in the proximity of elements that can modify significantly the rotors’ airflow, the inclusion of Aerodynamics Awareness within the motion planning process is required to ensure robust obstacle avoidance. To this end, a proper characterisation of the aerodynamic effects based on both theoretical and experimental considerations has been derived. This characterisation is taken into account in the trajectory generation process to discard states whose associated aerodynamic phenomena are not well compensated by the system controller and to explore alternatives that lead to the most efficient trajectories within the area of safe operation. Moreover, the motion planner also stands out for three other relevant features: the joint consideration of the multirotor and the robotic long-reach arm, the generation of efficient trajectories in terms of energy consumption, and the Dynamics Awareness of the strong coupling between the aerial platform and the robotic arm. The resulting motion planner has been successfully tested in a simulated environment that faithfully reflects an application scenario strongly affected by aerodynamic effects: the inspection of bridges to find potential cracks in the surface of pillars.
Lossless White Balance for Improved Lossless CFA Image and Video Compression

Color filter array is a spatial multiplexing of pixel-sized filters fabricated over pixel sensors in most color image sensors. The state-of-the-art lossless coding techniques of raw sensor data captured by such sensors leverage spatial or cross-color correlation using lifting schemes. In this paper, we propose a lifting-based lossless white balance algorithm. When applied to the raw sensor data, the spatial bandwidth of the implied chrominance signals decreases. We propose to use this white balance as a pre-processing step to lossless CFA subsampled image/video compression, improving the overall coding efficiency of the raw sensor data.
Interactive Visualization Approach to Support Exploratory Data Analysis: Illustration in Research Topic Distribution

Nowadays, data has an important role to support decision making in different domains. Especially in research area and digital literacy. Research topic analysis and exploration can help to establish research comprehensive roadmap. Data driven engineering is one of the trend of information technology that can not be ignored. Abundant data as valuable resources to get data obscure interesting pattern. Normally, we use big data to get important information of it. The problem of raw data is this kind of data need to be preprocessed, explored and analyzed. Big data is a popular approach to characterize the data. Interactive visualization is one of the solution to examine big number of data (big data). Interactive visualization as an explication to make Big data easier to be explored and analyzed.
Existential definability of modal frame classes

Abstract We prove an existential analogue of the Goldblatt‐Thomason Theorem which characterizes modal definability of elementary classes of Kripke frames using closure under model theoretic constructions. The less known version of the Goldblatt‐Thomason Theorem gives general conditions, without the assumption of first‐order definability, but uses non‐standard constructions and algebraic semantics. We present a non‐algebraic proof of this result and we prove an analogous characterization for an alternative notion of modal definability, in which a class is defined by formulas which are satisfiable under any valuation (the so‐called existential validity). Continuing previous work in which model theoretic characterization for this type of definability of elementary classes was proved, we give an analogous general result without the assumption of the first‐order definability. Furthermore, we outline relationships between sets of existentially valid formulas corresponding to several well‐known modal logics.
Forecasting Monthly Prices of Gold Using Artiﬁcial Neural Network

This study successfully i¬ tted an artii¬ cial neural network to a series on gold prices. The data used was monthly gold prices in US dollars and cents per troy ounce from October 2004 to February 2020. Of the 17 suggested Artii¬ cial Neural Network structures, the one with 2, 6 and 1 neurons in the input, hidden and output layers (ANN (2-6-1)) was adjudged the best because it had the least error, Mean Square Error (MSE) and Mean Absolute Error (MAE). The adequacy of the selected model was further coni¬ rmed by graphical examination of the actual values of gold prices and the ones predicted by the model as well as graphical residual analysis. Consequently, forecasts were made using the chosen network. The forecasts suggest a decline in gold prices in the coming months.Â Â Keywords: Gold, Forecasting, ANN.