Reviewerly/exHarmony huggingface dataset:
Multi-Bernoulli Mixture Filter: Complete Derivation and Sequential Monte Carlo Implementation

Multi-Bernoulli mixture (MBM) filter is one of the exact closed-form multi-target Bayes filters in the random finite sets (RFS) framework, which utilizes multi-Bernoulli mixture density as the multi-target conjugate prior. This filter is the variant of Poisson multi-Bernoulli mixture filter when the birth process is changed to a multi-Bernoulli RFS or a multi-Bernoulli mixture RFS from a Poisson RFS. On the other hand, labeled multi-Bernoulli mixture filter evolves to MBM filter when the label is discarded. In this paper, we propose a complete derivation of MBM filter where the derivation of update step does not use the probability generating functional. We also describe the sequential Monte Carlo implementation and adopt Gibbs sampling for truncating the MBM filtering density. Numerical simulation with a nonlinear measurement model shows that MBM filter outperforms the classical probability hypothesis density filter.
Sparse Acceleration Algorithm Based on Likelihood Upper Bound

It is a great challenge for intelligent analysis and discovery of useful information from a great massive video resources, and the target tracking become more and more popular as the important part of the intelligent video surveillance. The feature extracted by the traditional method cannot keep the invariance of the target when the target surfers occlusion, illumination and pose variation in the moving process, which leads to the failure of the tracker. To handle the problems mentioned above, we conduct a survey on the target tracking based on sparse representation. In view of the problem that a large amount of time is needed to solve the problem of sparse expression optimization, we propose an acceleration algorithm based on the observation likelihood upper bound. The effectiveness of the proposed algorithm is verified by experiments on a number of standard test sets.
A Software Process Improvement Model for Small Firms in Developing Countries

Managing software quality is a major challenge for software development firms. This has led many firms to adopt software process improvement programs such as the capability maturity model integration to improve the software development process. However, these models are often too cumbersome and costly, especially for small software firms in developing countries, to implement. This chapter proposes a simplified software process improvement model that prioritises key practices for software development, given the constraints that face small firms. Using data collected in four developing countries in the English-speaking Caribbean from 112 developer/user dyads on their software practices, the results show that software process coupled with supporting technology (e.g., project management tools) significantly impact the quality of the software product. Smaller projects (represented by size of the project team) are also associated with high quality software products than larger projects. Implications for SPI programs in small firms and future research is discussed.
Searching for Best Karatsuba Recurrences

Efficient circuits for multiplication of binary polynomials use what are known as Karatsuba recurrences. These methods divide the polynomials of size (i.e. number of terms) $$k \cdot n$$ into k pieces of size n. Multiplication is performed by treating the factors as degree-$$(k-1)$$ polynomials, with multiplication of the pieces of size n done recursively. This yields recurrences of the form $$ M(k n) \le \alpha M(n) + \beta n + \gamma ,$$ where M(t) is the number of binary operations necessary and sufficient for multiplying two binary polynomials with t terms each. Efficiently determining the smallest achievable values of (in order) $$\alpha , \beta , \gamma $$ is an unsolved problem. We describe a search method that yields improvements to the best known Karatsuba recurrences for k = 6, 7 and 8. This yields improvements on the size of circuits for multiplication of binary polynomials in a range of practical interest.
Manifest Contracts with Intersection Types

We present a manifest contract system PCFv$$\varDelta _\mathrm {H}$$ with intersection types. A manifest contract system is a typed functional calculus in which software contracts are integrated into a refinement type system and consistency of contracts is checked by combination of compile- and run-time type checking. Intersection types naturally arise when a contract is expressed by a conjunction of smaller contracts. Run-time contract checking for conjunctive higher-order contracts in an untyped language has been studied but our typed setting poses an additional challenge due to the fact that an expression of an intersection type $$\tau _{{\mathrm {1}}} \wedge \tau _{{\mathrm {2}}}$$ may have to perform different run-time checking whether it is used as $$\tau _{{\mathrm {1}}}$$ or $$\tau _{{\mathrm {2}}}$$. We build PCFv$$\varDelta _\mathrm {H}$$ on top of the $$\varDelta $$-calculus, a Church-style intersection type system by Liquori and Stolze. In the $$\varDelta $$-calculus, a canonical expression of an intersection type is a strong pair, whose elements are the same expressions except for type annotations. To address the challenge above, we relax strong pairs so that expressions in a pair are the same except for type annotations and casts, which are a construct for run-time checking. We give a formal definition of PCFv$$\varDelta _\mathrm {H}$$ and show its basic properties as a manifest contract system: preservation, progress, and value inversion. Furthermore, we show that run-time checking does not affect essential computation.
Gaze Tracking by Joint Head and Eye Pose Estimation Under Free Head Movement

Recent trends in the field of eye-gaze tracking have been shifting towards the estimation of gaze direction in everyday life settings, hence calling for methods that alleviate the constraints typically associated with existing methods, which limit their applicability in less controlled conditions. In this paper, we propose a method for eye-gaze estimation as a function of both eye and head pose components, without requiring prolonged user-cooperation prior to gaze estimation. Our method exploits the trajectories of salient feature trackers spread randomly over the face region for the estimation of the head rotation angles, which are subsequently used to drive a spherical eye-in-head rotation model that compensates for the changes in eye region appearance under head rotation. We investigate the validity of the proposed method on a publicly available data set.
Designing forward converters for data transmission systems in two-level RNS

Abstract We are considering the use of a two-stage Residue number system (RNS) in data transmission systems with a special set of modules {2 l 1 , 2 l 2 , 2 l 3 ,…, 2 l p − 1}. We propose the forward converter with such type of RNS and demonstrate its advantage in hardware costs and delay. This article discusses the FPGA simulation of a forward conversion circuit to a two-stage RNS using adders and the standard modulo function and their comparison in terms of delay and hardware costs.
Comprehensive Study of Existing Stream Ciphers

Now a day’s internet of things is one of the rapidly growing research field in the information technology domain. Offering security for this ever-augmenting technology is still remaining as the challenging task. Cryptographic techniques are used to authenticate and maintain confidentiality of message data. To maintain the security, system cryptographic algorithms and protocols are very essential. The system is mainly categorized into two groups namely stream cipher and block cipher. Stream cipher has its own advantage over block cipher. In this paper, we reviewed some of the stream ciphers. Selection of finest algorithms for the security of internet of things is very difficult in terms of energy and storage. The study aims to summarize some of the existing stream cipher techniques. This paper gives details on fundamental knowledge about encryption algorithms and its comparison. We also describe the design considerations of stream ciphers.
News Filter By Using Scraping Web with Crawling RegEx and TF-IDF Filter

The most important information in Education is in the form of announcement, as in the results of a poll site in Blitar Education Office which reached 69.8% on 10 November 2016. In fact, the information can be derived from other Education Offices, provinces, or CSR. Based on this problem, the researchers propose two ways to get relevant news from various sites. First, create a virtual robot for crawling. Second, do filtering and arranging a rank in HTML documents crawling results. Crawling is operated by scrapping method, which uses Regular Expression. Regular Expression has a function to cut HTML screen based on a predetermined pattern. URL that has been obtained is used for crawling the content details through the location of the original details one. Documents then are stored and ranked using TF-IDF. Next, the rank results sent to dewiberita.com using a post method agent request. On the first experiment, it is resulted that 100% RegEx parsing through the entire of URL address running successfully. Next on the second experiment which is using Recall and Precision, it produce a similar level until 71.4% if the news filtered visualization as maximize as a half of the total document.
Algebraic aspects of Bayesian modeling in psychology

Several Bayesian models in perceptual and cognitive psychology show a complex organization that is only partly captured by referring to Bayes rule and the associated concepts of prior, likelihood, and posterior distributions. In this article, an algebraic framework is constructed that may serve as a guide for representing and analyzing the internal organization of these models. The construction begins by defining a comprehensive class of probabilistic structures, called probability kernels, and by focusing on three basic operations on them, called projection, conditioning, and promotion. The expressive power of these operations is then illustrated by showing how suitable combinations of them cover typical moves of Bayesian computations, such as mixing or inducing probability distributions, marginalizing likelihood functions, and Bayes rule itself. As a central finding, we show that a suitable combination of the basic operations gives rise to a binary relation that organizes any complete set of probability kernels as a lattice, and we highlight the peculiarities of this algebraic structure. Lastly, we illustrate the analytic use of the proposed framework by applying it to two Bayesian models from the literature, one concerning spatial vision and the other concerning category representation.
Foresight Study of the Limits of Development and Application of the Modern Tools of GR Management in View of the Socio-Economic Specifics of Territories’ Development

The article presents the main conceptual and methodological directions of foresight study of the limits of development and application of the modern tools of GR management in view of the socio-economic specifics of territories’ development. The influence of the conditions of globalization, growth of uncertainty, and informatization of society’s life activities on effectiveness and flexibility of the policy of managing the interaction of all levels of authorities (municipal, regional, and federal) with business is reflected. Flexible policy of management in the system of interaction between public authorities and business synthesizes forecasting, planning, and controlling over the socio-economic specifics of development of territories, including analysis of the change of the situation of external environment and evaluation of dynamics of internal organization. Application of new modern tools of interaction of authorities and business as a form of GR management will allow determining the specifics of socio-economic development of territories. Foresight study of the limits of development and application of the modern tools of GR management in view of the socio-economic specifics of territories’ development will allow determining the risk of crises and evaluating, identifying, and eliminating the threats, as well as recognizing and using the opportunities in external environment and in internal environment of the system of interaction of public authorities and business.
Crowd emotion detection to light up a smart Christmas tree

The paper aims to describe part of a system created by Huawei, that proposes the first Christmas tree endowed with artificial intelligence. It is able to recognize facial emotions from images acquired by a mobile application and to light up itself with different lights and colors according to the prevalent sentiment. In this project, our role was to test the neural network integrated in the mobile application and able to recognize the sentiment of a facial expression. We have implemented a model based on a convolutional neural network and tested the accuracy of the recognition method, by creating an “ad-hoc” dataset of images.
Classes Lm and Ornstein–Uhlenbeck Type Processes

Any non-zero Lévy process does not have limit distribution as t →∞. But the Ornstein–Uhlenbeck process constructed from Brownian motion has a limit distribution as t →∞, which is Gaussian. Processes of Ornstein–Uhlenbeck type are analogues of the Ornstein–Uhlenbeck process with the role of Brownian motion played by general Lévy processes. In this chapter we shall construct them, give the condition under which they have limit distributions, and study the connection with classes L m.
Study on the effect of temperature on the leaching of contaminants from Ballangen Tailings Deposit, Norway-A statistical method

Abstract Temperature is one important factor that affects the leaching of contaminants from tailings deposit. However, randomness issue due to the precision of the instrument, the variation of ambient temperature, the individual skill of the lab technician etc., is inevitable and will affect the results when we evaluate the dependence of two parameters. In this study, a statistical method was developed to analyze the experimental data to reduce randomness. The experiment data from a laboratory batch leaching experiment on the tailings at Ballangen deposit, Northern Norway were used to analyze the dependency of concentrations of Cr, Cu, Ni and Zn in the leachate on temperature. The dependency of concentrations of Cr, Cu, Ni and Zn in the leachate is determined by testing the significance of the slope coefficient of regression analysis. The results show that the concentrations of Cr and Zn do not show dependency on the temperature. However, the temperature shows evident impact on the concentrations of Cu and Ni. High temperature will produce higher Cu and Ni concentration in the leachate.
Microsoft Access Data Curation Primer

This work was created as part of the Data Curation Network “Specialized Data Curation” Workshop #1 co-located with the Digital Library Federation (DLF) Forum 2018 in Las Vegas, Nevada on October 17-18, 2018.
부호화(Encoding)와 인출(Retrieval)이 기억력과 자아 효능감에 미치는 영향

This study aims to explore how the testing effect can effectively be used as students' review strategy by verifying the impact of schema encoding, only retrieval, and retrieval and feedback strategies on memory and self-efficacy. A convenience sampling method was employed to enrol 179 Year 5 student...
Meta-heuristic approaches to solve shortest lattice vector problem

We present the aptness of population based meta-heuristic approaches to compute a shortest non-zero vector in a lattice for solving the Shortest lattice Vector Problem (SVP). This problem has a great many applications such as optimization, communication theory, cryptography, etc. At the same time, SVP is notoriously hard to predict, both in terms of running time and output quality.The SVP is known to be NP-hard under randomized reduction and there is no polynomial time solution for this problem. Though LLL algorithm is a polynomial time algorithm, it does not give the optimal solutions. In this paper, we present the application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to solve the SVP on an appropriate search space. We have implemented the PSO, GA and LLL algorithm for the SVP. The comparison results shows that our algorithm works for all instances.
Using a Complex Technology in a Language Course

In this chapter, how the complex technology of Second Life affects participation in an English course is investigated. With the aim of exploring gender issues, the special affordance of Second Life, i.e. voice-morphing together with sound-isolated parcels, was used. The data set consists of about 33 hours of audio recordings and chat logs of 8,315 words. The results show that in audio10.8% of the course time deals with technological challenges, while in chat 69.2% of the words contribute to technology. Three challenges interfering with participation were identified: software complexity, unreliable functionality of Second Life, and hardware and connectivity issues. To deal with these problems, pedagogical facilitators, technological facilitators, and Second Life -experienced peers made a significant contribution. Based on the results, this chapter analyzes whether Second Life can be widely used in language education, how affordances of it can be learned and taught, and scenarios where Second Life can and cannot be used.
Brute Force Search Method for Cyberbullying Detection

In this chapter, the authors present a method for automatic detection of malicious internet contents, based on a combinatorial approach resembling brute force search algorithms, with application to language classification. The method automatically extracts sophisticated patterns from sentences and applies them in classification. The experiments performed on actual cyberbullying data showed advantage of this method to previous methods, including the one described in Chapter 4. Pros and cons of this method when compared to previous ones are also discussed in this chapter.
Trust throughput modeling pathways

To most individuals, “trust” can be viewed as a knowledge asset that may add, or rest, value to the company. The role of knowledge in achieving a judicious advantage is becoming an increasingly important issue with individuals and organizations. Trust supports an ethical valuation of people and organizations that fundamentally affects relationships with others. When trust is present, communication and problem solving are relatively easy. When distrust occurs, productivity and relational value can suffer significantly. Trust lowers transaction costs in more uncertain environments, thus providing individuals and organizations with a source of unique advantage. Moreover, distrust emerges when the suspicion arises that the disruption of expectations in one exchange is likely to generalize through all other exchanges.As such, the Throughput Modeling approach indicates how six different trust behaviors can be guided, how trust decision making can be improved and made defensible, and how special problems facing individuals, relational systems, and institutions can be dealt with via decision-making pathways leading to action.
Scalable Self-taught Deep-Embedded Learning Framework for Drug Abuse Spatial Behaviors Detection

Drug abuse has become an increasingly challenging issue national wide in the United States, while each state has its own legislation regarding such behavior which further stimulates different semantic representations of such behavior over space. To build an accurate and robust classifier to detect such behaviors with spatial variance remains challenging due to the existence of large noise in tweets and limited number of labeled data. Most efforts have utilized humans to label tweets for the base classifier training. The randomness of human labeled data would limit the generalization of base model trained. We propose a deep learning-based centroid-attention framework to consider the spatial variance. We further explore the effect of state-based exemplars on the base model. The performance of the base classifier is thus enhanced.
Hard Provability Logics

Let $$\mathsf{PL}(T,T')$$ and $$\mathsf{PL}_{_{\Sigma _1}}(T,T')$$ respectively indicate the provability logicProvability logic and $$\Sigma _1$$ -provability logicProvability logic of $$T$$ relative in $$T'$$ . In this paper we characterise the followingRelative provability logic relative provability logicsProvability logic: $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{HA},\mathbb {N})$$ , $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{HA},\mathsf{PA})$$ , $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{HA}^*,\mathbb {N})$$ , $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{HA}^*,\mathsf{PA})$$ , $$\mathsf{PL}(\mathsf{PA},\mathsf{HA})$$ , $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{PA},\mathsf{HA})$$ , $$\mathsf{PL}(\mathsf{PA}^*,\mathsf{HA})$$ , $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{PA}^*,\mathsf{HA})$$ , $$\mathsf{PL}(\mathsf{PA}^*,\mathsf{PA})$$ , $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{PA}^*,\mathsf{PA})$$ , $$\mathsf{PL}(\mathsf{PA}^*,\mathbb {N})$$ , $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{PA}^*,\mathbb {N})$$ (see Table 9.3). It turns out that all of these provability logicsProvability logic are decidable. The notion of reductionReduction for provability logicsProvability logic, first informally considered in (Ardeshir and Mojtahedi 2015). In this paper, we formalize a generalization of this notion (Definition 9.4.1) and provide several reductionsReduction of provability logicsProvability logic (see Diagram 9.5). The interesting fact is that $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{HA},\mathbb {N})$$ is the hardest provability logicProvability logic: the arithmetical completenesses of all provability logicsProvability logic listed above, as well as well-known provability logicsProvability logic like $$\mathsf{PL}(\mathsf{PA},\mathsf{PA})$$ , $$\mathsf{PL}(\mathsf{PA},\mathbb {N})$$ , $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{PA},\mathsf{PA})$$ , $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{PA},\mathbb {N})$$ and $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{HA},\mathsf{HA})$$ , are all propositionally reducible to the arithmetical completeness of $$\mathsf{PL}_{_{\Sigma _1}}(\mathsf{HA},\mathbb {N})$$ .
Fast and Reliable Acquisition of Truth Data for Document Analysis using Cyclic Suggest Algorithms

In document analysis the availability of ground truth data plays a crucial role for the success of a project. This is even more true at the rise of new deep learning methods which heavily rely on the availability of training data. But even for traditional, hand crafted algorithms that are not trained on data, reliable test data is important for the improvement and evaluation of the methods. Because ground truth acquisition is expensive and time consuming, semi-automatic methods are introduced which make use of suggestions coming from document analysis systems. The interaction between the human operator and the automatic analysis algorithms is the key to speed up the process while improving the quality of the data. The final confirmation of data may always be done by the human operator. This paper demonstrates a use case for acquisition of truth data in a mail processing system. It shows why a new, extended view on truth data is necessary in development and engineering of such systems. An overview over the tool and the data handling is given, the advantages in the workflow are shown, and consequences for the construction of analysis algorithms are discussed. It can be shown that the interplay between suggest algorithms and human operator leads to very fast truth data capturing. The surprising finding is the fact that if multiple suggest algorithms circularly depend on data, they are especially effective in terms of speed and accuracy.