Reviewerly/exHarmony huggingface dataset:
CRIME ANALYSIS USING MACHINE LEARNING

Crimes are the significant threat to the humankind. as crimes are increasing at a rapid rate approach for identifying trends in crime. Our project can predict regions which have high probability for crime occurrence and can visualize crime prone areas by visual representation of data by bar graphs, pie charts etc. It speed up the classification of criminal activities by calculating the average accuracy rate .It uses crime data set and predicts the types of crimes in a particular area which help in Accurate results based on the predictive analysis of logistic regression. The objective would be train a model for prediction using logistic regression classification algorithm. Logistic regression used for classification problems and it is a predictive analysis algorithm based on concept of probability. Crime analysis project is a systematic approach for identifying trends in crime. .It uses crime data set and predicts the types of crimes in a particular area which help in Accurate results based on the predictive analysis of logistic regression.
Prolegomena to a Task-Method-Knowledge Theory of Cognition

How can we integrate interrelated theories of individual elements of cognition? Computational models of reasoning processes encode an understanding of reasoning. Consequently, a computational modeling language may be ideally suited to the presentation of theories of cognition. By representing theories of a variety of phenomena in a single modeling language, we can potentially explore how these theories might interact. The Task-M ethod-Knowledge (TMK) modeling language evolves from artificial intelligence research on the subject of multistrategy reasoning. TMK models provide a compositional account of reasoning processes; they describe not only what the elements of a process are, but also how the functional properties of these elements combine to form the functional properties of the process as a whole. This paper explores the composition of theories of cognition within the TMK framework, drawing on some existing theories within cognitive science as examples.
What Makes A Good Fisherman? Linear Regression under Self-Selection Bias

In the classical setting of self-selection, the goal is to learn $k$ models, simultaneously from observations $(x^{(i)}, y^{(i)})$ where $y^{(i)}$ is the output of one of $k$ underlying models on input $x^{(i)}$. In contrast to mixture models, where we observe the output of a randomly selected model, here the observed model depends on the outputs themselves, and is determined by some known selection criterion. For example, we might observe the highest output, the smallest output, or the median output of the $k$ models. In known-index self-selection, the identity of the observed model output is observable; in unknown-index self-selection, it is not. Self-selection has a long history in Econometrics and applications in various theoretical and applied fields, including treatment effect estimation, imitation learning, learning from strategically reported data, and learning from markets at disequilibrium. In this work, we present the first computationally and statistically efficient estimation algorithms for the most standard setting of this problem where the models are linear. In the known-index case, we require poly$(1/\varepsilon, k, d)$ sample and time complexity to estimate all model parameters to accuracy $\varepsilon$ in $d$ dimensions, and can accommodate quite general selection criteria. In the more challenging unknown-index case, even the identifiability of the linear models (from infinitely many samples) was not known. We show three results in this case for the commonly studied $\max$ self-selection criterion: (1) we show that the linear models are indeed identifiable, (2) for general $k$ we provide an algorithm with poly$(d) \exp(\text{poly}(k))$ sample and time complexity to estimate the regression parameters up to error $1/\text{poly}(k)$, and (3) for $k = 2$ we provide an algorithm for any error $\varepsilon$ and poly$(d, 1/\varepsilon)$ sample and time complexity.
Evaluating Context for Deep Object Detectors

Which object detector is suitable for your context sensitive task? Deep object detectors exploit scene context for recognition differently. In this paper, we group object detectors into 3 categories in terms of context use: no context by cropping the input (RCNN), partial context by cropping the featuremap (two-stage methods) and full context without any cropping (single-stage methods). We systematically evaluate the effect of context for each deep detector category. We create a fully controlled dataset for varying context and investigate the context for deep detectors. We also evaluate gradually removing the background context and the foreground object on MS COCO. We demonstrate that single-stage and two-stage object detectors can and will use the context by virtue of their large receptive field. Thus, choosing the best object detector may depend on the application context.
Research on design method of human resource system test case in construction enterprise

Abstract In the process of human resource system test design in construction enterprises, various test design methods are often used, such as equivalence class division, boundary value analysis, causal graph, error inference and so on. How to choose the correct test design method is related to the rationality of test case design and high efficiency of test execution. Therefore, the selection of appropriate test design method plays a vital role in use case design. In this paper, the basic use of equivalence class partition and boundary value analysis and their relationship are briefly described. It is hoped that it can be used for reference in the study of human resource system test cases in construction enterprises.
HumanAL: Calibrating Human Matching Beyond a Single Task

This work offers a novel view on the use of human input as labels, acknowledging that humans may err. We build a behavioral profile for human annotators which is used as a feature representation of the provided input. We show that by utilizing black-box machine learning, we can take into account human behavior and calibrate their input to improve the labeling quality. To support our claims and provide a proof-of-concept, we experiment with three different matching tasks, namely, schema matching, entity matching and text matching. Our empirical evaluation suggests that the method can improve the quality of gathered labels in multiple settings including cross-domain (across different matching tasks).
Generative Evolutionary Strategy For Black-Box Optimizations

Many scientific and technological problems are related to optimization. Among them, black-box optimization in high-dimensional space is particularly challenging. Recent neural network-based black-box optimization studies have shown noteworthy achievements. However, their capability in high-dimensional search space is still limited. This study proposes a black-box optimization method based on the evolution strategy (ES) and the generative neural network (GNN) model. We designed the algorithm so that the ES and the GNN model work cooperatively. This hybrid model enables reliable training of surrogate networks; it optimizes multi-objective, high-dimensional, and stochastic black-box functions. Our method outperforms baseline optimization methods in this experiment, including ES, and Bayesian optimization.
An inscription on a book from the collection of A. I. Tunkov in the scientific library of the Tobolsk Historical and Architectural Museum-Reserve

Expositions in museums, libraries, other institutions of culture and arts as well as publications about book inscriptions are an illustration of the increased interest to this theme. The Scientific Library of the Tobolsk Historical and Architectural Museum- Reserve contains more than three thousand books with inscriptions and autographs of many famous figures of Siberia, Russia and abroad. Even if the addressee’s surname does not belong to the category of famous and celebrated, the donation record does not become less interesting. The publication presents a brief characteristic of the book collection of the producer A. I. Tunkov, found in the stock of the Scientific Library of the Tobolsk Historical and Architectural MuseumReserve, and attributes the discovered script of the actor M. M. Mikhailovich-Dolsky on the book “Picture Gallery or Systematic Collection of Drawings in all Branches of Human Knowledge” (St. Petersburg, 1872). The author managed to determine the names of the addressees of this inscription, the circumstances that gave rise to its origin. Work in the Scientific Archive of the Tobolsk Historical and Architectural Museum-Reserve and in the Russian State Archive of Literature and Art permitted to find out previously unknown facts from the biography of A. I. Tunkov.
Image-Schema Transfer: Towards Computational Facilitation of Analogical Problem Solving using a Diagrammatic Representation

This paper proposes an experimental system called 1ST for the facilitation of users’ analogical transfer by providing image-schemas. First, by taking the radiation problem as an example, we hypothesized that a proper image-schema can promote human analogical problem solving owing to its plasticity and ascertained the hypothesis based on a cognitive experiment. We then constructed the 1ST system which can provide image-schemas with plasticity, by using the extended techniques of analogical mapping and of constraintbased graphics.
Peran Inovasi Untuk Meningkatkan Literasi Masyarakat Melalui Pengembangan Perpustakaan Masjid Al Jarwal Ar Rahman Purwakarta

Science is a constituent part of human civilization and culture. Knowledge can be obtained from various things, one of which is from libraries. The existence of the library can not be separated from the civilization and culture of mankind. This study aims to conduct a study on the role of innovation to improve public literacy through library development. This study uses a qualitative approach by collecting data in the form of observations and interviews. The results of the innovation research are useful in the development of the AJAR Library which is a new library so that it is useful for improving literacy and providing continuous education for the community. The existence of the AJAR Library aims to arouse public interest and desire to continue reading and learning in order to realize sustainable education that has an impact on people's lives.
Fast Navigation with Icosahedral Golden Gates

An algorithm of Ross and Selinger for the factorization of diagonal elements of PU(2) to within distance $\varepsilon$ was adapted by Parzanchevski and Sarnak into an efficient probabilistic algorithm for any element of PU(2) using at most effective $3\log_p\frac{1}{\varepsilon^{3}}$ factors from certain well-chosen sets associated to a number field and a prime $p$. The icosahedral super golden gates are one such set associated to $\mathbb{Q}(\sqrt{5})$. We leverage recent work of Carvalho Pinto, Petit, and Stier to reduce this bound to $\frac{7}{3}\log_{59}\frac{1}{\varepsilon^3}$, and we implement the algorithm in Python. This represents an improvement by a multiplicative factor of $\log_259\approx5.9$ over the analogous result for the Clifford+$T$ gates. This is of interest because the icosahedral gates have shortest factorization lengths among all super golden gates.
Supplemental Material for Within-Person Variability Contributes to More Durable Learning of Faces

a URL, doi, or other permanent path for accessing the specified information in a public, openaccess repository, and the specified information must be provided in a format that is time-stamped, immutable, and permanent.Qualifying public, open-access repositories are committed to preserving data, materials, and/or registered analysis plans and keeping them publicly accessible via the web in perpetuity.Examples include the Open Science Framework (OSF) and the various Dataverse networks.Hundreds of other qualifying data/materials repositories are listed at http://re3data.org/.Preregistration of an analysis plan must take place via a publicly accessible registry system (e.g., OSF, ClinicalTrials.govor other trial registries in the WHO Registry Network, institutional registration systems).Personal websites and most departmental websites do not qualify as repositories.
The Supervisionary proof-checking kernel (or: a work-in-progress towards proof generating code)

Interactive theorem proving software is typically designed around a trusted proof-checking kernel, the sole system component capable of authenticating theorems. Untrusted automation procedures reside outside of the kernel, and drive it to deduce new theorems via an API. Kernel and untrusted automation are typically implemented in the same programming language -- the "meta-language" -- usually some functional programming language in the ML family. This strategy -- introduced by Milner in his LCF proof assistant -- is a reliability mechanism, aiming to ensure that any purported theorem produced by the system is indeed entailed by the theory within the logic. Changing tack, operating systems are also typically designed around a trusted kernel, a privileged component responsible for -- amongst other things -- mediating interaction betwixt user-space software and hardware. Untrusted processes interact with the system by issuing kernel system calls across a hardware privilege boundary. In this way, the operating system kernel supervises user-space processes. Though ostensibly very different, squinting, we see that the two kinds of kernel are tasked with solving the same task: enforcing system invariants in the face of unbounded interaction with untrusted code. Yet, the two solutions to solving this problem, employed by the respective kinds of kernel, are very different. In this abstract, we explore designing proof-checking kernels as supervisory software, where separation between kernel and untrusted code is enforced by privilege, not programming language module boundaries and type abstraction. We describe work on the Supervisionary proof-checking kernel, and briefly sketch its unique system interface. We then describe some potential uses of the Supervisionary kernel.
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning

To foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly, the field of explainable artificial intelligence (XAI) has resurfaced as a topic of active research, offering approaches to address the "how" and "why" of automated decision-making. Within this domain, counterfactual explanations (CFEs) have gained considerable traction as a psychologically grounded approach to generate post-hoc explanations. To do so, CFEs highlight what changes to a model's input would have changed its prediction in a particular way. However, despite the introduction of numerous CFE approaches, their usability has yet to be thoroughly validated at the human level. Thus, to advance the field of XAI, we introduce the Alien Zoo, an engaging, web-based and game-inspired experimental framework. The Alien Zoo provides the means to evaluate usability of CFEs for gaining new knowledge from an automated system, targeting novice users in a domain-general context. As a proof of concept, we demonstrate the practical efficacy and feasibility of this approach in a user study. Our results suggest that users benefit from receiving CFEs compared to no explanation, both in terms of objective performance in the proposed iterative learning task, and subjective usability. With this work, we aim to equip research groups and practitioners with the means to easily run controlled and well-powered user studies to complement their otherwise often more technology-oriented work. Thus, in the interest of reproducible research, we provide the entire code, together with the underlying models and user data.
Bayesian penalized model for classification and selection of functional predictors using longitudinal MRI data from ADNI

The main goal of this paper is to employ longitudinal trajectories in a significant number of sub-regional brain volumetric MRI data as statistical predictors for Alzheimer's disease (AD) classification. We use logistic regression in a Bayesian framework that includes many functional predictors. The direct sampling of regression coefficients from the Bayesian logistic model is difficult due to its complicated likelihood function. In high-dimensional scenarios, the selection of predictors is paramount with the introduction of either spike-and-slab priors, non-local priors, or Horseshoe priors. We seek to avoid the complicated Metropolis-Hastings approach and to develop an easily implementable Gibbs sampler. In addition, the Bayesian estimation provides proper estimates of the model parameters, which are also useful for building inference. Another advantage of working with logistic regression is that it calculates the log of odds of relative risk for AD compared to normal control based on the selected longitudinal predictors, rather than simply classifying patients based on cross-sectional estimates. Ultimately, however, we combine approaches and use a probability threshold to classify individual patients. We employ 49 functional predictors consisting of volumetric estimates of brain sub-regions, chosen for their established clinical significance. Moreover, the use of spike-and-slab priors ensures that many redundant predictors are dropped from the model.
Low Dimensional Invariant Embeddings for Universal Geometric Learning

This paper studies separating invariants: mappings on $D$ dimensional domains which are invariant to an appropriate group action, and which separate orbits. The motivation for this study comes from the usefulness of separating invariants in proving universality of equivariant neural network architectures. We observe that in several cases the cardinality of separating invariants proposed in the machine learning literature is much larger than the dimension $D$. As a result, the theoretical universal constructions based on these separating invariants is unrealistically large. Our goal in this paper is to resolve this issue. We show that when a continuous family of semi-algebraic separating invariants is available, separation can be obtained by randomly selecting $2D+1 $ of these invariants. We apply this methodology to obtain an efficient scheme for computing separating invariants for several classical group actions which have been studied in the invariant learning literature. Examples include matrix multiplication actions on point clouds by permutations, rotations, and various other linear groups. Often the requirement of invariant separation is relaxed and only generic separation is required. In this case, we show that only $D+1$ invariants are required. More importantly, generic invariants are often significantly easier to compute, as we illustrate by discussing generic and full separation for weighted graphs. Finally we outline an approach for proving that separating invariants can be constructed also when the random parameters have finite precision.
Organization of lifelong education of library staff in the electronic environment

The object of the report is to present a complex approach to lifelong professional education of library specialists in the digital environment, used in the Far Eastern State Scientific Library, to disclose the factors influencing quantitative and qualitative indicators in this sphere, to identify problematic and positive aspects based on the results of activities for the period of 2017–2021. The main attention is paid to the system of lifelong education of the staff of public libraries in the Khabarovsk Territory, closely related to the activities of the Center for Professional Development on the basis of the Far Eastern State Scientific Library, founded in 2002 with the support of the State Public Scientific and Technological Library of the Siberian Branch of the Russian Academy of Sciences and received the first license in the Far East to conduct educational activities on the basis of the library institution in 2004. Licensed programs of upgrading skills of library specialists of the region, short-term training in the framework of non-formal education, information and methodological support of educational activities (generation of information resources in modern formats in modern formats on current areas of library activity, development of network educational projects aimed at improving professional level of staff of the Far Eastern State Scientific Library and municipalities of the Khabarovsk Territory, support of professional skills competitions, analysis of the libraries activities) are examined. In the context of the digital space formation, the efforts of the Far Eastern State Library are aimed at interacting with partner libraries, providers of information resources, updating pedagogical tools with an emphasis on a competencybased approach, expanding the list of educational programs, introducing distance learning on additional professional programs, and providing open access to digitized educational resources.
Automatic element and mineral detection in thin sections using hyperspectral transmittance imaging microscopy (HyperTIM)

Abstract. In this study we present a novel method for the automatic detection of minerals and elements using hyperspectral transmittance imaging microscopy measurements of complete thin sections (HyperTIM). This is accomplished by using a hyperspectral camera system that operates in the visible and near-infrared (VNIR) range with a specifically designed sample holder, scanning setup, and a microscope lens. We utilize this method on a monazite ore thin section from Steenkampskraal (South Africa), which we analyzed for the rare earth element (REE)-bearing mineral monazite ((Ce,Nd,La)PO4), with high concentrations of Nd. The transmittance analyses with the hyperspectral VNIR camera can be used to identify REE minerals and Nd in thin sections. We propose a three-point band depth index, the Nd feature depth index (NdFD), and its related product the Nd band depth index (NdBDI), which enables automatic mineral detection and classification for the Nd-bearing monazites in thin sections. In combination with the average concentration of the relative Nd content, it permits a destruction-free, total concentration calculation for Nd across the entire thin section.
The Day of Yenisey – Ecological Feast at Kransoyarsk Area State Universal Scientific Library

In 2012, the ecological feast The Day of Yenisey was established in Krasnoyarsk Area to draw attention to the importance of the river for the region and the future generations. Kransoyarsk Area Scientific Library takes an active part in the feast by organizing book exhibitions and display, photo exhibitions, meetings with authors, online lectures and library lessons.
Desktop Voice Assistant With Speech Recognition Intelligence (DVAbot)

- In the advanced current days of reckless aheading technology we can perform tasks that we not once imagined that we could prepare that earlier but to complete these daydreams there is a requirement for a way by which we are able to automate our everyday tasks easily. Thus we developed applications like Voice Assistant that have the capability to communicate with us just by of human interaction. Various applications like Amazon Alexa, Microsoft Cortana are also used as an voice assistant .The voice application called “SIRI” help users to interact with other user’s mobile with vocal sound and also answers back to the voice instructions given by the user. We are going to develop a desktop voice assistant which will automate the tasks that are performed on desktop. It can modify the mode of communications between user and machine. This application is made in such a way that all of the facilities provided by the mobile phone can be retrieved by the user by giving the voice commands.