Reviewerly/exHarmony huggingface dataset:
A Hybrid Thread Modeling Method

In FY 2016, the research team evaluated Security Cards, STRIDE (Spoofing identity, Tampering with data, Repudiation, Information disclosure, Denial of service, Elevation of privilege), and persona non grata (PnG) for effectiveness in threat identification. Security Cards is an approach that emphasizes creativity and brainstorming over more structured approaches such as checklists. STRIDE involves modeling a system and subsystem and related data flows. PnGs represent archetypal users who behave in unwanted, possibly nefarious ways. The team used two scenarios: an aircraft maintenance scenario and a drone swarm scenario, both described in this technical note in detail, along with the project outcomes. No individual threat modeling method included all identified threats. The research team subsequently developed the Hybrid Threat Modeling Method (hTMM), considering the desirable characteristics for a Threat Modeling Method. At a high level, the hTMM includes the following steps, described in detail in the technical note: (1) Identify the system you will be threat modeling. (2) Apply Security Cards according to developers’ suggestions. (3) Prune PnGs that are unlikely or for which no realistic attack vectors could be identified. (4) Summarize results from the above steps, utilizing tool support. (5) Continue with a formal risk assessment method.
Іmplementation of innovative teaching technologies in the educational process of training of future geography teachers

Aim of the research: substantiation of features of introduction of innovative teaching technologies in the educational process of training of future geography teachers. Research methods. To achieve this goal, the following methods were used: theoretical, which made it possible to find out the state of the research problem; methodological, on the basis of synthesis, analysis, systematization and generalization of the studied sources for realization of introduction of innovative teaching technologies into educational process of training of future geography teachers. Results. The article is considered with the necessity of introducing innovative teaching technologies in the educational process of training future geography teachers on the example of teaching the discipline "Concepts of modern nature science". There were defined conditions under which effective introduction of innovative learning technologies into the educational process is possible. The prerequisites and criteria of the teacher's readiness for innovative activity are distinguished. The types of innovative technologies and possibilities of their application in the educational process of training of future geography teachers are analyzed, practical tasks for realization of specific innovative technologies of teaching are offered. The research concluded that the introduction of innovative learning technologies involves updating the content and forms of learning, connection theory with practice, combining traditional methods of learning with innovative, which will prepare competent and creative professionals who can actively act in accordance with requirements of the social and professional environment. Innovations in the system of training graduates of pedagogical specialties include not only the introduction of modern innovative technologies of teaching in the educational process of pedagogical universities, but also taking into account changes in the system of general secondary education.
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing

With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results.However, the gap between machine translation systems and human translators needs to be manually closed by postediting.In this paper, we propose an end-toend deep learning framework of the quality estimation and automatic post-editing of the machine translation output.Our goal is to provide error correction suggestions and to further relieve the burden of human translators through an interpretable model.To imitate the behavior of human translators, we design three efficient delegation modules -quality estimation, generative post-editing, and atomic operation post-editing and construct a hierarchical model based on them.We examine this approach with the English -German dataset from WMT 2017 APE shared task and our experimental results can achieve the state-of-the-art performance.We also verify that the certified translators can significantly expedite their postediting processing with our model in human evaluation.
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm

Sample- and computationally-efficient distribution estimation is a fundamental tenet in statistics and machine learning. We present SURF, an algorithm for approximating distributions by piecewise polynomials. SURF is: simple, replacing prior complex optimization techniques by straight-forward {empirical probability} approximation of each potential polynomial piece {through simple empirical-probability interpolation}, and using plain divide-and-conquer to merge the pieces; universal, as well-known polynomial-approximation results imply that it accurately approximates a large class of common distributions; robust to distribution mis-specification as for any degree $d \le 8$, it estimates any distribution to an $\ell_1$ distance $< 3$ times that of the nearest degree-$d$ piecewise polynomial, improving known factor upper bounds of 3 for single polynomials and 15 for polynomials with arbitrarily many pieces; fast, using optimal sample complexity, running in near sample-linear time, and if given sorted samples it may be parallelized to run in sub-linear time. In experiments, SURF outperforms state-of-the art algorithms.
Childhood Habituation in Evolution of Augmenting Topologies (CHEAT)

Neuroevolution is a field within machine learning that applies genetic algorithms to train artificial neural networks. Neuroevolution of Augmenting Topologies (NEAT) is a method that evolves both the topology of the network and trains the weights of the network at the same time, and has been found to successfully solve reinforcement learning problems efficiently and the XOR problem with a minimal topology. However, NEAT has not been shown to solve more complex labelling problems and has a vaguely motivated heuristic concept of speciation needed to keep a diverse population and protect new structural innovations from instant elimination. In this thesis a new algorithm was developed, the Childhood Habituation in Evolution of Augmenting Topologies (CHEAT) algorithm, which removes the need for the heuristic speciation concept and its associated hyper-parameters by splitting topology evolution and weight training into two distinct phases. CHEAT also allows for structured topology evolution by having the option of forcing fully connected layers. The algorithm was tested on the XOR problem and the spiral problem with two turns, with a result showing performances on par with NEAT for the XOR problem, and better results on the spiral problem where CHEAT is able to solve the problem while NEAT is not. It was found that without an early stopping criterion for gradient descent training, new structural innovations were quickly eliminated from the population before being optimally tuned, and thus the stopping criterion is vital to be able to remove the NEAT speciation heuristics. It was also found that restricting the algorithm to evolve in a structured manner by forcing fully connected layers was vital to solving any problems more complex than the XOR problem, likely due to the feature selection behaviour fully connected layers exhibit. The work done in this thesis opens further areas of research into evolving Artificial Neural Networks, where the most interesting leads lie in other weight training methods, different stopping criterion for gradient descent training, and finally letting the algorithm take control of evolution of its own hyper-parameters for automatic model construction. (Less)
LibGuides: 3D Printing Guide: Home

How to utilize the Learning Resource Center 3D printer. Includes direction on obtaining files for print and the procedures for requesting a print.
Holocene megaflood history and provenance of the upper Indus-River. Implication for human migration along the ancient Silk Road at Ladakh.

&amp;lt;p&amp;gt;&amp;amp;#160; &amp;amp;#160; &amp;amp;#160; &amp;amp;#160; &amp;amp;#160; The Indus River originating from the Manasarovar Lake runs along the Indus Tsangpo Suture Zone at Ladakh separating the Tethyan Himalaya in the south from the Karakoram Zone in the north. Due to the barrier created by the Pir Panjal Ranges and the Higher Himalaya, Ladakh falls in the rain shadow zone of ISM (Indian Summer Monsoon) with an average annual temperature of ~7.3&amp;amp;#176;C. Random catastrophic hydrological events are known to endanger lives and properties of people residing here. So, determination of frequency, recurrence and forcing mechanism of past extreme floods are crucial in this highly vulnerable area.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160; &amp;amp;#160; &amp;amp;#160; &amp;amp;#160; &amp;amp;#160; Here we studied Holocene mega flood history of the Upper Indus River at Ladakh using slack water deposits (SWDs). SWDs are composed of stacks of sand-silt couplets deposited during high flooding events. They are deposited instantly from suspension associated with sharp reduction of flow velocity due to local obstructions. Each couplet represent a flooding event. These events are dated employing Optically Stimulated Luminescence (OSL) using sand and AMS &amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;C using charcoal specks and hearth layers. The frequency of these events suggest higher occurrence of mega floods during pronounced northward penetration of ISM. Recurrence Interval (RI) analysis of these events suggest spatial variation in forcing mechanism between the trunk and the main tributary channel (Zanskar). Sedimentary provenance of these events are also analyzed using detrital zircon geochronology. The provenance analysis indicate more efficient sediment transportation along the Zanskar River as compared to the main Indus channel. Post LGM (Last Glacial Maximum) human migration along the channel is revealed from hearths found within these SWDs which generally occurs during post flooding episodes. Materials found within the hearths, chronology and the fashion of occurrence imply migration and cultural connectivity between the Indian sub-continent and the Central Asia along the ancient Silk Road at Ladakh as old as ~14 ka.&amp;lt;/p&amp;gt;
Priming Neuronal Machine Translation

Priming is a well known and studied psychology phenomenon based on the prior presentation of one stimulus (cue) to influence the processing of a response. In this paper, we propose a framework to mimic the process of priming in the context of neural machine translation (NMT). We evaluate the effect of using similar translations as priming cues on the NMT network. We propose a method to inject priming cues into the NMT networkand compare our framework to other mechanisms that perform micro-adaptation during inference. Overall, experiments conducted in a multi-domain setting confirm that adding priming cues in the NMT decoder can go a long way towards improving the translation accuracy.Besides, we show the suitability of our framework to gather valuable information for an NMT network from monolingual resources.
A Unified Cloud-Native Architecture For Heterogeneous Data Aggregation And Computation

Improving healthcare depends on collecting and analyzing different types of health related data such as Electronic Health Records (EHR), Patient Generated Health Data (PGHD), prescription and medication data and medical image data. Even though different solutions in terms of storage and processing have been designed and developed but each solution is usually designed for a specific type of data. Storing, processing, and analyzing all types of data using a single solution necessarily doesn't result in best performance and quality of analysis. To acquire the better quality, each types of data requires its own type of storage, data processing and machine learning solutions which cannot be integrated as a unified system in some cases. In order to have a unified system that serves all types of data we propose a modular cloud native architecture with autonomous modules in terms of control, deployment and management for each types of data.
Negotiated Positivism: The disregarded epistemology of Arne Furumark

Archaeological theory during the twentieth century is often presented according to a tri-partite scheme. This article serves to put this model into question through the explication of the epistemology of the Swedish classical archaeologist Arne Furumark. He introduced a heuristic model for ceramic studies in 1941 that bears the hallmarks of logical positivism. This early appropriation of analytical philosophy in classical archaeology does not resonate with the above-mentioned model of archaeological theory. However, Furumark did not adopt the agenda of processual archaeology wholeheartedly as the greater part of his research was founded on a culture historical framework. Furumark’s epistemology was negotiated between two archaeological paradigms, or two branches of positivism.
Neural Networks in Relation to Machine Learning and Artificial Intelligence in Bioinformatics

The utilization of prescient quality marks to help clinical choice is turning out to be increasingly significant. Profound learning has a gigantic potential in the expectation of aggregate from quality articulation profiles. Nonetheless, neural organizations are seen as secret elements, where precise forecasts are given with no clarification. The necessities for these models to become interpretable are expanding, particularly in the clinical field.
KAJIAN LITERATUR DESAIN PERPUSTAKAAN RAMAH DISABILITAS

The library is one of the places frequented by visitors with the aim of learning, reading, doing research, communicating and building relationships with others. Visitors who was came to the library come from a variety background. Ranging from children to the elderly, even from various regions and countries, and visitors with special needs or disabilities. Therefore, the function of the library as a learning tool must be able to serve various kinds of visitors so that it can be used as much as possible. To be able to serve a variety of visitor populations, especially for disabilities, adequate design is required. Like its design, its services, existing facilities, and library collections must be able to accommodate all visitors. Several studies have conducted to evaluate disability-friendly libraries. So that, this literature review journal conducted to collect and discuss previous research related to library evaluations in various disability-friendly places. In addition, researchers will also make a comparison of several library designs to its weaknesses and strengths they have to accommodate all visitor populations, especially for disabilities.Keywords: library, disability, service, facility
A Lattice Attribute-Based Encryption Scheme for Cloud Storage Based on R-LWE Problem

Abstract Attribute-Based Encryption (ABE) is generally applied on Cloud Storage to ensure the security and efficient access control of uploaded data. However, with the development of quantum computing, Diffie-Hellman Problem and Discrete Logarithm Problem, which all ABE scheme based on, have been solved. Therefore, anti-quantum ABE schemes become a research hotspot. However, current anti-quantum ABE schemes cannot make attribute revocation. In order to solve these problems, we propose a lattice attribute-based encryption scheme for cloud storage based on R-LWE problem (L-ABE). Firstly, we construct public/secret key pairs based on the hardness of Ring Learning with Error problem (R-LWE) to achieve quantum attack resistance effectively. Secondly, our scheme can achieve attribute revocation to ensure fine-grained access control. Finally, we prove that our scheme can resist chosen plaintext attack.
Empowerment in Digital Literacy Acquisition Programs

This chapter focuses on the experience of learners who became tutors in digital literacy acquisition programs in six states. Data were drawn from over 100 participants in a large, multi-year, Broadband Technology Opportunities Program (BTOP). The BTOP project included lead partners who provided adults with opportunities to learn to use computers and the Internet in settings that included libraries, community-based organizations, workforce development offices, adult education settings, and incarcerated re-entry programs. Similar implementation strategies were used across all programs They each: (1) used the same online platform designed specifically for adult learners, (2) provided digital literacy materials in English and Spanish, and (3) offered in-person tutor support and each learner was given the opportunity to work at his or her own pace and identify his or her own learning goals. A common customizable tutor training program was facilitated by tutor-coordinators to address the unique needs of different sites and implementation configurations. In almost every program site in the study, there were tutors who had started their participation in the program as learners. Tutor coordinators and individual tutors recognized something special in these learners and encouraged them to become tutors. Findings suggest that learners who became tutors built their self-confidence and continued to increase their application of digital literacies by tutoring others and widely applying what they had learned in new contexts.
Object Detection And Segmentation Using Optimized Deep Learning Algorithms

The effect of deep learning on video analysis including action detection and recognition was not that important due to the nature of the video data format and lack of annotations. Additionally, training profound neural networks on large-scale video datasets is extremely expensive in terms of computation power and cost. Low-level features such as intensity, orientation, and motion will render object identification for each pixel in image can solve the problem to a greater extent. The present article has reviewed few existing techniques adopted by earlier research that are implemented to process video data with prominence to detect various object classes. First, we proposed a semantic segmentation using deep learning technique that suggest exploring an alternate technique for object detection. Assuming pixel-wise annotations, an encoder-decoder network structure widely used for semantic video frame segmentation is built using 3D CNN to predict the pixel-wise labels for each video frame. Second, the paper proposed two optimized YOLO v2 and YOLO v3 object detection and classification algorithm to improve the efficiency of object classification. Finally, the performance of the proposed algorithms is evaluated using an experimental approach and shown that the proposed techniques proved their efficiency with 99% of accuracy levels in object detection and segmentation when compared with the other classification versions with 61%.
On Chinese Translation of English Financial News Based on Functional Translation Theory

At present, the huge shift of people"s economic concept and financial management mode propels the vigorous development of financial media.Financial news, as a special style under the news genre, is a report with regard to the latest facts and situations of economic decisions, economic activities, and economic phenomena.Undoubtedly, the quality of its translation directly influences the international communication for information, language and cultural exchange.In order to help translators obtain financial information more effectively and accurately, this article introduces the linguistic features of English financial news, which is beneficial to translate them reasonably.In addition, this article selects English news and corresponding Chinese translation texts from domestic authoritative bilingual news websites, analyzes the translation texts of English financial news under the guidance of functional translation theory.Afterwards, this article puts forward some translation strategies of English financial news from the perspective of functional translation theory, and explores the instructing role of functional translation theory in news translation at the same time.This article aims at proving the rationality and practicability of this theory, and establishing a sort of audience-oriented translation view focusing on the effect of news communication, which can provide some thoughts for the future of financial news translation practice.
Solving Historical Dictionary Codes with a Neural Language Model

We solve difficult word-based substitution codes by constructing a decoding lattice and searching that lattice with a neural language model.We apply our method to a set of enciphered letters exchanged between US Army General James Wilkinson and agents of the Spanish Crown in the late 1700s and early 1800s, obtained from the US Library of Congress.We are able to decipher 75.1% of the cipher-word tokens correctly.
Efforts to Prevent Cyber Attacks Could be the Cause of the Next Global Financial Crisis

Headlines like “A cyberattack could trigger the next financial crisis, new report says,”1 and “How a Cyber Attack Could Cause the Next Financial Crisis,”2 should be of concern to all of us and be important incentives to do something! You probably have not yet heard the ironic headline, “Efforts to Prevent Cyber Attacks Could be the Cause of the Next Global Financial Crisis.” But, that is already a problem that is emerging and needs to be given serious attention.
Research Of Development Of Communicative Competence Of Future Managers Pre-School Educational Institutions

The article presents the characteristics of the primary section of the development of communicative competence of future leaders of preschool educational organizations. The relevancy of the research topic resulted from a growing tendency of communication development in managing sphere, therefore, communicative competence of the management sector specialists is an essential requirement for their profession. The improvement of the vocational education quality is one of the essential issues of not only Russia, but also for the world society, as at the present time that countries, which prioritize the increase in High Technology and information and intellectual development of people, reach the success. Under such conditions pedagogical education intends to provide their graduates narrow background skills as well as the development of competences, especially communicative skill as a fundamental one. Though, the existing system of student's training in higher educational institutions could be described by the poor focus of educational process on the development of communicative competence among students, also lack of the system of essential managerial and pedagogical conditions needed to provide the development of it, limited usage of communicative potential and experience of students, while organizing the educational process by a teacher.