Reviewerly/exHarmony huggingface dataset:
Network Dynamics Governed by Lyapunov Functions: From Memory to Classification

In 1982 John Hopfield published a neural network model for memory retrieval, a model that became a cornerstone in theoretical neuroscience. A key ingredient of the Hopfield model was the use of a network dynamics that is governed by a Lyapunov function. In a recent paper, Krotov and Hopfield showed how a Lyapunov function governs a biological plausible learning rule for the neural networks' connectivity. By doing so, they bring an intriguing approach to classification tasks, and show the relevance of the broader framework across decades in the field.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS MACROMEDIA FLASH PADA MATERI SEL SISWA KELAS XI SMA

This research is a development research. The aim of this research is to produce a media study base on Macromedia Flash to increase the result of study for students class XI in cell course. This development research applies 4-D model which includes define stage, design stage, develop stage, and disseminate stage. Base on the result of media test, obtained a media study base on Macromedia Flash is valid according to validators and qualified: (1) the ability of teachers to manage learning was good criteria,(2) the activity of students in learning process was active,(3) classical learning completeness is achieved,(4) the respond of students in learning component was positive. The result of research in effective class base on descriptive analysis obtained result that a media study base on Macromedia Flash has been fulfilled, among others: (1) result study in class XI reach 92, 86 % students achieved completeness, (2) the ability of teachers to manage learning was good, (3) student’s activity during the class achieved good criteria, (4) the respond of students in learning was positive. Based on the results of analysis data can be concluded that media study learning model base on Macromedia Flash on cell course in class XI senior high school was good quality.
Team building with limited resources

The content of this article is focused on the study of team building technology. The basic system-forming signs of team building are considered, including in the field of application to team building in conditions of limited resources, ways to overcome the problems that arise when forming an effective team.
Design Space Exploration for Value Prediction in Security Applications

With the introduction of Spectre and Meltdown, two new attacks thattarget the speculative instructions due to Out-of-Order execution intoday's processors, a new way to handle speculative loads has ...
The Conceivability And Admissibility Of Forensic Evidence from IoT Devices in Digital Forensics

This article focuses on the prospects of IoT devices held by the users for their personal usages or in Workspace Mobility or Smart Home Solutions. IoT devices are now burgeoning its capability with the incorporation of Artificial Intelligence, Edge AI, RPA(Robotic Process Automation), AutoML, ARVR and NLP( Natural Language Processing). There are many superfluous IoT Devices and Industry 4.0 based Solutions in the market with great features in use such as Google Home Mini, Google Home Voice Controller, Amazon Echo plus Voice Controller, Kuri Mobile Robot, August Smart Lock than the others. These devices record, store or sometimes transmit the user inputs plus activities and it is unclear to what extent they record and store at each instance. There are countable instances of news and Forensic Use Cases from different parts of the globe which enlighten statements where The Court of Law has demanded the data recorded by the IoT devices as Digital Evidences to complete IOT Crimes Investigations.
Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis

In cyberattack detection and prevention systems, cybersecurity analysts always prefer solutions that are as interpretable and understandable as rule-based or signature-based detection. This is because of the need to tune and optimize these solutions to mitigate and control the effect of false positives and false negatives. Interpreting machine learning models is a new and open challenge. However, it is expected that an interpretable machine learning solution will be domain specific. For instance, interpretable solutions for machine learning models in healthcare are different than solutions in malware detection. This is because the models are complex, and most of them work as a black-box. Recently, the increased ability for malware authors to bypass antimalware systems has forced security specialists to look to machine learning for creating robust detection systems. If these systems are to be relied on in the industry, then, among other challenges, they must also explain their predictions. The objective of this paper is to evaluate the current state-of-the-art ML models interpretability techniques when applied to ML-based malware detectors. We demonstrate interpretability techniques in practice and evaluate the effectiveness of existing interpretability techniques in the malware analysis domain.
Joint Multi-Dimensional Model for Global and Time-Series Annotations.

Crowdsourcing is a popular approach to collect annotations for unlabeled data instances. It involves collecting a large number of annotations from several, often naive untrained annotators for each data instance which are then combined to estimate the ground truth. Further, annotations for constructs such as affect are often multi-dimensional with annotators rating multiple dimensions, such as valence and arousal, for each instance. Most annotation fusion schemes however ignore this aspect and model each dimension separately. In this work we address this by proposing a generative model for multi-dimensional annotation fusion, which models the dimensions jointly leading to more accurate ground truth estimates. The model we propose is applicable to both global and time series annotation fusion problems and treats the ground truth as a latent variable distorted by the annotators. The model parameters are estimated using the Expectation-Maximization algorithm and we evaluate its performance using synthetic data and real emotion corpora as well as on an artificial task with human annotations
Stratigraphic study of Qalqula Formation, Sulaymaniyah Governorate - NE Iraq

Qulqula Formation studied in Suren mountain - Sulaymaniyah- NE Iraq. 10 samples collected from the outcrop near Kani Seif Hamid area. Petrographic and mineralogical investigation carried out, thin sections, X- ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) with Energy Dispersive X-ray Analysis (EDX) samples were prepared and analysed in Wollongong University-Australia. Preliminary results show that the lower part of Qulqula Formation in the studied section undergo severe recrystallization and partly dolomitized. In addition, from chronostratigraphic viewpoint, Qulqula Formation in the studied area thought to represent Late Triassic age. This study report for the first time the occurrence of Calpionellids fossils, two species were identified; Tintinnopsella remanei Borza and Calpionellites major (Colom) which represent the Late Jurassic - Early Cetaceous age (i.e. Late Tithonian - Early Valanginian respectively). This coincided with the proposed age results from previous works.&#x0D; &#x0D; http://dx.doi.org/10.25130/tjps.23.2018.170
Variative application of teaching methods in the process of studying English by future economists

The article discusses the issues of variation application of teaching methods at the university on the example of learning English by future economists. It is noted that the traditional methods of teaching humanities and professional disciplines often represent the transfer of theoretical knowledge in lectures or practical classes. Variability is the use or combination of those methods that are necessary for the formation of a certain professionally significant quality. In this case, all methods are chosen taking into account the personal and activity approaches, and are aimed at the active interaction of students and teachers, taking into account their personal characteristics. The article also emphasizes the importance of gaming techniques, simulation games, in which the activities of specialists are modeled and a foreign vocabulary is practiced.
The Immersion of Directed Multi-graphs in Embedding Fields. Generalisations

The purpose of this paper is to outline a generalised model for representing hybrids of relational-categorical, symbolic, perceptual-sensory and perceptual-latent data, so as to embody, in the same architectural data layer, representations for the input, output and latent tensors. This variety of representation is currently used by various machine-learning models in computer vision, NLP/NLU, reinforcement learning which allows for direct application of cross-domain queries and functions. This is achieved by endowing a directed Tensor-Typed Multi-Graph with at least some edge attributes which represent the embeddings from various latent spaces, so as to define, construct and compute new similarity and distance relationships between and across tensorial forms, including visual, linguistic, auditory latent representations, thus stitching the logical-categorical view of the observed universe to the Bayesian/statistical view.
LibGuides: BEd Teacher Education: Working Remotely with SUC Library - FAQs

Library support for BEd Teacher Education students in Stranmillis University College Guidance on resources and using the Library remotely
Revisit Prediction by Deep Survival Analysis

In this paper, we introduce SurvRev, a next-generation revisit prediction model that can be tested directly in business. The SurvRev model offers many advantages. First, SurvRev can use partial observations which were considered as missing data and removed from previous regression frameworks. Using deep survival analysis, we could estimate the next customer arrival from unknown distribution. Second, SurvRev is an event-rate prediction model. It generates the predicted event rate of the next k days rather than directly predicting revisit interval and revisit intention. We demonstrated the superiority of the SurvRev model by comparing it with diverse baselines, such as the feature engineering model and state-of-the-art deep survival models.
A MACHINE LEARNING APPROACH FOR UNDERSTANDING GPA WITH STUDENTS’ EXPERIENCE USING HYBRID ALGORITHM

Foreseeing understudies' review has risen as a noteworthy zone of examination in training because of the craving to distinguish the fundamental factors that impact scholastic execution. Due to constrained accomplishment in foreseeing the Grade Point Average (GPA), the greater part of the earlier research has concentrated on anticipating grades in a particular arrangement of classes dependent on understudies' earlier exhibitions. The issues related with information driven models of GPA expectation are additionally opened up by a little example measure and a generally vast dimensionality of perceptions in an analysis. In this paper, we use the best in class machine learning systems to develop and approve a prescient model of GPA exclusively dependent on an arrangement of self-administrative learning practices decided in a moderately little example analyze. At last, the objective of level expectation in comparative examinations is to utilize the built models for the outline of mediation methodologies went for helping understudies in danger of scholarly disappointment. In such manner, we lay the numerical preparation for characterizing and identifying most likely accommodating mediations utilizing a probabilistic prescient model of GPA. We exhibit the use of this structure by characterizing fundamental intercessions and recognizing those mediations that are most likely supportive to understudies with a low GPA. The utilization of self-administrative practices is justified, in light of the fact that the proposed mediations can be effortlessly drilled by understudies.
Multi-level neural networks for PDEs with uncertain parameters

A novel multi-level method for partial differential equations with uncertain parameters is proposed. The principle behind the method is that the error between grid levels in multi-level methods has a spatial structure that is by good approximation independent of the actual grid level. Our method learns this structure by employing a sequence of convolutional neural networks, that are well-suited to automatically detect local error features as latent quantities of the solution. Furthermore, by using the concept of transfer learning, the information of coarse grid levels is reused on fine grid levels in order to minimize the required number of samples on fine levels. The method outperforms state-of-the-art multi-level methods, especially in the case when complex PDEs (such as single-phase and free-surface flow problems) are concerned, or when high accuracy is required.
AI, Combined with MRI and Patient Data, Can Predict Alzheimer’s Risk

Deep-learning model outperformed neuroradiologists in predicting risk for disease development.
Virtual Reality-Neural Networks for reconstruction of devastated cities by earthquakes: lacustrine deposits in Mexico City

The reconstruction of a city that has been devastated by an earthquake requires the intelligent management of resources and the establishment of work directions that return, as quickly as possible, the largest number of inhabitants to their routine life. The most important difficulty arising in the initial reconstruction after strong earthquakes period is to find the best assignment of available resources to operational areas. One of the aspects that must be taken care of immediately is the recovery of buried facilities that conduct water, gas or hydrocarbons. For solving this problem, a dynamic optimization model is introduced. The model uses detailed 4D-descriptions of the regions with i) higher vulnerability to collapse, break or smash pipelines, ii) specific characteristics of the ground motions and the properties of soils deposits, iii) strategic densities of superstructures and inhabitants, iv) operational areas and v) available resources to calculate the resource performance and efficiency for different tasks related to the response. Based on a specialized geo-datamart and a Virtual Reality– Neural Networks framework (where the causes-effects can be simulated), the analysts can define the regions where the complex geotechnical-earthquake-infrastructure interaction is more dangerous and the effect of associated damage (such as leaks of dangerous substances - water and gas pipelines), could be devastating due to the number of people living in the surrounding area.
One-Shot Image Classification by Learning to Restore Prototypes

One-shot image classification aims to train image classifiers over the dataset with only one image per category. It is challenging for modern deep neural networks that typically require hundreds or thousands of images per class. In this paper, we adopt metric learning for this problem, which has been applied for few- and many-shot image classification by comparing the distance between the test image and the center of each class in the feature space. However, for one-shot learning, the existing metric learning approaches would suffer poor performance because the single training image may not be representative of the class. For example, if the image is far away from the class center in the feature space, the metric-learning based algorithms are unlikely to make correct predictions for the test images because the decision boundary is shifted by this noisy image. To address this issue, we propose a simple yet effective regression model, denoted by RestoreNet, which learns a class agnostic transformation on the image feature to move the image closer to the class center in the feature space. Experiments demonstrate that RestoreNet obtains superior performance over the state-of-the-art methods on a broad range of datasets. Moreover, RestoreNet can be easily combined with other methods to achieve further improvement.
What’s in a Gist? Towards an Unsupervised Gist Representation for Few-Shot Large Document Classification

The gist can be viewed as an abstract concept that represents only the quintessential meaning derived from a single or multiple sources of information. We live in an age where vast quantities of information are widely available and easily accessible. Identifying the gist contextualises information which facilitates the fast disambiguation and prediction of related concepts bringing about a set of natural relationships defined between information sources. In this paper, we investigate and introduce a novel unsupervised gist extraction and quantification framework that represents a computational form of the gist based on notions from fuzzy trace theory. To evaluate our purposed framework, we apply the gist to the task of semantic similarity, specifically to few-shot large document classification where documents on average have a large number of words. The results show our proposed gist representation can effectively capture the essential information from a text document while dramatically reducing the features used.
An initial study on classification of sexual and bullying remarks in daily conversation

Sexual harassment and bullying within the company are social issues. This paper presents the method of classifying them from the daily conversation for the purpose of detecting such kind of remarks. The authors collect the three kinds of utterance data, create a classification dictionary and show the experimental results conducted using a Bayesian filter.
Estestvennonauchnyi muzei kak faktor formirovaniia nauchno-filosofskogo myshleniia studenta (k 65-letiiu MZ MGU im. M.V. Lomonosova)

The role of the museum display of the Natural Zoning and Soil Formation Department of the Space Earth Science and Sustainable Nature Management Sector of the Landholding museum of M.V. Lomonosov Moscow State University in the formation and development of the scientific conceptual apparatus of students and schoolchildren of the last stage of education is discussed. The scientific and artistic forms of providing the provisions of science are revealed. The role of scientific and philosophical creativity of V.I. Vernadsky, V.V. Dokuchaev, G.V. Dobrovolsky, Yu. K. Efremov, E.D. Nikitin, V.P. Belov in the educational process is shown on the example of the display. The importance of some fundamental provisions of the Russian science of the XX – early XXI centuries in the formation of scientific and philosophical outlook of visitors of the Landholding museum of M.V. Lomonosov Moscow State University is pointed out.
ТЕОРЕТИЧНІ МОДЕЛІ Й КОНЦЕПЦІЇ САМОРЕГУЛЬОВАНОГО НАВЧАННЯ

Theoretical approaches to determination of the concepts of the main theoretical models of self-regulated learning by the scientists in the end of the twentieth century have been studied in the article. The essence of the theoretical approached to this phenomenon studying have been clarified.The following self-regulated learning models and concepts have been characterized: “The Model of Dimensions of Academic Learning” (B. Zimmerman); “Good Strategy User” model (M. Pressley, J. Borkowski, W. Schneider); “The Six-Components Model of Self-Regulated Learning” (M. Boekarts); “The Modе1 or Self-Regulated Learning from Texts” (R.-J. Simons and F. De Jong); “The Concept of Self-Regulated Learning” (Michigan Group – M. Pintrich, W. McKeachie, T. Garcia, E. De Groot); and “The Model of Self-Regulation” (P. Winne). Similarities and differences between the presented models have been presented.Based of theoretical analysis, it has been determined that the mentioned above models have numerous similarities, particularly in the introductory assumptions. All authors have agreed that self-regulated learning causes much more efficient results rather than learning regulated externally. In addition, they have emphasized that deep metacognitive, declarative and procedural knowledge, appropriate level of motivation and willingness to execute this learning activity, are required for effective learning.Differences have been observed in the degree of importance that the authors have attached to subjective conditions (e.g. they are major in the SGU Model of M. Pressley, J. Borkowski, W. Schneider and the Model of Michigan Group), and to the specific manifestations of self-regulatory activity and their interrelations (e.g. The Model of R.-J. Simons and F. De Jong and the Model of P. Winne). The fact that during a short period of time in the end of the twentieth century, a number of theoretical models of self-regulated learning was formulated, allows us to assume that they were not appropriately analyzed and developed. It should also be noted a positive fact that the elaborated theoretical models stimulated and encouraged empirical researches aimed at confirmation or denial of their provisions. A prospective for further research can be seen in development and implementation of our own structural and functional model of self-regulated learning into educational process of students. Additional attention should be paid to characteristics of such mechanisms and elements of the process of self-regulated learning, that would encourage them to increase motivation and efficiency of the own learning activity.
ANALISIS SINGKAT PERBANDINGAN KATA BILANGAN BAHASA TIONGHOA DAN BAHASA INGGRIS

Chinese quantifiers is an important feature of Chinese. English and Chinese belong to different language systems, the number of concepts have different forms of manifestation. By comparing the position, grammatical features, expressive features, Rhetorical aspects of the brief Chinese quantifiers body research and quantifiers in their language, understand the English quantifier distinction thereby performing Chinese classifier and Error Analysis and Exploration Teaching Countermeasures. Quantifier has been teaching Chinese Foreign teaching process is an outstanding difficulties, so we should use a more reasonable approach, an appropriate use of contrast comparison theory, cultural sources and comes to explain quantifier, rhetorical effect, color sense, historical evolution, etc., staged by the method of teaching, teaching of Chinese as a Foreign quantifier error analysis, and thus a reasonable teaching.
Semantical Interpretations of the Temporal Logic Systems CL and Kb with the Gaps of Traditional Truth-values

Over the past several decades, in the field of temporal logic there have been created a great number of semantical theories that provide different truth conditions for tensed propositions. In this article we deal with five non-bivalent semantical interpretations of the temporal logic systems CL (Cocchiarella Linear) and Kb (Kripke Branching): 1) Ł3 by J. Łukasiewicz; 2) K3 by S. C. Kleene; 3) Ockhamism by A. Prior; 4) supervaluationism by R. Thomason; 5) relativism by J. MacFarlane. The aim of this article is to present a detailed typology of the five semantical theories based on these criteria: a) the ability to deal with the problem of retrospective evaluation of future contingent propositions; b) the ability to deal with the problem of divine omniscience and free will; c) their relation to the law of excluded middle; d) their relation to other formulas that are intuitively acceptable in an intederministic context. It is argued that the only theory that satisfies all four criteria is the relativism of J. MacFarlane; however, it faces some serious challenges when dealing with the problem of retrospective evaluation of future contingent propositions in the theological context.