Reviewerly/exHarmony huggingface dataset:
Emerging NUI-based Methods for User Authentication: A New Taxonomy and Survey

As the convenience and cost benefits of Natural User Interface (NUI) technologies are hastening their wide adoption, computing devices equipped with such interfaces are becoming ubiquitous. Used for a broad range of applications, from accessing email and bank accounts to home automation and interacting with a healthcare provider, such devices require, more than ever before, a secure yet convenient user authentication mechanism. This paper introduces a new taxonomy and presents a survey of “point-of-entry” user-device authentication mechanisms that employ a natural user interaction. The taxonomy allows a grouping of the surveyed techniques based on the sensor type used to capture user input, the actuator a user applies during interaction, and the credential type used for authentication. A set of security and usability evaluation criteria are then proposed based on the Bonneau, Herley, Van Oorschot and Stajano framework. An analysis of a selection of techniques and, more importantly, the broader taxonomy elements they belong to, based on these evaluation criteria, are provided. This analysis and taxonomy provide a framework for the comparison of different authentication alternatives given an application and a targeted threat model. Similarly, the taxonomy and analysis also offer insights into possibly unexplored, yet potentially rewarding, research avenues for NUI-based user authentication that could be explored.
Video Analysis by Deep Learning

The tasks of automatically classifying the content of videos or predicting the outcome of a series of events occurring in a sequence of frames, while may sound simple, are still very challenging research areas despite the vast improvement in computing hardware and the easy access to large sets of data. In our work, we extend machine learning techniques to comprehend videos by tackling three challenging tasks: video classification on the full-length video level, video classification both on the level of actions performed in certain frames and the full-length video level, and action prediction of upcoming events.Classification on the video level is a classic machine learning problem that has been addressed previously. We address this problem both using a standard deep learning approach, where a deep convolutional neural network (CNN) is trained on video frames then a Long Short Term Memory (LSTM) network is used to aggregate the features learned by the CNN into a single video label. And we introduce a different approach that uses still images of a data set that is independent on the video data set to train a CNN that is later used to classify a selection of video frames and make a conclusion about the video class. Our approach results in a classification accuracy that ranges between 91% and 94% when processing only 10 to 300 video frames, respectively, of the test videos on a subset of the YouTube Sports-1M dataset.Classification on the actions level and the video class level is not a well-addressed problem. We tackle the challenge by using a hybrid CNN-Hidden Markov Model (HMM) system where a dictionary of actions is constructed from the training data and is used to detect a sequence of video actions then map this actions sequence into a video class for the entire video. Our approach detects the actions in videos of the Actions for Cooking Eggs (ACE) data set with an accuracy of 79% while classifying the videos with a 100% accuracy.Finally, we address the problem of next action prediction by using the same hybrid CNN-HMM system to predict the next performed action when only part of the video is available. Our approach successfully predicts the next first and second performed actions in a video stream with a probability higher than 50% when 60% or more of the video is available for processing, with the prediction accuracy continuing to increase as the system gains access to more video frames.
Multi-Provider Secure Processing of Sensors Data

We describe the implementation of an approach for supporting secure query processing over sensors data in a multi-provider scenario. Our solution relies on the definition of authorizations regulating access to data according to three different visibility levels (no visibility, encrypted visibility, and plaintext visibility). Data processing is performed by multiple providers based on the restrictions imposed by authorizations, which may require to adjust data visibility on the fly. We describe the structure of the query optimizer and show how the operations of a computation can be assigned to different cloud providers to build an efficient, secure, and economical plan for collaborative data processing.
Kaskade: Graph Views for Efficient Graph Analytics

Graphs are an increasingly popular way to model real-world entities and relationships between them, ranging from social networks to data lineage graphs and biological datasets. Queries over these large graphs often involve expensive subgraph traversals and complex analytical computations. These real-world graphs are often substantially more structured than a generic vertex-and-edge model would suggest, but this insight has remained mostly unexplored by existing graph engines for graph query optimization purposes. Therefore, in this work, we focus on leveraging structural properties of graphs and queries to automatically derive materialized graph views that can dramatically speed up query evaluation. We present KASKADE, the first graph query optimization framework to exploit materialized graph views for query optimization purposes. KASKADE employs a novel constraint-based view enumeration technique that mines constraints from query workloads and graph schemas, and injects them during view enumeration to significantly reduce the search space of views to be considered. Moreover, it introduces a graph view size estimator to pick the most beneficial views to materialize given a query set and to select the best query evaluation plan given a set of materialized views. We evaluate its performance over real-world graphs, including the provenance graph that we maintain at Microsoft to enable auditing, service analytics, and advanced system optimizations. Our results show that KASKADE substantially reduces the effective graph size and yields significant performance speedups (up to 50X), in some cases making otherwise intractable queries possible.
Minimum Variance Solution of Underdetermined Systems of Linear Equations

A system of linear equations is said underdetermined when there are more unknowns than equations. Such systems may have infinitely many solutions. In this case, it is important to single out solutions possessing special features. A well known example is the Minimum Norm (MN) solution, which is the solution having the least Euclidean norm. In this note, we consider another useful solution, related with the MN one, which we call the Minimum Variance (MV) solution. We discuss some of its properties and derive a simple, closed form expression.
Architecture for Integrating Learning Platforms Using Adapter

The advantage of the electronic and mobile learning platforms is the dissemination of learning contents with ease, but these operate differently to exchange the learning the learning contents from the server to the client. integrating these learning platforms to operate as a single platform and exchange the contents based on learners' request could improve the learning efficiency and reduce the operational cost. this work introduces a Web Services approach based on the client-server model to develop an integrated architecture that join the two learning platforms. in this paper the architecture of the learning platforms is presented and explained. furthermore, an adapter in a form of Web services is developed and as a middleware for the client-server communication.
FEATURES OF SOCIO-CULTURAL ACTIVITY AS A FACTOR OF SOCIAL-EDUCATIONAL INFLUENCE ON PERSONALITY

The article reveals the essence of socio-cultural activity as a factor of social and educational influence on the personality in the process of socialization. Research methods used: analysis of psychological and pedagogical literature on the research problem; synthesis; generalisation. The socio-cultural activity is considered as a system of forms, means and methods governing the processes of social formation of human, as well as an independent subsystem of social systems of social preparation, social education and education. The modern theory of socio-cultural activity considers the process of educational influence of the environment on the individual complex, summarizing the knowledge of various sciences about humanity and culture. The basic principles of socio-cultural activity are characterized: the principle of cultural conformity, the principle of humanism; principle of nature correspondence; the principle of priority of universal human interests in the process of mastering spiritual and moral values; the principle of comprehensive cultural development, self-organization, self-affirmation of the individual; the principle of humanization of the content of socio-cultural activities and their educational potential, their submission to the interests, needs and attitudes of the individual; the principle of dialectical unity and continuity of cultural-historical, social-pedagogical and national-ethnic experience, traditions and innovations; the principle of socio-state co-management of socio-cultural activities, ensuring decentralization and sovereignty of regional policy in the socio-cultural sphere. The content peculiarities of socio-cultural activity are revealed. It is concluded that socio-cultural activity, in its essence, is an educational activity, has a human-like character, since human-oriented, the disclosure of the spiritual and creative potential embodied in it, acts as an important factor in the socio-educational impact on the person.
Indexing Graph Search Trees and Applications

We consider the problem of compactly representing the Depth First Search (DFS) tree of a given undirected or directed graph having $n$ vertices and $m$ edges while supporting various DFS related queries efficiently in the RAM with logarithmic word size. We study this problem in two well-known models: {\it indexing} and {\it encoding} models. While most of these queries can be supported easily in constant time using $O(n \lg n)$ bits\footnote{We use $\lg$ to denote logarithm to the base $2$.} of extra space, our goal here is, more specifically, to beat this trivial $O(n \lg n)$ bit space bound, yet not compromise too much on the running time of these queries. In the {\it indexing} model, the space bound of our solution involves the quantity $m$, hence, we obtain different bounds for sparse and dense graphs respectively. In the {\it encoding} model, we first give a space lower bound, followed by an almost optimal data structure with extremely fast query time. Central to our algorithm is a partitioning of the DFS tree into connected subtrees, and a compact way to store these connections. Finally, we also apply these techniques to compactly index the shortest path structure, biconnectivity structures among others.
Complementarity-Preserving Fracture Morphology for Archaeological Fragments

We propose to employ scale spaces of mathematical morphology to hierarchically simplify fracture surfaces of complementarity fitting archaeological fragments. This representation preserves complementarity and is insensitive to different kinds of abrasion affecting the exact fitting of the original fragments. We present a pipeline for morphologically simplifying fracture surfaces, based on their Lipschitz nature; its core is a new embedding of fracture surfaces to simultaneously compute both closing and opening morphological operations, using distance transforms.
Real-Time Object Identification with a Smartphone Knock (video)

We propose Knocker, a real-time object identification technique with smartphones. Knocker leverages unique impulse signals that are generated by knocking on an object with a smartphone. Knocker does not require any special augmentation for both smartphones and objects.
Research of Influence of Informatively-Communication Technologies is in Structural Subdivision of Higher Educational Establishment

Background:The rapid development of automation based on software can affect to the future of many professions and these risks we must be taken into consideration when forming a new modern specialist.Ukrainian medical education is not weak in comparing to other educational systems, but specialists of which we prepare must be the competitive in the world market of services.This can lead to the destruction of the stereotypes the traditional educational field but at the same time makes it possible to use of a variety of up-to-date information technologies.The questions arise whether the specialists of the longdistance learning really will be competitive?Is whether an educational base tallying the requirements of the market?To answer a number of questions should be analyzed in detail what is included in the concept of the longdistance learning, what are the prospects for development.Purpose: study of the scientific literature that relating to implementation the distance learning in the medical field of Ukraine and own experimental experience.
Risk watch: Copyright in legal practice

Issues concerning copyright laws can, perhaps unexpectedly, pop up in legal practice and care needs to be taken not to fall foul of them.
Descriptions of Elementary Particles plus Dark Matter plus Dark Energy and Explanations for Some Related Data

We suggest united models and specific predictions regarding elementary particles, dark matter, aspects of galaxy evolution, dark energy, and aspects of the cosmology timeline. Results include specific predictions for new elementary particles and specific descriptions of dark matter and dark energy. Some of our modeling matches known elementary particles and extrapolates to predict other elementary particles, including bases for dark matter. Some modeling explains observed ratios of effects of dark matter to effects of ordinary matter. Some models suggest aspects of galaxy formation and evolution. Some modeling correlates with eras of increases or decreases in the observed rate of expansion of the universe. Our modeling framework features mathematics for isotropic quantum harmonic oscillators and provides a framework for creating physics theories. Some aspects of our approach emphasize existence of elementary particles and de-emphasize motion. Some of our models complement traditional quantum field theory and, for example, traditional calculations of anomalous magnetic dipole moments.
The Parameterized Position Heap of a Trie

Let $\Sigma$ and $\Pi$ be disjoint alphabets of respective size $\sigma$ and $\pi$. Two strings over $\Sigma \cup \Pi$ of equal length are said to parameterized match (p-match) if there is a bijection $f:\Sigma \cup \Pi \rightarrow \Sigma \cup \Pi$ such that (1) $f$ is identity on $\Sigma$ and (2) $f$ maps the characters of one string to those of the other string so that the two strings become identical. We consider the p-matching problem on a (reversed) trie $\mathcal{T}$ and a string pattern $P$ such that every path that p-matches $P$ has to be reported. Let $N$ be the size of the given trie $\mathcal{T}$. In this paper, we propose the parameterized position heap for $\mathcal{T}$ that occupies $O(N)$ space and supports p-matching queries in $O(m \log (\sigma + \pi) + m \pi + \mathit{pocc}))$ time, where $m$ is the length of a query pattern $P$ and $\mathit{pocc}$ is the number of paths in $\mathcal{T}$ to report. We also present an algorithm which constructs the parameterized position heap for a given trie $\mathcal{T}$ in $O(N (\sigma + \pi))$ time and working space.
SASTRA ANAK SEBAGAI AWAL PENDIDIKAN KARAKTER DALAM CERPEN SERI “AKU MAU MENDENGARKAN”, “AKU BERANI BERTERIMAKASIH”, DAN “AKU MAU MEMAAFKAN” KARYA ADE YULIA

ABSTRACT This research is about how literature, especially children’s literature can be a character building for children. In literary works, the author has the purpose conveyed, moral, religious and political goals. To find out the objectives to be conveyed by the author, can be analyzed through pragmatics. In the series of short story entitled Mau Memaafkan ,”Aku Berani Berterimakasih” and  Aku Mau Memaafkan  by Ade Yulia, the author discusses more about moral goals / messages. is our learning to educate someone at the request given, learn to apologize and forgive the mistakes we make and encourage thanks for the help that others have given us. From good behavior, words and habits it will produce good morality and character also, because relating to literary works, especially children's literature can be the beginning of children's character building. Keywords: Children's Literature, Character Building, Short Story.
Penjadwalan Perkuliahan Dengan Metode Vertex Graph Coloring Dan Simulated Annealing

&lt;p&gt;&lt;em&gt;In college, lecture scheduling is very important in lecturing process, because the activities of lecturers and students depend on lecture schedule. To solve the problem, use Vertex GraphColoring and Simulated Annealing. In Vertex Graph Coloring, look for neighboring and neighboring vertices. While on Simulated Annealing, look for space and exchange positions randomly. The merger of Vertex Graph Coloring and Simulated Annealing aims to create optimum lecture schedule by looking at hard constraint and soft constraint. Testing is done at Faculty of Pharmacy University of North Sumatra, by making schedule from manual to computerized, so it is expected to make the schedule optimally and able to avoid hardconstaint and soft constraint.&lt;/em&gt;&lt;/p&gt;
Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language

This paper presents a more recent literature review on Natural Language Generation.In particular, we highlight the efforts for Brazilian Portuguese in order to show the available resources and the existent approaches for this language.We also focus on the approaches for generation from semantic representations (emphasizing the Abstract Meaning Representation formalism) as well as their advantages and limitations, including possible future directions.
Optimal $q$-Ary Error Correcting/All Unidirectional Error Detecting Codes

Codes that can correct up to $t$ symmetric errors and detect all unidirectional errors, known as $t$-EC-AUED codes, are studied in this paper. Given positive integers $q$, $a$ and $t$, let $n_q(a,t+1)$ denote the length of the shortest $q$-ary $t$-EC-AUED code of size $a$. We introduce combinatorial constructions for $q$-ary $t$-EC-AUED codes via one-factorizations of complete graphs, and concatenation of MDS codes and codes from resolvable set systems. Consequently, we determine the exact values of $n_q(a,t+1)$ for several new infinite families of $q,a$ and $t$.
Gender-Based Prespective On The Sadistic Scenes In Sam Taylor-Johnson’s Fifty Shades Of Grey: Reader-Response Theory

This research aims to determine the readers’ response to the Fifty Shades of Grey by E.L James which is analyzed with Reader Response theory by Richard beach. This research was conducted to identify the readers’ response to: 1) how are the responses of the audiences towards Fifty Shades of Grey movie 2) how is the response of the reviewer based on different gender background 3) how is the response of the viewer based on the different marital status background. This research used qualitative research methods. The data of this research consists of two kinds, namely primary data and secondary data. Primary data is readers’ reviews by using questioner towards Fifty Shades of Grey movie. While the secondary data obtained from other sources such as literary books, previous studies, journals, articles, internet, and others related to the study. The technique of collecting data is using document analysis. The result of this research shows that there are different views about Fifty Shades of Grey caused by the different backgrounds of the reviewers. Keywords: Fifty Shades of Grey, Readers’ Response
Smart Home Wireless Sensor Nodes Addressing the Challenges using Smart Objects and Artificial Intelligence

Smart homes are further development of intelligent buildings and home automation, where context awareness and autonomous behaviour are added. They are based on a combination of the Internet and emerging technologies like wireless sensor nodes. These wireless sensor nodes are challenging because they consume battery power, they use network bandwidth, and they produce wireless interferences. Currently, different methods exist for handling these challenges. These methods are, however, based on adjusting the transmitter frequency and using duty-cycling in combination with sleep mode approaches. This paper introduces an approach that considerably lowers the wireless sensor node power consumption and the amount of transmitted sensor events. It uses smart objects that include artificial intelligence to efficiently process the sensor event on location and thereby saves the costly wireless transportation of these events. In this paper it has been shown that this approach provides huge savings of power consumption and network load, which in turn reduces the interference level
An Analysis on Students’ Perception of the Application of Journalist Questions in Teaching Writing

This research aimed at knowing about the teaching learning process through journalist questions and studentsâ€™ perception of the application of journalist questions. This research is qualitative research. The subject of this research was the students of X IPS SMA NW Pringgabaya which consisted of 23 students. In addition, observation, questionnaire, and semi structured interview were used for collecting data. The finding revealed that the students have a good perception toward the implementation of journalist questions in teaching writing in the class. In addition, based on the result of observation, questionnaire and interview, the application of journalist questions was applied appropriately and the studentsâ€™ perception in teaching writing is good.â€
Learning Bilingual Word Embeddings Using Lexical Definitions

Bilingual word embeddings, which representlexicons of different languages in a shared em-bedding space, are essential for supporting se-mantic and knowledge transfers in a variety ofcross-lingual NLP tasks. Existing approachesto training bilingual word embeddings requireoften require pre-defined seed lexicons that areexpensive to obtain, or parallel sentences thatcomprise coarse and noisy alignment. In con-trast, we propose BilLex that leverages pub-licly available lexical definitions for bilingualword embedding learning. Without the needof predefined seed lexicons, BilLex comprisesa novel word pairing strategy to automati-cally identify and propagate the precise fine-grained word alignment from lexical defini-tions. We evaluate BilLex in word-level andsentence-level translation tasks, which seek tofind the cross-lingual counterparts of wordsand sentences respectively.BilLex signifi-cantly outperforms previous embedding meth-ods on both tasks.
Quantum filtering for a two-level atom driven by two counter-propagating photons

The purpose of this paper is to propose quantum filters for a two-level atom driven by two continuous-mode counter-propagating photons and under continuous measurements. Two scenarios of multiple measurements, 1) homodyne detection plus photodetection, and 2) two homodyne detections, are discussed. Filtering equations for both cases are derived explicitly. As demonstration, the two input photons with rising exponential and Gaussian pulse shapes are used to excite a two-level atom under two homodyne detection measurements. Simulations reveal scaling relations between atom-photon coupling and photonic pulse shape for maximum atomic excitation.