Miracle-dz/UniFAR-FTUs huggingface dataset:
Conformational changes of a Swi2/Snf2 ATPase during its mechano-chemical cycle

Remodelling protein nucleic acid interfaces is an important biological task, which is often carried out by nucleic acid stimulated ATPases of the Swi2/Snf2 superfamily. Here we study the mechano-chemical cycle of such an ATPase, namely the catalytic domain of the Sulfolobus solfataricus Rad54 homologue (SsoRad54cd), by means of fluorescence resonance energy transfer (FRET). The results of the FRET studies show that the enzyme can be found in (at least) two different possible conformations in solution. An open conformation, consistent with a recently reported crystal structure, is converted into a closed conformation after DNA binding. Upon subsequent binding of ATP no further change in conformation can be detected by the FRET measurements. Instead, a FRET detectable conformational change occurs after ATP hydrolysis and prior to ADP release, suggesting a powerstroke that is linked to phosphate release. Based on these data we will present a new model for the mechano-chemical cycle of this enzyme. This scheme in turn provides a working model for understanding the function of other members of the Swi2/Snf2 family.
Designing textual password systems for children

This paper describes two studies that looked at passwords for children. The first examined what children knew about the creation of textual passwords and then a second, follow on study, investigated the passwords that children chose to use. The two studies show that children have some understanding of how to make a good textual password but that the passwords they use are often in contradiction to their own knowledge of best practice. Based on the findings from these two studies, the authors propose three key design principles for systems that require textual passwords for children and where no alternative is available.
Personalized mobile physical activity recognition

Personalization of activity recognition has become a topic of interest recently. This paper presents a novel concept, using a set of classifiers as general model, and retraining only the weight of the classifiers with new labeled data from a previously unknown subject. Experiments with different methods based on this concept show that it is a valid approach for personalization. An important benefit of the proposed concept is its low computational cost compared to other approaches, making it also feasible for mobile applications. Moreover, more advanced classifiers (e.g. boosted decision trees) can be combined with the new concept, to achieve good performance even on complex classification tasks. Finally, a new algorithm is introduced based on the proposed concept, which outperforms existing methods, thus further increasing the performance of personalized applications.
Machine Learning and Text Segmentation in Novelty Detection

This paper explores a combination of machine learning, approximate text segmentation and a vector-space model to distinguish novel information from repeated information. In experiments with the data from the Novelty Track at the Text Retrieval Conference, we show improvements over a variety of approaches, in particular in raising precision scores on this data, while maintaining a reasonable amount of recall.
Mapping FrameNet and SUMO with WordNet Verb: Statistical Distribution of Lexical-Ontological Realization

Automatic acquisition of lexical knowledge is critical to a wide range of natural language processing tasks. Verb knowledge is especially important in semantic parsing. Verbs denote relational information of lexicogrammar and semantically state the participants and event involved in the meaning construed. This paper describes a statistical distribution approach to reuse and integrate information from the Suggested Upper Merged Ontology (SUMO), WordNet and FrameNet. The mapping between word-meanings, frame-semantics and world concepts suggests a heuristic approach for linking WordNet verbs and FrameNet frames providing a knowledge base for Semantic Role Labeling(SRL), identifying the appropriate range of possible semantic roles with respect to the event evoked by verb. This is accomplished through the verbs covered by both FrameNet and WordNet, taking the shared lexical knowledge as learning data to map SUMO concepts with FrameNet frames. The exploitation of the mapping aims at automatic populating WordNet data to FrameNet frames constructing a knowledge base for semantic parsing.
Rule extraction from support vector machines

Support vector machines (SVMs) are learning systems based on the statistical learning theory, which are exhibiting good generalization ability on real data sets. Nevertheless, a possible limitation of SVM is that they generate black box models. In this work, a procedure for rule extraction from support vector machines is proposed: the SVM+Prototypes method. This method allows to give explanation ability to SVM. Once determined the decision function by means of a SVM, a clustering algorithm is used to determine prototype vectors for each class. These points are combined with the support vectors using geometric methods to define ellipsoids in the input space, which are later transfers to if-then rules. By using the support vectors we can establish the limits of these regions.
Analysing Google rankings through search engine optimization data

Purpose – The purpose of this paper is to identify the most popular techniques used to rank a web page highly in Google.Design/methodology/approach – The paper presents the results of a study into 50 highly optimized web pages that were created as part of a Search Engine Optimization competition. The study focuses on the most popular techniques that were used to rank highest in this competition, and includes an analysis on the use of PageRank, number of pages, number of in‐links, domain age and the use of third party sites such as directories and social bookmarking sites. A separate study was made into 50 non‐optimized web pages for comparison.Findings – The paper provides insight into the techniques that successful Search Engine Optimizers use to ensure a page ranks highly in Google. Recognizes the importance of PageRank and links as well as directories and social bookmarking sites.Research limitations/implications – Only the top 50 web sites for a specific query were analyzed. Analysing more web sites a.
Transformation Algorithms for Data Streams

Next generation data processing systems must deal with very high data ingest rates and massive volumes of data. Such conditions are typically encountered in the intelligence community (IC) where analysts must search through huge volumes of data in order to gather evidence to support or refute their hypotheses. Their effort is made all the more difficult given that the data appears as unstructured text that is written in multiple languages using characters that have different encodings. Human analysts have not been able to keep pace with reading the data and a large amount of data is discarded even though it might contain key information. The goal of our project is to assess the feasibility of incrementally replacing humans with automation in key areas of information processing. These areas include document ingest, content categorization, language translation, and context- and temporally-based information retrieval
Heterogeneous Transfer Learning for Image Clustering via the SocialWeb

In this paper, we present a new learning scenario, heterogeneous transfer learning, which improves learning performance when the data can be in different feature spaces and where no correspondence between data instances in these spaces is provided. In the past, we have classified Chinese text documents using English training data under the heterogeneous transfer learning framework. In this paper, we present image clustering as an example to illustrate how unsupervised learning can be improved by transferring knowledge from auxiliary heterogeneous data obtained from the social Web. Image clustering is useful for image sense disambiguation in query-based image search, but its quality is often low due to imagedata sparsity problem. We extend PLSA to help transfer the knowledge from social Web data, which have mixed feature representations. Experiments on image-object clustering and scene clustering tasks show that our approach in heterogeneous transfer learning based on the auxiliary data is indeed effective and promising.
The HTK book

1 The Fundamentals of HTK 2 1.1 General Principles of HMMs . 3 1.2 Isolated Word Recognition . 3 1.3 Output Probability Specification . 6 1.4 Baum-Welch Re-Estimation . 6 1.5 Recognition and Viterbi Decoding . 9 1.6 Continuous Speech Recognition . 10 1.7 Speaker Adaptation . 13
Modeling and analysis of two-factor authentication protocol for USB digital evidence acquisition devices

This paper proposes two-factor authentication protocol for adding security layer to USB digital evidence acquisition devices, and preventing illegal usage of data stored in that devices from unauthorized users. By using one time password generated from unique parameters of individual user, the proposed protocol can be used to protect forensic acquisition software and sensitive information stored in storage devices from any unauthorized users. In the protocol design and experiment, we use a formal method tool to model and verify for ensuring the correctness of workflow as well as a security of the protocol. The result from simulation and state space analysis show that the proposed protocol is able to strengthen up the security of USB storage devices and satisfies basic security properties.
An overlay automata approach to regular expression matching

Regular expression (RegEx) matching, the core operation of intrusion detection and prevention systems, remains a fundamentally challenging problem. A desired RegEx matching scheme should satisfy four requirements: DFA speed, NFA size, automated construction, and scalable construction. Despite lots of work on RegEx matching, no prior scheme satisfies all four of these requirements. In this paper, we approach this holy grail by proposing OverlayCAM, a RegEx matching scheme that satisfies all four requirements. The theoretical underpinning of our scheme is OD2FA, a new automata model proposed in this paper that captures both state and transition replication inherent in DFAs. Our RegEx matching solution processes one input character per lookup like a DFA, requires only the space of an NFA, is grounded in sound automata models, is easy to deploy in existing network devices, and comes with scalable and automated construction algorithms.
CLASSIFYING EARTH-SURFACE FEATURES BYUSING A FULL SCALE-VECTOR APPROACH

This paper presents a new approach to the problem of classifying earth-surface features, using image analysis techniques. The discussion will be connned to the elementary case of classiication of surface materials where, without other a priori knowledge, only the gray-level histogram suuces. We perform the classiication of materials such as vegetation, roads, water, cities, etc, through a segmentation process. We show that the given image can be correctly partitioned into a number of regions each one showing a homogeneous intensity change model by using a three-step process. Initially the image is divided into a number of nonoverlapping windows and then we model the intensity changes for each window by using a feature vector. We show that we can get a suitable estimation of the generally-unknown number of regions with the help of an optimization criteria for merging windows. We also accomplish the automatic selection of estimates of the population parameters for each one of the homogeneous regions in the given image. For doing this, we use the identiication between rough estimates of those parameters computed on a coarse segmentation and the zero-crossings in rst and second derivative of the histogram smoothed at its corresponding scale vector. To obtain the nal segmentation of the given image, we use a iterated conditional mode (ICM) method.
The Practice of In-Depth Study in an Issues-Oriented Social Studies Classroom.

This paper describes research that sought to merge the definition of in-depth study and its theoretical underpinnings with the reality of classroom practice. It describes and analyzes one high school public issues class where single topics were developed in depth. The research seeks to answer the question: What does in-depth study look like in practice? In particular, it explores three questions: (1) How and why is knowledge selected, organized, and utilized in the depth classroom, and what meaning do students give it? (2) What is the nature of the social interaction in the classroom, and what meaning do students give it? and (3) What practical teaching dilemmas do teachers face in the everyday practice of in-depth study? Four common characteristics of in-depth study are identified: (1) use of knowledge that is complex, thick, and divergent about a single topic, concept, or event using sources that range beyond the textbook; (2) focus on essential and authentic issues or questions that contain elements of ambiguity, doubt, or controversy; (3) a spirit of inquiry that provides opportunities, support, and assessment mechanisms for students to manipulate ideas in ways that transform their meaning; and (4) sustained time on a single topic, concept, or event. In terms of theory, the research confirms the notion that thinking about and understanding knowledge depend on: (1) its organization around key ideas; (2) a functional base where the learner uses knowledge to solve problems; and (3) a social setting where the learner interacts with teachers and students. (DK) *****************************************************************Reproductions supplied by EDRS are the best that can be made from the original document. ***************************************::*******************************
Towards the automatic discovery of platform transformation templates of legacy object-oriented systems

Software modernization is needed to perform the evolution of a system when conventional practices can no longer achieve the desired evolution goal. In their initiative called architecture-driven modernization (ADM), the Object Management Group proposes to use MDA to perform this modernization. However, ADM needs new tools and techniques to migrate systems developed on a non-model-driven environment to a model-driven environment. One challenge to enable this migration is the discovery of a platform description model (PDM) from the implementation of a system. In this paper, we propose an approach to discover a view of the PDM from an object-oriented system source code. This view is given as a set of transformation templates parameterizing the source code of the system's implementation platform and expressed in the QVT language. The approach uses different analysis techniques and was validated on several systems written in Java and gives good results for a number of them.
A simple and efficient multi-way relay network coding scheme

For full-data exchange multi-way relay channels, a novel efficient multi-way relay network coding scheme is proposed. It is based on multi-stage two-way network coding, which is low-complexity and high reliable. For the channel without noise, the throughput per node per channel is increased by 50 % compared to plain routing, and even by 12.5% compared to the binary-signaling network coding scheme. Besides, due to the simple nature of 3-pulse amplitude modulation (3-PAM), it has much lower complexity and much higher reliability compared to the binary-signaling network coding scheme. Both theoretical analysis and computer simulations prove such throughput gain. On the additive white Gaussian noise (AWGN) channel, low density parity check (LDPC) codes are utilized in this scheme to improve the reliability. The simulation results show that the signal-to-noise ratio (SNR) gains to reference scheme increase as the number of the source nodes increases, and the SNR gains of the LDPC-coding scheme are even bigger than the uncoding scheme.
A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models

Total variation (TV) models are among the most popular and successful tools in signal processing. However, due to the complex nature of the TV term, it is challenging to efficiently compute a solution for large-scale problems. State-of-the-art algorithms that are based on the alternating direction method of multipliers (ADMM) often involve solving large-size linear systems. In this paper, we propose a highly scalable parallel algorithm for TV models that is based on a novel decomposition strategy of the problem domain. As a result, the TV models can be decoupled into a set of small and independent subproblems, which admit closed form solutions. This makes our approach particularly suitable for parallel implementation. Our algorithm is guaranteed to converge to its global minimum. With N variables and np processes, the time complexity is O(N/enp) to reach an e-optimal solution. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art algorithms, especially in dealing with high-resolution, megasize images.
Fundamental Challenges Toward Making the IoT a Reachable Reality

Constantly advancing integration capability is paving the way for the construction of the extremely large scale continuum of the Internet where entities or things from vastly varied domains are uniquely addressable and interacting seamlessly to form a giant networked system of systems known as the Internet-of-Things (IoT). In contrast to this visionary networked system paradigm, prior research efforts on the IoT are still very fragmented and confined to disjoint explorations of different applications, architecture, security, services, protocol, and economical domains, thus preventing design exploration and optimization from a unified and global perspective. In this context, this survey article first proposes a mathematical modeling framework that is rich in expressivity to capture IoT characteristics from a global perspective. It also sets forward a set of fundamental challenges in sensing, decentralized computation, robustness, energy efficiency, and hardware security based on the proposed modeling framework. Possible solutions are discussed to shed light on future development of the IoT system paradigm.
Effects of sodium gradient on the cytosolic free Ca2+ and contractility of the pregnant rat myometrium smooth muscle.

Experiments performed on sarcolemmal vesicles isolated from uterine smooth muscle suggested the existence of two Ca 2 " transporting systems (ATP-driven Ca 2+ pump and Na + Ca 2+ exchange) (Kosterin et al. 1984). The functional role of these two energy-dependent Ca :+ transporting systems in the regulation of [Ca 2 ' ], and in the contraction-relaxation cycle of intact smooth muscle has not been identified. A kinetic analysis of the two systems showed that the Na +-dependent Ca :+ transporting system had a lower affinity for Ca 2 ~ than the ATP-driven Ca 2 * pump (Km for Ca :+ was 30—60 /jmol I and 0.3—0.5/itnol 1 respectively) (Kosterin. 1990). These results were obtained in experiments performed on a model system of plasma membrane vesicles in which all physiologically relevant conditions, i. e. ionic gradients and membrane potential, were not strictly observed. Nevertheless, the data obtained suggested that, at least in the uterine smooth muscle Na + /Ca 2+ exchange does not play a dominant role in regulating cytosolic free Ca 2+. However, direct measurement of cytosolic free Ca 2 + in relation to sodium gradient was obviously required to support this view. In present work the effect of Na* gradient on the level of intracellular free concentration of Ca 2+ was studied in cell suspension isolated from pregnant rat myometrium using the Ca 2 *-sensitive fluorescent probe fura-2 (Grynkiewicz et al. 1985). Also, the role of the Na gradient in the regulation of mechanical activity of intact strips dissected from pregnant rat myometrium was investigated. Suspension of smooth muscle cells from pregnant rat myometrium was prepared by collagenase digestion as described elsewhere (Mollard et al. 1986). The viability of the isolated cells was assessed by trypan blue and ethidium bromide exclusion using the cytospectrofluorimetric method. According to these measurements at least 60—70 % of the cells excluded the dyes. If not indicated otherwise, the [Ca 2 + ], measurement studies were performed in the basal medium containing (mmol/1)
Students’ Use of Smartphones at a Historically Disadvantaged University

As technology becomes cheaper the potential for learning is greater, preparing and disseminating learning material through smartphones is likely to become popular. However, it was not clear whether students from the most disadvantaged backgrounds, have the right equipment (smartphones) to support learning at a historically disadvantaged university. This study assesses smartphone ownership as a potential tool for enhancing students’ learning at a relatively resource-poor higher education (HE) environment in rural KwaZuluNatal. Correspondence universities, such as ‘Unisa’ and examples worldwide in HE, use students’ smartphones for educational purposes. Primary data were collected from postgraduates and undergraduate students using a selfadministered structured questionnaire for all faculties. The unified theory of acceptance and use of technology (UTAUT) was employed to account for students’ perceived ease of use (EOU) and perceived usefulness (PU). The results revealed smartphone ownership and use was greater than the ownership and use of regular cellular phones. Regular cellular phone users commented on smartphone capabilities as enhancing learning.
Residual Salinity Detection on Road Surface Using Neural Network

This article discusses the use of microwaves to detect saline solutions on road surfaces in winter. The reflection characteristics of water and saline solutions at microwave frequencies depend on the respective dielectric constants. Therefore, the salinity and thickness of films seem to be detectable based on reflection characteristics. We confirm this ability through theoretical calculations and verification experiments. Field tests verify the practicality of this approach. Furthermore, we propose a new estimation method for the relationships salinity level and reflection characteristics using neural networks. It is shown that the proposed method has a good performance.
Forced spaser oscillations

We study oscillations of a spaser driven by an external optical wave. When the frequency of the external field is shifted from the frequency of an autonomous spaser, the spaser exhibits stochastic oscillations at low field intensity. The plasmon oscillations lock to the frequency of the external field only when the field amplitude exceeds a threshold value. We find a region of external field amplitude and the frequency detuning (the Arnold tongue) for which the spaser becomes synchronized with the external wave. We obtain the conditions upon the amplitude and frequency of the external field (the curve of compensation) at which the spaser’s dipole moment oscillates with a phase shift of π relatively to the external wave. For these values of the amplitude and frequency, the loss in the metal nanoparticles within the spaser is exactly compensated for by the gain. It is expected that if these conditions are not satisfied, then due to loss or gain of energy, the amplitude of the wave travelling along the system of spasers either tends to the curve of compensation or leave the Arnold tongue. We also consider cooperative phenomena showing that in a chain of interacting spasers, depending on the values of the coupling constants, either all spasers oscillate in phase or a nonlinear autowave travels in the system. In the latter scenario, the traveling wave is harmonic, unlike excitations in other nonlinear systems. Due to the nonlinear nature of the system, any initial distribution of spaser states evolves into one of these steady states.