Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE) RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost) and convolutional neural networks (CNN). To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.
Comparing effective and ineffective behaviors of student programmers

This paper reports on a quantitative evaluation of five years of data collected in the first three programming courses at Virginia Tech. The dataset involves a total of 89,879 assignment submissions by 1,101 different students. Assignment results were partitioned into two groups: scores above 80% (A/B) and scores below 80% (C/D/F). To investigate student behaviors that result in differing levels of achievement, all students who consistently received A/B scores and all students who consistently received C/D/F scores were removed from the dataset. A within-subjects comparison of the scores received by the remaining individuals was performed. Further, time and code-size data that is difficult to compare directly between different courses was normalized. This study revealed several significant results. When students received A/B scores, they started earlier and finished earlier than when the same students received C/D/F scores. They also wrote slightly more program code. They did not appear to spend any more time on their work, however. Approximately two-thirds of the A/B scores were received by individuals who started more than a day in advance of the deadline, while approximately two-thirds of the C/D/F scores were received by individuals who started on the last day or later. One possible explanation is that students who start earlier simply have more time to seek assistance when they get stuck.
Computational Aspects of Nearly Single-Peaked Electorates

Manipulation, bribery, and control are well-studied ways of changing the outcome of an election. Many voting systems are, in the general case, computationally resistant to some of these manipulative actions. However when restricted to single-peaked electorates, these systems suddenly become easy to manipulate. Recently, Faliszewski, Hemaspaandra, and Hemaspaandra studied the complexity of dishonest behavior in nearly single-peaked electorates. These are electorates that are not single-peaked but close to it according to some distance measure. In this paper we introduce several new distance measures regarding single-peakedness. We prove that determining whether a given profile is nearly single-peaked is NP-complete in many cases. For one case we present a polynomial-time algorithm. Furthermore, we explore the relations between several notions of nearly single-peakedness.
A Table-Driven Streaming XML Parsing Methodology for High-Performance Web Services

This paper presents a table-driven streaming XML parsing methodology, called TDX. TDX expedites XML parsing by pre-recording the states of an XML parser in tabular form and by utilizing an efficient runtime streaming parsing engine based on a push-down automaton. The parsing tables are automatically produced from the XML schemas of a WSDL service description. Because the schema constraints are pre-encoded in a parsing table, the approach effectively implements a schema-specific XML parsing technique that combines parsing and validation into a single pass. This significantly increases the performance of XML Web services, which results in better response time and may reduce the impact of the flash-crowd effect. To implement TDX, we developed a parser construction toolkit to automatically construct parsers in C code from WSDLs and XML schemas. We applied the toolkit to an example Web services application and measured the raw performance compared to popular high-performance parsers written in C/C++, such as eXpat, gSOAP, and Xerces. The performance results show that TDX can be an order of magnitude faster
Applying the analytic hierarchy process to the evaluation of customer-oriented success factors in mobile commerce

Analytic hierarchy process (AHP) is a multi-criteria decision method that utilizes structured pair-wise comparisons among systems of similar alternative strategies to produce a scale of preference. This study aimed elucidate the factors that affect success in mobile commerce, and then evaluate and rate these factors by analyzing components of commercial activity in the mobile Internet environment using the AHP. In general, the decision-making model followed by a customer engaged in purchasing activity consists of some major phases, characterized by several components and involving one or more alternative actions. The customer use information from outside sources to develop a set of criteria and evaluate alternatives. Identification of these criteria and alternatives is an important consideration in preparing to launch a mobile commerce business, from the view point of a company. We surveyed the relative importance of factors and alternatives by directing a questionnaire to CEOs, business managers and students who are using auction, mobile banking, ticketing, Internet searching in mobile Internet. The results show that the main factors that influence customers are related to trust and security. A structured analysis of such customer-oriented factors provides good insights, and help business managers to time the launch of mobile commerce businesses.
Span-Restorable Elastic Optical Networks Under Different Spectrum Conversion Capabilities

This paper deals with the design of a span-restorable (SR) elastic optical network under different spectrum conversion capabilities, including 1) no spectrum conversion, 2) partial spectrum conversion, and 3) full spectrum conversion. We develop Integer Linear Programming (ILP) models to minimize both the required spare capacity and the maximum number of link frequency slots used for each of the three spectrum conversion cases. We also consider using the Bandwidth Squeezed Restoration (BSR) technique to obtain the maximal restoration levels for the affected service flows, subject to the limited frequency slot capacity on each fiber link. Our studies show that the spectrum conversion capability significantly improves spare capacity efficiency for an elastic optical network.
Conformity to Masculine Norms and Intellectual Engagement

Research on the relationship between masculinity and intellectual engagement may be helpful in exploring the current challenges of male students in academic settings. Although the traditional male role in Western societies has often included notions of winning, competitiveness, and achievement, there is a growing research literature that documents male struggles with achievement, particularly in academic, intellectual, and occupational domains (Morris, 2011; Rosin, 2010; Sax, 2008a, b). In this study, the relationships between conformity to masculine norms and intellectual engagement were explored in a sample of diverse men in the United States. It was predicted that men who more strongly conformed to masculine norms would demonstrate lower intellectual engagement. As predicted, conformity to masculine norms was significantly predictive of lower intellectual engagement on three of the five constructs measured (Openness to Experience, Intellectual Complexity, and Love of Learning). Conformity to masculine norms was not a significant predictor of Need for Cognition or Curiosity.
Modelling a Tidal Turbine in Unsteady Flow

A brief review of the literature is provided on the characteristics of marine currents and the approaches used for simulating tidal turbines. The feasibility of using CFD models to simulate time-dependent turbulent flow around a tidal turbine is then explored. Two different approaches for specifying the structure of the turbulent inflow conditions in CFD models are compared: the von Krmn spectral approach and the Synthetic Eddy Method (SEM) of Jarrin et al. (2006). The former model is commonly employed in the wind industry and is coded into Garrad Hassans Tidal Bladed and NRELs TurbSim. Different approaches are also tested for decomposing the turbulence at the inlet into resolved and modelled components. The results from these tests indicate that the turbulence produced using the SEM inlet conditions is slightly less susceptible to decay with downstream distance than the von Krmn approach provided that the modelled turbulent kinetic energy accounts for only a small fraction of the total turbulence energy.
An Efficient User Authentication and User Anonymity Scheme with Provably Security for IoT-Based Medical Care System

In recent years, with the increase in degenerative diseases and the aging population in advanced countries, demands for medical care of older or solitary people have increased continually in hospitals and healthcare institutions. Applying wireless sensor networks for the IoT-based telemedicine system enables doctors, caregivers or families to monitor patients’ physiological conditions at anytime and anyplace according to the acquired information. However, transmitting physiological data through the Internet concerns the personal privacy of patients. Therefore, before users can access medical care services in IoT-based medical care system, they must be authenticated. Typically, user authentication and data encryption are most critical for securing network communications over a public channel between two or more participants. In 2016, Liu and Chung proposed a bilinear pairing-based password authentication scheme for wireless healthcare sensor networks. They claimed their authentication scheme cannot only secure sensor data transmission, but also resist various well-known security attacks. In this paper, we demonstrate that Liu–Chung’s scheme has some security weaknesses, and we further present an improved secure authentication and data encryption scheme for the IoT-based medical care system, which can provide user anonymity and prevent the security threats of replay and password/sensed data disclosure attacks. Moreover, we modify the authentication process to reduce redundancy in protocol design, and the proposed scheme is more efficient in performance compared with previous related schemes. Finally, the proposed scheme is provably secure in the random oracle model under ECDHP.
A practical group matching scheme for privacy-aware users in mobile social networks

Privacy issues in group matching problem have become one of the most important things in Mobile Social Networks (MSNs) currently. Mobile users may feel uncomfortable when releasing personal information to some irrelevant people or groups. In this paper, we propose a practical group matching scheme without employing any Trusted Third Party (TTP) for privacy-aware users in MSNs. We first propose a fuzzy matrix algorithm to generate user's authority instead of complex cryptographic computations to reduce the communication and computation overhead, and thus build a public set to store all the group members' profiles and authorities. As a result, our group matching does not need all the group members are online anymore. Moreover, we utilize the Ochiai similarity considering both of the number of common attributes and the size of each user's profile. The privacy and performance are analyzed and evaluated via detailed simulations.
Theoretical and Technical Orientations Toward Longitudinal Research in the Social Sciences

This paper views life as a developmental process, embedded in a multilevel context. It addresses a number of theoretical, modelling and measurement issues at a relatively abstract level, integrating insights from different disciplines that study developmental processes. At the theoretical level, the usefulness of the life course as an organising principle is discussed. At the analytical level, the main building blocks of the life course are identified, with the life event being the unit of analysis. Each life event has three core aspects: the time at occurrence, the likelihood of occurrence (risk of experiencing the event), and the reason for occurrence. Notions of time, risk, exposure, uncertainty, and interaction are discussed. These basic concepts neatly connect life course theory and life history models.
Block Type Pilot Protection Technique for Clipping in OFDM Systems

One of the drawbacks in an OFDM system is the high peak to average power ratio (PAPR). Among a number of techniques to reduce the high PAPR, clipping is the simplest and most easily implementable method. Clipping introduces the clipping noise, which affects pilot data as well as information data. As a result, the performance of channel estimation and system decreases due to a nonlinear distortion process. In this paper, the proposed block type pilot protection technique for clipping has the properties, such as distortionless pilot and no side information. And simulation results indicate that the proposed scheme has better BER performance and its complexity decreases.
Targeting endothelial junctional adhesion molecule-A/ EPAC/ Rap-1 axis as a novel strategy to increase stem cell engraftment in dystrophic muscles

Muscular dystrophies are severe genetic diseases for which no efficacious therapies exist. Experimental clinical treatments include intra‐arterial administration of vessel‐associated stem cells, called mesoangioblasts (MABs). However, one of the limitations of this approach is the relatively low number of cells that engraft the diseased tissue, due, at least in part, to the sub‐optimal efficiency of extravasation, whose mechanisms for MAB are unknown. Leukocytes emigrate into the inflamed tissues by crossing endothelial cell‐to‐cell junctions and junctional proteins direct and control leukocyte diapedesis. Here, we identify the endothelial junctional protein JAM‐A as a key regulator of MAB extravasation. We show that JAM‐A gene inactivation and JAM‐A blocking antibodies strongly enhance MAB engraftment in dystrophic muscle. In the absence of JAM‐A, the exchange factors EPAC‐1 and 2 are down‐regulated, which prevents the activation of the small GTPase Rap‐1. As a consequence, junction tightening is reduced, allowing MAB diapedesis. Notably, pharmacological inhibition of Rap‐1 increases MAB engraftment in dystrophic muscle, which results into a significant improvement of muscle function offering a novel strategy for stem cell‐based therapies.
The Effects of Traditional Anti-Virus Labels on Malware Detection Using Dynamic Runtime Opcodes

The arms race between the distributors of malware and those seeking to provide defenses has so far favored the former. Signature detection methods have been unable to cope with the onslaught of new binaries aided by rapidly developing obfuscation techniques. Recent research has focused on the analysis of low-level opcodes, both static and dynamic, as a way to detect malware. Although sometimes successful at detecting malware, static analysis still fails to unravel obfuscated code, whereas dynamic analysis can allow researchers to investigate the revealed code at runtime. Research in the field has been limited by the underpinning data sets; old and inadequately sampled malware can lessen the extrapolation potential of such data sets. The main contribution of this paper is the creation of a new parsed runtime trace data set of over 100 000 labeled samples, which will address these shortcomings, and we offer the data set itself for use by the wider research community. This data set underpins the examination of the run traces using classifiers on count-based and sequence-based data. We find that malware detection rates are lessened when samples are labeled with traditional anti-virus (AV) labels. Neither count-based nor sequence-based algorithms can sufficiently distinguish between AV label classes. Detection increases when malware is re-classed with labels yielded from unsupervised learning. With sequenced-based learning, detection exceeds that of labeling as simply “malware” alone. This approach may yield future work, where the triaging of malware can be more effective.
A method for building knowledge bases with morphological semantics

This paper presents a strategy for building a morphological machine dictionary of English efficiently to infer meanings of derivatives from a simple word (hereafter a semantic stem) by considering morphological affixes and their semantic classifications. The basic concept is to group the derivatives into one frame and to restrict the derivatives, accessible to a knowledge base, to the semantic stem. This approach enables us to simplify the structures of a morphological dictionary and the representation of the knowledge base.
From Microwave to TeraHerz: electromagnetic imaging for cultural heritage

Non-invasive inspections capable of imaging sub-surface features at different penetration depth and spatial resolution have a crucial role in the frame of cultural heritage, from archaeology to the analysis of artworks or their fragments. This motivates continuous research efforts finalized, on one hand, to improve the imaging capabilities of existing technologies and novel ones
Statistical Enrichment of Epigenetic States Around Triplet Repeats that Can Undergo Expansions

More than 30 human genetic diseases are linked to tri-nucleotide repeat expansions. There is no known mechanism that explains repeat expansions in full, but changes in the epigenetic state of the associated locus has been implicated in the disease pathology for a growing number of examples. A comprehensive comparative analysis of the genomic features associated with diverse repeat expansions has been lacking. Here, in an effort to decipher the propensity of repeats to undergo expansion and result in a disease state, we determine the genomic coordinates of tri-nucleotide repeat tracts at base pair resolution and computationally establish epigenetic profiles around them. Using three complementary statistical tests, we reveal that several epigenetic states are enriched around repeats that are associated with disease, even in cells that do not harbor expansion, relative to a carefully stratified background. Analysis of over one hundred cell types reveals that epigenetic states generally tend to vary widely between genic regions and cell types. However, there is qualified consistency in the epigenetic signatures of repeats associated with disease suggesting that changes to the chromatin and the DNA around an expanding repeat locus are likely to be similar. These epigenetic signatures may be exploited further to develop models that could explain the propensity of repeats to undergo expansions.
Fluoride release and uptake capacities of fluoride-releasing restorative materials.

Many fluoride-releasing dental materials are being sold on the basis of their cariostatic properties. However, the amount fluoride release of these materials is still uncertain. This study investigated the fluoride release and uptake characteristics of four flowable resin composites (Heliomolar Flow, Tetric Flow, Wave, Perma Flo), one flowable compomer (Dyract flow), one conventional glass ionomer cement mixed with two different powder/liquid ratios (ChemFlex Syringeable and ChemFlex Condensable), one packable resin composite (SureFil), one ion-releasing composite (Ariston pHc) and one resin-modified glass ionomer cement (Vitremer). Seven discs (6-mm diameter and 1.5-mm height) were prepared for each material. Each disc was immersed in 3.5 ml of deionized water within a plastic vial and stored at 37 degrees C. The deionized water was changed every 24 hours and the release of fluoride was measured for 30 days. At the end of this period, the samples were recharged with 2 ml of 1.23% acidulated phosphate fluoride (APF) gel for four minutes. Then, all samples were reassessed for an additional 10 days. The fluoride release of all samples was measured with a specific fluoride electrode and an ionanalyzer. Statistical analyses were conducted using two-way repeated measure ANOVA and Duncan's multiple range tests. For all tested materials, the greatest fluoride release was observed after the first day of the study (p<0.01) but gradually diminished with time. During the test period, Tetric Flow released the lowest amount of fluoride; however, no statistically significant difference was found from Wave and Heliomolar Flow (p>0.05). Ariston pHc released the highest amount of fluoride, followed by ChemFlex Syringeable, Vitremer and ChemFlex Condensable. There were statistically significant differences among these materials (p<0.05). Fluoride release of all materials were significantly increased after the first day following refluoridation and Ariston pHc released the greatest among all materials (p<0.01). At the end of two days of refluoridation, the fluoride release rate for each material dropped quickly and stabilized within three days.
Decomposing Tool-Action Observation: A Stereo-EEG Study

A description of the spatiotemporal dynamics of human cortical activity during cognitive tasks is a fundamental goal of neuroscience. In the present study, we employed stereo-EEG in order to assess the neural activity during tool-action observation. We recorded from 49 epileptic patients (5502 leads) implanted with intracerebral electrodes, while they observed tool and hand actions. We deconstructed actions into 3 events-video onset, action onset, and tool-object contact-and assessed how different brain regions respond to these events. Video onset, with actions not yet visible, recruited only visual areas. Aligning the responses at action onset, yielded activity in the parietal-frontal manipulation circuit and, selectively for tool actions, in the left anterior supramarginal gyrus (aSMG). Finally, by aligning to the tool-object contact that signals the achievement of the main goal of the observed action, activations were found in SII and dorsal premotor cortex. In conclusion, our data show that during tool-action observation, in addition to the general action observation network there is a selective activation of aSMG, which exhibits internally different patterns of responsiveness. In addition, neural responses selective for the contact between the tool and the object were also observed.
Memory efficient multi-rate regular LDPC decoder for CMMB

In this paper, we propose a memory efficient multi-rate Low Density Parity Check (LDPC) decoder for China Mobile Multimedia Broadcasting (CMMB). We find the best trade-off between the performance and the circuit area by designing a partially parallel decoder which is capable of passing multiple messages in parallel. By designing an efficient address generation unit (AGU) with an index matrix, we could reduce both the amount of memory requirement and the complexity of computation. The proposed regular LDPC decoder was designed in Verilog HDL and was synthesized by Synopsys¿ Design Compiler using Chartered 0.18 ¿m CMOS cell library. The synthesized design has the gate size of 455 K (in NAND2). For the two code rates supported by CMMB, the rate-1/2 decoder has a throughput of 14.32 Mbps, and the rate-3/4 decoder has a throughput of 26.97 Mbps. Compared with a conventional LDPC for CMMB, our proposed design requires only 0.39% of the memory.