Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Detection Based Spectrum Sensing for Cognitive Radio Network

Cognitive radio is an intelligent radio wireless communication technology in order to increase the spectrum efficiency. In this paper one of the most important cognitive radio task i.e. spectrum sensing is explained in detail. Spectrum sensing used to detect the presence of the primary users in a licensed spectrum, which is a fundamental problem for cognitive radio. Increasing efficiency of the spectrum usage is an urgent due to increasing demand for higher data rates, better quality of services and higher capacity. In this paper Energy Detection Technique is discussed in detail.
Systematic control and management of data integrity

Integrity has long been considered a fundamental requirement for secure computerized systems, and especially today's demand for data integrity is stronger than ever as many organizations are in-creasing their reliance on data and information systems. A number of recently enacted data privacy regulations also require high in-tegrity for personal data. In this paper, we discuss various issues concerning systematic control and management of data integrity with a primary focus on access control. We first examine some previously proposed integrity models and define a set of integrity requirements. We then present an architecture for comprehensive integrity control systems, which has its basis on data validation and metadata management. We also provide an integrity control policy language that we believe is flexible and intuitive.
An intronic open reading frame was released from one of group II introns in the mitochondrial genome of the haptophyte Chrysochromulina sp. NIES-1333

Mitochondrial (mt) genome sequences, which often bear introns, have been sampled from phylogenetically diverse eukaryotes. Thus, we can anticipate novel insights into intron evolution from previously unstudied mt genomes. We here investigated the origins and evolution of three introns in the mt genome of the haptophyte Chrysochromulina sp. NIES-1333, which was sequenced completely in this study. All the three introns were characterized as group II, on the basis of predicted secondary structure, and the conserved sequence motifs at the 5′ and 3′ termini. Our comparative studies on diverse mt genomes prompt us to propose that the Chrysochromulina mt genome laterally acquired the introns from mt genomes in distantly related eukaryotes. Many group II introns harbor intronic open reading frames for the proteins (intron-encoded proteins or IEPs), which likely facilitate the splicing of their host introns. However, we propose that a “free-standing,” IEP-like protein, which is not encoded within any introns in the Chrysochromulina mt genome, is involved in the splicing of the first cox1 intron that lacks any open reading frames.
Recycling Bits in LZ 77-Based Compression

We present a technique that exploits the multiplicity of the ways a text may be encoded using an LZ77-based compression method. With such methods, repeated parts of the text are encoded as length-distance pairs that refer to previously seen text. In general, given the maximum length of a repeated part, there may be more than one distance at which there is a copy of the repeated part. The compressor is free to select any of these distances since the decompressor is able to recover the same text anyway. The mere act of choosing one of these distances can be used to convey information to the decompressor. We present the details of our technique. We have integrated our technique in a high performance compressor in order to make measurements. The experiments show that our technique significantly improves compression rates on many files of the Calgary corpus.
The efficacy of vertical vs. horizontal plyometric training on speed, jumping performance and agility in soccer players

This study compared the efficacy of a vertical and horizontal plyometric training program on acceleration, speed, jumping performance, and agility in young soccer players during the in-season period. Thirty male soccer players (19.1 ± 5.8 years) were assigned into a horizontal plyometric, a vertical plyometric, or a control group. The horizontal plyometric group and vertical plyometric group participated in an eight-week training program that was performed one day/week (in conjunction with conventional soccer training) and consisted of horizontal (for the horizontal plyometric group) or vertical plyometric exercises (for the vertical plyometric group). Acceleration (10-m sprint), speed (30-m sprint), agility, and jumping performance (horizontal and vertical) were assessed prior and after the completion of plyometric training programs. Agility, 30-m sprint performance, and vertical jumping performance (p < 0.01) improved following the completion of training in both horizontal plyometric group and vertical plyometric group, whereas horizontal jumping performance improved only in horizontal plyometric group (p < 0.01). There were no significant effects on acceleration for both horizontal plyometric group and vertical plyometric group. Furthermore, no differences were observed between groups in all performance parameters. In control group, acceleration, speed, agility, and jumping performance remained stable throughout the study. To sum up, horizontal or vertical plyometric training programs, consisted of one session/week, may be used by coaches and fitness professionals as effective exercise modalities for preserving or improving jumping performance, acceleration, speed, and agility in young soccer players during the in-season period.
Combining Structured and Unstructured Randomness in Large Scale PCA

Principal Component Analysis (PCA) is a ubiquitous tool with many applications in machine learning including feature construction, subspace embedding, and outlier detection. In this paper, we present an algorithm for computing the top principal components of a dataset with a large number of rows (examples) and columns (features). Our algorithm leverages both structured and unstructured random projections to retain good accuracy while being computationally efficient. We demonstrate the technique on the winning submission the KDD 2010 Cup.
Shock-cloud interaction in the Vela SNR observed with XMM-Newton

We analyzed an XMM-Newton EPIC observation of a bright knot, named FilD, in the northern rim of the Vela SNR, where the shock has encountered a cloud. The good combination of sensitivity, spectral, and spatial resolution allowed us to describe the internal structure of the observed ISM clouds and to obtain estimates of their temperature, density, O, Ne, and Fe abundances, and of their extension along the line of sight. We also examined the interaction of the shock with the FilD knot and estimated that the time elapsed from the shock impact is about one cloud crushing time. Our analysis allowed us to conclude that the observed X-ray emission is best explained by the propagation of transmitted shocks through ISM inhomogeneities with an inward increasing density profile. We found that the interaction with the shock determines the heating of the inner part of the clouds and the evaporation of the outer part.
An improved feature ranking method for diagnosis of systematic timing uncertainty

For diagnosis of systematic modeling uncertainty, an earlier work proposes a path-based methodology that employs support vector classification analysis to rank so-called delay entities. This work explains that delay entities can be seen as path features that are used to encode the characteristics of a path. We present an improved path feature ranking algorithm based on support vector epsiv-insensitive regression. We also discuss how to check if a dataset is too noisy for the analysis. Experimental results are presented to explain the ranking methodology and demonstrate the effectiveness of the improved approach.
Mutation Testing: An Artificial Immune System Approach

Through time, many software engineering fundamentals change such as programming languages, paradigms and development teams. These changes affect test suites; if these suites do not evolve, they will wear out. Mutation testing is a computationally expensive, fault-based testing method. By using high quality test data however, only a minimal number of mutations are required, allowing changes in the development environment. Initially however, test data is rarely of high quality. To improve it necessitates more mutations. The focus of this conceptual paper is the adoption of an immune system metaphor as the basis for an alternative mutation testing system. It is envisaged that such a system, through careful monitoring of program development, will be able to provide a minimal set of effective mutations and test cases; a vaccine which can be applied to the software development process to help protect it
A reproducing kernel Hilbert space approach for speech enhancement

The problem of speech enhancement has drawn a considerable amount of research attention over the past few years. Among the techniques developed one finds subspace methods, which seem to offer a good compromise between signal distortion and residual noise level. In this contribution, we present a novel subspace approach to single-channel speech enhancement. The eigen decomposition which was originally performed in the input space is now being done in a reproducing kernel Hilbert space, where the speech nonlinearities can be considered. The proposed algorithm was tested in various noise conditions including white, car, pink and train station noises at various signal-to-noise ratios (SNRs). Objective results show that for white noise, the algorithm presents an average improvement of 73.26% while for colored noise an average improvement of 68.42% is achieved. Subjective tests made on speech, corrupted with white and colored noises, demonstrate that the proposed algorithm provides a significant improvement over other speech enhancement methods found in literature.
On popularity-based load balancing in content networks

The paper investigates the use of multipath routing and load balancing in content oriented architectures proposed for the next generation Internet. It discusses how various load balancing methods can influence the efficiency of in-network caching and overall network performance measured as file retrieval time. Proposed approach, called Popularity-Aware Load Balancing, differentiates popular and unpopular content, and applies dedicated balancing principle to each content set. Performance of the proposed approach is validated through developed simulator of the content-centric network. Obtained results prove that popularity-based load balancing applied for multi-path routing patterns can outperform single-path routing patterns in the content networks experiencing flash crowds.
Interesting region detection in aerial video using Bayesian topic models

Searching interesting regions in aerial video is a new and challenging problem. This paper presents an approach to detect visual interesting regions in aerial video using pLSA topic model. Traditional interesting region detection approaches just use bottom-up information, such as color, orientation and movement etc. Our proposed method can discover the semantic content of the whole image, the co-occurrence of local image patches via pLSA model, and consequently improve detection result significantly in real world scenes. First, we extract frames from aerial video as documents. Then we use vector quantized SIFT descriptors as words. Third, we discover topics (e.g. plants, roads, buildings) and the relation among them using pLSA model. Finally, we can detect interesting regions as we need according to calculated models. Experimental observations show the success of our approach on interesting region detection in aerial video.
Star Formation in Disk Galaxies Driven by Primordial H2

We show that gaseous H I disks of primordial composition irradiated by an external radiation field can develop a multiphase medium with temperatures between 102 and 104 K as a result of the formation of molecular hydrogen. For a given H I column density there is a critical value of the radiation field below which only the cold H I phase can exist. Because of a time-decreasing quasar background, the gas starts cooling slowly after recombination until the lowest stable temperature in the warm phase is reached at a critical redshift z = zcr. Below this redshift the formation of molecular hydrogen promotes a rapid transition toward the cold H I phase. We find that disks of protogalaxies with 1020 ≲ NH I ≲ 1021 cm-2 are gravitationally stable at T ~ 104 K and can start their star formation history only at z ≲ zcr ~ 2, after the gas in the central portion of the disk has cooled to temperatures T ≲ 200 K. Such a delayed starbust phase in galaxies of low gas surface density and low dynamical mass can disrupt the disks and cause them to fade away. These objects could contribute significantly to the faint blue galaxy population.
Effects of Unilateral and Bilateral Cochlear Implantation on Cortical Activity Measured by an EEG Neuroimaging Method in Children

Bilateral implantation of a cochlear implant (CI) after a >2 year period of unilateral hearing with a second implant has been shown to result in altered latencies in brainstem responses in children with congenital deafness [1, 2]. In this thesis, a neural source localization method was developed to investigate the effects of unilateral CI use on cortical development after the implantation of a 2 nd CI. The electroencephalography (EEG) source localization method is based on the linearly constrained minimum variance (LCMV) vector beamformer [3] and utilizes null constraints to minimize the electrical artifact produced by the CI. The accuracy of the method was assessed and optimized through simulations and comparisons to beamforming with magnetoencephalography (MEG) data. After using cluster analyses to ensure that sources compared across subjects originate from the same neural generators, a study was done to examine the effects of unilateral CI hearing on hemispheric lateralization to monaural responses. It was found that a >2 year period of unilateral hearing results in expanded projections from the 1 st implanted ear to the contralateral auditory area that is not reversed by implantation of a 2 nd CI. A subsequent study was performed to examine the effects of unilateral CI hearing on the contributions of the 1 st and 2 nd implanted ears to the binaural response. It was found that in children with > 2 years of unilateral hearing,
Maximum Entropy Learning Model for Biomedical Semantic Type Induction

In the biomedical literature the gene names tend to be used to refer to biomedical entities other than genes. Therefore, when performing information extraction tasks it is necessary to distinguish between all such biomedical entities. Our approach consists of training a Maximum Entropy classifier for this task using a large automatically-created training corpus, as opposed to manually annotated data. In order to create the training corpus, a rulebased baseline tagger is developed using the information from the Sequence Ontology that classifies biomedical entities into seven biotypes. Subsequently, this tagger is applied to a large corpus of biomedical text in order to annotate it, thus generating the training data for the machine learning system. The results obtained show that the maximum entropy classifier trained on automatically created data performs better than the baseline tagger itself, which demonstrates the efficiency of the adopted approach. The developed machine learning techniques are domain-independent and can be applied to text mining in any field.
Sources and fluxes of organic nitrogen in precipitation over the southern East Sea / Sea of Japan

We measured total dissolved reactive nitrogen in precipitation samples collected at Uljin, a Korean coastal site upwind of the southern East Sea/Sea of Japan (EJS), selected as a representative study site of atmospheric deposition over the northwestern Pacific margin. NO−3 was found to be the most abundant nitrogen species, followed by NH+4 and dissolved organic nitrogen (DON). Air-mass back-trajectory (AMBT) analysis revealed that a significant fraction of the inorganic nitrogen (NO−3 and NH + 4 ) originated from mainland Asia, whereas the DON was primarily derived from Korea. Using varimax-rotated factor analysis in combination with major ions as tracers, agricultural activities in Korea were identified as the primary sources of DON in these samples. In addition, a positive correlation was found at Uljin between the size of organic fraction in total reactive nitrogen and nitrogen to carbon atomic ratio in organic matter. This correlation has also been observed at other locations worldwide, implying the utilization potential of atmospheric organic nitrogen might increase with its proportion in total nitrogen. Combining wet deposition measurements in this study with literature values for dry deposition observed at a remote island in the EJS, the total atmospheric depositional flux of reactive nitrogen was estimated to be 115 mmol N m yr over the southern EJS. Our study sheds new light on the potentially significant contribution to primary productivity of the northwestern Pacific Ocean by atmospheric deposition of nitrogen, especially the organic fraction.
NIR Follow-Up of the VVDS 02hr Field: First Results

Abstract We present a new K-band survey covering 623 arcmin2 in the VVDS 0226-0430 deep field down to a limiting magnitude KVega ≤ 20.5. We use the spectroscopic sample extracted from this new K-band catalogue to assess the effectiveness of optical-near infrared color selections in identifying extreme classes of objects at high redshift.
The Effects of Digital Recording of Telephone Interviews on Survey Data Quality

Because digital recording is relatively unobtrusive, we assume it does not affect interviewerrespondent interaction and that recordings capture exactly how the interview would have proceeded otherwise. However interviewers may behave differently knowing they are recorded. Also, the technology is not completely unobtrusive and interviewers notice differences in the flow of the interview as recordings are saved on the computer. Finally, we also have to ask respondents for permission to record, which may affect their willingness to participate or their responses. The aim of this research is to examine how recording affects survey data quality. Sample cases in a public opinion study were randomly assigned to be recorded or not in order to isolate potential effects of the recording on participation rates and data quality. The primary outcome measures related to survey participation examined here include completion and refusal rates, break-offs during the interview, the time it takes an interviewer to convince a respondent to participate, and the time to completion. Since recording might also affect the type of responses people give, this paper also examines whether there are differences in survey satisficing (Krosnick, 1991) depending on whether the respondent is being recorded or not. The outcomes examined here include whether respondents who are being recorded provide more thoughtful answers. Measures of thoughtfulness include indicators of acquiescence bias, social desirability bias, responses to open-ended items, correct answers to factual questions, nondifferentiation of answers, and item nonresponse.
Evaluation of the Sheidow Park social problem solving program for primary school children: a field study

Research has demonstrated links between children's poor peer relations and various forms of child and adult psychosocial maladjustment. Social skills training programs have been developed to increase children's social competence and reduce the risk for later problems. The Sheidow Park Social Problem Solving Program is a curriculum based cognitive social skills training program, designed for Australian primary school children. The present research evaluated the effects of this program on a variety of dimensions of children's social competence. Subjects were Reception/Year 1 children in two classes of a South Australian suburban primary school. The teacher of one class implemented the social skills program, while the other class experienced no formal social skills intervention. The results indicated that the various measures of social competence employed were relatively independent of one another, supporting the need for a comprehensive range of measures in social skills training research. The Sheidow Park program demonstrated a significant effect on children's sense of social self-competence and the degree to which they perceived a variety of challenging social situations as difficult to deal with. However, the program had no effect on teacher and peer ratings of children's social competence or on children's satisfaction with their wider social network. The findings are explained within the context of attribution and cognitive dissonance theories, and the strengths and limitations of both the Sheidow Park program and the present research are discussed. Suggestions for future research and modifications to the program are made.
Learning a Real-Time 3 D Point Cloud Obstacle Discriminator via Bootstrapping Michael Samples and

Many recent mobile robotics applications have incorporated the use of 3D LIDAR sensors as an important component in scene understanding due to frequent data measurements and direct observation of geometric relationships. However, the sparseness of point cloud information and the lack of unique cues at an individual point level presents challenges in algorithm design for obstacle detection, segmentation, and tracking. Since individual measurements yield less information about the presence of obstacles, many algorithmic approaches model the joint posterior of point-labels. Such approaches can produce robust point labelings at higher computation cost. In this paper, we apply joint posterior approaches with smooth terrain priors for point cloud obstacle discrimination. The resulting labels are used to bootstrap efficient discriminators which require no human labeled data, yet are comparable in discriminative ability to the joint posterior approaches.
Silicon Enhances Water Stress Tolerance by Improving Root Hydraulic Conductance in Solanum lycopersicum L.

Silicon (Si) can improve drought tolerance in plants, but the mechanism is still not fully understood. Previous research has been concentrating on Si’s role in leaf water maintenance in Si accumulators, while little information is available on its role in water uptake and in less Si-accumulating plants. Here, we investigated the effects of Si on root water uptake and its role in decreasing oxidative damage in relation to root hydraulic conductance in tomato (Solanum lycopersicum ‘Zhongza No.9’) under water stress. Tomato seedlings were subjected to water stress induced by 10% (w/v) polyethylene glycol-6000 in the absence or presence of 2.5 mM added silicate. The results showed that Si addition ameliorated the inhibition in tomato growth and photosynthesis, and improved water status under water stress. The root hydraulic conductance of tomato plants was decreased under water stress, and it was significantly increased by added Si. There was no significant contribution of osmotic adjustment in Si-enhanced root water uptake under water stress. The transcriptions of plasma membrane aquaporin genes were not obviously changed by Si under water stress. Water stress increased the production of reactive oxygen species and induced oxidative damage, while added Si reversed these. In addition, Si addition increased the activities of superoxide dismutase and catalase and the levels of ascorbic acid and glutathione in the roots under stress. It is concluded that Si enhances the water stress tolerance via enhancing root hydraulic conductance and water uptake in tomato plants. Si-mediated decrease in membrane oxidative damage may have contributed to the enhanced root hydraulic conductance.