Reviewerly/exHarmony huggingface dataset:
Tackling the Challenges of FASTQ Referential Compression

The exponential growth of genomic data has recently motivated the development of compression algorithms to tackle the storage capacity limitations in bioinformatics centers. Referential compressors could theoretically achieve a much higher compression than their non-referential counterparts; however, the latest tools have not been able to harness such potential yet. To reach such goal, an efficient encoding model to represent the differences between the input and the reference is needed. In this article, we introduce a novel approach for referential compression of FASTQ files. The core of our compression scheme consists of a referential compressor based on the combination of local alignments with binary encoding optimized for long reads. Here we present the algorithms and performance tests developed for our reads compression algorithm, named UdeACompress. Our compressor achieved the best results when compressing long reads and competitive compression ratios for shorter reads when compared to the best programs in the state of the art. As an added value, it also showed reasonable execution times and memory consumption, in comparison with similar tools.
Practical quantum computers still at least a decade away

Significant technical and financial issues remain towards building a large, fault-tolerant quantum computer and one is unlikely to be built within the coming decade.
A computational strategy for the identification of pulmonary squamous cell carcinoma in computerized tomography images

The objective of the work is to propose a computational strategy to identify lung squamous cell carcinoma in three-dimensional databases (3D) of multislice computerized tomography. This strategy consists of the pre-processing, segmentation, and post-processing stages. During pre-processing, an anisotropic, gradient-based diffusion algorithm and a filter bank are used to address artifact and image noise issues. During segmentation, the technique called region growing is applied to pre-processed images. Finally, in the post-processing, a morphological dilation filter is used to process the segmented images. In order to make value judgments about the performance of the proposed strategy, the relative percentage error is used to compare the dilated segmentations of the squamous cell carcinoma with the segmentations of the squamous cell carcinoma generated, manually, by a pulmonologist. The combination of parameters linked to the highest PrE, allows establishing the optimal parameters of each of the algorithms that make up the proposed strategy.
CNN Soil Texture Classification

1-dimensional convolutional neural networks (CNN) for the classification of soil texture based on hyperspectral data.
An Accurate and Robust Gaze Estimation Method Based on Maximum Correntropy Criterion

Accurately estimating the user's gaze is important in many applications, such as humancomputer interaction.Due to great convenience, appearance-based methods for gaze estimation have been a popular subject of research for many years.However, the greatest challenges in the appearance-based gaze estimation in a desktop environment are how to simplify the calibration process and deal with other issues such as image noise and low resolution.To address the problems, we adopt a mapping relationship between the high-dimensional eye image features space and the low-dimensional gaze positions and propose a robust and accurate method for gaze estimation with a webcam.First, we utilize Kullback-Leibler divergence to reduce feature dimension and keep similarity between the feature space and the gaze space.Then, we construct the objective function using the maximum correntropy criterion instead of mean squared error, which can enhance the anti-noise ability, especially for outliers or pixel corruption.A regularization term is adopted to adaptively select the sparse training samples for gaze estimation.We conducted extensive experiments in a desktop environment, which verified that the proposed method was robust and efficient in dealing with sparse training samples, pixel corruption, and low-resolution problems in gaze estimation.
A Cyber Secured IoT: Fostering Smart Living and Safety of Fragile Individuals in Intelligent Environments

Due to better living conditions and progress in medicine older adults today are the main group of population in terms of growing speed. Furthermore, together with disabled people, they represent the most frail category of individuals. Indeed, they are likely to present different constellations of impairments (both at physical and cognitive level). Older adults and individuals with disabilities strongly benefit from being properly assisted or from prolonging their individual autonomy. Such interventions can be implemented levering on the IoT technologies. The present paper describes a project that aims at providing older and disabled people with smart buildings that will be equipped with IoT technologies, e.g., environmental and wearable sensors. The identification of such technologies will be based on co-design activities that will focus on their accessibility and usability. Concurrent laboratory tests will be carried out to assess the best methodologies of wireless communication between the smart devices. These ambient-assisted living tools will be installed in two real-world scenarios, i.e., a nursing home and a co-housing solution. Such tools will facilitate older and disabled people in carrying out daily activities, ensuring their safety and privacy protection. The outcomes of the project will provide pivotal information on how to improve human living in different environmental contexts.
Gentle Slow Start to Alleviate TCP Incast in Data Center Networks

Modern data center networks typically adopt symmetric topologies, such as leaf-spine and fat-tree. When a large number of transmission control protocol (TCP) flows in data center networks send data to the same receiver, the congestion collapse, called TCP Incast, frequently happens because of the huge packet losses and Time-Out. To address the TCP Incast issue, we firstly demonstrate that adjusting the increasing speed of the congestion window during the slow start phase is crucially important. Then we propose the Gentle Slow Start (GSS) algorithm, which adjusts the congestion window according to real-time congestion state in a gentle manner and smoothly switches from slow start to congestion avoidance phase. Furthermore, we present the implementation and design of Gentle Slow Start and also integrate it into the state-of-the-art data center transport protocols. The test results show that GSS effectively decreases the Incast probability and increases the network goodput by average 8x.
Coding of US Presidential discourse on protection

This dataset is based on NVivo coding of all clauses in US Presidential Papers (Public Papers of the Presidents of the United States 1989–2012 [Washington, D.C.: US Government Printing Office]) with the word protect in any of its forms. Clauses are coded for their referent objects (who is being protected). For those clauses that deal with the protection of global civilians (referent object is not US or allies, but people in other countries), coding is also done for the agent of protection and for the method of protection (protection by changing someone else's behaviour vs. protection by changing one's own behaviour).
Segmentation of brain tumors using a semi-automatic computational strategy

In this work, a semi-automatic computational strategy is proposed for brain tumor segmentation. The filtering (erosion + gaussian filters), segmentation (level set technique) and quantification (BT volume) stages are applied to magnetic resonance imaging in order to generate the three-dimensional morphology of brain tumors. The Jaccard's Similarity Index is considered to contrast manual segmentation with semi-automatic segmentations of brain tumor. In this sense, the highest Jaccard's Similarity Index provides the best parameters of the techniques that constitute the semi-automatic computational strategy. Results are promising, showing an excellent correlation between these segmentations. The volume is used for the brain tumors characterization.
Assessment of knowledge and confidence for E-learning

One of the most important goals in E-learning is to guarantee that participants reach the learning objectives. We have observed that having the knowledge of the subject is not sufficient for reaching learning objectives. The participants must also develop understanding that they know the subject, which we have named confidence. In this work, we have demonstrated that it is possible to assess both knowledge and confidence using only two different types of multiple-choice test questions. We have developed 1) a method to design questions to identify both knowledge and confidence and 2) a method to estimate actual knowledge and confidence from answers. We have evaluated our method using Monte-Carlo simulations. Our simulations demonstrated that it is always possible to obtain reliable estimations for knowledge and confidence using approximately 100 multiple choice test questions in a given subject.
The Role of Anonymity in the Flaming Activity on YouTube in Malaysia

&lt;p&gt;&lt;em&gt;The term flaming refers to offensive language such as swearing, insults and hating comments. Anonymity renders an environment that encourages irresponsible acts by people to display offensive behaviors. The aim of this study is to examine the role of anonymity in the flaming activity in Malaysia. The Uses and gratification Theory was proposed in order to explain flaming and its relation to anonymity. In-depth interview was conducted with 10 flamers of YouTube and the data was analyzed thematically. The results concludes that most of the flamers kept their identity anonymous due privacy concerns and for the freedom of speech. The rest of the flamers used their real name as a form of publicity, identity defining and to boost their self-confidence. This study contributes practically in the enrichment of the data on flaming for the concerning parties such as Malaysian Communications and Multimedia Commission, and Cyber Security Malaysia.&lt;/em&gt;&lt;/p&gt;
Study on Mapping of Information Literature Culture in South Coastal Communities West Java Province

This study aims to mapping the culture of information literacy on coastal communities in West Java region. While the urgency of this research is an effort to foster interest in reading the community, including in this case the people in the coastal areas should be the attention of all parties. The research method used in this study is the mix method (mix method) as a combination approach as an alternative to the use of a single method in a study. The mixed method will give better results because it has a wealth of data, because it can combine or combine. Based on the results of research conducted in the first stage it is known that the culture literasi community in the coastal area in general is still low. In addition, also support the regulation and legislation of local government or district government in the south coast of West Java, namely Sukabumi, Cianjur, Garut, Tasik and Pangandaran) in the effort to develop the culture of literasi in general is still low.
Graph-Based Segmentation with Local Band Constraints

Shape constraints are potentially useful high-level priors for object segmentation, allowing the customization of the segmentation to a given target object. In this work, we present a novel shape constraint, named Local Band constraint ( $${{\,\mathrm{LB}\,}}$$ ), for the generalized graph-cut framework, which in its limit case is strongly related to the Boundary Band constraint, preventing the generated segmentation to be irregular in relation to the level sets of a given reference cost map or template of shapes. The $${{\,\mathrm{LB}\,}}$$ constraint is embedded in the graph construction with additional arcs defined by a translation-variant adjacency relation, making it easy to combine with other high-level constraints. The $${{\,\mathrm{LB}\,}}$$ constraint demonstrates competitive results as compared to Geodesic Star Convexity, Boundary Band, and Hedgehog Shape Prior in Oriented Image Foresting Transform (OIFT) for various scenarios involving natural and medical images, with reduced sensibility to seed positioning.
Efficient Preprocessing Algorithm for Online Handwritten Arabic Strokes

Developing an online handwriting recognition system for Arabic script used in pen-based devices plays an important role in making these devices available and usable for the Arabic society. This paper develops a preprocessing algorithm for strokes written in Arabic script to improve the strokes recognition process, since there are some troubles on distinguishing the written stroke from similar characters. The uniqueness of the recommended preprocessing algorithm is the dealing with every stroke in the character separately. The algorithm is comprised of five essential phases that will settle on the strokes prepared and make the strokes ready for the next phase in recognition. Before the preprocessing algorithm, a basic step called stroke capturing is done, where data points are collected along the path of an input device (stylus pen or mouse) same time those characters are drawn.
Lebanese secondary physics teachers’ attitudes towards the use of ICT

Information and communication technology (ICT) is increasingly widespread, influencing educational systems worldwide. ICT educational tools can be a transformative tool that may facilitate meaningful learning in physics courses if educators are willing to embrace it with a positive attitude. The attitude of physics teachers has a tremendous effect on ICT integration in physics classrooms. This research paper is aimed to examine the attitudes towards the use of ICTs among Lebanese secondary physics teachers. A sample of 141 secondary physics teachers was assessed for their ICTs’ attitude using a Likert type questionnaire. The questionnaire focused on the teachers’ affect towards ICT, aware of usefulness and their confidence to use ICT. The results of both descriptive and inferential statistics showed a positive attitude towards ICT and no gender, years of experience or age differences among physics teachers’ attitudes. Implications for teacher training and suggestions for further research are provided. Keywords: Information and communication technology (ICT), ICT implementation, ICT attitude, Lebanese secondary physics teachers.
Potential capabilities of the Geogebra interactive environment during the implementation of the continuity of the "school-university" mathematical education

В. И. Сафонов, О. А. Бакаева
The Correlations between Reward and Elementary School Students’ Learning Motivation

The purpose of this study was to find the correlation between reward and student’s learning motivation. This research is quantitative research. The population in this study is fifth grade students in the seventh cluster of Taman District of Pekanbaru. The sampling technique used by the researchers is the simple random sampling. Researchers took a precision of 10% and the sample used was 115 students. Techniques of collecting data are in the form of questionnaires. Data were analysed using the product moment correlation technique. These results indicate that there is a positive and significant correlation between reward and students learning motivation. The significance value is 0,001 &lt; 0,05. The correlation between reward and students’ motivation is at the low level. The large donations or contributions between reward and student learning motivation is 10,2% from coefficient of determination.
Efficient Adaptively Secure Public-Key Trace and Revoke from Subset Cover Using D $$\acute{e}$$ e ´ j $$\grave{a}$$ a ` Q Framework

We provide an efficient and secure construction for the trace and revoke from subset cover ( $${\textsf {TRSC}}$$ ) systems in the public-key setting, having ciphertext size proportional to the number of revoked users and public parameter of constant size. The system is obtained by tweaking the identity based encryption scheme of Wee (TCC 2016) under the subset cover framework. Existing $${\textsf {TRSC}}$$ constructions are inefficient with respect to the size of the parameters and derive their security from the q-type assumptions in the random oracle model. Our construction is the first adaptively secure $${\textsf {TRSC}}$$ system to achieve such parameters without using any random oracles. In addition, we are able to eliminate the q-type assumptions by integrating the D $$\acute{e}$$ j $$\grave{a}$$ Q framework of Chase and Meiklejohn (EUROCRYPT 2014) and its extension by Wee (TCC 2016) in our construction and analyze its security under the hardness of static subgroup decision problems over bilinear group setting. Moreover, this is the first proposal to feature optimally short private keys, even in the standard security model without any security breach.
Vehicle Detection on Unmanned Aerial Vehicle Images based on Saliency Region Detection

The target detection and tracking technology of the unmanned aerial vehicle (UAV) is an important research direction in the field of UAV aerial photography.In order to effectively and accurately detect vehicles in a UAV platform and in complicated road environments, the authors proposed a vehicle detection method based on saliency region detection.First, the saliency map of the target is calculated by using the salient region detection method based on the optimized frequency-turned.Next, segmentation methods based on Boolean Map and OTSU are combined to determine the region of interest of the vehicle target in the saliency map image.Finally, a series of vehicle apparent features-based methods based on geometry, symmetry, and horizontal edge wave are used to determine the vehicle and eliminate the interference of roadside objects accurately.Experimental tests carried out from different datasets show excellent performance in multivehicle detection in terms of accuracy in complex traffic situations and under different scales and angles of aerial images, realizing fast vehicle detection on the UAV platform.
Efficient Unsupervised Image Segmentation by Optimum Cuts in Graphs

In this work, a method based on optimum cuts in graphs is proposed for unsupervised image segmentation, that can be tailored to different objects, according to their boundary polarity, by extending the Oriented Image Foresting Transform (OIFT). The proposed method, named UOIFT, encompasses as a particular case the single-linkage algorithm by minimum spanning tree (MST), establishing important theoretical contributions, and gives superior segmentation results compared to other approaches commonly used in the literature, usually requiring a lower number of image partitions to isolate the desired regions of interest. The method is supported by new theoretical results involving the usage of non-monotonic-incremental cost functions in directed graphs. The results are demonstrated using a region adjacency graph of superpixels in medical and natural images.
A Short Server-Aided Certificateless Aggregate Multisignature Scheme in the Standard Model

Aggregate signature scheme allows each signer to sign a different message and then all those signatures are aggregated into a single short signature. In contrast, multisignature scheme allows multisigners to jointly sign only one message. Aggregate multisignature scheme is a combination of both aforementioned signature schemes, where signers can choose to generate either a multisignature or an aggregate signature. This combination scheme has many concrete application scenarios such as Bitcoin blockchain, Healthcare, Multicast Acknowledgment Aggregation, and so on. On the other hand, to deal with the problems of expensive certificates in certified public key cryptography and key escrow in identity-based cryptography, the notion of certificateless public key cryptography has been introduced by Riyami and Paterson at Asiacrypt’03. In this paper, we propose the first certificateless aggregate multisignature scheme that achieves the constant-size of signature and is secure in the standard model under a generalization of the Diffie-Hellman exponent assumption. In our scheme, however, the signature is generated with the help of the authority.
Multi-feature Fusion for High Resolution Aerial Scene Image Classification

Remote sensing scene classification plays an important role in many applications. Obtaining a high discriminative feature representation is the key of scene classification. The information hidden in different layers of convolutional neural network (CNN) has great potential for enhancing the feature discrimination ability. In this paper, the features from convolutional layers and a set of local features are combined for scene classification. Specifically, deep hierarchical features from different convolutional layers are extracted by a pretrained CNN model, which is used as a feature extractor. A patch-based MS-CLBP method is adopted to acquire local representations. Then the holistic hierarchical and local visual representation is obtained after fisher vector (FV) encoding. Finally, an improved extreme learning machine (ELM) is adopted to classify the scene images based on the obtained FVs. Experimental results show that the proposed methods achieves excellent performance compared with the state-of-the-art classification methods.
Opening Up Open Access Institutional Repositories to Demonstrate Value: Two Universities’ Pilots on Including Metadata-Only Records

INTRODUCTION Institutional repository managers are continuously looking for new ways to demonstrate the value of their repositories. One way to do this is to create a more inclusive repository that provides reliable information about the research output produced by faculty affiliated with the institution. DESCRIPTION OF PROGRAM This article details two pilot projects that evaluated how their repositories could track faculty research output through the inclusion of metadata-only (no full-text) records. The purpose of each pilot project was to determine the feasibility and provide an assessment of the long-term impact on the repository’s mission statement, staffing, and collection development policies. NEXT STEPS This article shares the results of the pilot project and explores the impact for faculty and end users as well as the implications for repositories.
Recognition of human activity based on sparse data collected from smartphone sensors

This paper proposes a method of human activity monitoring based on the regular use of sparse acceleration data and GPS positioning collected during smartphone daily utilization. The application addresses, in particular, the elderly population with regular activity patterns associated with daily routines. The approach is based on the clustering of acceleration and GPS data to characterize the user’s pattern activity and localization for a given period. The current activity pattern is compared to the one obtained by the learned data patterns, generating alarms of abnormal activity and unusual location. The obtained results allow to consider that the usage of the proposed method in real environments can be beneficial for activity monitoring without using complex sensor networks.