Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Applied Sciences and Smart Technologies

The difficulty level of a subject is needed either to understand the student acceptance of the subject and the highest level of student achievement in it. Some factors are considered, what kind of instructions, the readiness of the instructor and students in teaching and learning, evaluation and monitoring systems, and student expectations. Many factors are involved, and educators should know this. It is better if they can discern which are the prime factors and which the secondary factors. The purpose of the study is to find out the determinant factors in establishing the difficulty level of the subject from the students’, teachers’ and infrastructure point of view using three machine learning techniques. The MSE and the variable importance measurement were used to predict between some factors such as Attendance, Instructors, and other factors as independent variables and the difficulty level of the subject as a dependent variable. The study result showed that Gradient Boosting Machine obtained the MSE value result 1.14 and 1.30 for training and validation dataset. The model generated five variable importance as an independent factor, i.e. Attendance, Instructor, The course can give a new perspective to students, The quizzes, assignments, projects and exams International Journal of Applied Sciences and Smart Technologies Volume 1, Issue 1, pages 65–82
Improving Healthcare System Usability Without Real Users : A Semi-Parallel Design Approach

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A Formalism for the Description of Protein Interaction Dedicated to Jerzy Tiuryn on the Occasion of his 60th Birthday

The Calculus of Looping Sequences is a formalism for describing evolution of biological systems by means of term rewriting rules. We propose to enrich this calculus by labelling elements of sequences. Since two elements with the same label are considered to be linked, this allows us to represent protein interaction at the domain level. Well-formedness of terms are ensured by both a syntactic constraint and a type system: we discuss the differences between these approaches through the description of a biological system, namely the EGF pathway.
Comparison of Exact and Approximate Multi-Objective Optimization for Software Product Lines by

Software product lines (SPLs) manage product variants in a systematical way and allow stakeholders to derive variants by selecting features. Finding a desirable variant is hard, due to the huge configuration space and usually conflicting objectives (e.g., lower cost and higher performance). This scenario can be reduced to a multi-objective optimization problem in SPLs. We address the problem using an exact and an approximate algorithm and compare their accuracy, time consumption, scalability and parameter setting requirements on five case studies with increasing complexity. Our empirical results show that (1) it is feasible to use exact techniques for small SPL multi-objective optimization problems, and (2) approximate methods can be used for large problems but require substantial effort to find the best parameter settings for acceptable approximation. Finally, we discuss the tradeoff between accuracy and time consumption when using exact and approximate techniques for SPL multi-objective optimization and guide stakeholders to choose one or the other in practice.
Key Management Using Biometrics

The security of cryptosystems lies in three main factors: the complexity of the encryption algorithm, the length of the key, and safe storage of the key. Safe storage of the key, which is known as key management, is the most vulnerable area in the encryption process. Since biometrics requires the physical presence of the user, we can use biometric data to protect the cipher key. But the main obstacle of the integration is that biometric data are noisy while cryptography requires keys to be exactly right. In this paper, we propose a key management scheme using biometrics, which can generate constant and accurate keys from noisy biometric samples every time. The proposed scheme includes key binding and key retrieval, utilizing a multivariate linear equation and its solution space to generate repeatable keys. Our experiments use fingerprint as an instance, and results show good performance in terms of both accuracy and speed. This scheme can also be applied to other biometric identifiers such as face ,iris, voice, etc., with most substeps being the same except the generation of false feature elements and the process of feature matching.
A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection

An analysis of high dimensional data can offer a detailed description of a system but is often challenged by the curse of dimensionality. General dimensionality reduction techniques can alleviate such difficulty by extracting a few important features, but they are limited due to the lack of interpretability and connectivity to actual decision making associated with each physical variable. Important variable selection techniques, as an alternative, can maintain the interpretability, but they often involve a greedy search that is susceptible to failure in capturing important interactions. This research proposes a new method that produces subspaces, reduced-dimensional physical spaces, based on a randomized search and forms an ensemble of models for critical subspaces. When applied to high-dimensional data collected from a composite metal development process, the proposed method shows its superiority in prediction and important variable selection. keywords: Dimensionality reduction; Important variable selection; Subspace-based modeling
Matching Protein -Sheet Partners by Neural Networks

Predicting the secondary structure (-helices,-sheets, coils) of proteins is an important step towards understanding their three dimensional con-formations. Unlike-helices that are built up from one contiguous region of the polypeptide chain,-sheets are more complex resulting from a combination of two or more disjoint regions. The exact nature of these long distance interactions remains unclear. Here we introduce two neural-network based methods for the prediction of amino acid partners in parallel as well as anti-parallel-sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a-sheet structure. Variations on these architecture , including also prooles and ensembles, are trained and tested via ve-fold cross validation using a large corpus of curated data. Prediction on both coupled and non-coupled residues currently approaches 84% accuracy, better than any previously reported method.
Teaching Old Dogs New Tricks

The authors employed a longitudinal in-depth action research method to explore the implementation of organizational learning in an Asian national police force. They aimed to get an interpretive, in-depth understanding of the related processes of transformational change, as well as the barriers to change, in a machine bureaucracy with entrenched structure and culture not ordinarily conducive to learning and adaptation. Second, they aimed to explore the applicability of universalist change management prescriptions in this context. The authors found several structural and cultural barriers to transformational change that were nevertheless being successfully contested through a bottom-up participative change process, the existence of change champions, experiences that challenged the prevailing culture, and change actions that were congruent with the organization’s authorizing environment. Second, they found that universalist change management prescriptions may not always be relevant because the nature, task, and culture of an organization influence what approaches are appropriate and applicable.
Join { the { Shortest { Queue Prefetching for VBR Video on Demand 1

We present a high-performance prefetching protocol for the delivery of prerecorded VBR video from a server across a packet-switched network to a large number of clients. Not only does the protocol give constant perceptual quality and almost 100% link utilization, but it also allows for immediate commencement of the video upon user request and near instantaneous response to pause/resume and temporal jumps. The protocol requires (1) that each client have a small amount of memory dedicated to the application (2) that there is one bottleneck shared link between the server and the clients. The protocol is based on the observation that there are frequent periods of time during which the shared link's bandwidth is under utilized. During these periods the server can prefetch frames from any of the ongoing videos and send the frames to the bu ers in the appropriate clients. The server chooses prefetched frames according to a join{the{shortest{queue (JSQ) policy. We present simulation results of our prefetch policy that are based on MPEG encoded traces. Our simulations show that JSQ prefetching performs favorably as compared with the smoothing{based protocols in the existing literature.
Dual PECCS: A Cognitive System for Conceptual Representation and Categorization

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Quantitative evaluation of image registration techniques in the case of retinal images

In human retina observation (with non mydriatic optical microscopes), an image registration process is often employed to enlarge the field of view. Analyzing all the images takes a lot of time. Numerous techniques have been proposed to perform the registration process. Its good evaluation is a difficult question that is then raising. This article presents the use of two quantitative criterions to evaluate and compare some classical featurebased image registration techniques. The images are first segmented and the resulting binary images are then registered. The good quality of the registration process is evaluated with a normalized criterion based on the ǫ dissimilarity criterion, and the Figure of merit criterion (fom), for 25 pairs of images with a manual selection of control points. These criterions are normalized by the results of the affine method (considered as the most Further author information: (Send correspondence to Y. Gavet) Y. Gavet: E-mail: gavet@emse.fr, Telephone: [33] 4 77 42 01 70 1 ha l-0 07 00 87 0, v er si on 1 24 M ay 2 01 2 Author manuscript, published in "Journal of Electronic Imaging 21, 2 (2012) 21118" DOI : 10.1117/1.JEI. 21.2. 021118
Survey on Spatio-Temporal Clustering

Clustering is one of the major data mining methods in large databases for knowledge discovery. The importance of spatial and spatio-temporal data mining is growing with the increasing attention to social media and vast amount of spatio-temporal data generated by mobile devices, GPS, weather forecasting. Thus, clustering plays important role in broad range of applications. Density based clustering DBSCAN is commonly used for spatial data mining. Various density based clustering algorithms and its modifications are used for spatio-temporal clustering. Literature survey, challenges, applications of spatio-temporal data are introduced in further
Ogunshile CompleX-Machine : An Automated Testing Tool Using X-Machine Theory

This paper is aimed at creating an Automatic Java XMachine testing tool for software development. The nature of software development is changing; thus, the type of software testing tools required is also changing. Software is growing increasingly complex and, in part due to commercial impetus for faster software releases with new features and value, increasingly in danger of containing faults. These faults can incur huge cost for software development organisations and users; Cambridge Judge Business School’s research estimated the cost of software bugs to the global economy is $312 billion. Beyond the cost, faster software development methodologies and increasing expectations on developers to become testers is driving demand for faster, automated, and effective tools to prevent potential faults as early as possible in the software development lifecycle. Using X-Machine theory, this paper will explore a new tool to address software complexity, changing expectations on developers, faster development pressures and methodologies, with a view to reducing the huge cost of fixing software bugs. Keywords—Conformance testing, finite state machine, software testing, X-Machine.
North Carolina macular dystrophy: phenotypic variability and computational analysis of disease-implicated non-coding variants

Purpose: North Carolina macular dystrophy (NCMD) is an autosomal dominant, congenital disorder affecting the central retina. Here, we report clinical and genetic findings in three families segregating NCMD and use epigenomic datasets from human tissues to gain insights into the effect of NCMD-implicated variants. Methods: Clinical assessment and genetic testing were performed. Publicly-available transcriptomic and epigenomic datasets were analyzed and the 'Activity-by-Contact' (ABC) method for scoring enhancer elements and linking them to target genes was used. Results: A previously-described, heterozygous, non-coding variant upstream of the PRDM13 gene was detected in all six affected study participants (chr6:100,040,987G>C [GRCh37/hg19]). Inter- and intra-familial variability were observed; the visual acuity ranged from 0.0 to 1.6 LogMAR and fundoscopic findings ranged from visually insignificant, confluent, drusen-like macular deposits to coloboma-like macular lesions. Variable degrees of peripheral retinal spots (which were easily detected on widefield retinal imaging) were observed in all study subjects. Notably, a 6-year-old patient developed choroidal neovascularization and required treatment with intravitreal bevacizumab injections. Computational analysis of the five single nucleotide variants that have been implicated in NCMD revealed that these non-coding changes lie within two putative enhancer elements; these elements are predicted to interact with PRDM13 in the developing human retina. PRDM13 was found to be expressed in the fetal retina, with highest expression in the amacrine precursor cell population. Conclusions: We provide further evidence supporting the role of PRDM13 dysregulation in the pathogenesis of NCMD and highlight the utility of widefield retinal imaging in individuals suspected to have this condition.
Modular protein-oligonucleotide signal exchange

The ability to detect a protein selectively and produce a predicted signal in real time is a long-lasting engineering challenge in the field of biochemistry. Such a mechanism typically requires a sensing module to recognize the input protein and a translation module to produce a programmable output signal that reflects the concentration of the input. Here we present a generic biomolecular reaction process that exchanges the concentration of an input protein with a DNA oligonucleotide. This approach uses the unique characteristic of DNA oligonucleotide aptamer that can either bind to a specific protein or to a complementary DNA oligonucleotide reversibly. We then pass the information of the protein concentration to the output signal through DNA strand displacement reactions. Using this strategy, we design and characterize four different exchange processes that can produce modular DNA oligonucleotides in response to different proteins such as clinically important human α-thrombin and vascular endothelial growth factor (VEGF). These exchange processes are capable of real time sensing and are modular such that they can be used for concurrent detection of different proteins with well-defined input-output characteristics. The novelty and simplicity of our approach encourage to develop advanced biochemical systems for point-of-care testing of infectious diseases and treatments.
Taxonomies of Visual Programming P and rogram Visualization

here has been a great interest recently in systems that use graphics to aid in the programming, ' debugging, and understanding of computer systems. The terms ''Visual Programming'' and 'Program Visualization'' have been applied to these systems. This paper attempts to provide-t more meaning to these terms by giving precise definitions, and then surveys a number of sys ems that can be classified as providing Visual Programming or Program Visualization. These systems are organized by classifying them into three different taxonomies.
Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications

result of the social structure of American society. ’’ This is true of the particular form or version of organized crime which he describes— it did not emanate from a transplanted Sicilian Mafia. That explanation, however, does not account for the many faces of organized crime in Australia, China, Russia, Japan, and many other places. Nor does it account for the growing phenomenon of transnational organized crime. Apart from any points of disagreement, this is a serious work of scholarship on a subject that has too often gotten short shrift in that respect. Organized Crime in Chicago is a good addition to the organized crime literature.
.1. Navigation Model 1.2. Paint Program Interface

Database visualization is an area of growing importance as database systems become larger and more accessible. DataSplash is an easy-to-use, integrated environment for navigating, creating, and querying visual representations of data. We will demonstrate the three main components which make up the DataSplash environment: a navigation system, a direct-manipulation interface for creating and modifying visualizations, and a directmanipulation visual query system.
sRoute: Treating the Storage Stack Like a Network

In a data center, an IO from an application to distributed storage traverses not only the network, but also several software stages with diverse functionality. This set of ordered stages is known as the storage or IO stack. Stages include caches, hypervisors, IO schedulers, file systems, and device drivers. Indeed, in a typical data center, the number of these stages is often larger than the number of network hops to the destination. Yet, while packet routing is fundamental to networks, no notion of IO routing exists on the storage stack. The path of an IO to an endpoint is predetermined and hard-coded. This forces IO with different needs (e.g., requiring different caching or replica selection) to flow through a one-size-fits-all IO stack structure, resulting in an ossified IO stack. This paper proposes sRoute, an architecture that provides a routing abstraction for the storage stack. sRoute comprises a centralized control plane and "sSwitches" on the data plane. The control plane sets the forwarding rules in each sSwitch to route IO requests at runtime based on application-specific policies. A key strength of our architecture is that it works with unmodified applications and VMs. This paper shows significant benefits of customized IO routing to data center tenants (e.g., a factor of ten for tail IO latency, more than 60% better throughput for a customized replication protocol and a factor of two in throughput for customized caching).
The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract)

In this extended abstract we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by presenting a benchmark set of domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. We conclude with a brief update on the latest ALE developments. All of the software, including the benchmark agents, is publicly available.
A novel haptic rendering technique using a hybrid surface representation addresses conventional limitations in haptic displays

explore, paint, and manipulate virtual 3D models in a natural way using a haptic display device. A haptic rendering algorithm must generate a force field to simulate the presence of these virtual objects and their surface properties (such as friction and texture), or to guide the user along a specific trajectory. We can roughly classify haptic rendering algorithms according to the surface representation they use: geometric haptic algorithms for surface data, and volumetric haptic algorithms based on volumetric data including implicit surface representation. Our algorithm is based on a hybrid surface representation—a combination of geometric (B-rep) and implicit (V-rep) surface representations for a given 3D object, which takes advantage of both surface representations. For the visual display, the geometric model can effectively represent the 3D model compared to volume rendering. Meanwhile, the implicit surface representation has many properties that address the limitations of current geometric haptic algorithms and introduce new haptic and visual effects. The benefits of using a hybrid representation include