Miracle-dz/UniFAR-FTUs huggingface dataset:
SAM‐T04: What is new in protein–structure prediction for CASP6

The SAM‐T04 method for predicting protein structures uses a single protocol across the entire range of targets, from comparative modeling to new folds. This protocol is similar to the SAM‐T02 protocol used in CASP5, but has improvements in the iterative search for similar sequences in finding and aligning templates, in creating fragment libraries, in generating protein conformations, and in scoring the conformations. The automatic procedure made some improvements over simply selecting an alignment to the highest‐scoring template, and human intervention made substantial improvements over the automatic procedure. The main improvements made by human intervention were from adding constraints to build (or retain) β‐sheets and from splitting multidomain proteins into separate domains. The uniform protocol was moderately successful across the entire range of target difficulty, but was somewhat less successful than other approaches in CASP6 on the comparative modeling targets. Proteins 2005;Suppl 7:135–142. © 2005 Wiley‐Liss, Inc.
Improved Zero Text Watermarking Algorithm against Meaning Preserving Attacks

Internet is largely composed of textual contents and a huge volume of digital contents gets floated over the Internet daily. The ease of information sharing and re-production has made it difficult to preserve author’s copyright. Digital watermarking came up as a solution for copyright protection of plain text problem after 1993. In this paper, we propose a zero text watermarking algorithm based on occurrence frequency of non-vowel ASCII characters and words for copyright protection of plain text. The embedding algorithm makes use of frequency non-vowel ASCII characters and words to generate a specialized author key. The extraction algorithm uses this key to extract watermark, hence identify the original copyright owner. Experimental results illustrate the effectiveness of the proposed algorithm on text encountering meaning preserving attacks performed by five independent attackers. Keywords—Copyright protection, Digital watermarking, Document authentication, Information security, Watermark.
cISP: A Speed-of-Light Internet Service Provider

Low latency is a requirement for a variety of interactive network applications. The Internet, however, is not optimized for latency. We thus explore the design of cost-effective wide-area networks that move data over paths very close to great-circle paths, at speeds very close to the speed of light in vacuum. Our cISP design augments the Internet's fiber with free-space wireless connectivity. cISP addresses the fundamental challenge of simultaneously providing low latency and scalable bandwidth, while accounting for numerous practical factors ranging from transmission tower availability to packet queuing. We show that instantiations of cISP across the contiguous United States and Europe would achieve mean latencies within 5% of that achievable using great-circle paths at the speed of light, over medium and long distances. Further, we estimate that the economic value from such networks would substantially exceed their expense.
Spectral clustering of protein sequences

A major challenge in bioinformatics is the grouping together of protein sequences into functionally similar families. Large scale clustering of protein sequences may help to identify novel relationships and may also be of use in structural genomics. This paper explores the use of graph-theoretic spectral methods for clustering protein sequences. Using the leading eigenvectors of a matrix derived from similarity information between protein sequences, we were able to obtain meaningful clusters on quite diverse sets of proteins. The results presented show how this method is often able to identify correctly the superfamilies to which the sequences belong.
Exploring MCDA methods with DecSpace

Multiple Criteria Decision Aiding (MCDA) is a domain of Operations Research and Management Science mainly devoted to the development of methods, techniques and tools aiming to help decision makers in complex decision processes. This paper describes DecSpace, a framework to explore a wide range of methods of that kind. DecSpace is a web-based framework, making it possible to explore solutions for problems involving one or more methods. Persistent workspaces also make it possible to reuse solutions. In this sense, DecSpace is intended for use in teaching and researching in MCDA methods, as well for professional use as a Decision Support System for engineering and management scenarios where decisions based on those methods are to be considered. DecSpace has an open architecture, making it possible to use methods implemented locally, fully integrated as part of the local application, or methods available from remote servers, if exposed as web services.
Path Planning for Mobile Robot Navigation using Voronoi Diagram and Fast Marching Santiago Garrido

This paper presents a new sensor based global Path Planner which operates in two steps. In the first step the safest areas in the environment are extracted by means of a Voronoi diagram. In the second step Fast Marching Method is applied to the Voronoi extracted areas in order to obtain the shortest path. In this way the trajectory obtained is the shortest between the safe possible ones. This two step method combines an extremely fast global planner operating on a simple sensor based environment modeling, while it operates at the sensor frequency. The main characteristics are speed and reliability, because the map dimensions are reduced to a unidimensional map and this map represents the safest areas in the environment for moving the robot.
Academic stress and life satisfaction as social sustainability among university students

This study was conducted to determine the relationship between academic stress and life satisfaction among university students in Sabah, Malaysia. A total of 400 students were selected as respondents and data were collected using questionnaires. Academic stress was measured using the Perception of Academic Stress Scale (PAS), which has four subscales: i) Performance stress; ii) Workload perception of workload and examinations; iii) Academic self-perception; and iv) Time constraints. Meanwhile, the Satisfaction with Life Scale was used to study student’s life satisfaction. The data obtained were analyzed using Pearson correlation and t-test. The results showed that performance stress is negatively associated with life satisfaction, while academic self-perception is positively associated with life satisfaction. The result also showed that perception of workload and examinations and time constraints not associated with life satisfaction. All in all, academic self-perception and performance stress have an association with life satisfaction.
Support Vector Machines Based on Convex Risk Functionals and General Norms

This paper studies unified formulations of support vector machines (SVMs) for binary classification on the basis of convex analysis, especially, convex risk functionals theory, which is recently developed in the context of financial optimization. Using the notion of convex risk functionals, a pair of primal and dual formulations of the SVMs are described in a general manner, and duality results and optimality conditions are established in some standard setting. Interpretability of dual formulations is related to properties of the convex risk functionals. Besides, robust optimization modelings are easily incorporated. The formulation uses arbitrary norms for regularizers and incorporates new families of norms (in place of the lp-norms) in SVMs. We evaluate the proximity between SVMs with an arbitrary norm and the l2-norm, which also plays a role in kernelization. Numerical results demonstrate that with the new family of polyhedral norms, regularizer tuning can be efficiently incorporated via (possibly, parametric) linear programming, resulting in a better performance than the l2-regularizer.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)

Even if there is a common agreement on the general benefits of Blockchain Technology, it is less obvious when the usage of this technology is valuable; i.e. when a Blockchain Technology fit is given for a certain scenario. To assess whether a Blockchain is suitable, various descriptions and frameworks already exist. Nevertheless, decisions when to implement Blockchain Technology are still hype-driven and based on known use cases instead of such frameworks. This study provides a state-of-the-art analysis of papers that offer assessments for a Blockchain Technology suitability. By drawing conclusions on five research problems in this field, a research agenda is derived and guidelines for a BCT framework are suggested. Accordingly, a framework should clearly state (a) the organisational level, (b) what to assess, and (c) how to assess it. Furthermore, a framework should be (d) case independent, and would offer value if (e) patterns are outlined to assess a BCT fit more easily.
Image Processing Model with K-support Norm

In recent years, 1 l norm is usually considered as the regularization term in the field of sparse representation. However, the non-zero entries obtained by the 1 l regularization term always neglect the correlations with each other. In fact, different relationships or structures among non-zero entries are necessary in many applications. K-support norm is firstly proposed in the field of sparse prediction. The most important property of the k-support norm is grouping feature of the largest entries in the obtained solution. In this paper, we present a new image processing model by introducing the k-support norm to image gradient domain. The proposed model can be applied to image denoising and edge detection simultaneously. Some examples demonstrate the effectiveness of the novel model and its improvements.
Shape Decomposition using Modal Analysis

We introduce a novel algorithm that decomposes a deformable shape into meaningful parts requiring only a single input pose. Using modal analysis, we are able to identify parts of the shape that tend to move rigidly. We define a deformation energy on the shape, enabling modal analysis to find the typical deformations of the shape. We then find a decomposition of the shape such that the typical deformations can be well approximated with deformation fields that are rigid in each part of the decomposition. We optimize for the best decomposition, which captures how the shape deforms. A hierarchical refinement scheme makes it possible to compute more detailed decompositions for some parts of the shape. Although our algorithm does not require user intervention, it is possible to control the process by directly changing the deformation energy, or interactively refining the decomposition as necessary. Due to the construction of the energy function and the properties of modal analysis, the computed decompositions are robust to changes in pose as well as meshing, noise, and even imperfections such as small holes in the surface.
Intrinsic Plagiarism Detection using Complexity Analysis

We introduce Kolmogorov Complexity measures as a way of extracting structural information from texts for Intrinsic Plagiarism Detection. Kolmogorov complexity measures have been used as features in a variety of machine learning tasks including image recognition, radar signal classification, EEG classification, DNA analysis, speech recognition and some text classification tasks (Chi and Kong, 1998; Zhang, Hu, and Jin, 2003; Bhattacharya, 2000; Menconi, Benci, and Buiatti, 2008; Frank, Chui, and Witten, 2000; Dalkilic et al., 2006; Seaward and Saxton, 2007; Seaward, Inkpen, and Nayak, 2008). Intrinsic Plagiarism detection uses no external corpus for document comparison and thus plagiarism must be detected solely on the basis of style shifts within the text to be analyzed. Given the small amount of text to be analyzed, feature extraction is of particular importance. We give a theoretical background as to why complexity measures are meaningful and we introduce some experimental results on the PAN'09 Intrinsic Plagiarism Corpus. We show complexity features based on the Lempel-Ziv compression algorithm slightly increase performance over features based on normalized counts. Furthermore we believe that more sophisticated compression algorithms which are suited to com- pressing the English language show great promise for feature extraction for various text classification problems.
EasyCluster 2 : Clustering and Assembly of Large-scale Roche 454 Transcriptome Data for Gene Validation and Alternative Splicing Analysis

Motivation Expressed sequence tags and full-length cDNAs represent an invaluable source of evidence for inferring reliable gene structures and discovering potential alternative splicing events (1). However, to fully exploit their biological potential, correct and reliable EST clusters are required. To fill this gap we developed the program EasyCluster that resulted the most accurate when compared to software at the state of the art in this field (2). Recent technological advances are dramatically increasing the number of available transcriptome reads. EST-like sequences can now be generated by pyrosequencing using Roche 454 platforms which generate approximately one million reads per run. Handling such huge amount of EST-like data is basic to detect alternative splicing events, improve gene annotations or simply create gene-oriented clusters for expression studies. Sometimes EST-like data provide a fragmented overview of their genomic loci of origin and, thus, transcript assembly may be an optimal solution to annotate user-produced sequences. For these reasons we propose here a new implementation of EasyCluster able to manage genome scale transcriptome data and generate reliable gene-oriented clusters from 454 reads. The new version of EasyCluster software can facilitate downstream analyses because it enables the assembly of full-length transcripts per cluster, improves the clustering procedure using available annotations and embeds a graphical browser to provide an overview of results at genome level.
Affordable Safe & Secure Mobility Evolution State-ofthe-art and technology Y 1 Deliverable D 6 . 6

THIS DOCUMENT CONTAINS INFORMATION, WHICH IS PROPRIETARY TO THE ASSUME CONSORTIUM. NEITHER THIS DOCUMENT NOR THE INFORMATION CONTAINED HEREIN SHALL BE USED, DUPLICATED OR COMMUNICATED BY ANY MEANS TO ANY THIRD PARTY, IN WHOLE OR IN PARTS, EXCEPT WITH THE PRIOR WRITTEN CONSENT OF THE ASSUME CONSORTIUM THIS RESTRICTION LEGEND SHALL NOT BE ALTERED OR OBLITERATED ON OR FROM THIS DOCUMENT. THE RESEARCH LEADING TO THESE RESULTS HAS RECEIVED FUNDING FROM VARIOUS NATIONAL AUTHORITIES IN THE FRAMEWORK OF THE ITEA 3 PROGRAMME (PROJECT NUMBER 14014).
A (cid:12)lter model for mobile processes y

This paper presents a (cid:12)lter model for (cid:25) -calculus and shows its full abstraction with respect to a ‘may’ operational semantics. The model is introduced in the form of a type assignment system. Types are related by a preorder that mimics the operational behaviour of terms. A subject expansion theorem holds. Terms are interpreted as (cid:12)lters of types: this interpretation is compositional. The proof of full abstraction relies on a notion of realizability of types and on the construction of terms, which test when an arbitrary term has a (cid:12)xed type.
THE IMPACT ON THE PRODUCT DEVELOPMENT PROCESS WHEN OFFSHORING OR OUTSOURCING

This paper investigates the impact on the product development process when a company offshores or outsources a part of it abroad. Data was collected through interviews conducted in five companies. The study showed that the impact can be both negative and positive. The key impact was the possibility of a more complex development process due to cross-cultural virtual communication and collaboration. This meant a spiral development process is harder to offshore or outsource than a generic one. However, this complexity can be lessened by making the process or product simpler and by codifying and documenting processes and tasks to provide a common frame of reference. However, the case companies encountered several problems with implementing these control actions; for example culture, miscommunication, quality and transport time. This seemed to be caused by a brief preparation phase which meant the need for codification, documentation and simplification to counteract the added complexity was not conducted until the company had already moved the function or product part abroad. Furthermore, a thorough investigation of whether this was possible or desired ways to counteract this before mentioned complexity was left as a ‘learning by doing’ experience. These results suggest that offshoring/outsourcing a certain part of a product is only possible if it involves discrete tasks, and when the company knows where the relevant knowledge resides and how it can be shared. Doing so can even increase product complexity if the outsourcing is done to gain new competences. In the case of offshoring/outsourcing the development of a part of a product, product integration is vital. When offshoring/outsourcing a whole function, separation and codification seem to be the key issue for success. Partial functional offshoring/outsourcing due to cost reasons seems to lead to simpler products due to the before mentioned added complexity. Here integrated processes become the key factor.
Private Analysis of Graph Structure

We present efficient algorithms for releasing useful statistics about graph data while providing rigorous privacy guarantees. Our algorithms work on datasets that consist of relationships between individuals, such as social ties or email communication. The algorithms satisfy edge differential privacy, which essentially requires that the presence or absence of any particular relationship be hidden. Our algorithms output approximate answers to subgraph counting queries. Given a query graph H, for example, a triangle, k-star, or k-triangle, the goal is to return the number of edge-induced isomorphic copies of H in the input graph. The special case of triangles was considered by Nissim et al. [2007] and a more general investigation of arbitrary query graphs was initiated by Rastogi et al. [2009]. We extend the approach of Nissim et al. to a new class of statistics, namely k-star queries. We also give algorithms for k-triangle queries using a different approach based on the higher-order local sensitivity. For the specific graph statistics we consider (i.e., k-stars and k-triangles), we significantly improve on the work of Rastogi et al.: our algorithms satisfy a stronger notion of privacy that does not rely on the adversary having a particular prior distribution on the data, and add less noise to the answers before releasing them. We evaluate the accuracy of our algorithms both theoretically and empirically, using a variety of real and synthetic datasets. We give explicit, simple conditions under which these algorithms add a small amount of noise. We also provide the average-case analysis in the Erdős-Rényi-Gilbert G(n,p) random graph model. Finally, we give hardness results indicating that the approach Nissim et al. used for triangles cannot easily be extended to k-triangles (hence justifying our development of a new algorithmic approach).
The Expected Lifetime of \single-address-space" Operating Systems

Trends toward shared-memory programming paradigms, large (64-bit) address spaces, and memory-mapped les have led some to propose the use of a single virtual-address space, shared by all processes and processors. To simplify address-space management, some have claimed that a 64-bit address space is suuciently large that there is no need to ever re-use addresses. Unfortunately, there has been no data to either support or refute these claims, or to aid in the design of appropriate address-space management policies. In this paper, we present the results of extensive kernel-level tracing of the workstations on our campus, and discuss the implications for single-address-space operating systems. We found that single-address-space systems will probably not outgrow the available address space, but only if reasonable space-allocation policies are used, and only if the system can adapt as larger address spaces become available.
About retraining rule in multi–expert intelligent system for semi–supervised learning using SVM classifiers

This paper proposes three methods in order to retrain classifiers in a multi–expert scenario, when new (unknown) data are available. In fact, when a multi–expert system is adopted, the collective behaviour of classifiers can be used for both recognition aims and selection of the most profitable samples for system retraining. More specifically a misclassified sample for a particular expert can be used to update the expert itself if the collective behaviour of the multi–expert system allows the classification of the sample with high confidence. In addition, this paper provides a comparison between the new approach and those available in the literature for semi–supervised learning using the SVM classifier by taking into account four different combination techniques at abstract and measurement levels. The experimental results, which have been obtained using the handwritten digits of the CEDAR database, demonstrate the effectiveness of the proposed approach.
RECOMMENDATION NETWORKS AND THE LONG TAIL OF ELECTRONIC COMMERCE 1

It has been conjectured that the peer-based recommendations associated with electronic commerce lead to a redistribution of demand from popular products or “blockbusters” to less popular or “niche” products, and that electronic markets will therefore be characterized by a “long tail” of demand and revenue. We test this conjecture using the revenue distributions of books in over 200 distinct categories on Amazon.com and detailed daily snapshots of co-purchase recommendation networks in which the products of these categories are situated. We measure how much a product is influenced by its position in this hyperlinked network of recommendations using a variant of Google’s PageRank measure of centrality. We then associate the average influence of the network on each category with the inequality in the distribution of its demand and revenue, quantifying this inequality using the Gini coefficient derived from the category’s Lorenz curve. We establish that categories whose products are influenced more by the recommendation network have significantly flatter demand and revenue distributions, even after controlling for variation in average category demand, category size, and price differentials. Our empirical findings indicate that doubling the average network influence on a category is associated with an average increase of about 50 percent in the relative revenue for the least popular 20 percent of products, and with an average reduction of about 15 percent in the relative revenue for the most popular 20 percent of products. We also show that this effect is enhanced by higher assortative mixing and lower clustering in the network, and is greater in categories whose products are more evenly influenced by recommendations. The direction of these results persists over time, across both demand and revenue distributions, and across both daily and weekly demand aggregations. Our work illustrates how the microscopic economic data revealed by online networks can be used to define and answer new kinds of research questions, offers a fresh perspective on the influence of networked IT artifacts on business outcomes, and provides novel empirical evidence about the impact of visible recommendations on the long tail of electronic commerce.