Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimization Dijkstra Algorithm Based on Two-Function Limitation Strategy

There are two problems in searching the shortest path by Dijkstra algorithm. One is that the practical application of the result is not satisfactory because of the fixed weights; the other is that the time complexity is very high because of searching all nodes. So two-function limitation strategy is presented in this paper. One is that the weights are dynamic evaluated by adaptive function in order to limit the number of bad paths and improve the practicality of Dijkstra algorithm. The other is that the number of reaching nodes is limited by heuristic function according to dynamic weights in order to reduce the time complexity. According to the simulation results, we get the conclusion that two-function limitation strategy can reduce the number of bad paths by 89.2% and improve search efficiency by 58.3%. Keywords-shortest path; Dijkstra algorithm; adaptive function; heuristic function; road weights
Therapeutic Challenges to Retinitis Pigmentosa: From Neuroprotection to Gene Therapy

Syndromic retinitis pigmentosa (RP) is the result of several mutations expressed in rod photoreceptors, over 40 of which have so far been identified. Enormous efforts are being made to relate the advances in unraveling the patho-physiological mechanisms to therapeutic approaches in animal models, and eventually in clinical trials on humans. This review summarizes briefly the current clinical management of RP and focuses on the new exciting treatment possibilities. To date, there is no approved therapy able to stop the evolution of RP or restore vision. The current management includes an attempt at slowing down the degenerative process by vitamin supplementation, trying to treat ocular complications and to provide psychological support to blind patients. Novel therapeutic may be tailored dependant on the stage of the disease and can be divided in three groups. In the early stages, when there are surviving photoreceptors, the first approach would be to try to halt the degeneration by correction of the underlying biochemical abnormality in the visual cycle using gene therapy or pharmacological treatment. A second approach aims to cope with photoreceptor cell death using neurotrophic growth factors or anti-apoptotic factors, reducing the production of retino-toxic molecules, and limiting oxidative damage. In advanced stages, when there are few or no functional photoreceptors, strategies that may benefit include retinal transplantation, electronic retinal implants or a newly described optogenetic technique using a light-activated channel to genetically resensitize remnant cone-photoreceptor cells.
Sharing the Collective Intelligence between E-mail Applications

This position paper explores the idea of sharing the collective intelligence among different e-mail applications. The goal of such a sharing is to improve the performance of the e-mail services, like categorization, actor role and community detection on the large scale. We describe our approach to the cross-application categorization represented as the learning of multiple tasks with different category sets. Our multi-task learning approach is based on the ensemble principle and extends the conventional Adaboost technique from one- to multi-task setting.
Mining Bug Repositories--A Quality Assessment

The process of evaluating, classifying, and assigning bugs to programmers is a difficult and time consuming task which greatly depends on the quality of the bug report itself. It has been shown that the quality of reports originating from bug trackers or ticketing systems can vary significantly. In this research, we apply information retrieval (IR) and natural language processing (NLP) techniques for mining bug repositories. We focus particularly on measuring the quality of the free form descriptions submitted as part of bug reports used by open source bug trackers. Properties of natural language influencing the report quality are automatically identified and applied as part of a classification task. The results from the automated quality assessment are used to populate and enrich our existing software engineering ontology to support a further analysis of the quality and maturity of bug trackers.
To Call or Not to Call It Terrorism: Public Debate on Ideologically-motivated Acts of Violence in Finland, 1991–2015

ABSTRACT This article looks at how domestic acts of ideologically-motivated violence have been treated in Finnish public discussion with a particular focus on how the word “terrorism” has and has not been used to characterize such incidents. The work demonstrates that Finnish public debate has, with certain notable exceptions, primarily avoided labelling any violent attacks in the country as terrorism. This reluctance stems from Finnish traditions of crisis management, counterterrorism, and politics. Furthermore, the propensity to use the term “terrorism” cannot be explained by such characteristics of the attack itself as the number of casualties or whether it was perpetrated by ingroup or outgroup members. Rather than “what has happened,” the question of calling or not calling an act terrorism comes down to the question of “what needs to be done. ” The article contributes to academic debate by investigating a surprisingly under-researched aspect of how the term terrorism is used in public discussion while also shedding new light on the debate in Finland, a country seldom touched upon by research of terrorism and political violence.
Improvement of Nearest-Neighbor Classifiers via Support Vector Machines

Theoretically well-founded, Support Vector Machines (SVM) are well-known to be suited for efficiently solving classification problems. Although improved generalization is the main goal of this new type of learning machine, recent works have tried to use them differently. For instance, feature selection has been recently viewed as an indirect consequence of the SVM approach. In this paper, we also exploit SVMs differently from what they are originally intended. We investigate them as a data reduction technique, useful for improving case-based learning algorithms, sensitive to noise and computationally expensive. Adopting the margin maximization principle for reducing the Structural Risk, our strategy allows not only to eliminate irrelevant instances but also to improve the performances of the standard k-Nearest-Neighbor classifier. A wide comparative study is presented on several benchmarks of UCI repository, showing the utility of our approach.
Allele frequency net: a database and online repository for immune gene frequencies in worldwide populations

The allele frequency net database (http://www.allelefrequencies.net) is an online repository that contains information on the frequencies of immune genes and their corresponding alleles in different populations. The extensive variability observed in genes and alleles related to the immune system response and its significance in transplantation, disease association studies and diversity in populations led to the development of this electronic resource. At present, the system contains data from 1133 populations in 608 813 individuals on the frequency of genes from different polymorphic regions such as human leukocyte antigens, killer-cell immunoglobulin-like receptors, major histocompatibility complex Class I chain-related genes and a number of cytokine gene polymorphisms. The project was designed to create a central source for the storage of frequency data and provide individuals with a set of bioinformatics tools to analyze the occurrence of these variants in worldwide populations. The resource has been used in a wide variety of contexts, including clinical applications (histocompatibility, immunology, epidemiology and pharmacogenetics) and population genetics. Demographic information, frequency data and searching tools can be freely accessed through the website.
A Self-Optimizing Cloud Computing System for Distributed Storage and Processing of Semantic Web Data

Clouds are dynamic networks of common, off-the-shell computers to build computation farms. The rapid growth of databases in the context of the semantic web requires efficient ways to store and process this data. Using cloud technology for storing and processing Semantic Web data is an obvious way to overcome difficulties in storing and processing the enormously large present and future datasets of the Semantic Web. This paper presents a new approach for storing Semantic Web data, such that operations for the evaluation of Semantic Web queries are more likely to be processed only on local data, instead of using costly distributed operations. An experimental evaluation demonstrates the performance improvements in comparison to a naive distribution of Semantic Web data.
Pattern classification using neural networks

The author extends a previous review and focuses on feed-forward neural-net classifiers for static patterns with continuous-valued inputs. He provides a taxonomy of neural-net classifiers, examining probabilistic, hyperplane, kernel, and exemplar classifiers. He then discusses back-propagation and decision-tree classifiers; matching classifier complexity to training data; GMDH (generalized method of data handling) networks and high-order nets; K nearest-neighbor classifiers; the feature-map classifier; the learning vector quantizer; hypersphere classifiers; and radial-basis function classifiers. <<ETX>>
Localization of ubiquitous environment based mobile robot

In this paper, a localization method is provided using different-type sensors in ubiquitous environment. Ubiquitous based mobile robot requires information from sensors and structured environment to recognize its own position. In order to recognize the position of mobile robot, it is necessary data structure to integrate information from mobile sensors and the structured environment. An integration method is provided to take full advantage of characteristics of sensors and the structured environment. In this paper, odometric sensor is used as a mobile sensor and starLITE are used as the structured environment. Data of two sources are used as input of the proposed integration method. An extended Kalman filter (EKF) is used as integration filter to estimate sensor data error. The integration mechanism is implemented using P3DX mobile robot in ubiquitous environment.
Establishing the most effective way to deliver the sketch procedure to enhance interviewee free recall

ABSTRACT The relative effectiveness of three sketching procedures for enhancing the recall of a witnessed event was assessed. Participants (N = 123) viewed a mock crime video and were asked to recall its contents using one of three sketching procedures (i.e. Sketch and Free Recall; Sketch then Free Recall; Sketch and Explain then Free Recall) or two comparison procedures (i.e. Mental Reinstatement of Context; Control). Participants who were administered the Sketch and Explain then Free Recall procedure recalled more correct details than those who were administered the other four procedures (all ds > 0.70); the greater number of correct details was observed primarily for object and action details. There was minimal difference in incorrect details recalled or accuracy rate between all five procedures. The implications of using different sketching procedures for enhancing recall at the onset of investigative interviews are discussed.
Verifiable Searchable Symmetric Encryption from Indistinguishability Obfuscation

Searchable symmetric encryption (SSE) allows a client to encrypt his data in such a manner that the data can be efficiently searched. SSE has practical application in cloud storage, where a client outsources his encrypted data to a cloud server while maintaining the searchable ability over his data. Most of the current SSE schemes assume that the cloud server is honest-but-curious. However, the cloud may actively cheat on the search process to keep its cost low. In this paper, we focus on the malicious cloud model and propose a new verifiable searchable symmetric encryption scheme. Our scheme is built on the secure indistinguishability obfuscation (iO) and can be considered as the first step to apply iO in the SSE field. Moreover, our scheme can be easily extended to multiple functionalities, such as conjunctive and boolean queries. Furthermore, it can be extended to realize a publicly verifiable SSE. Thorough analysis shows that our scheme is secure and achieves a better performance.
Generic Congestion Control Through Network Coordination ( Extended

With the rapid emergence of new services and the quick change of traffic in Internet, the widely used end-to-end congestion control scheme becomes insufficient. This paper proposes a new congestion control architecture based on the coordination among the core router, the edge router, and the host. The core router notifies its immediately upstream nodes when it detects incipient congestion. Those nodes then constrain their sending rates to prevent congestion. No per-flow state information is maintained in routers. The application itself need not take care of the rate adaptation. The architecture provides a uniform solution accommodating both responsive and unresponsive traffic with trivial overhead. It reduces the packet loss to zero, and leads to higher network resource utilization. Response time to congestion is greatly reduced. The architecture has been implemented in the Linux kernel. Some preliminary experiments show its advantages.
Evidence for the Chronic in Vivo Production of Human T Cell Leukemia Virus Type I Rof and Tof Proteins from Cytotoxic T Lymphocytes Directed against Viral Peptides

Human T cell leukemia virus type I (HTLV-I) is a persistent virus that causes adult T cell leukemia and tropical spastic paraparesis/HTLV-I–associated myelopathy. Studies on rabbits have shown that viral proteins encoded by the open reading frames pX-I and pX-II are required for the establishment of the persistent infection. To examine the in vivo production of these proteins in humans, we have investigated whether cytotoxic T lymphocytes isolated from HTLV-I–infected individuals recognized pX-I and pX-II peptides. CD8+ T lymphocytes to pX-I and pX-II peptides were detected in HTLV-I–infected individuals, whatever their clinical status, and even in the absence of any antigenic restimulation. These findings indicate that the HTLV-I pX-I and pX-II proteins are chronically synthesized in vivo, and are targets of the natural immune response to the virus.
Laws of the iterated logarithm for symmetric jump processes

Based on two-sided heat kernel estimates for a class of symmetric jump processes on metric measure spaces, the laws of the iterated logarithm (LILs) for sample paths, local times and ranges are established. In particular, the LILs are obtained for $\beta$-stable-like processes on $\alpha$-sets with $\beta>0$.
Team splitting and the ‘borderline personality’: a relational reframe

The discussion of splitting within mental health treatment teams emerged from British object relations theory, a movement concerned with understanding the interplay between individuals in terms of their inner lives. Over time, the terminology used in this domain has become reified and splitting has been discussed in terms that are apt to frame the process as patient-precipitated. This is apt to encourage simplistic inferences, and attribute to patients responsibility for events that are beyond their control. In this article, the historical development of the psychoanalytic account of team splitting is examined and contemporary critiques are reviewed. It is suggested that the resultant one-person picture overlooks the ways that psychoanalytic theory has been subject to controversy and revision. In the last section of the article, I propose that relational innovations in psychoanalytic theory provide a way to rediscover the richness of early descriptions of splitting.
Immigrant threat and Latino/a disadvantage: disentangling the impact of immigration attitudes on ethnic sentencing disparities

ABSTRACT The criminal justice system has increasingly been relied upon to address immigration apprehension, resulting in concerns that this institution will be abused in an effort to indirectly address this perceived social problem. The consequences of such an approach will likely extend to Latino/a populations as a result of rhetoric linking ethnicity, immigration, and crime. Despite popular theoretical frameworks suggesting that disadvantage will vary according to the size of the population and the extent of perceived threats toward this minority, many neglect attitudinal measures or fail to measure actual criminological outcomes. This project addresses this oversight by exploring potential mediating effects of attitudes on the relationship between population measures and ethnic sentencing disparities. After fitting multilevel models nesting cases within counties and states, the results indicate that there is significant variation across all levels. While greater disparities in Latino/a sentencing were found in counties with greater Latino/a populations, this relationship was nonlinear. Additionally, state level measures of immigrant threat attitudes appear to be stronger predictors of Latino/a sentencing disparities. These contextual effects are more influential than offender level ethnicity, supporting threat hypotheses and suggesting that measurement of this concept should not be limited to offender ethnicity and population characteristics alone.
Joint Optimization of LDPC Codes with EXIT Chart

A joint optimization method of LDPC codes using EXIT chart is introduced in this paper. Based on the design method proposed by Brink, we take into account the variable nodes degree and check nodes degree together in the code design process, other than fixing the check nodes degree in advance. Compared with the non-joint design method, the joint optimization method can generate LDPC codes with nearer Shannon limit performance.
A Fault Diagnosis Method Based on Composite Model and SVM for Fermentation Process

A method of fault diagnosis based on composite model and support vector machines for fermentation process is proposed to overcome its difficulty in direct measurement of state parameters. In order to obtain the process state, composite model is presented by combining mass equations of bioreactors with RBF neural network that serve as estimators of unmeasured process kinetic parameters. Then Support vector machines are used to analyze and recognize fault patterns, making use of estimated state variables on line. The proposed method is applied to glutamic acid fermentation process, and the simulation results show its feasibility and effectiveness.
Quantifying the complexity of short-term heart period variability through k nearest neighbor local linear prediction

The complexity of short-term heart period (HP) variability was quantified exploiting the paradigm that associates the degree of unpredictability of a time series to its dynamical complexity. Complexity was assessed through k-nearest neighbor local linear prediction. A proper selection of the parameter k allowed us to perform either linear or nonlinear prediction, and the comparison of the two approaches to infer the presence of nonlinear dynamics. The method was validated on simulations reproducing linear and nonlinear time series with varying levels of predictability. It was then applied to HP variability series measured from healthy subjects during head-up tilt test, showing that short-term HP complexity increases significantly from the supine to the upright position, and that nonlinearities are involved in the generation of HP dynamics in both positions.
Evaluation of Static and Dynamic Scheduling for Media Processors

This paper presents the results of an architecture style evaluation that compares the performance of static scheduling and dynamic scheduling for media processors. Existing programmable media processors have predominantly used staticallyscheduled architectures. As future media processors progress to higher frequencies and higher degrees of parallelism, the dynamic aspects of processing become more pronounced and dynamic hardware support may be needed to achieve high performance. This paper explores many of the dynamic aspects of media processing by evaluating various fundamental architecture styles and the frequency effects on those architecture models. The results indicate that dynamic out-of-order scheduling methods enable significantly higher degrees of parallelism and are less susceptible to high frequency effects. Consequently, dynamic scheduling support may be necessary for maximizing performance in future media processors.
Does Africa grow slower than Asia and Latin America

In this paper we address the question whether countries on the African continent have lower average growth rates in real GDP per capita than countries in Asia and Latin America. In contrast to previous studies, we do not aggregate the data, nor do we a priori assign countries to clusters. Instead, we put forward a so-called latent class panel time series model, which allows a data-based classification of countries to clusters with growth levels that differ across the clusters. Our empirical results suggest that twenty-six African countries can be assigned to the low growth cluster, but that eleven African countries show growth levels which are comparable with many countries in Asia and Latin America. We also present results for sub-periods, which demonstrate that the relative performance of African countries has improved considerably over time.
Noncooperative two-level nonlinear programming through strategic oscillation based on PSO and clustering

In this paper, we consider a computational method of Stackelberg solutions for noncooperative two-level nonlinear programming problems (TLNLPPs) which contain two decision makers with different priorities. For the purpose of deriving (approximate) Stackelberg solutions to TLNLPPs efficiently, we propose a solution method based on particle swarm optimization and clustering using strategic oscillation.
A Path Planner in Changing Environments by Using W-C Nodes Mapping Coupled with Lazy Edges Evaluation

This paper presents a path planner based on PRM framework for robots operating in changing environments, in which obstacles can move randomly and robots may change their original shapes, e.g. a robot manipulator grasps an object. W-C nodes mapping coupled with lazy edges evaluation is used to ensure a generated path containing only valid nodes and edges when constructed probabilistic roadmap becomes partially invalid in changing environments. Our method combines merits of DRM and Lazy PRM methods. W-C nodes mapping, which is constructed in pre-processing phase by mapping every basic cell in workspace to nodes of roadmap, is preserved to indicate invalid nodes of roadmap fast whenever obstacles move. W-C edges mapping, which is another mapping relationship of DRM method, is skipped since it is much more complicated and time-consuming for construction. The simplified mapping with smaller size can be recomputed or modified fast when robots change their original shapes. Instead, lazy edges evaluation is used to ensure all edges valid along a found path. Simulated experiments for a dual-manipulator system show that our method is efficient for path planning in changing environments