Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Factors for the Progression of Mild Cognitive Impairment in Different Types of Neurodegenerative Disorders

Objective Mild cognitive impairment (MCI) is a transitional state between normal aging and early dementia. It has a heterogeneous etiology and clinical course. This study aimed to examine the factors associated with the progression of MCI in different types of dementia disorders. Method A retrospective, longitudinal, observational study of outpatients with MCI was conducted at a medical center in northern Taiwan. Patient medical records were reviewed, and risk factors were analyzed by multivariate analysis. Results Among 279 patients with MCI, 163 (58.4%), 68 (24.4%), and 48 (17.2%) were diagnosed with Alzheimer's disease, vascular cognitive impairment, and Lewy body diseases, respectively. During the observation period, 37.2% of patients progressed to dementia. Older age and a higher Clinical Dementia Rating Scale-Sum of Boxes were associated with the risk of progression. Hyperlipidemia was associated with a decreased risk. Converters were more likely to receive an antidementia prescription. Conclusion Our study suggests the importance of comprehensive clinical profiling, risk factor assessment, and detailed drug history evaluations in improving our understanding and management of dementia subtypes.
Knowledge base completion using distinct subgraph paths

Graph feature models facilitate efficient and interpretable predictions of missing links in knowledge bases with network structure (i.e. knowledge graphs). However, existing graph feature models---e.g. Subgraph Feature Extractor (SFE) or its predecessor, Path Ranking Algorithm (PRA) and its variants---depend on a limited set of graph features, connecting paths. This type of features may be missing for many interesting potential links, though, and the existing techniques cannot provide any predictions at all then. In this paper, we address the limitations of existing works by introducing a new graph-based feature model - Distinct Subgraph Paths (DSP). Our model uses a richer set of graph features and therefore can predict new relevant facts that neither SFE, nor PRA or its variants can discover by principle. We use a standard benchmark data set to show that DSP model performs better than the state-of-the-art - SFE (ANYREL) and PRA - in terms of mean average precision (MAP), mean reciprocal rank (MRR) and Hits@5, 10, 20, with no extra computational cost incurred.
Automatically evolving difficult benchmark feature selection datasets with genetic programming

There has been a wealth of feature selection algorithms proposed in recent years, each of which claims superior performance in turn. A wide range of datasets have been used to compare these algorithms, each with different characteristics and quantities of redundant and noisy features. Hence, it is very difficult to comprehensively and fairly compare these feature selection methods in order to find which are most robust and effective. In this work, we examine using Genetic Programming to automatically synthesise redundant features for augmenting existing datasets in order to more scientifically test feature selection performance. We develop a method for producing complex multi-variate redundancies, and present a novel and intuitive approach to ensuring a range of redundancy relationships are automatically created. The application of these augmented datasets to well-established feature selection algorithms shows a number of interesting and useful results and suggests promising directions for future research in this area.
Improving Continuous Integration with Similarity-Based Test Case Selection

Automated testing is an essential component of Continuous Integration (CI) and Delivery (CD), such as scheduling automated test sessions on overnight builds. That allows stakeholders to execute entire test suites and achieve exhaustive test coverage, since running all tests is often infeasible during work hours, i.e., in parallel to development activities. On the other hand, developers also need test feedback from CI servers when pushing changes, even if not all test cases are executed. In this paper we evaluate similarity-based test case selection (SBTCS) on integration-level tests executed on continuous integration pipelines of two companies. We select test cases that maximise diversity of test coverage and reduce feedback time to developers. Our results confirm existing evidence that SBTCS is a strong candidate for test optimisation, by reducing feedback time (up to 92% faster in our case studies) while achieving full test coverage using only information from test artefacts themselves.
Camera Model Identification with Convolutional Neural Networks and Image Noise Pattern

—Camera source detection has drawn a lot of attention in past decade. It enables us to solve a wide range of problems, from crime evidence identiﬁcation to photo tampering detection. In this paper, some main methods people used in this area for past decade will be reviewed in the Introduction and Method sections. Also in the Method and Result sections, I compared and improved state-of-the-art approaches to solve camera model identiﬁcation problem. In the latter part, I proposed a novel approaches based on Convolutional Neural Networks and Image Noise Pattern. Results on the dataset from Kaggle shows that to identify source camera, Convolutional Neural Networks can be applied to the pattern noise rather than directly to the original pictures to achieve at least similar or even better performance.
Implementing modular class-based reuse mechanisms on top of a single inheritance VM

Code reuse is a good strategy to avoid code duplication and speed up software development. Existing object-oriented programming languages propose different ways of combining existing and new code such as e.g., single inheritance, multiple inheritance, Traits or Mixins. All these mechanisms present advantages and disadvantages and there are situations that require the use of one over the other. To avoid the complexity of implementing a virtual machine (VM), many of these mechanisms are often implemented on top of an existing high-performance VM, originally meant to run a single inheritance object-oriented language. These implementations require thus a mapping between the programming model they propose and the execution model provided by the VM. Moreover, reuse mechanisms are not usually composable, nor it is easy to implement new ones for a given language. In this paper, we propose a modular meta-level runtime architecture to implement and combine different code reuse mechanisms. This architecture supports dynamic combination of several mechanisms without affecting runtime performance in a single inheritance object-oriented VM. It includes moreover a reflective Meta-Object Protocol to query and modify classes using the programming logical model instead of the underlying low-level runtime model. Thanks to this architecture, we implemented Stateful Traits, Mixins, CLOS multiple inheritance, CLOS Standard Method Combinations and Beta prefixing in a modular and composable way.
Choosing Models to Explore Financial Supply Chain Relationships

A residential mortgage-backed security resMBS, is a complex financial product that is constructed by pooling cash flows from a collection of mortgage loans. Figure 1 shows a simplified view of the complex product structure, composed of tranches (slices) of securities, on the right, with a prioritized waterfall payment structure. The cash flows from the pool of mortgages, on the left, both the principal and interest payments, are allocated to the securities based on the waterfall payment priority. The tranches served first are referred to as senior debt, or Class A or B securities, followed by mezzanine and then unsecured or equity debt. The securities are rated for their credit worthiness and are packaged within a prospectus that is issued, with the securities sold to investors. These products can be issued by government sponsored enterprises (GSEs such as Fannie Mae) or they may be issued as private label securities.
Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to transfer domain knowledge from existing well-defined tasks to new ones where labels are unavailable. In the real-world applications, as the domain (task) discrepancies are usually uncontrollable, it is significantly motivated to match the feature distributions even if the domain discrepancies are disparate. Additionally, as no label is available in the target domain, how to successfully adapt the classifier from the source to the target domain still remains an open question. In this paper, we propose the Re-weighted Adversarial Adaptation Network (RAAN) to reduce the feature distribution divergence and adapt the classifier when domain discrepancies are disparate. Specifically, to alleviate the need of common supports in matching the feature distribution, we choose to minimize optimal transport (OT) based Earth-Mover (EM) distance and reformulate it to a minimax objective function. Utilizing this, RAAN can be trained in an end-to-end and adversarial manner. To further adapt the classifier, we propose to match the label distribution and embed it into the adversarial training. Finally, after extensive evaluation of our method using UDA datasets of varying difficulty, RAAN achieved the state-of-the-art results and outperformed other methods by a large margin when the domain shifts are disparate.
On the behavior of the infinite restricted boltzmann machine for clustering

Clustering is a core problem within a wide range of research disciplines ranging from machine learning and data mining to classical statistics. A group of clustering approaches so-called nonparametric methods, aims to cluster a set of entities into a beforehand unspecified and unknown number of clusters, making potentially expensive pre-analysis of data obsolete. In this paper, the recently, by Cote and Larochelle introduced infinite Restricted Boltzmann Machine that has the ability to self-regulate its number of hidden parameters is adapted to the problem of clustering by the introduction of two basic cluster membership assumptions. A descriptive study of the influence of several regularization and sparsity settings on the clustering behavior is presented and results are discussed. The results show that sparsity is a key adaption when using the iRBM for clustering that improves both the clustering performances as well as the number of identified clusters.
Relationship between beliefs about medicines, adherence to treatment, and disease activity in patients with rheumatoid arthritis under subcutaneous anti-TNFα therapy

Objective In patients with rheumatoid arthritis (RA), nonadherence to treatment is often related to patients’ beliefs and concerns regarding their medication. This study aimed to analyze the correlations regarding patients’ medication beliefs, medication adherence, and objective measures of disease activity and safety in patients with RA established on subcutaneous (SC) anti-tumor necrosis factor α (TNFα) therapy. Methods This Phase IV, noninterventional, non–drug-specific study enrolled patients with RA being treated with stable-dose SC anti-TNFα (adalimumab, etanercept, golimumab, and certolizumab pegol). At initial visit and 6 and 12 months later, patients completed the Beliefs about Medicines Questionnaire-Specific section, assessing perceptions of personal need for anti-TNFα therapy (anti-TNFα-Necessity) and concerns (anti-TNFα-Concerns), Medication Adherence Rating Scale (MARS), mean Disease Activity Score in 28 joints (DAS28), and other scales. Longitudinal data were analyzed by linear mixed models. Results A total of 460 patients were included. At initial visit, anti-TNFα-Necessity beliefs were high (mean ± SD: 4.3 ± 0.55) vs anti-TNFα-Concerns (2.8 ± 0.78). Medication adherence (MARS) was high (4.8 ± 0.39). All scores remained stable over the 1-year follow-up period. Anti-TNFα-Necessity beliefs and anti-TNFα-Concerns were not related to each other, but strongly correlated with medication adherence. While concerns worsened with disease activity, clinical status, and low quality of life, necessity beliefs remained unaffected. Conclusion In patients with RA established on stable-dose SC anti-TNFα, anti-TNFα-Necessity beliefs persistently outweighed anti-TNFα-Concerns, but both correlated with adherence. These findings may be of use in subsequent studies looking to predict adherence in patients starting treatment with SC anti-TNFα.
D-ball: virtualized sports in diminished reality

Many video games have been created that virtually represent sports from the real world. We present a new type of sports called "D-Ball", based on ballgames, that uses diminished reality (DR) technology. We created a prototype for a new DR system that uses a head-mounted smartphone to show specific objects and features from the real environment that have been extracted based on their color. Players can only see the balls, lines, and markers attached to players that have this color. D-Ball allows the players to experience a ballgame as if they were in a video game from the 70s. Players can customize their own appearance in D-Ball by changing the colored markers attached to their bodies. Furthermore, the difficulty of D-Ball can be modified by changing the ratio between the completely diminished and the completely real aspects of reality. In this paper, we propose a new method for DR in virtualized sports and discuss the game and prototype design of D-Ball as an example of this method.
EGOlink: Supporting Editors of Online Historical Sources through Automatic Link Discovery

We propose a tool for analyzing EGO (European History Online) document collection according to the link structure. The analysis tool is pre-step towards building a (semi-)automatic approach for linking EGO articles to each other as well as to other external resources. Our aim is to assist the editorial office in performing the linking task and to increase the inter-connectivity of EGO articles.
Progressive Sampling Strategies for Disk Light Sources

This technical memo compares six different strategies for sampling a disk area light source. We test padding the domain with zero values, rejection sampling, polar mapping, concentric mapping, and two new strategies we call polar4 and concentric4 mapping. We test the six strategies with nine different sample sequences on pixels with full illumination and partial shadow (penumbra), and find the best combination of sampling strategy and sample sequence. One of the new sampling strategies gives more than 3 times error reduction in some cases.
Testing Service Oriented Architectures Using Stateful Service Virtualization via Machine Learning

Today's enterprise software systems are much complicated than the past. Increasing number of dependent applications, heterogeneous technologies and wide usage of Service Oriented Architectures (SOA), where numerous services communicate with each other, makes testing of such systems challenging. For testing these software systems, the concept of service virtualization is gaining popularity. Service virtualization is an automated technique to mimic the behavior of a given real service. Services can be classified as stateless or stateful services. Many services are stateful in nature. Although there are works in the literature for virtualization of stateless services, no such solution exists for stateful services. To the best of our knowledge, this is the first work for stateful service virtualization. We employ classification based and sequence-to-sequence based machine learning algorithms in developing our solutions. We demonstrate the validity of our approach on two data sets collected from real life services and obtain promising results.
Exploring OS-based full-system deterministic replay

Modern computer systems have various sources of nondeterminism such as external inputs, concurrency in software and hardware, asynchronous interrupts and timing variations. With these sources of nondeterminism, many errors and bugs can remain undetected during development, manifesting in the form of corrupt data, hangs, crashes or other catastrophic results. Deterministic full-system replay helps in identifying the cause of such failures by reproducing a previously happened execution. Existing full-system deterministic replay schemes are based on either a special hardware implementation or a virtualization platform. Though beneficial, either they require non-trivial modifications to hardware or suffer from lack of reproducibility. This paper presents an innovative operating system (OS) based replay framework called Software Black Box (SBB) which is the first attempt to provide full-system replay without requiring any special hardware implementation or virtualization. It can reproduce the entire execution of a computer system including, user-level processes, OS functions and device drivers with instruction level accuracy. We implemented a prototype of SBB for Linux operating system ported to the ARM uniprocessor environment and evaluated its performance using Phoronix benchmark suites and some networking workloads. The results are promising, making it suitable for many purposes including debugging, testing, security and performance analysis.
Applying Subsequence Matching to Collaborative Filtering: Extended Abstract

Neighbourhood-based approaches, although they are one of the most popular strategies in the recommender systems area, continue using classic similarities that leave aside the sequential information of the users interactions. In this extended abstract, we summarise the main contributions of our previous work where we proposed to use the Longest Common Subsequence algorithm as a similarity measure between users, by adapting it to the recommender systems context and proposing a mechanism to transform users interactions into sequences. Furthermore, we also introduced some modifications on the original LCS algorithm to allow non-exact matchings between users and to bound the similarities obtained in the [0,1] interval. Our reported results showed that our LCS-based similarity was able to outperform different state-of-the-art recommenders in two datasets in both ranking and novelty and diversity metrics.
Towards coherent single-document summarization: an integer linear programming-based approach

Automatic Text Summarization (ATS) is a viable option to reduce the content of textual documents, e.g., as a possible preprocessing step in many text mining applications. Single-document extractive summarizers have been developed based on different approaches, but many of them have the drawback of producing summaries with low coherence among the selected sentences in the generated summaries. In this paper, we present an unsupervised summarization system as an attempt towards coherent extractive single-document summarization. This system relies on Integer Linear Programming (ILP) as an optimization technique for selecting the smallest subset of sentences of a document maximizing the coverage of relevant concepts. Furthermore, our solution uses a graph-based algorithm for two goals: representing both sentences and concepts and enabling local coherence scoring among the sentences in the generated summaries. The proposed system is evaluated on two single-document benchmark datasets (DUC 2001-2002) using ROUGE measures, and compared with other state-of-the-art summarizers. The achieved results are very competitive.
Towards an approach to elicit domain requirements from social networks: the case of emergency systems

The1 growth of social networks impacts on several areas of our society. They can be used during software development, more specifically in the requirements elicitation activity, for identifying, complementing and validating the domain requirements. In this paper, we propose an approach that shows how social networks can be used as a source for capturing domain requirements. The aim is to perform the initial modeling of the system domain, providing a systematic methodology (process) for rapidly capturing relevant features that would not be straightforwardly elicited using traditional approaches. We apply this approach to the emergency systems domain (more specifically, floods in coastal areas), extracting information from the Twitter social network. The result is a domain model whose features can be reused in several applications of that domain. The application of the approach has been evaluated for its usefulness by domain experts and replicated to verify the generation of similar results at different time periods.
Technology Comprehension: Scaling Making into a National Discipline

We account for the first research results from a governmentinitiated experiment that scales Making to a national discipline. The Ministry of Education, in Denmark, has introduced Technology Comprehension as a new discipline for lower secondary education. Technology Comprehension is first experimented as an elective subject in 13 schools. The discipline combines elements from computing, design, and the societal aspect of technology and, thus, resonates with the existing FabLearn and Making initiatives in Scandinavia. We report the identified opportunities and challenges based on interviews, surveys, and a theme discussion with experienced teachers from the 13 schools. The main takeaways are: First, the teachers did not perceive Technology Comprehension as a distinguished discipline, which calls for more research on how Making is scaled into a national discipline. Second, Technology Comprehension opens up for interdisciplinary and engaging learning activities, but teachers need scaffolding and support to actualise these opportunities. Third, Technology Comprehension challenges teachers' existing competencies in relation to the discipline and students' prerequisites and needs. Teachers need pedagogical means to take the societal aspect into account within the discipline. Finally, we argue for further research on supporting teachers when scaling Technology Comprehension on a national level.
Fuzzy logic for pipelines risk assessment

Article history: Received December 20, 2011 Received in Revised form March, 25, 2012 Accepted 20 April 2012 Available online April 23 2012 Pipelines systems are identified to be the safest way of transporting oil and natural gas. One of the most important aspects in developing pipeline systems is determining the potential risks that implementers may encounter. Therefore, risk analysis can determine critical risk items to allocate the limited resources and time. Risk Analysis and Management for Critical Asset Protection (RAMCAP) is one of the best methodologies for assessing the security risks. However, the most challenging problem in this method is uncertainty. Therefore, fuzzy set theory is used to model the uncertainty. Thus, Fuzzy RAMCAP is introduced in order to risk analysis and management for pipeline systems. Finally, a notional example from pipeline systems is provided to demonstrate an application of the proposed methodology. © 2012 Growing Science Ltd. All rights reserved.
SWARD: A Secure WAke-up RaDio against Denial-of-Service on IoT devices

Wake-up radios are mechanisms that control the sleep and active modes of energy-constrained Internet of Things (IoT) nodes. These radios detect pre-determined wake-up tokens and switch the devices to an active state. Such systems are vulnerable to a kind of Denial-of-Service attacks called Denial-of-Sleep, where attackers continuously send wake-up tokens to deplete the battery of the nodes. We propose a protocol to mitigate these attacks that includes a novel solution to generate hard-to-guess wake-up tokens at every wake-up. Simulations show that under standard operating conditions, it has a negligible energy overhead (0.03%), while it increases the lifetime of an IoT node by more than 40 times under Denial-of-Sleep attack. Finally we compare our protocol to related work against Denial-of-Sleep attacks, and explain why it is both more resilient and more energy-efficient than existing approaches.