nils-herrmann/clean_scirepeval huggingface dataset:
Multilabel Classification via Co-Evolutionary Multilabel Hypernetwork

Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn an integrated classification model for all labels. The proposed Co-MLHN exploits arbitrary order label correlations and has linear computational complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against state-of-the-art multilabel learning algorithms, in terms of both classification performance and scalability with respect to the number of labels.
Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing

Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5 m, 1 m, and 8 m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5 m for localization and 0.9 m for tracking in the office environment. When using only the magnetism, Magicol consumes 9 × less energy in tracking compared to WiFi-based tracking.
Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems

An efficient optimization method called ‘Teaching–Learning-Based Optimization (TLBO)’ is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods.
A Theoretic Framework of K-Means-Based Consensus Clustering

Consensus clustering emerges as a promising solution to find cluster structures from data. As an efficient approach for consensus clustering, the Kmeans based method has garnered attention in the literature, but the existing research is still preliminary and fragmented. In this paper, we provide a systematic study on the framework of K-meansbased Consensus Clustering (KCC). We first formulate the general definition of KCC, and then reveal a necessary and sufficient condition for utility functions that work for KCC, on both complete and incomplete basic partitionings. Experimental results on various real-world data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with substantial missing values.
A Framework for Evaluating Mobile App Repackaging Detection Algorithms

Because it is not hard to reverse engineer the Dalvik bytecode used in the Dalvik virtual machine, Android application repackaging has become a serious problem. With repackaging, a plagiarist can simply steal others’ code violating the intellectual property of the developers. More seriously, after repackaging, popular apps can become the carriers of malware, adware or spy-ware for wide spreading. To maintain a healthy app market, several detection algorithms have been proposed recently, which can catch some types of repackaged apps in various markets efficiently. However, they are generally lack of valid analysis on their effectiveness. After analyzing these approaches, we find simple obfuscation techniques can potentially cause false negatives, because they change the main characteristics or features of the apps that are used for similarity detections. In practice, more sophisticated obfuscation techniques can be adopted (or have already been performed) in the context of mobile apps. We envision this obfuscation based repackaging will become a phenomenon due to the arms race between repackaging and its detection. To this end, we propose a framework to evaluate the obfuscation resilience of repackaging detection algorithms comprehensively. Our evaluation framework is able to perform a set of obfuscation algorithms in various forms on the Dalvik bytecode. Our results provide insights to help gauge both broadness and depth of algorithms’ obfuscation resilience. We applied our framework to conduct a comprehensive case study on AndroGuard, an Android repackaging detector proposed in Black-hat 2011. Our experimental results have demonstrated the effectiveness and stability of our framework.
Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.
A survey of software testing practices in alberta

Software organizations have typically de-emphasized the importance of software testing. In this paper, the results of a regional survey of software testing and software quality assurance techniques are described. Researchers conducted the study during the summer and fall of 2002 by surveying software organizations in the Province of Alberta. Results indicate that Alberta-based organizations tend to test less than their counterparts in the United States. The results also indicate that Alberta software organizations tend to train fewer personnel on testing-related topics. This practice has the potential for a two-fold impact: first, the ability to detect trends that lead to reduced quality and to identify the root causes of reductions in product quality may suffer from the lack of testing. This consequence is serious enough to warrant consideration, since overall quality may suffer from the reduced ability to detect and eliminate process or product defects. Second, the organization may have a more difficult time adopting methodologies such as extreme programming. This is significant because other industry studies have concluded that many software organizations have tried or will in the next few years try some form of agile method. Newer approaches to software development like extreme programming increase the extent to which teams rely on testing skills. Organizations should consider their testing skill level as a key indication of their readiness for adopting software development techniques such as test-driven development, extreme programming, agile modelling, or other agile methods.
Program-flow attestation of IoT systems software

Remote attestation is the process of measuring the integrity of a device over the network, by detecting modification of software or hardware from the original configuration. Several remote software-based attestation mechanisms have been introduced, that rely on strict time constraints and other impractical constraints that make them inconvenient for IoT systems. Although some research is done to address these issues, they integrated trusted hardware devices to the attested devices to accomplish their aim, which is costly and not convenient for many use cases. In this paper, we propose “Dual Attestation” that includes two stages: static and dynamic. The static attestation phase checks the memory of the attested device. The dynamic attestation technique checks the execution correctness of the application code and can detect the runtime attacks. The objectives are to minimize the overhead and detect these attacks, by developing an optimized dynamic technique that checks the application program flow. The optimization will be done in the prover and the verifier sides.
Reliable Client Accounting for P2P-Infrastructure Hybrids

Content distribution networks (CDNs) have started to adopt hybrid designs, which employ both dedicated edge servers and resources contributed by clients. Hybrid designs combine many of the advantages of infrastructurebased and peer-to-peer systems, but they also present new challenges. This paper identifies reliable client accounting as one such challenge. Operators of hybrid CDNs are accountable to their customers (i.e., content providers) for the CDN’s performance. Therefore, they need to offer reliable quality of service and a detailed account of content served. Service quality and accurate accounting, however, depend in part on interactions among untrusted clients. Using the Akamai NetSession client network in a case study, we demonstrate that a small number of malicious clients used in a clever attack could cause significant accounting inaccuracies. We present a method for providing reliable accounting of client interactions in hybrid CDNs. The proposed method leverages the unique characteristics of hybrid systems to limit the loss of accounting accuracy and service quality caused by faulty or compromised clients. We also describe RCA, a system that applies this method to a commercial hybrid content-distribution network. Using trace-driven simulations, we show that RCA can detect and mitigate a variety of attacks, at the expense of a moderate increase in logging overhead.
Recommendation System Using Bloom Filter in Mapreduce

Many clients like to use the Web to discover product details in the form of online reviews. The reviews are provided by other clients and specialists. Recommender systems provide an important response to the information overload problem as it presents users more practical and personalized information facilities. Collaborative filtering methods are vital component in recommender systems as they generate high-quality recommendations by influencing the likings of society of similar users. The collaborative filtering method has assumption that people having same tastes choose the same items. The conventional collaborative filtering system has drawbacks as sparse data problem & lack of scalability. A new recommender system is required to deal with the sparse data problem & produce high quality recommendations in large scale mobile environment. MapReduce is a programming model which is widely used for large-scale data analysis. The described algorithm of recommendation mechanism for mobile commerce is user based collaborative filtering using MapReduce which reduces scalability problem in conventional CF system. One of the essential operations for the data analysis is join operation. But MapReduce is not very competent to execute the join operation as it always uses all records in the datasets where only small fraction of datasets are applicable for the join operation. This problem can be reduced by applying bloomjoin algorithm. The bloom filters are constructed and used to filter out redundant intermediate records. The proposed algorithm using bloom filter will reduce the number of intermediate results and will improve the join performance.
Multi-View 3D Entangled Forest for Semantic Segmentation and Mapping

Applications that provide location related services need to understand the environment in which humans live such that verbal references and human interaction are possible. We formulate this semantic labelling task as the problem of learning the semantic labels from the perceived 3D structure. In this contribution we propose a batch approach and a novel multi-view frame fusion technique to exploit multiple views for improving the semantic labelling results. The batch approach works offline and is the direct application of an existing single-view method to scene reconstructions with multiple views. The multi-view frame fusion works in an incremental fashion accumulating the single-view results, hence allowing the online multi-view semantic segmentation of single frames and the offline reconstruction of semantic maps. Our experiments show the superiority of the approaches based on our fusion scheme, which leads to a more accurate semantic labelling.
What maintains parental support for vaccination when challenged by anti-vaccination messages? A qualitative study.

This study sought to explore how parents respond to competing media messages about vaccine safety. Six focus groups with mothers of infants were shown television vignettes of typical pro- and anti-vaccination claims. Thematic analysis of transcripts was undertaken. Mothers expressed surprise and concern about alleged vaccine risks but quickly reinstated their support for vaccination by deference to authority figures; type-casting immunisation opponents; and notions of anticipatory regret, good parenting and social responsibility. We conclude that personal experiences, value systems and level of trust in health professionals are fundamental to parental decision making about vaccination. Vaccination advocacy should increase the focus on matters of process such as maintaining trust and public confidence, particularly in health professionals. Stories about people affected by vaccine-preventable diseases need to re-enter the public discourse.
Long-Term Treatment with High-Dose Thiamine in Parkinson Disease: An Open-Label Pilot Study.

To investigate the potential clinical, restorative, and neuroprotective effects of long-term treatment with thiamine in Parkinson disease (PD).



Observational open-label pilot study.



Outpatient neurologic rehabilitation clinic.



Starting in June 2012, we have recruited 50 patients with PD (33 men and 17 women; mean age, 70.4 ± 12.9 years; mean disease duration, 7.3 ± 6.7 years). All the patients were assessed at baseline with the Unified Parkinson's Disease Rating Scale (UPDRS) and the Fatigue Severity Scale (FSS) and began treatment with 100 mg of thiamine administered intramuscularly twice a week, without any change to personal therapy. All the patients were re-evaluated after 1 month and then every 3 months during treatment.



Thiamine treatment led to significant improvement of motor and nonmotor symptoms: mean UPDRS scores (parts I-IV) improved from 38.55 ± 15.24 to 18.16 ± 15.08 (p = 2.4 × 10(-14), t test for paired data) within 3 months and remained stable over time; motor UPDRS part III score improved from 22.01 ± 8.57 to 9.92 ± 8.66 (p = 3.1 × 10(-22)). Some patients with a milder phenotype had complete clinical recovery. FSS scores, in six patients who had fatigue, improved from 53.00 ± 8.17 to 23.60 ± 7.77 (p < 0.0001, t test for paired data). Follow-up duration ranged from 95 to 831 days (mean, 291.6 ± 207.2 days).



Administration of parenteral high-dose thiamine was effective in reversing PD motor and nonmotor symptoms. The clinical improvement was stable over time in all the patients. From our clinical evidence, we hypothesize that a dysfunction of thiamine-dependent metabolic processes could cause selective neural damage in the centers typically affected by this disease and might be a fundamental molecular event provoking neurodegeneration. Thiamine could have both restorative and neuroprotective action in PD.
A study of attention-based neural machine translation model on Indian languages

Neural machine translation (NMT) models have recently been shown to be very successful in machine translation (MT). The use of LSTMs in machine translation has significantly improved the translation performance for longer sentences by being able to capture the context and long range correlations of the sentences in their hidden layers. The attention model based NMT system has become state-of-the-art, performing equal or better than other statistical MT approaches. In this paper, we studied the performance of the attention-model based NMT system on the Indian language pair, Hindi and Bengali. We analysed the types of errors that occur in morphologically rich languages when there is a scarcity of large parallel training corpus. We then carried out certain post-processing heuristic steps to improve the quality of the translated statements and suggest further measures.
Analysis of the Paragraph Vector Model for Information Retrieval

Previous studies have shown that semantically meaningful representations of words and text can be acquired through neural embedding models. In particular, paragraph vector (PV) models have shown impressive performance in some natural language processing tasks by estimating a document (topic) level language model. Integrating the PV models with traditional language model approaches to retrieval, however, produces unstable performance and limited improvements. In this paper, we formally discuss three intrinsic problems of the original PV model that restrict its performance in retrieval tasks. We also describe modifications to the model that make it more suitable for the IR task, and show their impact through experiments and case studies. The three issues we address are (1) the unregulated training process of PV is vulnerable to short document over-fitting that produces length bias in the final retrieval model; (2) the corpus-based negative sampling of PV leads to a weighting scheme for words that overly suppresses the importance of frequent words; and (3) the lack of word-context information makes PV unable to capture word substitution relationships.
Review. The neurobiology of pathological gambling and drug addiction: an overview and new findings.

Gambling is a prevalent recreational behaviour. Approximately 5% of adults have been estimated to experience problems with gambling. The most severe form of gambling, pathological gambling (PG), is recognized as a mental health condition. Two alternate non-mutually exclusive conceptualizations of PG have considered it as an obsessive-compulsive spectrum disorder and a 'behavioural' addiction. The most appropriate conceptualization of PG has important theoretical and practical implications. Data suggest a closer relationship between PG and substance use disorders than exists between PG and obsessive-compulsive disorder. This paper will review data on the neurobiology of PG, consider its conceptualization as a behavioural addiction, discuss impulsivity as an underlying construct, and present new brain imaging findings investigating the neural correlates of craving states in PG as compared to those in cocaine dependence. Implications for prevention and treatment strategies will be discussed.
NetCDF: an interface for scientific data access

The network common data form (NetCDF), a data abstraction for storing and retrieving multidimensional data, is described. NetCDF is distributed as a software library that provides a concrete implementation of that abstraction. The implementation provides a machine-independent format for representing scientific data. Together, the abstraction, library, and data format support the creation, access, and sharing of scientific information. NetCDF is useful for supporting objects that contain dissimilar kinds of data in a heterogeneous network environment and for writing application software that does not depend on application-specific formats. Independence from particular machine representations is achieved by using a nonproprietary standard for external data representation. The discussion covers NetCDF data abstraction and interface; dimensions, variables, and attributes; direct access and hyperslab access, the NetCDF library; the data format; ncdump and ncgen utilities; experience, usability, and performance; limitations of NetCDF; and future plans.<<ETX>>
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations

In this work we explore the generalization characteristics of unsupervised representation learning by leveraging disentangled VAE’s to learn a useful latent space on a set of relational reasoning problems derived from Raven Progressive Matrices. We show that the latent representations, learned by unsupervised training using the right objective function, significantly outperform the same architectures trained with purely supervised learning, especially when it comes to generalization.
Text mining in healthcare. Applications and opportunities.

Healthcare information systems collect massive amounts of textual and numeric information about patients, visits, prescriptions, physician notes and more. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. However, the documents that comprise the health record vary in complexity, length and use of technical vocabulary. This makes knowledge discovery complex. Commercial text mining tools provide a unique opportunity to extract critical information from textual data archives. In this paper, we share our experience of a collaborative research project to develop predictive models by text mining electronic clinical records. We provide an overview of the text mining process, examples of existing studies, experiences of our collaborative project and future opportunities.
Bayesian Reinforcement Learning in Factored POMDPs

Bayesian approaches provide a principled solution to the explorationexploitation trade-off in Reinforcement Learning. Typical approaches, however, either assume a fully observable environment or scale poorly. This work introduces the Factored Bayes-Adaptive POMDP model, a framework that is able to exploit the underlying structure while learning the dynamics in partially observable systems. We also present a belief tracking method to approximate the joint posterior over state and model variables, and an adaptation of the Monte-Carlo Tree Search solution method, which together are capable of solving the underlying problem near-optimally. Our method is able to learn efficiently given a known factorization or also learn the factorization and the model parameters at the same time. We demonstrate that this approach is able to outperform current methods and tackle problems that were previously infeasible.