nils-herrmann/clean_scirepeval huggingface dataset:
Forecasting exchage rates using machine learning models with time-varying volatility

This thesis is focused on investigating the predictability of exchange rate returns on monthly and daily frequency using models that have been mostly developed in the machine learning field. The forecasting performance of these models will be compared to the Random Walk, which is the benchmark model for financial returns, and the popular autoregressive process. The machine learning models that will be used are Shrinkage and Selection Operator (LASSO) and Bayesian Additive Regression trees (BART). A characterizing feature of financial returns data is the presence of volatility clustering, i.e. the tendency of persistent periods of low or high variance in the time series. This is in disagreement with the machine learning models which implicitly assume a constant variance. We therefore extend these models with the most widely used model for volatility clustering, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) process. This allows us to jointly estimate the time varying variance and the parameters of the machine learning using an iterative procedure. These GARCH-extended machine learning models are then applied to make one-step-ahead prediction by recursive estimation that the parameters estimated by this model are also updated with the new information. In order to predict returns, information related to the economic variables and the lagged variable will be used. This study is repeated on three different exchange rate returns: EUR/SEK, EUR/USD and USD/SEK in order to obtain robust results. Our result shows that machine learning models are capable of forecasting exchange returns both on daily and monthly frequency. The results were mixed, however. Overall, it was GARCH-extended SVR that shows great potential for improving the predictive performance of the forecasting of exchange rate returns. 2 3 Acknowledgement It gives me a great pleasure in acknowledging my supervisor Prof. Mattias Villani, Linköping University, for his guidance, patience and encouragement in completion of this thesis. His support from initial to final stage has enabled me to have better understanding of this topic. I would also like to thank Marianna Blix Grimaldi, Sveriges Riksbank, for introducing such an interesting topic and providing me opportunity to do it. Her comments and suggestions were helpful in improving this study.
A Comparison of the Electroencephalogram between Institutionalized and Community Children in Romania

Electroencephalographic (EEG) data were collected from a sample of institutionalized infants and young children in Bucharest, Romania, and were compared with EEG data from age-matched children from the local community who had never been institutionalized and who were living with their families in the Bucharest area. Compared with the never-institutionalized group, the institutionalized group showed a pattern of increased low-frequency (theta) power in posterior scalp regions and decreased high-frequency (alpha and beta) power, particularly at frontal and temporal electrode sites. This finding is consistent with EEG studies of children facing environmental adversity and children with learning disorders. The institutionalized group also showed less marked hemispheric EEG asymmetries than the never-institutionalized group, particularly in the temporal region. The results are discussed in the context of two models: that the pattern of EEG in the institutionalized children reflects a maturational lag in nervous system development, or that it reflects tonic cortical hypoactivation.
DeepAM: a heterogeneous deep learning framework for intelligent malware detection

With computers and the Internet being essential in everyday life, malware poses serious and evolving threats to their security, making the detection of malware of utmost concern. Accordingly, there have been many researches on intelligent malware detection by applying data mining and machine learning techniques. Though great results have been achieved with these methods, most of them are built on shallow learning architectures. Due to its superior ability in feature learning through multilayer deep architecture, deep learning is starting to be leveraged in industrial and academic research for different applications. In this paper, based on the Windows application programming interface calls extracted from the portable executable files, we study how a deep learning architecture can be designed for intelligent malware detection. We propose a heterogeneous deep learning framework composed of an AutoEncoder stacked up with multilayer restricted Boltzmann machines and a layer of associative memory to detect newly unknown malware. The proposed deep learning model performs as a greedy layer-wise training operation for unsupervised feature learning, followed by supervised parameter fine-tuning. Different from the existing works which only made use of the files with class labels (either malicious or benign) during the training phase, we utilize both labeled and unlabeled file samples to pre-train multiple layers in the heterogeneous deep learning framework from bottom to up for feature learning. A comprehensive experimental study on a real and large file collection from Comodo Cloud Security Center is performed to compare various malware detection approaches. Promising experimental results demonstrate that our proposed deep learning framework can further improve the overall performance in malware detection compared with traditional shallow learning methods, deep learning methods with homogeneous framework, and other existing anti-malware scanners. The proposed heterogeneous deep learning framework can also be readily applied to other malware detection tasks.
Logic programming as a service

New generations of distributed systems are opening novel perspectives for logic programming (LP): on the one hand, service-oriented architectures represent nowadays the standard approach for distributed systems engineering; on the other hand, pervasive systems mandate for situated intelligence. In this paper we introduce the notion of Logic Programming as a Service (LPaaS) as a means to address the needs of pervasive intelligent systems through logic engines exploited as a distributed service. First we define the abstract architectural model by re-interpreting classical LP notions in the new context; then we elaborate on the nature of LP interpreted as a service by describing the basic LPaaS interface. Finally, we show how LPaaS works in practice by discussing its implementation in terms of distributed tuProlog engines, accounting for basic issues such as interoperability and configurability.
Brushless Three-Phase Synchronous Generator Under Rotating Diode Failure Conditions

In brushless excitation systems, the rotating diodes can experience open- or short-circuits. For a three-phase synchronous generator under no-load, we present theoretical development of effects of diode failures on machine output voltage. Thereby, we expect the spectral response faced with each fault condition, and we propose an original algorithm for state monitoring of rotating diodes. Moreover, given experimental observations of the spectral behavior of stray flux, we propose an alternative technique. Laboratory tests have proven the effectiveness of the proposed methods for detection of fault diodes, even when the generator has been fully loaded. However, their ability to distinguish between cases of diodes interrupted and short-circuited, has been limited to the no-load condition, and certain loads of specific natures.
Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries.

In this paper, we focus on three aspects: (1) to annotate a set of standard corpus in Chinese discharge summaries; (2) to perform word segmentation and named entity recognition in the above corpus; (3) to build a joint model that performs word segmentation and named entity recognition.



Two independent systems of word segmentation and named entity recognition were built based on conditional random field models. In the field of natural language processing, while most approaches use a single model to predict outputs, many works have proved that performance of many tasks can be improved by exploiting combined techniques. Therefore, in this paper, we proposed a joint model using dual decomposition to perform both the two tasks in order to exploit correlations between the two tasks. Three sets of features were designed to demonstrate the advantage of the joint model we proposed, compared with independent models, incremental models and a joint model trained on combined labels.



Micro-averaged precision (P), recall (R), and F-measure (F) were used to evaluate results.



The gold standard corpus is created using 336 Chinese discharge summaries of 71 355 words. The framework using dual decomposition achieved 0.2% improvement for segmentation and 1% improvement for recognition, compared with each of the two tasks alone.



The joint model is efficient and effective in both segmentation and recognition compared with the two individual tasks. The model achieved encouraging results, demonstrating the feasibility of the two tasks.
A 0.97–1.53-GHz Tunable Four-Pole Bandpass Filter With Four Transmission Zeroes

In this letter, a miniature 0.97–1.53-GHz tunable four-pole bandpass filter with constant fractional bandwidth is demonstrated. The filter consists of three quarter-wavelength resonators and one half-wavelength resonator. By introducing cross-coupling, two transmission zeroes are generated and are located at both sides of the passband. Also, source–load coupling is employed to produce two extra transmission zeroes, resulting in a miniature (<inline-formula> <tex-math notation="LaTeX">$0.09\lambda _{{\text {g}}}\times 0.1\lambda _{{\text {g}}}$ </tex-math></inline-formula>) four-pole, four-transmission zero filter with high selectivity. The measured results show a tuning range of 0.97–1.53 GHz with an insertion loss of 4.2–2 dB and 1-dB fractional bandwidth of 5.5%. The four transmission zeroes change with the passband synchronously, ensuring high selectivity over a wide tuning range. The application areas are in software-defined radios in high-interference environments.
AI-Based Software Defect Predictors: Applications and Benefits in a Case Study

the later stages of the development life cycle versus during production (Brooks 1995). Therefore, testing is one of the most critical and time-consuming phases of the software development life cycle, which accounts for 50 percent of the total cost of development (Brooks 1995). The testing phase should be planned carefully in order to save time and effort while detecting as many defects as possible. Different verification, validation, and testing strategies have been proposed so far to optimize the time and effort utilized during the testing phase: code reviews (Adrian, Branstad, and Cherniavsky 1982; Shull et al. 2002), inspections (Fagan 1976), and automated tools (Menzies, Greenwald, and Frank 2007; Nagappan, Ball, and Murphy 2006; Ostrand, Weyuker, and Bell 2005). Defect predictors improve the efficiency of the testing phase in addition to helping developers assess the quality and defectproneness of their software product (Fenton and Neil 1999). They also help managers in allocating resources. Most defect prediction models combine well-known methodologies and algorithms such as statistical techniques (Nagappan, Ball, and Murphy 2006; Ostrand, Weyuker, and Bell 2005; Zimmermann et al. 2004) and machine learning (Munson and Khoshgoftaar 1992; Fenton and Neil 1999; Lessmann et al. 2008; Moser, Pedrycz, and Succi 2008). They require historical data in terms of software metrics and actual defect rates, and combine these metrics and defect information as training data to learn which modules seem to be defect prone. Based on the knowledge from training data and software metrics acquired from a recently completed project, such tools can estimate defect-prone modules of that project.
Parallel implementation of the TestU01 statistical test suite

As the need of high quality random number generators is constantly increasing especially for cryptographic algorithms, the development of high throughput randomness generators has to be combined with the development of high performance statistical test suites. Unfortunately the implementations of the most popular batteries of test suites are not focused on efficiency and high performance, do not benefit of the processing power offered by today's multi-core processors and tend to become bottlenecks in the processing of large volumes of data generated by various random number generators. Hence there is a stringent need for providing highly efficient statistical tests and our research efforts and results on improving and parallelizing the TestU01 test suite intend to fill this need. Experimental results show that the parallel version of TestU01 takes full advantage of the system's available processing power, reducing the execution time up to 4 times on the tested multicore systems.
Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks

Excitatory control of inhibitory neurons is poorly understood due to the difficulty of studying synaptic connectivity in vivo. We inferred such connectivity through analysis of spike timing and validated this inference using juxtacellular and optogenetic control of presynaptic spikes in behaving mice. We observed that neighboring CA1 neurons had stronger connections and that superficial pyramidal cells projected more to deep interneurons. Connection probability and strength were skewed, with a minority of highly connected hubs. Divergent presynaptic connections led to synchrony between interneurons. Synchrony of convergent presynaptic inputs boosted postsynaptic drive. Presynaptic firing frequency was read out by postsynaptic neurons through short-term depression and facilitation, with individual pyramidal cells and interneurons displaying a diversity of spike transmission filters. Additionally, spike transmission was strongly modulated by prior spike timing of the postsynaptic cell. These results bridge anatomical structure with physiological function.
A Comprehensive Literature Review of File Carving

File carving is a recovery technique allowing file recovery without knowledge about contextual information such as file system metadata. Due to recent advancements in research, file carving has become an essential technique for both general data recovery and digital forensics investigations. During the last few years a considerable amount of publications has been published on the topic of file carving. Out of around 130 publications in this field we selected 70 key papers with major contributions to the topic in order to identify potential fields of future research activities. The first contribution of this paper is a survey on state-of-the-art literature supporting researchers and practitioners in gaining a comprehensive view on the progress in file carving research. In addition to that, the second major contribution of this paper is a (preliminary) file carving ontology. The purpose of the ontology presented within this paper is to push forward recovery approaches that are based on knowledge bases processible by computer systems.
MATLAB Implementation of Memristorbased Chua ’ s Circuit and its Chaos Control

Recently, there has been an extensive interest in research on the analysis and control of chaotic behaviour in nonlinear systems. Various control problems can be defined for such systems, such as targeting the trajectories to a desired point, stabilizing unstable periodic orbits etc. In this paper, necessity of Chaos control in Memristor based circuit is discussed. A MATLAB Simulink model is presented for a Memristor based Chua’s circuit & feedback control method is implemented for the purpose of Chaos control in the same model.
PLASMA: Portable Programming for SIMD Heterogeneous Accelerators

Data-parallel accelerators have emerged as highperformance alternatives to general-purpose processors for many applications. The Cell BE, GPUs from NVIDIA and ATI, and the like can outperform conventional superscalar architectures, but only for applications that can take advantage of these accelerators’ SIMD architectures, large number of cores, and local memories. Coupled with the SIMD extensions on general-purpose processors, these heterogeneous computing architectures provide a powerful platform to accelerate data-parallel programs. Unfortunately, each accelerator provides its own programming model, and programmers are often forced to confront issues of distributed memory, multithreading, load-balancing and computation scheduling. This necessitates a framework which can exploit different types of parallelism across heterogeneous functional units and supports multiple types of high-level programming languages including stream programming or traditional shared or distributed memory programming framework or prototyping languages such as MATLAB. Towards this goal, in this paper, we present PLASMA, a programming framework that enables the writing of portable SIMD programs. The main component of PLASMA is an intermediate representation (IR), which provides succinct and clean abstractions to enable programs to be compiled to different accelerators. With the assistance of a runtime, these programs can then be automatically multithreaded, run on multiple heterogeneous accelerators transparently and are oblivious of distributed memory. We demonstrate a prototype compiler and runtime that targets PLASMA programs to scalar processors, processors with SIMD extensions and GPUs.
Laparoscopic cholecystectomy, Calot's triangle, and variations in cystic arterial supply

The extrahepatic biliary tree with the exact anatomic features of the arterial supply observed by laparoscopic means has not been described heretofore. Iatrogenic injuries of the extrahepatic biliary tree and neighboring blood vessels are not rare. Accidents involving vessels or the common bile duct during laparoscopic cholecystectomy, with or without choledocotomy, can be avoided by careful dissection of Calot's triangle and the hepatoduodenal ligament.  We performed 244 laparoscopic cholecystectomies over a 2-year period between January 1, 1995 and January 1, 1997.  In 187 of 244 consecutive cases (76.6%), we found a typical arterial supply anteromedial to the cystic duct, near the sentinel cystic lymph node. In the other cases, there was an atypical arterial supply, and 27 of these cases (11.1%) had no cystic artery in Calot's triangle. A typical blood supply and accessory arteries were observed in 18 cases (7.4%).  Young surgeons who are not yet familiar with the handling of an anatomically abnormal cystic blood supply need to be more aware of the precise anatomy of the extrahepatic biliary tree.
GPS-based position control and waypoint navigation system for quadrocopters

Motivation for the investigation of position and waypoint controllers is the demand for Unattended Aerial Systems (UAS) capable of fulfilling e.g. surveillance tasks in contaminated or in inaccessible areas. Hence, this paper deals with the development of a 2D GPS-based position control system for 4 Rotor Helicopters able to keep positions above given destinations as well as to navigate between waypoints while minimizing trajectory errors. Additionally, the novel control system enables permanent full speed flight with reliable altitude keeping considering that the resulting lift is decreasing while changing pitch or roll angles for position control. In the following chapters the control procedure for position control and waypoint navigation is described. The dynamic behavior was simulated by means of Matlab/Simulink and results are shown. Further, the control strategies were implemented on a flight demonstrator for validation, experimental results are provided and a comparison is discussed.
Exploring consumer adoption of mobile payments - A qualitative study

This paper examines consumer adoption of a new electronic payment service, mobile payments. The empirical data for the explorative study was collected by establishing six focus group sessions. The results suggest that the relative advantages of mobile payments include time and place independence, availability, possibilities for remote purchases, and queue avoidance. The interviewees found mobile payments to be mostly compatible with digital content and service purchases and to complement small value cash payments. Interestingly, the findings suggest that the relative advantages of mobile payments depend on certain situational factors such as lack of other payment methods or urgency. There are, however, several barriers to the adoption of mobile payments, including premium pricing of the payments, complexity of payment procedures, a lack of widespread merchant acceptance, and perceived risks.
Theoretical study on dynamical planar-chirality switching in checkerboard-like metasurfaces

In this paper, we show that the handedness of a planar chiral checkerboard-like metasurface can be dynamically switched by modulating the local sheet impedance of the metasurface structure. We propose a metasurface design to realize the handedness switching and theoretically analyze its electromagnetic characteristic based on Babinet’s principle. Numerical simulations of the proposed metasurface are performed to validate the theoretical analysis. It is demonstrated that the polarity of asymmetric transmission for circularly polarized waves, which is determined by the planar chirality of the metasurface, is inverted by switching the sheet impedance at the interconnection points of the checkerboard-like structure. The physical origin of the asymmetric transmission is also discussed in terms of the surface current and charge distributions on the metasurface.
PINK1, Parkin, and Mitochondrial Quality Control: What can we Learn about Parkinson’s Disease Pathobiology?

The first clinical description of Parkinson's disease (PD) will embrace its two century anniversary in 2017. For the past 30 years, mitochondrial dysfunction has been hypothesized to play a central role in the pathobiology of this devastating neurodegenerative disease. The identifications of mutations in genes encoding PINK1 (PTEN-induced kinase 1) and Parkin (E3 ubiquitin ligase) in familial PD and their functional association with mitochondrial quality control provided further support to this hypothesis. Recent research focused mainly on their key involvement in the clearance of damaged mitochondria, a process known as mitophagy. It has become evident that there are many other aspects of this complex regulated, multifaceted pathway that provides neuroprotection. As such, numerous additional factors that impact PINK1/Parkin have already been identified including genes involved in other forms of PD. A great pathogenic overlap amongst different forms of familial, environmental and even sporadic disease is emerging that potentially converges at the level of mitochondrial quality control. Tremendous efforts now seek to further detail the roles and exploit PINK1 and Parkin, their upstream regulators and downstream signaling pathways for future translation. This review summarizes the latest findings on PINK1/Parkin-directed mitochondrial quality control, its integration and cross-talk with other disease factors and pathways as well as the implications for idiopathic PD. In addition, we highlight novel avenues for the development of biomarkers and disease-modifying therapies that are based on a detailed understanding of the PINK1/Parkin pathway.