nils-herrmann/clean_scirepeval huggingface dataset:
Cuff-less, non-invasive and continuous blood pressure monitoring using indirect methods

Cuff-based methods for measuring blood pressure (BP) have shown some limitations particularly when continuous measurement is required. Moreover, the inflation of the cuff is one of the main disturbing factors as expressed among patients. In this study, a promising approach based on changes in pulse transit time (PTT) for continuous, non-invasive and indirect measurement of BP is proposed to overcome the shortcomings of cuff-based methods. The PTT can be measured using plethysmography (PPG) waveform and the QRS peak of electrocardiogram (ECG) signal and indirectly interpreted as systolic BP. Time synchronization of the two sensors plays an important role in improving the accuracy of measurement. Therefore, it is aimed to employ a tablet-based system for collecting the wireless devices' data at a specific time. It is also our objective to include a triggering mechanism option using ambient sensors in order to start the process of data collection. Bluetooth/IEEE 802.15.4 wireless technologies will be used for real-time sensing and time synchronization. We have found that the proposed indirect measurement of BP can provide continuous information of the BP in a more convenient way from the patients' point of view. Moreover, the result of our indirect measurement of BP indicates that there are some short term fluctuations in the BP.
Employees' Intended Information Security Behaviour in Real Estate Organisations: a Protection Motivation Perspective

Due to the amount of identifiable customer personal, financial and other information stored by real estate organisations in their information systems, the threats are real. Challenges to secure the organisational (and customer) data are compounded by the nature of the industry (e.g. the core business and employees’ qualifications are non-security-related). To investigate the factors that influence real estate employees’ intended information security behaviour, we propose a research model based on Protection Motivation Theory (PMT) where we also include previous incidents as constituting threat appraisal components. Our findings from a survey of 105 real estate business employees in Australia reveal that perceived vulnerability, perceived severity, previous incidents, and response efficacy have a positive impact on real estate employees’ information security behavioural intention whereas self-efficacy does not. Our study also determines that response cost has a negative significant effect on intended information security behaviour.
Deep Big Multilayer Perceptrons for Digit Recognition

The competitive MNIST handwritten digit recognition benchmark has a long history of broken records since 1998. The most recent advancement by others dates back 8 years (error rate 0.4%). Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the MNIST handwritten digits benchmark with a single MLP and 0.31% with a committee of seven MLP. All we need to achieve this until 2011 best result are many hidden layers, many neurons per layer, numerous deformed training images to avoid overfitting, and graphics cards to greatly speed up learning.
Learning Rare Word Representations using Semantic Bridging

We propose a methodology that adapts graph embedding techniques (DeepWalk (Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as crosslingual vector space mapping approaches (Least Squares and Canonical Correlation Analysis) in order to merge the corpus and ontological sources of lexical knowledge. We also perform comparative analysis of the used algorithms in order to identify the best combination for the proposed system. We then apply this to the task of enhancing the coverage of an existing word embedding’s vocabulary with rare and unseen words. We show that our technique can provide considerable extra coverage (over 99%), leading to consistent performance gain (around 10% absolute gain is achieved with W2V-GN-500K cf.§3.3) on the Rare Word Similarity dataset.
The cultural construction of self-enhancement: an examination of group-serving biases.

Self-serving biases, found routinely in Western samples, have not been observed in Asian samples. Yet given the orientation toward individualism and collectivism in these 2 cultures, respectively, it is imperative to examine whether parallel differences emerge when the target of evaluation is the group. It may be that Asians show a group-serving bias parallel to the Western self-serving bias. In 2 studies, group-serving biases were compared across European Canadian, Asian Canadian, and Japanese students. Study 1 revealed that Japanese students evaluated a family member less positively than did both groups of Canadian students. Study 2 replicated this pattern with students' evaluations of their universities. The data suggest that cultural differences in enhancement biases are robust, generalizing to individuals' evaluations of their groups.
Systems of Systems Engineering: Basic Concepts, Model-Based Techniques, and Research Directions

The term “System of Systems” (SoS) has been used since the 1950s to describe systems that are composed of independent constituent systems, which act jointly towards a common goal through the synergism between them. Examples of SoS arise in areas such as power grid technology, transport, production, and military enterprises. SoS engineering is challenged by the independence, heterogeneity, evolution, and emergence properties found in SoS. This article focuses on the role of model-based techniques within the SoS engineering field. A review of existing attempts to define and classify SoS is used to identify several dimensions that characterise SoS applications. The SoS field is exemplified by a series of representative systems selected from the literature on SoS applications. Within the area of model-based techniques the survey specifically reviews the state of the art for SoS modelling, architectural description, simulation, verification, and testing. Finally, the identified dimensions of SoS characteristics are used to identify research challenges and future research areas of model-based SoS engineering.
Design, Prototyping, and Analysis of a Low Cost Axial-Flux Coreless Permanent-Magnet Generator

This paper presents the design, prototyping, and analysis of a relatively small and cheap axial-flux three-phase coreless permanent-magnet generator. The excitation of the machine is done by rectangular flat shaped neodymium-iron-boron magnets. A two-dimensional model of the machine is analyzed with finite-element software to obtain the machine parameters. One special feature of the constructed generator is in the design and prototyping of nonferromagnetic holders to counteract centrifugal forces acting on magnets, especially at high speeds. By implementing the nonferromagnetic holders, one can expect to construct high-speed axial-flux permanent-magnet generators at low cost.
An intelligent load shedding (ILS) system application in a large industrial facility

Conventional methods of system load shedding are too slow and do not effectively calculate the correct amount of load to be shed. This results in either excessive or insufficient load reduction. In recent years, load shedding systems have been repackaged using conventional under-frequency relay and/or breaker interlocks schemes integrated with programmable logic controllers to give a new look to an antiquated load preservation methodology. A truly modern and intelligent load shedding system with a computerized power management system should provide fast and optimal load management by utilizing system topology and actual operating conditions tempered with knowledge of past system disturbances. This paper demonstrates the need for a modern load shedding scheme and introduces the new technology of intelligent load shedding. Comparisons of intelligent load shedding with conventional load shedding methods are made from perspectives of system design, system engineering, project implementation, and system operation. A case study of the application of an intelligent load shedding scheme in a large industrial facility is provided.
Coupling Distributed and Symbolic Execution for Natural Language Queries

Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning. An executor for table querying typically requires multiple steps of execution because queries may have complicated structures. In previous studies, researchers have developed either fully distributed executors or symbolic executors for table querying. A distributed executor can be trained in an end-to-end fashion, but is weak in terms of execution efficiency and explicit interpretability. A symbolic executor is efficient in execution, but is very difficult to train especially at initial stages. In this paper, we propose to couple distributed and symbolic execution for natural language queries, where the symbolic executor is pretrained with the distributed executor’s intermediate execution results in a step-by-step fashion. Experiments show that our approach significantly outperforms both distributed and symbolic executors, exhibiting high accuracy, high learning efficiency, high execution efficiency, and high interpretability.
Wasserstein Adversarial Mixture Clustering

Clustering complex data is a key element of unsupervised learning which is still a challenging problem. In this work, we introduce a deep approach for unsupervised clustering based on a latent mixture living in a low-dimensional space. We achieve this clustering task through adversarial optimization of the Wasserstein distance between the real and generated data distributions. The proposed approach also allows both dimensionality reduction and model selection. We achieve competitive results on difficult datasets made of images, sparse and dense data.
Detecting source code similarity using code abstraction

Various approaches have been proposed to develop effective methods to measure program similarity. Even commercial tools and freeware tools are available for measuring program similarity based on source code comparison. These tools are quite useful to handle small to middle scale software products, but limited for large scale software products. In addition, these tools may report similarity measures with less credentials for the source code either obfuscated by malicious users or generated by automatic program template generation tools. To handle large scale software, more drastic measures should be provided. In this paper, we propose an automatic abstraction method to summarize source code. We eliminate a large portion of source code which is less relevant to similarity comparison. With this abstraction, our similarity comparison method can provide more robust measures for obfuscation and automatic code generation. We evaluate our abstraction method by running through source comparison tool --- MOSS, a web-based similarity detection tool. According to our experiment with multiple versions of Apache HTTP server, Putty SSH client, and Lighttpd server, our abstraction method reports quite reliable results with abstracted source code, which are only 23--35% of original source code. As the execution time for pattern match is linearly proportional to the length of the source code, our method can reduce the execution time as much as the percentage of source code reduction.
Content Based Image Retrieval Systems using SIFT : A Survey

this paper provides the novel information about Image Retrieval and Content based image retrieval (CBIR) system since it is now a big need of society. CBIR is a method for finding similar images from large image databases. Different approaches are used for Content based image retrieval, out of which Scale invariant feature transform is very popular. In this paper, we discuss the Industrial product development using SIFT Algorithm and results obtained in several experiments proposed to evaluate the application of Scale Invariant Feature Transform (SIFT) in content based Image Retrieval (CBIR) systems.  Content Based Image Retrieval (CBIR), Scale Invariant Feature Transform (SIFT)
Toward Open Set Recognition

To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of “closed set” recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is “open set” recognition, where incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. This paper explores the nature of open set recognition and formalizes its definition as a constrained minimization problem. The open set recognition problem is not well addressed by existing algorithms because it requires strong generalization. As a step toward a solution, we introduce a novel “1-vs-set machine,” which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. This methodology applies to several different applications in computer vision where open set recognition is a challenging problem, including object recognition and face verification. We consider both in this work, with large scale cross-dataset experiments performed over the Caltech 256 and ImageNet sets, as well as face matching experiments performed over the Labeled Faces in the Wild set. The experiments highlight the effectiveness of machines adapted for open set evaluation compared to existing 1-class and binary SVMs for the same tasks.
Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control.
Sentiment Classification Using Machine Learning Techniques with Syntax Features

Sentiment classification has adopted machine learning techniques to improve its precision and efficiency. However, the features are always produced by basic words-bag methods without much consideration for words' syntactic properties, which could play an important role in the judgment of sentiment meanings. To remedy this, we firstly generate syntax trees of the sentences, with the analysis of syntactic features of the sentences. Then we introduce multiple sentiment features into the basic words-bag features. Such features were trained on movie reviews as data, with machine learning methods (Naive Bayes and support vector machines). The features and factors introduced by syntax tree were examined to generate a more accurate solution for sentiment classification.
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning

Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses. We assessed three unique radiomics-based predictive models, each of which employed different fundamental design principles ranging from a pattern recognition method via deep-learning convolution neural network (DL-CNN), to a more deterministic radiomics feature-based approach and then a bridging method between the two, utilizing a system which extracts radiomics features from the image patterns. Our study indicates that the computerized assessment using radiomics information from the pre- and post-treatment CT of bladder cancer patients has the potential to assist in assessment of treatment response.
SDN-based distributed mobility management for 5G networks

Software-Defined Networking (SDN) is transforming the networking ecosystem. SDN allows network operators to easily and quickly introduce new services and flexibly adapt to their requirements, while simplifying the network management to reduce the cost of operation, maintenance and deployment. On the other hand, mobility is a key aspect for the future mobile networks. In this context, Distributed Mobility Management (DMM) has been recently introduced as a new trend to overcome the limitations of the today's mobility management protocols which are highly centralized and hierarchical. Driven from the fact that DMM and SDN share the same principle in which the data and control plane are decoupled, we propose a DMM solution based on SDN architecture called S-DMM. This solution offers a lot of advantages including no need to deploy any mobility-related component at the access router, independence of the underlying technologies, and per-flow mobility support. On one hand, the numerical results prove that S-DMM is more scalable than the legacy DMM. On the other hand, the experiment results from a real implementation show that S-DMM comes at no performance penalty (in terms of handover latency and end-to-end delay) compared to legacy DMM, yet at a slightly better management cost, which makes S-DMM a promising candidate for a mobility management solution in the context of 5G networks.
Unrealistic optimism about susceptibility to health problems

In this study, 100 college students compared their own chances of experiencing 45 different health- and life-threatening problems with the chances of their peers. They showed a significant optimistic bias for 34 of these hazards, consistently considering their own chances to be below average. Attempts to account for the amount of bias evoked by different hazards identified perceived controllability, lack of previous experience, and the belief that the problem appears during childhood as factors that tend to increase unrealistic optimism. The investigation also examined the importance of beliefs and emotions as determinants of self-reported interest in adopting precautions to reduce one's risk. It found that: (a) beliefs about risk likelihood, beliefs about risk severity, and worry about the risk all made independent contributions to interest in risk reduction; (b) unrealistic optimism undermined interest in risk reduction indirectly by decreasing worry; and (c) beliefs about risk likelihood and severity were not sufficient to explain the amount of worry expressed about different hazards.
Should I do that? using relational reinforcement learning and declarative programming to discover domain axioms

Robots assisting humans in complex domains need the ability to represent, reason with, and learn from, different descriptions of incomplete domain knowledge and uncertainty. This paper focuses on the challenge of incrementally and interactively discovering previously unknown axioms governing domain dynamics, and describes an architecture that integrates declarative programming and relational reinforcement learning to address this challenge. Answer Set Prolog (ASP), a declarative programming paradigm, is used to represent and reason with incomplete domain knowledge for planning and diagnostics. For any given goal, unexplained failure of plans created by ASP-based inference is taken to indicate the existence of unknown domain axioms. The task of discovering these axioms is formulated as a reinforcement learning problem, and a relational representation is used to incrementally generalize from specific axioms identified over time. These generic axioms are then added to the ASP-based representation for subsequent inference. The architecture's capabilities are demonstrated and evaluated in two domains, Blocks World and Robot Butler.
A 5GS/s voltage-to-time converter in 90nm CMOS

A voltage-to-time converter (VTC) is presented for use in a time-based analog-to-digital converter (ADC). The converter runs with a 5 GHz input clock to provide a maximum conversion rate of 5 GS/s. A novel architecture enables the VTC to provide an adjustable linear delay versus input voltage characteristic. The circuit is realized in a 90nm CMOS process. After calibration, the converter achieves better than 3.7 effective bits for input frequencies up to 1.75 GHz, making it suitable for use in a time-based ADC with up to 4-bit resolution.
Reynolds flocking in reality with fixed-wing robots: Communication range vs. maximum turning rate

The success of swarm behaviors often depends on the range at which robots can communicate and the speed at which they change their behavior. Challenges arise when the communication range is too small with respect to the dynamics of the robot, preventing interactions from lasting long enough to achieve coherent swarming. To alleviate this dependency, most swarm experiments done in laboratory environments rely on communication hardware that is relatively long range and wheeled robotic platforms that have omnidirectional motion. Instead, we focus on deploying a swarm of small fixed-wing flying robots. Such platforms have limited payload, resulting in the use of short-range communication hardware. Furthermore, they are required to maintain forward motion to avoid stalling and typically adopt low turn rates because of physical or energy constraints. The tradeoff between communication range and flight dynamics is exhaustively studied in simulation in the scope of Reynolds flocking and demonstrated with up to 10 robots in outdoor experiments.
Approaches to Interpreter Composition

In this paper, we compose six different Python and Prolog VMs into 4 pairwise compositions: one using C interpreters; one running on the JVM; one using meta-tracing interpreters; and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs.
Spacer-First Damascene-Gate FinFET Architecture Featuring Stringer-Free Integration

This letter presents a new Damascene-gate FinFET process that inherently suppresses stringers, resulting from gate and spacers patterning. The so-called spacer-first integration scheme relies on the engineering of a hydrogen silsesquioxane layer by electron beam lithography followed by two selective compartmentalized development steps to successively release the Damascene-gate cavity and the source/drain (S/D) contact regions. In contrast to the existing gate-first and gate-last integration approaches, the resulting FinFET process does not impose any restriction or interdependency on the sizing of the fins, gate, spacers, and S/D regions. A complete morphological and electrical validation is proposed in the particular case of wrap-around self-aligned metallic Schottky S/D contacts.