nils-herrmann/clean_scirepeval huggingface dataset:
Coupling Mechanism in Hybrid SIW–CPW Forward Couplers for Millimeter-Wave Substrate Integrated Circuits

Various publications address the suppression of the undesired mode coupling in laterally bounded conductor-backed coplanar waveguides (CPWs). In this paper, we present an integrated waveguide structure in which this coupling effect of different guided modes is deliberately exploited. Efficient forward coupling at arbitrary levels is achieved between a substrate integrated rectangular waveguide (SIW) and a CPW on its broad wall. Such hybrid integrated couplers combine the SIW's low-loss property with the CPW's flexibility and monolithic microwave integrated circuit compatibility. They are attractive for transitions, six-ports, antenna feeding networks, active antennas, and distributed phase shifters or amplifiers in millimeter-wave applications. The coupling mechanism is studied and a rapid design becomes feasible by use of the provided theory. Two couplers (a 3- and 0-dB version with 4.95- and 9.8-mm-long coupling sections) were designed and fabricated on alumina substrate for operation around 50 GHz. The 3-dB coupler achieves a wide relative bandwidth of 25% at 0.5-dB amplitude imbalance due to the asymmetric coupling structure, whereas the 0-dB coupler (transition) has a 7% relative bandwidth at less than -0.2-dB coupling loss. Results obtained from millimeter-wave probe measurements on realized prototypes agree well with our theory and simulations.
Deception and design: the impact of communication technology on lying behavior

Social psychology has demonstrated that lying is an important, and frequent, part of everyday social interactions. As communication technologies become more ubiquitous in our daily interactions, an important question for developers is to determine how the design of these technologies affects lying behavior. The present research reports the results of a diary study, in which participants recorded all of their social interactions and lies for seven days. The data reveal that participants lied most on the telephone and least in email, and that lying rates in face-to-face and instant messaging interactions were approximately equal. This pattern of results suggests that the design features of communication technologies (e.g., synchronicity, recordability, and copresence) affect lying behavior in important ways, and that these features must be considered by both designers and users when issues of deception and trust arise. The implications for designing applications that increase, decrease or detect deception are discussed.
Gait asymmetry in patients with Parkinson’s disease and elderly fallers: when does the bilateral coordination of gait require attention?

While it is known that certain pathologies may impact on left–right symmetry of gait, little is known about the mechanisms that contribute to gait symmetry or how high in the hierarchy of the control of gait symmetry is regulated in humans. To assess the contribution of cognitive function to gait symmetry, we measured gait asymmetry (GA) in three subject groups, patients with Parkinson’s disease (PD, n = 21), idiopathic elderly fallers (n = 15), and healthy elderly controls (n = 11). All subjects walked under two walking conditions: usual walking and dual tasking (cognitive loading) condition. For each subject, the swing time (SW) was calculated and averaged across strides for the left and right feet (SWL and SWR). GA was defined as: $$ 100 \times {\left| {\ln ({\text{SWR}}/{\text{SWL}})} \right|.} $$ For both the PD patients and the elderly fallers GA values were significantly higher during the usual walking condition, as compared with the control group (P < 0.01). In addition, for both the PD patients and the elderly fallers, GA significantly increased when they walked and performed a dual task, compared with the usual walking condition (P < 0.003). In contrast, dual tasking did not affect the GA of the healthy controls (P = 0.518). GA was associated with gait speed and gait variability, but no correlations were found between GA and the asymmetry of the classic PD motor symptoms. Thus, the results suggest that the ability to generate a steady, rhythmic walk with a bilaterally coordinated gait does not rely heavily on mental attention and cognitive resources in healthy older adults. In contrast, however, when gait becomes impaired and less automatic, GA apparently relies on cognitive input and attention.
Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO2 Memristive Spiking-Neuron

Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1010–1011 neurons is still of great challenge. Recently, phase change memristor neuron has been proposed to realize a human-brain level neural network operating at a high speed while consuming a small amount of power and having a high integration density. Although memristor neuron can be scaled down to nanometer, integration of 1010–1011 neurons still faces many problems in circuit complexity, chip area, power consumption, etc. In this work, we propose a CMOS compatible HfO2 memristor neuron that can be well integrated with silicon circuits. A hybrid Convolutional Neural Network (CNN) based on the HfO2 memristor neuron is proposed and constructed. In the hybrid CNN, one memristive neuron can behave as multiple physical neurons based on the Time Division Multiplexing Access (TDMA) technique. Handwritten digit recognition is demonstrated in the hybrid CNN with a memristive neuron acting as 784 physical neurons. This work paves the way towards substantially shrinking the amount of neurons required in hardware and realization of more complex or even human cerebral cortex level memristive neural networks.
Alignment for Advanced Machine Learning Systems

We survey eight research areas organized around one question: As learning systems become increasingly intelligent and autonomous, what design principles can best ensure that their behavior is aligned with the interests of the operators? We focus on two major technical obstacles to AI alignment: the challenge of specifying the right kind of objective functions, and the challenge of designing AI systems that avoid unintended consequences and undesirable behavior even in cases where the objective function does not line up perfectly with the intentions of the designers. Open problems surveyed in this research proposal include: How can we train reinforcement learners to take actions that are more amenable to meaningful assessment by intelligent overseers? What kinds of objective functions incentivize a system to “not have an overly large impact” or “not have many side effects”? We discuss these questions, related work, and potential directions for future research, with the goal of highlighting relevant research topics in machine learning that appear tractable today.
Incremental learning for ν-Support Vector Regression

The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ν-Support Vector Classification (ν-SVC) (Schölkopf et al., 2000), ν-SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ν-SVC algorithm (AONSVM) to ν-SVR will not generate an effective initial solution. It is the main challenge to design an incremental ν-SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ν-SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ν-SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ν-SVR algorithms with both cold and warm starts.
BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system

In the last years, the concept of smart buildings has been proposed and proved to be an effective solution to tackle the problem of reducing the power consumption of complex (both residential and commercial) buildings, while providing the users with a very high level of comfort. In this context, knowing the exact position of users inside the buildings has been identified as a needed feature to optimize the behavior of the building itself. Recently, using the occupants mobile devices as sensors has been validated as an effective solution to have accurate occupancy detection systems, even if no energy efficient solution in therm of battery consumption has been found so far. On the contrary, with this work, we present BLUE-SENTINEL, an accurate and power efficient method to identify the occupants of each room of a smart building using mobile devices as source of information. The proposed approach faces the occupancy detection problem with a good accuracy by exploiting iBeacon, a very recent low-power technology proposed by Apple. In particular, since the iBeacon protocol is built upon Bluetooth Low Energy (BLE), it represents a very highly power-efficient solution. In addition to this, the iBeacon technology is characterized by a good level of compatibility and portability, supporting both iOS- and Android-based devices. The proposed approach has been validated in a real environment with a prototype system released as open source showing how this technology is suitable for the occupancy detection in a smart building.
A hybrid neuro-evolutive algorithm for neural network optimization

This paper proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes, moreover has the ability to reuse the genotypes and automatically build modular, hierarchical and recurrent neural networks. A genetic algorithm (GA) evolves a Lindenmayer System that is used to design the neural network's architecture. This basic neural codification confers scalability and search space reduction in relation to other methods. Furthermore, the system uses a parallel genome scan engine that increases both the implicit parallelism and convergence of the GA. The fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. The NEA was tested on five real-world classification datasets and three well-known datasets for time series forecasting (TSF). The results are statistically compared against established state-of-the-art algorithms and various forecasting methods (ADANN, ARIMA, UCM, and Forecast Pro). In most cases, our NEA outperformed the other methods, delivering the most accurate classification and time series forecasting with the least computational effort. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process.
KenLM: Faster and Smaller Language Model Queries

We present KenLM, a library that implements two data structures for efficient language model queries, reducing both time and memory costs. The PROBING data structure uses linear probing hash tables and is designed for speed. Compared with the widelyused SRILM, our PROBING model is 2.4 times as fast while using 57% of the memory. The TRIE data structure is a trie with bit-level packing, sorted records, interpolation search, and optional quantization aimed at lower memory consumption. TRIE simultaneously uses less memory than the smallest lossless baseline and less CPU than the fastest baseline. Our code is open-source1, thread-safe, and integrated into the Moses, cdec, and Joshua translation systems. This paper describes the several performance techniques used and presents benchmarks against alternative implementations.
Xface: MPEG-4 based open source toolkit for 3D Facial Animation

In this paper, we present our open source, platform independent toolkit for developing 3D talking agents, namely Xface. It relies on MPEG-4 Face Animation (FA) standard. The toolkit currently incorporates three pieces of software. The core Xface library is for developers who want to embed 3D facial animation to their software as well as researchers who want to focus on related topics without the hassle of implementing a full framework from scratch. XfaceEd editor provides an easy to use interface to generate MPEG-4 ready meshes from static 3D models. Last, XfacePlayer is a sample application that demonstrates the toolkit in action. All the pieces are implemented in C++ programming language and rely on only operating system independent libraries. The main design principles for Xface are ease of use and extendibility.
Knowledge Discovery in Biomedical Data Facilitated by Domain Ontologies

In some real-world areas, it is important to enrich the data with external background knowledge so as to provide context and to facilitate pattern recognition. These areas may be described as data rich but knowledge poor. There are two challenges to incorporate this biological knowledge into the data mining cycle: (1) generating the ontologies; and (2) adapting the data mining algorithms to make use of the ontologies. This chapter presents the state-of-the-art in bringing the background ontology knowledge into the pattern recognition task for biomedical data.
Measurement of the elastic properties and intrinsic strength of monolayer graphene.

We measured the elastic properties and intrinsic breaking strength of free-standing monolayer graphene membranes by nanoindentation in an atomic force microscope. The force-displacement behavior is interpreted within a framework of nonlinear elastic stress-strain response, and yields second- and third-order elastic stiffnesses of 340 newtons per meter (N m(-1)) and -690 Nm(-1), respectively. The breaking strength is 42 N m(-1) and represents the intrinsic strength of a defect-free sheet. These quantities correspond to a Young's modulus of E = 1.0 terapascals, third-order elastic stiffness of D = -2.0 terapascals, and intrinsic strength of sigma(int) = 130 gigapascals for bulk graphite. These experiments establish graphene as the strongest material ever measured, and show that atomically perfect nanoscale materials can be mechanically tested to deformations well beyond the linear regime.
Markov chain Monte Carlo method and its application

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Automated regression testing of BPMN 2.0 processes: a capture and replay framework for continuous delivery

Regression testing is a form of software quality assurance (QA) that involves comparing the behavior of a newer version of a software artifact to its earlier correct behavior, and signaling the QA engineer when deviations are detected. Given the large potential in automated generation and execution of regression test cases for business process models in the context of running systems, powerful tools are required to make this practically feasible, more specifically to limit the potential impact on production systems, and to reduce the manual effort required from QA engineers. 
 In this paper, we present a regression testing automation framework that implements the capture & replay paradigm in the context of BPMN 2.0, a domain-specific language for modeling and executing business processes. The framework employs parallelization techniques and efficient communication patterns to reduce the performance overhead of capturing. Based on inputs from the QA engineer, it manipulates the BPMN2 model before executing tests for isolating the latter from external dependencies (e.g. human actors or expensive web services) and for avoiding undesired side-effects. Finally, it performs a regression detection algorithm and reports the results to the QA engineer. 
 We have implemented our framework on top of a BPMN2-compliant execution engine, namely jBPM, and performed functional validations and evaluations of its performance and fault-tolerance. The results, indicating 3.9% average capturing performance overhead, demonstrate that the implemented framework can be the foundation of a practical regression testing tool for BPMN 2.0, and a key enabler for continuous delivery of business process-driven applications and services.
Build watson: An overview of DeepQA for the Jeopardy! Challenge

Computer systems that can directly and accurately answer peoples' questions over a broad domain of human knowledge have been envisioned by scientists and writers since the advent of computers themselves. Open domain question answering holds tremendous promise for facilitating informed decision making over vast volumes of natural language content. Applications in business intelligence, healthcare, customer support, enterprise knowledge management, social computing, science and government would all benefit from deep language processing. The DeepQA project (www.ibm.com/deepqa) is aimed at illustrating how the advancement and integration of Natural Language Processing (NLP), Information Retrieval (IR), Machine Learning (ML), massively parallel computation and Knowledge Representation and Reasoning (KR&R) can greatly advance open-domain automatic Question Answering. An exciting proof-point in this challenge is to develop a computer system that can successfully compete against top human players at the Jeopardy! quiz show (www.jeopardy.com). Attaining champion-level performance Jeopardy! requires a computer to rapidly answer rich open-domain questions, and to predict its own performance on any given category/question. The system must deliver high degrees of precision and confidence over a very broad range of knowledge and natural language content and with a 3-second response time. To do this DeepQA generates, evidences and evaluates many competing hypotheses. A key to success is automatically learning and combining accurate confidences across an array of complex algorithms and over different dimensions of evidence. Accurate confidences are needed to know when to "buzz in" against your competitors and how much to bet. Critical for winning at Jeopardy!, High precision and accurate confidence computations are just as critical for providing real value in business settings where helping users focus on the right content sooner and with greater confidence can make all the difference. The need for speed and high precision demands a massively parallel compute platform capable of generating, evaluating and combing 1000's of hypotheses and their associated evidence. In this talk I will introduce the audience to the Jeopardy! Challenge and describe our technical approach and our progress on this grand-challenge problem.
Solving University Scheduling Problem with a Memetic Algorithm

Received May 31, 2012 Revised Jun 8, 2012 Accepted Jun 15, 2012 Scheduling problem is one of the Non-deterministic Polynomial (NP) problems. This means that using a normal algorithm to solve NP problems is so time-consuming a process (it may take months or even years with available equipment), and thus such an algorithm is regarded as an impracticable way of dealing with NP problems. The m thod of Memetic Algorithm presented in this paper is different from other available algorithms. In this algorithm the problem of a univ ersity class Scheduling is solved through applying a new chromosome structure, modifying the normal genetic methods and adding a local search, which is la med to considerably improve the solution. We included the teacher, clas s and course information with their maximal constraints in the proposed algo rithm, and it produced an optimized scheduling table for a weekly program of the university after creating the initial population of chromosomes and running genetic operators. The results of the study show a high eff ici ncy for the proposed algorithm compared with other algorithms considerin g maximum Constraints.  Keyword:
Analysis and comparison of MIMO radar waveforms

Choosing a proper waveform is a critical task for the implementation of multiple-input multiple-output (MIMO) radars. In addition to the general requirements for radar waveforms such as good resolution, low sidelobes, etc, MIMO radar waveforms also should possess good orthogonality. In this paper we give a brief overview of MIMO radar waveforms, which are classified into four categories: (1) time division multiple access (TDMA), (2) frequency division multiple access (FDMA), (3) Doppler division multiple access (DDMA), and (4) code division multiple access (CDMA). A special circulating MIMO waveform is also addressed The properties as well as application limitations of different waveforms are analyzed and compared. Some simulations results are also presented to illustrate the respective performance of different waveforms.
A Hybrid Approach for Arabic Text Summarization Using Domain Knowledge and Genetic Algorithms

Text summarization is the process of producing a shorter version of a specific text. Automatic summarization techniques have been applied to various domains such as medical, political, news, and legal domains proving that adapting domain-relevant features could improve the summarization performance. Despite the existence of plenty of research work in the domain-based summarization in English and other languages, there is a lack of such work in Arabic due to the shortage of existing knowledge bases. In this paper, a hybrid, single-document text summarization approach (abbreviated as (ASDKGA)) is presented. The approach incorporates domain knowledge, statistical features, and genetic algorithms to extract important points of Arabic political documents. The ASDKGA approach is tested on two corpora KALIMAT corpus and Essex Arabic Summaries Corpus (EASC). The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) framework was used to compare the automatically generated summaries by the ASDKGA approach with summaries generated by humans. Also, the approach is compared against three other Arabic text summarization approaches. The (ASDKGA) approach demonstrated promising results when summarizing Arabic political documents with average F-measure of 0.605 at the compression ratio of 40%.