nils-herrmann/clean_scirepeval huggingface dataset:
Among the Machines: Human-Bot Interaction on Social Q&A Websites

With the rise of social media and advancements in AI technology, human-bot interaction will soon be commonplace. In this paper we explore human-bot interaction in STACK OVERFLOW, a question and answer website for developers. For this purpose, we built a bot emulating an ordinary user answering questions concerning the resolution of git error messages. In a first run this bot impersonated a human, while in a second run the same bot revealed its machine identity. Despite being functionally identical, the two bot variants elicited quite different reactions.
Enhancing Automatic Wordnet Construction Using Word Embeddings

Researchers have shown that a wordnet for a new language, possibly resource-poor, can be constructed automatically by translating wordnets of resource-rich languages. The quality of these constructed wordnets is affected by the quality of the resources used such as dictionaries and translation methods in the construction process. Recent work shows that vector representation of words (word embeddings) can be used to discover related words in text. In this paper, we propose a method that performs such similarity computation using word embeddings to improve the quality of automatically constructed wordnets.
79GHz-band radar cross section measurement for pedestrian detection

This paper presents radar cross section (RCS) measurement for pedestrian detection in 79GHz-band radar system. For a human standing at 6.2 meters, the RCS distribution's median value is -11.1 dBsm and the 90 % of RCS fluctuation is between -20.7 dBsm and -4.8 dBsm. Other measurement results (human body poses beside front) are shown. And we calculated the coefficient values of the Weibull distribution fitting to the human body RCS distribution.
The influence of leadership styles on employees ’ job satisfaction in public sector organizations in Malaysia

Leadership is a process influence between leaders and subordinates where a leader attempts to influence the behaviour of subordinates to achieve the organizational goals. Organizational success in achieving its goals and objectives depends on the leaders of the organization and their leadership styles. By adopting the appropriate leadership styles, leaders can affect employee job satisfaction, commitment and productivity. Two hundred Malaysian executives working in public sectors voluntarily participated in this study. Two types of leadership styles, namely, transactional and transformational were found to have direct relationships with employees’ job satisfaction. The results showed that transformational leadership style has a stronger relationship with job satisfaction. This implies that transformational leadership is deemed suitable for managing government organizations. Implications of the findings were discussed further.
Earthquake detection through computationally efficient similarity search

Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection—identification of seismic events in continuous data—is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity that overcomes the disadvantages of existing detection methods. Our method, called Fingerprint And Similarity Thresholding (FAST), can analyze a week of continuous seismic waveform data in less than 2 hours, or 140 times faster than autocorrelation. FAST adapts a data mining algorithm, originally designed to identify similar audio clips within large databases; it first creates compact “fingerprints” of waveforms by extracting key discriminative features, then groups similar fingerprints together within a database to facilitate fast, scalable search for similar fingerprint pairs, and finally generates a list of earthquake detections. FAST detected most (21 of 24) cataloged earthquakes and 68 uncataloged earthquakes in 1 week of continuous data from a station located near the Calaveras Fault in central California, achieving detection performance comparable to that of autocorrelation, with some additional false detections. FAST is expected to realize its full potential when applied to extremely long duration data sets over a distributed network of seismic stations. The widespread application of FAST has the potential to aid in the discovery of unexpected seismic signals, improve seismic monitoring, and promote a greater understanding of a variety of earthquake processes.
MEMS IMU for AHRS applications

Northrop Grumman, LITEF is developing MEMS (micro-electro-mechanical systems) based Inertial Measurement Units (IMU) for future attitude and heading reference systems (AHRS) with a target accuracy of 5 deg/h for the gyroscopes and 2.5 mg for the accelerometers. Within the technology development phase, prototype single axis gyroscopes have been realized and extensively tested for effects including temperature, acoustic and vibration sensitivities. These devices employ micro-machined all-silicon gyroscope sensor chips processed with deep reactive ion etching (DRIE). Silicon fusion bonding ensures pressures smaller than 3middot10-2 mbar. Sophisticated analog electronics and digital signal processing condition the capacitive pick-off signals and realize full closed loop operation. The current results with overall bias error smaller than 2 deg/h to 5 deg/h, scale factor error <1200 ppm, measurement range >1000 deg/s and angular random walk <0.4 radic/vh indicate that stable production of 5 deg/h gyroscopes is realistic. The fabrication technology for capacitive, pendulous accelerometer chips is based on that used for the gyros with only an increase in the enclosed pressure to obtain overcritical damping. Pulse width modulation (PWM) within a digital control loop is used to realize closed loop operation. Accelerometer chips have been tested over temperature with a residual bias error <2.0 mg and a scale factor error <1400 ppm. These sensor chips have been integrated into an IMU whereby the power budget and size of the sensor electronics have been optimized. In this paper the salient features of the gyro and accelerometer designs are presented together with an overview of the IMU system architecture. Measurement results, with a focus on environmental characteristics and robustness, are included.
The Revised ARPANET Routing Metric

The ARPANET routing metric was revised in July 1987, resulting in substantial performance improvements, especially in terms of user delay and effective network capacity. These revisions only affect the individual link costs (or metrics) on which the PSN (packet switching node) bases its routing decisions. They do not affect the SPF (“shortest path first”) algorithm employed to compute routes (installed in May 1979). The previous link metric was packet delay averaged over a ten second interval, which performed effectively under light-to-moderate traffic conditions. However, in heavily loaded networks it led to routing instabilities and wasted link and processor bandwidth.
The revised metric constitutes a move away from the strict delay metric: it acts similar to a delay-based metric under lightly loads and to a capacity-based metric under heavy loads. It will not always result in shortest-delay paths. Since the delay metric produced shortest-delay paths only under conditions of light loading, the revised metric involves giving up the guarantee of shortest-delay paths under light traffic conditions for the sake of vastly improved performance under heavy traffic conditions.
Verb Physics: Relative Physical Knowledge of Actions and Objects

Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, “Tyler entered his house” implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.
A temperature compensated digitally trimmable on-chip IC oscillator with low voltage inhibit capability

The design of a temperature compensated IC on-chip oscillator and a low voltage detection circuitry sharing the bandgap reference is described. The circuit includes a new bandgap isolation strategy to reduce oscillator noise coupled through the current sources. The IC oscillator provides a selectable clock (11.6 MHz or 21.4 MHz) with digital trimming to minimize process variations. After fine-tuning the oscillator to the target frequency, the temperature compensated voltage and current references guarantees less than /spl plusmn/2.5% frequency variation from -40 to 125/spl deg/C, when operating from 3 V to 5 V of power supply. The low voltage detection circuit monitors the supply voltage applied to the system and generates the appropriate warning or even initiates a system shutdown before the in-circuit SoC presents malfunction. The module was implemented in a 0.5 /spl mu/m CMOS technology, occupies an area of 360 /spl times/ 530 /spl mu/m/sub 2/ and requires no external reference or components.
AN AUTOMATED VIRUS CLASSIFICATION SYSTEM

In recent years, significant interest has developed around automated malware classification methods and an industry-wide naming convention. However, in the anti-virus industry, virus naming is not a uniformly standardized process and only worsens with each new malware sample. Virus naming cannot be reliable unless the virus analyst can tell if a new sample is part of an existing family in a reasonable amount of time. Previous research in automatic classification has produced several interesting classification methods; however, to our knowledge, none of the methods can deal with an entire virus collection or produce meaningful results in a reasonable amount of time. In this paper, we introduce an innovative classification system that uses an average desktop machine. The classification system compares new and unknown samples with all existing malware, and within a few minutes, returns matches for that sample based on evolutionary behaviour of existing malware. Compared to previous methods, our method is independent of the malware class and language. We describe three approximate matching algorithms and evaluate their run time and storage space requirements. We also discuss how these methods are applied in several malware-handling tasks including sample clustering, outbreak detection, automatic virus naming, and phylogeny tree.
MVE - A Multi-View Reconstruction Environment

We present MVE, the Multi-View Environment. MVE is an end-to-end multi-view geometry reconstruction software which takes photos of a scene as input and produces a surface triangle mesh as result. The system covers a structure-from-motion algorithm, multi-view stereo reconstruction, generation of extremely dense point clouds, and reconstruction of surfaces from point clouds. In contrast to most image-based geometry reconstruction approaches, our system is focused on reconstruction of multi-scale scenes, an important aspect in many areas such as cultural heritage. It allows to reconstruct large datasets containing some detailed regions with much higher resolution than the rest of the scene. Our system provides a graphical user interface for structure-from-motion reconstruction, visual inspection of images, depth maps, and rendering of scenes and meshes.
A randomized, wait-list controlled clinical trial: the effect of a mindfulness meditation-based stress reduction program on mood and symptoms of stress in cancer outpatients.

The objective of this study was to assess the effects of participation in a mindfulness meditation-based stress reduction program on mood disturbance and symptoms of stress in cancer outpatients.



A randomized, wait-list controlled design was used. A convenience sample of eligible cancer patients enrolled after giving informed consent and were randomly assigned to either an immediate treatment condition or a wait-list control condition. Patients completed the Profile of Mood States and the Symptoms of Stress Inventory both before and after the intervention. The intervention consisted of a weekly meditation group lasting 1.5 hours for 7 weeks plus home meditation practice.



Ninety patients (mean age, 51 years) completed the study. The group was heterogeneous in type and stage of cancer. Patients' mean preintervention scores on dependent measures were equivalent between groups. After the intervention, patients in the treatment group had significantly lower scores on Total Mood Disturbance and subscales of Depression, Anxiety, Anger, and Confusion and more Vigor than control subjects. The treatment group also had fewer overall Symptoms of Stress; fewer Cardiopulmonary and Gastrointestinal symptoms; less Emotional Irritability, Depression, and Cognitive Disorganization; and fewer Habitual Patterns of stress. Overall reduction in Total Mood Disturbance was 65%, with a 31% reduction in Symptoms of Stress.



This program was effective in decreasing mood disturbance and stress symptoms in both male and female patients with a wide variety of cancer diagnoses, stages of illness, and ages. cancer, stress, mood, intervention, mindfulness.
An Unsupervised Neural Attention Model for Aspect Extraction

Aspect extraction is an important and challenging task in aspect-based sentiment analysis. Existing works tend to apply variants of topic models on this task. While fairly successful, these methods usually do not produce highly coherent aspects. In this paper, we present a novel neural approach with the aim of discovering coherent aspects. The model improves coherence by exploiting the distribution of word co-occurrences through the use of neural word embeddings. Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space. In addition, we use an attention mechanism to de-emphasize irrelevant words during training, further improving the coherence of aspects. Experimental results on real-life datasets demonstrate that our approach discovers more meaningful and coherent aspects, and substantially outperforms baseline methods on several evaluation tasks.
Variational Bi-LSTMs

Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in general) model sequences along the forward time direction. Bidirectional LSTMs (Bi-LSTMs) on the other hand model sequences along both forward and backward directions and are generally known to perform better at such tasks because they capture a richer representation of the data. In the training of Bi-LSTMs, the forward and backward paths are learned independently. We propose a variant of the Bi-LSTM architecture, which we call Variational Bi-LSTM, that creates a channel between the two paths (during training, but which may be omitted during inference); thus optimizing the two paths jointly. We arrive at this joint objective for our model by minimizing a variational lower bound of the joint likelihood of the data sequence. Our model acts as a regularizer and encourages the two networks to inform each other in making their respective predictions using distinct information. We perform ablation studies to better understand the different components of our model and evaluate the method on various benchmarks, showing state-of-the-art performance.
An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation

The multilevel capability of metal oxide resistive switching memory was explored for the potential use as a single-element electronic synapse device. TiN/HfOx/AlOx/ Pt resistive switching cells were fabricated. Multilevel resistance states were obtained by varying the programming voltage amplitudes during the pulse cycling. The cell conductance could be continuously increased or decreased from cycle to cycle, and about 105 endurance cycles were obtained. Nominal energy consumption per operation is in the subpicojoule range with a maximum measured value of 6 pJ. This low energy consumption is attractive for the large-scale hardware implementation of neuromorphic computing and brain simulation. The property of gradual resistance change by pulse amplitudes was exploited to demonstrate the spike-timing-dependent plasticity learning rule, suggesting that metal oxide memory can potentially be used as an electronic synapse device for the emerging neuromorphic computation system.
Waveform Inspired Models and the Harmonic Balance Emulator

The paper presents the power amplifier design. The introduction of a practical harmonic balance capability at the device measurement stage brings a number of advantages and challenges. Breaking down this traditional barrier means that the test-bench engineer needs to become more aware of the design process and requirements. The inverse is also true, as the measurement specifications for a harmonically tuned amplifier are a bit more complex than just the measurement of load-pull contours. We hope that the new level of integration between both will also result in better exchanges between both sides and go beyond showing either very accurate, highly tuned device models, or using the device model as the traditional scapegoat for unsuccessful PA designs. A nonlinear model and its quality can now be diagnosed through direct comparison of simulated and measured wave forms. The quality of a PA design can be verified by placing the device within the measurement system, practical harmonic balance emulator into the same impedance state in which it will operate in the actual realized design.
Wearable Photoplethysmographic Sensors — Past and Present

Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue

Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM.



A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity.



Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.
Deep Text Classification Can be Fooled

In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack. Specifically, confronted with different adversarial scenarios, the text items that are important for classification are identified by computing the cost gradients of the input (white-box attack) or generating a series of occluded test samples (blackbox attack). Based on these items, we design three perturbation strategies, namely insertion, modification, and removal, to generate adversarial samples. The experiment results show that the adversarial samples generated by our method can successfully fool both state-of-the-art character-level and wordlevel DNN-based text classifiers. The adversarial samples can be perturbed to any desirable classes without compromising their utilities. At the same time, the introduced perturbation is difficult to be perceived.
Particle filter theory and practice with positioning applications

The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear Bayesian filtering problem, and there is today a rather mature theory as well as a number of successful applications described in literature. This tutorial serves two purposes: to survey the part of the theory that is most important for applications and to survey a number of illustrative positioning applications from which conclusions relevant for the theory can be drawn. The theory part first surveys the nonlinear filtering problem and then describes the general PF algorithm in relation to classical solutions based on the extended Kalman filter (EKF) and the point mass filter (PMF). Tuning options, design alternatives, and user guidelines are described, and potential computational bottlenecks are identified and remedies suggested. Finally, the marginalized (or Rao-Blackwellized) PF is overviewed as a general framework for applying the PF to complex systems. The application part is more or less a stand-alone tutorial without equations that does not require any background knowledge in statistics or nonlinear filtering. It describes a number of related positioning applications where geographical information systems provide a nonlinear measurement and where it should be obvious that classical approaches based on Kalman filters (KFs) would have poor performance. All applications are based on real data and several of them come from real-time implementations. This part also provides complete code examples.