nils-herrmann/clean_scirepeval huggingface dataset:
Quantifying and Testing Indirect Effects in Simple Mediation Models When the Constituent Paths Are Nonlinear.

Most treatments of indirect effects and mediation in the statistical methods literature and the corresponding methods used by behavioral scientists have assumed linear relationships between variables in the causal system. Here we describe and extend a method first introduced by Stolzenberg (1980) for estimating indirect effects in models of mediators and outcomes that are nonlinear functions but linear in their parameters. We introduce the concept of the instantaneous indirect effect of X on Y through M and illustrate its computation and describe a bootstrapping procedure for inference. Mplus code as well as SPSS and SAS macros are provided to facilitate the adoption of this approach and ease the computational burden on the researcher.
Modelling Interaction of Sentence Pair with Coupled-LSTMs

Recently, there is rising interest in modelling the interactions of two sentences with deep neural networks. However, most of the existing methods encode two sequences with separate encoders, in which a sentence is encoded with little or no information from the other sentence. In this paper, we propose a deep architecture to model the strong interaction of sentence pair with two coupled-LSTMs. Specifically, we introduce two coupled ways to model the interdependences of two LSTMs, coupling the local contextualized interactions of two sentences. We then aggregate these interactions and use a dynamic pooling to select the most informative features. Experiments on two very large datasets demonstrate the efficacy of our proposed architecture and its superiority to state-ofthe-art methods.
Input Warping for Bayesian Optimization of Non-stationary Functions

Bayesian optimization has proven to be a highly effective methodology for the global optimization of unknown, expensive and multimodal functions. The ability to accurately model distributions over functions is critical to the effectiveness of Bayesian optimization. Although Gaussian processes provide a flexible prior over functions, there are various classes of functions that remain difficult to model. One of the most frequently occurring of these is the class of non-stationary functions. The optimization of the hyperparameters of machine learning algorithms is a problem domain in which parameters are often manually transformed a priori, for example by optimizing in “log-space,” to mitigate the effects of spatially-varying length scale. We develop a methodology for automatically learning a wide family of bijective transformations or warpings of the input space using the Beta cumulative distribution function. We further extend the warping framework to multi-task Bayesian optimization so that multiple tasks can be warped into a jointly stationary space. On a set of challenging benchmark optimization tasks, we observe that the inclusion of warping greatly improves on the state-of-the-art, producing better results faster and more reliably.
Methodological rigour within a qualitative framework.

This paper discusses the literature on establishing rigour in research studies. It describes the methodological trinity of reliability, validity and generalization and explores some of the issues relating to establishing rigour in naturalistic inquiry.



Those working within the naturalistic paradigm have questioned the issue of using validity, reliability and generalizability to demonstrate robustness of qualitative research. Triangulation has been used to demonstrate confirmability and completeness and has been one means of ensuring acceptability across paradigms. Emerging criteria such as goodness and trustworthiness can be used to evaluate the robustness of naturalistic inquiry.



It is argued that the transference of terms across paradigms is inappropriate; however, if we reject the concepts of validity and reliability, we reject the concept of rigour. Rejection of rigour undermines acceptance of qualitative research as a systematic process that can contribute to the advancement of knowledge. Emerging criteria for demonstrating robustness in qualitative inquiry, such as authenticity, trustworthiness and goodness, need to be considered. Goodness, when not seen as a separate construct but as an integral and embedded component of the research process, should be useful in assuring quality of the entire study. Triangulation is a tried and tested means of offering completeness, particularly in mixed-method research. When multiple types of triangulation are used appropriately as the 'triangulation state of mind', they approach the concept of crystallization, which allows for infinite variety of angles of approach.



Qualitative researchers need to be explicit about how and why they choose specific legitimizing criteria in ensuring the robustness of their inquiries. A shift from a position of fundamentalism to a more pluralistic approach as a means of legitimizing naturalistic inquiry is advocated.
A Hierarchical Thread Scheduler and Register File for Energy-Efficient Throughput Processors

Modern graphics processing units (GPUs) employ a large number of hardware threads to hide both function unit and memory access latency. Extreme multithreading requires a complex thread scheduler as well as a large register file, which is expensive to access both in terms of energy and latency. We present two complementary techniques for reducing energy on massively-threaded processors such as GPUs. First, we investigate a two-level thread scheduler that maintains a small set of active threads to hide ALU and local memory access latency and a larger set of pending threads to hide main memory latency. Reducing the number of threads that the scheduler must consider each cycle improves the scheduler’s energy efficiency. Second, we propose replacing the monolithic register file found on modern designs with a hierarchical register file. We explore various trade-offs for the hierarchy including the number of levels in the hierarchy and the number of entries at each level. We consider both a hardware-managed caching scheme and a software-managed scheme, where the compiler is responsible for orchestrating all data movement within the register file hierarchy. Combined with a hierarchical register file, our two-level thread scheduler provides a further reduction in energy by only allocating entries in the upper levels of the register file hierarchy for active threads. Averaging across a variety of real world graphics and compute workloads, the active thread count can be reduced by a factor of 4 with minimal impact on performance and our most efficient three-level software-managed register file hierarchy reduces register file energy by 54%.
Analysis of hyperbolic and circular positioning algorithms using stationary signal-strength-difference measurements in wireless communications

Referring received signal strength (RSS) to a signal propagation model to find user location is one of the most promising strategies in wireless communications. This paper develops a simple method based on relative signal-strength measurements, that is, the differences in stationary signal strength measured at the user location from multiple base transceiver stations (BTSs). The stationary signal strength is the averaged RSS and also is a stationary Gaussian process. In using this method, it is vitally important to confirm that some uncertain propagation parameters can be canceled out while a signal propagation model is merged into our method for locating users. In this way, the differences in stationary signal strength lead to two solutions: One is the distance difference between pairs of BTSs, and the other is the distances from the user location to the multiple BTSs. Consequently, the hyperbolic positioning algorithm due to the distance-difference solution and the circular positioning algorithm due to the distances solution can be presented, respectively. Afterward, some experimental results were drawn from a field trial in a real propagation environment. Results show that the hyperbolic and circular positioning algorithms can locate the user to within about 350 and 300 m in 67 percentile, respectively. Compared with the numerical result reported in the literature on existing methods based on RSS only, our method is superior. Despite the result not meeting Federal Communications Commission (FCC) requirements, this method proved to be sufficiently simple and efficient in terms of the computation at burden and network signaling load.
Complex-valued convolutional neural networks for real-valued image classification

In this paper, complex-valued convolutional neural networks are presented, by giving the full deduction of the gradient descent algorithm for training this type of networks. The performances of convolutional neural networks in the real-valued domain for image classification gave rise to the idea of extending them to the complex-valued domain, also. Real-valued image classification experiments done using the MNIST and CIFAR-10 datasets have shown an improvement in performance of complex-valued convolutional neural networks over their real-valued counterparts.
A New Auto Exposure System to Detect High Dynamic Range Conditions Using CMOS Technology

This paper proposes a new auto-exposure algorithm that can accurately detect high-contrast lighting conditions and improve the dynamic range of output images for a camera system. The proposed method calculates the difference between the mean value and the median value of the brightness level of captured images to estimate lighting conditions. After that, a multiple exposure mechanism is carried out to improve the details of output pictures. Simulation results show that the system works well with CMOS sensors used in mobile phones and surveillance cameras. Besides, the proposed algorithm is fast and simple and therefore can be fitted in most CMOS platforms that have limited capabilities.
A stream function solver for liquid simulations

This paper presents a liquid simulation technique that enforces the incompressibility condition using a stream function solve instead of a pressure projection. Previous methods have used stream function techniques for the simulation of detailed single-phase flows, but a formulation for liquid simulation has proved elusive in part due to the free surface boundary conditions. In this paper, we introduce a stream function approach to liquid simulations with novel boundary conditions for free surfaces, solid obstacles, and solid-fluid coupling.
 Although our approach increases the dimension of the linear system necessary to enforce incompressibility, it provides interesting and surprising benefits. First, the resulting flow is guaranteed to be divergence-free regardless of the accuracy of the solve. Second, our free-surface boundary conditions guarantee divergence-free motion even in the un-simulated air phase, which enables two-phase flow simulation by only computing a single phase. We implemented this method using a variant of FLIP simulation which only samples particles within a narrow band of the liquid surface, and we illustrate the effectiveness of our method for detailed two-phase flow simulations with complex boundaries, detailed bubble interactions, and two-way solid-fluid coupling.
In-situ vehicular antenna integration and design aspects for vehicle-to-vehicle communications

Vehicle-to-vehicle (V2V) communications aim to enhance driver safety and traffic efficiency by using the recently designated frequency bands in the 5.9 GHz range in Europe. Due to the time-frequency selective fading behavior of the vehicular communication channel, multi-antenna techniques can provide enhanced link conditions by means of diversity processing. This paper highlights the integration of a four-element (N =4) linear array antenna into the roof-top compartment of a vehicle to conduct Multiple-Input Multiple-Output (MIMO) high-resolution mobile-to-mobile channel measurements.
SimRank: a measure of structural-context similarity

The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects:" This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.
LoRa for the Internet of Things

New transceiver technologies have emerged which enable power efficient communication over very long distances. Examples of such Low-Power Wide-Area Network (LPWAN) technologies are LoRa, Sigfox and Weightless. A typical application scenario for these technologies is city wide meter reading collection where devices send readings at very low frequency over a long distance to a data concentrator (one-hop networks). We argue that these transceivers are potentially very useful to construct more generic Internet of Things (IoT) networks incorporating multi-hop bidirectional communication enabling sensing and actuation. Furthermore, these transceivers have interesting features not available with more traditional transceivers used for IoT networks which enable construction of novel protocol elements. In this paper we present a performance and capability analysis of a currently available LoRa transceiver. We describe its features and then demonstrate how such transceiver can be put to use efficiently in a wide-area application scenario. In particular we demonstrate how unique features such as concurrent non-destructive transmissions and carrier detection can be employed. Our deployment experiment demonstrates that 6 LoRa nodes can form a network covering 1.5 ha in a built up environment, achieving a potential lifetime of 2 year on 2 AA batteries and delivering data within 5 s and reliability of 80%.
Energy-Efficient Communication Protocol for Wireless Microsensor Networks

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adap tive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
Effort-aware just-in-time defect prediction: simple unsupervised models could be better than supervised models

Unsupervised models do not require the defect data to build the prediction models and hence incur a low building cost and gain a wide application range. Consequently, it would be more desirable for practitioners to apply unsupervised models in effort-aware just-in-time (JIT) defect prediction if they can predict defect-inducing changes well. However, little is currently known on their prediction effectiveness in this context. We aim to investigate the predictive power of simple unsupervised models in effort-aware JIT defect prediction, especially compared with the state-of-the-art supervised models in the recent literature. We first use the most commonly used change metrics to build simple unsupervised models. Then, we compare these unsupervised models with the state-of-the-art supervised models under cross-validation, time-wise-cross-validation, and across-project prediction settings to determine whether they are of practical value. The experimental results, from open-source software systems, show that many simple unsupervised models perform better than the state-of-the-art supervised models in effort-aware JIT defect prediction.
Better Word Representations with Recursive Neural Networks for Morphology

Vector-space word representations have been very successful in recent years at improving performance across a variety of NLP tasks. However, common to most existing work, words are regarded as independent entities without any explicit relationship among morphologically related words being modeled. As a result, rare and complex words are often poorly estimated, and all unknown words are represented in a rather crude way using only one or a few vectors. This paper addresses this shortcoming by proposing a novel model that is capable of building representations for morphologically complex words from their morphemes. We combine recursive neural networks (RNNs), where each morpheme is a basic unit, with neural language models (NLMs) to consider contextual information in learning morphologicallyaware word representations. Our learned models outperform existing word representations by a good margin on word similarity tasks across many datasets, including a new dataset we introduce focused on rare words to complement existing ones in an interesting way.
Predicting personality with social media

Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions; it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Facebook profile. We will describe the type of data collected, our methods of analysis, and the results of predicting personality traits through machine learning. We then discuss the implications this has for social media design, interface design, and broader domains.
A Metric for Distributions with Applications to Image Databases

Bombay, India We introduce a new distance between two distributions that we call the Earth Mover’s Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distribution into the other by moving “distribution mass” around. This is a special case of the transportation problem from linear optimization, for which efficient algorithms are available. The EMD also allows for partial matching. When used to compare distributions that have the same overall mass, the EMD is a true metric, and has easy-to-compute lower bounds. In this paper we focus on applications to image databases, especially color and texture. We use the EMD to exhibit the structure of color-distribution and texture spaces by means of MultiDimensional Scaling displays. We also propose a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search.
A myosignal-based powered exoskeleton system

Integrating humans and robotic machines into one system offers multiple opportunities for creating assistive technologies that can be used in biomedical, industrial, and aerospace applications. The scope of the present research is to study the integration of a human arm with a powered exoskeleton (orthotic device) and its experimental implementation in an elbow joint, naturally controlled by the human. The Human–Machine interface was set at the neuromuscular level, by using the neuromuscular signal (EMG) as the primary command signal for the exoskeleton system. The EMG signal along with the joint kinematics were fed into a myoprocessor (Hill-based muscle model) which in turn predicted the muscle moments on the elbow joint. The moment-based control system integrated myoprocessor moment prediction with feedback moments measured at the human arm/exoskeleton and external load/exoskeleton interfaces. The exoskeleton structure under study was a two-link, two-joint mechanism, corresponding to the arm limbs and joints, which was mechanically linked (worn) by the human operator. In the present setup the shoulder joint was kept fixed at given positions and the actuator was mounted on the exoskeleton elbow joint. The operator manipulated an external weight, located at the exoskeleton tip, while feeling a scaled-down version of the load. The remaining external load on the joint was carried by the exoskeleton actuator. Four indices of performance were used to define the quality of the human/machine integration and to evaluate the operational envelope of the system. Experimental tests have shown that synthesizing the processed EMG signals as command signals with the external-load/human-arm moment feedback, significantly improved the mechanical gain of the system, while maintaining natural human control of the system, relative to other control algorithms that used only position or contact forces. The results indicated the feasibility of an EMG-based power exoskeleton system as an integrated human–machine system using highlevel neurological signals.
Determining Optical Flow

Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantized rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.
Mobile device fingerprinting considered harmful for risk-based authentication

In this paper, we present a critical assessment of the use of device fingerprinting for risk-based authentication in a state-of-practice identity and access management system. Risk-based authentication automatically elevates the level of authentication whenever a particular risk threshold is exceeded. Contemporary identity and access management systems frequently leverage browser-based device fingerprints to recognize trusted devices of a certain individual. We analyzed the variability and the predictability of mobile device fingerprints. Our research shows that particularly for mobile devices the fingerprints carry a lot of similarity, even across models and brands, making them less reliable for risk assessment and step-up authentication.