Reviewerly/exHarmony huggingface dataset:
Enactments and the design of trail running equipment: An example of carrying systems

Sports equipment brands have increasingly turned to experience-centered design, meaning the integration of users' activity into the design process. From an enactive perspective, this research investigated two entries of collecting and analyzing interactions between trail runners and their equipment. The paper articulates two studies. Study 1 analyzed traces of enactments on online forums and showed that trail runners reported the issues they enacted while running and reflexively posted the traces of their activity by highlighting the flaws in their carrying systems. Study 2 presents a field test protocol for assessing different carrying systems. The results showed four typical sequences of enactment that characterized the runners' activity. The outcomes of these two studies of runners' enactments while using equipment suggest a method that designers can appropriate to analyze experiential data, which can then be integrated into the conception process.
Goal-directed control in Pavlovian-instrumental transfer.

The current article concerns human outcome-selective Pavlovian-instrumental transfer (PIT), where Pavlovian cues selectively invigorate instrumental responses that predict common rewarding outcomes.Several recent experiments have observed PIT effects that were insensitive to outcome devaluation manipulations, which has been taken as evidence of an automatic "associative" mechanism.Other similar studies observed PIT effects that were sensitive to devaluation, which suggests a more controlled, goal-directed process.Studies supporting the automatic approach have been criticised for using a biased baseline, while studies supporting the goal-directed approach have been criticised for priming multiple outcomes at test.The current experiment addressed both of these issues.Participants first learned to perform two instrumental responses to earn two outcomes each (R1-O1/O3, R2-O2/O4), before four Pavlovian stimuli (S1-S4) were trained to predict each outcome.One outcome that was paired with each instrumental response (O3 and O4) was then devalued, so that baseline response choice at test would be balanced.Instrumental responding was then assessed in the presence of each individual Pavlovian stimulus, so that only one outcome was primed per trial.PIT effects were observed for the valued outcomes, ts > 3.99, ps < .001,but not for the devalued outcomes, F < 1, BF10 = 0.29.Hence, when baseline response choice was equated and only one outcome was primed per test trial, PIT was sensitive to outcome devaluation.The data therefore support goal-directed models of PIT.
True2F: Backdoor-Resistant Authentication Tokens

We present True2F, a system for second-factor authentication that provides the benefits of conventional authentication tokens in the face of phishing and software compromise, while also providing strong protection against token faults and backdoors.To do so, we develop new lightweight two-party protocols for generating cryptographic keys and ECDSA signatures, and we implement new privacy defenses to prevent cross-origin tokenfingerprinting attacks.To facilitate real-world deployment, our system is backwards-compatible with today's U2F-enabled web services and runs on commodity hardware tokens after a firmware modification.A True2F-protected authentication takes just 57ms to complete on the token, compared with 23ms for unprotected U2F.
Feature‐based recursive observer design for homography estimation and its application to image stabilization

Abstract This paper presents a new algorithm for online estimation of a sequence of homographies applicable to image sequences obtained from robotic vehicles equipped with vision sensors. The approach taken exploits the underlying Special Linear group structure of the set of homographies along with gyroscope measurements and direct point‐feature correspondences between images to develop temporal filter for the homography estimate. Theoretical analysis and experimental results are provided to demonstrate the robustness of the proposed algorithm. The experimental results show excellent performance and robustness even in the case of very fast camera motions (relative to frame rate) and severe occlusions.
Modelling equivalence classes of feature models with concept lattices to assist their extraction from product descriptions

Software product line engineering gathers a set of methods to help create, manage and maintain a collection of similar software systems. Variability modelling is a focal point of this paradigm, where feature models (FMs) are the prevalent notation. Migration from single system development to software product lines is a spreading topic in software engineering. To ease the migration, research has been done to automatically extract FMs from software descriptions, but most of these approaches are defined in a functional manner based on an ad-hoc variability analysis. In this paper, we propose a theoretical view on FM extraction from software descriptions based on Formal Concept Analysis (FCA). It is a structural framework for variability representation which allows to lay down theoretical foundation to variability extraction. We propose an original mapping between relationships expressed in FMs and the ones emphasised in FCA conceptual structures. We show that conceptual structures represent equivalence classes of FMs that steer the user choices during their synthesis, and propose a reverse engineering method based on them. We discuss its applicability and show that the combinatorial explosion of concept lattices can be avoided by the use of two sub-orders embodying the necessary information concerning variability.
Towards Real Time Multi-robot Routing using Quantum Computing Technologies

In this paper, we investigate the potential for current quantum computing technologies to provide good solutions to the NP-hard problem of routing multiple robots on a grid in real time. A hybrid quantum-classical approach has been presented in detail. Classical computation is used to generate candidate paths, while quantum annealing is used to select the optimal combination of paths. This second process is generally the most time consuming when performed clasically. The performance is benchmarked classically and on a D-Wave 2000Q with up to 200 robots and has shown that producing valid solutions for the problem of multi-robot routing is achievable with the current quantum annealing technology. The current limitations of using quantum annealing are also discussed.
SDR Implementation of a D2D Security Cryptographic Mechanism

Device-to-device (D2D) communication represents a promising technique to enable devices to communicate directly without the interaction of access points or base stations.The ad hoc and proximity nature of this communication introduce some very important security vulnerabilities.Key management, access control, privacy, secure routing, and transmission need dedicated signaling procedures and optimized implementation mechanisms that are appropriate for the mobile, low-energy, and low-processing power environment.This paper proposes a security mechanism for D2D communication involving the use of physically unclonable functions (PUF) for unique key generation, elliptic-curve cryptography (ECC) and Diffie-Hellman key exchange (DHKE) for key management, and Salsa20/20 as stream cyphering encryption method, suitable for confidentiality of the wireless transmissions.All these methods are implemented and tested on a software defined radio (SDR) communication platform consisting of a Zync-based system-onchip (SoC), complemented by radio frequency (RF) daughter boards from analog devices-an integration using hardware and software co-design.
Efficient phase unwrapping algorithm based on cubature information particle filter applied to unwrap noisy continuous phase maps

This paper presents a new phase unwrapping algorithm for wrapped phase fringes through combining a cubature information particle filter with an efficient local phase gradient estimator and an efficient quality-guided strategy based on heap-sort. The cubature information particle filter that not only is independent from noise statistics but also is not constrained by the nonlinearity of the model constructed is applied to retrieve unambiguous phase from modulus 2π wrapped fringe patterns through constructing a recursive cubature information particle filtering phase unwrapping procedure to perform simultaneously phase unwrapping and noise filtering for the first time to our knowledge, which can be expected to obtain more robust solutions from wrapped phase fringes. Phase gradient estimate is one of the key steps in almost all phase unwrapping algorithms and is directly related to the precision and the efficiency of phase unwrapping procedure. Accordingly, an efficient local phase gradient estimator that is more efficient than ones published previously is deduced to obtain phase gradient information required by the proposed algorithm, which can drastically decrease time consumption of unwrapping procedure and drastically improve the efficiency of the algorithm. The efficient quality-guided strategy based on heap-sort guarantees that the proposed algorithm efficiently unwraps wrapped pixels along the path from the high-reliance regions to the low-reliance regions of wrapped phase images. In addition, the accelerated version of the proposed algorithm is further developed through combing with reversible modulo wavelet operators to solve phase unwrapping problem of wrapped phase images in wavelet transform domain, which can reduce the amount of wrapped pixels that need to be unwrapped, and can further decrease time consumption of unwrapping procedure performing on wrapped phase images. This algorithm and its accelerated version under the frame of wavelet transform are demonstrated with various types of wrapped phase images, showing acceptable solutions.
Three-Dimensional Pose Estimation for Laboratory Mouse From Monocular Images

Video-based activity and behavior analysis of mice has garnered wide attention in biomedical research. Animal facilities hold large numbers of mice housed in “home-cages” densely stored within ventilated racks. Automated analysis of mice activity in their home-cages can provide a new set of sensitive measures for detecting abnormalities and time-resolved deviation from the baseline behavior. Large-scale monitoring in animal facilities requires minimal footprint hardware that integrates seamlessly with the ventilated racks. The compactness of hardware imposes the use of fisheye lenses positioned in close proximity to the cage. In this paper, we propose a systematic approach to accurately estimate the 3D pose of the mouse from single-monocular fisheye-distorted images. Our approach employs a novel adaptation of a structured forest algorithm. We benchmark our algorithm against existing methods. We demonstrate the utility of the pose estimates in predicting mouse behavior in a continuous video.
Improving the Sharpness of Digital Image Using an Amended Unsharp Mask Filter

Many of the existing imaging systems produce images with blurry appearance due to various existing limitations.Thus, a proper sharpening technique is usually used to increase the acutance of the obtained images.The unsharp mask filter is a well-known sharpening technique that is used to recover acceptable quality results from their blurry counterparts.However, this filter often introduces an overshoot effect, which is an undesirable effect that makes the recovered edges appear with visible white shades around them.In this article, an amended unsharp mask filter is developed to sharpen different digital images without introducing the overshoot effect.In the developed filter, the image is smoothed by using the traditional bilateral filter and then blurred using a modified Butterworth filter instead of blurring it with a Gaussian low-pass filter only as in the traditional unsharp mask filter.Using this approach allowed to eliminate the overshoot effect and to recover better quality results.The proposed filter is assessed by using two modern image quality assessment metrics, real and synthetic-blurred images, and is compared with three renowned image sharpening techniques.Various experiments and comparisons showed that the proposed filter produced promising results with both real and synthetic-blurred images.
Proximal Point Approximations Achieving a Convergence Rate of O(1/k) for Smooth Convex-Concave Saddle Point Problems: Optimistic Gradient and Extra-gradient Methods.

We study the iteration complexity of the optimistic gradient descent-ascent (OGDA) method and the extra-gradient (EG) method for finding a saddle point of a convex-concave unconstrained min-max problem. To do so, we first show that both OGDA and EG can be interpreted as approximate variants of the proximal point method. This is similar to the approach taken in [Nemirovski, 2004] which analyzes EG as an approximation of the `conceptual mirror prox'. In this paper, we highlight how gradients used in OGDA and EG try to approximate the gradient of the Proximal Point method. We then exploit this interpretation to show that both algorithms produce iterates that remain within a bounded set. We further show that the primal dual gap of the averaged iterates generated by both of these algorithms converge with a rate of $\mathcal{O}(1/k)$. Our theoretical analysis is of interest as it provides a the first convergence rate estimate for OGDA in the general convex-concave setting. Moreover, it provides a simple convergence analysis for the EG algorithm in terms of function value without using compactness assumption.
A Framework for Early Detection of Antisocial Behavior on Twitter Using Natural Language Processing

Online antisocial behavior is a social problem and a public health threat. A manifestation of such behavior may be fun for a perpetrator, however, can drive a victim into depression, self-confinement, low self-esteem, anxiety, anger, and suicidal ideation. Online platforms such as Twitter and Facebook can sometimes become breeding grounds for such behavior. These platforms may have measures in place to deter online antisocial behavior, however, such behavior still prevails. Most of the measures rely on users reporting to platforms for intervention. In this paper, we advocate a more proactive approach based on natural language processing and machine learning that can enable online platforms to actively look for signs of antisocial behavior and intervene before it gets out of control. By actively searching for such behavior, social media sites can possibly prevent dire situations that can lead to someone committing suicide.
SOS: Safe, Optimal and Small Strategies for Hybrid Markov Decision Processes

For hybrid Markov decision processes, UPPAAL Stratego can compute strategies that are safe for a given safety property and (in the limit) optimal for a given cost function. Unfortunately, these strategies cannot be exported easily since they are computed as a very long list. In this paper, we demonstrate methods to learn compact representations of the strategies in the form of decision trees. These decision trees are much smaller, more understandable, and can easily be exported as code that can be loaded into embedded systems. Despite the size compression and actual differences to the original strategy, we provide guarantees on both safety and optimality of the decision-tree strategy. On the top, we show how to obtain yet smaller representations, which are still guaranteed safe, but achieve a desired trade-off between size and optimality.
How Well Do Embedding Models Capture Non-compositionality? A View from Multiword Expressions

In this paper, we apply various embedding methods on multiword expressions to study how well they capture the nuances of non-compositional data. Our results from a pool of word-, character-, and document-level embbedings suggest that Word2vec performs the best, followed by FastText and Infersent. Moreover, we find that recently-proposed contextualised embedding models such as Bert and ELMo are not adept at handling non-compositionality in multiword expressions.
Fairwashing: the risk of rationalization

Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and generally complex -- produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques can be beneficial by providing interpretability, they can be used in a negative manner to perform fairwashing, which we define as promoting the false perception that a machine learning model respects some ethical values. In particular, we demonstrate that it is possible to systematically rationalize decisions taken by an unfair black-box model using the model explanation as well as the outcome explanation approaches with a given fairness metric. Our solution, LaundryML, is based on a regularized rule list enumeration algorithm whose objective is to search for fair rule lists approximating an unfair black-box model. We empirically evaluate our rationalization technique on black-box models trained on real-world datasets and show that one can obtain rule lists with high fidelity to the black-box model while being considerably less unfair at the same time.
HyperAdam: A Learnable Task-Adaptive Adam for Network Training

Deep neural networks are traditionally trained using humandesigned stochastic optimization algorithms, such as SGD and Adam. Recently, the approach of learning to optimize network parameters has emerged as a promising research topic. However, these learned black-box optimizers sometimes do not fully utilize the experience in human-designed optimizers, therefore have limitation in generalization ability. In this paper, a new optimizer, dubbed as HyperAdam, is proposed that combines the idea of “learning to optimize” and traditional Adam optimizer. Given a network for training, its parameter update in each iteration generated by HyperAdam is an adaptive combination of multiple updates generated by Adam with varying decay rates . The combination weights and decay rates in HyperAdam are adaptively learned depending on the task. HyperAdam is modeled as a recurrent neural network with AdamCell, WeightCell and StateCell. It is justified to be state-of-the-art for various network training, such as multilayer perceptron, CNN and LSTM.
Automating Analysis and Feedback to Improve Mathematics Teachers’ Classroom Discourse

Our work builds on advances in deep learning for natural language processing to automatically analyze transcribed classroom discourse and reliably generate information about teachers’ uses of specific discursive strategies called ”talk moves.” Talk moves can be used by both teachers and learners to construct conversations in which students share their thinking, actively consider the ideas of others, and engage in sustained reasoning. Currently, providing teachers with detailed feedback about the talk moves in their lessons requires highly trained observers to hand code transcripts of classroom recordings and analyze talk moves and/or one-on-one expert coaching, a time-consuming and expensive process that is unlikely to scale. We created a bidirectional long short-term memory (bi-LSTM) network that can automate the annotation process. We have demonstrated the feasibility of this deep learning approach to reliably identify a set of teacher talk moves at the sentence level with an F1 measure of 65%.
Multimineral Fingerprinting of Transhimalayan and Himalayan Sources of Indus-Derived Thal Desert Sand (Central Pakistan)

As a Quaternary repository of wind-reworked Indus River sand at the entry point in the Himalayan foreland basin, the Thal Desert in northern Pakistan stores mineralogical information useful to trace erosion patterns across the western Himalayan syntaxis and the adjacent orogenic segments that fed detritus into the Indus delta and huge deep-sea fan throughout the Neogene. Provenance analysis of Thal Desert sand was carried out by applying optical and semi-automated Raman spectroscopy on heavy-mineral suites of four eolian and 11 fluvial sand samples collected in selected tributaries draining one specific tectonic domain each in the upper Indus catchment. In each sample, the different types of amphibole, garnet, epidote and pyroxene grains—the four dominant heavy-mineral species in orogenic sediment worldwide—were characterized by SEM-EDS spectroscopy. The chemical composition of 4249 grains was thus determined. Heavy-mineral concentration, the relative proportion of heavy-mineral species, and their minerochemical fingerprints indicate that the Kohistan arc has played the principal role as a source, especially of pyroxene and epidote. Within the western Himalayan syntaxis undergoing rapid exhumation, the Southern Karakorum belt drained by the Hispar River and the Nanga Parbat massif were revealed as important sources of garnet, amphibole, and possibly epidote. Sediment supply from the Greater Himalaya, Lesser Himalaya, and Subhimalaya is dominant only for Punjab tributaries that join the Indus River downstream and do not contribute sand to the Thal Desert. The detailed compositional fingerprint of Thal Desert sand, if contrasted with that of lower course tributaries exclusively draining the Himalaya, provides a semi-actualistic key to be used, in conjunction with complementary provenance datasets and geological information, to reconstruct changes in paleodrainage and unravel the relationship between climatic and tectonic forces that controlled the erosional evolution of the western Himalayan-Karakorum orogen in space and time.
Text Similarity Measurement of Semantic Cognition Based on Word Vector Distance Decentralization With Clustering Analysis

Text similarity measurement, which is a basic task in natural language processing, is widely used in text information mining, news classification and clustering, artificial intelligence, and other fields. This paper proposes a text similarity measure method named word vector distance decentralization (WVDD) which can deal with complex semantic relations, including sentence components, word order and weights for Chinese language. Then, the clustering analysis is performed for the obtained similarity results. A K-means algorithm based on Spark architecture for parallel computing is adopted to accelerate clustering speed here. In experimental verification, the test sets are significant number of customer comments posted on the Jingdong website, which is a comprehensive online shopping mall. F-measure is used to evaluate the accuracy of the results obtained by the proposed method. The superiority of the proposed method is verified and compared with the sentence vector model (Doc2vec) and bag-of-words model. The proposed method can be applied to analyze network language, such as customers' comments online and web chat data.