Reviewerly/exHarmony huggingface dataset:
Studying language in context using the temporal generalization method

The temporal generalization method (TGM) is a data analysis technique that can be used to test if the brain's representation for particular stimuli (e.g. sounds, images) is maintained, or if it changes as a function of time (King J-R, Dehaene S. 2014 Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci.18, 203-210. (doi:10.1016/j.tics.2014.01.002)). The TGM involves training models to predict the stimuli or condition using a time window from a recording of brain activity, and testing the resulting models at all possible time windows. This is repeated for all possible training windows to create a full matrix of accuracy for every combination of train/test window. The results of a TGM indicate when brain activity patterns are consistent (i.e. the trained model performs well even when tested on a different time window), and when they are inconsistent, allowing us to track neural representations over time. The TGM has been used to study the representation of images and sounds during a variety of tasks, but has been less readily applied to studies of language. Here, we give an overview of the method itself, discuss how the TGM has been used to analyse two studies of language in context and explore how the TGM could be applied to further our understanding of semantic composition. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.
Optimizing energy efficiency of CNN-based object detection with dynamic voltage and frequency scaling

Abstract On the one hand, accelerating convolution neural networks (CNNs) on FPGAs requires ever increasing high energy efficiency in the edge computing paradigm. On the other hand, unlike normal digital algorithms, CNNs maintain their high robustness even with limited timing errors. By taking advantage of this unique feature, we propose to use dynamic voltage and frequency scaling (DVFS) to further optimize the energy efficiency for CNNs. First, we have developed a DVFS framework on FPGAs. Second, we apply the DVFS to SkyNet, a state-of-the-art neural network targeting on object detection. Third, we analyze the impact of DVFS on CNNs in terms of performance, power, energy efficiency and accuracy. Compared to the state-of-the-art, experimental results show that we have achieved 38% improvement in energy efficiency without any loss in accuracy. Results also show that we can achieve 47% improvement in energy efficiency if we allow 0.11% relaxation in accuracy.
Fusion Kalman and Weighted UFIR State Estimator With Improved Accuracy

In this article, estimates of the Kalman filter (KF) and weighted unbiased finite impulse response (UFIR) filter are fused in discrete time-varying state-space to improve the robustness in uncertain environments associated with industrial applications. The weighted UFIR filter is derived using the Frobenius norm and termed as Frobenius finite impulse response (FFIR) filter. It is confirmed that the FFIR filter has better performance under the uncertainties and errors in the noise statistics, while the KF filter is best when the model and noise are exactly known. Based on a numerical example of a hover system, we show that the FFIR filter is able to outperform the UFIR filter and that the fusion KF/FFIR filter is able to outperform both of them. An experimental verification provided for the drone velocity estimation under the hover operation conditions has proved a better accuracy and robustness of the proposed fusion KF/FFIR filter.
Exploration of Subjective Color Perceptual-Ability by EEG-Induced Type-2 Fuzzy Classifiers

Perceptual-ability informally refers to the ability of a person to recognize a stimulus. This article deals with color perceptual-ability measurement of subjects using brain response to basic color (red, green, and blue) stimuli. It also attempts to determine the subjective ability to recognize the base colors in the presence of noise tolerance of the base colors, referred to as recognition tolerance. Because of intrasession and intersession variations in subjective brain signal features for a given color stimulus, there exists uncertainty in perceptual-ability. In addition, small variations in the color stimulus result in wide variations in brain signal features, introducing uncertainty in the perceptual-ability of the subject. Type-2 fuzzy logic has been employed to handle the uncertainty in color perceptual-ability measurements due to: 1) variations in brain signal features for a given color and 2) the presence of colored noise on the base colors. Because of the limited power of uncertainty management of the interval type-2 fuzzy sets and high computational overhead of its general type-2 counterpart, we develop a semi-general type-2 fuzzy classifier to recognize the base color. It is important to note that the proposed technique transforms a vertical slice-based general type-2 fuzzy set (GT2FS) into an equivalent interval type-2 counterpart to reduce the computational overhead, without losing the contributions of the secondary memberships. The proposed Semi-GT2FSs-induced classifier yields superior performance in classification accuracy with respect to existing type-1, type-2, and other well-known classifiers. The brain-understanding of a perceived base or noisy base colors is also obtained by exact low-resolution electromagnetic topographic analysis (e-LORETA) software. This is used as the reference for our experimental results of the semi-general type-2 classifier in color perceptual-ability detection. Statistical tests undertaken confirm the superiority of the proposed classifier over its competitors. The proposed technique is expected to have interesting applications in identifying people with excellent color perceptual-ability for chemical, pharmaceutical, and textile industries.
Efficient Hardware Implementations of Grain-128AEAD

We implement the Grain-128AEAD stream cipher in hardware, using a 65 nm library. By exploring different optimization techniques, both at RTL level but also during synthesis, we first target high throughput, then low power. We reach over 33 GB/s targeting a high-speed design, at expense of power and area. We also show that, when targeting low power, the design only requires 0.23 $${\upmu }$$ W running at 100 kHz. By unrolling the design, the energy consumed when encrypting a fixed length message decreases, making the 64 parallelized version the most energy efficient implementation, requiring only 11.2 nJ when encrypting a 64 kbit message. At the same time, the best throughput/power ratio is achieved at a parallelization of 4.
Proving expected sensitivity of probabilistic programs with randomized variable-dependent termination time

The notion of program sensitivity (aka Lipschitz continuity) specifies that changes in the program input result in proportional changes to the program output. For probabilistic programs the notion is naturally extended to expected sensitivity. A previous approach develops a relational program logic framework for proving expected sensitivity of probabilistic while loops, where the number of iterations is fixed and bounded. In this work, we consider probabilistic while loops where the number of iterations is not fixed, but randomized and depends on the initial input values. We present a sound approach for proving expected sensitivity of such programs. Our sound approach is martingale-based and can be automated through existing martingale-synthesis algorithms. Furthermore, our approach is compositional for sequential composition of while loops under a mild side condition. We demonstrate the effectiveness of our approach on several classical examples from Gambler's Ruin, stochastic hybrid systems and stochastic gradient descent. We also present experimental results showing that our automated approach can handle various probabilistic programs in the literature.
Multi-stage hydrothermal processes at the Laozuoshan gold deposit in NE China: Insights from textures and compositions of sulfide assemblages

The Laozuoshan gold deposit is a large gold deposit which experienced the early hydrothermal mineralization and the late hydrothermal mineralization in Jiamusi Massif, Northeast China. The Jiamusi Massif hosts numerous gold deposits that are commonly considered as products of the metamorphic hydrothermal process during two oceanic subductions episodes. In this study, we report detailed textures observations, identify the sulfides phases by the laser Raman spectroscopy, and obtain in situ composition data with electron probe micro analysis to further understand the formation and metasomatism of multi-stage sulfides deposition in the Laozuoshan gold deposit. The paragenetic relationship between pyrite and marcasite at the nanoscale was also clarified by focused ion beam scanning electron microscopy and transmission electron microscopy. According to the observed textural relationships among ore and gangue minerals, the early hydrothermal mineralization period can be divided into three stages (Stage I to Stage III), and the late hydrothermal mineralization period into four stages (Stage IV to Stage VII). The sulfides are mainly pyrrhotite (Po-1) in Stage I, with minor chalcopyrite, arsenopyrite, and pyrite. The Ag-rich (averaging 15.44 wt%) electrum (Au-1) is hosted in arsenopyrite, which is also rich in cobalt (averaging 2.69 wt%). Pyrite (Py-2) is the main sulfide in Stage II, with minor chalcedony and calcite. The mineral stability relationships in both Stage I and Stage II confirm that there was an increase in sulfur and oxygen fugacities and a decrease in temperature. The sulfides are mainly arsenopyrite (Apy-2) in Stage III, with minor native golds and tellurbismuth. The native golds (Au-2) average 8.02 wt% silver. Arsenopyrite geothermometers indicate precipitation temperatures between 420 and 390 °C for Stage III. The sulfur and oxygen fugacities of the ore-related fluids were below the pyrite-pyrrhotite buffer during Stage III, revealing a reduced fluids system. In the late hydrothermal mineralization period, multiple magmatic activities caused multi-stage hydrothermal fluids events. In Stage VI, the pyrrhotite replaced by marcasite and pyrite through the dissolution-reprecipitation mechanism, indicating the increase in pH and oxygen fugacity of the fluids, and alteration from weakly acidic and weakly reduced conditions to neutral and weakly oxidizing conditions. The mineralogical and compositional evolution of the sulfides in the Laozuoshan gold deposit constrain the physicochemical conditions of multi-stage ore-forming fluids, which is of considerable significance in understanding the metamorphic-hydrothermal superimposed gold-bearing fluids evolved in the Jiamusi Massif.
Adapting to an Emerging Social Media Landscape: The Rise of Informalization of Company Communication in Tourism

This study investigates the evolvement of informalization of company communication on social media over time, based on actual social media data from the tourism industry. The development in the use of emoticons and emoji by companies is examined, as an expression of informalization and humanization of online company communication. We selected 33 companies from the tourism industry in The Netherlands and investigated their Facebook and Twitter messages supplemented with the messages of consumers who interacted with these companies, for the period 2011–2016. Results show that the use of emoticons and emoji in online company communication increased significantly over the period covered in this study, demonstrating a higher level of informalization of company communication. Since this is a key factor for improving relational outcomes, this finding has scholarly as well as managerial relevance. We discuss the implications of the results for the presence of organizations on social media.
Scalability Analysis of Blockchain on a Serverless Cloud

While adopting Blockchain technologies to automate their enterprise functionality, organizations are recognizing the challenges of scalability and manual configuration that the state of art present. Scalability of Hyperledger Fabric is an open challenge recognized by the research community. We have automated many of the configuration steps of installing Hyperledger Fabric Blockchain on AWS infrastructure and have benchmarked the scalability of that system. We have used the UCR (University of California Riverside) Time Series Archive with 128 timeseries datasets containing over 191,177 rows of data totaling 76,453,742 numbers. Using an automated Serverless approach, we have loaded this dataset, by chunks, into different AWS instances, triggering the load by SQS messaging. In this paper, we present the results of this benchmarking study and describe the approach we took to automate the Hyperledger Fabric processes using serverless Lambda functions and SQS triggering. We will also discuss what is needed to make the Blockchain technology more robust and scalable.
Partial Recovery of Erdős-Rényi Graph Alignment via k-Core Alignment

We determine information theoretic conditions under which it is possible to partially recover the alignment used to generate a pair of sparse, correlated Erdos-Renyi graphs. To prove our achievability result, we introduce the k-core alignment estimator. This estimator searches for an alignment in which the intersection of the correlated graphs using this alignment has a minimum degree of k. We prove a matching converse bound. As the number of vertices grows, recovery of the alignment for a fraction of the vertices tending to one is possible when the average degree of the intersection of the graph pair tends to infinity. It was previously known that exact alignment is possible when this average degree grows faster than the logarithm of the number of vertices.
Swing-In-Place (SIP): A Less Fatigue Walking-in-Place Method With Side-Viewing Functionality for Mobile Virtual Reality

The concept of mobile Virtual Reality (VR) headset that utilize mobile smartphone as the main display and processing device has successfully achieved a low-cost but yet feasible method to implement VR for a mobile user. However, locomotion in mobile VR is still a challenge because the ways to interact with the smartphone are limited. Walking-in-place (WIP) can be used as a hands-free input method to control locomotion inside a mobile VR environment, at the same time, enhance user experience by increasing the sense of immersion. Commonly, WIP implementation in mobile VR uses the inertia sensors of smartphone to detect WIP, thus no additional apparatus is needed. However, common WIP implementation navigates based on user's gaze direction, but in reality, we may walk and look into different direction at the same time. Besides that, fatigue caused by WIP is another important issue to be considered for prolongs usage of VR application. So, we present a less fatigue WIP gesture, swing-in-place (SIP) with a WIP implementation that allows users to travel and look in different directions during locomotion. Our implementation used only the accelerometer and gyroscope in single smartphone. Evaluation results show that SIP able to provide a similar feeling of immersion and it is less fatigue when compared with a common WIP method used in mobile VR. The side-viewing functionality is liked by the users.
Dementia Scale Classification Based on Ubiquitous Daily Activity and Interaction Sensing

This paper investigates the integration of different approaches to automatically predict high/low-score on the dementia scale. We propose two different approaches to predict this value by capturing the following: (1) the participant's interaction behavior with a humanoid robot and (2) the indoor daily activity in the residence using ubiquitous sensors. The interaction and indoor activity data set were obtained by recording 32 participants living in common residences, including 17 with symptoms of dementia, as indicated through a cognitive test (Revised Hasegawa Dementia Scale). To obtain the interaction features, we extracted the turn-taking features of interaction with a mobile-typed humanoid robot. To extract the indoor activity features, we collected the location data of each participant in the residence using the received signal strength indicators (RSSIs) of Bluetooth signals from different access points (e.g., shared spaces or the participant's room). In the experimental evaluation, we trained binary classification models for classifying the score on the dementia scale from these datasets. The results show that the best classification accuracy (0.875) is achieved when interaction and activity features are fused using a random forest classifier.
A cross-region transfer learning method for classification of community service cases with small datasets

The precise classification of community service cases is the most fundamental aspect of intelligent community service systems. However, data imbalance makes it challenging to achieve the desired level of precise classification. Existing transfer learning methods use open Internet knowledge for identifying case features and mining potential feature relations. However, community service cases have the characteristics such as short text length and non-public content, which restrict the transfer learning modes. In this paper, a cross-region transfer learning method is proposed to solve the classification problem of cases with small datasets in data imbalance situation while considering the perspective of regional cooperation. First, an ontology modeling method is applied to standardize the case features, reducing the effect of semantic ambiguity on transfer results. Secondly, to improve the effectiveness of source domain classification, this paper utilizes an extended marginal fisher analysis where the distance is measured by the inner product between data. Next, the mapping matrix from target domain to source domain is learned through domain adaptation. Finally, the method is verified based on the empirical data from Lanzhou and Beidaihe in China. Experimental results on classifiers show the proposed method helps regions to improve case classification rates significantly through knowledge complementation. The proposed approach can be followed to build case-based community service systems of reasonable accuracy and limited sample sizes.
Prostate MR Image Segmentation With Self-Attention Adversarial Training Based on Wasserstein Distance

Prostate diseases are very common in men. Accurate segmentation of the prostate plays a significant role in further clinical treatment and diagnosis. There have been some methods that combine the segmentation network and generative adversarial network, using the adversarial training to boost the performance of segmentation network. However, the traditional adversarial training is unstable, which is hard to train. This attribute can easily lead to training failure. In this paper, we propose a segmentation network with self-attention adversarial training based on Wasserstein distance to tackle the problem. First, a segmentation network with residual connection and attention mechanism is deployed to generate the prostate segmentation prediction. Then, a self-attention discriminator network is added to the segmentation network to discriminate the prediction from ground truth. In the discriminator network, we replace the cross-entropy loss function with Wasserstein distance loss function which is better to measure the difference between distributions. The comparative experiments suggest our method is more stable than traditional adversarial training and achieves state-of-the-art performance.
Strong Password Generation Based On User Inputs

Every person using different online services is concerned with the security and privacy for protecting individual information from the intruders. Many authentication systems are available for the protection of individuals' data, and the password authentication system is one of them. Due to the increment of information sharing, internet popularization, electronic commerce transactions, and data transferring, both password security and authenticity have become an essential and necessary subject. But it is also mandatory to ensure the strength of the password. For that reason, all cyber experts recommend intricate password patterns. But most of the time, the users forget their passwords because of those complicated patterns. In this paper, we are proposing a unique algorithm that will generate a strong password, unlike other existing random password generators. This password will he based on the information, i.e. (some words and numbers) provided by the users so that they do not feel challenged to remember the password. We have tested our system through various experiments using synthetic input data. We also have checked our generator with four popular online password checkers to verify the strength of the produced passwords. Based on our experiments, the reliability of our generated passwords is entirely satisfactory. We also have examined that our generated passwords can defend against two password cracking attacks named the "Dictionary attack" and the "Brute Force attack". We have implemented our system in Python programming language. In the near future, we have a plan to extend our work by developing an online free to use user interface. The passwords generated by our system are not only user-friendly but also have achieved most of the qualities of being strong as well as non- crackable passwords.
Using Stochastic Gradient Decent Algorithm For Incremental Matrix Factorization In Recommendation System

In recent years, with the development and daily update of user feedback data, the recommendation systems require algorithms able to process data accurately and fast. To tackle this data stream problem, online model updates for new data points come to be available. Another issue is how to evaluate algorithms in a streaming data environment while conventional Collaborative Filtering algorithms are proposed for stationary data. Furthermore, traditional evaluation methodologies are only useful in offline experiments. In this research, we propose a novel incremental model base on stochastic gradient decent algorithm for Matrix Factorization. In addition, we also show a prequential evaluation protocol for recommender systems, applicable for streaming data environments. Comparing with other state-of-the-art models, our algorithm has combative accuracy, while being significantly faster.
Comparative study of AR versus video tutorials for minor maintenance operations

Augmented Reality (AR) has become a mainstream technology in the development of solutions for repair and maintenance operations. Although most of the AR solutions are still limited to specific contexts in industry, some consumer electronics companies have started to offer pre-packaged AR solutions as alternative to video-based tutorials (VT) for minor maintenance operations. In this paper, we present a comparative study of the acquired knowledge and user perception achieved with AR and VT solutions in some maintenance tasks of IT equipment. The results indicate that both systems help users to acquire knowledge in various aspects of equipment maintenance. Although no statistically significant differences were found between AR and VT solutions, users scored higher on the AR version in all cases. Moreover, the users explicitly preferred the AR version when evaluating three different usability and satisfaction criteria. For the AR version, a strong and significant correlation was found between the satisfaction and the achieved knowledge. Since the AR solution achieved similar learning results with higher usability scores than the video-based tutorials, these results suggest that AR solutions are the most effective approach to substitute the typical paper-based instructions in consumer electronics.