Reviewerly/exHarmony huggingface dataset:
LCSS-Based Algorithm for Computing Multivariate Data Set Similarity: A Case Study of Real-Time WSN Data

Multivariate data sets are common in various application areas, such as wireless sensor networks (WSNs) and DNA analysis. A robust mechanism is required to compute their similarity indexes regardless of the environment and problem domain. This study describes the usefulness of a non-metric-based approach (i.e., longest common subsequence) in computing similarity indexes. Several non-metric-based algorithms are available in the literature, the most robust and reliable one is the dynamic programming-based technique. However, dynamic programming-based techniques are considered inefficient, particularly in the context of multivariate data sets. Furthermore, the classical approaches are not powerful enough in scenarios with multivariate data sets, sensor data or when the similarity indexes are extremely high or low. To address this issue, we propose an efficient algorithm to measure the similarity indexes of multivariate data sets using a non-metric-based methodology. The proposed algorithm performs exceptionally well on numerous multivariate data sets compared with the classical dynamic programming-based algorithms. The performance of the algorithms is evaluated on the basis of several benchmark data sets and a dynamic multivariate data set, which is obtained from a WSN deployed in the Ghulam Ishaq Khan (GIK) Institute of Engineering Sciences and Technology. Our evaluation suggests that the proposed algorithm can be approximately 39.9% more efficient than its counterparts for various data sets in terms of computational time.
Practical Deployability of Permissioned Blockchains

Ever since the evolution of cryptocurrencies, there has been profound interest in employing the underlying Blockchain technology for enterprise applications. Enterprises are keen on embracing the advantages of Blockchain in applications ranging from FinTech, Supply chain, IoT, Identity Management, Notary, Insurance and to many other domains. Blockchain is often spoken of as the third disruption after computers and the internet, and is being studied for application in several domains. A blockchain, as used in most cryptocurrencies, does not require any authorization for participants to join or leave the system, and hence is referred to as a permission-less blockchain. However, enterprise applications cannot operate in such models. Enterprise applications operate in a regulated, permissioned blockchain setting. This paper provides an industry focused insight into the practicality and feasibility of permissioned blockchains in real-world applications. In particular, we consolidate some non-trivial challenges that should be addressed in making the permissioned blockchain practically deployable in enterprises.
Scale-Aware Attention Network for Crowd Counting

In crowd counting datasets, people appear at different scales, depending on their distance to the camera. To address this issue, we propose a novel multi-branch scale-aware attention network that exploits the hierarchical structure of convolutional neural networks and generates, in a single forward pass, multi-scale density predictions from different layers of the architecture. To aggregate these maps into our final prediction, we present a new soft attention mechanism that learns a set of gating masks. Furthermore, we introduce a scale-aware loss function to regularize the training of different branches and guide them to specialize on a particular scale. As this new training requires ground-truth annotations for the size of each head, we also propose a simple, yet effective technique to estimate it automatically. Finally, we present an ablation study on each of these components and compare our approach against the literature on 4 crowd counting datasets: UCF-QNRF, ShanghaiTech A & B and UCF_CC_50. Without bells and whistles, our approach achieves state-of-the-art on all these datasets. We observe a remarkable improvement on the UCF-QNRF (25%) and a significant one on the others (around 10%).
Activation and superactivation of single-mode Gaussian quantum channels

Activation of quantum capacity is a surprising phenomenon according to which the quantum capacity of a certain channel may increase by combining it with another channel with zero quantum capacity. Superactivation describes an even more particular occurrence, in which both channels have zero quantum capacity, but their composition has a nonvanishing one. We investigate these effects for all single-mode phase-insensitive Gaussian channels, which include thermal attenuators and amplifiers, assisted by a two-mode positive-partial-transpose channel. Our result shows that activation phenomena are special but not uncommon. We can reveal superactivation in a broad range of thermal attenuator channels, even when the transmissivity is quite low, or the thermal noise is high. This means that we can transmit quantum information reliably through very noisy Gaussian channels with the help of another Gaussian channel, whose quantum capacity is also zero. We further show that no superactivation is possible for entanglement-breaking Gaussian channels in physically relevant circumstances by proving the nonactivation property of the coherent information of bosonic entanglement-breaking channels with finite input energy.
Overcoming Data Transfer Bottlenecks in DNN Accelerators via Layer-Conscious Memory Managment

Deep Neural Networks (DNNs) are rapidly evolving to satisfy the performance and accuracy requirements in many real world applications. The evolution renders DNNs more and more complex in terms of network topology, data sizes and layer types. Currently most state-of-the-art DNN accelerators adopt a uniform memory hierarchy (UMH) design methodology, which means that the data transferring of all convolutional and fully connected layers must go through the same memory levels. Unfortunately, for some layers, the performance is always bounded by off-chip memory transferring. It is caused by the saturating of data reuse happening in on-chip buffers, resulting in underutilization of on-chip memory. To address this issue, we propose a layer-conscious memory hierarchy (LCMH) methodology for DNN accelerators. LCMH could determine the memory levels of all the layers according to their requirements for off-chip memory bandwidth and on-chip buffer size for the data sources. As a result, the off-chip memory footprints of memory bounded layers could be avoided by keeping the data of them on chip. In addition, we provide architectural support for the accelerators equipped with LCMH. Experimental results show that designs with layer- conscious memory management could achieve up to 36% speedup compared with the designs wth UMH and 5% improvement over state-of-the-art designs.
Distributed Protocols for Leader Election

We do a game-theoretic analysis of leader election, under the assumption that each agent prefers to have some leader than no leader at all. We show that it is possible to obtain a fair Nash equilibrium, where each agent has an equal probability of being elected leader, in a completely connected network, in a bidirectional ring, and a unidirectional ring, in the synchronous setting. In the asynchronous setting, Nash equilibrium is not quite the right solution concept. Rather, we must consider ex post Nash equilibrium; this means that we have a Nash equilibrium no matter what a scheduling adversary does. We show that ex post Nash equilibrium is attainable in the asynchronous setting in all the networks we consider, using a protocol with bounded running time. However, in the asynchronous setting, we require that n &gt; 2. We show that we can get a fair ex post ϵ-Nash equilibrium if n =2 in the asynchronous setting under some cryptographic assumptions (specifically, the existence of a one-way functions), using a commitment protocol . We then generalize these results to a setting where we can have deviations by a coalition of size k . In this case, we can get what we call a fair k -resilient equilibrium in a completely connected network if n &gt; 2 k ; under the same cryptographic assumptions, we can a get a k -resilient equilibrium in a completely connected network, unidirectional ring, or bidirectional ring if n &gt; k . Finally, we show that under minimal assumptions, not only do our protocols give a Nash equilibrium, they also give a sequential equilibrium, so players even play optimally off the equilibrium path.
Fair sampling of ground-state configurations of binary optimization problems

Although many efficient heuristics have been developed to solve binary optimization problems, these typically produce correlated solutions for degenerate problems. Most notably, transverse-field quantum annealing-the heuristic employed in current commercially available quantum annealing machines-has been shown to often be exponentially biased when sampling the solution space. Here we present an approach to sample ground-state (or low-energy) configurations for binary optimization problems. The method samples degenerate states with almost equal probability and is based on a combination of parallel tempering Monte Carlo with isoenergetic cluster moves. We illustrate the approach using two-dimensional Ising spin glasses, as well as spin glasses on the D-Wave Systems quantum annealer chimera topology. In addition, a simple heuristic to approximate the number of solutions of a degenerate problem is introduced.
An Empirical Comparison of Missing Value Imputation Techniques on APS Failure Prediction

The Air Pressure System (APS) is a type of function used in heavy vehicles to assist braking and gear changing.The APS failure dataset consists of the daily operational sensor data from failed Scania trucks.The dataset is crucial to the manufacturer as it allows to isolate components which caused the failure.However, missing values and imbalanced class problems are the two most challenging limitations of this dataset to predict the cause of the failure.The prediction results can be affected by the way of handling these missing values and imbalanced class problem.In this paper, we have examined and presented the impact of five different missing value imputation techniques namely: Expectation Maximization, Mean Imputation, Soft Impute, MICE, and Iterative SVD in producing significantly better results.We have also performed an empirical comparison of their performance by applying five different classifiers namely: Naive Bayes, KNN, SVM, Random Forest, and Gradient Boosted Tree on this highly imbalanced dataset.The primary aim of this study is to observe the impact of the mentioned missing value imputation techniques in the enhancement of the prediction results, performing an empirical comparison to figure out the best classification model and imputation technique.We found that the MICE imputation and the random under-sampling techniques are the highest influential techniques for improving the prediction performance and false negative rate.
Characterization of Alzheimer conditions in MR images using volumetric and sagittal brainstem texture features

Brainstem analysis in Magnetic Resonance Images is essential to detect Alzheimer's condition in the preclinical stages. In this work, an attempt has been made to segment the brainstem in sagittal (2D) and volumetric (3D) images and evaluate texture changes to differentiate Alzheimer's disease (AD) stages. The images obtained from a public access database are spatial normalized, skull stripped and contrast enhanced. Morphological Reconstruction based Fast and Robust Fuzzy ‘C’ Means technique is used to cluster the brain tissue in preprocessed images into three groups namely cerebrospinal fluid, grey matter and white matter. Brainstem is segmented from the white matter tissue using connected component labelling. Texture features from volumetric and sagittal brainstem slices are extracted and its statistical significance is evaluated. Results show that the proposed approach is able to segment the brainstem from all the considered images. Variation in texture is observed to be less than 2% among sagittal brainstem slices. Additionally, midsagittal and volumetric features are correlated, suggesting that midsagittal brainstem structure gives an estimate of brainstem volume. Texture features extracted from midsagittal slice shows significant variation (p < 0.05) and is able to differentiate AD classes. Midsagittal brainstem texture features are able to capture the changes occurring in the early stages of disease condition. As the distinction of AD in preclinical stage is complex and clinically significant, this approach could be useful for early diagnosis of the disease.
Vulnerability Analysis of AR.Drone 2.0, an Embedded Linux System

The goal of this work was to identify and try to solve some of the vulnerabilities present in the AR Drone 2.0 by Parrot. The approach was to identify how the system worked, find and analyze vulnerabilities and flaws in the system as a whole and in the software, and find solutions to those problems. Analyzing the results of some tests showed that the system has an open WiFi network and the communication between the controller and the drone are unencrypted. Analyzing the Linux operating system that the drone uses, we see that "Pairing Mode" is the only way the system protects itself from unauthorized control. This is a feature that can be easily bypassed. Port scans reveal that the system has all the ports for its services open and exposed. This makes it susceptible to attacks like DoS and takeover. This research also focuses on some of the software vulnerabilities, such as Busybox that the drone runs. Lastly, this paper discuses some of the possible methods that can be used to secure the drone. These methods include securing the messages via SSH Tunnel, closing unused ports, and re-implementing the software used by the drone and the controller.
Set of 4d–3 observables to determine any pure qudit state

We present a tomographic method which requires only $4d-3$ measurement outcomes to reconstruct \emph{any} pure quantum state of arbitrary dimension $d$. Using the proposed scheme we have experimentally reconstructed a large number of pure states of dimension $d=7$, obtaining a mean fidelity of $0.94$. Moreover, we performed numerical simulations of the reconstruction process, verifying the feasibility of the method for higher dimensions. In addition, the \emph{a priori} assumption of purity can be certified within the same set of measurements, what represents an improvement with respect to other similar methods and contributes to answer the question of how many observables are needed to uniquely determine any pure state.
From loop fusion to kernel fusion: a domain-specific approach to locality optimization

Optimizing data-intensive applications such as image processing for GPU targets with complex memory hierarchies requires to explore the tradeoffs among locality, parallelism, and computation. Loop fusion as one of the classical optimization techniques has been proven effective to improve locality at the function level. Algorithms in image processing are increasing their complexities and generally consist of many kernels in a pipeline. The inter-kernel communications are intensive and exhibit another opportunity for locality improvement at the system level. The scope of this paper is an optimization technique called kernel fusion for data locality improvement. We present a formal description of the problem by defining an objective function for locality optimization. By transforming the fusion problem to a graph partitioning problem, we propose a solution based on the minimum cut technique to search fusible kernels recursively. In addition, we develop an analytic model to quantitatively estimate potential locality improvement by incorporating domain-specific knowledge and architecture details. The proposed technique is implemented in an image processing DSL and source-to-source compiler called Hipacc, and evaluated over six image processing applications on three Nvidia GPUs. A geometric mean speedup of up to 2.52 can be observed in our experiments.11Artifact available at:https://doi.org/10.5281/zenodo.2240193
On The Current State of Scholarly Retrieval Systems

The enormous growth in the size of scholarly literature makes its retrieval challenging. To address this challenge, researchers and practitioners developed several solutions. These include indexing solutions e.g. ResearchGate, Directory of Open Access Journals (DOAJ), Digital Bibliography &amp; Library Project (DBLP) etc., research paper repositories e.g. arXiv.org, Zenodo, etc., digital libraries, scholarly retrieval systems, e.g., Google Scholar, Microsoft Academic Search, Semantic Scholar etc., digital libraries, and publisher websites. Among these, the scholarly retrieval systems, the main focus of this article, employ efficient information retrieval techniques and other search tactics. However, they are still limited in meeting the user information needs to the fullest. This brief review paper is an attempt to identify the main reasons behind this failure by reporting the current state of scholarly retrieval systems. The findings of this study suggest that the existing scholarly retrieval systems should differentiate scholarly users from ordinary users and identify their needs. Citation network analysis should be made an essential part of the retrieval system to improve the search precision and accuracy. The paper also identifies several research challenges and opportunities that may lead to better scholarly retrieval systems.
Scan angle error measurement based on phase-stepping algorithms in scanning beam interference lithography

In this paper, a doubled-period grating (DPG) method is proposed to measure the scan angle error in scanning beam interference lithography (SBIL), together with a high-resolution two-dimensional stage and phase-stepping algorithms. A reference grating, which was the doubled period of the interference field, is adapted to diffract the incident left and right beams to form an interferogram captured by a CCD camera. The phase-stepping algorithm was applied to calculate the phase of the interferogram. First, by translating the stage, the reference grating lines were adjusted parallel to the scan direction by comparing the phase of the interferogram between the starting point and the ending point. Next, by rotating the stage step by step, the phase of the interferogram was obtained at each step as well as the phase slope. The scan angle error was considered to be least when the slope was minimum. Finally, a grating mask with a size of 100×100 mm2 was fabricated to verify the feasibility of the method. The scan angle error was measured with a precision of less than 12.65 μrad, which indicates the effectiveness of the proposed method to fabricate high-quality gratings in the future.
Design Science Research for Computational Thinking in Constructionist Education: A Pragmatist Perspective

[full article, abstract in English; abstract in Lithuanian]&#x0D; The article examines the modern computer-based educational environment and the requirements of the possible cognitive interface that enables the learner’s cognitive grounding by incorporating abductive reasoning into the educational process. Although the main emphasis is on cognitive and physiological aspects, the practical tools for enabling computational thinking in a modern constructionist educational environment are discussed. The presented analytical material and developed solutions are aimed at education with computers. However, the proposed solutions can be generalized in order to create a computer-free educational environment. The generalized paradigm here is pragmatism, considered as a philosophical assumption. By designing and creating a pragmatist educational environment, a common way of organizing computational thinking that enables constructionist educational solutions can be found.
Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors

Text reviews can provide rich useful semantic information for modeling users and items, which can benefit rating prediction in recommendation. Different words and reviews may have different informativeness for users or items. Besides, different users and items should be personalized. Most existing works regard all reviews equally or utilize a general attention mechanism. In this paper, we propose a hierarchical attention model fusing latent factor model for rating prediction with reviews, which can focus on important words and informative reviews. Specially, we use the factor vectors of Latent Factor Model to guide the attention network and combine the factor vectors with feature representation learned from reviews to predict the final ratings. Experiments on real-world datasets validate the effectiveness of our approach.
An Adaptive Moment estimation method for online AUC maximization

Area Under the ROC Curve (AUC) is a widely used metric for measuring classification performance. It has important theoretical and academic values to develop AUC maximization algorithms. Traditional methods often apply batch learning algorithm to maximize AUC which is inefficient and unscalable for large-scale applications. Recently some online learning algorithms have been introduced to maximize AUC by going through the data only once. However, these methods sometimes fail to converge to an optimal solution due to the fixed or rapid decay of learning rates. To tackle this problem, we propose an algorithm AdmOAM, Adaptive Moment estimation method for Online AUC Maximization. It applies the estimation of moments of gradients to accelerate the convergence and mitigates the rapid decay of the learning rates. We establish the regret bound of the proposed algorithm and implement extensive experiments to demonstrate its effectiveness and efficiency.
SVD based Privacy Preserving Recommendation Model using Optimized Hybrid Item-based Collaborative Filtering

Recommender system is one of the most decision support system which solves the problem of filtering required information from huge collected information as per the user's interest, choice or item's property. Collaborative based filtering recommender system is one of best filtering approaches which is very effective in wide range applications. Item Based Collaborative Filtering (IBCF) approach solves scalability and data sparsity issue with better accuracy. In this paper, we have developed optimized hybrid item based collaborative filtering recommendation model using binary rating matrix along with Jaccard similarity and used basic Singular Value Decomposition method (SVD) for privacy preserving. In this model, binary rating matrix is first examined and relationships among various items are identified by optimizing nearest neighbors as parameter. Then we use these relationships that help with recommendations for the user by achieving high privacy and better accuracy of the model.
Users as co-designers: Visual–spatial experiences at Whitworth Art Gallery

The importance of building users in the architectural field has been increasingly acknowledged in architectural research. Some studies have applied actor-network theory (ANT) to highlight the ways through which users are embedded in relation to the built environment. However, many of these studies are limited in recognizing the diverse ways users manipulate these relations as they experience the same built environment differently. This study draws on a recent ANT scholarship by summarizing the ethnographic research of Whitworth Art Gallery in Manchester, United Kingdom. Findings show that users see and react to the same built environment in different ways that are not limited to their alignment or misalignment with the design. They extend to include diverse interpretations and reactions (destructing, ignoring, noticing, attending, and interacting). The different user experiences explored in this research demonstrate three main themes, namely, entanglements, continuity, and transformation, which suggest new ways of recognizing the need for increasingly adaptable and inviting designs that address different user preferences and desires.