Reviewerly/exHarmony huggingface dataset:
Neuron-Level Fuzzy Memoization in RNNs

Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the accuracy of future predictions and, therefore, they are very effective for sequence processing problems.
Blockchain and Public Procurement

Public procurement relies in an apparent irreconcilability between competition, which implies some confidentiality, and transparency. The latest Public Procurement Directives have made e-procurement a mandatory feature. Since blockchain technology has been developed and designed to accomplish integrity, transparency, efficiency and data accuracy, goals which are very much appreciated in public procurement, an interesting question then arises: is there room to apply this technology within public procurement procedures? Will smart contracts be an interesting tool within public procurement? Considering public duties such as data protection, which must be complied with by contracting authorities, and some blockchain features such as non-withdrawable information and the likely broad access to the information there enclosed, one can be drawn to conclude that there is no possible conciliation between these two procedures. The mandatory e-procurement implies some neighbouring problems with this technology. Yet, are there any technological solutions for some of the drawbacks?
An Efficient Gaussian Sum Filter Based on Prune-Cluster-Merge Scheme

The main problem for the state estimation with Gaussian mixture model is the exponentially growing number of Gaussian components. To solve this problem, an efficient Gaussian sum filter (GSF) based on the prune-cluster-merge (PCM) scheme-based Gaussian mixture reduction method is proposed. First, an adaptive weight-censored pruning strategy named as the j-th order statistical technique is presented to delete components with little contributes to the posterior distribution. Then, the Gauss clustering method is proposed to partition the remainder components into clusters based on the newly defined distribution similarity criterion. Integrating the acquired clusters with the covariance intersection algorithm, the components in the same cluster are merged into a standard Gaussian component by keeping the shape of the original distribution. Meanwhile, an extended integral square error cost function is constructed to optimize the performance of the cluster-merge operation. Finally, an efficient Gaussian sum filter is derived by combining the PCM scheme with extended Kalman filters. Numerical results show that the proposed filter can not only keep a better approximation to the original distribution with fewer Gaussian components comparing with the number-limited GSF and Runnalls's GSF, but achieve a higher cost-effectiveness than the particle filter.
Modelling Soil Cation Exchange Capacity in Different Land-Use Systems using Artificial Neural Networks and Multiple Regression Analysis

Cation exchange capacity (CEC), as an important indicator of soil quality, represents the ability of the soil to hold positively charged ions.In this study, CEC was successfully predicted using different statistical methods, including artificial neural networks (ANNs) involving multi-layer perceptron (MLP), radial basis function (RBF), multiple linear regression (MLR) and nonlinear regression (NLR).About 293 soil samples were collected from North East India, which are under three land uses (shifting agriculture (jhum), forest and cash crops).Also, 70% of the samples (205 samples) was selected as the calibration set and the remaining 30% (88 samples) used as the prediction set.Soil pH, texture, bulk density (BD) and organic carbon (OC) were used as predictor variables to estimate CEC.The CEC-pedotransfer function (CEC-PTF) performance was evaluated with the coefficient of determination (R 2 ), root mean square error (RMSE) and standard error for the estimate (SEE) between the observed and predicted values.The results indicated that the nonlinear model (R 2 = 0.91 and SEE = 1.82 for training) for cash-crop system, and RBF (R 2 = 0.91 and SEE = 3.83 for training) for jhum system were the best models to estimate CEC.In contrast, RBF (R 2 = 0.67 and SEE = 14.87 for training) for forest system was the worst model to estimate CEC.The results confirm that clay and OC were the most influential variables to predict CEC in the cashcrop system, whereas BD and OC were more suitable for jhum system.Although the ANNs provided suitable predictions of the entire dataset, NLR gave a formula to estimate soil CEC using commonly tested soil properties.Thus, NLR provided a reasonable estimate of CEC for most soils analysed.
A Graphical PIN Entry System with Shoulder Surfing Resistance

Personal identification number or PIN based authentication systems are most commonly used authentication systems. Due to maturity and simplicity, these authentication systems are vastly deployed in many different areas such as automatic teller machine (ATM), point of sale (POS), electronic door access system and in different kinds of mobile applications. However, due to limited password space and small password length, they are highly susceptible to different kinds of shoulder surfing attacks. In this paper, we have proposed a graphical PIN entry scheme that provides resistance against shoulder surfing attacks. To alleviate the shoulder surfing attack in our proposed scheme, we have used specialized interface design and indirect PIN entry method. For indirect PIN entry method we have used extra information in the form of reference location, which is not observable for the attacker. We have implemented the prototype of the proposed authentication scheme using C# and conducted a user study to evaluate the usability of our proposed scheme. The results of the user study show that this scheme provides a reasonable balance between security and usability.
Latin script keyboards for South Asian languages with finite-state normalization

The use of the Latin script for text entry of South Asian languages is common, even though there is no standard orthography for these languages in the script.We explore several compact finite-state architectures that permit variable spellings of words during mobile text entry.We find that approaches making use of transliteration transducers provide large accuracy improvements over baselines, but that simpler approaches involving a compact representation of many attested alternatives yields much of the accuracy gain.This is particularly important when operating under constraints on model size (e.g., on inexpensive mobile devices with limited storage and memory for keyboard models), and on speed of inference, since people typing on mobile keyboards expect no perceptual delay in keyboard responsiveness.
Publicly verifiable data transfer and deletion scheme for cloud storage

With the rapid development of cloud storage, more and more resource-constraint data owners can employ cloud storage services to reduce the heavy local storage overhead. However, the local data owners lose the direct control over their data, and all the operations over the outsourced data, such as data transfer and deletion, will be executed by the remote cloud server. As a result, the data transfer and deletion have become two security issues because the selfish remote cloud server might not honestly execute these operations for economic benefits. In this article, we design a scheme that aims to make the data transfer and the transferred data deletion operations more transparent and publicly verifiable. Our proposed scheme is based on vector commitment (VC), which is used to deal with the problem of public verification during the data transfer and deletion. More specifically, our new scheme can provide the data owner with the ability to verify the data transfer and deletion results. In addition, by using the advantages of VC, our proposed scheme does not require any trusted third party. Finally, we prove that the proposed scheme not only can reach the expected security goals but also can satisfy the efficiency and practicality.
A Note on the Relevance of Semilattice Relevance Logic

A propositional logic has the variable sharing property if φ →ψ is a theorem only if φ and ψ share some propositional variable(s). In this note, I prove that positive semilattice relevance logic (R+u) and its extension with an involution negation (R¬u) have the variable sharing property (as these systems are not subsystems of R, these results are not automatically entailed by the fact that R satisfies the variable sharing property). Typical proofs of the variable sharing property rely on ad hoc, if clever, matrices. However, in this note, I exploit the properties of rather more intuitive arithmetical structures to establish the variable sharing property for the systems discussed.
Cooperative Transport of Large Objects by a Pair of Unmanned Aerial Systems using Sampling-based Motion Planning

Cooperative carrying of large, cable-suspended payloads by a pair of cooperating unmanned aerial vehicles (UAVs) is tackled in this paper. The proposed system, involving a sampling-based motion planning algorithm and a model predictive control-based coordination of UAVs, aims to achieve a smooth and reliable flight performance in environments with obstacles. The motion planning is designed to satisfy constraints on relative positions of UAVs, which are defined from the cooperative transport task and by onboard mutual localization, which is used for real-time estimation of states of neighboring robots carrying the object. Besides, a guiding principle with a cost-driven expansion is employed to steer the growth of a Rapidly-exploring Random Tree (RRT) to keep the coupled system of UAVs and the object as close to desired mutual positions as possible. A significant deviation of the controlled system from the desired configuration, by increasing or decreasing the relative distance between UAVs carrying the cable-suspended object, is achieved only if it is required by environment constraints (e.g. in narrow passages), while the allowed limits are always satisfied. Using the guiding principle enables us to find feasible solutions of the problem in a reasonable short time using onboard computer even in environments with a complicated structure of obstacles. The proposed system was evaluated in numerous simulations, compared with state-of-the-art solutions using statistical sets of results, and its performance and reliability were verified in experiments in real-world conditions.
Predicting the Difficulty of EFL Tests Based on Corpus Linguistic Features and Expert Judgment

This study examines the relationships among various major factors that may affect the difficulty level of language tests in an attempt to enhance the robustness of item difficulty estimation, which constitutes a crucial factor ensuring the equivalency of high-stakes tests. The observed difficulties of the reading and listening sections of two EFL tests were compared using corpus linguistic features and expert judgments, i.e., native and nonnative speakers' perceived difficulty of the test items. The research findings are as follows: Some corpus features and the predicted difficulties demonstrated a moderate to high correlation with the test sections' observed difficulty. The native and nonnative speakers' predicted difficulties significantly explained the observed difficulty of the test sections, where the nonnative speakers' predicted difficulty explained a similar variance. When entered separately, the corpus features showed a stronger explanatory power than the predicted difficulties. The corpus features and predicted difficulty together accounted for the largest variance, which was more than half of the variance of the test sections. The current study suggests that corpus features and expert judgment capture different aspects of item difficulty and future research in this area needs to consider how these two can be combined for robust item difficulty estimation.
Overview of Content-Based Click-Through Rate Prediction Challenge for Video Recommendation

Content cold-start is a core problem in recommendation field, by which service providers can mine the potential profit from content that has not yet been discovered by most users, and provide more accurate personalized service to their users. In video recommendation, video and audio features should cover enough semantic information in the purpose of recommendation, thus should take an non-negligible role for content cold-start. This paper summarizes the Content Based Video Relevance Prediction Challenge held by Hulu, a top online streaming video platform in US, in ACM Multimedia conference 2019. The challenge is a content-based CTR prediction task for video recommendation, where millions of user interaction data and thousands of video features are released for research purpose on related topics.
Tradeoff Analysis for Conflicting Software Non-Functional Requirements

The need for particular software non-functional requirements (NFRs) leads a project team to use specific design strategies to fulfill these NFRs. However, some of the strategies may cause deficiencies in other software NFRs. These strategies are called conflicting strategies. Making the tradeoff decision of these conflicting strategies for NFRs is crucial. Therefore, a conflicting NFRs tradeoff framework (CNTF) and the corresponding method are proposed. Firstly, the NFRs are obtained from stakeholders. Fuzzy set theory is used to express stakeholder assessments on the importance of each NFR. The assessment results are ranked using a fuzzy ranking method. Then, adapting from production theory in microeconomics, the degree of satisfaction by which NFRs are affected by conflicting strategies are plotted as tradeoff curves. Borrowing the concept from the linear programming in management science, a tradeoff method is presented to help software project teams make the best tradeoff decision for conflicting NFRs. Finally, feasibility and limitations of the CNTF are elaborated in a case study.
Close the Gap between Deep Learning and Mobile Intelligence by Incorporating Training in the Loop

Pre-trained deep learning models can be deployed on mobile devices to conduct inference. However, they are usually not updated thereafter. In this paper, we take a step further to incorporate training deep neural networks on battery-powered mobile devices and overcome the difficulties from the lack of labeled data. We design and implement a new framework to enlarge sample space via data paring and learn a deep metric under the privacy, memory and computational constraints. A case study of deep behavioral authentication is conducted. Our experiments demonstrate accuracy over 95% on three public datasets, a sheer 15% gain from traditional multi-class classification with less data and robustness against brute-force attacks with 99% success. We demonstrate the training performance on various smartphone models, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer. We also profile memory, energy and computational overhead. Our results indicate that training consumes lower energy than watching videos so can be scheduled intermittently on mobile devices.
Nonreference Image Quality Evaluation Algorithm Based on Wavelet Convolutional Neural Network and Information Entropy

The image quality evaluation method, based on the convolutional neural network (CNN), achieved good evaluation performance. However, this method can easily lead the visual quality of image sub-blocks to change with the spatial position after the image is processed by various distortions. Consequently, the visual quality of the entire image is difficult to reflect objectively. On this basis, this study combines wavelet transform and CNN method to propose an image quality evaluation method based on wavelet CNN. The low-frequency, horizontal, vertical, and diagonal sub-band images decomposed by wavelet transform are selected as the inputs of convolution neural network. The feature information in multiple directions is extracted by convolution neural network. Then, the information entropy of each sub-band image is calculated and used as the weight of each sub-band image quality. Finally, the quality evaluation values of four sub-band images are weighted and fused to obtain the visual quality values of the entire image. Experimental results show that the proposed method gains advantage from the global and local information of the image, thereby further improving its effectiveness and generalization.
Head Movement Control of Powered Wheelchair

This paper presents an efficient method to implement head gesture recognition to be the base of a framework for the physically disabled people. Currently, the most common way to control an powered wheelchair is to use a joystick. However, there are some individuals unable to operate joystick-driven powered wheelchairs due to sever physical disabilities. This paper proposes a novel head pose control algorithm to assist disabled patients (persons who cannot move their hands or legs but are able to make head movement). It this paper an interface that enables an powered wheelchair to be operated by gestures, such as head movements, is presented. The control system translates the position of the users head into speed and directional control of the wheelchair. The proposed algorithm was implemented/tested in real-time on the powered wheelchair.
A Syntactically Expressive Morphological Analyzer for Turkish

We present a broad coverage model of Turkish morphology and an open-source morphological analyzer that implements it.The model captures intricacies of Turkish morphologysyntax interface, thus could be used as a baseline that guides language model development.It introduces a novel fine part-of-speech tagset, a fine-grained affix inventory and represents morphotactics without zero-derivations.The morphological analyzer is freely available.It consists of modular reusable components of human-annotated gold standard lexicons, implements Turkish morphotactics as finite-state transducers using OpenFst and morphophonemic processes as Thrax grammars. 1
A new joint CTC-attention-based speech recognition model with multi-level multi-head attention

Abstract A method called joint connectionist temporal classification (CTC)-attention-based speech recognition has recently received increasing focus and has achieved impressive performance. A hybrid end-to-end architecture that adds an extra CTC loss to the attention-based model could force extra restrictions on alignments. To explore better the end-to-end models, we propose improvements to the feature extraction and attention mechanism. First, we introduce a joint model trained with nonnegative matrix factorization (NMF)-based high-level features. Then, we put forward a hybrid attention mechanism by incorporating multi-head attentions and calculating attention scores over multi-level outputs. Experiments on TIMIT indicate that the new method achieves state-of-the-art performance with our best model. Experiments on WSJ show that our method exhibits a word error rate (WER) that is only 0.2% worse in absolute value than the best referenced method, which is trained on a much larger dataset, and it beats all present end-to-end methods. Further experiments on LibriSpeech show that our method is also comparable to the state-of-the-art end-to-end system in WER.
Application of the polaron picture in the two-qubit quantum Rabi model

The polaron picture is employed to investigate and elucidate the physics of the two-qubit quantum Rabi model, which describes two identical qubits coupled to a common harmonic oscillator. This approach enables us to obtain the ground-state energy and some other simpler physical observables with high accuracy in all regimes of the coupling strengths $g$, while there is no constraint to the ration of tunneling frequency $\mathrm{\ensuremath{\Omega}}$ and field frequency $\ensuremath{\omega}$, which is not simultaneously possible using previous methods. In addition, we also discover a phenomenon that is not present in the one-qubit Rabi model: with the increase of coupling strength $g$, there is a transition of the ground state of the system from a multipolaron state to a bipolaron state. However, the tunneling frequency $\mathrm{\ensuremath{\Omega}}$ counteracts this process. Specifically, when the tunneling frequency $\mathrm{\ensuremath{\Omega}}=0$, the system always stays in the bipolaron state.
Teacher's Readiness to Work under the Conditions of Educational Space Digitalization

The information environment of the Internet, turns into the powerful educational tool. This article explores the issues of digital transformation of an educational space. These are the results of the monitoring study on the problems of the teacher's readiness to effectively use digital tools for the organization of the educational process: electronic forms of textbooks, educational applications, online services and educational platforms. The advantages and disadvantages of digital transformation are further explored. The binary learning effect of digital educational tools is acknowledged, as the Educational nature is not only the content of the resource, but also the process of working with it, which contributes to the development of not only the student, but also the professional skills of the teacher. The rapid development of digital technologies and methods of network integration, allow for the implementation of advanced learning technologies that take into account the processes of human self-organization and social communication in the conditions of a digital transformation.
Measuring user similarity using check-ins from LBSN: a mobile recommendation approach for e-commerce and security services

Social friendship and geographical position information often reflects individuals' personal preferences and other types of knowledge that can be used to extract their similarity for recommendation systems. This paper finds that users are more likely to move around some specific centres or check in at some hotspots; a few individuals check in frequently, whereas most locations are rarely visited. Based on these findings, we propose a multi-centre clustering algorithm to capture users' mobile patterns and develop a user similarity measurement method. Complexity analysis shows the method's efficiency in handling large datasets and experimental results demonstrate its good applicability.