Reviewerly/exHarmony huggingface dataset:
The Table to Tablet (T2T) Speech and Language Therapy Software Development Roadmap

Few studies have analyzed gains in using computers in speech and language therapy interventions for children with speech and/or language disorders when compared to a control group, but virtual tutors and computer-based visual feedback have been gaining interest in the literature. Previous systematic reviews mainly focused on development technological details of computer-based speech training systems or the potential of integrating mobile technology into education and rehabilitation, but recent systematic reviews have also evaluated the efficacy of computer-based speech and language therapy for children and how digital technology can support different activities, at school or elsewhere.This study aimed to analyze a continuous communication and joint team approach to develop solutions focused on the real needs of end users, which digitally emulate reliable and validated physical intervention materials for children with speech sound disorders (SSD).The Table to Tablet (T2T) software was developed using a design-based research methodology, which included four phases: activities development; ethnographic pretesting with a sample from the target population; software development; and beta-testing. The technology used to develop the software, the method used to ensure satisfaction and replay ability of the intervention materials, and results from the ethnographic and beta-testing phases are presented.Nineteen activities were developed during the first phase, which were then tested, with 7 service users, using a physical prototype. The beta-test approach included extensive testing and reformulation, supported by direct, nonparticipant observation and data collection using a questionnaire designed for children. Feedback was used to improve the software and interaction with users.The use of T2T-based intervention programmes by speech and language therapists (SLTs) will allow these professionals to make a better and more effective communication intervention, based on proven methodologies, that coexists in a structured physical and a digital version. These versions provide a full, 6-week intervention program, with minimal effort in preparing the session by the SLTs while delivering a very consistent intervention, with high replay value. A continuous communication and joint team approach was beneficial to the project and to the development of a solution focused on the real needs of SLTs and children with SSD. All problems were approached as a team with different skills and expertise, which minimized errors (eg, the developer making choices that would save him from spending time doing something that would not be used) and time spent. To add to this, the importance of integrating the end users as testers and collecting their opinions and actions per session allowed the production of better-targeted activities.ClinicalTrials.gov NCT02490826; https://clinicaltrials.gov/ct2/show/NCT02490826.
Deep Learning Architectures for Vector Representations of Patients and Exploring Predictors of 30-Day Hospital Readmissions in Patients with Multiple Chronic Conditions

This empirical study of a complex group of patients with multiple chronic concurrent conditions (diabetes, cardiovascular and kidney diseases) explores the use of deep learning architectures to identify patient segments and contributing factors to 30-day hospital readmissions. We implemented Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) on sequential Electronic Health Records data at the Danderyd Hospital in Stockholm, Sweden. Three distinct sub-types of patient groups were identified: chronic obstructive pulmonary disease, kidney transplant, and paroxysmal ventricular tachycardia. The CNN learned about vector representations of patients, but the RNN was better able to identify and quantify key contributors to readmission such as myocardial infarction and echocardiography. We suggest that vector representations of patients with deep learning should precede predictive modeling of complex patients. The approach also has potential implications for supporting care delivery, care design and clinical decision-making.
Stereovision-based surface deformation detection of offshore wind turbine model under ship impact

In order to detect surface deformations on the tower of offshore wind turbine model under ship impact, a surface deformation detection method based on the parallel stereovision model is proposed in the present paper. Combination usage of five constraint algorithms effectively adjust the disparity range and eliminate the false disparities in the disparity map, and accomplish the aim of disparity optimization. Based on the parallel stereovision model, 3D point clouds of deformable surface are computed and the deformable surfaces are reconstructed by Delaunay triangulation. Moreover, a stereovision measurement system used for detecting surface deformation is integrated based on MATLAB, the comparison experiments of distance measurement and a surface deformation detection test of offshore wind turbine model under ship impact are performed in lab. Experimental results show that 3D surface rebuilt by the proposed stereovision method can accurately reveal the surface deformation on the protective device of the wind turbine model under ship impact, verifying the reliability and effectiveness of the proposed stereovision method in the present paper.
Partial key exposure attacks on RSA: Achieving the Boneh–Durfee bound

Thus far, several lattice-based algorithms for partial key exposure attacks on RSA, i.e., given the most/least significant bits (MSBs/LSBs) of a secret exponent d and factoring an RSA modulus N, have been proposed such as Blömer and May (Crypto'03), Ernst et al. (Eurocrypt'05), and Aono (PKC'09). Due to Boneh and Durfee's small secret exponent attack, partial key exposure attacks should always work for d<N0.292 even without any partial information. However, it was difficult task to make use of the given partial information without losing the quality of Boneh–Durfee's attack. In particular, known partial key exposure attacks fail to work for d<N0.292 with only few partial information. Such unnatural situation stems from the fact that the additional information makes underlying modular equations involved. In this paper, we propose improved attacks when a secret exponent d is small. Our attacks are better than all known previous attacks in the sense that our attacks require less partial information. Specifically, our attack is better than all known ones for d<N0.5625 and d<N0.368 with the MSBs and the LSBs, respectively. Furthermore, our attacks fully cover the Boneh–Durfee bound, i.e., they always work for d<N0.292. At a high level, we obtain the improved attacks by fully utilizing unraveled linearization technique proposed by Herrmann and May (Asiacrypt'09). Although Herrmann and May (PKC'10) already applied the technique to Boneh–Durfee's attack, we show elegant and impressive extensions to capture partial key exposure attacks. More concretely, we construct structured triangular matrices that enable us to recover more useful algebraic structures of underlying modular polynomials. We embed the given MSBs/LSBs to the recovered algebraic structures and construct our partial key exposure attacks. In this full version, we provide overviews and explicit proofs of the triangular matrix constructions. We believe that the additional explanations help readers to understand our techniques.
Link Prediction in Knowledge Graphs: A Hierarchy-Constrained Approach

Link prediction over a knowledge graph aims to predict the missing head entities <inline-formula><tex-math notation="LaTeX">$h$</tex-math></inline-formula> or tail entities <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> and missing relations <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> for a triple <inline-formula><tex-math notation="LaTeX">$(h,r,t)$</tex-math></inline-formula> . Recent years have witnessed great advance of knowledge graph embedding based link prediction methods, which represent entities and relations as elements of a continuous vector space. Most methods learn the embedding vectors by optimizing a margin-based loss function, where the margin is used to separate negative and positive triples in the loss function. The loss function utilizes the general structures of knowledge graphs, e.g., the vector of <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> is the translation of the vector of <inline-formula><tex-math notation="LaTeX">$h$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> , and the vector of <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> should be the nearest neighbor of the vector of <inline-formula><tex-math notation="LaTeX">$h+r$</tex-math></inline-formula> . However, there are many particular structures, and can be employed to promote the performance of link prediction. One typical structure in knowledge graphs is hierarchical structure, which existing methods have much unexplored. We argue that the hierarchical structures also contain rich inference patterns, and can further enhance the link prediction performance. In this paper, we propose a hierarchy-constrained link prediction method, called hTransM, on the basis of the translation-based knowledge graph embedding methods. It can adaptively determine the optimal margin by detecting the single-step and multi-step hierarchical structures. Moreover, we prove the effectiveness of hTransM theoretically, and experiments over three benchmark datasets and two sub-tasks of link prediction demonstrate the superiority of hTransM.
Attention Based Visual Analysis for Fast Grasp Planning With a Multi-Fingered Robotic Hand

We present an attention based visual analysis framework to compute grasp-relevant information which helps to guide grasp planning using a multi-fingered robotic hand. Our approach uses a computational visual attention model to locate regions of interest in a scene and employ a deep convolutional neural network to detect grasp type and grasp attention point for a sub-region of the object in a region of interest. We demonstrate the proposed framework with object grasping tasks, in which the information generated from the proposed framework is used as prior information to guide grasp planning. The effectiveness of the proposed approach is evaluated in both simulation experiments and real-world experiments. Experimental results show that the proposed framework can not only speed up grasp planning with more stable configurations, but also handle unknown objects. Furthermore, our framework can handle cluttered scenarios. A new Grasp Type Dataset (GTD) which includes six commonly used grasp types and covers 12 household objects is also presented.
Efficient Lightweight Hardware Structures of Point Multiplication on Binary Edwards Curves for Elliptic Curve Cryptosystems

This paper presents efficient lightweight hardware implementations of the complete point multiplication on binary Edwards curves (BECs). The implementations are based on general and special cases of binary Edwards curves. The complete differential addition formulas have the cost of [Formula: see text] and [Formula: see text] for general and special cases of BECs, respectively, where [Formula: see text] and [Formula: see text] denote the costs of a field multiplication, a field squaring and a field multiplication by a constant, respectively. In the general case of BECs, the structure is implemented based on 3 concurrent multipliers. Also in the special case of BECs, two structures by employing 3 and 2 field multipliers are proposed for achieving the highest degree of parallelization and utilization of resources, respectively. The field multipliers are implemented based on the proposed efficient digit–digit polynomial basis multiplier. Two input operands of the multiplier proceed in digit level. This property leads to reduce hardware consumption and critical path delay. Also, in the structure, based on the change of input digit size from low digit size to high digit size the number of clock cycles and input words are different. Therefore, the multiplier can be flexible for different cryptographic considerations such as low-area and high-speed implementations. The point multiplication computation requires field inversion, therefore, we use a low-cost Extended Euclidean Algorithm (EEA) based inversion for implementation of this field operation. Implementation results of the proposed architectures based on Virtex-5 XC5VLX110 FPGA for two fields [Formula: see text] and [Formula: see text] are achieved. The results show improvements in terms of area and efficiency for the proposed structures compared to previous works.
Explicit equivalence of quadratic forms over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>

We propose a randomized polynomial time algorithm for computing non-trivial zeros of quadratic forms in 4 or more variables over Fq(t), where Fq is a finite field of odd characteristic. The algorithm is based on a suitable splitting of the form into two forms and finding a common value they both represent. We make use of an effective formula for the number of fixed degree irreducible polynomials in a given residue class. We apply our algorithms for computing a Witt decomposition of a quadratic form, for computing an explicit isometry between quadratic forms and finding zero divisors in quaternion algebras over quadratic extensions of Fq(t).
Controversial ‘pixel’ prior rule for JPEG adaptive steganography

Currently, the most successful model for image adaptive steganography is the framework of minimal distortion, in which a reasonable definition of costs can improve the security level. In the authors' previous work, they developed a rule for cost reassignment in spatial domain called the ‘controversial pixel prior (CPP)’ rule, which defines controversial pixels by utilizing the controversies among several comparable schemes. The CPP rule gives controversial pixels higher modification priorities. In this study, they investigate migrating the CPP rule from the spatial domain to the joint photographic experts group (JPEG) domain and name it the J-CPP rule. In JPEG images, the cover elements are discrete cosine transform (DCT) coefficients and variant factors mayinfluence the distortion definition includingquantisation step, inter-blocks correlation and block energy. However, there is no evidence to reveal which factor is of highest priority for promoting security. In this work, they investigate which factor is more helpful in promoting J-CPP rule, and they finally determine to set the spatial block residual as a penalty to perfect J-CPP rule. Through extensive experiments on different JPEG steganographic algorithms and steganalysis features, they demonstrate that the J-CPP rule can improve the security of JPEG adaptive steganography.
Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation

The difficulty of annotating training data is a major obstacle to using CNNs for low-level tasks in video. Synthetic data often does not generalize to real videos, while unsupervised methods require heuristic losses. Proxy tasks can overcome these issues, and start by training a network for a task for which annotation is easier or which can be trained unsupervised. The trained network is then fine-tuned for the original task using small amounts of ground truth data. Here, we investigate frame interpolation as a proxy task for optical flow. Using real movies, we train a CNN unsupervised for temporal interpolation. Such a network implicitly estimates motion, but cannot handle untextured regions. By fine-tuning on small amounts of ground truth flow, the network can learn to fill in homogeneous regions and compute full optical flow fields. Using this unsupervised pre-training, our network outperforms similar architectures that were trained supervised using synthetic optical flow.
Engineering of heralded narrowband color-entangled states

Efficient heralded generation of entanglement together with its manipulation is of great importance for quantum communications. In addition, states generated with bandwidths naturally compatible with atomic transitions allow a more efficient mapping of light into matter which is an essential requirement for long distance quantum communications. Here we propose a scheme where the indistinguishability between two spontaneous four-wave mixing processes is engineered to herald generation of single-photon frequency-bin entangled states, i.e., single-photons shared by two distinct frequency modes. We show that entanglement can be optimised together with the generation probability, while maintaining absorption negligible. Besides, the scheme illustrated for cold rubidium atoms is versatile and can be implemented in several other physical systems.
Fuzzy Pushdown Termination Games

The computational study on finite/infinite-state systems, probabilistic systems, and finite-state fuzzy systems, has received much attention recently. In contrast, there are very few results for algorithmic analysis of infinite-state fuzzy systems. In this paper, we introduce fuzzy pushdown termination games (FPDTGs), which are an extension of fuzzy pushdown automata with a game feature and can serve as a formal model of infinite-state fuzzy systems. We investigate some computational issues of the games under termination objectives for two players: the goal of player-1 is to maximize the truth value of eventually terminating at some given configurations with the empty stack, while player-2 aims at the opposite. Some interesting results are obtained. For example, we show that both players have optimal memoryless strategies and the same value. The problem of computing the value can be solved in exponential time when the triangular norm is chosen as the minimum one. Furthermore, we present efficient algorithms for computing the values of two special subclasses of FPDTGs. The potential for practical use of our model is demonstrated by a case study on a manufacturing system.
Precision-Enhanced Differentially-Private Mining of High-Confidence Association Rules

Association rule mining discovers patterns in large data repositories, and benefits diverse application domains such as healthcare, marketing, etc. However, mining datasets that contain data about individuals may cause significant privacy breaches. Recent research addresses the privacy threats that arise when mining sensitive data, and several techniques allow data mining with differential privacy guarantees. However, existing methods only discover rules that have very large support, i.e., occur in a large fraction of the dataset transactions (typically, more than 50 percent). This is a serious limitation, as numerous high-quality rules do not reach such high frequencies (e.g., rules about rare diseases, or luxury merchandise). We propose a method that focuses on mining high-quality association rules with moderate and low frequencies. We employ a novel technique for rule extraction that combines the exponential mechanism of differential privacy with reservoir sampling. The proposed algorithm allows us to directly mine association rules, without the need to compute noisy supports for large numbers of itemsets. Our experimental evaluation shows that our technique is able to sample low- and moderate-support rules with good precision, clearly outperforming existing solutions.
Traceable Anonymous Authentication and Key Exchange Protocol for Privacy-Aware Cloud Environments

Anonymous authentication with key exchange is an important technique for any subject who attempts to access sensitive cloud services without compromising his/her identity. However, most so-called anonymous authentication schemes, also known as dynamic authentication protocols, only consider and offer preauthentication anonymity to resist ID-theft attacks. This motivates the author to propose a new anonymous authentication with key exchange scheme achieving both preauthentication and postauthentication user anonymity. By using a registered security token hardware together with a rememberable password, our scheme allows a user to generate a pseudoidentity for cloud authentication. To reduce communication overheads with cloud servers, our scheme utilizes an offline password update procedure and provides fast error detection in both login and password-update processes. Still, to make our scheme more suitable for privacy-aware cloud environments, the functionality of tracing real identities of anonymous users is provided. Additionally, we formally prove the authenticated key exchange (AKE) security of the proposed scheme in the random oracle model and discuss some potential attacks. The comparison results also clearly reveal that our scheme has better functionalities and security properties among related works.
Efficient QoE-Aware Scheme for Video Quality Switching Operations in Dynamic Adaptive Streaming

Dynamic Adaptive Streaming over HTTP (DASH) is a popular over-the-top video content distribution technique that adapts the streaming session according to the user's network condition typically in terms of downlink bandwidth. This video quality adaptation can be achieved by scaling the frame quality, spatial resolution or frame rate. Despite the flexibility on the video quality scaling methods, each of these quality scaling dimensions has varying effects on the Quality of Experience (QoE) for end users. Furthermore, in video streaming, the changes in motion over time along with the scaling method employed have an influence on QoE, hence the need to carefully tailor scaling methods to suit streaming applications and content type. In this work, we investigate an intelligent DASH approach for the latest video coding standard H.265 and propose a heuristic QoE-aware cost-efficient adaptation scheme that does not switch unnecessarily to the highest quality level but rather stays temporarily at an intermediate quality level in certain streaming scenarios. Such an approach achieves a comparable and consistent level of quality under impaired network conditions as commonly found in Internet and mobile networks while reducing bandwidth requirements and quality switching overhead. The rationale is based on our empirical experiments, which show that an increase in bitrate does not necessarily mean noticeable improvement in QoE. Furthermore, our work demonstrates that the Signal-to-Noise Ratio (SNR) and the spatial resolution scalability types are the best fit for our proposed algorithm. Finally, we demonstrate an innovative interaction between quality scaling methods and the polarity of switching operations. The proposed QoE-aware scheme is implemented and empirical results show that it is able to reduce bandwidth requirements by up to 41% whilst achieving equivalent QoE compared with a representative DASH reference implementation.