Reviewerly/exHarmony huggingface dataset:
Integrated Learning via Randomized Forests and Localized Regression With Application to Medical Diagnosis

The tree-based machine learning functions on the divide-and-conquer principle and is known to perform well in certain applications. In this paper, we first give a new data partitioning rule using the mean of the data columns to grow the tree till the child nodes are small in size. Then, the local regression is applied to leave nodes to enhance the resolution of the node outputs. Randomization is introduced at tree growth and forest creation. The local prediction accuracies on the leaves are used to select a subset of the test data for actual predictions. The case study on the diagnosis of autistic spectrum disorder shows that the proposed method achieves the prediction accuracy of the ensemble at above 96% with reduced variance, which is much better than those reported in the literature.
Robust and flexible method for calibrating the focal length of on-orbit space zoom camera

For the on-orbit space zoom camera, the camera focal length is in a constant process of change; accordingly, compared with calibrating other camera intrinsic parameters, calibrating the focal length has a practical significance for the space zoom camera. With the vanishing points obtained from the solar panel of human-made space satellites, this paper introduces a focal length self-calibration method for the on-orbit space zoom camera. First, the geometrical relationship and infinite homography of vanishing points at various camera positions are used to derive the method. To improve the accuracy and robustness performance of this approach, an optimization method is then proposed to nonlinearly optimize the camera focal length. Finally, simulation and real physical experiments demonstrate that the proposed method is flexible and accurate with good anti-noise interference and real-time capacity. The method proposed in this paper makes more realistic sense for a number of important space tasks.
Criticism and justification of negotiated compromises

Abstract The paper focuses on conflicts about an already negotiated compromise, taking as its example a debate in Dutch parliament about the approval of the Paris Agreement on climate change of 2015. It deals with a variety of worries that opponents of approval may advance and the arguments in its defense thus invited. It concludes with a profile of dialogue providing reasonable options for those involved in such a conflict.
Translating JSON Schema logics into OWL axioms for unified data validation on a digital manufacturing platform

JSON (JavaScript Object Notation) is a prevalent data format used in cloud-based platforms that support composable digital manufacturing workflows. The current work presents a method to translate the logics found in JSON Schema into OWL axioms, in order to facilitate ontology-based unified data validation with JSON data. The specific contributions of this paper include the demonstration of using a formal ontology for the logic translation and data validation, a technique for disambiguating implicit relations found in JSON Schema as explicit OWL properties, and mapping JSON Schema validation keywords to equivalent OWL expressions.
Business Model Characteristics for Local IaaS Providers for Counteracting the Dominance of the Hyperscalers

The Infrastructure as a Service (IaaS) market is dominated by only a few globally acting hyperscalers. The rest consists of a multitude of smaller providers whose IaaS services are restricted to one country or region. As basic IaaS services have become a commodity, the price has turned into the most important decision criterion for customers. For this reason, the central concern of IaaS providers is to achieve economies of scale. However, because of their marginal size, the locally operating IaaS providers are unable to compete in this situation. Accordingly, a growing market consolidation among the local IaaS providers can be expected within the next years. To compete with the further increase in dominance of the hyperscalers, this paper investigates business model characteristics applying to local IaaS providers. The hypotheses were derived from 21 expert interviews with representatives from 17 cloud providers. Due to the exploratory character of this study, the research approach followed the guidelines of the grounded theory method.
Multiplier-Free Implementation of Galois Field Fourier Transform on a FPGA

A novel approach to implementing Galois field Fourier transform (GFT) is proposed that completely eliminates the need for any finite field multipliers by transforming the symbols from a vector representation to a power representation. The proposed method is suitable for implementing GFTs of prime and nonprime lengths on modern FPGAs that have a large amount of on-chip distributed embedded memory. For GFT of length 255 that is widely used in many applications, the proposed memory-based implementation exhibits 25% improvement in latency, 27% improvement in throughput, and 56% reduction in power consumption compared to a finite field multiplier-based implementation.
An efficient graph reduction framework for interactive texture segmentation

The performance of graph cut based interactive object segmentation approach depends highly on the size of the image. For high-resolution images, it requires an unacceptable amount of storage and time and becomes even more complex when the image has textural contents. In this work, we present an efficient approach to graph cut based texture segmentation by extracting texture features and reducing the overall size of the graph. To extract texture features, Non-decimated Complex Wavelet Transform (NDCWT) is employed whose sub-bands present the texture attributes at different scales and orientations. Moreover, to deal with the huge computational burdens caused by large images, we consider partitioning of the image area where the image is divided into equal-sized non-overlapping blocks and each such block is mapped to a node in the graph. This will not only reduce the size of the graph but also expedite the optimization process significantly. To achieve pixel level accuracy without using any boundary editing, the blocks lying on the boundary are identified and the final segmentation is obtained by applying the standard graph cut on the pixels of such blocks. Thus our approach is able to retain full resolution accuracy with minimal user interaction as opposed to the pre-segmentation or super-pixel based graph cut approaches. Experiments have been conducted on various gray scale as well as color texture images from Brodatz, Berkeley and MSRC datasets which reveal that the proposed approach not only reduces the segmentation time and memory consumption but also improves segmentation accuracy significantly.
High performance distributed deep learning

The current wave of advances in Deep Learning (DL) has led to many exciting challenges and opportunities for Computer Science and Artificial Intelligence researchers alike. Modern DL frameworks like Caffe2, TensorFlow, Cognitive Toolkit (CNTK), PyTorch, and several others have emerged that offer ease of use and flexibility to describe, train, and deploy various types of Deep Neural Networks (DNNs). In this tutorial, we will provide an overview of interesting trends in DNN design and how cutting-edge hardware architectures are playing a key role in moving the field forward. We will also present an overview of different DNN architectures and DL frameworks. Most DL frameworks started with a single-node/single-GPU design. However, approaches to parallelize the process of DNN training are also being actively explored. The DL community has moved along different distributed training designs that exploit communication runtimes like gRPC, MPI, and NCCL. In this context, we will highlight new challenges and opportunities for communication runtimes to efficiently support distributed DNN training. We also highlight some of our co-design efforts to utilize CUDA-Aware MPI for large-scale DNN training on modern GPU clusters. Finally, we include hands-on exercises in this tutorial to enable the attendees to gain first-hand experience of running distributed DNN training experiments on a modern GPU cluster.
Fast Secure Comparison for Medium-Sized Integers and Its Application in Binarized Neural Networks

In 1994, Feige, Kilian, and Naor proposed a simple protocol for secure 3-way comparison of integers a and b from the range [0, 2]. Their observation is that for $$p=7$$ , the Legendre symbol $$\left( x\mid p\right) $$ coincides with the sign of x for $$x=a-b\in [-2,2]$$ , thus reducing secure comparison to secure evaluation of the Legendre symbol. More recently, in 2011, Yu generalized this idea to handle secure comparisons for integers from substantially larger ranges [0, d], essentially by searching for primes for which the Legendre symbol coincides with the sign function on $$[-d,d]$$ . In this paper, we present new comparison protocols based on the Legendre symbol that additionally employ some form of error correction. We relax the prime search by requiring that the Legendre symbol encodes the sign function in a noisy fashion only. Practically, we use the majority vote over a window of $$2k+1$$ adjacent Legendre symbols, for small positive integers k. Our technique significantly increases the comparison range: e.g., for a modulus of 60 bits, d increases by a factor of 2.8 (for $$k=1$$ ) and 3.8 (for $$k=2$$ ) respectively. We give a practical method to find primes with suitable noisy encodings. We demonstrate the practical relevance of our comparison protocol by applying it in a secure neural network classifier for the MNIST dataset. Concretely, we discuss a secure multiparty computation based on the binarized multi-layer perceptron of Hubara et al., using our comparison for the second and third layers.
Integrating a gradient–based difference operator with machine learning techniques in right heart segmentation

In this research a three step method for right heart segmentation based on a gradient-based difference operator and machine learning techniques is reported. The proposed method is applied to human heart multi-slice computerized tomography (MSCT) volumes. The first step is the preprocessing, where a gradient-based difference operator is applied to exploit the functional relationship between the original input image and its edge enhanced version. In the second step, the least squares support vector machines (LSSVM) are used with a double purpose. First, an appropriate volume-of-interest is automatically established in order to isolate the structure to segment. Second, another LSSVM is trained for locating the voxels required for initializing the seed based clustering procedure. In the third step (segmentation step), the preprocessed volumes are subsequently processed with an unsupervised clustering technique based on simple linkage region growing. Dice score is used as a metric function to compare the segmentations obtained using the proposed method with respect to ground truth volumes traced by a cardiologist. The right atrium, pulmonary valve, right ventricle and venae cavae are segmented from 80 cardiac MSCT volumes. Reported metrics confirm that this method is a promising technique for right heart segmentation.
Robust visual tracking via global context regularized Locality-constrained Linear Coding

Locality-constrained Linear Coding (LLC) based visual tracking can give a better and faster tracking performance than traditional sparse representation based tracking methods. However, the existing LLC based methods often use the anchor points near the target to build the sparse coding dictionary for local sparse coding. It may cause a problem that it is hard to discriminate the difference between the negative and positive anchor points in sparse coding dictionary when facing severe background clutter, illumination change and occlusion. In this paper, we propose a context aware sparse coding method to achieve robust visual tracking. The proposed method can prevent the negative anchor points from disturbing the classifier accuracy because it uses a global context regularizer to constrain the sparse coding value of those negative anchor points that are similar to the positive anchor points. Experiment results show that our method can achieve a better tracking performance than state-of-the-art tracking methods do.
Standardizing a Shoe Insole Based on ISO/IEEE 11073 Personal Health Device (X73-PHD) Standards

Personal healthcare systems play fundamental role in shaping the future of healthcare. With the explosion of Digital Twin [1] including wearable technology, many healthcare systems depend on personal health devices (PHD) to perform their functions. It is thus important to standardize the utilized PHDs. In this paper, we propose the X73-PHD standard for a smart shoe insole as this promising device is not yet standardized. We provide a background on shoe insoles and their importance in gait analysis. The main contribution of this paper is providing the detailed design of X73-PHD standard for the shoe insole (SI) through the three main parts of X73-PHD standards, which are: domain information model (DIM), service model (SM), and communication model (CM). Besides,we explain the hardware architecture and the functional model of the SI used in this work. Finally, the standard implementation for the SI is presented.
An Exploratory Study on Migrating Single-Products towards Product Lines in Startup Contexts

A majority of technology startups fail; inadequate software engineering practices are known to be a contributing factor. The smooth transitioning towards software product lines in particular is a major stumbling block for startups that must broaden their product portfolio to deal with divergent demands imposed by the market. We conducted a preliminary study within two software engineering startups, which revealed the motivating factors and benefits that would lead to the migration from a single product into a product line. Despite the benefits, tackling the challenges foreseen and the identification of features and their relations in the current product is the crucial first step towards implementing an appropriate highly-configurable product portfolio.
Most specific consequences in the description logic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.gif" display="inline" id="d1e19" overflow="scroll"><mml:mi mathvariant="script">E</mml:mi><mml:mi mathvariant="script">L</mml:mi></mml:math>

The notion of a most specific consequence with respect to some terminological box is introduced, conditions for its existence in the description logic EL and its variants are provided, and means for its computation are developed. Algebraic properties of most specific consequences are explored. Furthermore, several applications that make use of this new notion are proposed and, in particular, it is shown how given terminological knowledge can be incorporated in existing approaches for the axiomatization of observations. For instance, a procedure for an incremental learning of concept inclusions from sequences of interpretations is developed.
Low Space Complexity <inline-formula> <tex-math notation="LaTeX">$GF(2^m)$ </tex-math> </inline-formula> Multiplier for Trinomials Using <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math> </inline-formula>-Term Karatsuba Algorithm

We propose bit-parallel GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) multipliers for irreducible trinomials using an n-term Karatsuba algorithm and Mastrovito approach, which are generalizations of the newly proposed multiplication scheme for a specific trinomial. The complexities of the proposed multipliers for GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) depend on the choice of an irreducible trinomial x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> + x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> + 1 defining GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) and values n, m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> such that m = nm <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> or m = nm <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> + 1. It is possible to achieve a space-time tradeoff by choosing proper values for k, n, and m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . For the purpose of a specific comparison, we compare the proposed multipliers with the best-known multipliers for an odd m ∈ [399, 450] for which there exists an irreducible trinomial of degree m. As a result, the proposed multipliers achieve the lowest space complexities among similar bit-parallel multipliers (they have roughly 40% reduced space complexities compared with the fastest multiplier). On the other hand, their time complexities match or are at most 2T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> higher than the fastest multipliers, where T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> is the delay of one 2-input XOR gate.
Developing Soft and Technical Skills Through Multi-Semester, Remotely Mentored, Community-Service Projects

For the past four years, we have taught a reimagined software design course with typical and atypical components. Projects form the core of the course: Students work in teams of 4-6 people to develop non-mission-critical software for local non-profits, building their engagement with the community and helping them understand the broader impact of their work. These projects require multiple semesters to complete. Since students typically enroll for the course for one semester, this model gives students the novel experience of legacy software. We also provide each team with an alumni mentor who helps them navigate not only technical problems but also the challenges of working with a real-world, non-technical client. These aspects of the course also develop our students' soft skills. They learn to work with a team, to communicate with non-technical clients, to work with remote collaborators (or mentors), and to think ahead to those who will take on the project in the next semester. As we tell our students, these skills are often as crucial as their technical skills. In this paper, we report on the design of the course and describe some of the challenges associated with this model (e.g., projects that inadvertently reveal information, clients who switch management or expectations, and projects that become obsolete) and provide suggestions for those who might want to adopt a similar approach.
The Role of Optimum Connectivity in Image Segmentation: Can the Algorithm Learn Object Information During the Process?

Image segmentation is one of the most investigated research topics in Computer Vision and yet presents challenges due to the difficulty of modeling all possible appearances of objects in images. In this sense, it is important to investigate methods that can learn object information before and during delineation. This paper addresses the problem by exploiting optimum connectivity between image elements (pixels and superpixels) in the image domain and feature space to improve segmentation. The study uses the Image Foresting Transform (IFT) framework to explain and implement all methods and describes some recent advances related to superpixel and object delineation. It provides a guideline to learn prior object information from the target image only based on seed pixels, superpixel clustering, and classification, evaluates the impact of using object information in several connectivity-based delineation methods using the segmentation by a deep neural network as baseline, and shows the potential of a new paradigm, namely Dynamic Trees, to learn object information from the target image only during delineation.
A transformation model based on dual-tree complex wavelet transform for non-rigid registration of 3D MRI images

Image registration is regarded as an important component of medical procedures. The present study aimed to introduce a new transformation model based on dual-tree complex wavelet transform (DT-CWT). To this aim, parametric registration methods was revised based on the function expansion theory and the gradient descent algorithm was used to introduce a general formulation for transformation models based on spatio-spectral transforms. Then, the performance of the proposed method was evaluated on a public dataset of 3D real magnetic resonance images (MRI) and compared with the transformation model based on wavelets. Finally, the performance of the proposed method was compared with the current state-of-the-art methods (IRTK, SyN and SPM-DARTEL). Based on the experimental results, the proposed method could deliver superior registration performance compared with the previous methods.
New Bi-Signature Scheme Based on GHZ States and W States

A new quantum bi-signature scheme based on GHZ states and W states is proposed. In the proposed scheme, Alice and Bob sign one same message and send their signatures to Charlie. Different from some typical quantum signature schemes, the new quantum bi-signature scheme firstly sets up a secure channel and the three parties verify each other with the correlation of GHZ states. Then Alice, Bob and Charlie utilize the measurement outcomes of W states to implement signature and verification. The proposed scheme without any key converts the message with quantum one-way function to improve the security. The new quantum bi-signature scheme can solve the most issues of two-way choice in real life, and analysis results show that the proposed scheme is secure and efficient. Furthermore, the proposed scheme can be implemented with the existing physical technologies.
An UML to OWL based approach for extracting Moodle’s Ontology for Social Network Analysis

The main analyses in Social Network Analysis field are focusing on measuring the frequency of social interactions that are modeled only with numbers; 0 or 1 on the graph adjacency matrix. Therefore, ontologies have drawn attention to empower and enhance the Social Network Analysis with semantics. By exploring theses semantics, besides we better view and analyze the interactions between students and teachers inside the Learning Management System, the network graph structure and analyses change. In this paper, we propose a methodology to build Moodle’s OWL (Web Ontology Language) ontology for Social Network Analysis ends. The proposed ontology gives explicit meaning to the relationships inside Moodle instead of modeling them as nodes and edges with implicit semantics like the case in classical Social Network Analysis. The semantics of relationships influences the graph structure and statistics by making some nodes more central and influential and other nodes less central and influential. The built ontology is basically made by exploring the UML (Unified Modeling Language) class diagram that we created from Moodle Mount Orange School and is our main metadata source.