Reviewerly/exHarmony huggingface dataset:
A Distributed Computing Framework Based on Variance Reduction Method to Accelerate Training Machine Learning Models

To support large-scale intelligent applications, distributed machine learning based on JointCloud is an intuitive solution scheme. However, the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly, which highly demand on computing and memory resources. To overcome the challenges, we propose a computing framework for L-BFGS optimization algorithm based on variance reduction method, which can utilize a fixed big learning rate to linearly accelerate the convergence speed. To validate our claims, we have conducted several experiments on multiple classical datasets. Experimental results show that the computing framework accelerate the training process of solver and obtain accurate results for machine learning algorithms.
Distance Bounds for High Dimensional Consistent Digital Rays and 2-D Partially-Consistent Digital Rays

We consider the problem of digitalizing Euclidean segments. Specifically, we look for a constructive method to connect any two points in ℤ^d. The construction must be consistent (that is, satisfy the natural extension of the Euclidean axioms) while resembling them as much as possible. Previous work has shown asymptotically tight results in two dimensions with Θ(log N) error, where resemblance between segments is measured with the Hausdorff distance, and N is the L₁ distance between the two points. This construction was considered tight because of a Ω(log N) lower bound that applies to any consistent construction in ℤ². In this paper we observe that the lower bound does not directly extend to higher dimensions. We give an alternative argument showing that any consistent construction in d dimensions must have Ω(log^{1/(d-1)} N) error. We tie the error of a consistent construction in high dimensions to the error of similar weak constructions in two dimensions (constructions for which some points need not satisfy all the axioms). This not only opens the possibility for having constructions with o(log N) error in high dimensions, but also opens up an interesting line of research in the tradeoff between the number of axiom violations and the error of the construction. In order to show our lower bound, we also consider a colored variation of the concept of discrepancy of a set of points that we find of independent interest.
Innovation and Development Process of Software Development Methods under Digital Background

The progress of digitalization of software engineering technology has always been the thrust of industrial growth. In the process of continuously improving the technical level of software development, the problems encountered have gradually emerged. In the digital age, traditional information communication media such as language, text, sound and image all use digital technology to transform each other. There are still many problems in the development of software development methods, which also requires that software development methods can conform to the trend of the times and constantly solve the existing problems while developing. Computer software plays a very core role in the development of computer industry, and it is also a powerful driving force for the rapid development of information and digital technology. With the development of digital technology, it is helpful to cultivate more high-end design talents to explore the innovative development of software development teaching. Based on the development and application of digital technology in computer software engineering, this paper expounds the methods and technologies commonly used in engineering development, and puts forward the corresponding optimization strategies for the application of digital technology.
Keeping It Real!

In this paper we discuss the necessity to preserve the sense of presence in virtual reality (VR). A high sense of presence has proven advantages but is also very fragile to interruptions. We outline scenarios where interaction and communication between persons inside and outside virtual environments are necessary and assess challenges for maintaining the immersed user’s sense of presence in such cases. We also use existing literature to outline an experiment that allows us to try out different methods of collaboration between immersed users and external facilitators in order to discern their effect on presence.
Characterizing Sub-Cohorts via Data Normalization and Representation Learning

The process of identifying a cohort of interest is a very challenging task. It requires manually inspecting many patient records of complex structure that might include medical coding errors and missing data. This paper presents a computational pipeline for refining the process of cohort selection based on medical concepts recorded in the electronic health records (EHRs). The pipeline extracts EHR data for a given cohort and normalizes this data using standard vocabularies. Then a stacked denoising autoencoder is used to embed the normalized patient vectors in a low dimensional space, where the patients are subsequently clustered into sub-cohorts. The goal is to represent the cohort in a standard format and abstract variants of sub-populations. As a use-case, we applied the pipeline to 1.8 million Veterans diagnosed with major depressive disorder (MDD), and identified four meaningful sub-cohorts using the features learned by the autoencoder. Then, each sub-cohort was explored using a set of keywords for interpretation.
überSpark

Today's computing ecosystem, comprising commodity heterogeneous interconnected computing (CHIC) platforms, is increasingly being employed for critical applications, consequently demanding fairly strong end-to-end assurances. However, the generality and system complexity of today's CHIC stack seem to outpace existing tools and methodologies toward provable end-to-end guarantees. This paper describes our on-going research, and presents überSpark, a system architecture that argues for structuring the CHIC stack around Universal Object Abstractions (üobjects), a fundamental system abstraction and building block towards practical and provable end-to-end guarantees.
Review on Student Profile in Educational Research

This study reviewed recently published scientific literature on the use of student profile (SP) in educational research. After systematically searching online bibliographic databases, 23 relevant papers were located and included in the study. The results indicate that (1) SP construction was mainly based on the theory about learning theories, particularly social constructivism; (2) SP analysis was mainly about learning style, academic achievement, learning motivation, learning anxiety, etc.; (3) The E-learning platforms that used to collect data can be divided into two groups: adaptive learning styles and intelligent learning styles; (4) The computing mechanism includes Bayesian networks, decision trees, etc. As SP is still at its early developing stage, it has some research limitations: not only lack of studies in investigating all cognitive aspects, but also lack of factors that considered in constructing an overall student profile. SP would be employed extensively in education field in the near future if it can address the challenges mentioned above.
Multi-Feature Fusion Identification of Important Nodes in Traffic Network

Different modeling and analysis methods describe different characteristics of the traffic network and lead to different conclusions for the identification of important road sections and intersections. In this paper, the road network and bus network of Langfang city are integrated to model the composite traffic network, and the actual traffic flow data are added to analyze. A bioinformatics approach called SPRING is introduced to determine the importance of certain nodes. This method gives the ranking result of node importance with a combination of various characteristics, which include the similarity of network characteristics and traffic conditions between certain nodes and others. Through comparing the node importance of multi-feature fusion and the node importance of single traditional network characteristic, it is concluded that multi-feature fusion method has the feasibility, advantages, and disadvantages in identification of important nodes and shows effectiveness when considering the real traffic situation and the demand of traffic analysis.
Implementación de la Realidad Aumentada en un sistema opto mecatrónico de grabado láser

The main objective of this work is to develop an interactive mobile application for monitoring, description and training in the operation of a laser engraving machine using augmented reality technology. The CAD design of the machine is used, generating a visualization of each of the elements that make it up with a description of its main characteristics. Also, an option is included where you can observe the 3D animation of the engraving process. All of the above is generated through the use of open source software (Unity, Vuforia and C #) thus creating a practical option for training in the handling of this type of machinery. This type of technology can be implemented in different educational and industrial environments. For the convenience of use, the generated application has a user-friendly virtual environment and is developed for the main digital platforms (Android, iOS, Windows).
Why Majority Rule Does Not Work in Quantum Computing: A Pedagogical Explanation

To increase the reliability of computations result, a natural idea is to use duplication: we let several computers independently perform the same computations, and then, if their results differ, we select the majority’s result. Reliability is an important issue for quantum computing as well, since in quantum physics, all the processes are probabilistic, so there is always a probability that the result will be wrong. It thus seems natural to use the same majority rule for quantum computing as well. However, it is known that for general quantum computing, this scheme does not work. In this paper, we provide a simplified explanation of this impossibility.
Latent Factor Model with User and Fused Item Embeddings for Recommendation

Collaborative filtering (CF) is commonly used in recommender systems, and latent factor model (LFM) is one of the most popular methods for CF. Though LFMs achieve great success in CF, most of them are not satisfactory enough for data with negative feedback. Recently, embedding techniques are applied in LFMs and enhance the performance. However, it is hard to keep the convergence speed and evaluation metrics in balance since the joint learning embeddings bring a large number of trainable parameters. By using items and users embeddings, in this paper, we propose an improved LFM. Different from the existing embedding based LFMs, the proposed model learns item latent representations from the combination of co-liked item co-occurrence matrix (LICM) and co-disliked item co-occurrence matrix (DICM). This property reduces the trainable parameters and brings a quick convergence. Experimental results on MovieLens datasets show that the proposed model performs well.
Cross-Lingual Transfer for Hindi Discourse Relation Identification

Discourse relations between two textual spans in a document attempt to capture the coherent structure which emerges in language use. Automatic classification of these relations remains a challenging task especially in case of implicit discourse relations, where there is no explicit textual cue which marks the discourse relation. In low resource languages, this motivates the exploration of transfer learning approaches, more particularly the cross-lingual techniques towards discourse relation classification. In this work, we explore various cross-lingual transfer techniques on Hindi Discourse Relation Bank (HDRB), a Penn Discourse Treebank styled dataset for discourse analysis in Hindi and observe performance gains in both zero shot and finetuning settings on the Hindi Discourse Relation Classification task. This is the first effort towards exploring transfer learning for Hindi Discourse relation classification to the best of our knowledge.
General Virtual Images Construction Using Pixel Scrambling for Face Recognition

Face recognition encounters the problem that multiple samples of the same object may be very different owing to the deformation of appearances. To synthesize reasonable virtual images of objects is a good way to solve it, which has been successfully used in face recognition. However, the schemes proposed are usually not suitable for other applications. In this paper, we propose a novel and general scheme to generate virtual images for deformable faces. We introduce the idea of pixel scrambling to our scheme and allow the pixels within an image block to be randomly rearranged to reflect possible variations of the appearance of objects. We demonstrate that virtual images obtained using pixel scrambling and original images are complementary in representing the appearance of objects. Extensive classification experiments on face databases show that the proposed virtual image scheme is very competent and can be combined with a number of classifiers to achieve surprising accuracy improvement.
A Deep Profiling and Visualization Framework to Audit Clinical Assessment Variation

Clinical assessment variation (CAV) has a profound impact on patient outcomes, and appropriate tooling is critically needed to help understand and guide necessary interventions. In this study, we propose an intuitive approach to visualizing CAV and summarizing the contexts pertinent to decision-making. By superimposing the response variable and clusters learned according to the explanatory variables, a color-coded 2D scatter plot can be rendered to show the spatial proximity and semantic composition of the clusters. Without loss of generality, an example application on preoperative patient assessment demonstrated the approach can assist in auditing inconsistent human decisions and informing the reconciliation process. The methods will also benefit refining of clinical assessment guidelines by systematically eliciting practice-based knowledge.
Design and verification of DEFLATE acceleration as an architected instruction in z15

The IBM z15 processor chip contains a new hardware component to perform DEFLATE compliant compression and decompression. The Integrated Accelerator for zEnterprise Data Compression is based on a high-frequency DEFLATE pipeline and includes a hardware generator for dynamic Huffman tables. Accessible as an architected instruction, this engine has been designed for straight forward exploitation by software and is easily available to any application in the problem state. A brand-new hardware/firmware integration model has been developed to provide this complex functionality without imposing restrictions on data patterns or data sizes and without impacting system responsiveness. This article describes the concept, implementation, and verification of DEFLATE compliant compression acceleration in z15 across both hardware and firmware. It illustrates various challenges that result from incorporating complex data-dependent and data-intense functionality like DEFLATE as an architected instruction and discusses how solutions in hardware/firmware codesign have been applied to overcome these challenges.
The Impact of AI on Disintermediation Processes in the Tourism Industry

As an innovative technology, artificial intelligence (AI) leads to disruption and automation of almost every process and business model in almost any industry today. AI contributes to the process of disintermediation of value chains resulting in shrinking the producer-customer links. The chapter examines the influence of AI on disintermediation in the tourism industry. It investigates the changes and transformation of the value creation process and marketing in the tourism industry affected by AI technology. AI could transform and revolutionize every segment of the tourism industry. Thus, it can make tourism more efficient, with new value-added customer services. AI is considered an enabling tool for the creation of the so-called “smart tourism” as a new age of tourism development. As a digital tool, AI enables the tourism industry to get insight into the customer, understand tourist profiles and provide consumers with personalized and niche travel experiences.
Plan distance heuristics for task fusion in distributed temporal continuous planning

Automating planning for large teams of heterogeneous robots is a growing challenge, as robot capabilities diversify and domain complexities are incorporated. Temporal and continuous features accurately model real-world constraints, but add computational complexity. Distributed planning methods, suc h as the Coalition Formation then Planning framework, allocate tasks to robot teams and plan each task separately to accelerate planning. However, the task decomposition limits cooperation between coalitions allocated to different tasks and results in lower quality plans that require more actions and time to complete. Task Fusion estimates couplings between tasks and fuses coupled coalition-task pairs to improve cooperation and produce higher quality plans. Task Fusion relies on existing heuristics, which were ineffective and often resulted in worse results than the baseline framework. This manuscript introduces new heuristics that outperform the existing methods in two complex heterogeneous multi-robot domains that incorporate temporal and continuous constraints.
F-Transform and Convolutional NN: Cross-Fertilization and Step Forward

We propose to assign the F-transform kernels to the CNN weights and compare them with commonly used initialization. By this, we develop a new initialization mechanism where the F-transform convolution kernels are used in the convolutional layers. Based on a series of experiments, we demonstrate the suitability of the F-transform-based deep neural network in the domain of image processing with the focus on classification. Moreover, we support our insight by revealing the similarity between the F-transform and first-layer kernels in certain deep neural networks.
Interactive Archaeological Storytelling: Using Immersive Technologies to Visit the Ancient Site of Roca Vecchia (Lecce, ITA)

Immersive technologies have the potential to significantly improve the way of presenting cultural heritage storytelling and conveying the results of the archaeological research to a wide generalist audience. This work describes the methodological approach we applied to develop an augmented realty and a virtual reality application to support the narrative of the archaeological site of Roca Vecchia (Lecce, Italy). Starting from the archaeological evidence, we virtually rebuilt the area as it was supposed to be at the time of the Middle Bronze Age and we developed immersive and interactive applications to let visitors vividly experience some of the historical events happened around the mid 2nd millennium BC. The methodologies described and the applications developed are the result of the ongoing collaborative research leaded by a multidisciplinary team made up of archaeologists, humanists, engineers and artists.
Modified Multi-cohort Intelligence Algorithm with Panoptic Learning for Unconstrained Problems

In this paper, we present a new optimization algorithm referred to as Modified Multi-cohort Intelligence with Panoptic Learning (Multi-CI-PL). This proposed algorithm is a modified version of Multi-cohort Intelligence (Multi-CI), where Panoptic Learning (PL) is incorporated into Multi-CI which makes every cohort candidate learn the most from the best candidate but at same time partially learn from other candidates. A variety of well-known set of unconstrained test problems have been successfully solved by using the proposed algorithm and compared with several other evolutionary algorithms. The Multi-CI-PL approach has resulted in competent and sufficiently robust results. The associated strengths, weaknesses, and possible real-world extensions are also discussed.
Tuple Measure Model Based on CFI-Apriori Algorithm

Aiming at the loss of valuable information in the traditional Apriori algorithm. A new improved algorithm CFI-Apriori (Candidate frequent item sets-Apriori) is proposed to solve this problem, A tuple measurement model based on the algorithm is created. Compared with traditional Apriori algorithm, CFI-Apriori algorithm stores infrequent candidate item sets and creates two tuple measurement models based on probability weight and information entropy. The measurement models could screen out high-value target tuples to provide a basis for efficient use of data. Simulation results show that the improved algorithm and tuple measure model can utilize data efficiently, which is more efficient than traditional methods.
A Comparative Study of dotNetRDF And Semweb.NET Semantic Web Libraries

Many open sources tools have been developed to simplify ontology development tasks including editing, storing, maintaining, querying and so forth. Furthermore, many studies have been conducted to evaluate such tools in the past years. However there has not been enough work done on comparison and evaluation of dot NET-based tools for ontology processing. This study experimented ontology development in two dot NET semantic web libraries, namely, SemWeb.NET and dotNetRDF. Thereafter, their capabilities in terms of programming constructs, ontology storage and query supports, usability, scalability documentation level are evaluated and compared. The comparative analysis from the experimental results show that SemWeb.NET is a cross platform library as it can be executed on multiple operating systems while dotNetRDF runs only on Microsoft Windows system. dotNetRDF provides some graphical user interface tools such as rdfEditor and SPARQL GUI which enable the visual edition and query of ontologies, whereas, SemWeb.NET only offer a command line interface to process ontologies. Furthermore, The dotNetRDF library is well documented and updated which make it easier to develop ontologies in dotNetRDF compared to SemWeb.NET. Moreover, although both libraries can create and process ontologies of RDF format their memory sizes are very limited.
Bergman and Scheele: Childhoods

Torbern Olof Bergman was born on March 20, 1735. As Sweden used the older Julian calendar until February 1753, Bergman’s birthday was March 9 by that calendar. He was born on the Katrineberg royal residence.
Contour-based Medical Image Fusion for Biopsy

Implementing prostate biopsy with the guidance of magnetic resonance imaging (MRI) can reduce the risk and improve the accuracy in prostate cancer therapy. How to combine information from MRI and transrectal ultrasonography (TRUS) plays an important role in image fusion-guided biopsy. In this study, we introduce a neural network based on U-net for prostate segmentation in MRI and TRUS. On the basis of the contour generated from segmentation masks, we implement the thin plate spline-robust point matching for non-rigid registration. We validate our method using animal experiments with simulated lesions and obtain convincing results. The results show that our model achieve a mean intersection over union (mIOU) of 0.819 for TRUS segmentation and a mIOU of 0.878 for MRI segmentation. We also modify root mean square error (RMSE) and achieve the result of 0.0082 in registration which is more accurate than rigid registration.