Reviewerly/exHarmony huggingface dataset:
The Design of Digital Book Content for Assessment and Evaluation Courses by Adopting Superitem Concept Based on Kvisoft Flipbook Maker in era of Industry 4.0

Functionally, Assessment and Evaluation digital book based on kvisoft flipbook maker can attract students to read and make them easier to gain knowledge about the concepts of assessment and evaluation courses quickly whenever and wherever they are. Yet sometimes the material content contained in Assessment and Evaluation digital book is still not able to direct the mindset of students gradually from the basic level to high level. Therefore, Assessment and Evaluation digital book made using the kvisoft flipbook maker application, also needs to develop its content by adopting the superitem concept, so that it can improve students' thinking through the exploration of their mindset from simple to complex levels. Based on those problems, this research aims to provide an overview of the digital book content design for Assessment and Evaluation courses created with Kvisoft Flipbook Maker application by adopting the super item concept. The approach used was the development research with the Borg and Gall development design, which focuses on the stages of designing. The research subjects involved in this research were four experts to conduct an initial trial on the design of digital book content. They are two information technology education experts and two education evaluation experts. Data collection techniques were carried out by distributing questionnaires. The analytical technique used was descriptive statistics, by determining the percentage description. The results of this research indicated that a quality level in the design of digital book material content included in the good category.
Literal Occurrences of Multiword Expressions: Rare Birds That Cause a Stir

Abstract Multiword expressions can have both idiomatic and literal occurrences. For instance pulling strings can be understood either as making use of one’s influence, or literally. Distinguishing these two cases has been addressed in linguistics and psycholinguistics studies, and is also considered one of the major challenges in MWE processing. We suggest that literal occurrences should be considered in both semantic and syntactic terms, which motivates their study in a treebank. We propose heuristics to automatically pre-identify candidate sentences that might contain literal occurrences of verbal VMWEs, and we apply them to existing treebanks in five typologically different languages: Basque, German, Greek, Polish and Portuguese. We also perform a linguistic study of the literal occurrences extracted by the different heuristics. The results suggest that literal occurrences constitute a rare phenomenon. We also identify some properties that may distinguish them from their idiomatic counterparts. This article is a largely extended version of Savary and Cordeiro (2018).
Leveraging Wikidata to Enhance Authority Records in the EHRI Portal

This paper will discuss how the European Holocaust Research Infrastructure (EHRI) leveraged Wikidata to expand and enhance its authority records for Holocaust-era camps and ghettos. As an international project integrating descriptions for Holocaust-related archival material held in institutions across the world, EHRI faces challenges in developing name authorities that can be efficiently and effectively applied across multiple languages and various metadata standards. The integration of the EHRI name authorities for camps and ghettos into Wikidata demonstrates how the linked open data capacities of Wikidata can be used to enhance local metadata and expand the reach of that data.
Measuring linguistic complexity in long‐term L2 speakers of English and L1 attriters of German

Linguistic complexity is neither easily defined nor measured. The challenge in finding reliable ways to measure linguistic complexity is even more pronounced when the variation of contexts in which complexity is measured is taken into account. This paper therefore aims at finding measures for assessing syntactic and lexical complexity that are sensitive and non‐overlapping even in a less frequently studied context: spontaneous speech of L1 attriters and highly proficient L2 English speakers. To evaluate operationalizations of syntactic and lexical complexity, several previously proposed measures are applied to a corpus of spontaneous speech and a principal component analysis is conducted. The results show which of the measures group together as underlying variables of syntactic and lexical complexity and how this can inform future operationalizations of complexity.
Measuring Semantic Abstraction of Multilingual

In this paper, we investigate whether multilingual neural translation models learn stronger semantic abstractions of sentences than bilingual ones. We test this hypotheses by measuring the perplexity of such models when applied to paraphrases of the source language. The intuition is that an encoder produces better representations if a decoder is capable of recognizing synonymous sentences in the same language even though the model is never trained for that task. In our setup, we add 16 different auxiliary languages to a bidirectional bilingual baseline model (English-French) and test it with in-domain and out-of-domain paraphrases in English. The results show that the perplexity is significantly reduced in each of the cases, indicating that meaning can be grounded in translation. This is further supported by a study on paraphrase generation that we also include at the end of the paper.
Understanding Generalization of Deep Neural Networks Trained with Noisy Labels.

Over-parameterized deep neural networks trained by simple first-order methods are known to be able to fit any labeling of data. Such over-fitting ability hinders generalization when mislabeled training examples are present. On the other hand, simple regularization methods like early-stopping can often achieve highly nontrivial performance on clean test data in these scenarios, a phenomenon not theoretically understood. This paper proposes and analyzes two simple and intuitive regularization methods: (i) regularization by the distance between the network parameters to initialization, and (ii) adding a trainable auxiliary variable to the network output for each training example. Theoretically, we prove that gradient descent training with either of these two methods leads to a generalization guarantee on the clean data distribution despite being trained using noisy labels. Our generalization analysis relies on the connection between wide neural network and neural tangent kernel (NTK). The generalization bound is independent of the network size, and is comparable to the bound one can get when there is no label noise. Experimental results verify the effectiveness of these methods on noisily labeled datasets.
Numerical stability of circular Hilbert transform and its application to signal decomposition

In this paper, we propose a novel method of computing Hilbert transform based on the mechanical quadrature method. Experiments show that the method outperforms the library function ‘hilbert’ in Matlab when the values of functions being handled are very large or approach to infinity, that is a problem we have to deal with when we compute, for instance, outer functions. As an application, we use the method to obtain the unwinding series of signals, which is a positive frequency decomposition of signals and is dependent of extraction of outer functions involving computation of Hilbert transform. The experimental results show better stability. Then, we give the transient time frequency distribution of the unwinding series of signals. Finally, experiments of noisy signals are given, and the results show that the introduced method can well resist some disturbance.
Testbed Design and Performance Emulation in Fog Radio Access Networks

F-RAN, as a promising technology to achieve low latency and high spectral and energy efficiency, has attracted significant attention. Although performance analysis and resource allocation for F-RANs have been well researched, performance emulation based on a testbed is still challenging. In this article, a testbed for F-RANs has been designed and implemented based on OAI. In order to present the prototype implementation and emulation results, a high-definition video acquisition demonstration based on F-RANs has been built. Given that AI-enabled techniques can cope with dynamic network environments and intractable performance evaluation with low complexity, both an edge caching driven redirection mechanism and transmission mode selection have been evaluated, and the corresponding AI-enabled video quality assessment has been analyzed. The emulation results show that HDV acquisition in F-RANs can reduce both end-to-end latency and frame loss rate while maintaining high video quality.
7. V-line and conical Radon transforms with applications in imaging

This chapter is dedicated to the recent developments and current knowledge on the V-line and conical Radon transforms (VLT and CRT). The primary motivation for the study of such operators has been their relation to several imaging modalities based on physics of scattered particles. We discuss many of these applications and explain why the measured data is modeled through VLT and CRT. The mathematical results presented in the chapter are primarily focused on a special class of operators, which use integration trajectories with vertices inside the image domain. The text is supplemented with an extensive list of references on both theoretical and applied aspects of the subject.
Privacy-aware relationship semantics–based XACML access control model for electronic health records in hybrid cloud

State-of-the-art progress in cloud computing encouraged the healthcare organizations to outsource the management of electronic health records to cloud service providers using hybrid cloud. A hybrid cloud is an infrastructure consisting of a private cloud (managed by the organization) and a public cloud (managed by the cloud service provider). The use of hybrid cloud enables electronic health records to be exchanged between medical institutions and supports multipurpose usage of electronic health records. Along with the benefits, cloud-based electronic health records also raise the problems of security and privacy specifically in terms of electronic health records access. A comprehensive and exploratory analysis of privacy-preserving solutions revealed that most current systems do not support fine-grained access control or consider additional factors such as privacy preservation and relationship semantics. In this article, we investigated the need of a privacy-aware fine-grained access control model for the hybrid cloud. We propose a privacy-aware relationship semantics–based XACML access control model that performs hybrid relationship and attribute-based access control using extensible access control markup language. The proposed approach supports fine-grained relation-based access control with state-of-the-art privacy mechanism named Anatomy for enhanced multipurpose electronic health records usage. The proposed (privacy-aware relationship semantics–based XACML access control model) model provides and maintains an efficient privacy versus utility trade-off. We formally verify the proposed model (privacy-aware relationship semantics–based XACML access control model) and implemented to check its effectiveness in terms of privacy-aware electronic health records access and multipurpose utilization. Experimental results show that in the proposed (privacy-aware relationship semantics–based XACML access control model) model, access policies based on relationships and electronic health records anonymization can perform well in terms of access policy response time and space storage.
Pose-Guided Spatial Alignment and Key Frame Selection for One-Shot Video-Based Person Re-Identification

One-shot video-based person re-identification exploits the unlabeled data by using a singlelabeled sample for each individual to train a model and to reduce the need for laborious labeling.Although recent works focusing on this task have made some achievements, most state-of-the-art models are vulnerable to misalignment, pose variation and corrupted frames.To address these challenges, we propose a one-shot video-based person re-identification model based on pose-guided spatial alignment and KFS.First, a spatial transformer sub-network trained using pose-guided regression is employed to perform the spatial alignment.Second, we propose a novel training strategy based on KFS.Key frames with abruptly changing poses are deliberately identified and selected to make the network adaptive to pose variation.Finally, we propose a frame feature pooling method by incorporating long short-term memory with an attention mechanism to reduce the influence of corrupted frames.Comprehensive experiments are presented based on the MARS and DukeMTMC-VideoReID datasets.The mAP values for these datasets reach 46.5% and 68.4%, respectively, demonstrating that the proposed model achieves significant improvements over state-of-the-art one-shot person re-identification methods.
Automatic recognition system of emotions expressed through the face using machine learning: Application to police interrogation simulation

During our work, we developed Automatic recognition system of emotions expressed through the face. This system will be adapted to judicial police interrogation or interviews simulation. This system is based on a functional division: detection and follow-up the face, acquisition of facial expressions starting from video images sequences, and finally, characteristics extraction and emotions expressed recognition. The deep convolution network model is formed primarily on FER2013. It is used to train and validate the model. We use OpenCV library and its implementation of the Viola and Jones algorithm for face detection.
Estimating Confidence of Individual User Predictions in Item-based Recommender Systems

This paper focuses on recommender systems based on item-item collaborative filtering (CF). Although research on item-based methods is not new, current literature does not provide any reliable insight on how to estimate confidence of recommendations. The goal of this paper is to fill this gap, by investigating the conditions under which item-based recommendations will succeed or fail for a specific user. We formalize the item-based CF problem as an eigenvalue problem, where estimated ratings are equivalent to the true (unknown) ratings multiplied by a user-specific eigenvalue of the similarity matrix. We show that the magnitude of the eigenvalue related to a user is proportional to the accuracy of recommendations for that user. We define a confidence parameter called the eigenvalue confidence index, analogous to the eigenvalue of the similarity matrix, but simpler to be computed. We also show how to extend the eigenvalue confidence index to matrix-factorization algorithms. A comprehensive set of experiments on five datasets show that the eigenvalue confidence index is effective in predicting, for each user, the quality of recommendations. On average, our confidence index is 3 times more correlated with MAP with respect to previous confidence estimates.
Multi-tissue Partitioning for Whole Slide Images of Colorectal Cancer Histopathology Images with Deeptissue Net

Tissue composition plays an essential role in diagnosis and prognosis of colorectal cancer (CRC). Studies have shown that the relative proportion of tissue composition on colorectal specimens is potentially prognostic of outcome in CRC patients. Some of the important tissue partitions include blood vessel, tumor epithelium, adipose tissue, mucosal glands, mucus, muscle, stroma, necrosis, immune cell, and background/other tissues. A challenge in accurately determining quantitative measurements of tissue composition however is in the need for automated tissue partitioning image analysis tools. Towards this goal, we present a Deeptissue Net, a deep learning strategy which involves integrating DenseNet with Focal Loss. In order to show the effectiveness of Deeptissue Net, the model was trained with 40 WSIs from one site and tested on 620 WSIs from two sites. 10 distinct tissue partitions are blood vessel, tumor epithelium, adipose tissue, mucosal glands, mucus, muscle, stroma, necrosis, immune cell, and background/other tissues. The ground truth for training and evaluating Deeptissue Net involved careful annotation of the different tissue compartments by expert pathologists. The Deeptissue net was trained with the tissue partitions delineated for the 10 classes on the 40 WSIs and subsequently evaluated on the remaining $$N=620$$ datasets. By measuring with confusion matrices, the Deeptissue Net achieves the accuracy of 0.72, 0.84, and 0.88 in classifying mucus, stroma, and necrosis on the 2nd batch of Dataset 1; 0.85 and 0.96 in classifying mucus and muscle on Dataset 2, respectively, which significantly outperformed DenseNet and ResNet.
Evolving controllably difficult datasets for clustering

Synthetic datasets play an important role in evaluating clustering algorithms, as they can help shed light on consistent biases, strengths, and weaknesses of particular techniques, thereby supporting sound conclusions. Despite this, there is a surprisingly small set of established clustering benchmark data, and many of these are currently handcrafted. Even then, their difficulty is typically not quantified or considered, limiting the ability to interpret algorithmic performance on these datasets. Here, we introduce HAWKS, a new data generator that uses an evolutionary algorithm to evolve cluster structure of a synthetic data set. We demonstrate how such an approach can be used to produce datasets of a pre-specified difficulty, to trade off different aspects of problem difficulty, and how these interventions directly translate into changes in the clustering performance of established algorithms.
The University of Helsinki Submissions to the WMT19 News Translation Task

In this paper, we present the University of Helsinki submissions to the WMT 2019 shared task on news translation in three language pairs: English-German, English-Finnish and Finnish-English.This year, we focused first on cleaning and filtering the training data using multiple data-filtering approaches, resulting in much smaller and cleaner training sets.For English-German, we trained both sentence-level transformer models and compared different document-level translation approaches.For Finnish-English and English-Finnish we focused on different segmentation approaches, and we also included a rule-based system for English-Finnish.
TeIL: a type-safe imperative tensor intermediate language

Each of the popular tensor frameworks from the machine learning domain comes with its own language for expressing tensor kernels. Since these tensor languages lack precise specifications, it is impossible to understand and reason about tensor kernels that exhibit unexpected behaviour. In this paper, we give examples of such kernels.
Towards Integration of Blockchain and IoT: A Bibliometric Analysis of State-of-the-Art

Since its inception, Blockchain has proven itself as an emerging technology that revolutionizes diverse industries. Among others, Internet of Things (IoT) is one of the application domains that reaps large benefits from Blockchain. The Blockchain’s potential to overcome different challenges of IoT services has shifted the research interests of many scientists towards addressing the integration of two disruptive technologies, i.e., IoT and Blockchain. This resulted in publishing more research papers in this emerging field. Thus, there is a need to conduct research studies through which a broad overview of research contributions in this field could be investigated. To respond to this need, a number of review papers have been published recently, each of which has considered the integration of IoT and Blockchain from a different perspective. Nonetheless, none of them has reported a bibliometric analysis of the state-of-the-art in the integration of IoT and Blockchain. This gap stimulated us to investigate a thorough analysis of the current body of knowledge in this field, through a bibliometric study. In this paper, we conducted a bibliometric analysis on the Scopus database to assess all scientific papers that addressed the integration of IoT and Blockchain. We have analyzed those collected papers against four criteria including annual publication and citation patterns, most-cited papers, most frequently used keywords, and most popular publication venues. The results disseminate invaluable insights to the researchers before establishing a research project on IoT and Blockchain integration.
Integer Compression in NVRAM-centric Data Stores

Lightweight integer compression algorithms play an important role in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. Thus, there is a large number of algorithms to choose from, while different algorithms are tailored to different data characteristics. As we show in this paper, with the availability of byte-addressable non-volatile random-access memory (NVRAM), a novel type of main memory with specific characteristics increases the overall complexity in this domain. In particular, we provide a detailed evaluation of state-of-the-art lightweight integer compression schemes and database operations on NVRAM and compare it with DRAM. Furthermore, we reason about possible deployments of middle- and heavyweight approaches for better adaptation to NVRAM characteristics. Finally, we investigate a combined approach where both volatile and non-volatile memories are used in a cooperative fashion that is likely to be the case for hybrid and NVRAM-centric database systems.
Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy.
A novel light field imaging based 3D geometry measurement technique for turbomachinery blades

This paper presents a novel snapshot three-dimensional (3D) imaging technique for fast turbomachinery blade geometrical measurements. The proposed measurement system employs a microlens array based light-field camera to capture 3D blade geometry into one light-field image, from which 100 new perspective images can be generated and the blade 3D point cloud can be recovered through sophisticated light-field rendering algorithms. The measurement accuracy is determined to be approximately by measuring a series of standard gauge blocks. Performance of the proposed technique is compared against a coordinate-measuring machine (CMM) and laser scanner by measuring a turbine blade. It reveals that the single light-field camera 3D measurement system can produce data point in with an average measurement accuracy of , which is considerably more efficient than the current CMM and laser scanning techniques.