Reviewerly/exHarmony huggingface dataset:
Smart homes for the future of dementia care

Around 850 000 people in the UK have dementia. Due to an ageing population, prevalence is forecast to increase to over 1 million people by 2025. The associated burden on patients, carers, and health-care systems has become an urgent priority to address. Research to investigate how technology might be able to reduce this burden and help people with dementia live at home is underway. Technology has been in use for the self-management of other diseases for some time (eg, devices that automatically monitor glucose and deliver insulin for patients with diabetes), but the role of technology in dementia care is now being fully explored in a new £20 million project.
Provably Secure (Broadcast) Homomorphic Signcryption

Signcryption has drawn a lot of attention due to its useful applications in many areas, in particular for applications where the computation and communication resources are constrained, for example, for lightweight devices. The traditional signcryption scheme does not support the homomorphic property. Recent work by Rezaeibagha et al. (Provsec 2017) offered a provably secure homomorphic signcryption scheme, in which for the first time, provided a scheme provably secure under some restriction. In this paper, we show that the homomorphic signcryption can be extended to provably secure broadcast signcryption scheme. We allow the broadcasted signcrypted data items to be aggregated without requiring decryption that is a desirable feature in distributed environments.
Fomentar la participación en clase de los estudiantes universitarios y evaluarla

The objective of this research is to analyze a system the aim of which is to encourage the participation of students based on critical reflection. The method consists mainly of presenting material in the classroom to be constructively criticized by the students and assigning the task of noting down the participation by at least two randomly selected students. It is a relatively objective system of evaluating participation. The methodology is based on a mixed method, qualitative-quantitative. Participant observation together with a survey of 507 students was used, with a response rate of 55% of the students. 66% consider that students’ critical capacity must be developed in a high or very high degree (compared to 9% in low or very low degree) and 51% agree in a high o very high degree with the rotating system of recording the student’s participation in class (compared to 19% in a low or very low degree). There is a significant association between the size of the class group and the grade obtained in participation (r = -0.96), with worse scores in the most numerous groups. It is surprising that students with low participation (from 0 to 3 interventions) are the most numerous group, with 42.2%. It reflects that the method has excluded or almost excluded a very high percentage of students. If you add the average and high participation, the method works more or less well for a majority of students, the 58.2%, but we must make the maximum effort to integrate the large group of excluded people.
Multimodal Machine Translation with Embedding Prediction

Tosho Hirasawa, Hayahide Yamagishi, Yukio Matsumura, Mamoru Komachi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop. 2019.
Cyber Gremlin: social networking, machine learning and the global war on Al-Qaida-and IS-inspired terrorism

Abstract Among our mundane and technical concepts, machine learning is currently one of the most important and widely used, but least understood. To date, legal scholars have conducted comparatively little work on its cognate concepts. This article critically examines the use of machine learning technologies to suppress or block access to al-Qaida and IS-inspired propaganda. It will: (i) demonstrate that, insofar as law and policy dictate that machine learning systems comply with desired constitutional norms, automated-decision making systems are not as effective as critics would like; (ii) emphasize that, under the current envisaged ‘proactive’ role of networking sites, equating radical and extreme ideas and ideology with ‘violence’ is a practical reality; and (iii) outline a workable strategy for cross-border legal and technical counterterrorism that satisfies the requirements for algorithmic fairness.
The Effects of the Floating Action Button on Quality of Experience

Google’s Material Design, created in 2014, led to the extended application of floating action buttons (FAB) in user interfaces of web pages and mobile applications. FAB’s roll is to trigger an activity either on the present screen, or it can play out an activity that makes another screen. A few specialists in user experience (UX) and user interface (UI) design are sceptical regarding the usability of FAB in the interfaces of both web pages and mobile applications. They claim that the use of FAB easily distracts users and that it interferes with using other important functions of the applications, and it is unusable in applications designed for iOS systems. The aim of this paper is to investigate by an experiment the quality of experience (QoE) of a static and animated FAB and compare it to the toolbar alternative. The experimental results of different testing methods rejected the hypothesis that the usage and animation of this UI element has a positive influence on the application usability. However, its static and animated utilization enhanced the ratings of hedonic and aesthetic features of the user experience, justifying the usage of this type of button.
Cluster-Scheduling Big Graph Traversal Task for Parallel Processing in Heterogeneous Cloud Based on DAG Transformation

Task scheduling is the key to the full utilization of heterogeneous cloud capabilities for parallel processing of big graphs. Most graph processing systems adopt single-granularity scheduling mechanisms without considering the heterogeneity of the cloud, leading to poor performance. To alleviate it by learning from the excellent directed acyclic graph (DAG)-based scheduling techniques accumulated in traditional parallel computing, we first present a streaming DAG-construction heuristic. It transforms a big graph along with graph traversal algorithms to be carried out into a DAG. We then propose a three-phase heterogeneous-aware cluster-scheduling algorithm to schedule the DAG into a heterogeneous cloud for parallel processing. In the first phase, we design a parallel linear clustering algorithm to cluster the DAG into a series of linear clusters with different granularities. In the second phase, we design a heterogeneous-aware load balancing algorithm to map these clusters to different computational nodes of the cloud. In the last phase, we design a task ordering algorithm to assigns these clusters as-early-as-possible start times. The experimental results show that our scheme can generate high-quality schedules and improve the efficiency and performance of parallel processing of big graphs in the heterogeneous cloud.
Cooperative Generator-Discriminator Networks for Abstractive Summarization with Narrative Flow

We introduce Cooperative Generator-Discriminator Networks (Co-opNet), a general framework for abstractive summarization with distinct modeling of the narrative flow in the output summary. Most current approaches to abstractive summarization, in contrast, are based on datasets whose target summaries are either a single sentence, or a bag of standalone sentences (e.g., extracted highlights of a story), neither of which allows for learning coherent narrative flow in the output summaries. To promote research toward abstractive summarization with narrative flow, we first introduce a new dataset, Scientific Abstract SummarieS (SASS), where the abstracts are used as proxy gold summaries for scientific articles. We then propose Co-opNet, a novel transformer-based framework where the generator works with the discourse discriminator to compose a long-form summary. Empirical results demonstrate that Co-opNet learns to summarize with considerably improved global coherence compared to competitive baselines.
The Effectiveness of Physics Mobile Learning (PML) with HomboBatu theme to Improve the Ability of Diagram Representation and Critical Thinking of Senior High School Students

This study aimed to determine the effectiveness of Physics Mobile Learning (PML) with HomboBatu theme smartphone Android-Assisted to improve the ability of diagram representation and critical thinking of students of Grade X in senior high school.The research type used was experimental research with pretestposttest control group design.The sampling technique selection used cluster random sampling.The subjects were 32 students of Grade X MIPA 4, SMAN 6 Yogyakarta as control class with learning treatment of Powerpoint media and 22 students of Grade X MIPA 7, SMAN 6 Yogyakarta as experimental class with learning treatment of PML smartphone Android-Assisted with BatuHombo theme.Test instruments consisted of pretest and posttest.Data analysis used was analysis of N-Gain and Hotteling's Trace multivariate test (MANOVA).The results showed that learning with PML-themed HomboBatu media was more effective in increasing the representation ability of N-Gain diagrams by 0.78 in the high category.However, there was no significant difference in N-Gain scores in the experimental class and control class in improving critical thinking skills.This Physics Mobile Learning can be used by students or teachers to study Indonesian culture (Nias) for its learning media as well as for learning experiences that train the ability of diagram representation and critical thinking.
Sharing Linkable Learning Objects with the Use of Metadata and a Taxonomy Assistant for Categorization

In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning courses content of the Drupal-based Content Management System G-Lorep for academic learning is exchanged designing an object incorporating metadata to support the reuse and the classification in its context. In such an Artificial Intelligence environment, the exchange of Linkable Learning Objects can be used for dialogue between Learning Systems to obtain information, especially with the use of semantic or structural similarity measures to enhance the existent Taxonomy Assistant for advanced automated classification.
An incremental learning method based on formal concept analysis for pattern recognition in nonstationary sensor-based smart environments

Smart homes are typical nonstationary environments that keep generating new data. Ideally, predictive models learned from historical data should automatically adapt to new data without retraining. However, for many data mining algorithms, adding new data with new features to an existing model means we need to retrain the entire model. Compared with static ones, the models having incremental learning mechanisms are more suitable for handling streaming data in the aspect of self-adaptation. Therefore, we propose an incremental algorithm based on the formal concept analysis (FCA) for mining sequential patterns and apply it to recognize various human behavioral patterns in nonstationary sensor-based smart homes. It can automatically adapt to new training data with new classes or features to enhance the currently built model and have competitive recognition results compared to other classical graphical models.
Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings

This paper describes our system (Fermi) for Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media of SemEval-2019. We participated in all the three sub-tasks within Task 6. We evaluate multiple sentence embeddings in conjunction with various supervised machine learning algorithms and evaluate the performance of simple yet effective embedding-ML combination algorithms. Our team Fermi’s model achieved an F1-score of 64.40%, 62.00% and 62.60% for sub-task A, B and C respectively on the official leaderboard. Our model for sub-task C which uses pre-trained ELMo embeddings for transforming the input and uses SVM (RBF kernel) for training, scored third position on the official leaderboard. Through the paper we provide a detailed description of the approach, as well as the results obtained for the task.
Large-Scale Coarse-to-Fine Object Retrieval Ontology and Deep Local Multitask Learning

Object retrieval plays an increasingly important role in video surveillance, digital marketing, e-commerce, etc. It is facing challenges such as large-scale datasets, imbalanced data, viewpoint, cluster background, and fine-grained details (attributes). This paper has proposed a model to integrate object ontology, a local multitask deep neural network (local MDNN), and an imbalanced data solver to take advantages and overcome the shortcomings of deep learning network models to improve the performance of the large-scale object retrieval system from the coarse-grained level (categories) to the fine-grained level (attributes). Our proposed coarse-to-fine object retrieval (CFOR) system can be robust and resistant to the challenges listed above. To the best of our knowledge, the new main point of our CFOR system is the power of mutual support of object ontology, a local MDNN, and an imbalanced data solver in a unified system. Object ontology supports the exploitation of the inner-group correlations to improve the system performance in category classification, attribute classification, and conducting training flow and retrieval flow to save computational costs in the training stage and retrieval stage on large-scale datasets, respectively. A local MDNN supports linking object ontology to the raw data, and an imbalanced data solver based on Matthews' correlation coefficient (MCC) addresses that the imbalance of data has contributed effectively to increasing the quality of object ontology realization without adjusting network architecture and data augmentation. In order to evaluate the performance of the CFOR system, we experimented on the DeepFashion dataset. This paper has shown that our local MDNN framework based on the pretrained NASNet architecture has achieved better performance (14.2% higher in recall rate) compared to single-task learning (STL) in the attribute learning task; it has also shown that our model with an imbalanced data solver has achieved better performance (5.14% higher in recall rate for fewer data attributes) compared to models that do not take this into account. Moreover, MAP@30 hovers 0.815 in retrieval on an average of 35 imbalanced fashion attributes.
Construction of Recognition Algorithms Based on the Two-Dimensional Functions

The problem of pattern recognition is considered by defining in the space of interconnected features. A new approach to constructing a model of recognition algorithms is proposed, taking into account the interconnectedness of the features of the images under consideration. A distinctive feature of the proposed model of algorithms is the determination of a suitable set of two-dimensional threshold functions in the construction of an extreme recognition algorithm. The purpose of this article is to develop a model of modified RA based on the calculation of estimates. To test the efficiency of the proposed model of RA, the experimental studies were carried out to solve a number of model problems. The analysis of the obtained results shows that the considered models of algorithms are effectively used in those cases when there is certain dependence between the features of the objects.
New method for GIS vector data protection based on the use of secondary watermark

The paper provides a study on the watermarking method for vector map data based on a cyclic shift of a polygon vertex list. We propose a method modification allowing to automate authentication procedure, as well as to increase robustness against map contents modification. The main idea of the improved method is to use a noise-like image as a secondary carrier for a watermark, represented in the form of a bit sequence. An algorithm for construction of a noise-like image carrying a watermark sequence, as well as an algorithm for extraction of such a sequence, are given. An experimental study explores the information capacity of the carrier image and its robustness against quantization and interpolation noise adding, i.e. distortions simulating the embedding into map objects. The efficiency of the method is also demonstrated on real cartographic data. Conclusions comprise recommendations for rational choice of embedding parameters depending on the number of polygons on the vector map.
ScrambleDB: Oblivious (Chameleon) Pseudonymization-as-a-Service

Abstract Pseudonymization is a widely deployed technique to de-sensitize data sets by consistently replacing identifying attributes with non-sensitive surrogates. However, all existing solutions are impractical to deploy in settings where data is accumulated from distributed sources: they either require sharing the same secret key with all sources, or rely on a fully trusted service to consistently compute these pseudonyms. Further, the consistency of pseudonyms, which is required to maintain the data’s utility, comes with inherent and severe privacy limitations. This paper solves the key management and privacy challenges by introducing oblivious pseudonymization-as-a-service. Therein, the pseudonymization is outsourced to a central, yet fully oblivious entity, i.e., the service neither learns the sensitive information nor the pseudonyms it produces. Further, to obtain better privacy we no longer require pseudonyms to be computed consistently and instead introduce a dedicated join procedure. When data is stored at rest, all data is pseudonymized in a fully unlinkable manner. Only when certain subsets of the data are needed, the linkage is established through a controlled and nontransitive join operation. We formally define the desired security properties in the UC framework and propose a generic protocol that provably satisfies them. The core of our scheme is a 3-party oblivious and convertible PRF, which we believe to be of independent interest.
Multi-adversarial Faster-RCNN for Unrestricted Object Detection

Conventional object detection methods essentially suppose that the training and testing data are collected from a restricted target domain with expensive labeling cost. For alleviating the problem of domain dependency and cumbersome labeling, this paper proposes to detect objects in an unrestricted environment by leveraging domain knowledge trained from an auxiliary source domain with sufficient labels. Specifically, we propose a multi-adversarial Faster-RCNN (MAF) framework for unrestricted object detection, which inherently addresses domain disparity minimization for domain adaptation in feature representation. The paper merits are in three-fold: 1) With the idea that object detectors often becomes domain incompatible when image distribution resulted domain disparity appears, we propose a hierarchical domain feature alignment module, in which multiple adversarial domain classifier submodules for layer-wise domain feature confusion are designed; 2) An information invariant scale reduction module (SRM) for hierarchical feature map resizing is proposed for promoting the training efficiency of adversarial domain adaptation; 3) In order to improve the domain adaptability, the aggregated proposal features with detection results are feed into a proposed weighted gradient reversal layer (WGRL) for characterizing hard confused domain samples. We evaluate our MAF on unrestricted tasks, including Cityscapes, KITTI, Sim10k, etc. and the experiments show the state-of-the-art performance over the existing detectors.
Acute Lymphoblastic Leukemia Cell Detection in Microscopic Digital Images Based on Shape and Texture Features

Leukemia or blood cancer is a disease that affects a large population, especially children. Fast and early detection of four main types of leukemia is crucial for successful treatment and patient’s recovery. Leukemia can be detected in microscope blood images by detecting blasts, i.e. not fully developed white blood cells. Computer-aided diagnostic systems can improve the quality and speed of abnormal lymphocytes detection. In this paper we proposed a method for automatic detection of one type of leukemia, acute lymphoblastic leukemia, by classifying white blood cells into normal cells and blasts. The proposed method uses shape and texture features as input vector for support vector machine optimized by bare bones fireworks algorithm. Based on the results obtained on the standard benchmark set, ALL-IDB, our proposed method shows a competitive accuracy of classification comparing to other state-of-the-art method.
Measure of not-completely-positive qubit maps: The general case

We show that the set of not-completely-positive (NCP) maps is unbounded, unless further assumptions are made. This is done by first proposing a reasonable definition of a valid NCP map, which is nontrivial because NCP maps may lack a full positivity domain. The definition is motivated by specific examples. We prove that for valid NCP maps, the eigenvalue spectrum of the corresponding dynamical matrix is not bounded. Based on this, we argue that in general the volume measure of qubit maps, including NCP maps, is not well defined.
Parameter Uncertainty in the Kalman--Bucy Filter

In standard treatments of stochastic filtering one first has to estimate the parameters of the model.Simply running the filter without considering the reliability of this estimate does not take into account this additional source of statistical uncertainty.We propose an approach to address this problem when working with the continuous time Kalman-Bucy filter, by making evaluations via a nonlinear expectation.We show how our approach may be reformulated as an optimal control problem, and proceed to analyse the corresponding value function.In particular we present a novel uniqueness result for the associated Hamilton-Jacobi-Bellman equation.
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation

We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function. The new likelihood has two benefits: it leads to well-calibrated uncertainty estimates and allows for an efficient latent variable augmentation. The augmented model has the advantage that it is conditionally conjugate leading to a fast variational inference method via block coordinate ascent updates. Previous approaches suffered from a trade-off between uncertainty calibration and speed. Our experiments show that our method leads to well-calibrated uncertainty estimates and competitive predictive performance while being up to two orders faster than the state of the art.
About a New Model of Engineering Training

The aim of the article is to present a model of training engineers based on new approaches to engineering education, taking into account the modern achievements of psychology. The authors believe that the main reason for the insufficient level of engineering training is the discrepancy between the essence of the knowledge transferred to students (both form and content) to the specifics of engineering activity. Therefore, the most important task is to eliminate this contradiction. The basis of the proposed model of engineering training is the design of the educational process in the logic of engineering process in the development of innovative technology. This learning technology called “Simulation-Activity Technology of Engineering Training” (SATET) has been developed and tested at the National Research Nuclear University MEPhI in training engineersdevelopers of innovative technology. The foundations laid in the proposed technology of engineering training, and the mechanisms for its implementation can be considered as a start for the development of a new paradigm of training engineers.