Reviewerly/exHarmony huggingface dataset:
The effect of mobile-assisted language learning (MALL) on EFL learners’ collocation learning

The aim of this study was to identify the effectiveness of a mobile application in vocabulary knowledge improvement of English as a Foreign Language (EFL) learners.This study adopted a quasi-experimental research design consisting of an experimental and a control group with 73 participants studying at two state universities in Turkey.During the application that lasted for 14 weeks, the experimental group tried to learn 40 collocations via CollocatApp, while control group used worksheets.The data were gathered through Collocation Achievement Test (CAT) developed by the researchers and Vocabulary Knowledge Scale (VKS) (Wesche & Paribakht, 1996).CAT was used for measuring receptive vocabulary knowledge of learners, and VKS was used for testing productive vocabulary knowledge.The findings showed that there was a significant difference between the experimental group and control group in terms of receptive vocabulary knowledge in the post-test.However, there was no difference between groups in retention tests.At the dimension of productive vocabulary knowledge, it was seen that there was no difference between the use of mobile applications and worksheets in terms of productive vocabulary knowledge.Thus, it was concluded that using mobile applications was an effective way of improving vocabulary knowledge receptively for only short-term memory.
Text-Image-Video Summary Generation Using Joint Integer Linear Programming

Automatically generating a summary for asynchronous data can help users to keep up with the rapid growth of multi-modal information on the Internet. However, the current multi-modal systems usually generate summaries composed of text and images. In this paper, we propose a novel research problem of text-image-video summary generation (TIVS). We first develop a multi-modal dataset containing text documents, images and videos. We then propose a novel joint integer linear programming multi-modal summarization (JILP-MMS) framework. We report the performance of our model on the developed dataset.
Ciphertext-Policy Hierarchical Attribute-Based Encryption Against Key-Delegation Abuse for IoT-Connected Healthcare System

With the maturity of technologies such as Internet of Things (IoT) and Cloud Computing, more and more medical institutions share patient's medical data in the IoT-connected healthcare system, whereas it also brings hidden dangers to data security and privacy protection. The application of Hierarchical Attribute-Based Encryption (HABE) in IoT-connected healthcare system helps to solve the problem of data sharing with a large number of users. Delegation is a function of HABE, which can realize the transfer of hierarchical user access rights, effectively reducing the workload of the trusted authority. However, when “delegation” is used to generate decryption keys for users who do not comply with the access structure, the “key-delegation abuse” problem arises, which can seriously damage the privacy of patient's data. Nevertheless, it has not attracted much attention in previous research. This paper proposes the problem of key-delegation abuse in Ciphertext-Policy Hierarchical Attribute-Based Encryption (CP-HABE). After the cryptanalysis of typical schemes, we conclude two reasons for key-delegation abuse in CP-HABE, which are randomizing the original key elements or keeping parts of them in a new decryption key. This paper proposes a new mechanism specifically by using directed graph and construct a CP-HABE scheme against key-delegation abuse (CP-HABE-AKDA). Aiming at the scenario of key leaking, we further present the traceable CP-HABE-AKDA solution for the IoT-connected healthcare system, which has the additional function of tracking and verifying the identity of key leaker.
Learning Self-Awareness for Autonomous Vehicles: Exploring Multisensory Incremental Models

The technology for autonomous vehicles is close to replacing human drivers by artificial systems endowed with high-level decision-making capabilities. In this regard, systems must learn about the usual vehicle's behavior to predict imminent difficulties before they happen. An autonomous agent should be capable of continuously interacting with multi-modal dynamic environments while learning unseen novel concepts. Such environments are not often available to train the agent on it, so the agent should have an understanding of its own capacities and limitations. This understanding is usually called self-awareness. This paper proposes a multi-modal self-awareness modeling of signals coming from different sources. This paper shows how different machine learning techniques can be used under a generic framework to learn single modality models by using Dynamic Bayesian Networks. In the presented case, a probabilistic switching model and a bank of generative adversarial networks are employed to model a vehicle's positional and visual information respectively. Our results include experiments performed on a real vehicle, highlighting the potentiality of the proposed approach at detecting abnormalities in real scenarios.
Smart Homes

Smart home innovations are both a research topic and an industry reality. In this article, we highlight smart home research from the Proceedings of the ACM Journal on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) presented at the September 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and smart home industry updates from the CES conference in January 2020.
Lifelong Machine Learning Architecture for Classification

Benefiting from the rapid development of big data and high-performance computing, more data is available and more tasks could be solved by machine learning now. Even so, it is still difficult to maximum the power of big data due to each dataset is isolated with others. Although open source datasets are available, algorithms’ performance is asymmetric with the data volume. Hence, the AI community wishes to raise a symmetric continuous learning architecture which can automatically learn and adapt to different tasks. Such a learning architecture also is commonly called as lifelong machine learning (LML). This learning paradigm could manage the learning process and accumulate meta-knowledge by itself during learning different tasks. The meta-knowledge is shared among all tasks symmetrically to help them to improve performance. With the growth of meta-knowledge, the performance of each task is expected to be better and better. In order to demonstrate the application of lifelong machine learning, this paper proposed a novel and symmetric lifelong learning approach for sentiment classification as an example to show how it adapts different domains and keeps efficiency meanwhile.
Success prediction of android applications in a novel repository using neural networks

Abstract Nowadays, Android applications play a major role in software industry. Therefore, having a system that can help companies predict the success probability of such applications would be useful. Thus far, numerous research works have been conducted to predict the success probability of desktop applications using a variety of machine learning techniques. However, since features of desktop programs are different from those of mobile applications, they are not applicable to mobile applications. To our knowledge, there has not been a repository or even a method to predict the success probability of Android applications so far. In this research, we introduce a repository composed of 100 successful and 100 unsuccessful apps of Android operating system in Google PlayStore TM including 34 features per application. Then, we use the repository to a neural network and other classification algorithms to predict the success probability. Finally, we compare the proposed method with the previous approaches based on the accuracy criterion. Experimental results show that the best accuracy which we achieved is 99.99%, which obtained when we used MLP and PCA, while the best accuracy achieved by the previous work in desktop platforms was 96%. However, the time complexity of the proposed approach is higher than previous methods, since the time complexities of NPR and MLP are O $$( n^3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>3</mml:mn> </mml:msup> </mml:mrow> </mml:math> ) and O $$( nph^koi$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mi>p</mml:mi> <mml:msup> <mml:mi>h</mml:mi> <mml:mi>k</mml:mi> </mml:msup> <mml:mi>o</mml:mi> <mml:mi>i</mml:mi> </mml:mrow> </mml:math> ), respectively.
2.5D Navigation Graph and Improved A-Star Algorithm for Path Planning in Ship inside Virtual Environment

A new method entitled 2.5D navigation graph for walkable domain modelling, and an improved A-star algorithm for path planning in large ship inside 3D virtual environment (VE) are developed. Firstly, the authors partitioned the XOZ plane with squared cells and then converted the 3D virtual scene model composed of triangular facets into cuboidal obstacles in each grid via projection and intersection algorithm. The top face of each cuboid obstacle is a candidate walkable area for the character in 3D VE. Top facets of all obstacles in the 2.5D navigation graph form the walkable domain of the 3D VE. Furthermore, the obstacle for the next stay position is found by the improved A-star algorithm among the obstacles which reposes on the surrounding eight cells. Experimental results indicate that the provided 2.5D navigation graph and the improved A-star method can deal with the problem of path planning for ship inside 3D VE. Especially, the proposed scheme has adequately taken the horizontal and vertical characteristics of the ship inside environment, the body size of the character, and the physiological specialty and walking customs of humankind into account.
HyperNews: Simultaneous News Recommendation and Active-Time Prediction via a Double-Task Deep Neural Network

Personalized news recommendation can help users stay on top of the current affairs without being overwhelmed by the endless torrents of online news. However, the freshness or timeliness of news has been largely ignored by current news recommendation systems. In this paper, we propose a novel approach dubbed HyperNews which explicitly models the effect of timeliness on news recommendation. Furthermore, we introduce an auxiliary task of predicting the so-called "active-time" that users spend on each news article. Our key finding is that it is beneficial to address the problem of news recommendation together with the related problem of active-time prediction in a multi-task learning framework. Specifically, we train a double-task deep neural network (with a built-in timeliness module) to carry out news recommendation and active-time prediction simultaneously. To the best of our knowledge, such a "kill-two-birds-with-one-stone" solution has seldom been tried in the field of news recommendation before. Our extensive experiments on real-life news datasets have not only confirmed the mutual reinforcement of news recommendation and active-time prediction but also demonstrated significant performance improvements over state-of-the-art news recommendation techniques.
Monarch V2

Monarch is a wearable electronics prototype that enables the wearer to amplify or extend body language through the use of a muscle-activated kinetic textile for the purpose of augmented social interaction. This pictorial details the second prototype stage with a focus on addressing the wearability [5,14], technical, and production challenges resulting from the first prototype [7]. The purpose of these improvements is to enable a small batch production of the prototype for further testing in daily life. Design decisions are brought to the foreground for observation and reflection, including those surrounding material choices and production methods. The result is a detailed visual account of the generative and evaluative discoveries [12] as well as a contribution of several recommendations that can be applied to small batch production of wearable electronic prototypes in a research lab context.
Quantum capacity of a bosonic dephasing channel

We study the quantum capacity of a continuous-variable dephasing channel, which is a notable example of a non-Gaussian quantum channel. We prove that a single-letter formula applies. The optimal input state is found to be diagonal in the Fock basis and with a distribution that is a discrete version of a Gaussian. We discuss how its mean and variance are related to the dephasing rate and input energy. We then show that by increasing the input energy, the capacity saturates to a finite value. We also show that it decays exponentially for large values of dephasing rates.
The visual analytics of big, open public transport data – a framework and pipeline for monitoring system performance in Greater Sydney

Many cities, countries and transport operators around the world are striving to design intelligent transport systems. These systems capture the value of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility, interests, economic activity and lifestyles. They aim to provide services that can enable transportation customers and managers to be better informed and make safer and more efficient use of infrastructure.In developing principles, guidelines, methods and tools to enable synergistic work between humans and computer-generated information, the science of visual analytics continues to expand our understanding of data through effective and interactive visual interfaces.In this paper, we describe an application of visual analytics related to the study of movement and transportation systems. This application documents the use of rapid, 2D and 3D web visualisation and data analytics libraries and explores their potential added value to the analysis of big public transport performance data. A novel approach to displaying such data through a generalisable framework visualisation system is demonstrated. This framework recalls over a year’s worth of public transport performance data at a highly granular level in a fast, interactive browser-based environment.Greater Sydney, Australia forms a case study to highlight potential uses of the visualisation of such large, passively-collected data sets as an applied research scenario. In this paper, we argue that such highly visual systems can add data-driven rigour to service planning and longer-term transport decision-making. Furthermore, they enable the sharing of quality of service statistics with various stakeholders and citizens and can showcase improvements in services before and after policy decisions. The paper concludes by making recommendations on the value of this approach in embedding these or similar web-based systems in transport planning practice, performance management, optimisation and understanding of customer experience.
Data Pricing -- From Economics to Data Science

Data are invaluable. How can we assess the value of data objectively and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, data management, data mining, electronic commerce, and marketing. In this tutorial, we present a unified and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing, review the development and evolution of pricing models, and compare the proposals of marketplaces of data. We cover both digital products, such as ebooks and MP3 music, and data products, such as data sets, data queries and machine learning models. We also connect data pricing with the highly related areas, such as cloud service pricing, privacy pricing, and decentralized privacy preserving infrastructure like blockchains.
Optimization by Neural Networks in the Coherent Ising Machine and its Application to Wireless Communication Systems

Recently, new optimization machines based on non-silicon physical systems, such as quantum annealing machines, have been developed, and their commercialization has been started. These machines solve the problems by searching the state of the Ising spins, which minimizes the Ising Hamiltonian. Such a property of minimization of the Ising Hamiltonian can be applied to various combinatorial optimization problems. In this paper, we introduce the coherent Ising machine (CIM), which can solve the problems in a milli-second order, and has higher performance than the quantum annealing machines especially on the problems with dense mutual connections in the corresponding Ising model. We explain how a target problem can be implemented on the CIM, based on the optimization scheme using the mutually connected neural networks. We apply the CIM to traveling salesman problems as an example benchmark, and show experimental results of the real machine of the CIM. We also apply the CIM to several combinatorial optimization problems in wireless communication systems, such as channel assignment problems. The CIM's ultra-fast optimization may enable a real-time optimization of various communication systems even in a dynamic communication environment.
Model-Agnostic Metalearning-Based Text-Driven Visual Navigation Model for Unfamiliar Tasks

As vision and language processing techniques have made great progress, mapless-visual navigation is occupying uppermost position in domestic robot field.However, most current end-to-end navigation models tend to be strictly trained and tested on identical datasets with stationary structure, which leads to great performance degradation when dealing with unseen targets and environments.Since the targets of same category could possess quite diverse features, generalization ability of these models is also limited by their visualized task description.In this article we propose a model-agnostic metalearning based textdriven visual navigation model to achieve generalization to untrained tasks.Based on meta-reinforcement learning approach, the agent is capable of accumulating navigation experience from existing targets and environments.When applied to finding a new object or exploring in a new scene, the agent will quickly learn how to fulfill this unfamiliar task through relatively few recursive trials.To improve learning efficiency and accuracy, we introduce fully convolutional instance-aware semantic segmentation and Word2vec into our DRL network to respectively extract visual and semantic features according to object class, creating more direct and concise linkage between targets and their surroundings.Several experiments have been conducted on realistic dataset Matterport3D to evaluate its target-driven navigation performance and generalization ability.The results demonstrate that our adaptive navigation model could navigate to text-defined targets and achieve fast adaption to untrained tasks, outperforming other state-of-the-art navigation approaches.
Applying k-vertex cardinality constraints on a Neo4j graph database

As with any other database solution, graph databases also need to be able to implement business rules related to a given application domain. At the moment, aside from integrity constraints, there is a limited number of mechanisms for business rules implementation in Graph Database Management Systems (GDBMSs). The underlying property graph data model does not include any formal notation on how to represent different constraints. Specifically, this paper discusses the problem of representing cardinality constraints in graph databases. We introduce the novel concept of k-vertex cardinality constraints, which enable us to specify the minimum and maximum number of edges between a vertex and a subgraph. We also propose an approach, which includes the representation of cardinality constraints through the property graph data model, and demonstrate its implementation through a series of stored procedures in Neo4j GDBMS. The proposed approach is then evaluated by performing experiments on synthetic and real datasets to test the influence of checking cardinality constraints on query execution times (QETs) when adding new edges. Additionally, a comparison is performed on synthetic datasets with varying outgoing vertex degrees in order to gain an insight into how increasing the vertex degree affects QETs. In general, the results obtained for each test scenario show that the implemented k-vertex cardinality constraints model does not significantly affect QETs. Also, the results indicate that the model is dependent on the order of the underlying k-vertex cardinality constraints and outgoing vertex degree in the dataset.
Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

Synthetic aperture radar (SAR) image classification is an important part in the understanding and interpretation of SAR images. Each patch in SAR images has a scene category, but usually contains multiple land-cover classes or latent properties, which can be represented by topics in the probabilistic topic model (PTM). The representation and selection of discriminative features in PTM have a large impact on the classification results. Most of the existing feature learning methods do not make full use of high-level structure feature and the feature correlation within similar images to mine discriminative features. Therefore, this article proposes a discriminative sketch topic model with structural constraint (C-SSTM) for SAR image classification. In the proposed model, each image patch is characterized by structural and texture features. In particular, the sketch structural feature is based on the sketch map to represent the image local structure pattern. Then, the local image manifold information is preserved in terms of structure and texture. In the structural constraint, the texture and structure of each image patch are combined to learn discriminative latent semantic topics between image patches. Finally, each image patch is quantified by discriminative latent semantic topics instead of low-level representation. The experimental results tested on synthetic and real SAR images demonstrate that the proposed C-SSTM is able to learn effective structural feature representation from SAR images. Compared with other related approaches, C-SSTM produces competitive classification accuracies with high time efficiency.
A Simulation Study on the Risk Assessment of the Modernization of Traditional Sports Culture Based on a Cellular Automaton Model

Based on the cellular automaton model, the risk of modernization of traditional sports culture is evaluated and measured by using the methods of documentary, interview, and simulation experiments. The results of simulation experiment show that, in the complex system of the modernization of traditional sports culture, the mutation factors will lead to the sudden decline of the survival suitability of traditional sports culture and the risk of being unable to evolve; in the process of the modernization of traditional sports culture, there are two kinds of risks: self-extinction of dying out and self-extinction of homogenization; risk of dislocation failure lies in the government’s support policies; risk of imbalance between fitness and scale allocation lies in modernity of derivative traditional sports culture. Countermeasures. The government should measure the environmental carrying capacity of the development of traditional sports culture in the region according to the regional resource situation; on this basis, heterogeneous sports activities are selectively introduced, the vitality of local traditional sports culture is enhanced, and the policy support and intervention time are reasonably controlled, to promote the balanced allocation of the suitability and scale of the derivative traditional sports culture.