Reviewerly/exHarmony huggingface dataset:
Subject Clitics in Microcontact: A Case Study from Heritage Friulian in Argentina and Brazil

Abstract In this paper we present data from first generation immigrants (G1) and second and third generation heritage speakers of Friulian, a Rhaeto-Romance language spoken in North-Eastern Italy and also found in Argentina and Brazil. The target phenomenon is subject clitics ( SCL s). We show that SCL s in heritage Friulian are in a process of being reanalyzed from being agreement markers to pronouns. While SCL s are obligatory in Friulian as spoken in Italy, they are often dropped in heritage Friulian in Argentina and Brazil; this phenomenon, we argue, needs to be interpreted as the drop of pronominal subjects, and not of agreement-like SCL s. We also demonstrate that the use of SCL s (reanalyzed as pronominal subjects) is conditioned both by grammatical factors (it happens more in some grammatical persons than in others) and by discourse factors (they are used more in the case of a continuation topic than in other topicalization contexts). This means that in heritage Friulian, discourse constraints on the expression of subjects are not being lost or weakened; in fact, against the general grammaticalization trend of pronominal forms, new discourse constraints are introduced.
Opportunities and Risks in Higher Education in the Postpandemic Period

In the first half of the 2020, we all have witnessed the measures taken to stop the spread of the novel Coronavirus which causes the COVID-19 disease and slow down the pandemic in almost the whole world. One of the most significant measures which was taken to prevent gatherings in public spaces is to stop in person education altogether and move education into digital platforms. On the other hand, in the second half of the 2020, we expect to observe a controlled normalization process until a vaccine is found. Considering that until a vaccine is found, the pandemic will keep affecting people and it will take time to turn back into regular social life, higher education institutions are also expected to adopt into this controlled social life which will last a while longer and keep increasing its educational and scientific activities in this environment. Although, it is still not certain how the higher education institutions will take shape and take its place in the “new normal”, it is possible to say that there are many risks and opportunities waiting for the higher education institutions. While developments in distance and digital learning may be considered as a big opportunity for the universities, travel restrictions, economic stagnation, problems faced in distance learning and obstacles which was encountered in the internationalization process of higher education may be considered as ominously awaiting risks for the higher education institutions.
A stochastic first-order trust-region method with inexact restoration for finite-sum minimization

Abstract We propose a stochastic first-order trust-region method with inexact function and gradient evaluations for solving finite-sum minimization problems. Using a suitable reformulation of the given problem, our method combines the inexact restoration approach for constrained optimization with the trust-region procedure and random models. Differently from other recent stochastic trust-region schemes, our proposed algorithm improves feasibility and optimality in a modular way. We provide the expected number of iterations for reaching a near-stationary point by imposing some probability accuracy requirements on random functions and gradients which are, in general, less stringent than the corresponding ones in literature. We validate the proposed algorithm on some nonconvex optimization problems arising in binary classification and regression, showing that it performs well in terms of cost and accuracy, and allows to reduce the burdensome tuning of the hyper-parameters involved.
Cloud-based Secure Healthcare Framework by using Enhanced Ciphertext Policy Attribute-Based Encryption Scheme

Cloud computing is an emerging technology that has been used to provide better healthcare services to the users because of its convenient and economical features.Noted that the healthcare services required fast and reliable data sharing at anytime from anywhere for better monitoring and decision making in medical requirements.However, the privacy and integrity of electronic healthcare record become a significant issue during data sharing and outsourcing in Cloud.The data privacy of clients/patients is important in healthcare services where exposure of the data to unauthorized parties is unexceptional.In order to address this security loophole, this paper presents a Cloud-based Secure Healthcare Framework (SecHS) to offer safe access to healthcare and medical data.Specifically, this paper enhance the Ciphertext Policy Attribute-Based Encryption (CP-ABE) scheme by adding two more modules which aims to provide fine-grained access control and offer privacy and integrity of data.It facilitates encryption and hashing schemes.The proposed framework is compared with existing frameworks that used CP-ABE scheme.It shows the SecHS offers better features towards securing the healthcare services data.Optimistically, data security requirements such as privacy, integrity and fine-grained access control are required to effectively proposed for assuring data sharing in the Cloud environment.
Nowhere to Leak: A Multi-client Forward and Backward Private Symmetric Searchable Encryption Scheme

Symmetric Searchable Encryption (SSE) allows users to outsource encrypted data to a possibly untrusted remote location while simultaneously being able to perform keyword search directly through the stored ciphertexts. An ideal SSE scheme should reveal no information about the content of the encrypted information nor about the searched keywords and their mapping to the stored files. However, most of the existing SSE schemes fail to fulfil this property since in every search query, some information potentially valuable to a malicious adversary is leaked. The leakage becomes even bigger if the underlying SSE scheme is dynamic. In this paper, we minimize the leaked information by proposing a forward and backward private SSE scheme in a multi-client setting. Our construction achieves optimal search and update costs. In contrast to many recent works, each search query only requires one round of interaction between a user and the cloud service provider. In order to guarantee the security and privacy of the scheme and support the multi-client model (i.e. synchronization between users), we exploit the functionality offered by AMD’s Secure Encrypted Virtualization (SEV).
Analysis of trigonometric chaotic sequence by proposing an index-based bit level scrambling image encryption

Chaotic map is applied to numerous research fields, such as encryption of data and information. In this paper, a novel bit-level scrambling encryption based on three-dimensional trigonometric chaotic sequence is proposed. This encryption is operated on grayscale images because pixel value of which is between 0 and 255, which can be converted into 8-bit binary number, then each image can be converted into eight binary images. Combine binary images a three-dimensional matrix as input, which have three index values width, height, and page. Generate three numbers by the chaotic sequence to locate the value of the matrix need to be changed. After the value is located, change its value 0 to 1 or 1 to 0, this is the basic encryption scheme. This paper makes a three-dimensional binary matrix with 32 images to test the encryption scheme, and get average value of images’ number of pixel change rate at 0.9603, unified averaged change intensity at 31.27%, information entropy at 7.9891, also the histograms. And correlation coefficients of each pixel from three directions are small. There are comparisons with other encryptions too. The key space of this encryption is more than 10[Formula: see text].
Fake News and the Convention on Cybercrime

The COVID-19 pandemic and the recent term of the United States President, Donald Trump, brought the term “fake news” to the attention of the broader community. Some jurisdictions have developed anti-fake news legislation, whilst others have used existing cybercrime legislation. A significant deficiency is the lack of a clear definition of fake news. Just because a person calls something “fake news” does not mean that it is indeed false. Especially during pandemics, the primary aim should be to have misinformation and disinformation removed quickly from the web rather than prosecute offenders. The most widely accepted international anti-cybercrime treaty is the Convention on Cybercrime developed by the Council of Europe, which is silent on fake news, the propagation of which may be a cybercrime. There is an Additional Protocol that deals with hate speech, which the authors consider to be a subset of fake news. Using examples from Southeast Asia, the paper develops a comprehensive definition of what constitutes fake news. It ensures that it covers the various flavours of fake news that have been adopted in various jurisdictions. Hate speech can be considered a subset of fake news and is defined as the publication or distribution of fake news with the intention to incite hatred or violence against ethnic, religious, political, and other groups in society. The paper proposes some offences, including those that should be applied to platform service providers. The recommendations could be easily adapted for inclusion in the Convention on Cybercrime or other regional conventions. Such an approach is desirable as cybercrime, including propagating fake news, is not a respecter of national borders, and has widespread deleterious effects. Keywords: Fake news; hate speech; Convention on Cybercrime; draft legislation
A Crisis of Governance – Or an Opportunity?

The National Health Service Track and Trace Programme has been one of the most pivotal and controversial tools the UK government has deployed to combat the COVID-19 pandemic. This article reviews the challenges of the development and implementation of the NHS COVID-19 mobile application, and reviews these in the context of the following 3 key areas: outsourcing of public sector, organisational structure and leadership, digital framework and governance. The paper argues that the current pandemic has demonstrated weaknesses in each of the above-mentioned areas, and that is an urgent need to strategically address these in order to prepare for the next public health crisis.
Single Image Depth Estimation Using Edge Extraction Network and Dark Channel Prior

The key to the depth estimation from a single image lies in inferring the distance of various objects without copying texture while maintaining clear object boundaries. In this paper, we propose depth estimation from a single image using edge extraction network and dark channel prior (DCP). We build an edge extraction network based on generative adversarial networks (GANs) to select valid depth edges from a number of edges in an image. We use DCP to generate a transmission map that is able to represent distance from the camera. Transmission map is generated by conducting minimum value filtering on DCP. First, we concatenate the transmission map with the original RGB image to form a tensor, i.e. RGB + T. Second, we generate an initial depth image from the tensor through the generator by inferring depth from stacked residual blocks. Third, we compare the edge map of the initial depth image with that of the input RGB image to select valid depth edges. Both edge maps are generated by the edge extraction network. Finally, we distinguish real and fake on the generated depth image using a discriminator, and enhances the performance of the generator. Various experiments on NYU, Make3D and MPI Sintel datasets demonstrate that the proposed network generates clear edges in depth images as well as outperforms state-of-the-art methods in terms of visual quality and quantitative measurements.
Phone-Pointing Remote App: Using Smartphones as Pointers in Gesture-Based IoT Remote Controls

Remote control mobile applications for operating Internet of Things (IoT) devices using smartphones are commonly based on a touch user interface. The effort of using such apps is often disproportionate to the simplicity of carrying out the actions manually. For example, turning a light on or off via menus and forms of a standard remote app might not be very convenient. A voice user interface, while easier to use, gives rise to other issues, including user privacy and distracting others nearby. This paper proposes a new type of universal IoT remote control applications for smartphones: phone-pointing remote apps. Using a phone-pointing remote app, users can physically point their smartphones at IoT devices to select them, and operate them via movement gestures, without needing to turn on the phone screen or talk, and with no need for any additional hardware. This new approach provides a unique combination of advantages. It is simple, intuitive, fast, and voiceless. Instead of using the touchscreen or the microphone as the input source, phone-pointing remote apps will use a combination of standard smartphone sensors, including the GNSS sensor, Wi-Fi scanner, Bluetooth receiver, camera, barometer, magnetometer, g-force meter, accelerometer, and gyroscope. An analysis of the proposed model in light of relevant results from related studies provides positive preliminary indications regarding the feasibility of this novel approach.
Mobile Learning Approaches and Its Impact on Student‘s Education — A Survey

As mobile devices have become a part of our everyday life, there is a lot of research going on how to make this technology more productive for the students. Most of the research studies have indicated that, mobile devices are very useful for students while a few research studies negates its effectiveness. In this paper, we have presented the literature survey conducted on the use of mobile technologies in the field of Teaching and Learning (T&amp;L). These studies are broadly classified into three areas: Impact and learning environment; specific applications; and mobile learning framework. We will be exploring all these areas in this paper. Our review in impact and environment of mobile learning indicates that the use of mobile technology is nourishing the knowledge and learning of students in most cases. Our review on specific applications of mobile learning reveals positive impacts on learning in various fields but the key concern raised was difficulties in implementing them and not having proper control guideline. Moreover, our review on frameworks for mobile learning indicates that there are different frameworks developed for different applications. Most of the applications are limited to non-technical areas. However, we can observed that there is a lack of connectivity between course development and framework for mobile learning. Therefore, the future course developers need to address the gap by taking into account of learners’ behavior in using mobile devices and their cultures to enrich student engagement and learning.
Cloud Native Privacy Engineering through DevPrivOps

Cloud native information systems engineering enables scalable and resilient software architectures powering major online offerings. Today, these are built following agile development practices. At the same time, a growing demand for privacy-friendly services is articulated by societal norms and policy through effective legislative frameworks. In this paper, we (i) identify conceptual dimensions of cloud native privacy engineering – that is, bringing together cloud computing fundamentals and privacy regulation – and propose an integrative approach to be addressed to overcome the shortcomings of existing privacy enhancing technologies in practice and evaluating existing system designs. Furthermore, we (ii) propose a reference software development lifecycle called DevPrivOps to enhance established agile development methods with respect to privacy. Altogether, we show that cloud native privacy engineering opens up key advances to the state of the art of privacy by design and by default using latest technologies.
Predicting and Evaluating the Online News Popularity based on Random Forest

Abstract With the rapid development of science and technology, the internet has become a large media of information spread. There is a large quantity message on this platform. And online articles are the main form of information propagation. If the press can know what kind of articles will be more popular, they can construct an article that can help them spread the information they want to spread. Therefore, it’s very important to predict the popularity of these articles. Some models in machine learning could be applied to this problem. In this paper, it will introduce an approach based on Random Forest. To avoid too much calculation, the experiment first uses PCA to make dimension reduction. Then the model evaluation uses the ROC area values to assess the accuracy of the model. Its performance is better than CART and C4.5.
Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming

Many algorithms in probabilistic sampling-based motion planning have been proposed to create a path for a robot in an environment with obstacles. Due to the randomness of sampling, they can efficiently compute the collision-free paths made of segments lying in the configuration space with probabilistic completeness. However, this property also makes the trajectories have some unnecessary redundant or jerky motions, which need to be optimized. For most robotics applications, the trajectories should be short, smooth and keep away from obstacles. This paper proposes a new trajectory optimization technique which transforms a polygon collision-free path into a smooth path, and can deal with trajectories which contain various task constraints. The technique removes redundant motions by quadratic programming in the parameter space of trajectory, and converts collision avoidance conditions to linear constraints to ensure absolute safety of trajectories. Furthermore, the technique uses a projection operator to realize the optimization of trajectories which are subject to some hard kinematic constraints, like keeping a glass of water upright or coordinating operation with dual robots. The experimental results proved the feasibility and effectiveness of the proposed method, when it is compared with other trajectory optimization methods.
Understanding the Effect of Bias in Deep Anomaly Detection

Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting training of deep anomaly detection models with additional labeled anomaly samples. However, the labeled data often does not align with the target distribution and introduces harmful bias to the trained model. In this paper, we aim to understand the effect of a biased anomaly set on anomaly detection. Concretely, we view anomaly detection as a supervised learning task where the objective is to optimize the recall at a given false positive rate. We formally study the relative scoring bias of an anomaly detector, defined as the difference in performance with respect to a baseline anomaly detector. We establish the first finite sample rates for estimating the relative scoring bias for deep anomaly detection, and empirically validate our theoretical results on both synthetic and real-world datasets. We also provide an extensive empirical study on how a biased training anomaly set affects the anomaly score function and therefore the detection performance on different anomaly classes. Our study demonstrates scenarios in which the biased anomaly set can be useful or problematic, and provides a solid benchmark for future research.
Design and Control of a Service Robot with Modular Cargo Spaces

In this paper, we present a general purpose mobile manipulation robot mainly designed for autonomous handling of various service tasks in indoor spaces. The suggested robot has a small footprint and omnidirectional mobility to navigate through tight indoor spaces while avoiding obstacles, and is equipped with a dexterous manipulator and modular cargo space which can be used to carry and deploy multiple items at once. The robot is capable of perception, navigation and manipulation using the modular software codebase we have developed for general service robots, and has demonstrated the capability of picking up, storing and deploying of multiple objects autonomously using its modular cargo space.
Minimizing Spontaneous Raman Scattering Noise for Quantum Key Distribution in WDM Networks

We model the SRS impact on QKD when sharing C-band for quantum and classical communications in WDM networks and find the optimal wavelength allocation leading to &gt;26% increase of achievable transmission distance for quantum signals.
A Reversible Data Hiding Algorithm Based on Secret Sharing

In traditional secret sharing schemes, all shared images containing secret segments are needed to recover secret information.In this paper, a reversible data hiding scheme based on Shamir secret sharing is used.Secret information can be recovered even if only part of the encrypted sharing is owned.This method can reduce the vulnerability of traditional encryption sharing schemes to attack.Before uploading the secret information to the cloud server, embed the encrypted n segments of secret information into n different pictures.The receiver downloads t images from the cloud server (t < n), extracts the encrypted information using the watermark extraction algorithm, and obtains the original secret information after decryption through Shamir secret sharing.
Collaborative Filtering Recommendation Algorithm Based on User Explicit Preference

Collaborative filtering algorithm is one of the widely used methods in recommender system, However, collaborative filtering has the problem of data sparsity and the calculation of similarity is simple relatively, which can't describe the similarity between users well. Considering that user's preferences to projects may be affected by project attributes, this paper will introduce project attribute information to mine the user's attribute preference and makes up for the problem in similarity calculation by the user's ratings. By describing the user's attribute preference, we propose a collaborative filtering algorithm CF-UEP based on the user's explicit attribute preference. Through experimental comparison, we prove that our method is more advanced. The algorithm solves the data sparse problem effectively.
QoE-Based MEC-Assisted Predictive Adaptive Video Streaming for On-Road Driving Scenarios

This letter studies a multi-access edge computing assisted predictive adaptive video streaming scheme for on-road driving scenarios based on deep reinforcement learning (DRL). By judiciously designing the state and the reward, the user movement awareness is integrated into our scheme, which enables it to make proactive decisions that can maximize the long-term quality of experience. To enhance the learning efficiency, we introduce the post-decision state into DRL. The effectiveness of the scheme has been validated by simulation results.