Reviewerly/exHarmony huggingface dataset:
Compression effects and scene details on the source camera identification of digital videos

The continuous growth of technologies like 4G or 5G has led to a massive use of mobile devices such as smartphones and tablets. This phenomenon, combined with the fact that people use mobile phones for a longer period of time, results in mobile phones becoming the main source of creation of visual information. However, its reliability as a true representation of reality cannot be taken for granted due to the constant increase in editing software. This makes it easier to alter original content without leaving a noticeable trace in the modification. Therefore, it is essential to introduce forensic analysis mechanisms to guarantee the authenticity or integrity of a certain digital video, particularly if it may be considered as evidence in legal proceedings. This paper explains the branch of multimedia forensic analysis that allows to determine the identification of the source of acquisition of a certain video by exploiting the unique traces left by the camera sensor of the mobile device in visual content. To do this, a technique that performs the identification of the source of acquisition of digital videos from mobile devices is presented. It involves 3 stages: (1) Extraction of the sensor fingerprint by applying the block-based technique. (2) Filtering the strong component of the PRNU signal to improve the quality of the sensor fingerprint. (3) Classification of digital videos in an open scenario, that is, where the forensic analyst does not need to have access to the device that recorded the video to find out the origin of the video. The main contribution of the proposed technique eliminates the details of the scene to improve the PRNU fingerprint. It should be noted that these techniques are applied to digital images and not to digital videos. In this work, we show that it is necessary to take this improvement into account to improve the identification of digital videos. Experimental results are also presented that support the validity of the techniques used and show promising results.
A Deep Q-Learning Sanitization Approach for Privacy Preserving Data Mining

With the establishment of the 5G network, a number of data-intensive applications will be developed. Privacy of information over the network is increasingly relevant, and require protection. The privacy of information while utilizing data is a trade-off that needs to be addressed. In this paper, we propose data privacy of 5G connected devices over heterogeneous networks (5G-Hetnets). A deep Q learning (DQL) based technique is applied to sensitize sensitive information from a given database while keeping the balance between privacy protection and knowledge discovery during the sanitization process. It takes transaction states as input and results in state and action pair. The DQL discovers the transactions dynamically, then the sanitization operation hide the sensitive information by minimizing side effects. The proposed approach shows significant improvement of performance compared to greedy and meta-heuristics and heuristics approaches.
Dependency parsing of biomedical text with BERT

Abstract Background: Syntactic analysis, or parsing, is a key task in natural language processing and a required component for many text mining approaches. In recent years, Universal Dependencies (UD) has emerged as the leading formalism for dependency parsing. While a number of recent tasks centering on UD have substantially advanced the state of the art in multilingual parsing, there has been only little study of parsing texts from specialized domains such as biomedicine. Methods: We explore the application of state-of-the-art neural dependency parsing methods to biomedical text using the recently introduced CRAFT-SA shared task dataset. The CRAFT-SA task broadly follows the UD representation and recent UD task conventions, allowing us to fine-tune the UD-compatible Turku Neural Parser and UDify neural parsers to the task. We further evaluate the effect of transfer learning using a broad selection of BERT models, including several models pre-trained specifically for biomedical text processing. Results: We find that recently introduced neural parsing technology is capable of generating highly accurate analyses of biomedical text, substantially improving on the best performance reported in the original CRAFT-SA shared task. We also find that initialization using a deep transfer learning model pre-trained on in-domain texts is key to maximizing the performance of the parsing methods.
Mitigation of Odometer Fraud for In-Vehicle Security Using the Discrete Hartley Transform

Odometer fraud is a serious offense in the automotive sector and indicates the disconnection, resetting, or alteration of a vehicle's odometer and the related sensor with the intent to change the number of miles/Kms indicated or recorded to report false information. This paper focuses specifically on the threat scenario where the odometer sensor (i.e., Hall Sensor) is manipulated or replaced to implement an odometer fraud. This paper proposes a technique to mitigate odometer fraud by performing a physical layer authentication of the Hall Sensor, which takes in consideration the limitation of the in-vehicle networks and microprocessors. In particular, the Discrete Hartley Transform (DHT) in combination with machine learning algorithms is used to perform the authentication on an experimental data set of 12 Hall Sensors, which has been collected by the authors. The results shows that features extracted with DHT have more discriminating power than the original time domain and the frequency domain representations based on the Fast Fourier Transform (FFT) especially in presence of noise.
Controlled Functional Encryption Revisited: Multi-Authority Extensions and Efficient Schemes for Quadratic Functions

Abstract In a Functional Encryption scheme (FE), a trusted authority enables designated parties to compute specific functions over encrypted data. As such, FE promises to break the tension between industrial interest in the potential of data mining and user concerns around the use of private data. FE allows the authority to decide who can compute and what can be computed, but it does not allow the authority to control which ciphertexts can be mined. This issue was recently addressed by Naveed et al., that introduced so-called Controlled Functional encryption (or C-FE), a cryptographic framework that extends FE and allows the authority to exert fine-grained control on the ciphertexts being mined. In this work we extend C-FE in several directions. First, we distribute the role of (and the trust in) the authority across several parties by defining multi-authority C-FE (or mCFE). Next, we provide an efficient instantiation that enables computation of quadratic functions on inputs provided by multiple data-owners, whereas previous work only provides an instantiation for linear functions over data supplied by a single data-owner and resorts to garbled circuits for more complex functions. Our scheme leverages CCA2 encryption and linearly-homomorphic encryption. We also implement a prototype and use it to showcase the potential of our instantiation.
Study of blockchain based smart grid for energy optimization

In order to establish an effective fusion of green technology along with renewable energy technologies, the idea of smart grid is represented as a modern view of the traditional power system. It serves as a solution to the problems of energy supply in the future by means of the combination of Internet of Things technology and Wireless Sensor Networks. In this path, to make sure that energy should be made available anywhere at any instance, Energy Internet is rising as an innovative solution. These innovations ultimately aim to create a sustainable society. Yet, the integration and syncing of relatively large amount of increasing interconnection may become a challenge related to the conventional centralised grid structure. Similarly, protection and confidentiality problems in the utilization and trade of electricity statistics pose significant challenges to implementation of the smart grid. Therefore, from its centralised form, the smart grid undergoes a transition from its centralised form to the decentralised topology. Blockchain Technology has some outstanding features for applicability in the promising smart grid idea. Here, we present a detailed study on blockchain based smart grid application. We studied a range of recent blockchain centred research works accessible in various literature dealing with safety concerns associated in smart grid idea in order to be solved by blockchain. It also summarises a range of similar realistic designs, trials and solutions that have recently emerged. Use case in each area is studied to identify effective blockchain architecture, type of block structure and associated technologies. The energy trading process can be enhanced by using credit-based payment system. Effective record aggregation systems using blockchain technology needs to be considered to solve the confidentiality and safety problems of the grid. Energy allocation systems can distantly manage the power stream in a specific region by tracking the usage data for that sector by blockchain. Finally, the key research concerns and future directions for blockchain application to smart grid security problems are addressed.
A Neural Local Coherence Analysis Model for Clarity Text Scoring

Local coherence relation between two phrases/sentences, such as cause-effect, and contrast gives a strong influence on whether a text is well-structured or not.This paper follows the assumption and presents a method for scoring text clarity by utilizing local coherence between adjacent sentences.We hypothesize that the coherence knowledge learned from the different domain data is beneficial for capturing a well-structured text and thus helpful for scoring text clarity.We propose a text clarity scoring method that utilizes local coherence analysis with an out-domain setting, i.e., the training data for the source and target tasks are different from each other.The method based on the pre-trained language model BERT firstly trains the local coherence model as an auxiliary manner and then re-trains it together with the text clarity scoring model.The experimental results by using the PeerRead benchmark dataset show the improvement compared with a single model, scoring text clarity model 1 .
Vector AR3-APP – A Good-Practice Example of Learning with Augmented Reality

Abstract After a discussion about the possibilities and status of augmented reality in education, a good practice example of an augmented reality application is presented. This case study examines the use of an augmented reality app in higher education to support abstract STEM content, such as vectors. Based on this example, the implementation of such apps in didactic concepts and self-directed learning will be discussed. Furthermore, aspects of integration into digital learning and teaching will be addressed.
Fighting coronavirus with faith

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Performance comparison of relational databases SQL Server, MySQL and PostgreSQL using a web application and the Laravel framework

Many database implementations are supported by application frameworks that can affect their performance. The paper presents a comparison of the performance of SQL Server, MySQL and PostgreSQL relational databases based on an application written in PHP using the Laravel framework. The time of performance for various types of queries, both simple and using column and table concatenation was evaluated. The obtained results for the same database structures differed depending on the operations performed on the databases. Looking at the entirety of the research conducted, it can be concluded that in the case of databases in which the number of records is not too large (up to 1000 records) and the technical parameters of the device on which the database is running are of low or medium class, MySQL performs very well.
Six textbook mistakes in computational physics

This article discusses several erroneous claims which appear in textbooks on numerical methods and computational physics. These are not typos or mistakes an individual author has made, but widespread misconceptions. In an attempt to stop these issues from further propagating, we discuss them here, along with some background comments. In each case, we also provide a correction, which is aimed at summarizing material that is known to experts but is frequently mishandled in the introductory literature. To make the mistakes and corrections easy to understand, we bring up specific examples drawn from elementary physics and math. We also take this opportunity to provide pointers to the specialist literature for readers who wish to delve into a given topic in more detail.
Towards Assessing Critical Infrastructures’ Cyber-Security Culture During COVID-19 Crisis: A Tailor-made Survey

This paper outlines the design and development of a survey targeting the cyber-security culture assessment of critical infrastructures during the COVID-19 crisis, when living routine was seriously disturbed and working reality fundamentally affected. Its foundations lie on a security culture framework consisted of 10 different security dimensions analysed into 52 domains examined under two different pillars: organizational and individual. In this paper, a detailed questionnaire building analysis is being presented while revealing the aims, goals and expected outcomes of each question. It concludes with the survey implementation and delivery plan following a number of pre-survey stages each serving a specific methodological purpose.
Predicting short-term next-active-object through visual attention and hand position

Human intention prediction is of great significance in many applications, such as human-robot interaction, intelligent rehabilitation robots. This paper studies the problem of short-term next-active-object prediction in egocentric images. The short-term next-active-object refers to the object that a human is going to interact with in the short-term future, which is an embodiment of human intention. Most current methods usually use object-centered cues, such as the deviation of object appearance change and the unique shape of the egocentric object trajectory, to predict the next-active-object. In this paper, inspired by the fact that human intention is also revealed by human-centered cues, we propose a deep neural network model that integrates the cues from visual attention and hand positions to predict the next-active-object. Firstly, the probability maps of visual attention and hand positions are constructed, and then the probability distribution of next-active-object is generated. We experimentally compare our method with several baseline methods using two datasets and confirm its effectiveness. In addition, ablation experiments are conducted, and crucial points concerning the next-active-object are discussed.
Playing to distraction: towards a robust training of CNN classifiers through visual explanation techniques

The field of deep learning is evolving in different directions, with still the need for more efficient training strategies. In this work, we present a novel and robust training scheme that integrates visual explanation techniques in the learning process. Unlike the attention mechanisms that focus on the relevant parts of images, we aim to improve the robustness of the model by making it pay attention to other regions as well. Broadly speaking, the idea is to distract the classifier in the learning process to force it to focus not only on relevant regions but also on those that, a priori, are not so informative for the discrimination of the class. We tested the proposed approach by embedding it into the learning process of a convolutional neural network for the analysis and classification of two well-known datasets, namely Stanford cars and FGVC-Aircraft. Furthermore, we evaluated our model on a real-case scenario for the classification of egocentric images, allowing us to obtain relevant information about peoples' lifestyles. In particular, we work on the challenging EgoFoodPlaces dataset, achieving state-of-the-art results with a lower level of complexity. The obtained results indicate the suitability of our proposed training scheme for image classification, improving the robustness of the final model.
Functorial semantics for partial theories

We provide a Lawvere-style definition for partial theories, extending the classical notion of equational theory by allowing partially defined operations. As in the classical case, our definition is syntactic: we use an appropriate class of string diagrams as terms. This allows for equational reasoning about the class of models defined by a partial theory. We demonstrate the expressivity of such equational theories by considering a number of examples, including partial combinatory algebras and cartesian closed categories. Moreover, despite the increase in expressivity of the syntax we retain a well-behaved notion of semantics: we show that our categories of models are precisely locally finitely presentable categories, and that free models exist.
Integration of Mobile Augmented Reality (MAR) Applications into the 5E Learning Model in Biology Teaching

This research aims to determine the effect of MAR′s integration into 5E learning in teaching biology. Particularly, its impact on the academic success of the preservice teachers and their attitudes towards digital technology is studied. To this end, the convergent parallel mixed research method is used. The study was conducted with the 31 preservice science teachers in a General Biology Laboratory Course in the 2018-2019 academic year. As the data collection tools, “Achievement Test with Open-Ended Questions”, “Attitude Scale towards Digital Technology” and “Semi-Structured Interview Form” were used. The evaluation of the research results indicates that MAR practices in the biology laboratory have increased the academic success and positively affected the attitudes of preservice teachers towards digital technology. Moreover, preservice teachers expressed that applications of MAR facilitated their learning and understanding, making the lessons more attractive. Accordingly, the use MAR applications are recommended in teaching Biology to examine various anatomical structures.
Accelerating Haze Removal Algorithm Using CUDA

The dark channel prior (DCP)-based single image removal algorithm achieved excellent performance. However, due to the high complexity of the algorithm, it is difficult to satisfy the demands of real-time processing. In this article, we present a Graphics Processing Unit (GPU) accelerated parallel computing method for the real-time processing of high-definition video haze removal. First, based on the memory access pattern, we propose a simple but effective filter method called transposed filter combined with the fast local minimum filter algorithm and integral image algorithm. The proposed method successfully accelerates the parallel minimum filter algorithm and the parallel mean filter algorithm. Meanwhile, we adopt the inter-frame atmospheric light constraint to suppress the flicker noise in the video haze removal and simplify the estimation of atmospheric light. Experimental results show that our implementation can process the 1080p video sequence with 167 frames per second. Compared with single thread Central Processing Units (CPU) implementation, the speedup is up to 226× with asynchronous stream processing and qualified for the real-time high definition video haze removal.
The technological impact on real estate investing: robots vs humans: new applications for organisational and portfolio strategies

Purpose The goal of this paper is to present a roadmap for real estate operating companies (REOCs) to transform themselves into tech-centric enterprises. Design/methodology/approach This qualitative approach is based on the impact of technology on physical real estate assets and organisational structures as reviewed in industry and academic literature, professional experience and current property technology (PropTech) applications. Findings New technologies are rapidly changing how investors, tenants and managers use, invest and finance property. The revolutionary change for the industry will be in its organisational and industry structure, away from the traditional hierarchical-mechanistic form to a virtual open-agile-innovative organisational form. Research limitations/implications Research limitations come from the lack of real estate companies utilising the hybrid flipped form of organisational structures. Practical implications Due to the current state of the economy, effects of the pandemic and rapid adoption of new technologies, real estate companies are likely to radically change the way they are organised, how they add value, innovate and their leadership/management style. Social implications The revolution in real estate technologisation will not come from the application of these technologies but the rapid change in ideological thought and management leadership style and culture. Originality/value The introduction of artificial intelligence/machine learning (AI/ML), blockchain, virtual reality, tablets, cell phones, applications, 5G, etc. is putting pressure on real estate organisations to change. These changes are long overdue and the future, modern real estate company will take a hybrid PropTech form – a company focussed on delivering high-quality products and services to its clients in real time.
Children's memory of a story experienced with virtual reality versus traditional media technology

Immersive Virtual Reality Technology (IVR) is a visual multi-sensory computer-simulated environment that perceptually surrounds an individual, creating the illusion that one has “stepped inside” and is included in, and interacting with the generated world. Although IVR has been suggested as a tool to enhance learning, existing work has not examined how IVR presentations, compared with other types of storytelling, facilitate or interfere with children’s memory formation. Here, we present data from a study of seventy 6- and 7-year-old children randomly assigned to experience a story in one of three modalities: IVR, video, or a paper-based book. We assessed the children’s story recall and their ability to identify the protagonist’s emotions. Results showed that, overall, children in the IVR condition performed better in the memory-recall task than the children in the video and book conditions. The most pronounced difference in memory performance was between the IVR and book conditions. In the IVR versus video conditions, 6-year-olds performed significantly better in the IVR condition than in the video condition, while 7-year-olds performed similarly in both digital-story conditions. We found no effects of condition on children’s attribution of emotions to the story’s protagonist. We conclude that IVR may enhance children’s ability to learn story content in certain situations.