Reviewerly/exHarmony huggingface dataset:
RONFC: A Novel Enabler-Independent NFC Protocol for Mobile Transactions

The use of near field communication (NFC) technology for contactless mobile transactions has become popular in the past decade with the availability of this technology in mobile devices.Today, there are millions of the NFC-enabled mobile handsets in the market, with mobile handset manufacturers and mobile network operators enabling m-wallet solutions using the secure elements (SEs) that they own, thus can remotely control, on the devices.While this approach gives full control to the SE owner to activate any mobile transaction system on a device, having a more flexible approach would increase the benefits that end users could obtain from this technology in a variety of use cases.In this paper, we introduce a novel protocol for the NFC-based mobile transaction procedure, which uses tamper-resistant SEs that are already installed at the transaction terminals, and is mobile handset manufacturer and mobile network operator-independent.We evaluate and show the feasibility of the use of our proposed model with common mobile electronic payment scenarios.The evaluation results demonstrate that the proposed solution is promising for adoption as a secure NFC transaction model, which will have applications in various security-sensitive IoT scenarios, including but not limited to, mobile identification, healthcare, payment, and access control.
Readability of web content

Readability is determined by the characteristics of the text that influence their understanding.The web is composed of content on various topics and the results retrieved in the top positions by the main search engines are expected to be those with the highest number of views.In this study, we analyzed the readability of web pages according to the topic to which it belongs and their position in the search result.For that, we collected the top-20 results retrieved by Google to 23,779 queries from 20 topics and used several readability metrics.The results of the analysis showed that the content from organizations (like colleges and other institutions) and health-related content have lower readability values.Categories Games and Home are on the opposite side.For the categories identified as having less readability, tools can be developed that help the user understand their content.We also found that top-ranked pages have higher values of readability.One can conclude that, directly or indirectly, readability is a factor that seems to be being considered by the Google search engine or has an influence on page popularity.
Mobile Friendly or Attacker Friendly?

In the last few years, traffic generated by mobile devices has surpassed desktop visits. In order to provide users with the best browsing experience, many website owners specifically tailor their site to mobile devices. While some websites make use of reactive designs, many others opt to create an entirely new "mobile-first" website, typically hosted on a subdomain of the desktop site. These mobile-first sites provide a unique viewpoint on how organizations handle security: the mobile version of a site is typically developed several years after the desktop site by the same organization. Through a large-scale security analysis on 10,222 domains with both a desktop and mobile-first version, we find several strong indicators that security is generally applied consistently across the different parts of an organization's web estate. Overall, we find relatively few differences between the desktop and mobile versions of a website, both on the adoption and the implementation of security features, indicating that these are applied reactively rather than proactively during the design phase.
Rely-Guarantee Reasoning About Concurrent Memory Management in Zephyr RTOS

Formal verification of concurrent operating systems (OSs) is challenging, and in particular the verification of the dynamic memory management due to its complex data structures and allocation algorithm. Up to our knowledge, this paper presents the first formal specification and mechanized proof of a concurrent buddy memory allocation for a real-world OS. We develop a fine-grained formal specification of the buddy memory management in Zephyr RTOS. To ease validation of the specification and the source code, the provided specification closely follows the C code. Then, we use the rely-guarantee technique to conduct the compositional verification of functional correctness and invariant preservation. During the formal verification, we found three bugs in the C code of Zephyr.
Continuous Authentication Based on User Interaction Behavior

Continuous authentication (CA) is the process which continuously verifying a user based on their on-going interaction with a computer system. In this paper, we propose an adaptive continuous authentication method based on the changes of context, in which providing protection for the user's on-going interaction with computer in different contexts. In order to prevent a situation where an attacker tries to avoid detection by limiting to one input device, we considered both keystroke and mouse usage behavior patterns. In this research, collecting 30 users' data in an uncontrolled environment, extracting the user behavior feature from data by a new feature extraction method, using fusion technology to identify users, and then, according to the recognition result we can judge whether the current user is a real user or not. The experiment result shows that our scheme has a false acceptance rate (FAR) of 0%, a false rejection rate (FRR) of 2.04%, and the authentication time that between 10 seconds and 60 seconds for authentication.
Work memories in Super 8: Searching a frame quality metric for movie restoration assessment

This paper describes the digital acquisition and restoration of a Super 8 documentary on the origins of citizens’ awareness on paper recycling in the city of Brescia, Lombardy, northern Italy. This film is currently stored in Fondazione ASM archives, and its acquisition is part of a larger project of preservation of industrial memories in Italy. Here we describe the tools used for the acquisition and the post-processing with a Spatial Colour Algorithm (SCA) for unsupervised colour and contrast enhancement. This method provides a semi-automatic image restoration, whose quality has been tested through different evaluations. In this paper, some advantages of the use of SCAs in the restoration pipeline are presented, together with a preliminary discussion on the problem of assessing frame restoration results. To this aim, we present objective vs. subjective evaluation, and some issues associated with this complex task. The most used full-reference and no-reference metrics have been used, together with alternative low-level descriptors. Data and examples are presented and discussed.
Limitations of benchmark sets and landscape features for algorithm selection and performance prediction

Benchmark sets and landscape features are used to test algorithms and to train models to perform algorithm selection or configuration. These approaches are based on the assumption that algorithms have similar performances on problems with similar feature sets. In this paper, we test different configurations of differential evolution (DE) against the BBOB set. We then use the landscape features of those problems and a case base reasoning approach for DE configuration selection. We show that, although this method obtains good results for BBOB problems, it fails to select the best configurations when facing a new set of optimisation problems with a distinct array of landscape features. This demonstrates the limitations of the BBOB set for algorithm selection. Moreover, by examination of the relationship between features and algorithm performance, we show that there is no correlation between the feature space and the performance space. We conclude by identifying some important open questions raised by this work.
Analysing exposure diversity in collaborative recommender systems—Entropy fusion approach

Recommender Systems are considered as essential business tools to leverage the potential growth of on-line services. Neighbourhood based collaborative filtering, a successful recommendation approach has mainly focused on improving accuracy of predictions. From user point of view, it is more valuable to obtain novel and diverse recommendations rather than monotonic preferences. Ratings given by a user for different categories of items are considered as a tool to access user exposure diversity which signifies his creative and divergent thinking. On the other hand, pair of items is concordant if highly correlated users agree in rating the items. Based on the user exposure diversity and item concordance, the neighbourhood selection process of item based collaborative recommender systems is refined. Rating predictions are made based on the newly selected neighbours. The performance of the proposed approach is investigated for accuracy and diversity of predictions on Movielens data sets. The results demonstrate that the proposed approach outperforms the state of the art recommendation approaches which address accuracy–diversity trade off. Statistical analysis is done to prove the efficiency of the proposed approach.
Analyzing Code-Switching Rules for English–Hindi Code-Mixed Text

In this work, we have proposed an efficient and less resource intensive strategy for parsing and analyzing switching points in code-mixed data. Specifically, we have explored the rules of code-switching in Hindi–English code-mixed data. The work involves code-mixed text extraction, translation of the extracted texts to its pure form, forming word pairs, annotation of these using of Parts-of-Speech tags and recognition of the rules that govern switching in code-mixed text. We have created three models, viz. baseline model, lexicon-based model, and machine learning-based model, and found out the individual accuracies of these models.
Spatially constrained regionalization with multilayer perceptron

Abstract In this article, multilayer perceptron (MLP) network models with spatial constraints are proposed for regionalization of geostatistical point data based on multivariate homogeneity measures. The study focuses on non‐stationarity and autocorrelation in spatial data. Supervised MLP machine learning algorithms with spatial constraints have been implemented and tested on a point dataset. MLP spatially weighted classification models and an MLP contiguity‐constrained classification model are developed to conduct spatially constrained regionalization. The proposed methods have been tested with an attribute‐rich point dataset of geological surveys in Ukraine. The experiments show that consideration of the spatial effects, such as the use of spatial attributes and their respective whitening, improve the output of regionalization. It is also shown that spatial sorting used to preserve spatial contiguity leads to improved regionalization performance.
Deep Attentive Feature Learning for Histopathology Image Classification

In this paper, we present a new deep learning-based approach for histopathology image classification. Our method is built upon standard convolutional neural networks (CNNs), and incorporates two separate attention modules for more effective feature learning. In particular, the attention modules infer the attention maps along different dimensions, which help focus the CNNs on critical image regions, as well as highlight discriminative feature channels while suppressing the irrelevant information with respect to the classification task. The attention modules are light-weight, and enhances the feature representation with small extra computational overhead. Experimental results on the publicly available BreakHis dataset demonstrate that our method outperforms the state-of-the-arts by a large margin.
Application of Artificial Intelligence to Adaptive Instruction - Combining the Concepts

In recent years, instructional systems for individuals and teams, including virtual environments, serious games, simulator-based training and on-the-job/live training, have been supplemented by Adaptive Instructional Systems (AISs). Artificial Intelligence (AI) techniques and Machine Learning (ML) techniques have been proposed, and are increasingly used, for a number of functions of AISs. This paper aims to combine, on the one hand, the concepts of AI and ML, and, on the other hand, adaptive instruction. The emphasis is put on simulator-based training in a professional context, predominantly skill learning by practicing tasks in simulated environments, either as an individual student or as part of a team. The major goals of this paper are: (1) to provide a basic description of available ML techniques, (2) to sketch the potential use of machine learning techniques in adaptive instruction, and (3) to provide examples of applications from the literature. This paper neither introduces a new AI approach to adaptive instruction, nor does it extensively review the literature of such approaches.
Empirical evidence of the effectiveness of primitive granularity control for hyper-heuristics

The set of primitive operations available to a generative hyper-heuristic can have a dramatic impact on the overall performance of the heuristic search in terms of efficiency and final solution quality. When constructing a primitive set, users are faced with a tradeoff between generality and time spent searching. A set consisting of low-level primitives provides the flexibility to find most or all potential solutions, but the resulting heuristic search space might be too large to find adequate solutions in a reasonable time frame. Conversely, a set of high-level primitives can enable faster discovery of mediocre solutions, but prevent the fine-tuning necessary to find the optimal heuristics. By varying the set of primitives throughout evolution, the heuristic search can utilize the advantages of both high-level and low-level primitive sets. This permits the heuristic search to either quickly traverse parts of the search space as needed or modify the minutiae of the search to find optimal solutions in reasonable amounts of time not feasible with implicit levels of primitive granularity. This paper demonstrates this potential by presenting empirical evidence of improvements to solvers for the Traveling Thief Problem, a combination of the Traveling Salesman Problem and the Knapsack Problem, a recent and difficult problem designed to more closely emulate real world complexity.
Image Forensics from Chroma Subsampling of High-Quality JPEG Images

The JPEG compression format provides a rich source of forensic traces that include quantization artifacts, fingerprints of the container format, and numerical particularities of JPEG compressors. Such a diverse set of cues serves as the basis for a forensic examiner to determine origin and authenticity of an image. In this work, we present a novel artifact that can be used to fingerprint the JPEG compression library. The artifact arises from chroma subsampling in one of the most popular JPEG implementations. Due to integer rounding, every second column of the compressed chroma channel appears on average slightly brighter than its neighboring columns, which is why we call the artifact a "chroma wrinkle". We theoretically derive the chroma wrinkle footprint in DCT domain, and use this footprint for detecting chroma wrinkles. The artifact is detected with more than 90% accuracy on images of JPEG quality 75 and above. Our experiments indicate that the artifact can also be used for manipulation localization, and that it is robust to several global postprocessing operations.
VISA: A Supervised Approach to Indexing Video Lectures with Semantic Annotations

Many universities adopt educational systems where the teacher lecture is video recorded and the video lecture is made available to students with minimum post-processing effort. These cost-effective solutions suffer from the limited amount of annotations associated with the video content, which strongly limits the usability of the service when students need to retrieve specific portions of video, e.g., to revise unclear aspects covered in the past lectures. This paper presents, as a real case study, the system developed and implemented in our university for video lecture annotation and indexing. The original video recordings, which last around 1.5 hour, are first partitioned into smaller segments and then annotated by mapping their content with the entities in a multilingual knowledge base. To this purpose, the proposed approach analyzes both the transcription of the teacher's speech and the text appearing in the video (e.g., the slide content, the note written on the whiteboard) by means of an ad hoc Named Entity Recognition and Disambiguation (NERD) step. NERD relies on a supervised classification approach tailored to the domain under analysis. More specifically, to identify the most salient entities of the knowledge base matching the video content it considers not only text similarity measures but also the semantic pertinence of the candidate entities to the main subject of the video lectures. The performance of the proposed system was validated on a ground truth against the techniques available in the general entity annotation system GERBIL. The preliminary results demonstrate the effectiveness of the proposed approach.
A Decennary Survey on Artificial Intelligence Methods for Image Segmentation

The technique of breaking down an image into categorial regions containing each pixel with similar attributes is termed as image segmentation (IS). It is the preliminary step of image processing. This technique can be used for both grey-scale and colour images. This technique is applied everywhere, even in our personal Smartphone's camera while capturing pictures. And image segmentation is the most innovative problem under the computer vision domain. This paper provides various techniques that are available in the field of image segmentation and their pros and cons. A lot of research is being done by applying artificial intelligence techniques for the image segmentation problems. In this paper, an overview of artificial intelligence algorithm techniques such as machine learning, deep learning, meta-heuristics approaches that was used in the past decade has been discussed, and a comparative study about the same is carried out and the problems and recommendations for selection of appropriate method for image segmentation have been dealt with.
Research on Neural Machine Translation Model

Abstract In neural machine translation (NMT), cyclic neural networks, especially long-term and short-term memory networks and gated recurrent neural networks, have been regarded as the latest methods for sequence modeling and transduction problems for a long time, such as language modeling and machine translation. When the cyclic neural network is running, the sequence information is processed one by one, strictly following the order from left to right or from right to left, processing one word at a time, and parallel operation cannot be realized, resulting in slow running speed. With the rapid development of neural machine translation (NMT) network architecture, cyclic neural network has been effectively replaced by convolution network and self - attention. Convolution neural network has replaced the divine circulation neural network due to its parallel computation of convolution. The Transformer model replaces the long-term and short-term memory network with a complete self-attention structure, and abandons the traditional encoder and decoder model which must combine the inherent mode of convolutional neural network or circular neural network and only uses the self-attention mechanism. Although the biggest innovation of Transformer architecture is to use full self - attention, there are several other factors, such as multi-head attention and residual connection. The model flexibly combine several common building blocks in the Transformer architecture with the cyclic neural network. By borrowing the framework of the Transformer architecture without using full self - attention, experiments show that the cyclic model can be very close to the performance of the Transformer Our model achieved 26.7 BLEU in the WMT 2014 English to German translation task and 37.8 BLEU in the WMT 2014 English to French translation task. Using these two scores alone is very close to the score of the Transformer architecture using full attention, so even if the cyclic neural network is used instead of full self - attention, it can perform well on the data set.
Software Reusability Metrics Estimation From the Social Media by Using Evolutionary Algorithms

Software expansion is rising with the help of the standard paradigm in the 21st century. The maximum contribution of software growth focuses mainly on object-oriented development methodologies. This paradigm helps the developer to develop code quickly and makes sure that the platform assists in producing a quality product. The software reusability metrics play a crucial role for software development. To overcome the scalability issues, researchers and developers both adopt a CK metrics suite to extract the software metrics to extract the features from the repositories. The main objective of this article is to extract the set of metrics from social media by using novel evolutionary techniques. Dissimilar features within this area are examined with a suitable research query that discovers the potential and extent.
Effects of Weight and Balance of Head Mounted Display on Physical Load

Recently, VR technology has advanced rapidly, and many Head Mounted Displays (HMD) are sold by companies all over the world. In order to have good experiences in VR environment, comfortability of wearing HMD is essential. However, few studies investigated the feelings of fatigue on commercially available HMDs. In this research, we focused on the effects of weight and balance of HMD on physical load. We performed experiment with 188 participants using shooting game played under different weights and balances. After playing the game, the participants were asked to answer a questionnaire to assess the level of physical loads that they perceived. As the results, we clarified that the weight of HMD affects the feelings of fatigue, and its degree varies depending on the position of the center of gravity. The results suggest the joint torque at the neck can be an indicator of the physical load of HMD. Our results will be highly suggestive for designing new HMD.
Rammar: RAM Assisted Mask R-CNN for FMCW Sensor Based HGD System

Recently, hand gesture detection (HGD) system have become increasingly interesting to researchers in the field of human-computer interfaces. However, the traditional HGD has low robustness and detection accuracy, as well as privacy protection problem. Therefore, we present Rammar, a residual attention module (RAM) assisted Mask R-CNN, for frequency modulated continuous wave (FMCW) sensor based on HGD system. Firstly, by analyzing the time domain and frequency domain of the FMCW sensor signal, the three-dimensional feature maps of Range-Time-Map (RTM), Doppler-Time-Map (DTM) and Angle-Time-Map (ATM) of each hand gesture are obtained, respectively, avoiding insufficient information of single dimension parameter. Secondly, RTM, DTM and ATM images of each hand gesture are simultaneously sent to Rammar for training. To focus on the features of gesture, RAM in Rammar employs average-pooling and max-pooling to extract time and spatial features. Finally, the extracted three-dimensional feature maps are merged in the fully connected layer. The experimental results show that Rammar not only makes the average detection accuracy of the hand gestures to 98.1&#x0025;(increased by 5&#x0025;), but also reduces the detection time effectively.