Reviewerly/exHarmony huggingface dataset:
Reversible and Plausibly Deniable Covert Channels in One-Time Passwords Based on Hash Chains

Covert channels enable stealthy communications over innocent appearing carriers. They are increasingly applied in the network context. However, little work is available that exploits cryptographic primitives in the networking context to establish such covert communications. We present a covert channel between two devices where one device authenticates itself with Lamport’s one-time passwords based on a cryptographic hash function. Our channel enables plausible deniability jointly with reversibility and is applicable in different contexts, such as traditional TCP/IP networks, CPS/IoT communication, blockchain-driven systems and local inter-process communications that apply hash chains. We also present countermeasures to detect the presence of such a covert channel, which are non-trivial because hash values are random-looking binary strings, so that deviations are not likely to be detected. We report on experimental results with MD5 and SHA-3 hash functions for two covert channel variants running in a localhost setup. In particular, we evaluate the channels’ time performance, conduct statistical tests using the NIST suite and run a test for matching hash values between legitimate and covert environments to determine our channels’ stealthiness.
Symbolic Execution Based Intra-Procedural Analysis for Search for Defects

Svace is a static analysis tool for bug detection in C/C++/Java source code. To analyze a program, Svace performs an intra-procedure analysis of individual functions, starting from the leaves of a call-graph and moving towards the roots, and uses summaries of previously analyzed procedures at call-cites. In this paper, we overview the approaches and techniques employed by Svace for the intra-procedural analysis. This phase is performed by an analyzer engine and an extensible set of detectors. The core engine employs a symbolic execution approach with state merging. It uses value numbering to reduce the set of symbolic expressions, maintains points-to relationship graph for memory modeling, and performs strong and weak updates of program values. Detectors are responsible for discovering and reporting bugs. They calculate different properties of program values using a variety of abstract domains. All detectors work simultaneously orchestrated by the engine. Svace analysis is unsound and employs a variety of heuristics to speed-up. We designed Svace to analyze big projects (several MLOCs) in just a few hours and report as many warnings as possible, while keeping a good quality of reports ≥ 65 of true positives). For example, Tizen 5.5 (20MLOC) analysis takes 8.6 hours and produces 18,920 warnings, more than 70% of which are true-positive.
Short-term effects of aligning prisms on the objective and subjective fixation disparity in far distance

&#x0D; &#x0D; &#x0D; &#x0D; Fixation disparity (FD) refers to a suboptimal condition of binocular vision. The oculomotor aspect of FD refers to a misadjustment in the vergence angle between the two visual axes that is measured in research with eye trackers (objective fixation disparity, oFD). The sensory aspect is psychophysically tested using dichoptic nonius lines (subjective fixation disparity, sFD). Some optometrists use nonius tests to determine the prisms for constant wear aiming to align the eyes. However, they do not (yet) use eye trackers. We investigate the effect of aligning prisms on oFD and sFD for 60 sec exposure duration of prisms determined with the clinically established Cross test in far distance vision. Without prisms, both types of FD were correlated with the aligning prism, while with prisms the FD was close to zero (these analyses included all base-in and base-out cases). The effect of base-in prisms on oFD was proportional to the amount of the aligning prism for the present 60 sec exposure, similar as for the 2- 5 sec exposure in Schmid et al. (2018). Thus, within 1 minute of prism exposure, no substantial vergence adaptation seems to occur in the present test conditions. Further studies may investigate intra- individual responses to different exposure times of aligning prisms in both prism directions. &#x0D; &#x0D; &#x0D; &#x0D;
Explainable Artificial Intelligence. Model Discovery with Constraint Programming

This paper explores a yet another approach to Explainable Artificial Intelligence. The proposal consists in application of Constraint Programming to discovery of internal structure and parameters of a given black-box system. Apart from specification of a sample of the input and output values, some presupposed knowledge about the possible internal structure and functional components is required. This knowledge can be parameterized with respect to functional specification of internal components, connections among them, and internal parameters. Models of constraints are put forward and example case studies illustrate the proposed ideas.
The impact of sentiment analysis from user on Facebook to enhanced the service quality

Facebook's influence on the modern social media platform is undoubtedly enormous. While it has gotten a backlash for its inability to control its influence over important affairs, there are still many questions regarding people's perception of Facebook and their sentiment over Facebook. This paper's role in this ongoing debate is to give a glimpse of people's sentiment and perception of Facebook in recent times. By collecting samples data from Facebook's Top Page, this paper hopes to represent a significant amount of people's aspirations towards this company. By processing the data with a processing tool to construct and model out the data and a sentiment analyzer tool helps determine the sentiment, this paper can deduce a 600-comment worth of processed data. The results from the 600 sampled comments concluded that the sentiments towards Facebook are 41.50% negative comments, 22.83% neutral comments, and 35.67% positive comments.
Design of Electronic Contract Architecture Based on Blockchain Technology

In view of the cumbersome, time-consuming., inefficient, resource-consuming and costly signing process of traditional paper contracts, the paper analyzes the design principles and designs relevant architectures for electronic contracts, which can help enterprises save a lot of resources and improve efficiency. The design covers presentation layer, interface layer, cache management, system management, storage service, multi-chain monitoring and management, secure storage and security management, etc., which can help the electronic contract make full use of its advantages.
Quantitative Performance Review of Wheeled Mobile Robot Path Planning Algorithms

Path planning evaluates and identifies an obstacle free path for a wheeled mobile robot (WMR) to traverse within its workspace. It emphasizes metric like, start and goal coordinate, static or dynamic workspace, static or dynamic obstacles, computational time and local minimum problem. Path planning play a significant role toward WMR effective traverse within it workspace like industrial, military, hospital, school and office. In this workspace, path planning is an optimal method to increase the productivity of WMR to achieve it specific task. Hence, in this paper, we present a review of path planning algorithms (classical algorithms, heuristics and intelligent algorithms, and machine learning algorithm) for mobile robot using statistical method. Regarding our objective, we use this statistical method to evaluate the success of these algorithms base on the following metrics: architecture (hybrid or standalone), algorithm sub-category (global or local or combine), workspace (static or dynamic), obstacle type (static or dynamic), number of obstacle (≤ 2, ≤ 5, &amp;gt; 5) and test workspace (virtual or real-world). Research materials are sourced from recognized databases where relevant research articles are obtained and analyzed. Result shows hybrid of machine learning approach with heuristic and intelligent algorithm has superior performance where they are applied compare to other hybrid. Also, in complex workspace Q-learning algorithm outperforms other algorithms. To conclude future research is discussed to provide reference for hybrid of Q-learning algorithm with Cuckoo Search, Shuffled Frog Leaping and Artificial Bee Colony algorithm to improve its performance in complex workspace.
Superpixel Generation by the Iterative Spanning Forest Using Object Information

Superpixel segmentation methods aim to partition the image into homogeneous connected regions of pixels (i.e., superpixels) such that the union of its comprising superpixels precisely defines the objects of interest. However, the homogeneity criterion is often based solely on color, which, in certain conditions, might be insufficient for inferring the extension of the objects (e.g., low gradient regions). In this dissertation, we address such issue by incorporating prior object information — represented as monochromatic object saliency maps — into a state-of-the-art method, the Iterative Spanning Forest (ISF) framework, resulting in a novel framework named Object-based ISF (OISF). For a given saliency map, OISF-based methods are capable of increasing the superpixel resolution within the objects of interest, whilst permitting a higher adherence to the map’s borders, when color is insufficient for delineation. We compared our work with state-of-the-art methods, considering two classic superpixel segmentation metrics, in three datasets. Experimental results show that our approach presents effective object delineation with a significantly lower number of superpixels than the baselines, especially in terms of preventing superpixel leaking.
Research on Road Slope Compound Monitoring System Based on Internet of Things Technology

Abstract Because the stability of the slope is affected by many factors, in order to be able to collect as much information as possible about the stability of the slope, a multi-parameter, multi-device composite system integration based on the Internet of Things technology is proposed. Monitoring systems and slope monitoring projects require low real-time data because most monitoring cycles are long. However, due to the particularity of the monitoring process environment, there are higher requirements for energy saving, scalability, and robustness of wireless transmission networks. RF433MHz technology can meet the problem of wireless transmission in slope monitoring in many aspects such as power consumption, network scalability, network networking capabilities, and operating costs, so it is very suitable for slope monitoring and early warning systems with multiple parameters and multiple measurement points. Through engineering tests, the system works stably and the validity and accuracy of the monitoring data. The researched road slope composite monitoring system based on the Internet of Things technology has certain engineering application value.
Oversampling technique in student performance classification from engineering course

&lt;span&gt;The first year of an engineering student was important to take proper academic planning. All subjects in the first year were essential for an engineering basis. Student performance prediction helped academics improve their performance better. Students checked performance by themselves. If they were aware that their performance are low, then they could make some improvement for their better performance. This research focused on combining the oversampling minority class data with various kinds of classifier models. Oversampling techniques were SMOTE, Borderline-SMOTE, SVMSMOTE, and ADASYN and four classifiers were applied using MLP, gradient boosting, AdaBoost and random forest in this research. The results represented that Borderline-SMOTE gave the best result for minority class prediction with several classifiers.&lt;/span&gt;
Visualizing Children Stories with Generated Image Sequences

Visualizing children's stories is an important task to support distance learning and education during the current COVID-19 pandemic outbreak. This task can be regarded as a rapid transition to overcome any significant disruption to the provision of visual learning for children. However, such a task needs a deep understanding of the characters and events being involved in a story text. It also includes the distinction between common actions and complex ones. The distinction helps in visualizing the objects and their relationships in the scene in a consistent and coherent way. In addition, this process requires natural language processing and understanding as well as a dynamic sequence of images rather than presenting a single image. Specifically, handling datasets with high semantic complexity as in the case of children's stories needs detailed image sequence to support the visualization task. Some recent works have utilized Generative Adversarial Networks (GANs) which show some promising upshots to tackle this challenge. Despite these works success to generate images for isolated sentences, they fail to generate images that are well consistent to the description and coherent to the whole story. To address this issue, we propose a method to generate single images and sequences of images for common and non-common actions, respectively. This yields different sequences of images which we evaluate based on a coherency metric. Our obtained results show the effectiveness of the proposed method.
Image Steganography Using Auto Encoder-Decoder Based Deep Learning Method

Image steganography is the process of communicating hidden secret image embedded in a cover image in plain sight without arousing any suspicions. In the recent times, deep learning methods have gained popularity and is widely used in the field of steganography. In this paper, an auto encoder-decoder based deep convolutional neural network is proposed to embed the secret image inside the cover image and to extract the secret image from the generated stego image. Training and testing is done on a subset of the ImageNet dataset. To evaluate the proposed method, Peak Signal-Noise Ratio (PSNR) and Mean Squared Error (MSE) metrics are used. The proposed method has proved to achieve higher invisibility, security and robustness. The capacity of the method is higher when compared to traditional Least Significant Bit substitution methods.
Discriminative Multi-Stream Postfilters Based on Deep Learning for Enhancing Statistical Parametric Speech Synthesis

Statistical parametric speech synthesis based on Hidden Markov Models has been an important technique for the production of artificial voices, due to its ability to produce results with high intelligibility and sophisticated features such as voice conversion and accent modification with a small footprint, particularly for low-resource languages where deep learning-based techniques remain unexplored. Despite the progress, the quality of the results, mainly based on Hidden Markov Models (HMM) does not reach those of the predominant approaches, based on unit selection of speech segments of deep learning. One of the proposals to improve the quality of HMM-based speech has been incorporating postfiltering stages, which pretend to increase the quality while preserving the advantages of the process. In this paper, we present a new approach to postfiltering synthesized voices with the application of discriminative postfilters, with several long short-term memory (LSTM) deep neural networks. Our motivation stems from modeling specific mapping from synthesized to natural speech on those segments corresponding to voiced or unvoiced sounds, due to the different qualities of those sounds and how HMM-based voices can present distinct degradation on each one. The paper analyses the discriminative postfilters obtained using five voices, evaluated using three objective measures, Mel cepstral distance and subjective tests. The results indicate the advantages of the discriminative postilters in comparison with the HTS voice and the non-discriminative postfilters.
On the Exploration of Incremental Learning for Fine-grained Image Retrieval.

In this paper, we consider the problem of fine-grained image retrieval in an incremental setting, when new categories are added over time. On the one hand, repeatedly training the representation on the extended dataset is time-consuming. On the other hand, fine-tuning the learned representation only with the new classes leads to catastrophic forgetting. To this end, we propose an incremental learning method to mitigate retrieval performance degradation caused by the forgetting issue. Without accessing any samples of the original classes, the classifier of the original network provides soft labels to transfer knowledge to train the adaptive network, so as to preserve the previous capability for classification. More importantly, a regularization function based on Maximum Mean Discrepancy is devised to minimize the discrepancy of new classes features from the original network and the adaptive network, respectively. Extensive experiments on two datasets show that our method effectively mitigates the catastrophic forgetting on the original classes while achieving high performance on the new classes.
A Survey on Privacy Issues of Augmented Reality Applications

Privacy is one of the biggest concerns of the coming decade, ranking third among concerns of consumers. Data breaches and leaks are constantly in the news with companies like Facebook and Amazon being outed for their excessive data collection. With companies and governmental agencies tracking and monitoring individuals to a great degree, there are concerns that contemporary technologies that feed into these systems can be misused or misappropriated further. Frameworks currently in place fail to address many of these consumer's concerns and even the legal framework could use further elaboration to better control the way data is handled. In this paper, We address the current industrial standards, frameworks, and concerns of one of the biggest technology trends right now, the Augmented Reality. The expected prevalence of augmented reality applications necessitates a deeper study not only of their security but the expected challenges of users using such applications as well.
Optimising Criteria for Manual Smear Review Following Automated Blood Count Analysis: A Machine Learning Approach

The complete blood count (CBC) performed by automated haematology analysers is the most common clinical procedure in the world. Used for health checkup, diagnosis and patient follow-up, the CBC impacts the majority of medical decisions. If the analysis does not fit an expected setting, the laboratory staff manually reviews a blood smear, which is highly time-consuming. Criteria for reviewing CBCs are based on international consensus guidelines and locally adjusted to account for laboratory resources and populations characteristics. Our objective is to provide a clinical laboratory decision support tool to identify which CBC variables are linked to an increased risk of abnormal manual smear and at which threshold values. Thus, we treat criteria adjustment as a feature selection problem. We propose a cost-sensitive Lasso-penalised additive logistic regression combined with stability selection, adapted to the peculiarities of data and context: class-imbalance, categorisation of continuous predictors, required stability and enhanced interpretability. Using simulated and real CBC data, we show that our proposal is competitive in terms of predictive performance (compared to deep neural networks) and model selection performance (provided that there is sufficient data in the neighbourhood of the true thresholds). The R code is publicly available as an open source project.
A gamification framework for research productivity enhancement on the higher education institution

&lt;span lang="IN"&gt;The condition of research productivity in higher education institutions in Indonesia is still not ideal. Departing from this problem, this &lt;/span&gt;&lt;span lang="EN-GB"&gt;paper&lt;/span&gt;&lt;span lang="IN"&gt; aims &lt;/span&gt;&lt;span lang="EN-GB"&gt;to&lt;/span&gt;&lt;span lang="IN"&gt; contribute in the form of a framework used as the main alternative in optimizing research productivity, including the number of publications in higher education institutions. The mechanism proposed is a framework that uses designs derived from games or better known as gamification. Based on the preliminary testing of the proposed framework, it shows that each construct of the framework has a positive impact on research productivity enhancement as the final goal. One of the constructs is Network has a positive impact of 0.415 on Behavior, the Behavior has a positive impact of 0.403 on research productivity enhancement. This also applies to other constructs. Except for the Points that do&lt;/span&gt;&lt;span lang="IN"&gt;not have good reliability, it will become homework in future studies.&lt;/span&gt;
COVID-19 Community Archives and the Platformization of Digital Cultural Memory

In this study we aim to understand how GitHub is used by COVID-19 interest groups for organizing community archives to protect their knowledge from the Chinese government's censorship efforts.We introduce two case studies of such COVID-19 community archives published with GitHub that appeared online in early 2020.Using public GitHub repository documentation and web archive web crawls from the Internet Archive's Wayback Machine, we describe how these digital community archives emerge and exist on the platform, how knowledge of them circulated on other US based social media sites and show strategies and tactics these volunteers used to keep these community archives alive, resist censorship, and guard the safety of these collections.We argue that these COVID-19 community archives are at risk because of their platform accessibility as much as the content they document, and that understanding how organizers use GitHub's platform affordances is essential to theorizing how platforms are impacting approaches to preserving cultural memory.
A Concept Extraction System with Rich Multimedia Learning Resources for Children

COVID-19 pandemic imposed a new way of teaching children. Distance education through the Internet will then be adopted in many countries as the main source of education. The whole world is transformed from physical teaching in schools' premises to virtual teaching through the web by using software like ZOOM or Microsoft team. These software connect people to discuss and share documents, however, they do not generate contents. Instructors need digital materials to deliver effectively through and explain concepts. They cannot prepare them by themselves as they get used to rely on printed textbooks in their schools. The objective of this work is to propose and implement a new online learning platform that uses multimedia in teaching. It focuses on teaching the Arabic language through simple stories for children and none-Arabic speakers in Qatar University. The platform receives as input the story script, automatically extract the main concepts, and associate them with images. A multimodal text-image corpus is built with structured contents allowing the instructors to find the most representative visual concepts associated with keywords. The corpus can be enriched with additional stories and images. Search engines are used to retrieve these images that the instructor need to approve them to be used in education. The platform opens a new era of distance education and improve the education process during the COVID-19 pandemic.
News Crawling Based on Python Crawler

Abstract News is an important form to reflect current politics, which attracts people’s attention. The emergence of crawlers provides a convenient way for people to obtain useful information from mass news. Through Python language, this paper takes the relevant news of business logistics hub as an example to carry out the experiment of network news crawling. This paper introduces the workflow, design and implementation of the crawler in detail. The experiment proves that the designed crawler can get news quickly and can provide people with the information they need.