Reviewerly/exHarmony huggingface dataset:
An Adaptive Physical Layer Key Extraction Scheme for Smart Homes

The wireless transmission of home area networks contributes to the vulnerabilities of smart homes. As a result, smart homes are vulnerable to malicious attacks like eavesdropping. To secure the transmissions, secret keys should be established for smart home devices. In practical smart home applications, smart home devices might be resource-constrained devices. Thus, the lightweight key establishment schemes are preferred. Typically, lightweight physical layer key extraction schemes can be employed by the smart home devices. However, the key generation rate of the schemes is low. To improve the key generation rate, in this paper we propose an adaptive physical layer key extraction scheme for smart homes by making use of the Received Signal Strength (RSS). Specifically, in our scheme, two smart home devices collect the RSS measurements by probing with each other. Then, the collected measurements are quantized by running the proposed Self-adaptive Lossless Quantization algorithm. Completing these operations, the two smart home devices can obtain the secret key. In our scheme, we design the self-adaptive lossless quantization algorithm to improve the key generation rate. To show the practicality of the new scheme, we implement it using the Ralink WiFi cards. Additionally, we analyse the performance of our scheme and compare it with that of other related works.
A Novel Algorithm for Efficient Labeling and Its Application to On-Road Risk Detection

This paper reports on the application of deep learning for on-road risk detection. In the proposed system, a USB-camera is mounted on a mobility scooter to provide video data in real-time, and a convolutional neural network (CNN) is used to detect possible risks. Each frame is classified to 11 categories, including normal, left/right attention, left/right warning, etc. A bottleneck problem in deep learning is the collection of labeled data. During the initial experiment, we collected video data containing more than 130,000 frames to train the CNN. A great number of data will be needed in the process of commercializing the system. To solve this problem, we propose a novel method that enables us to assign labels efficiently. Using this proposed method, we found experimentally that we can obtain labels of all data by labeling manually less than 10% of all the data. In addition, the CNN obtained via transfer learning, based on the well-known AlexNet, performs very well. The average performance of several runs is about 95.83% for testing data. Considering that 30 frames are captured in each second, this accuracy means that three consecutive mistakes are almost impossible, if we use the CNN for real-time risk detection.
Secure Reversible Data Hiding in the Medical Image using Histogram Shifting and RC4 Encryption

Information about medical images is personal and confidential, making security one of the demands that must be fulfilled in the delivery and storage of medical images. To secure this information, information is generally embedded in the image. But the embedding process can change the structure of the image so that it can affect the diagnosis. To prevent this, the medical image must be completely retrieved. This research proposes a reversible data hiding (RDH) technique by means of shifting histograms to store information about medical images. In order for information security to increase, RC4 encryption is performed on that information before pinning it. Experimental results show that RC4 encryption can increase the entropy value and bit error rate so that it is difficult to break. While the quality of RDH image output gets a high PSNR and SSIM score. The medical image can also be retrieved entirety, which is proven by the perfect PSNR and SSIM values.
Deep Learning-based Automatic Modulation Recognition Algorithm in Internet of Things

Automatic modulation recognition (AMR) is one of the most promising topics in internet of things (IoT), which endows the capability of adaptive modulation to adapt various complicate environment. This paper proposes a deep learning-based method to distinguish frequency shift keying (FSK), phase shift keying (PSK) and quadrature amplitude modulation (QAM) with high accuracy. We train convolution neural network (CNN) on amplitude and phase (AP) samples, which are two-dimension matrices consisting of amplitudes and phases extracted from complex-valued baseband signals. The performance of our proposed method is confirmed in both light-of-sight (LOS) and non-light-of-sight (NLOS) channel. In addition, considering that IoT devices may not have powerful computing and sufficient power, it is proposed that the application of AP samples with appropriate dimension for CNN training can reduce consumed power of IoT devices with maintaining algorithm performances.
HyperMI: A Privilege-level VM Protection Approach against Compromised Hypervisor

Sensitive data in guest virtual machine is easy to be leaked once hypervisor is compromised. Therefore, VM protection is important to protect the memory of VM from compromised hypervisor's malicious access causally. Previous efforts employ extra customized hardware or employ new software relying on a higher privilege level than hypervisor. This paper proposes HyperMI, a novel approach that provides a privilege-level secure execution environment for VM protection in cloud computing against compromised hypervisor. It provides HyperMI world, effective VM isolation and event-driven VM monitoring in order to prevent customers' sensitive data in VM from being leaked. HyperMI world, placed at the same privilege level with hypervisor, is a privilege-level secure isolated execution environment. As TCB, it is used for VM isolation and VM monitoring. What's the most important, HyperMI focuses on decoupling the function of interaction between hypervisor and VM and decoupling the function of address mapping of VM from hypervisor. As a result, HyperMI can only controls page mapping of VM and EPT updating when a page is allocated to a VM. We have implemented a prototype for KVM hypervisor with multiple Linux as guest OSes, which can be used in commercial cloud computing industry with portability and compatibility for all kinds of CPU platforms. The security analysis shows that this approach can provide protection for VM with effective isolation and event-driven monitoring, and the performance evaluation confirms the efficiency of HyperMI.
Result and Effectiveness of Malicious E-mail Response Training in a Hospital.

Malicious e-mails sent intentionally to institutions have caused an increase in data breaches. Measures against these methods must be taken by healthcare institutions to prevent leakage of sensitive personal medical information. As a form of training, we conducted a phishing simulation to gauge the response of the hospital staff to similar email attacks, and to raise awareness about information security protocols.
A Secure and Practical Blockchain Scheme for IoT

With features such as decentralization, consistency, tamper resistance, non-repudiation, and pseudonym, blockchain technology has the potential to strengthen the Internet of Things (IoT) significantly, thus opening an intriguing research area in the integration of blockchain and IoT. However, most existing blockchain schemes were not dedicated to the IoT ecosystem and hence could not meet the specific requirements of IoT. This paper aims to fix the gap. Inspired by Chainspace, a blockchain platform which could be applicable in IoT, VChain is proposed, a novel blockchain scheme suitable for IoT which is more secure, concrete, and practical compared with Chainspace. Specifically, in VChain, a two-layer BFT-based consensus protocol with HoneyBadger BFT protocol is proposed and a collective signature scheme as building blocks. The designs above allow for supporting faulty-shards-tolerance and asynchronous network model, which could not be sustained in Chainspace, and keeping high efficiency as well. Moreover, the sharding strategy presented in VChain, different from that in RapidChain, which adopts the energy-consuming PoW mechanism for sharding, is environmentfriendly and thus makes VChain fit for IoT well. Last but not least, VChain also inherits the merits of Chainspace to separate the execution and verification of smart contracts for privacy.
On the equivalence of authentication codes and robust (2, 2)-threshold schemes

Abstract In this paper, we show a “direct” equivalence between certain authentication codes and robust threshold schemes. It was previously known that authentication codes and robust threshold schemes are closely related to similar types of designs, but direct equivalences had not been considered in the literature. Our new equivalences motivate the consideration of a certain “key-substitution attack.” We study this attack and analyze it in the setting of “dual authentication codes.” We also show how this viewpoint provides a nice way to prove properties and generalizations of some known constructions.
Information Entropy Differential Privacy: A Differential Privacy Protection Data Method Based on Rough Set Theory

Data have become an important asset for analysis and behavioral prediction, especially correlations between data. Privacy protection has aroused academic and social concern given the amount of personal sensitive information involved in data. However, existing works assume that the records are independent of each other, which is unsuitable for associated data. Many studies either fail to achieve privacy protection or lead to excessive loss of information while applying data correlations. Differential privacy, which achieves privacy protection by injecting random noise into the statistical results given the correlation, will improve the background knowledge of adversaries. Therefore, this paper proposes an information entropy differential privacy solution for correlation data privacy issues based on rough set theory. Under the solution, we use rough set theory to measure the degree of association between attributes and use information entropy to quantify the sensitivity of the attribute. The information entropy difference privacy is achieved by clustering based on the correlation and adding personalized noise to each cluster while preserving the correlations between data. Experiments show that our algorithm can effectively preserve the correlation between the attributes while protecting privacy.
Exploiting Offset-guided Network for Pose Estimation and Tracking

Human pose estimation has witnessed a significant advance thanks to the development of deep learning. Recent human pose estimation approaches tend to directly predict the location heatmaps, which causes quantization errors and inevitably deteriorates the performance within the reduced network output. Aim at solving it, we revisit the heatmap-offset aggregation method and propose the Offset-guided Network (OGN) with an intuitive but effective fusion strategy for both two-stages pose estimation and Mask R-CNN. For two-stages pose estimation, a greedy box generation strategy is also proposed to keep more necessary candidates while performing person detection. For mask R-CNN, ratio-consistent is adopted to improve the generalization ability of the network. State-of-the-art results on COCO and PoseTrack dataset verify the effectiveness of our offset-guided pose estimation and tracking.
A Framework for Hoax News Detection and Analyzer used Rule-based Methods

Currently, the era where social media can present various facilities can answer the needs of the community for information and utilization for socio-economic interests.But the other impact of the presence of social media opens an ample space for the existence of information or hoax news about an event that is troubling the public.The hoax also provides cynical provocation, which is inciting hatred, anger, incitement to many people, directly influencing behavior so that it responds as desired by the hoax makers.Fake news is playing an increasingly dominant role in spreading Misinformation by influencing people's Perceptions or knowledge to distort their awareness and decision-making.A framework is develope dataset collection of hoax gathered using web crawlers from several websites, using classification techniques.This hoax news will be categorized into several detection parameters including, page URL, title hoax news, publish date, author, and content.Matching each word hoax using the similarity algorithm to produce the accuracy of the hoax news uses the rule-based detection method.Experiments were carried out on eleven thousand-hoax news used as training datasets and testing data sets; this data set for validation using similarity algorithms, to produce the highest accuracy of hoax text similarity.In this study, each hoax news will label into four categories, namely, Fact, Hoax, Information, Unknown.Contributions propose Automatic detection of hoax news, Automatic Multilanguage Detection, and a collection of datasets that we gather ourselves and validation that results in four categories of hoax news that have measured in terms of text similarity using similarity techniques.Further research can be continued by adding objects hate speech, black campaign, blockchain technique to ward off hoaxes, or can produce algorithms that produce better text accuracy.
Improving an AI-Based Algorithm to Automatically Generate Concept Maps

Concept maps have been used to assist learners as an effective learning method in identifying relationships between information, especially when teaching materials have many topics or concepts. However, making a manual concept map is a long and tedious task. It is time-consuming and demands an intensive effort in reading the full content and reasoning the relationships among concepts. Due to this inefficiency, many studies are carried out to develop intelligent algorithms using several data mining techniques. In this research, the authors aim at improving Text Analysis-Association Rules Mining (TA-ARM) algorithm using the weighted K-nearest neighbors (KNN) algorithm instead of the traditional KNN. The weighted KNN is expected to optimize the classification accuracy, which will, eventually, enhance the quality of the generated concept map.
Comparative between optimization feature selection by using classifiers algorithms on spam email

&lt;span lang="EN-US"&gt;Spam mail has become a rising phenomenon in a world that has recently witnessed high growth in the volume of emails. This indicates the need to develop an effective spam filter. At the present time, Classification algorithms for text mining are used for the classification of emails. This paper provides a description and evaluation of the effectiveness of three popular classifiers using optimization feature selections, such as Genetic algorithm, Harmony search, practical swarm optimization, and simulating annealing. The research focuses on a comparison of the effect of classifiers using K-nearest Neighbor (KNN), Naïve Bayesian (NB), and Support Vector Machine (SVM) on spam classifiers (without using feature selection) also enhances the reliability of feature selection by proposing optimization feature selection to reduce number of features that are not important.&lt;/span&gt;
Internal and collective interpretation for improving human interpretability of multi-layered neural networks

The present paper aims to propose a new type of information-theoretic method to interpret the inference mechanism of neural networks. We interpret the internal inference mechanism for itself without any external methods such as symbolic or fuzzy rules. In addition, we make interpretation processes as stable as possible. This means that we interpret the inference mechanism, considering all internal representations, created by those different conditions and patterns. To make the internal interpretation possible, we try to compress multi-layered neural networks into the simplest ones without hidden layers. Then, the natural information loss in the process of compression is complemented by the introduction of a mutual information augmentation component. The method was applied to two data sets, namely, the glass data set and the pregnancy data set. In both data sets, information augmentation and compression methods could improve generalization performance. In addition, compressed or collective weights from the multi-layered networks tended to produce weights, ironically, similar to the linear correlation coefficients between inputs and targets, while the conventional methods such as the logistic regression analysis failed to do so.
DEFINING REQUIREMENTS TO DEVELOP INFORMATION SECURITY CONCEPT N HYBRID THREATS CONDITIONS. PART 2

Current article provides the trends of the hybrid threats landscape for 2020 and further. The key aspects of the implementation of hybrid threats and ways of counteracting them in cyberspace, which are closely related to the constant change of directions of cyber-attacks, improving their efficiency and speed of implementation, the use of artificial intelligence systems to ensure the security of information resources and the creation of adaptive systems of adversity to information and cyber threats , the use of machine learning techniques for a better understanding of the current state of threats, the application of artificial intelligence principles and responsive scenarios for predicting cyberattacks, developing customized action plans that rely on artificial intelligence systems to improve threat detection and response speed, utilize counterintelligence and counter-methods to respond quickly to any spyware before initiating active action, enhancing communication between law enforcement agencies to form a unified approach for interaction between law enforcement agencies at international and local levels, government organizations, the corporate sector and experts in the field of security. &nbsp; The article defines the necessity for development of experts validated strategies to protect against information and cyber-attacks by criminals. The article describes the object of protection with determination of purpose and basic functions of the system, group of tasks solved in the system, classification of users of the system, organizational structure of service personnel, structure and composition of a complex of software and hardware, types of information assets stored and processed in system, structure of information flows, characteristics of channels of interaction with other systems and entry points.
Real-Time Indonesian Language Speech Recognition with MFCC Algorithms and Python-Based SVM

Abstract — Automatic Speech Recognition (ASR) is a technology that uses machines to process and recognize human voice. One way to increase recognition rate is to use a model of language you want to recognize. In this paper, a speech recognition application is introduced to recognize words "atas" (up), "bawah" (down), "kanan" (right), and "kiri" (left). This research used 400 samples of speech data, 75 samples from each word for training data and 25 samples for each word for test data. This speech recognition system was designed using Mel Frequency Cepstral Coefficient (MFCC) as many as 13 coefficients as features and Support Vector Machine (SVM) as identifiers. The system was tested with linear kernels and RBF, various cost values, and three sample sizes (n = 25, 75, 50). The best average accuracy value was obtained from SVM using linear kernels, a cost value of 100 and a data set consisted of 75 samples from each class. During the training phase, the system showed a f1-score (trade-off value between precision and recall) of 80% for the word "atas", 86% for the word "bawah", 81% for the word "kanan", and 100% for the word "kiri". Whereas by using 25 new samples per class for system testing phase, the f1-score was 76% for the "atas" class, 54% for the "bawah" class, 44% for the "kanan" class, and 100% for the "kiri" class.
Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm

Despite great scalability on large data and their ability to understand correlations between topics, spectral topic models have not been widely used due to the absence of reliability in real data and lack of practical implementations.This paper aims to solidify the foundations of spectral topic inference and provide a practical implementation for anchor-based topic modeling.Beginning with vocabulary curation, we scrutinize every single inference step with other viable options.We also evaluate our matrix-based approach against popular alternatives including a tensor-based spectral method as well as probabilistic algorithms.Our quantitative and qualitative experiments demonstrate the power of Rectified Anchor Word algorithm in various real datasets, providing a complete guide to practical correlated topic modeling.
Classification of Arabic Writing Styles in Ancient Arabic Manuscripts

This paper proposes a novel and an effective approach to classify ancient Arabic manuscripts in "Naskh" and "Reqaa" styles.This work applies SIFT and SURF algorithms to extract the features and then uses several machine learning algorithms: Gaussian Naïve Bayes (GNB), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) classifiers.The contribution of this work is the introduction of synthetic features that enhance the classification performance.The training phase encompasses four training models for each style.For testing purposes, two famous books from the Islamic literature are used: 1) Al-kouakeb Al-dorya fi Sharh Saheeh Al-Bokhary; and 2) Alfaiet Ebn Malek: Mosl Al-tolab Le Quaed Al-earab.The experimental results show that the proposed algorithm yields a higher accuracy with SIFT than with SURF which could be attributed to the nature of the dataset.
Self-Actualization of a Young Teacher’s Personality in Multicultural Educational Environment

The process of self-actualization of a teacher within a multicultural educational context creates the foundation for effective cross-cultural communication, tolerance, the development of professional multicultural competencies as well as fosters respect for ethnic minorities. This article demonstrates the importance of teacher self-actualization through the prism of scientific propositions. The latter implies the construction of a new theoretical framework, in which the teacher’s personality is viewed as a "new" set of characteristics, personality traits and abilities necessary for the performance of professional activities. The materials provided in the article might be of interest for pre-school educators, child psychologists, university lecturers as well as for wider audiences. The problem of the study lies in the fact that beginning teachers often fail to realize the full potential of their professional activities, thus raising the questions: what are the common features of a beginning teacher’s personality self-actualization and who contributes to the improvement of quality of the educational process?
State Detection of Electrical Equipment Based on Infrared Thermal Imaging Technology

With the increasing demand of power supply reliability for electrical equipment in power grid and the continuous development of infrared thermal imaging technology, infrared thermal imaging technology has been widely used in electrical equipment detection. Using infrared instruments to detect and diagnose thermal fault of electrical equipment has become one of the mainstream methods of electric equipment inspection. However, at present, fault detection relies heavily on personnel’s experience and has low detection efficiency. In order to improve the intelligent level of power system and solve the problem of accurate detection of thermal faults of electrical equipment in substations, this paper applies Faster RCNN algorithm to infrared detection to realize automatic detection of electrical equipment faults. The average recognition accuracy of equipment can reach more than 85%, which has good effect.