Reviewerly/exHarmony huggingface dataset:
Steganographic on Pixel Value Differencing in Iris Biometric

Biometric technology was widely used in more than decade ago and become convenient since human uses their own physical traits and behaviour rather than using keys and IDs to enter the systems. As the arising of biometric use, the issues of security in biometric data and the system points exist. The security and integrity of biometric data presents a major challenge as many benefits of biometric may quite easily become impediment. Every point of vulnerability in the biometrics system consist of specific defend techniques to prevent the misidentification of the real person. In biometric security system, techniques such as watermarking and steganography have been used in attempt to improve security of biometric data. Steganography has become one of the technique used in defending data and system in biometric security The techniques of steganography is divided into two embedding methods which are the least significant bits (LBS) and pixel value differencing (PVD). LBS is most popular techniques for data hiding field and data embedding since it embeds the fixed-length secret bits in the same fixed-length of pixel. While, PVD works better by providing a high quality stego image, besides the high capacity of concealed information. PVD provides high capacity and produces a stego image with good pixels value. PVD technique is also resistant to statistical attacks. It embeds the secret data in the difference as well as in the sum of the two contiguous pixels. In steganography technique, standard measurement to test the performance of stego image quality due to the hiding of message by using the Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). The aim is to achieve higher value of PSNR that indicates a better reconstruction. The expected findings shows a better results of stego image quality based on PSNR and MSE. As a conclusion, the study of the project brings a good impact of higher security towards steganography field in biometrics applications.
Person re-identification through face detection from videos using Deep Learning

In a networked video surveillance system, the videos collected from different surveillance cameras are stored in a centralized server. These videos can be made available for scrutiny, during an event of security threat. Real time analysis of these surveillance videos can be helpful in preventing crimes in the areas under surveillance. A major challenge in video analytics is object detection from video frames. Identifying people in the videos has many applications like handling security through surveillance cameras, crime detection, personalized assistance for the needy, product purchase promotion, employee monitoring, etc. Accurate person reidentification from videos has huge potential that can revolutionize the way businesses work today. This paper discusses a method for person re-identification from the videos collected from surveillance cameras. The critical tasks in this process are face detection from videos and prediction of persons using Convolutional Neural Network models developed using the cropped face images from the face detection stage.
Predicting Your Future Audience: Experiments in Picking the Best Topic for Future Content

This work in progress reports on ongoing experimentation with machine learning approaches on time series data, where the time series is a quantification of the success of content about a certain topic published on a certain digital channel over a past time period. The experiment tests how accurate predictive analytical approaches can be to predict the future success of a piece of media content published on the Web or social media platform according to its topics. Our intention is to enable a new innovation in media organizations’ content publication strategies, where the choice of media for a future publication can be informed by such predictive capabilities in order to maximize the potential content's reach to a digital audience.
Resilient Neural Forecasting Systems

Industrial machine learning systems face data challenges that are often under-explored in the academic literature. Common data challenges are data distribution shifts, missing values and anomalies. In this paper, we discuss data challenges and solutions in the context of a Neural Forecasting application on labor planning. We discuss how to make this forecasting system resilient to these data challenges. We address changes in data distribution with a periodic retraining scheme and discuss the critical importance of model stability in this setting. Furthermore, we show how our deep learning model deals with missing values natively without requiring imputation. Finally, we describe how we detect anomalies in the input data and mitigate their effect before they impact the forecasts. This results in a fully autonomous forecasting system that compares favourably to a hybrid system consisting of the algorithm and human overrides.
Frequency Domain Design Method of Wavelet Basis Based on Pulsar Signal

During pulsar navigation, the high-frequency noise carried by the pulsar profile signal reduces the accuracy of the pulse TOA (Time of Arrival) estimation. At present, the main method to remove signal noise by using wavelet transform is to redesign the function of the threshold and level of wavelet transform. However, the signal-to-noise ratio and other indicators of the filtered signal need to be further optimised, so a more appropriate wavelet basis needs to be designed. This paper proposes a wavelet basis design method based on frequency domain analysis to improve the denoising effect of pulsar signals. This method first analyses the pulsar contour signal in the frequency domain and then designs a Crab pulsar wavelet basis (CPn, where n represents the wavelet basis length) based on its frequency domain characteristics. In order to improve the real-time performance of the algorithm, a wavelet lifting scheme is implemented. Through simulation, this method analyses the pulsar contour signal data at home and abroad. Results show the signal-to-noise ratio can be increased by 4 dB, the mean square error is reduced by 61% and the peak error is reduced by 45%. Therefore, this method has better filtering effect.
Water logging problems in Egypt’s Deserts: Case study Abu Mena archaeological site using geospatial techniques

Groundwater table rise below cultivated, industrial and urban areas, and archaeological sites is a worldwide environmental phenomenon. Groundwater level rise can cause damages to the foundations of the archaeological monuments due to water logging and salt accumulation. Several factors contribute to the groundwater rise. Important factors are those related to hydrological and hydrogeological conditions as well as the inadequate sewerage, excessive irrigation and poor drainage systems. Reclamation projects in West Al-Nubaria area, including Abu Mena archaeological site in Burg Al-Arab to the southwest of Alexandria lead to urban and agricultural sprawl. The groundwater table has risen remarkably and reached mostly the ground surface causing the problems of water logging. Intense groundwater exploitation carried out without careful planning has however brought about a severe deterioration of land and water resources. Water logging, salt accumulation and the deterioration of land and water resources can be prevented, or at least mitigated, through implementation of some guidelines that control irrigation, drainage and sewage processes. The current work aims at investigating the groundwater table rise and its related problems at the Abu Mena archaeological and cultural site and to explore the reason(s) behind this problem. According to the results of study, some measures for the management of water irrigation, drainage and sewage systems will be recommended.
Image Watermarking Using APDCBT in Selected Pixel Blocks

In the most recent decade, a change called all stage discrete cosine biorthogonal change (APDCBT) showed up in the field of computerized picture preparing. It is primarily used to settle the antagonism found in discrete cosine change (DCT), particularly in a low piece rate. In this paper, the APDCBT is utilized for watermark addition dependent on chose areas of a picture. The addition is relying upon the homogeneity of each piece of the picture. So as to decide the degree of homogeneity, there are two criteria: mean and standard deviation are connected for the force of the picture. Medium recurrence groups of APDCBT in picture pixel squares are utilized to hold the installed watermark. At that point, the change is inversed to acquire the resultant watermarked picture. The heartiness of APDCBT against watermark expulsion assaults is tried and the trial results demonstrated the predominance of the APDCBT over conventional DCT in the watermark implanting framework. All the more explicitly, when the LSB reset assault connected. For example, when the differentiation change assault connected, the normal of Normalized Cross Correlation (NCC) estimations of removed watermark pictures with the first watermark it was 0.992 for the proposed strategy. This a guaranteed outcome, on the off chance that it is contrasted and the NCC of the DCT strategy, which was 0.423. The proposed technique can be utilized for copyright protection purpose.
Evaluation of Image Feature Detection and Matching Algorithms

Image features detection and matching algorithms play an important role in the field of machine vision. Among them, the computational efficiency and robust performance of the features detector descriptor selected by the algorithm have a great impact on the accuracy and time consumption of image matching. This paper comprehensively evaluates typical SIFT, SURF, ORB, BRISK, KAZE, AKAZE algorithms. The Oxford dataset is used to compare the robustness of various algorithms under illumination transformation, rotation transformation, scale transformation, blur transformation, and viewpoint transformation. Jitter video is also used to compare the anti-jitter ability for these algorithms. The indicators compared include: time of detecting features, time of matching images, total running time, number of detected feature points, accuracy, number of repeated feature points, and repetition rate. Experimental results show that, Under different transformations, each algorithm has its own advantages and disadvantages.
Digital Microscopy Image Enhancement Technique for Microstructure Image Analysis of Bottom Ash Particle Polymer Composites

PolymerMicroscopy image enhancement compositesPolymer composite areMicrostructure compositeBottom ash particle materialsImage analysis with matrices of polymersPolymer with reinforcements using fibers or particles or both together. This study uses polypropylene polymer compositePolymer composite material with reinforcement of bottom ash particlesBottom ash particle. In the manufacture of polypropylene-shaped sheet (lamina) polymer compositePolymer composite materials with bottom ash particlesBottom ash particle, the process of assessing the composite characteristics of the polypropylene matrix such as the level of distribution of bottom ash particlesBottom ash particle as fillers in the polypropylene matrix through microscopic imagesMicroscopic image is necessary. Analysis of the level of distribution of reinforcement particle is used to avoid the formation of filler particle agglomerates during the dispersionDispersion of various types of fillers in polymerPolymer resins. Bottom ash particlesBottom ash particle themselves have an irregular shape that requires particular analysis to distinguish a single particle and an agglomerate particle. Before the analysis step, the most crucial step at the beginning is to carry out the process of improving the quality of the input imageImage, which aims to improve imageImage contrast and eliminate unwanted noiseNoise. This chapter proposes an image enhancementImage enhancement method to enhance the quality of the microstructure imageMicrostructure image of a polymer compositePolymer composite that is analyzed using a digital microscope. The proposed method combines the multiresolution approach and the anisotropic diffusion method. From the experiment, this image enhancementImage enhancement method gives the best performance when it is compared with other techniques in the state of the arts.
Fine grained pointing recognition for natural drone guidance

Human action recognition systems are typically focused on identifying different actions, rather than fine grained variations of the same action. This work explores strategies to identify different pointing directions in order to build a natural interaction system to guide autonomous systems such as drones. Commanding a drone with hand-held panels or tablets is common practice but intuitive user-drone interfaces might have significant benefits. The system proposed in this work just requires the user to provide occasional high-level navigation commands by pointing the drone towards the desired motion direction. Due to the lack of data on these settings, we present a new benchmarking video dataset to validate our framework and facilitate future research on the area. Our results show good accuracy for pointing direction recognition, while running at interactive rates and exhibiting robustness to variability in user appearance, viewpoint, camera distance and scenery.
A fast parallel sparse polynomial GCD algorithm

We present a parallel GCD algorithm for sparse multivariate polynomials with integer coefficients. The algorithm combines a Kronecker substitution with a Ben-Or/Tiwari sparse interpolation modulo a smooth prime to determine the support of the GCD. We have implemented our algorithm in C for primes of various sizes and have parallelized it using Cilk C. We compare our implementation with Maple and Magma's serial implementations of Zippel's GCD algorithm.
A Teaching Reform and Practice to Improve Student's Ability of Practice and Innovation in Computer Major

Local colleges should cultivate high-quality IT applied talents, the key is to cultivate the students' ability of practice and innovation. In order to cultivate these abilities, we put forward a set of effective solutions, which is to improve the teaching quality of programming courses and strengthen the practical teaching link of programming courses. To this end, we implemented a series of teaching reforms, including refactoring talent training plan and practical teaching system, setting up the programming courses' teaching team, enhancing the teaching team's teaching level and practice guiding ability, building a personalized teaching auxiliary platform, building three-dimensional and network teaching resources, building school's innovative practice base, implementing three-dimensional and deep project education program, and establishing the operation mechanism of joint examination of programming courses. Since the implementation of the reform, we have achieved excellent results, including improving the quality of course teaching, and improving students' ability of computational thinking, programming practice, employment quality, and the ability of science and technology to serve society.
Quantitative approaches to productivity and borrowing in Maltese derivation

Abstract This paper applies quantitative measures of productivity to a selection of Maltese derivational affixes borrowed from Italian/Sicilian. The productivity rankings of the selected affixes are compared to their Italian sources revealing identical rankings for the most productive affixes and slightly deviating rankings for the less productive affixes. It is argued that morphological productivity scores and the subsequent rankings form a pattern that is established in the recipient language by the sum of matter borrowings of formations involving the respective affixes. In conjunction with a discussion of the mixed lexicon and morphology of Maltese, a case is made that converging or diverging productivity rankings can be explained by well-studied variables in language contact studies: intensity of contact, availability of registers/repertoires, and route of borrowing (direct/indirect). In addition it is shown that the cross-linguistic application of other quantitative measures, including a newly proposed Integration Factor and Seifart’s criteria for direct and indirect borrowing, can help uncover where the distributions of donor and recipient language affixes diverge, providing an exploratory tool for morphologist and contact linguists alike.
Logic and Semantic Technologies for Computer Science Education

We report on some projects to develop software rooted in formal logic and semantics in order to enhance education in computer science and mathematics. The goal is to let students actively engage with the course material by solving concrete problems where the correctness of a solution is automatically checked; furthermore, if a solution is not correct or the student gets stuck, the software shall provide additional insight and hints that aid the student towards the desired result. In particular, we describe our experience with the RISCAL software, a model checker for mathematical theories and algorithms, in university courses on logic, formal methods, and formal modeling.
Remote laboratories for engineering education: status and prospects

The analysis of the state and prospects of the theory, the types of structures of remote laboratories for engineering education, the structures of models of cognitive objects of study using remote laboratories is performed. Based on the HCT development strategy, the faculty of Engineering Technology and Science needs to use remote laboratories. Training course ELE2603 - Instrumentation and Control in the Bachelor of Electrical Engineering Technology (BELET) program for prototyping remote laboratory work selected.
Generative Network Based Image Encryption

The algorithm which we proposed in this paper is a combined approach of Image steganography and Generative Adversarial Network. In the recommended encryption algorithm, the image matrix is processed through multiple layers of encryption where it encodes the input image matrix using steganography inside a completely new random image. Then, the newly generated image is passed through the GAN (Generative Adversarial Network) layer where the input image matrix is encrypted into a completely new model. Hence, one needs to have the same trained model to encrypt and decrypt the image, which makes it a symmetric approach where the trained model STEG file will act as a key.
A risk management framework for security and integrity of networks and services

It is clearly acknowledged that, in complex sectors like telecommunications, to consider an infrastructure as fully secure, although desirable, is not realistic. The current European regulation on public communications networks is aware of this assumption and currently requires that Telecommunications Service Providers (TSPs) take appropriate technical and organizational measures to manage the risks posed to the security of networks and services. In this context, risk management has become both a key aspect for dealing with security and a main trust vector included particularly in regulations. In this context, our paper concerns the establishment of a national security risk management framework to comply with national and European regulations for TSPs. This framework is composed of two parts: a security risk management tool to be used by the TSPs and an analysis tool to be used by the regulatory authority to gather and assess the risk management reports from the TSPs. The latter is specifically used to benchmark the security level of TSPs and the security of the sector as a whole. This paper reports on the design of this framework and the challenges emerging after an entire regulatory cycle.
Single Tweakey Cryptanalysis of Reduced-Round SKINNY-64

SKINNY is a lightweight tweakable block cipher which received a great deal of cryptanalytic attention following its elegant structure and efficiency. Inspired by the SKINNY competitions, multiple attacks on it were reported in different settings (e.g. single vs. related-tweakey) using different techniques (impossible differentials, meet-in-the-middle, etc.). In this paper we revisit some of these attacks, identify issues with several of them, and offer a series of improved attacks which were experimentally verified. Our best attack can attack up to 18 rounds using $$2^{60}$$ chosen ciphertexts data, $$2^{116}$$ time, and $$2^{112}$$ memory.
AI 2000: A Decade of Artificial Intelligence

In the past decades, artificial intelligence has dramatically changed the way we work and live. Moreover, it is increasingly becoming a national strategy for its rapid development and broad application in industries. However, the way artificial intelligence advances itself is sorely lacking until now. One of the most important reasons is the deficiency of timely and reliable knowledge graph in this field. To illustrate the problem, we introduce an academic knowledge graph of AI, named AI 2000, which combines techniques in data mining, bibliometrics, natural language processing, etc. In this work, we try to link the entities of scholars, academic papers, researches, etc. in the field of artificial intelligence. AI 2000 aims at serving as a medium for us to explore the evolution of artificial intelligence in the past decade and looking forward to its future trend. The methodology illustrates its timeliness and reliability, and analysis demonstrates high quality and availability. It is freely available at AMiner1.
Reinforcement Learning Aided Network Architecture Generation for JPEG Image Steganalysis

The architectures of convolutional neural networks used in steganalysis have been designed heuristically. In this paper, an automatic Network Architecture Generation algorithm based on reinforcement learning for JPEG image Steganalysis (JS-NAG) has been proposed. Different from the automatic neural network generation methods in computer vision which are based on the strong content signals, steganalysis is based on the weak embedded signals, thus needs specific design. In the proposed method, the agent is trained to sequentially select some high-performing blocks using Q-learning to generate networks. An early stop strategy and a well-designed performance prediction function have been utilized to reduce the search time. To generate the optimal networks, hundreds of networks have been searched and trained on 3 GPUs for 15 days. To further improve the detection accuracy, we make an ensemble classifier out of the generated convolutional neural networks. The experimental results have shown that the proposed method significantly outperforms the current state-of-the-art CNN based methods.
A passive user‐side solution for evil twin access point detection at public hotspots

Summary This paper proposes a passive user‐side solution, called Wi‐Fi legal access point (AP) finder (LAF), to the notorious evil twin access point problem, which in turn can result in diverse security problems, such as fraud, identity theft, and man‐in‐the‐middle attacks. Due to the severe security threats created by evil twins, many promising solutions have been proposed. However, the majority of these solutions are designed for the administrators of wireless networks, not for Wi‐Fi users. Hence, they are either too expensive or need some data that are usually not accessible to normal users. LAF utilizes the TCP three‐way handshake‐related packets and packet forwarding property created by evil twins to find legal APs, called good twins, at public hotspots or unencrypted WLANs; thus, it does not need any data or assistance from wireless network administrators. LAF does not send exploring packets actively; hence, evil twins cannot sense its existence. No matter when and where a user needs to utilize an AP to connect to the Internet at a hotspot, he can just use LAF to find out a legal AP to connect to. Experimental results show that LAF can quickly and accurately find legal APs after observing only a few packets.