Reviewerly/exHarmony huggingface dataset:
A Semi-blind Watermarking Method for Authentication of Face Images Using Autoencoders

Recent advents of the internet have made accessibility of people to digital data such as audio, images, and videos much easier. Meanwhile, one of the cases that adversaries take advantage of is the people's face images that are available across the web. Digital watermarking is used to authenticate the original owner of the images and protect their copyright. With the help of digital watermarking, hidden data is embedded inside the image. Recently, neural networks such as autoencoders are one of the most popular models that are used in many fields. Neural networks are capable of understanding all kinds of raw data such as images and videos. In this paper, we present a method for embedding the user's national ID in their face images using autoencoders. The proposed autoencoder is trained with a dataset contains face images. The image is coded into some code using the autoencoders' encoder. Then, the national ID is embedded in this code and the modified code is reconstructed using the decoder to form the watermarked image. To extract the watermark, the watermarked image is encoded with the encoder and the watermark is extracted. Experiment results show that our model recovers the watermark with high accuracy and it is resistant against JPEG attacks. Moreover, the quality of the watermarked images is acceptable, and their SSIM compare to the original image is about 90%.
LookLike: Similarity-based Trust Prediction in Weighted Sign Networks

Trust network is widely considered to be one of the most important aspects of social networks. It has many applications in the field of recommender systems and opinion formation. Few researchers have addressed the problem of trust/distrust prediction and, it has not yet been established whether the similarity measures can do trust prediction. The present paper aims to validate that similar users have related trust relationships. To predict trust relations between two users, the LookLike algorithm was introduced. Then we used the LookLike algorithm results as new features for supervised classifiers to predict the trust/distrust label. We chose a list of similarity measures to examined our claim on four real-world trust network datasets. The results demonstrated that there is a strong correlation between users' similarity and their opinion on trust networks. Due to the tight relation between trust prediction and truth discovery, we believe that our similarity-based algorithm could be a promising solution in their challenging domains.
Security definitions, entropy measures and constructions for implicitly detecting data corruption

We discuss security definitions, entropy measures and cryptographic constructions associated with the recently proposed implicit data integrity methodology. Such methodology is applied in order to detect data corruption without producing, storing or verifying mathematical summaries of the content such as Message Authentication Codes (MACs) or checksums. The main idea is that, whereas typical user data demonstrate patterns such as repeated bytes or words, decrypted data resulting from corrupted ciphertexts no longer demonstrate such patterns. Thus, by checking the entropy of decrypted ciphertexts, corruption can be possibly detected. The paper expands on earlier contributions, arguing for the need of a new notion of security based on the assumption that it is computationally difficult for an adversary to corrupt some ciphertext so that the resulting plaintext demonstrates specific patterns. A second contribution of the paper is a proposal for a new entropy measure that is applicable to short messages. The entropy measure we propose is called "pattern entropy index" and can be efficiently computed for messages that can be as small as 64 bytes. Third, we extend the security analysis of the known cryptographic construction called IVP (Integrity Via Preprocessing). We show that IVP supports implicit data integrity and is secure in input perturbing and oracle replacing adversary models. The cryptographic strength of IVP is 32.169 bits, which is sufficient for defending against online data corruption and content replay attacks. Computationally, IVP is much lighter than other authenticated encryption approaches requiring only two additional encryption rounds in the critical path of a 128-bit block cipher such as AES.
Virtual Reality as a tool for active learning and student engagement: industrial engineering experience

The opportunity to bring real experience to the classroom in order to promote active learning and student engagement is achieved through extended realities as virtual reality (VR). The use of VR cases in engineering education has promote a flexible learning space (real industrial facilities) and the increasing of the students´ motivation. This research had the objective to evaluate the impact of using VR cases versus using traditional cases in two different campi of a private university, in the same academic course during 2 weeks. The methodology has 3 phases: (1) case design with VR and with the traditional paper case format, (2) application of the cases (VR and traditional) in two groups (no control group, it was the same group). (3) Results analysis based on observations, direct surveys, and students reports. This experience contributed to understand the dynamics of the case methodology under two perspectives: students and professors. While real experience, innovation, and fun represent the student’s perspective using VR to understand layout facilities and work design as real application concept, from a professor’s perspective this experience derived in more student engagement, critical thinking and systemic perspective. In addition, the process to develop VR cases was for itself an interesting challenge from academic and industrial possibilities, but allows us to develop active learning in-and out- of the classroom.
High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data

Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies.
Technological Paradigms of Digital Competences Development

Since the technological paradigms are the theoretical basis for digitization, the purpose of the study is to develop a unified methodological approach to the definition of the content and processes of digital competencies formation on the basis of these paradigms. The unity of the approach is achieved by clarifying the concept, defining the inventory, composition, structure of digital competencies within the boundaries of technological paradigms and on the basis of the European Qualification Framework (EQF). The authors have used the methods of comparative and semantic analysis, contextual analysis of policy-based documents at the supranational level to justify the methodological provisions of the development of digital competencies in the Russian policy as part of key objectives and priorities of training personnel possessing competence for the development and implementation of digital technologies, increasing the digital literacy among the population. The practical significance of the results of the study is connected with the effectiveness of the strategies for the development of industries and subjects of the Russian Federation, the validity of the choice of educational programs.
Autonomous Obstacle Avoidance Algorithm of UAVs for Automatic Terrain Following Application

Autonomous obstacle avoidance flight is a key capability for unmanned aerial vehicles (UAVs) in the automatic terrain following application, which ensures that the UAVs can perform complex, versatile and difficult movements in the flight. Existing obstacle avoidance methods such as the visual SLAM, generally require artificially specifying the feature values that need to be extracted and are susceptible to illumination and obstacle positions. While artificial intelligence technology has made breakthroughs in many fields, the advantages of the neural networks overcome these shortcomings. Therefore, we propose an autonomous obstacle avoidance algorithm using deep reinforcement learning method. Concretely, a virtual three-dimensional visual simulation environment is established firstly, which simulates the flight states of the UAV and output the state image in real time according to the control decision. A deep convolutional neural network is then built as the brain of the intelligent agent, which takes the state image of the UAV as input, and outputs the discretized control decision to control the UAV. Moreover, The Deep Q Network method is employed to train the convolutional neural network in an autonomous way, that is the intelligent agent can try to control the UAV to avoidance the obstacle by itself. After training, the UAV is controlled by the trained convolutional neural network to complete the autonomous obstacle avoidance task during the flight. This algorithm is realized through continuous self-learning and self-evolution, which enables the UAVs to utilize visual information in complex scenes, adjust flight height in real time according to terrain height and obstacle height. The research results will have a strong application prospects in both military and civilian areas.
Recommendation Algorithm Combining Tag Data and Naive Bayes Classification

Collaborative filtering algorithm is the most popular algorithm applied to recommendation systems. However, it has been plagued by the cold start problem which seriously affects the effectiveness of recommendation. Aiming at users' cold start problem, we proposed a recommendation algorithm which combined tag data and Naive Bayes classification. Tag data was used to represent the users' attribute characteristics, and new and old users were matched by Naive Bayes classifier, which utilized the similar user groups to infer the interests of new users. After determining the categories of new users, we calculated the average rating information of users in this category for items to achieve Top-N recommendation. Experiments showed that the algorithm can achieve better RMSE in the problem of the cold start of users, and the recommendation accuracy was significantly improved.
Dataset Cleaning — A Cross Validation Methodology for Large Facial Datasets using Face Recognition

In recent years, large “in the wild” face datasets have been released in an attempt to facilitate progress in tasks such as face detection, face recognition, and other tasks. Most of these datasets are acquired from webpages with automatic procedures. As a consequence, noisy data are often found. Furthermore, in these large face datasets, the annotation of identities is important as they are used for training face recognition algorithms. But due to the automatic way of gathering these datasets and due to their large size, many identities folder contain mislabeled samples which deteriorates the quality of the datasets. In this work, it is presented a semi-automatic method for cleaning the noisy large face datasets with the use of face recognition. This methodology is applied to clean the CelebA dataset and show its effectiveness. Furthermore, the list with the mislabelled samples in the CelebA dataset is made available.
A High-Performance VLSI Architecture for a Self-Feedback Convolutional Neural Network

This brief studies the problem of developing an area-time efficient VLSI architecture for a novel self-feedback Convolutional Neural Network (CNN). Self-feedback CNNs offer the promise of high-precision object detection amidst occlusions. However, the size of a typical network required for practical applications presents a challenge for embedded system development. We first present the structure of the self-feedback CNN. We then present an efficient systolic array architecture for the self-feedback CNN with low on-chip memory requirement. The self-feedback CNN has been tested on the KITTI benchmark dataset and it achieves high accuracy for detecting occluded cyclists and pedestrians. FPGA implementation of the proposed architecture on Xilinx Virtex7 XC7VX485T achieves roughly 1.14 Tera Operations per second (TOP/s) at 386 MHz with 9× reduction in on-chip memory requirement compared to recent CNN architectures.
Evolving Metric Learning for Incremental and Decremental Features

Online metric learning has been widely exploited for large-scale data classification due to the low computational cost. However, amongst online practical scenarios where the features are evolving ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , some features are vanished and some new features are augmented), most metric learning models cannot be successfully applied to these scenarios, although they can tackle the evolving instances efficiently. To address the challenge, we develop a new online Evolving Metric Learning (EML) model for incremental and decremental features, which can handle the instance and feature evolutions simultaneously by incorporating with a smoothed Wasserstein metric distance. Specifically, our model contains two essential stages: a Transforming stage (T-stage) and a Inheriting stage (I-stage). For the T-stage, we propose to extract important information from vanished features while neglecting non-informative knowledge, and forward it into survived features by transforming them into a low-rank discriminative metric space. It further explores the intrinsic low-rank structure of heterogeneous samples to reduce the computation and memory burden especially for highly-dimensional large-scale data. For the I-stage, we inherit the metric performance of survived features from the T-stage and then expand to include the new augmented features. Moreover, a smoothed Wasserstein distance is utilized to characterize the similarity relationships among the heterogeneous and complex samples, since the evolving features are not strictly aligned in the different stages. In addition to tackling the challenges in one-shot case, we also extend our model into multi-shot scenario. After deriving an efficient optimization strategy for both T-stage and I-stage, extensive experiments on several datasets verify the superior performance of our EML model.
A P2P Optimistic Fair-Exchange (OFE) Scheme for Personal Health Records Using Blockchain Technology

In today’s digital world, it is common to exchange sensitive data between different parties. There are many examples of sensitive data or documents that require a digital exchange, such as banking information, insurance data, health records. In many cases, the exchange exists between unknown and untrusted parties. Therefore, it is essential to execute the data exchange over a fair non-repudiation protocol. In this paper, we propose a P2P fair non-repudiation data exchange scheme by leveraging Blockchain and distributed ledger technology. The proposed scheme combines on-chain and off-chain communication patterns to enable the exchange of personal health records between patients and health care providers. We provide an informal security analysis for the proposed scheme. Moreover, we propose a design and implementation agnostic to existing Blockchain platforms to enable unbiased evaluation of the proposed scheme.
Methods of Classification and Assessment of Critical Information Infrastructure Objects

assessing the criticality of critical infrastructure is an important task in the complex of tasks for ensuring the protection of critical infrastructure of the state. It is important to understand the difference in methodological approaches to criticality and risk assessment. In the paper general characteristics of the approach to the classification of critical information infrastructure objects are given. Principals and assumptions are described. The identification and categorization procedure are presented.The paper also presents Cybersecurity Guidelines for Critical Information Infrastructure in Ukraine. The provisions of the Cybersecurity Guidelines for Critical Infrastructure Facilities of Ukraine are intended to implement cybersecurity activities aimed at managing cybersecurity risks of critical information infrastructure facilities. The guidance should be used to organize systematic, targeted cybersecurity risk management activities.
Overview of advanced neural network architectures

Though initially outclassed by more computationally efficient machine learning algorithms, neural network–based deep learning strategies have come of age with modern implementations and hardware and are now capable of addressing a range of previously intractable problems. These include methods for dealing with complex spatial or sequential data, strategies for coping with sets of data in which not all of the examples are annotated, and ways of generalizing across different databases. Other methods take inspiration from biological strategies such as operant conditioning or evolutionary principles. These and other techniques described here form the basis for many of the advances in the use of artificial intelligence in the service of pathology, as well as medicine in general.
Primo on the Go: A Usability Study of the Primo Mobile Interface

Primo Analytics from two campuses of the California State University system has revealed that a dramatically low proportion of Primo searches originate from mobile devices. The current study focuses on usability concerns as one area that may be preventing students from searching the Primo mobile interface. The sample for this study includes students from two campuses with a variety of experience with library instruction and searching the library’s discovery system. Participants were given the same set of six common academic tasks to complete. Both mobile and desktop participants found three tasks relatively easy to complete. When there were differences in completion rates between the mobile and desktop users, the desktop users were overall more successful. No task was consistently difficult for desktop users to complete, while mobile users found two of the tasks very challenging to complete. Based on the findings, the authors make recommendations for improving the experience of mobile users.
IoT Security Issues and Possible Solution Using Blockchain Technology

The world is movingOyshi, Marzan Tasnim along with the technical advancement where smart home and smart cities are creating smart lifestyle and Internet of things (IoT) is being one of the most popular technologies in this decade.Bonny, Moushumi Zaman IoT is making life better by connecting devices and producing billions of data. But for the heterogeneous connectivity, Saha, Susmitas smaller size, memory, storage and less security measurement, it is easy to hack and manipulate the IoT devices. To have a safe and secured connection, it is a must to ensure the data security in IoT too.Tumpa, Zerin Nasrin This research proposes a novel method to secure the IoT devices using blockchain technology.
Investigating Peers in Social Networks: Reliable or Unreilable

Social networking sitesNeethu, M. R. have made its impact in converting a passive reader into content contributor.Harini, N. This has turned online social networks into a commercial sphere. The site demands creation of public profiles. The main goal of online social networks is to share content with maximum users.Abirami, K. This enormous sharing of information can lead to malicious users leverage the privacy of an individual. An attempt has been made in this paper to improve privacy preservation by calculating the reliability of a user, can entrust on his/her peer using the content sharing, Machine Learning and tone analysis. The recorded metrics and transaction details are stored in IPFS.
Research on Automatic Generation and Analysis Technology of Network Attack Graph

In view of the problem that the overall security of the network is difficult to evaluate quantitatively, we propose the edge authority attack graph model, which aims to make up for the traditional dependence attack graph to describe the relationship between vulnerability behaviors. This paper proposed a network security metrics based on probability, and proposes a network vulnerability algorithm based on vulnerability exploit probability and attack target asset value. Finally, a network security reinforcement algorithm with network vulnerability index as the optimization target is proposed based on this metric algorithm.
Reversible Majority Voter Based on Fredkin Gates

As known Q-bits, Q-bytes, Quantum computing, Reversible calculations have recently become the perspective IT technologies. In such area, special elements are used, for example, Feynman gates, Toffoli gates, Fredkin gates etc. However, quantum calculations requires the solving a lot of problems of data representation. Nonetheless, reversible approach can be successfully used in binary case for the design of checkable and energy-efficient ("green") circuits. In such direction, so-called "adiabatic" and "billiard" logic is of most interest. However, binary reversible "billiard" gates have more inputs and outputs than usual gates, they have "trash" outputs. At the same time, the reliability problems of such circuits have not yet been fully resolved. The article discusses the question of tripling Fredkin’s circuits. The author develops and studies Reversible Majority Voter based on Fredkin’s gates. The analysis of three channels requires six additional bits, and the circuit has five Fredkin elements and five garbage outputs.