Reviewerly/exHarmony huggingface dataset:
FakeTransformer: Exposing Face Forgery From Spatial-Temporal Representation Modeled By Facial Pixel Variations

With the rapid development of generation model, AI-based face manipulation technology, which called DeepFakes, has become more and more realistic. This means of face forgery can attack any target, which poses a new threat to personal privacy and property security. Moreover, the misuse of synthetic video shows potential dangers in many areas, such as identity harassment, pornography and news rumors. Inspired by the fact that the spatial coherence and temporal consistency of physiological signal are destroyed in the generated content, we attempt to find inconsistent patterns that can distinguish between real videos and synthetic videos from the variations of facial pixels, which are highly related to physiological information. Our approach first applies Eulerian Video Magnification (EVM) at multiple Gaussian scales to the original video to enlarge the physiological variations caused by the change of facial blood volume, and then transform the original video and magnified videos into a Multi-Scale Eulerian Magnified Spatial-Temporal map (MEMSTmap), which can represent time-varying physiological enhancement sequences on different octaves. Then, these maps are reshaped into frame patches in column units and sent to the vision Transformer to learn the spatio-time descriptors of frame levels. Finally, we sort out the feature embedding and output the probability of judging whether the video is real or fake. We validate our method on the FaceForensics++ and DeepFake Detection datasets. The results show that our model achieves excellent performance in forgery detection, and also show outstanding generalization capability in cross-data domain.
A Variant of Material Connexive Logic

The relationship between formal (standard) logic and informal (common-sense, everyday) reasoning has always been a hot topic. In this paper, we propose another possible way to bring it up inspired by connexive logic. Our approach is based on the following presupposition: whatever method of formalizing informal reasoning you choose, there will always be some classically acceptable deductive principles that will have to be abandoned, and some desired schemes of argument that clearly are not classically valid. That way, we start with a new version of connexive logic which validates Boethius’ (and thus, Aristotle’s) Theses and quashes their converse from right to left. We provide a sound and complete axiomatization of this logic. We also study the implication-negation fragment of this logic supplied with Boolean negation as a second negation.
SMS Spam and Ham Detection Using Naïve Bayes Algoritm

ML is termed as Machine Learning; it is the study of computer algorithms which automatically improves through experience. The usage of mobile phones is growing popular in our everyday life. Short Message Service are viewed as most generally applied correspondence administration which is less costly. In any case, this has prompted an increment in cell phones assaults like SMS Spam. Here, Naive bayes algorithm is used in order to differentiate between spam and ham SMS. Spam is the unnecessary fraud messages received whereas ham is legitimate message. The algorithm used here is machine learning classification algorithm, and it is implemented here and can be used in differentiating between spam and ham messages with the help of SMS spam collection data set provided. We train the machine by providing that data set such that it learns from that data and will be able to draw conclusions on its own. Now a days it is very much crucial to identify the spam messages to reduce many frauds happening around the globe. This algorithm can classify with an accuracy of 98.13%.
Fault-Tolerance of Binarized and Stochastic Computing-based Neural Networks

Both binarized and stochastic computing-based neural networks exploit bit-wise operations to replace expensive full-precision multiplications with simple XNOR gates and thus, offer low-cost hardware implementation. In stochastic computing, arithmetic computations are performed on sequences of random bits which can approximate any real values. Stochastic computing-based neural networks benefit from approximate computing and promote fault-tolerant architectures against soft errors in noisy environments. On the other hand, in binarized neural networks, real values are deterministically binarized using the sign function. As a result, any bit-flip in the binarized values dramatically changes the outcome of arithmetic computations and makes binarized neural networks more vulnerable against soft errors. In this paper, we compare these two neural networks against each other in terms of fault-tolerance and hardware complexity (i.e., area and energy efficiency).
Design of the Information Security System Based on the Encryption Mechanism

In the context of the accelerating process of information technology in the international society, information security has become the focus of attention of the whole society. How to ensure communication security in the application of information communication has become the main direction of research and exploration. Therefore, on the basis of understanding the good cryptographic system, this paper analyzes how to design the practical information security system for the two commonly used cryptographic algorithms.
Pathological Analysis of Blood Cells Using Deep Learning Techniques

Introduction: The major area of work of pathologists is concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing the slides using a microscope and other instruments. But this method suffers from a lot of problems, like there is no standard way of diagnosing, human errors and it puts a heavy load on the laboratory men to diagnose such a large number of slides daily. Method: The slide viewing method is widely used and converted into digital form to produce high resolution images. This enables the area of deep learning and machine learning to deep dive into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are used in accordance with proposed algorithm, then model classifies the blood images with an accuracy of 95.24%. Result: After training the models on 20 epochs. The plots of training accuracy, testing accuracy and corresponding training loss, testing loss for proposed model is plotted using matplotlib and trends. Discussion: The performance of proposed model is better than existing standard architectures and other work done by various researchers. Thus, the proposed model enables the development of pathological system which will reduce human errors and daily load on laboratory men. This can also in turn help pathologists in carrying out their work more efficiently and effectively. Conclusion: In the present study, a neural based network has been proposed for classification of blood cells images into various categories. These categories have significance in the medical sciences. When input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are used in accordance with proposed algorithm, then model classifies the images with an accuracy of 95.24%. This accuracy is better than standard architectures.. Further it can be seen that the proposed neural network performs better than present related works carried by various researchers.
Human-machine system as a control shell in the implementation of mooring operations

Abstract Mooring is a complex and responsible process for a boat master, so modern trends determine the need to create automated and automatic systems for mooring vessels to minimize the human factor. The study researched the vessel ergatic control complex and the towing group with the use of mathematical apparatus in the implementation of mooring operations. The human-machine or “ergatic” system is the control shell under the active influences of the bridge complexes and is an auxiliary tool for the watchman on the navigation bridge. This is the essence – a human, as a controlling and observing link, is an integral part of the human-machine complex. Automation, even in systems with artificial intelligence, will not soon replace the flexibility of the human brain, despite possible and sometimes very serious human mistakes. Taking into account the fact that the automated mooring system applies all kinds of monitoring sensors to prevent docking impact, the control and responsibility for the mooring process still lie with the navigator operator.
A Holistic Approach to Data Access for Cloud-Native Analytics and Machine Learning

Cloud providers offer a variety of storage solutions for hosting data, both in price and in performance. For Analytics and machine learning applications, object storage services are the go-to solution for hosting the datasets that exceed tens of gigabytes in size. However, such a choice results in performance degradation for these applications and requires extra engineering effort in the form of code changes to access the data on remote storage. In this paper, we present a generic end-to-end solution that offers seamless data access for remote object storage services, transparent data caching within the compute infrastructure, and data-aware topologies that boost the performance of applications deployed in Kubernetes. We introduce a custom-implemented cache mechanism that supports all the requirements of the former and we demonstrate that our holistic solution leads up to 48% improvement for Spark implementation of the TPC-DS benchmark and up to 191% improvement for the training of deep learning models from the MLPerf benchmark suite.
Crop Suggestion using Data Mining Approaches

A solution using Association Rule Mining and Clustering is proposed where, given the conditions, a farmer will be suggested the suitable crop(s) that he can cultivate based on the past years' data. The implementation was done using Apriori, Frequent Pattern Growth (FP Growth) and K-Means algorithm. Data Mining techniques used prove to be very useful in helping the farmers in growing the suitable crops.
An Improved Image Compression Model Enabled by Adaptive Active Contour and Supervised Learning-Based ROI Classification

This paper plans to develop a novel image compression model with four major phases. (i) Segmentation (ii) Feature Extraction (iii) ROI classification (iv) Compression. The image is segmented into two regions by Adaptive ACM. The result of ACM is the production of two regions, this model enables separate ROI classification phase. For performing this, the features corresponding to GLCM are extracted from the segmented parts. Further, they are subjected to classification via NN, in which new training algorithm is adopted. As a main novelty JA and WOA are merged together to form J-WOA with the aim of tuning the ACM (weighting factor and maximum iteration), and training algorithm of NN, where the weights are optimized. This model is referred as J-WOA-NN. This classification model exactly classifies the ROI regions. During the compression process, the ROI regions are handled by JPEG-LS algorithm and the non-ROI region are handled by wavelet-based lossy compression algorithm. Finally, the decompression model is carried out by adopting the same reverse process.
SodsBC/SodsBC++ &amp; SodsMPC: Post-quantum Asynchronous Blockchain Suite for Consensus and Smart Contracts

SodsBC is the first asynchronous permissioned blockchain consensus (asynchronous BFT, aBFT in short) utilizing the concurrent preprocessing model to achieve post-quantum security while keeping high performance simultaneously. SodsBC concurrently preprocesses common random coins (from a global secret sharing pool) for the consensus necessary randomness usage, and also preprocesses symmetric encryption keys for considerable censorship resilience. The finalization of a stage of the global secret sharing pool utilizes the aBFT itself, as a transaction in the new block. SodsBC (and its followed work SodsBC++) is faster than the quantum-sensitive aBFT competitors. SodsMPC is a post-quantum smart contract system, in which all participants execute a contract by secure multi-party computation (MPC) protocols. MPC ensures the contract execution correctness while keeping the data privacy. Moreover, SodsMPC expresses the logic of a contract by a blind polynomial with secret-shared coefficients, and utilizes a finite state machine (FSM) to simplify the blind polynomial for better efficiency. When using MPC to compute this blind polynomial, the contract business logic privacy is obtained. SodsMPC participants also preprocess random permutation matrices to hide the real contract input relation, which protects the contract user anonymous identity.
Mobile learning as an active didactic strategy in the teaching-learning process

An educational experience is presented whose objective is to apply Mobile Learning as a didactic learning strategy mediated by wireless mobile devices, which empowers the educational process through gamification, where the student of a postgraduate program learns in a formal context in the middle of the game. A diagnosis is made, then through the systematization of experiences, it is possible to define, develop and implement the didactic strategy in a group of 33 graduate students. The outcomes achieved from evaluating the perception of students show that the changes in traditional teaching-learning didactic strategies for others mediated by Information and Communication Technologies (ICT) are favorable to activating their learning, increase interest in the contents of the program, reduce disapproval and stimulate the teaching-learning process through gamification, learning through games but in a formal, non-playful context.
The Quantum Computer and the Security of Information Systems

Many researchers and laboratories have been engaged for years in a competition for a single objective, which is the quantum computer. Recently, Google has announced that it has achieved quantum supremacy with its quantum computer "Sycamore". According to Google, such a machine can make an astronomical quantity of calculations considerably faster than any conventional computer. A technology which could be a real revolution in several domains, Computing, Experimentation Technics, Artificial Intelligence, Medical, Chemical, Banking...etc. But, the main question is: when can it be a reality? In the present paper, we will explore quantum phenomena and explain principles of quantum computer especially the qubit (quantum bit), to finally explain whether or not the quantum computer is a reality and could it be a danger for the information systems? We will explore the subject by presenting existing works, and by defining the framework of laws to achieve a quantum computer that may solves all our possible problems.
Dialogue-based Continuous Update of User Portraits

With the development of Internet-based services, user portraits, which are used to describe user interests and preferences, have been widely used as precision marketing references for service providers. Meanwhile, Virtual Personal Assistants (VPA) are extensively used in satisfying daily requirements of users by natural language based interactions. Dialogues generated during the interaction between a user and a VPA are conversations that contain rich information on implicit and explicit user demands and decisions on whether recommended services are accepted or not, in other words, these dialogues contain rich user preferences. In this paper, we propose a novel Multi-domain Dialogue-based User Portrait (MDUP) model and the corresponding method for constructing and updating MDUP of a user by historical dialogue mining. Based on the conventional Key-Value structure, we add four key features into MDUP, including: (1) The mentioned frequency of each preference attribute is recorded; (2) Update history of user preferences keeps complete in MDUP and could be used to explore user preference evolution; (3) Quantitative constraints on each preference attribute are imported to accurately express fine-grained user preferences; and (4) External knowledge graphs are utilized to enrich the semantic relationships between user preference attributes belonging to different domains. A text mining based algorithm for mining user preferences from historical dialogues and generating/updating MDUP is elaborately introduced. Experiments are conducted to validate the effectiveness of the proposed MDUP model and the corresponding algorithm. Downstream application scenarios of the MDUP model are briefly discussed to illustrate the usability of the model, including MDUP-based service recommendation, incorporating MDUP into the dialogue strategy of task-based VPA, and perceiving underlying evolution patterns of user preferences.
Postgraduate Students’ Individual Educational Paths: Design Principles and Implementation Conditions (on the Example of a Technical University)

The article addresses the importance of introducing individual educational paths (IEP) in training research and pedagogical personnel in postgraduate studies. The idea of an individual approach of learning is enshrined in a number of documents including the Federal Law “On Education in the Russian Federation”. However, in practice, there are obstacles to the implementation of IEP: the presence of regulatory documents governing the organization of the educational process in higher education, the predominance of the group form of organizing classes, the unpreparedness of teachers for the implementation of the personalized learning model. We find it reasonable to start the implementation of IEP at the level of postgraduate training, given the relative small number of students, as well as their cognitive independence and well-developed educational skills. The design of the IEP will make it possible to better take into account the initial competencies of postgraduate students, their cognitive needs and goals in further career advancement (orientation towards teaching, research, production activities), which are largely determined by the nature and level of professional education. The article reveals the specifics of the training of research and pedagogical personnel in the postgraduate study of a technical university, identifies the factors that have an impact on the way postgraduate students build their learning trajectories, substantiates the principles of IEP design, and describes the stages of personalized learning technology. The research data on the readiness of postgraduate students to design IEP on their own are presented. The results of the survey of post graduate students and their supervisors indicate that there are sufficient grounds for the successful implementation of a personalized learning model using IEP. Postgraduate students of a technical university are well versed in information resources, actively use University’s services of the electronic educational environment. They are able to determine the goals of educational and scientific activities, assess their competencies, and analyze achievements and failures. Most of scientific advisors are ready to assist postgraduate students in IEP designing. The article analyzes the difficulties that universities may encounter when introducing a new teaching model, and substantiates the conditions for the successful implementation of IEP in postgraduate studies.
Mattila–Sjölin Type Functions: A Finite Field Model

Let $\phi (x, y)\colon \mathbb {R}^{d}\times \mathbb {R}^{d}\to \mathbb {R}$ be a function. We say ϕ is a Mattila–Sjölin type function of index γ if γ is the smallest number satisfying the property that for any compact set $E\subset \mathbb {R}^{d}$ , ϕ(E,E) has a non-empty interior whenever $\dim _{H}(E)>\gamma $ . The usual distance function, ϕ(x,y) = |x − y|, is conjectured to be a Mattila–Sjölin type function of index $\frac {d}{2}$ . In the setting of finite fields $\mathbb {F}_{q}$ , this definition is equivalent to the statement that $\phi (E, E)=\mathbb {F}_{q}$ whenever |E|≫ qγ. The main purpose of this paper is to prove the existence of such functions with index $\frac {d}{2}$ in the vector space $\mathbb {F}_{q}^{d}$ .
Research on Vehicle Control System of Construction Machinery Based on Machine Vision

Abstract With the continuous development of construction machinery technology, great breakthroughs have been made in the application of electronic information technology. The control and operation system of construction machinery vehicles refers to the system that applies and receives certain instructions and makes the construction machinery produce specified actions according to the requirements of operators through its power transmission device. Compared with the previous construction machinery, the developing construction machinery has made good achievements in high efficiency, intensification, cost saving and environmental protection. The traditional speed measuring method can not meet the requirement of measuring accuracy of actual walking speed of construction machinery, and can not accurately reflect the actual walking speed and instantaneous motion state of construction machinery. This paper analyzes a kind of construction machinery vehicle using machine vision. It obtains important information through image processing, adopts intelligent control technology based on machine vision as an important control means of power drive, and can judge and control driving according to road conditions, thus improving the accuracy and stability of industrial autonomous transportation..
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e19" altimg="si10.svg"><mml:mi>C</mml:mi></mml:math>-differential bent functions and perfect nonlinearity

Drawing inspiration from Nyberg’s paper (Nyberg, 1991) on perfect nonlinearity and the c-differential notion we defined in Ellingsen et al. (2020), in this paper we introduce the concept of c-differential bent functions in two different ways (thus extending Kumar et al. (1985) classical definition). We further extend the notion of perfect c-nonlinear introduced in Ellingsen et al. (2020), also in two different ways, and show that, in both cases, the concepts of c-differential bent and perfect c-nonlinear are equivalent (under some natural restriction of the parameters). Some constructions of functions with these properties are also provided; one such construction provides a large class of PcN functions with respect to all c in some subfield of the field under consideration. We also show that both our classes of 0-differential bents are supersets of permutation polynomials, and that Maiorana–McFarland bent functions are not differential bent (of the first kind).