Reviewerly/exHarmony huggingface dataset:
Key Mismatch Attack on ThreeBears, Frodo and Round5

In the last years, several key reuse attacks were proposed against Round 2 candidates of the NIST Post-Quantum Cryptography Standardization Process. In these attacks, the adversary has access to the key mismatch oracle which tells her if a given ciphertext decrypts to a given message under the targeted secret key. One of the so far non-targeted candidates is ThreeBears, which is a key encapsulation mechanism based on the integer module learning with errors (I-MLWE) problem. In this paper, we present a first key mismatch attack against the ThreeBears cryptosystem. Our attack recovers the whole secret key with probability of 100% and requires about $$2^{11}$$ queries on average. Besides that, we use our technique to target other Round 2 candidates Frodo and Round5, and we improve the state-of-the-art results for them.
BlockELM A Public Blockchain Freight Exchange Protocol

Freight exchanges are central to the logistics industry, as they reduce empty runs and meet spot demands.To improve their efficiency in terms of automation and enhance trust between the participants, we propose a decentralized freight exchange implemented using public blockchains.With our solution, we also address shortcomings of public blockchains, such as scalability and privacy.We present two artifacts: a general architecture for an electronic logistics marketplace (ELM) and a concrete implementation as the proof of concept for a freight exchange.The solution is implemented using two off-the-shelf public blockchains and a public distributed file system.Additionally, we investigate the implications for the general ELM model and show that an ELM based on a blockchain can be viewed as infrastructure rather than a market participant.
Video Segmentation Techniques for Instructional Videos –Survey

Low cost smart phones and easy internet access have caused an increase in viewership of e-learning video. Usually the memory size of mobile phones is less therefore, it becomes extremely important to reduce size of these instructional videos. Video segmentation is the fundamental task of reducing size of e-learning videos. This paper gives an overview of existing techniques used for video segmentation of e-learning videos. Most of the methods used so far for segmenting instructional video are broadly categorized into i) feature extraction based segmentation ii) motion based segmentation.The performance, comparative merits and limitations of each approach is thoroughly examined and contradicted. The analysis is beneficial for appropriate use of existing methods and for enhancing their performance or forming new methods on the basis of existing methods by combining one or two methods together.
Mathematics and Computational Intelligence Synergies for Emerging Challenges

Nowadays, in all applied fields, there exists a wide range of challenges that need the development of techniques with solid mathematical foundations, in order to give answers to different problems.Many of these problems require advanced mathematical techniques, among which we can mention fuzzy techniques capable of dealing with imprecise, uncertain or incomplete information or methods based on other branches of computational intelligence (CI), such as various kinds of connectionist systems, and algorithms that copy the evolution of biological species, or the behavior of whole populations, and which often help solve "unsolvable" (intractable) mathematical problems in the form of high quality and efficient approximation.A special class of these methods is the group of memetic algorithms, which always combine CI in the form of the latter class of methods with the classic optimization techniques developed by classical mathematics, such as gradientbased techniques, or local subgraph optimization.Combining the advanced computer science techniques provided by CI with mathematical foundations makes them more efficient, robust and solid.As a consequence, the results of those processes in which they are applied are improved, compared to both purely mathematical and purely CI methods, this way making them more efficient, more reliable and much wider applicable.
Research on the Cultivation of English Majors’ English and American Cultural Cognition in Basic English Teaching Based on Big Data

Abstract For keeping in line with the international trend, English major students not only need to exercise their own listening, speaking, reading, writing, translation ability, but also need to have a full understanding of British and American culture to carry out targeted exchanges according to the actual needs. According to big data analysis, in the basic English teaching, the presentation of British and American cultural content can help students understand the basic cultural characteristics of British and American areas to cultivate students’ understanding of British and American culture. Based on the characteristics of basic English teaching, this paper discusses the cultivation methods of students’ British and American cultural cognition.
A tier-based typed programming language characterizing Feasible Functionals

The class of Basic Feasible Functionals BFF$_2$ is the type-2 counterpart of the class FP of type-1 functions computable in polynomial time. Several characterizations have been suggested in the literature, but none of these present a programming language with a type system guaranteeing this complexity bound. We give a characterization of BFF$_2$ based on an imperative language with oracle calls using a tier-based type system whose inference is decidable. Such a characterization should make it possible to link higher-order complexity with programming theory. The low complexity (cubic in the size of the program) of the type inference algorithm contrasts with the intractability of the aforementioned methods and does not overly constrain the expressive power of the language.
Digital Straight Segment Filter for Geometric Description

In this paper, an algorithmic scheme is proposed to estimate different local characteristics of image structures using discrete geometry tools. The arithmetic properties of Digital Straight Lines and their link with the Farey sequences allow the introduction of a new directional filter. In an incremental process, it provides local geometric information at each point in an image, such as the length, orientation and thickness of the longest Digital Straight Segment passing through that point. Experiments on binary and grayscale images are proposed and show the interest of this tool. Comparisons to a well-known morphological filter for grayscale images are also presented.
A new Eulerian temporal filtering method for boosting endoscopic video motion magnification

Endoscopic video sequences provide surgeons with much structural information (e.g., vessels and neurovascular bundles) that guides them to accurately manipulate various surgical tools and avoid surgical risks. Unfortunately, it is difficult for surgeons to intuitively perceive these small structures with tiny pulsation motion on endoscopic images. This work proposes a new endoscopic video motion magnification method to accurately generate the amplified pulsation motion that can be intuitively and easily visualized by surgeons. The proposed method explores a new temporal filtering for Eulerian motion magnification method to precisely magnify the tiny pulsation motion and simultaneously suppress noise and artifacts in endoscopic videos. We evaluate our approach on surgical endoscopic videos acquired in robotic prostatectomy. The experimental results demonstrate that our proposed temporal filtering method essentially outperforms other filters in current video motion magnification approaches, while it provides better visual quality and quantitative assessment than other methods.
Analysis and Comparison of Audiovisual Quality Assessment Datasets

Estimating perceived audiovisual quality by user is a vital component in many multimedia networks and applications. The need of ground truth data for quality assessment leads to elaboration of subjective quality assessment databases. Over the years, several datasets dedicated to audiovisual quality have been released in the public domain that are built upon extensive psychophysical experiences. In this paper, we study four audiovisual quality datasets: PLYM, TUM1080p50, VQEG MM2, and INRS. We also present detailed description of each dataset with the intention to facilitate their use for the researchers’ community. Moreover, different summarization tables have been reported in order to accomplish clear and fair comparison between the audiovisual quality databases. It is hoped that the present work will help researchers in selecting the appropriate databases for their experimentations on objective audiovisual quality metrics.
Cryptosystem Conversion, Packing and Matrix Processing of Homomorphically Encrypted Data: Application to IOT Devices

In this paper, we propose a secure protocol that allows processing encrypted data emitted by an IOT device with low computational capabilities. Its originality is threefold. It first relies on a new fast algorithm which makes possible the conversion of Combined Linear Congruential Generator (CLCG) encrypted IOT data into data homomorphically encrypted with the Damgard-Jurik (D-J) cryptosystem. In second, an original data packing strategy is given so as to reduce communication and computation complexity as well as process several D-J encrypted data at once by means of matrix operations. In third, we introduce a crypto-watermarking based integrity control mechanism. This one combines the lightweight hash function Quark with LSB substitution so as to offer the capability to check the integrity of CLCG encrypted data. We illustrate the deployment of our protocol, in the case an honest-but-curious or malicious third party wants to process encrypted data issued from a real connected knee prosthesis. We theoretically and experimentally demonstrate the performance of our solution. This one can nearly process 500 samples every second. Beyond, our proposal is suited to the general case of IOT.
Improved Uncertainty Post-Calibration via Rank Preserving Transforms

Modern machine learning models with high accuracy often exhibit poor uncertainty calibration: the output probabilities of the model do not reflect its accuracy, and tend to be over-confident. Existing post-calibration methods such as temperature scaling recalibrate a trained model using rather simple calibrators with one or few parameters, which can have a rather limited capacity. In this paper, we propose Neural Rank Preserving Transforms (NRPT), a new post-calibration method that adjusts the output probabilities of a trained classifier using a calibrator of higher capacity, while maintaining its prediction accuracy. NRPT learns a calibrator that preserves the rank of the probabilities through general monotonic transforms, individualizes to the original input, and allows learning with any loss function that encourages calibration. We show experimentally that NRPT improves the expected calibration error (ECE) significantly over existing post-calibration methods such as (local) temperature scaling on large-scale image and text classification tasks. The performance of NRPT can further match ensemble methods such as deep ensembles, while being much more parameter-efficient. We further demonstrate the improved calibration ability of NRPT beyond the ECE metric, such as accuracy among top-confidence predictions, as well as optimizing the tradeoff between calibration and sharpness.
Efficient Empowerment Estimation for Unsupervised Stabilization

Intrinsically motivated artificial agents learn advantageous behavior without externally-provided rewards. Previously, it was shown that maximizing mutual information between agent actuators and future states, known as the empowerment principle, enables unsupervised stabilization of dynamical systems at upright positions, which is a prototypical intrinsically motivated behavior for upright standing and walking. That follows from the coincidence between the objective of stabilization and the objective of empowerment. Unfortunately, sample-based estimation of this kind of mutual information is challenging. Recently, various variational lower bounds (VLBs) on empowerment have been proposed as solutions; however, they are often biased, have high sample complexity, and are unstable in training. In this work, we propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel, which allows us to efficiently calculate an unbiased estimator of empowerment by convex optimization. We demonstrate our solution for sample-based unsupervised stabilization on different dynamical control systems and show the advantages of our method by comparing it to the existing VLB approaches. Specifically, we show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images. Consequently, our method opens a path to wider and easier adoption of empowerment for many applications.
ESEM code generator for Mplus

ESEM code generator for Mplus. A convenient tool for ESEM, Bi-factor ESEM, ESEM within CFA, and Hierarchical ESEM code generation in Mplus.
Recommendation System for Department Selection at Vocational High School (VHS) Using the Yager Model Fuzzy Multi-Attribute Decision Making (FMADM) Method

Abstract The rapid growth of technology and knowledge requires efforts to improve human resources, one of which is improving the quality of vocational education. Vocational education is the implementation of formal education channels held in higher education one of which is the Vocational High School (VHS). VHS graduates with quality potential can increase superior human resources. The problems encountered are students who want to enter vocational schools, often have difficulty in choosing majors by their potential, mistakes in choosing majors cause students who have entered vocational schools to become graduates who are less superior. One of the solutions to this problem is to create a system that can recommend majors that are in sequence with student potential. One of the benefits if the students know the major recommendations according to student abilities, then when students carry out academic leraning will produce optimal achievement and be able to develop their abilities to the maximum and become superior vocational high school graduates. The method used in the calculation of this study is the Yager Model Fuzzy Multi-Attribute Decision Making (FMADM) method to provide the results of the major’s recommendations by student potential. The attributes used in the selection of these majors are academic value, talent value, interest value, intelligence quotient (IQ), parental support levels, achievement motivation, student’s attitude, student’s independence, and student’s health. Testing the recommendation system for the selection of VHS majors using the Yager Model FMADM method is obtained in the first scenario, the results of the recommendations at the top 10 results in an appropriate conformity value of 95.00% and not 5.00%. In the second scenario, the results of the recommendations at level 3 result in an appropriate suitability value of 82.50% and an unsuitable value of 17.50%. Based on this suitability, the FMADM Model Yager method can be implemented and produce recommendations according to student potential.
Precision analysis model and experimentation of vision reconstruction with two cameras and 3D orientation reference

Abstract Active vision reconstruction is widely used in industrial manufacturing and three-dimensional inspection. The reconstruction accuracy is an important problem to be investigated for the inspection process. The paper conducts an analysis study of the reconstruction error for the vision reconstruction with a planar laser, two cameras and a 3D orientation board. The variation principles of the spatial coordinates caused by the variations of the extrinsic parameters of the cameras, intrinsic parameters of the internal camera, and image coordinate points of the internal camera, are modeled and analyzed in this paper. The analysis is also proved by the verification experiments, which provides the application potential for other active-vision-based reconstructions.
Secure SMS transmission based on social network messages

Nowadays, short message communication has a wide range of applications in modern lives. Authentication, announcements, reports, and personal communications are the common instances of application of this widely used digital media. At transmission time, the message content spreads in the air without any intrinsic security countermeasure. As a result, the content of messages is interceptable without the need for complicated equipment. In modern methods, security has migrated from hardware to software, especially in smartphones. In this research, a method for securing short message communications is introduced which uses four distinguished cryptographic key generation factors to produce a couple of unique and dynamic encryption and decryption keys. The evaluation results show that the proposed method uses secure encryption and decryption keys, is resistant against a wide range of cryptographic and network attacks, outstandingly reduces the cost of establishing secure sessions, and highly satisfies the users with the delivered level of security and usability.
Monte-Carlo Planning and Learning with Language Action Value Estimates

Interactive Fiction (IF) games provide a useful testbed for language-based reinforcement learning agents, posing significant challenges of natural language understanding, commonsense reasoning, and non-myopic planning in the combinatorial search space. Agents based on standard planning algorithms struggle to play IF games due to the massive search space of language actions. Thus, language-grounded planning is a key ability of such agents, since inferring the consequence of language action based on semantic understanding can drastically improve search. In this paper, we introduce Monte-Carlo planning with Language Action Value Estimates (MC-LAVE) that combines a Monte-Carlo tree search with language-driven exploration. MC-LAVE invests more search effort into semantically promising language actions using locally optimistic language value estimates, yielding a significant reduction in the effective search space of language actions. We then present a reinforcement learning approach via MC-LAVE, which alternates between MC-LAVE planning and supervised learning of the self-generated language actions. In the experiments, we demonstrate that our method achieves new high scores in various IF games.
On identification of neural correlates of reflection in simple recurrent neural networks

Abstract Reflection, that in a general sense means internal representation of the external world, refers to one of awareness levels observed in animals. In this paper we demonstrate the ability of a homogeneous recurrent neural network to solve a problem that requires a reflection. The delayed matching to sample test was chosen as a task which is impossible to pass without an internal representation of an external world. Experiments showed that simple recurrent neural networks can form these representations and store them as neuron firing patterns for several clock cycles. Although the trained network was able to distinguish these patterns easily, the identification of certain stimulus by neuron firing was not practically possible due to minor differences in the level of synchronous firing of a given neuron for different stimuli. Neural networks were shown to be applicable for modeling reflexive abilities, so these simple models may also be used for creation of general technique that ultimately can be applied to recognizing neural correlates of human consciousness.
Psychological Principles of Organization of the Deductive Process at the English Lessons at Secondary Schools

The aim of our article is: to characterize a phase character of the educational activity at secondary schools, to describe a cognitive sphere of the person, which has a fundamental load in terms of constructing a model of the world, modeling the stages of logical memorization, to distinguish some routes of cognitive process according to psychological principles of providing deductive techniques, to propose psychological principles, which are the basis of providing deductive models at the English lessons at secondary schools. Methods of the research. The following theoretical methods of the research were used to solve the tasks formulated in the article: a categorical method, structural and functional methods, the methods of the analysis, systematization, modeling, generalization. The results of the research. Educational activity has a phase character. The first phase is characterized by awareness of the situation, the general conscious­ness increases to perform a long period of mental activity. In the measures of the second phase there is a process of unstable adaptation – the system of motives, needs and interests in close interaction with volitional activity having been created the preconditions for further adaptation to the whole process of educational activity. The third phase is a period of stable adaptation, when the goal is fully realized and conditions for its realization appear, the entire system of levels of the activity comes in line with the main goal of learning. It was shown that emphatic types of memory are motion, emotional, ver­bal-logical, sensory (visual, auditory, olfactory), figurative, mechanical, logical, long-term, short-term, involuntary, non-voluntary, etc. All of them, one this way or another one, are involved into the process of perception and processing the information. Sometimes the classification is used by different types of the analyzers: visual, auditory, motion, mixed, etc. It should be fixed in mind that the efficiency of logical memory is twenty times higher, better than mechanical one. In the article it was proposed the authors’ own vision of the stages of logical memorization. Conclusions. Cognitive motivation is recommended by us to be formed on the basis of the following psychological principles, which are the basis of pro­viding deductive models at the English lessons at secondary schools: with the help of specially organized didactic actions; by creation of problem situations; by introduction of special forms and methods of active learning, in which the prin­ciple of problem education is realized; by development of cognitive motivation in communication between a teacher and pupils, as well as schoolchildren with each other; by revitalization of teacher’s pedagogical skills, his / her ability to organize correctly the activities of pupils.
Synthetic Gaze Data Augmentation for Improved User Calibration

In this paper, we focus on the calibration possibilitiesó of a deep learning based gaze estimation process applying transfer learning, comparing its performance when using a general dataset versus when using a gaze specific dataset in the pretrained model. Subject calibration has demonstrated to improve gaze accuracy in high performance eye trackers. Hence, we wonder about the potential of a deep learning gaze estimation model for subject calibration employing fine-tuning procedures. A pretrained Resnet-18 network, which has great performance in many computer vision tasks, is fine-tuned using user’s specific data in a few shot adaptive gaze estimation approach. We study the impact of pretraining a model with a synthetic dataset, U2Eyes, before addressing the gaze estimation calibration in a real dataset, I2Head. The results of the work show that the success of the individual calibration largely depends on the balance between fine-tuning and the standard supervised learning procedures and that using a gaze specific dataset to pretrain the model improves the accuracy when few images are available for calibration. This paper shows that calibration is feasible in low resolution scenarios providing outstanding accuracies below 1.5\(^\circ \) of error.