Reviewerly/exHarmony huggingface dataset:
Learning Free-Form Deformation for 3D Face Reconstruction from In-The-Wild Images

The 3D Morphable Model (3DMM), which is a Principal Component Analysis (PCA) based statistical model that represents a 3D face using linear basis functions, has shown promising results for reconstructing 3D faces from single-view in-the-wild images. However, 3DMM has restricted representation power due to the limited number of 3D scans and global linear basis. To address the limitations of 3DMM, we propose a straightforward learning-based method that reconstructs a 3D face mesh through Free-Form Deformation (FFD) for the first time. FFD is a geometric modeling method that embeds a reference mesh within a parallelepiped grid and deforms the mesh by moving the sparse control points of the grid. As FFD is based on mathematically defined basis functions, it has no limitation in representation power. Thus, we can recover accurate 3D face meshes by estimating the appropriate deviation of control points as deformation parameters. Although both 3DMM and FFD are parametric models, deformation parameters of FFD are easier to interpret in terms of their effect on the final shape. This practical advantage of FFD allows the resulting mesh and control points to serve as a good starting point for 3D face modeling, in that ordinary users can fine-tune the mesh by using widely available 3D software tools. Experiments on multiple datasets demonstrate how our method successfully estimates the 3D face geometry and facial expressions from 2D face images, achieving comparable performance to the state-of-the-art methods.
k-Provability in $$\hbox {PA}$$

Abstract We study the decidability of k -provability in $$\hbox {PA}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>PA</mml:mtext> </mml:math> —the relation ‘being provable in $$\hbox {PA}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>PA</mml:mtext> </mml:math> with at most k steps’—and the decidability of the proof-skeleton problem—the problem of deciding if a given formula has a proof that has a given skeleton (the list of axioms and rules that were used). The decidability of k -provability for the usual Hilbert-style formalisation of $$\hbox {PA}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>PA</mml:mtext> </mml:math> is still an open problem, but it is known that the proof-skeleton problem is undecidable for that theory. Using new methods, we present a characterisation of some numbers k for which k -provability is decidable, and we present a characterisation of some proof-skeletons for which one can decide whether a formula has a proof whose skeleton is the considered one. These characterisations are natural and parameterised by unification algorithms.
Statistical Analysis of Pseudorandom Sequences and Stegocontainers

In the theoretical part of the paper, the scope of application of pseudorandom numbers and methods of their generation, as well as methods of statistical testing of pseudorandom sequences (PS) are considered. In the practical part of the work, the quality of PS obtained by Mersenne Twister [1] generator and the cryptographic generator of the RNGCryptoServiceProvider class of the. NET platform is evaluated. Based on the conducted research, the results of testing are obtained, which show that the quality of pseudorandom sequences generated by the cryptographic random number generator is higher than PS generated by Mersenne Twister. Additionally, based on statistical analysis by NIST and TestU01, a study is conducted in an attempt to establish the statistical indistinguishability of sets of empty- and stegocontainers created using a two-dimensional associative masking mechanism [2-4] based on a gamma of at least 500 KB in length. Research work was carried out under the guidance of R.F. Gibadullin, Associate Professor of the Department of Computer Systems of Kazan National Research Technical University named after A.N.Tupolev-KAI.
An Effective Algorithm for Extracting Maximal Bipartite Cliques

The reduction of bipartite clique enumeration problem into a clique enumeration problem is a well-known approach for extracting maximal bipartite cliques. In this approach, the graph inflation is used to transform a bipartite graph to a general graph, then any maximal clique enumeration algorithm can be used. However, between every two vertices (in the same set), the traditional inflation algorithm adds a new edge. Therefore incurring high computation overhead, which is impractical and cannot be scaled up to handle large graphs. This paper proposes a new algorithm for extracting maximal bipartite cliques based on an efficient graph inflation algorithm. The proposed algorithm adds the minimal number of edges that are required to convert all maximal bipartite cliques to maximal cliques. The proposed algorithm has been evaluated, using different real world benchmark graphs, according to the correctness of the algorithm, running time (in the inflation and enumeration steps), and according to the overhead of the inflation algorithm on the size of the generated general graph. The empirical evaluation proves that the proposed algorithm is accurate, efficient, effective, and applicable to real world graphs more than the traditional algorithm.
Testing Artificial Intelligence in High-Performance, Tactical Aircraft

The United States Air Force Test Pilot School (USAF TPS), in partnership with Lockheed Martin Skunk Works, developed and tested a suite of algorithms and artificial intelligence (AI) agents flown on an F-16D. The system was designed to autonomously locate and navigate to an emitter and optically recognize its associated vehicle under the project Have SPI-DERs. This project was the first to explore the use of AI agents trained in simulation using deep reinforcement learning and tested on a high-performance aircraft in the real world performing routing and computer vision tasks. During flight test, the Have SPIDERS project set out to demonstrate the SUT's ability to find and fix the emitter through three phases. The first phase passively detected the emitter using a small antenna array and routed the aircraft using a neural network to reduce the uncertainty volume of the emitter's location. The second phase routed the aircraft using a second neural network to an image point where the emitter could be optically identified. The final phase used a third neural network to optically recognize a vehicle collocated with the emitter. The first two phases each used two feedforward neural networks functioning as actor-critic algorithms. The third phase used a RetinaNet architecture. This paper uses the Have SPIDERS project as a vehicle to discuss lessons learned that can be applied to future AI flight test. These lessons focus on six areas for consideration during system development, test planning, and execution: understanding system under test (SUT) development, instrumenting the AI to understand its behavior, planning for unpredictability, simulation-to-real-world generalization, hardware choices, and manned surrogate testing considerations.
The Evolution of Competence Research in the International Context

The article presents the history of the development of competence assessment from the XX century till now. On the basis of the completed study the author developed the periodisation of national and international research projects in order to systematize various projects and highlight the stages of their development. The main focus of most projects is the assessment of competences and skills, literacy, respondents’ socio-demographic characteristics. All these components allow to analyse the received data as full as possible. The recent showed studies focus on the ability of using the existing skills and competences in real-life situations. The abovementioned periodisation gives an opportunity to researchers to obtain structured information of the projects and use it in their own research.
Intelligent Cyclic Spectrum Features Based Modulation Recognition Design

This paper proposes an intelligent deep learning aided modulation recognition system. In this design, we utilize the deep residual shrinkage network (DRSN) to identify the modulation types with the cyclic spectrum (CS) features as the data set. With the aim to reduce the computational complexity, we first use a half part of the XY-plane of the 3-dimensional CS, which is transformed into a gray-scale image to compose the dataset. Thanks to the statistical characteristics evaluation with the CS, the data set is noise-resilient. Then we develop the DRSN with soft thresholding and attention mechanism to combat the noise and interference, and to reserve key features of received modulated signals. Simulation results demonstrate that the proposed system can achieve a higher classification accuracy than counterpart methods with lower computational complexity.
The Optimization Analysis for the Original and Manipulation Identification of Illegally Filmed Images

Illegally filmed images, the sharing of non-consensually filmed images over social media, and the secret recording and distribution of celebrity images are increasing. To catch distributors of illegally filmed images, many investigation techniques based on an analysis of the file attribute information of the original images have been introduced. As forensic science advances, various types of anti-forensic technologies are being produced, requiring investigators to open and analyze all videos from the suspect’s storage devices, raising the question of the invasion of privacy during the investigation. The suspect can even file a lawsuit, which makes issuing a warrant and conducting an investigation difficult. Thus, it is necessary to detect the original and manipulated images without needing to directly go through multiple videos. We propose an optimization analysis and detection method for extracting original and manipulated images from seized devices of suspects. In addition, to increase the detection rate of both original and manipulated images, we suggest a precise measurement approach for comparative thresholds. Thus, the proposed method is a new digital forensic methodology for comparing and identifying original and manipulated images accurately without the need for opening videos individually in a suspect’s mobile device.
Obstacle Judgment Model of In-vehicle Voice Interaction System Based on Eye-tracking

Many obstacles have happened in in-vehicle voice user interaction (VUI) systems, which affect the drivers' safety. By studying human mental models, researchers in design have found the most threatening drivers' safety is not the Natural Language Processing (NLP) error that many technicians are only working to improve. Therefore, enhancing the collaboration between driver and in-vehicle VUI system needs to study human-computer interaction, not just to rely on technological improvement. In this study, we propose using eye-tracking technology to evaluate the impact of VUI obstacles on drivers quantitatively. Then we establish an obstacle judgment model of in-vehicle voice interaction systems based on eye-tracking and predict four types of VUI obstacles with about 93% accuracy. At last, a human-computer collaboration method for the in-vehicle voice system based on this model is discussed.
Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks

The amount of data generated by modern communication devices is enormous, reaching petabytes. The rate of data generation is also increasing at an unprecedented rate. Though modern technology supports storage in massive amounts, the industry is reluctant in retaining the data, which includes the following characteristics: redundancy in data, unformatted records with outdated information, data that misleads the prediction and data with no impact on the class prediction. Out of all of this data, social media plays a significant role in data generation. As compared to other data generators, the ratio at which the social media generates the data is comparatively higher. Industry and governments are both worried about the circulation of mischievous or malcontents, as they are extremely susceptible and are used by criminals. So it is high time to develop a model to classify the social media contents as fair and unfair. The developed model should have higher accuracy in predicting the class of contents. In this article, tensor flow based deep neural networks are deployed with a fixed Epoch count of 15, in order to attain 25% more accuracy over the other existing models. Activation methods like “Relu” and “Sigmoid”, which are specific for Tensor flow platforms support to attain the improved prediction accuracy.
Cluster Performance by Dynamic Load and Resource-Aware Speculative Execution

Big data is one of the fastest-growing technologies, which can handle huge amounts of data from various sources, such as social media, weblogs, banking and business sectors, etc. A Hadoop MapReduce job can be delayed if one of its many tasks is being assigned to an unreliable or congested machine. To solve this straggler problem, a novel algorithm design of speculative execution schemes for parallel processing clusters, from an optimization perspective, under different loading conditions is proposed. For the lightly loaded case, a task cloning scheme, namely the combined file task cloning algorithm, which is based on maximizing the overall system utility, and a straggler-detection algorithm is proposed based on a workload threshold. The detection and cloning of tasks assigned with the stragglers only will not be enough to enhance the performance unless cloning of tasks is allocated in a resource-aware method. So, a method is proposed which identifies and optimizes the resource allocation by considering all possible aspects of cluster performance balancing. One of the main issues appears due to the pre-configuration of the distinct map and reduces slots based on the number of files in the input folder. This can cause severe under-utilization of the slot as map slots might not be fully utilized to the input splits. To solve this issue, an alternative technique of Hadoop slot allocation is introduced in this paper by keeping the efficient management of the slots model. The combined file task cloning algorithm combines the files which are less than the size of a single data block and executes them in the highest performing data node. On implementing these efficient cloning and combining techniques on a heavily loaded cluster after detecting the straggler, the machine is found to reduce the elapsed time of execution to an average of 40%. The detection algorithm improves the overall performance of the heavily loaded cluster by 20% of the total elapsed time in comparison with the native Hadoop algorithm.
Power Side-Channel Analysis of RNS GLV ECC Using Machine and Deep Learning Algorithms

Many Internet of Things applications in smart cities use elliptic-curve cryptosystems due to their efficiency compared to other well-known public-key cryptosystems such as RSA. One of the important components of an elliptic-curve-based cryptosystem is the elliptic-curve point multiplication which has been shown to be vulnerable to various types of side-channel attacks. Recently, substantial progress has been made in applying deep learning to side-channel attacks. Conceptually, the idea is to monitor a core while it is running encryption for information leakage of a certain kind, for example, power consumption. The knowledge of the underlying encryption algorithm can be used to train a model to recognise the key used for encryption. The model is then applied to traces gathered from the crypto core in order to recover the encryption key. In this article, we propose an RNS GLV elliptic curve cryptography core which is immune to machine learning and deep learning based side-channel attacks. The experimental analysis confirms the proposed crypto core does not leak any information about the private key and therefore it is suitable for hardware implementations.
Counterfactual-Augmented Data for Multi-Hop Knowledge Base Question Answering

The rise of the counterfactual concept promoted the study of reasoning, and we applied it to Knowledge Base Question Answering (KBQA) multi-hop reasoning as a way of data augmentation for the first time. Intuitively, we propose a model-agnostic Counterfactual Samples Synthesizing(CSS) training scheme. The CSS uses two augmentation methods Q-CSS and T-CSS to augment the training set. That is, for each training instance, we create two augmented instances, one per augmentation method. Furthermore, perform the Dynamic Answer Equipment(DAE) algorithm to dynamically assign ground-truth answers for the expanded question, constructing counterfactual examples. After training with the supplemented examples, the KBQA model can focus on all key entities and words, which significantly improved model’s sensitivity. Experimental verified the effectiveness of CSS and achieved consistent improvements across settings with different extents of KB incompleteness.
Hostel’s Room Allocation System: A Framework Using Single-layer Fuzzy Logic

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LPCC-Net: RGB Guided Local Point Cloud Completion for Outdoor 3D Object Detection

Due to hardware limitations, point clouds collected by Li-DAR devices are sparse, making it challenging to locate faraway objects accurately. In this paper, we propose an RGB-guided local point cloud completion network, which aims to improve off-the-shelf 3D object detectors by selectively densifying the collected point clouds. Rather than predicting per-pixel depth in 2D images and projecting them back to pseudo-3D point clouds, our proposed method directly predicts the existence of points in 3D space around input points. Towards this goal, we create a semi-dense labeled local points completion dataset and design a new loss for training the network in a semi-supervised manner. Extensive experiments show that the proposed method can produce reasonable and accurate dense 3D point clouds from sparse inputs, improving off-the-shelf 3D object detectors on the KITTI 3D detection benchmark. The source code of our method will be available at https://github.com/emdata-ailab/LPCC-Net.
Hardware-Aware Low-Light Image Enhancement via One-Shot Neural Architecture Search with Shrinkage Sampling

Low-light image enhancement has traditionally been tackled by training a heuristically designed neural network architecture. Despite the success of these approaches, the heuristic design pattern inherently not only hinders further optimization of network architectures, but also limits the factors that the designer can take into consideration. As a result, these methods are difficult to achieve a balance between enhancing performance and hardware related performance. In this paper, we equip a basic enhancing algorithm with a neural architecture search technique. This technique helps to automatically search an optimal hardware-aware architecture while also increases neglectable computation burden. In this work, we propose a shrinkage sampling strategy to drastically decrease the computation cost of neural architecture search while improving the quality of search. Extensive experiments on various benchmarks demonstrate that our algorithm achieves state-of-the-art performance with higher speed.
Thoreau and the Confucian Four Books

Abstract Henry David Thoreau read three English translations of the Confucian Four Books and produced undated translations from a French translation of these texts. This study examines the relationship between Thoreau and Confucian thought via his engagement with this set of translations. Selections from the English translations were reprinted in the “Ethnical Scriptures” columns of the Transcendentalist periodical, The Dial . This study examines this understudied column, considering the possible impact the passages made on Thoreau's thought. Next, Thoreau's translations from the French are addressed, with the question of dating the translations explored, given its necessity for a robust appreciation of their role in the arc of his thought. Then, the connection between Confucian and Thoreauvian thought about civil disobedience is analyzed, with reference to a recent debate in the literature. Recommendations for further scholarship are offered, emphasizing links between Confucianism and Walden in general, and Mencius and Thoreau in particular.
A Hybrid Stochastic-Deterministic Minibatch Proximal Gradient Method for Efficient Optimization and Generalization

Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid stochastic-deterministic minibatch proximal gradient ( <small>HSDMPG</small> ) algorithm for strongly convex problems with linear prediction structure, e.g., least squares and logistic/softmax regression. <small>HSDMPG</small> enjoys improved computational complexity that is data-size-independent for large-scale problems. It iteratively samples an evolving minibatch of individual losses to estimate the original problem, and can efficiently minimize the sampled subproblems. For strongly convex loss of <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> components, <small>HSDMPG</small> attains an <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -optimization-error within <inline-formula><tex-math notation="LaTeX">$\mathcal {O} \left(\kappa \log ^{\zeta +1}\left(\frac{1}{\epsilon }\right)\frac{1}{\epsilon }\bigwedge n\log ^{\zeta }\left(\frac{1}{\epsilon }\right)\right)$</tex-math></inline-formula> stochastic gradient evaluations, where <inline-formula><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> is condition number, <inline-formula><tex-math notation="LaTeX">$\zeta =1$</tex-math></inline-formula> for quadratic loss and <inline-formula><tex-math notation="LaTeX">$\zeta =2$</tex-math></inline-formula> for generic loss. For large-scale problems, our complexity outperforms those of SVRG-type algorithms with/without dependence on data size. Particularly, when <inline-formula><tex-math notation="LaTeX">$\epsilon =\mathcal {O}(1/\sqrt{n})$</tex-math></inline-formula> which matches the intrinsic excess error of a learning model and is sufficient for generalization, our complexity for quadratic and generic losses is respectively <inline-formula><tex-math notation="LaTeX">$\mathcal {O} (n^{0.5}\log ^{2}(n))$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathcal {O} (n^{0.5}\log ^{3}(n))$</tex-math></inline-formula> , which for the first time achieves optimal generalization in less than a single pass over data. Besides, we extend <small>HSDMPG</small> to online strongly convex problems and prove its higher efficiency over the prior algorithms. Numerical results demonstrate the computational advantages of <small>HSDMPG</small> .
Improvement of the Approximate Method for the Comparison Operation in the RNS

The operation of number comparison is widely used in the implementation of most modern algorithms. The implementation of the algorithm for number comparison in the Residue Number System (RNS) consists of two stages. The first stage is calculating the positional characteristic of the modular number. The second stage is comparing positional characteristics of modular numbers in the positional number system. The article studies various comparison algorithms with software implementation in the Python programming language. The research consists in testing algorithms with different bit width and length of the RNS module vector. Also in this work, the improvement of the approximate method on the reduction of requirements for accuracy is carried out. The use of this method is based on low precision requirements for the comparison operation. It was found that in a certain range accuracy is not critical, which makes it possible to apply this method in a fairly wide range of problems.