Reviewerly/exHarmony huggingface dataset:
Self-supervised optical flow derotation network for rotation estimation of a spherical camera

In this paper, we propose a self-supervised optical flow-based approach to learn the rotation of an arbitrarily moving spherical camera. Nowadays, deep learning has enabled efficient learning of camera rotation efficiently. However, most approaches are fully supervised and require large datasets with ground-truth labels of the rotation, and these labels are difficult to acquire. We attempt to solve this problem by using a derotation operation of the spherical optical flow on a unit sphere. This operation decouples the camera rotation from the mixture of translational and rotational components, removing the effect of 3D information for rotation estimation. Therefore, we integrate a derotation layer into a convolutional neural network for regressing the camera rotation. This layer can be adopted for only spherical cameras, which can capture all-round information, and thus enables the network to be learned the camera rotation without using labeled training datasets. We experimentally demonstrate that our approach achieves the comparable performance for the rotation estimation to that of a fully supervised approach and that it outperforms a previously proposed approach. Moreover, transfer learning is conducted in new environments to confirm the benefit of the self-supervised learning.
Semantic Segmentation of Open Pit Mining Area Based on Remote Sensing Shallow Features and Deep Learning

Mineral resources are an important part of natural resources, and a good order of mining activities is an important prerequisite to ensure the safety of mining production, maintain the fairness of mining market, and steadily promote the construction of ecological civilization. Optical remote sensing image is one of the main carriers to reflect the mining activities of open-pit mining area. Deep learning technology is widely used in semantic segmentation of open-pit mining area. However, due to the complex surface environment of mining area, its segmentation accuracy needs to be further improved. In this paper, taking gaofen-2 optical remote sensing image as the data source, the remote sensing image sample set of open-pit mining area is constructed by manual annotation. Based on the sample set, the shallow texture features of the image are constructed, and part of the sample sets are put into the deep neural network for training. Combining the shallow texture features with the deep features of the deep neural network, a semantic segmentation model for pixel level open-pit mining area extraction is proposed by using U-net analysis model, and compared with the other two methods. The experimental results show that the overall accuracy of this method is 89.3%, and the average accuracy is 88.78%, which are better than the other two methods.
Designing the educational process based on virtual reality technologies

The article discusses the use of virtual reality technologies in the school informatics course in teaching modeling. The greatest attention is paid to methodological recommendations for the elective course "Technologies of multidimensional modeling based on a virtual reality system for a smart home". In the classroom, students get acquainted with the basic concepts related to virtual reality, modeling objects and processes in 3D editors and based on a modern game engine. For students in the lesson, it is proposed to work with additive technologies on the example of studying a 3D printing device using virtual reality technology. Students participate in all stages of designing and developing a model in the cloud environment and then printing it on a 3D printer. Training within the elective course ends with a consideration of the basic concepts of the internet of things, the possibilities of augmented reality technologies in this area and the construction of a smart device using an educational kit.
Convolution Autoencoder-Based Sparse Representation Wavelet for Image Classification

In this paper, we propose an effective Convolutional Autoencoder (AE) model for Sparse Representation (SR) in the Wavelet Domain for Classification (SRWC). The proposed approach involves an autoencoder with a sparse latent layer for learning sparse codes of wavelet features. The estimated sparse codes are used for assigning classes to test samples using a residual-based probabilistic criterion. Intensive experiments carried out on various datasets revealed that the proposed method yields better classification accuracy while exhibiting a significant reduction in the number of network parameters, compared to several recent deep learning-based methods.
On the Power of an Honest Majority in Three-Party Computation Without Broadcast

Fully secure multiparty computation (MPC) allows a set of parties to compute some function of their inputs, while guaranteeing correctness, privacy, fairness, and output delivery. Understanding the necessary and sufficient assumptions that allow for fully secure MPC is an important goal. Cleve (STOC’86) showed that full security cannot be obtained in general without an honest majority. Conversely, by Rabin and Ben-Or (FOCS’89), assuming a broadcast channel and an honest majority enables a fully secure computation of any function. Our goal is to characterize the set of functionalities that can be computed with full security, assuming an honest majority, but no broadcast. This question was fully answered by Cohen et al. (TCC’16) – for the restricted class of symmetric functionalities (where all parties receive the same output). Instructively, their results crucially rely on agreement and do not carry over to general asymmetric functionalities. In this work, we focus on the case of three-party asymmetric functionalities, providing a variety of necessary and sufficient conditions to enable fully secure computation. An interesting use-case of our results is server-aided computation, where an untrusted server helps two parties to carry out their computation. We show that without a broadcast assumption, the resource of an external non-colluding server provides no additional power. Namely, a functionality can be computed with the help of the server if and only if it can be computed without it. For fair coin tossing, we further show that the optimal bias for three-party (server-aided) r-round protocol remains $$\varTheta \left( 1/r\right) $$ (as in the two-party setting).
Multi-Agent Intermittent Interaction Planning via Sequential Greedy Selections Over Position Samples

In this work, we propose a method to solve the interaction planning problem for a set of mobile agents with obstacles and agent collisions via a core path planner and constrained random position sampling approach. The interaction constraint is posed in the form of an arbitrary number of discretized times in which we enforce a desired topological condition. The general objective function, to be maximized subject to the interaction constraint, is coverage of an environmental process here modeled as a Gaussian mixture model. The main tool we use to select positions and the times of interaction is the greedy algorithm, along with a submodular objective function and matroid constraint. Through this we guarantee strong theoretical lower bounds on sub-optimality. Simulations, including several Monte Carlo trials, are presented to corroborate our proposed methods.
Pixel based method for Text to Image Encryption

Normally, the encryption and decryption is done only to convert the text into an encrypted form (i.e.) the confused form of text. In this type of method a hacker may easily hack the text using the public key or private key. So in this paper a new technique called Text to image encryption has been proposed. This will convert the plain text or information into an image format. That image will hide the encrypted text. If the user wants to view the text, first the image is divided into blocks. Each color component will be modified using the secret key. It will be difficult to the hackers to hack the information. This method can be used for large set of databases.
Segmented Inscription Recognition Index for Measuring the Inscription Quality Extracted From 3-D Data

This paper proposes a novel measuring method for evaluating the inscriptions extracted from the three-dimensional scanning data of monuments based on the ground truth engravings. The inscription prediction affects inscription recognition differently regarding the prediction region. That is, regional importance in inscription recognition varies. Based on the observation, This paper proposes to divide the 2-class labeled ground truth into four areas according to the regional importance. For each classified area, the true positive, false negative, false positive, and true negative were weighted differently and used to determine a modified confusion matrix. After configuring the confusion matrix, the F1 score was calculated. The experiments showed that the proposed measure was more similar to the subjective inscription recognition by people than the mean opinion score.
Tezla, an Intermediate Representation for Static Analysis of Michelson Smart Contracts

This paper introduces Tezla, an intermediate representation of Michelson smart contracts that eases the design of static smart contract analysers. This intermediate representation uses a store and aims to preserve the semantics, flow and resource usage of the original smart contract. This enables properties like gas consumption to be statically verified. We provide an automated decompiler of Michelson smart contracts to Tezla. In order to support our claim about the adequacy of Tezla, we develop a static analyser that takes advantage of the Tezla representation of Michelson smart contracts to prove simple but non-trivial properties.
Edge-Aware Convolution for RGB-D Image Segmentation

Convolutional Neural Networks using RGB-D images as input have shown superior performance in recent research in the field of semantic segmentation. In RGB-D data, the depth channel encodes information from the 3D spatial domain, which has an inherent difference with the color channels. It thus needs to be treated in a special way, rather than just processed as another channel of the input signal. Under this purpose, we propose a simple but not trivial edge-aware convolutional kernel to utilize the geometric information contained in the depth channel to extract feature maps in a more effective manner. The edge-aware convolutional kernel is built upon regular convolutional kernel, thus, it can be used to restructure existing CNN models to achieve stable and effective feature extraction for RGB-D data. We compare our result with a previous method that is closely related to our to show our method can provide more effective and stable feature extraction.
Development of a method for adjusting the coordinates of the center of attention in the absence of fixation of the head

Abstract In this paper, the movement of the pupils is a biometric indicator that analyzes the emotional state of a person. Eyetracking using infrared illumination based on the determination of the displacement vector between the centers of the pupil and the shine of the cornea gives results with high accuracy, provided that the human head is fixed. A method for determining the coordinates of the center of attention in the absence of fixation of the head without using infrared illumination is proposed. The accuracy of determining the coordinates of the center of attention is improved by moving from a coordinate system associated with a fixed head to a coordinate system associated with a moving center of the pupil.
Apodini

Web services use protocols and middleware mechanisms to offer interfaces that enable the interoperability of heterogeneous components. Interface definition languages are used to describe service interfaces derived from system requirements and from system architecture. System designers faces two challenges: to replace an existing middleware and simultaneously supporting multiple types of middleware. This paper describes Apodini, an internal domain specific language to deal with these challenges. Apodini enables system designers to express the nonfunctional requirements and functionality of web service interfaces in a single domain specific language. Apodini is independent of any specific middleware, protocol, or interface definition language to enable extensible system design and implementation.
Text-to-Image Generation via Semi-Supervised Training

Synthesizing images from text is an important problem and has various applications. Most of the existing studies of text-to-image generation utilize supervised methods and rely on a fully-labeled dataset, but detailed and accurate descriptions of images are onerous to obtain. In this paper, we introduce a simple but effective semi-supervised approach that considers the feature of unlabeled images as "Pseudo Text Feature". Therefore, the unlabeled data can participate in the following training process. To achieve this, we design a Modality-invariant Semantic- consistent Module which aims to make the image feature and the text feature indistinguishable and maintain their semantic information. Extensive qualitative and quantitative experiments on MNIST and Oxford-102 flower datasets demonstrate the effectiveness of our semi-supervised method in comparison to supervised ones. We also show that the proposed method can be easily plugged into other visual generation models such as image translation and performs well.
Differentiated Instructions in Teaching English for Students with Autism Spectrum Disorder

This study aims to explore the use of differentiated instructions in teaching English for students with Autism Spectrum Disorder (ASD) and investigate English teachers’ views on the extent of differentiated instructions in teaching English for students with ASD. The participants of this study was two English lecturers of English language education department in a private inclusive university in Indonesia. Employing qualitative method, this study used the data gathered from the interviews with the participants. This study used thematic analysis and generated five themes regarding to the use of differentiated instructions and teachers’ views on it. The first three themes were used to explain the extent of differentiated instructions in teaching English for students with ASD. The first three themes are the participants used differentiated class-assignments for students with ASD in their English class, they used differentiated content materials for students with ASD, and lastly, they provided outside-class assistance for students with ASD. The last two themes were used to reveal teachers’ vi.ws on the extent of differentiated instructions in teaching English for students with ASD. First, differentiated instructions lessened the level of anxiety of students with ASD, and second, differentiated instructions provided a positive learning environment for students with ASD. Based on the results and limitation of the study, some implications, contributions, and suggestions for future studies are presented.
Efficient Homomorphic Comparison Methods with Optimal Complexity.

Comparison of two numbers is one of the most frequently used operations, but it has been a challenging task to efficiently compute the comparison function in homomorphic encryption (HE) which basically supports addition and multiplication. Recently, Cheon et al. (Asiacrypt 2019) introduced a new approximate representation of the comparison function with a rational function, and showed that this rational function can be evaluated by an iterative algorithm. Due to this iterative feature, their method achieves a logarithmic computational complexity compared to previous polynomial approximation methods; however, the computational complexity is still not optimal, and the algorithm is quite slow for large-bit inputs in HE implementation.
UTFPR at SemEval 2020 Task 12: Identifying Offensive Tweets with Lightweight Ensembles

Offensive language is a common issue on social media platforms nowadays.In an effort to address this issue, the SemEval 2020 event held the OffensEval 2020 shared task where the participants were challenged to develop systems that identify and classify offensive language in tweets.In this paper, we present a system that uses an Ensemble model stacking a BOW model and a CNN model that led us to place 29th in the ranking for English sub-task A.
Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds.

The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of convolutional neural networks. Inspired by recent interest in geometric deep learning, which aims to generalize convolutional neural networks to manifold and graph-structured domains, we define a geometric scattering transform on manifolds. Similar to the Euclidean scattering transform, the geometric scattering transform is based on a cascade of wavelet filters and pointwise nonlinearities. It is invariant to local isometries and stable to certain types of diffeomorphisms. Empirical results demonstrate its utility on several geometric learning tasks. Our results generalize the deformation stability and local translation invariance of Euclidean scattering, and demonstrate the importance of linking the used filter structures to the underlying geometry of the data.
Revisiting Fairness in MPC: Polynomial Number of Parties and General Adversarial Structures

We investigate fairness in secure multiparty computation when the number of parties $$n = {{\,\mathrm{poly}\,}}(\lambda )$$ grows polynomially in the security parameter, $$\lambda $$ . Prior to this work, efficient protocols achieving fairness with no honest majority and polynomial number of parties were known only for the AND and OR functionalities (Gordon and Katz, TCC’09). We show the following:
IDNet: A Single-Shot Object Detector Based on Feature Fusion

This paper proposes a novel single shot network for object detection. The proposed network, termed IDNet, explores the strategies of the feature fusion to alleviate the scale variation problem in object detection. IDNet mainly consists of two feature fusion modules: an indirect feature fusion module (IF) and a direct feature fusion module (DF). The IF shares long-range dependencies within pyramidal layers and based on these information, IDNet learns to emphasize informative regions and suppress the less useful ones on each layer. The DF is a feature fusion strategy based on modified lateral connection inspired by feature pyramid networks (FPN). It utilizes the averaging operation to reduce the change of feature maps' order of magnitude during fusing features to further improve the performance for detecting small instances. Comprehensive experiments are performed and the results indicate the effectiveness of IDNet, which reaches 80.3 mAP on PASCAL VOC 2007 benchmark.
A correlation-based entity embedding approach for robust entity linking

Entity alignment is a crucial tool in knowledge discovery to reconcile knowledge from different sources. Recent state-of-the-art approaches leverage joint embedding of knowledge graphs (KGs) so that similar entities from different KGs are close in the embedded space. Whatever the joint embedding technique used, a seed set of aligned entities, often provided by (time-consuming) human expertise, is required to learn the joint KG embedding and/or a mapping between KG embeddings. In this context, a key issue is to limit the size and quality requirement for the seed. State-of-the-art methods usually learn the embedding by explicitly minimizing the distance between aligned entities from the seed and uniformly maximizing the distance for entities not in the seed. In contrast, we design a less restrictive optimization criterion that indirectly minimizes the distance between aligned entities in the seed by globally maximizing the dimension-wise correlation among all the embeddings of seed entities. Within an iterative entity alignment system, the correlation-based entity embedding function achieves state-of-the-art results and is shown to significantly increase robustness to the seed's size and accuracy. It ultimately enables fully unsupervised entity alignment using a seed automatically generated with a symbolic alignment method based on entities' names.
Using Digital Footprints in Social Research: an Interdisciplinary Approach

The aim of the study is to clarify the concept of the digital footprint in jurisprudence and social sciences and determine its meaning for interdisciplinary research. The subject of this work is the analysis of the concepts of “digital footprint”, “digital reputation”, “digital image of a person”. The article notes that special machine techniques are required to process digital traces. The ways of storage, transmission and use of digital traces are considered. The research identified the main problems of using digital information in public relations. Digital traces can be used in various studies in the humanities, social sciences: sociology, law, economics, psychology using interdisciplinary research methods. The result of the study was the conclusion that digital information does not always mean a digital footprint. Digital information will have a digital footprint only when it is transferred from one user to another in an online environment, uploaded to a social network, etc. (in the case when it is possible to "trace" its movement). Thus, the "digital footprint" is the ability of information recorded in digital form, to leave special "marks" on the route from subscriber to subscriber, the ability to track such marks, receive information about its movement and transformation in order to collect, process and analyze these data.
A Blockchain Model in Tamarin and Formal Analysis of Hash Time Lock Contract

Formal analysis and verification methods can aid the design and validation of security properties in blockchain based protocols. However, to generate a reasonable and correct verification, a proper model for the blockchain is needed. In this paper, we give a blockchain model in Tamarin. Based on our model we analyze and give a formal verification for the hash time lock contract, an atomic cross chain trading protocol. The result shows that our model is able to identify an underlying assumption for the hash time lock contract and that the model is useful for analyzing blockchain based protocols.
Research and Development of QR Code Steganography Based on JSteg Algorithm in DCT Domain

Using steganography for data hiding is becoming a main subject to ensure both information security and picture quality. Traditional steganography algorithms usually convert secret information into a binary string and embed it in the pixel data of the cover image. In order to ensure the information security as well as convenient transmission, this work studies the steganography algorithm of embedding the QR code containing secret information into the cover image, based on the JSteg algorithm. Secret messages with different sizes have been tested by many cover images and standard parameters have adopted to verify the efficiency. According to the experimental results, all the PSNR in a value that is greater than 47.6 dB. The proposed method has high security and more imperceptibility.
The Video Sequences Quality with Wavelet Coding at Different Channel Bandwidth

The article presents the results of assessing the quality of an image transmitted over a communication channel with different bandwidth, if it is encoded with the developed wavelet codec. The degradation of the video stream quality in the case of narrowing the traffic channel with partial packet loss, if each packet contained one sub-band, was estimated. It is shown that the average compression ratio of the video codec based on the wavelet transform is 227.