Reviewerly/exHarmony huggingface dataset:
A Diagrammatic Approach to Information Flow in Encrypted Communication

We give diagrammatic tools to reason about information flow within encrypted communication. In particular, we are interested in deducing where information flow (communication or otherwise) has taken place, and fully accounting for all possible paths. The core mathematical concept is using a single categorical diagram to model the underlying mathematics, the epistemic knowledge of the participants, and (implicitly) the potential or actual communication between participants. A key part of this is a ‘correctness’ or ‘consistency’ criterion that ensures we accurately & fully account for the distinct routes by which information may come to be known (i.e. communication and / or calculation). We demonstrate how this formalism may be applied to answer questions about communication scenarios where we have the partial information about the participants and their interactions. Similarly, we show how to analyse the consequences of changes to protocols or communications, and to enumerate the distinct orders in which events may have occurred. We use various forms of Diffie-Hellman key exchange as an illustration of these techniques. However, they are entirely general; an extended version of this paper [8] provides similar analyses of other protocols.
UAV Tracking based on Unscented Kalman Filter for Sense and Avoid Applications

In this paper the performance of the Unscented Kalman Filter (UKF) based tracking is investigated for different Sense and Avoid (SAA) flight scenarios. The improvement of the tracking accuracy through integrating the range rate information into the estimation process with UKF is illustrated. The robustness of this method is further evaluated with a data set provided by Airbus, simulating a range of typical approach scenarios with a detection angle of -110° to +110°.
Fall Detection and Recognition from Egocentric Visual Data: A Case Study

Falling is among the most damaging events for elderly people, which sometimes may end with significant injuries. Due to fear of falling, many elderly people choose to stay more at home in order to feel safer. In this work, we propose a new fall detection and recognition approach, which analyses egocentric videos collected by wearable cameras through a computer vision/machine learning pipeline. More specifically, we conduct a case study with one volunteer who collected video data from two cameras; one attached to the chest and the other one attached to the waist. A total of 776 videos were collected describing four types of falls and nine kinds of non-falls. Our method works as follows: extracts several uniformly distributed frames from the videos, uses a pre-trained ConvNet model to describe each frame by a feature vector, followed by feature fusion and a classification model. Our proposed model demonstrates its suitability for the detection and recognition of falls from the data captured by the two cameras together. For this case study, we detect all falls with only one false positive, and reach a balanced accuracy of 93% in the recognition of the 13 types of activities. Similar results are obtained for videos of the two cameras when considered separately. Moreover, we observe better performance of videos collected in indoor scenes.
Predicting Evaluations of Essay by Computational Graph-Based Features

How to effectively evaluate students' essays based on a series of relatively objective writing criteria has always been a topic of discussion. With the development of automatic essay scoring, a key question is whether the writing quality can be evaluated systematically based on the scoring rubric. To address this issue, we used an innovative set of graph-based features to predict the quality of Chinese middle school students' essays. These features are divided into four sub-dimensions: basic characteristics, main idea, essay content, and essay development. The results show that graph-based features were significantly better at predicting human essay scores than the baseline features. This indicates that graph-based features can be used to reliably and systematically evaluate the quality of an essay based on the scoring rubric, and it can be used as an alternative tool to replace or supplement human evaluation.
Decoding Toric Codes on Three Dimensional Simplical Complexes

Three dimensional (3D) toric codes are a class of stabilizer codes with local checks and come under the umbrella of topological codes. While decoding algorithms have been proposed for the 3D toric code on a cubic lattice, there have been very few studies on the decoding of 3D toric codes over arbitrary lattices. Color codes in 3D can be mapped to toric codes. However, the resulting toric codes are not defined on cubic lattice. They are arbitrary lattices with triangular faces. Decoding toric codes over an arbitrary lattice will help in studying the performance of color codes. Furthermore, gauge color codes can also be decoded via 3D toric codes. Motivated by this, we propose an efficient algorithm to decode 3D toric codes on arbitrary lattices (with and without boundaries).
Filter Design for Two-Channel Filter Banks on Directed Bipartite Graphs

Graph signal processing on directed graphs is more challenging that on undirected graph, primarily because the graph matrices, e.g., adjacency or Laplacian, associated with the latter is non-symmetric. The eigenvalues that represent the spectral frequencies are therefore complex in general, and the design of spectral filters for complex frequencies presents more challenges. In this work we consider the design of two-channel filter banks for directed bipartite graphs. By decomposing any graph into a series of bipartite graphs, the basic two-channel system can be applied in cascade for arbitrary graphs. The graph filters are constructed using ladder structures. The design of the kernels in the ladder structures is achieved via a least-squares formulation. Analytical formulas are derived for the design of the kernel coefficients.
Time Scheduling and Finance Management: University Student Survival Kit

With plenty of opportunities for new social interactions, events, and other perspectives, it may be a challenge for students to balance both time and money simultaneously. Even though there is a variety of planning and budgeting application in the market to help students in terms of education and personal life, students ought to download multiple applications that only have a particular function. With this multifunctional application- StuLogger, it aims to improve student knowledge and promotes self-reflection, which may encourage students ’perception of their time spent and allow them to track their financial activity efficiently. It allows users to set up income and expense from various options such as food, transportation, bms, and others. Besides that, the app comes with a calendar, notes and reminder to allow users to organize their daily activities. The study employed a mixed-method approach in which both survey and interview were conducted online by students from private university.
On Dropping Clusters to Regularize Graph Convolutional Neural Networks

Dropout has been widely adopted to regularize graph convolutional networks (GCNs) by randomly zeroing entries of the node feature vectors and obtains promising performance on various tasks. However, the information of individually zeroed entries could still present in other correlated entries by propagating (1) spatially between entries of different node feature vectors and (2) depth-wisely between different entries of each node feature vector, which essentially weakens the effectiveness of dropout. This is mainly because in a GCN, neighboring node feature vectors after linear transformations are aggregated to produce new node feature vectors in the subsequent layer. To effectively regularize GCNs, we devise DropCluster which first randomly zeros some seed entries and then zeros entries that are spatially or depth-wisely correlated to those seed entries. In this way, the information of the seed entries is thoroughly removed and cannot flow to subsequent layers via the correlated entries. We validate the effectiveness of the proposed DropCluster by comprehensively comparing it with dropout and its representative variants, such as SpatialDropout, Gaussian dropout and DropEdge, on skeleton-based action recognition.
Sampling from manifold-restricted distributions using tangent bundle projections

A common problem in Bayesian inference is the sampling of target probability distributions at sufficient resolution and accuracy to estimate the probability density and to compute credible regions. Often by construction, many target distributions can be expressed as some higher-dimensional closed-form distribution with parametrically constrained variables, i.e., one that is restricted to a smooth submanifold of Euclidean space. I propose a derivative-based importance sampling framework for such distributions. A base set of n samples from the target distribution is used to map out the tangent bundle of the manifold, and to seed nm additional points that are projected onto the tangent bundle and weighted appropriately. The method essentially acts as an upsampling complement to any standard algorithm. It is designed for the efficient production of approximate high-resolution histograms from manifold-restricted Gaussian distributions and can provide large computational savings when sampling directly from the target distribution is expensive.
The state of website security response headers in Indonesia banking

The modern banking industry provides its best services through various transaction channels, one of them and currently plays an important role is an online service using a website. This online service presents new problems, especially related to malware attacks and data stealing. The risk of this attack can cause financial losses for banks and bank customers. There are many ways in banking to protect their online services to reduce and mitigate the risks. Banking as a service provider does invest in hardware and software security to achieve safe and comfortable facilities for customers. On the client-side, the role of the browser on the client becomes essential because the process of data theft through web vulnerabilities often occurs on the client-side. On the server-side, they are adding declarations of security response headers on the web to reduce the risk of attack. The security response header is indicating the commitment of web service providers to provide a secure environment. In this paper, the security header implementation will be measured on the banking website in Indonesia.
Every Layer Counts: Multi-Layer Multi-Head Attention for Neural Machine Translation

The neural framework employed for the task of neural machine translation (NMT) usually consists of a stack of multiple encoding and decoding layers.However, only the source feature representation from the top-level encoder layer is leveraged by the decoder subnetwork during the generation of target sequence.These models do not fully exploit the useful source representations learned by the lower-level encoder layers.Furthermore, there is no guarantee that the top-level encoder layer encodes all the necessary source information required by the decoder for the target generation.Inspired by recent advances in deep representation learning, this paper proposes a Multi-Layer Multi-Head Attention (MLMHA) module to exploit the different source representations from the multi-layer encoder subnetwork.Specifically, the decoder is allowed a more direct access to multiple encoder layers during the target generation.This technique further improves the translation performance of the model.Also, exposing multiple encoder layers enhances the flow of gradient information between the two subnetworks.Experimental results on two IWSLT language translation tasks (Spanish-English and English-Vietnamese) and WMT'14 English-German demonstrate the effectiveness of allowing the decoder access to representations from multiple encoder layers.Specifically, the MLMHA approaches explored in this paper achieve improvements up to +0.71, +0.75 and +0.49BLEU points over the Transformer baseline model on the English-German, Spanish-English, and English-Vietnamese translation tasks respectively.
Attention-Based Method for Categorizing Different Types of Online Harassment Language

In the era of social media and networking platforms, Twitter has been doomed for abuse and harassment toward users specifically women. Monitoring the contents including sexism and sexual harassment in traditional media is easier than monitoring on the online social media platforms like Twitter, because of the large amount of user generated content in these media. So, the research about the automated detection of content containing sexual or racist harassment is an important issue and could be the basis for removing that content or flagging it for human evaluation. Previous studies have been focused on collecting data about sexism and racism in very broad terms. However, there is no much study focusing on different types of online harassment attracting natural language processing techniques. In this work, we present an multi-attention based approach for the detection of different types of harassment in tweets. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classification specific multi-attention mechanism. Moreover, we tackle the problem of imbalanced data, using a back-translation method. Finally, we present a comparison between different approaches based on the Recurrent Neural Networks.
Soft bodies as input reservoir: role of softness from the viewpoint of reservoir computing

Biological systems are composed of a large number of continuum elements and they effectively realize their flexible and smooth functionalities by capitalizing on the rich dynamics occurring in their soft bodies. In particular, the diverse spatiotemporal patterns of the continuum body have recently been demonstrated as highly useful in implementing a certain class of computation, implying that soft bodies can work as computational devices like nervous systems. However, unlike nervous systems whose activities are basically chaotic and spontaneous, soft bodies are passive and eventually stop their movements without undergoing external force. In addition, both nervous systems and soft bodies are tightly coupled in biological systems and seamlessly contribute to establishing the functionalities as a whole, making it difficult to clarify the specific roles of each element and the degrees of contribution. In this paper, we quantitatively investigate a general role of the soft bodies by introducing a new concept called <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">input</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reservoir</sub> and examining the contribution of the passive elements to information processing. Input reservoir refers to a dynamical system that acts as a filter with a fading memory property whose output is projected onto a chaotic dynamical system and works as an interface between the external stimulus and the chaotic system. Soft bodies can be considered as one type of input reservoir in terms of function because soft bodies are placed between the environment and the chaotic nervous system. We show that the performance of a certain type of temporal task can be improved by installing an input reservoir and training the internal weights of the connected chaotic neural network using a reservoir computing method called <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">innate</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">training</sub> . Moreover, we demonstrate that complicated spatiotemporal patterns collected by sensing the soft body dynamics significantly contribute to increasing the computational capacity of the whole by projecting them onto a chaotic neural network and modifying the network weights by innate training. These results imply that soft continuum elements in biological systems have a significant effect in the system's information processing even when the soft elements are firmly joined with the nervous system.
Dragon Crypto – An Innovative Cryptosystem

In recent years cyber-attacks are continuously developing. This means that hackers can find their way around the traditional cryptosystems. This calls for new and more secure cryptosystems to take their place. This paper outlines a new cryptosystem based on the dragon curve fractal. The security level of this scheme is based on multiple private keys, that are crucial for effective encryption and decryption of data. This paper discusses, how core concepts emerging from fractal geometry can be used as a trapdoor function for this cryptosystem.
Filtering and Storing User Preferred Data: an Apache Spark Based Approach

This work-in-progress paper focuses on a filtering technique based on user preferences. It uses parallel processing and machine learning to effectively filter out user preferred data from a large raw data set. Although large volumes of data are generated, a user is often interested in only a select type (classes) of such data. The motivation behind this research is to devise an effective and efficient filtering technique for extracting user preferred data from large data sets. Storing only filtered data and discarding the remaining data can decrease latency in searching for specific information within a data set. It can also decrease the size of the storage required for storing these data. Such a filtering method that uses data classification techniques can give rise to high processing latencies. An algorithm and system that use both parallel processing and machine learning are presented. A proof-of-concept prototype is built on the Apache Spark parallel processing platform. Analysis of the results of preliminary experiments demonstrates the viability of the investigated technique.
Blockchain-Based Secure Cloud Data Deduplication with Traceability

Data deduplication technology makes the same data only keep one physical copy in the cloud, which can effectively eliminate data redundancy and achieve efficient storage. Data deduplication, especially encrypted data duplication, has become a research hotspot. Convergent encryption can effectively solve the existing problem of encrypted data deduplication. However, the cloud server cannot access plaintext data in the ciphertext environment, and it cannot check the consistency of the ciphertext and file label. Therefore, when the duplicate faking attack occurs, how to effectively trace malicious users becomes a critical problem. In this paper, we firstly introduce the blockchain technology into the scenario of secure data deduplication, and construct a specific deduplication scheme to solve the problem of malicious user tracing. We make use of the traceability of the blockchain to form a tamper-proof data chain. When the duplicate faking attack occurs, we can trace the identity of the malicious user and find out all the data files stored in the cloud server.
Aggregation in Value-Based Argumentation Frameworks

Value-based argumentation enhances a classical abstract argumentation graph - in which arguments are modelled as nodes connected by directed arrows called attacks - with labels on arguments, called values, and an ordering on values, called audience, to provide a more fine-grained justification of the attack relation. With more than one agent facing such an argumentation problem, agents may differ in their ranking of values. When needing to reach a collective view, such agents face a dilemma between two equally justifiable approaches: aggregating their views at the level of values, or aggregating their attack relations, remaining therefore at the level of the graphs. We explore the strenghts and limitations of both approaches, employing techniques from preference aggregation and graph aggregation, and propose a third possibility aggregating rankings extracted from given attack relations.
Unsupervised cycle-consistent person pose transfer

Person pose transfer, i.e., transferring the pose of a given person to a target pose, is a challenging task due to the complex interplay of appearance, pose, and background. Most of the previous works adopted the supervised framework and required paired person images with the same identity and different poses, which largely limits their applications. Besides, the background of the generated image may be altered from the original one due to some over-fitting issues, which is unfavorable for the pose transfer task. To tackle the above problems, we propose an unsupervised cycle-consistent person pose transfer approach. It is trained with unpaired cross-identity person images and can well preserve the background information. Compared with previous methods, our proposed approach achieves better results in the cross-identity person pose transfer task and similar results in self-identity one. Moreover, our method can serve as an effective data augmentation scheme for person recognition tasks, which is validated by extensive experiments on pedestrian re-identification and detection.
The Experience of the FRC CSC RAS in Creating a Digital Platform for Scientific Research in the Cause of Digital Transformation

The article presents the results of scientific research of the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences in terms of the formation and use of digital platforms for scientific research. The main factors and prerequisites that determine the relevance of this direction in the framework of the processes of digital transformation of society are given. The high-performance platform of the FRC CSC RAS is described, including the technologies for its functioning and the organization of parallel computing in the cloud. The algorithms for managing service-oriented processes in hybrid computing environments of digital platforms are shown. For the control loop of a multi-disciplinary and multidisciplinary digital platform, methods have been developed for semantic modeling of processes for converting data on components in the structure of processed information. When creating models of data processing and transmission, we used the experience of applying the concepts of Information Centric Network and Intent-Based Networking. The proposed new method of reproducing the processes of information transfer provides coordinated management in a single information management environment of the ecosystem of the digital platform. Methodical approaches to assessing the effectiveness of a high-performance platform for scientific research based on the accumulated backlog and experience in creating information management systems, situational centers, management decision support systems are presented.
Machine Learning in Object Recognition and Segmentation by Certain Class Neural Networks

In this article we present a new approach to object segmentation. We use a Convolutional Neural Network with a specific architecture called U-net to recognize an object in a colored image and efficiently segment it with a new approach which we find more accurate than the one we applied in our earlier work. We focused on fast and memory-efficient segmentation process applied after training of U-net, using a single point to represent the object. The main purpose of this work is to prove that proposed simplified object representation in training of neural network leads to accurate results in meaning of counting instances of an object and in a field of segmentation process. We present results of series of experiments analyzing accuracy of computations achieved using our algorithm on nine neural networks trained on different values of various parameters.