Reviewerly/exHarmony huggingface dataset:
Detecting dense text in natural images

Most existing text detection methods are mainly motivated by deep learning‐based object detection approaches, which may result in serious overlapping between detected text lines, especially in dense text scenarios. It is because text boxes are not commonly overlapped, as different from general objects in natural scenes. Moreover, text detection requires higher localisation accuracy than object detection. To tackle these problems, the authors propose a novel dense text detection network (DTDN) to localise tighter text lines without overlapping. Their main novelties are: (i) propose an intersection‐over‐union overlap loss, which considers correlations between one anchor and GT boxes and measures how many text areas one anchor contains, (ii) propose a novel anchor sample selection strategy, named CMax‐OMin, to select tighter positive samples for training. CMax‐OMin strategy not only considers whether an anchor has the largest overlap with its corresponding GT box (CMax), but also ensures the overlapping between one anchor and other GT boxes as little as possible (OMin). Besides, they train a bounding‐box regressor as post‐processing to further improve text localisation performance. Experiments on scene text benchmark datasets and their proposed dense text dataset demonstrate that the proposed DTDN achieves competitive performance, especially for dense text scenarios.
The Difference Between a Click and a Cart-Add: Learning Interaction-Specific Embeddings

For large-scale online marketplaces with over millions of items, users come to rely on personalized recommendations to find relevant items from their massive inventory. One hallmark of the shopping experience in such online marketplaces is the many ways a user can interact with an item they are interested in: they can click it, favorite it, add it to cart, purchase it, etc. We hypothesize that the different ways in which a user interacts with an item indicates different kinds of intent. Consequently, a user’s recommendations should be based not only on items from their past activity, but also the way in which they interacted with these items. Co-occurrence based methods have been successfully used to give recommendations that incorporate interaction types, such as the popular “Because you purchased X, you may also purchase Y”. In this paper, we propose a novel method that generalizes upon the co-occurrence methods to learn interaction-based item embeddings that encode the co-occurrence patterns of not only the item itself, but also the interaction type. The learned embeddings provide a convenient way of approximating the likelihood that one item-interaction pair would co-occur with another by way of a simple inner product. To show their effectiveness, we deploy the interaction-based embeddings in an industry-scale recommendation system that serves live traffic on Etsy.com. We find that taking interaction types into account shows significant improvements in accurately modeling user shopping behavior for both online and offline metrics.
An Operation-Minimized FPGA Accelerator Design by Dynamically Exploiting Sparsity in CNN Winograd Transform

To address the challenges of high computational complexity incurred in deep convolutional neural networks (CNNs), implementations by both the Fast Winograd Transform algorithm and the sparsity exploration method have been attempted in order to reduce the hardware operation overhead. Yet, the previous studies have been mainly concentrated on dealing with the fixed sparsity patterns of the weight filter. In this paper, we focus the effort specifically towards exploiting the characteristics of varying sparsity patterns existing in the input/output Activations of the Winograd-transformed network. To this end, a dynamically compressing approach for multiplication with the sparsity-changing matrix is proposed. Such a processing flow features in data indexing and restoring. Because they are dynamically generated during the inference process, the inputs/outputs are highly dependent on the actual data being processed. Unlike the static pattern of a weight matrix just requiring the offline compression, a real-time compression processor module is devised and employed to deal with the dynamic matrix pattern for updating online the inputs/outputs within FPGAs Block RAMs. In the next layer computation, only the valid data needs to be restored by following the necessary index information and broadcasting to those corresponding sparse weight matrices, which in turn generates the next batch inputs/outputs. The design has realized a typical CNN such as VGG on Xilinx Virtex 7 FPGA device for verification and achieves an overall performance of 629.4 GOPS. Meanwhile, the preliminary experimental results demonstrate 2.2 (up to 5.5) times improvement in terms of equivalent GOPS per DSP Block achieved with our adaptive sparsity exploitation approach, when compared to the other conventional counterparts.
Time Series Human Motion Prediction Using RGB Camera and OpenPose

The report projects that by 2050, the population aged 60 and above will reach 2.1 billion. This aging society is more likely to suffer from locomotive syndrome. In order to reduce the spread of locomotive syndrome, it is best to increase awareness before the citizens become elderly. We propose the system to predict human motion as the first step to realize the locomotive syndrome estimation. Previous researches were using the Kinect camera which has a depth sensor that the camera used to detect the pose of a human body. However, in this research we are using an RGB camera as a reliable alternative. We set a goal to predict 1 second ahead of the motion which includes simple motions such as hand gesture and walking movement. We used OpenPose to extract the features of a human body pose including 14 points. YOLOv3 is used to crop the main feature in the frames before OpenPose process the frame. Distance and direction which are calculated from the features by comparing two consecutive frames as the input of Recurrent Neural Network Long Short-term Memory (RNN-LSTM) model and Kalman Filter. Mostly, Kalman Filter show better accuracy then RNN-LSTM and based on the human motions, motion such as hand gesture and moving to the right side are easier than more complex motion like hand gesture and moving to the left side. We confirmed the validity of RGB-camera based method in the simple human motion case from the result.
Developing Consumer Electronics Software Using A-SPICE: Infotainment Development Experience

This article presents one experience with the design and implementation of a consumer electronics (CE) software module using automotive software process improvement and capability determination (A-SPICE) methodology. Electronic program guide (EPG) engine software module is a very stable, configurable product that ensures high performance and satisfies different television standards. It is designed to operate on a wide range of hardware platforms, especially in automotive multimedia systems. Since the EPG engine had to be integrated into the automotive infotainment system, that module was developed in compliance with the Hersteller Initiative Software-Manufacturer's software initiative metrics and A-SPICE standard. Application lifecycle management tool was used in the development process for project management and for generating data for analysis purposes. The software itself was tested during the whole project lifecycle using several static and dynamic code analysis methods. Difficulties concerning testing procedures, code quality, and complying with A-SPICE standard were detected and resolved. Since the CEs software development is not that strict as automotive one, this article points out the advantages of introducing the A-SPICE methodology in the CE field.
Fraud detection using heavy hitters

The high asymmetry of international termination rates, where calls are charged with higher values, are fertile ground for the appearance of frauds in Telecom Companies. In this paper, we present three different and complementary solutions for a real problem called Interconnect Bypass Fraud. This problem is one of the most common in the telecommunication domain and can be detected by the occurrence of abnormal behaviours from specific numbers. Our goal is to detect as soon as possible numbers with abnormal behaviours, e.g. bursts of calls, repetitions and mirror behaviours. Based on this assumption, we propose: (i) the adoption of a new fast forgetting technique that works together with the Lossy Counting algorithm; (ii) the proposal of a single pass hierarchical heavy hitter algorithm that also contains a forgetting technique; and (iii) the application of the HyperLogLog sketches for each phone number. We used the heavy hitters to detect abnormal behaviours, e.g. burst of calls, repetition and mirror. The hierarchical heavy hitters algorithm is used to detect the numbers that make calls for a huge set of destinations and destination numbers that receives a huge set of calls to provoke a denial of service. Additionally, to detect the cardinality of destination numbers of each origin number we use the HyperLogLog algorithm. The results shows that these three approaches combined complements the techniques used by the telecom company and make the fraud task more difficult.
Additional Vocational Education During Realization of Federal Projects

Breakthrough development of the Russian economy is the global goal which is continuously mentioned by the President in his speeches. Personnel potential of specialists of real sector of the economy has the determinant value for the successful realization of this goal. Specialists with great experience and knowledge in their field of work are of great value. There are a lot of people of pre-retirement age among them. A large number of aged specialists need support to keep in line with the modern trends in work skills improvement in conditions of total automation and digitization because of age and other limitations. VSTU actively takes part in the realization of the Federal project for advanced vocational education of pre-retirement age people and advanced training of companies’ personnel within the Federal project aimed at improvement of work efficiency and employment. To reach the goal the University has developed and implemented several dozen of advanced training programs especially oriented on the most effective and innovative solutions of professional problems by personnel thus contributing to meeting the challenges of the development of the modern economy.
Enhancing Privacy in a Blockchain-based Public Key Infrastructure

With the increasing need for, and the popularity of asymmetric cryptosystems, the need for a secure and efficient means of disseminating authentic public keys in a closed network is also on the increase[1]. Blockchain technology has been proposed as a solution to the security problems in the conventional models of a Public Key Infrastructure, as it offers transparency in the issuance of certificates and eliminates single points of failure. Blockchain excludes manipulation by making it computationally infeasible to edit a public key once it is published, thereby facilitating frequent key-updates. But this raises a concern about two issues - privacy and the proof of ownership, and ensuring one excludes the other. To reconcile these two seemingly irreconcilable problems, we propose a new key generation and update procedure, which will enable an identity to update its key with privacy and also to flawlessly prove the ownership of its past keys. Elliptic Curve Cryptographic methods are incorporated to make the cryptosystem secure. Further, the existence check of identities is optimised using multiple instances of bloom filter probabilistic data structures.
The Adoption of Automation in Cyber Forensics

Cybercrime has increased considerably over the past years, emphasizing the need for efficient investigations. Currently, some tools and processes are manual and lead to long and inaccurate investigations. This report provides a descriptive review of the of published research in the field of cyber forensics, in order to identify current practices, challenges and the adoption of automation. A pragmatist approach was taken to accommodate the multitude of theories and views presented in the literature. The research illustrates how the use of technology could simplify an investigators task and solving difficulties that currently exist in cyber forensics. The paper concludes in motivating for the use of automated practices in the cyber forensic process.
Blockchain in Action: Real-World Applications

Now that we are nearing the end of our exploration of the workings and details of blockchainBlockchain, it would be worthwhile to overview instances in which this technology has lent itself well to execution and been applied to advantage. As is often the case with potentially disruptive innovations, adoption proceeds slowly as actors learn about the technology and how best to harness its potential.
Blockchain Properties for Near-Planetary, Interplanetary, and Metaplanetary Space Domains

Blockchain technologies have demonstrated new and interesting ways to construct terrestrial economies, including the well-known public cryptocurrencies, but also as a means to facilitate business-to-business and business-to-consumer transactions. Some have recently proposed uses for blockchains in space. This paper identifies desirable blockchain properties that could be used to construct future in-space economies. Three general economic domains are anticipated, each with their own communications limitations that drive the selection of applicable blockchain properties: operations in orbits around planetary bodies where real-time communication is possible (for example, within cislunar space around Earth or cisdeimotic space around Mars), operations across interplanetary space between spheres of influence where pseudo-real-time communication is possible (for example, Earth–Mars or Earth–asteroids), and operations in metaplanetary space within the solar system but beyond the limit of pseudo-real-time communication. Blockchain properties are suggested for each domain, with a focus on the selection of blockchain consensus algorithms. An evaluation is performed on the readiness of extant technologies to operate in each domain.
Training of Specialists in On-Board Communication Systems

The article is devoted to improving the quality of management of an educational organization by automating personalized data collection, storage and ranking in the preparation of specialists in the on-Board communication systems. The authors have developed a module of the university's corporate system - an electronic portfolio of students, which implements a multi-criteria optimization method for calculating students' ratings based on the collected and stored data. The object of the study is multicriteria ranking methods. The subject of the research is the automated calculation of the student rating in the electronic portfolio. The aim of the study is to develop tools for collecting, storing and processing data on individual student achievements and the implementation of a mathematical method of multicriteria optimization for ranking students based on portfolio data. The practical significance of the study is to provide a tool for effective management of the learning process. Method and toolkit. A prototype of the module of the corporate system of the university “Electronic Portfolio” based on the developed configuration in “1C: Enterprise 8.3” is presented. For the ranking of students in the module for data analysis, a special case of the method of ranking alternatives is implemented - pairwise comparison according to their relative importance, using a unified scale of relations. Results. The authors described the scheme of functioning of is “Electronic portfolio”, showed the scheme of interaction of processes on formation of portfolio, presented the scheme of business processes at calculation of an individual rating. A fragment of the sample on which the performance of the multi-criteria optimization block was checked is shown. The criteria taking part in calculations and the rule of selection of alternatives for finding the optimal solution are de-scribed. Discussion and Conclusion. The paper presents the results of the IP block on ranking the list of students. The results of the calculations coincide with the practical results.
GPU Acceleration of Sparse Neural Networks

In this paper, we use graphics processing units (GPU) to accelerate sparse and arbitrary structured neural networks. Sparse networks have nodes in the network that are not fully connected with nodes in preceding and following layers, and arbitrary structure neural networks have different number of nodes in each layers. Sparse Neural networks with arbitrary structures are generally created in the processes like neural network pruning and evolutionary machine learning strategies. We show that we can gain significant speedup for full activation of such neural networks using graphical processing units. We do a prepossessing step to determine dependency groups for all the nodes in a network, and use that information to guide the progression of activation in the neural network. Then we compute activation for each nodes in its own separate thread in the GPU, which allows for massive parallelization. We use CUDA framework to implement our approach and compare the results of sequential and GPU implementations. Our results show that the activation of sparse neural networks lends very well to GPU acceleration and can help speed up machine learning strategies which generate such networks or other processes that have similar structure.
Fast and Accurate Estimation of Typed Graphlets

Typed graphlets are small typed (labeled, colored) induced subgraphs and were recently shown to be the fundamental building blocks of rich complex heterogeneous networks. In many applications, speed is more important than accuracy, and it is sufficient to trade-off a tiny amount of accuracy for a significantly faster method. In this work, we propose fast and accurate estimators for typed graphlets. The typed graphlet estimation techniques naturally support general heterogeneous graphs with any arbitrary number of types, which include bipartite, k-partite, k-star, labeled graphs, and attributed networks as special cases. The experiments demonstrate the effectiveness of the typed graphlet estimation techniques.
A Machine Learning Based Heuristic to Predict the Efficacy of Online Sale

It is difficult to decide upon the efficacy of an online sale simply from the discount offered on commodities. Different features have different influence on the price of a product which must be taken into consideration when determining the significance of a discount. In this paper we have proposed a machine learning based heuristic to quantify the "significance" of the discount offered on any commodity. Our proposed technique can quantify the significance of the discount based on features and the original price, and hence can guide a buyer during a sale season by predicting the efficacy of the sale. We have applied this technique on the Flipkart Summer Sale dataset using Support Vector Machine, which predicts the efficacy of the sale with an accuracy of 91.11%. Our result shows that very few mobile phones have a significant discount during the Flipkart Summer Sale.
A New Cryptographic Scheme Using the Chebyshev Polynomials

A secure environment is central to the growth and development of any nation. Crime and terrorism impede the stability and governance of any security sector. Hence, the need for security of information today has made cryptography to be very applicable in almost every sphere of life. Thus, constant improvement, modification, and formulation of new cryptographic schemes are keys to securing lives and belongings. In this work, we developed a new cryptographic scheme, employing Chebyshev polynomial as a fundamental component of the encrypting function and Laplace transforms as the transformational tool.
Combinatorial views on persistent characters in phylogenetics

The so-called binary perfect phylogeny with persistent characters has recently been thoroughly studied in computational biology as it is less restrictive than the well-known binary perfect phylogeny. Here, we focus on the notion of (binary) persistent characters, i.e. characters that can be realized on a phylogenetic tree by at most one 0→1 transition followed by at most one 1→0 transition in the tree, and analyze these characters under different aspects. First, we illustrate the connection between persistent characters and Maximum Parsimony, where we characterize persistent characters in terms of the first phase of the famous Fitch algorithm. Afterwards we focus on the number of persistent characters for a given phylogenetic tree. We show that this number solely depends on the balance of the tree. To be precise, we develop a formula for counting the number of persistent characters for a given phylogenetic tree based on an index of tree balance, namely the Sackin index. Lastly, we consider the question of how many (carefully chosen) binary characters together with their persistence status are needed to uniquely determine a phylogenetic tree and provide an upper bound for the number of characters needed.
Holistic Assessment of Structure Discovery Capabilities of Clustering Algorithms

Existing cluster validity indices often possess a similar bias as the clustering algorithm they were introduced for, e.g. to determine the optimal number of clusters. We suggest an efficient and holistic assessment of the structure discovery capabilities of clustering algorithms based on three criteria. We determine the robustness or stability of cluster assignments and interpret it as the confidence of the clustering algorithm in its result. This information is then used to label the data and evaluate the consistency of the stability-assessment with the notion of a cluster as an area of dense and separated data. The resulting criteria of stability, structure and consistency provide interpretable means to judge the capabilities of clustering algorithms without the typical biases of prominent indices, including the judgment of a clustering tendency.
Probability theory for analyzing nature-inspired algorithms

Probabilistic components are essential in nature-inspired algorithms because almost all metaheuristic algorithms use some forms of randomization and random initialization in terms of uniform distributions. This chapter introduces the fundamentals of probability distributions, Markov chains, Monte Carlo, and random walks. These concepts will form the foundations for algorithm analysis.
Selecting Representative Thumbnail Image and Video Clip from a Video via Bullet Screen

Finding representative thumbnails or key video clips in a long video usually requires laborious manual editing and compilation. Although there have been deep-learning-based methods that aim to understand videos and pictures, these approaches rely on a large number of computing resources and training data, and the results may still be unsatisfactory. This paper tackles the task of thumbnail selection and highlighting video selection from a different perspective — we leverage on the bullet screen, an emerging new feature on the online video streaming sites that are popular in East Asia, to select thumbnails and video clips. We compared the proposed method with a thumbnail and video clip selecting tool developed by KKStream, a leading streaming service provider in East Asia. We recruited 100 individuals to conduct subjective tests. The experimental results show that most participants are satisfied with the thumbnails and video clips selected by our method, suggesting that the bullet screen could be a valuable resource for video understanding.