Reviewerly/exHarmony huggingface dataset:
Novel Image Encryption Using a Pseudoset Generated by Chaotic Permutation Multicircular Shrinking With a Gradual Deletion of the Input Set

High-level security with a large keyspace and a short processing time is needed in digital image encryption. Generally, an encryption method that produces a large keyspace is characterized by a relatively slow encryption process. In this paper, we propose a new image encryption method that uses two chaotic pseudosets. A gradual deletion of the input set (GDIS) is introduced to enhance the process of chaotic permutation multicircular shrinking (CPMCS), herein referred to as GDIS CPMCS, to diffuse the image pixels and control the shift distance of the row and column rotations of an image. The proposed encryption scheme offers some advantages: it has a larger keyspace than the referenced image encryption schemes and a shorter processing time than CPMCS. The processing time of the proposed image encryption method with the GDIS CPMCS algorithm is 16.7 times faster for a gray image and 43 times faster for a color image than that in our previous work. Based on histogram and entropy analyses, the proposed scheme is also robust to statistical analysis. Moreover, the ciphered image has a very high degree of randomness according to the National Institute of Standards and Technology (NIST) randomness test results. In terms of differential analysis, a one-bit change in the original image leads to a substantial change in the ciphered image, as indicated by the unified average change intensity (UACI) and number of pixel change rate (NPCR) scores of 33.45% and 99.61%, respectively. Furthermore, GDIS CPMCS is robust to salt-and-pepper, Poisson, Gaussian, and speckle noise, with peak signal-to-noise ratios (PSNRs) higher than 14. The scheme is also robust to data loss since a reconstructed image with 50% data loss can be recognized, as indicated by a PSNR of 11.4.
COMBINING AGILE APPROACHES AND RISK MANAGEMENT FOR MECHATRONIC PRODUCT DEVELOPMENT – A CASE STUDY

Abstract As the benefits and potentials of agile approaches become increasingly clearer, also hardware product development seeks to apply those methods and procedures. However, mechatronic hardware products are often subject to stricter safety regulations so that a consequent risk management is essential. This paper presents a risk management method suitable for agile product development projects. The method together with an adapted agile procedure were applied and evaluated in a successful industrial case study. This lead to the extended application of agile approaches in other parts of the company.
Mind the Gap - A Benchmark for Dense Depth Prediction Beyond Lidar

The large interest in autonomous vehicles is a significant driver for computer vision research. Current deep learning approaches are capable of impressive feats, like dense full frame depth prediction from a single image. While impressive results have been achieved, it is not yet clear if they are sufficient for autonomous driving. The problem remains that existing evaluation benchmarks and metrics are not yet capable of fully addressing this issue. This work takes a step towards answering this question. Current evaluation methods are incapable of proving or refuting suitability for potentially hazardous real world situations. This is due to a) the large gaps in the currently used Lidar ground truth data, which cannot test many difficult and relevant cases and b) the global summary metrics used, which are intangible with respect to rigorous performance guarantees. In this work we provide a new benchmark based on commercially available dense light-field depth data, which closes these gaps in the evaluation. We implement domain-specific and interpretable error metrics, which allow for strict assertions over the performance of tested methods. The leaderboard for dense depth prediction is publicly available. The approach is also transferable to other depth estimation tasks. Such stringent evaluations are indispensable when testing and demonstrating performance for potentially hazardous applications like autonomous driving, and are a critical aspect for the assessment of autonomous systems by regulatory bodies as well as for public acceptance.
A New Information-Theoretic Method for Advertisement Conversion Rate Prediction for Large-Scale Sparse Data Based on Deep Learning

With the development of online advertising technology, the accurate targeted advertising based on user preferences is obviously more suitable both for the market and users. The amount of conversion can be properly increased by predicting the user's purchasing intention based on the advertising Conversion Rate (CVR). According to the high-dimensional and sparse characteristics of the historical behavior sequences, this paper proposes a LSLM_LSTM model, which is for the advertising CVR prediction based on large-scale sparse data. This model aims at minimizing the loss, utilizing the Adaptive Moment Estimation (Adam) optimization algorithm to mine the nonlinear patterns hidden in the data automatically. Through the experimental comparison with a variety of typical CVR prediction models, it is found that the proposed LSLM_LSTM model can utilize the time series characteristics of user behavior sequences more effectively, as well as mine the potential relationship hidden in the features, which brings higher accuracy and trains faster compared to those with consideration of only low or high order features.
Modularized Multi-Stage Binarized Deep-Model Compression and Performance Recovery

Despite the approaching Internet of Things (IoT) era, most smart devices cannot directly execute complicated deep neural networks (DNNs) due to their limited memory sizes and computing power. Even with model compression, compressing and executing a non-binarized DNN model often needs a codebook for weight calculation, which typically requires acceleration from a Graphics Processing Unit (GPU). Parameter binarization is one commonly used method to reduce computing time and space, but it often seriously compromises the accuracy of the binarized DNN (BNN), which tends to adopt single-stage non-modularized design. To address these issues, we propose a modularized multi-stage BNN model compression with flexible combination of parameter and structure compression to minimize model size while maintaining maximum accuracy possible. The compressed BNNs do not need any codebooks so they better fit into embedded platforms without any GPU acceleration. From our evaluations, the size of a BNN model can be reduced to 1/57 size at the cost of only about a 5% decrease in accuracy, which outperforms the state-of-the-art single-stage compression on BNN models. Furthermore, the proposed method can recover the accuracy of a BNN by 5% even after its size has been further compressed to approximately half its size, demonstrating its potentials in leveraging edge intelligence.
Decision Support System with K-Means Clustering Algorithm for Detecting the Optimal Store Location Based on Social Network Events

Nowadays, the business market is more complicated and comprises many challenges; it became more competitive and surrounded by high-risk patterns. Seeking for new technologies and adopting innovation is becoming an important and crucial issue to eliminate the complexity of the decision-making process and failure probability. Decision support system (DSS) is a computerized system that encompasses mathematical and analytical models, knowledge base and a user interface to help managers for making better decisions. This research aims to develop a decision support system based on K-means clustering algorithm to detect the optimal store location through social network events. Also, this research explains how to extract data from one social network channel "Instagram" using the "Octoparse API" as a web data extraction tool. K-means algorithm identifies k- number of centroids, and allocates every data point to the nearest cluster. As a result, we analyzed 12754 posts started on the 1st of January 2019. Cleaned data are transformed using Minimax and K-means algorithms. As an output, we have got json format data file with centres which are placed on the map to provide a better understanding. The Result of this research is a visualized map pointed with places to define the optimal location of a specific store at the selected region. The practical value of this DSS tool is to help users to make a more valuable and accurate decision which lead to a decrease in the probability of ineffective business decision and minimize the business losses.
Optimizing the Role of Orchestrator for Integration Aneka PaaS with AWS Cloud

The adoption of Hybrid cloud in enterprises has grown rapidly every year. Hybrid cloud solution implements the better of Private and Public cloud while proving that running of multiple application in different environment to provide segregation of personal useful data into a private environment from the raw meta data to reside on the cost-effective public cloud. This paper covers the deployment of hybrid cloud application with the use of Aneka PaaS (Platform as a Service) computing and Amazon Web Services Elastic Cloud Computing (AWS EC2) cloud service. The deployed application is automated using Orchestra- tor- an automation tool that allows EC2 resources to be deployed that enables uninterrupted running off application without having to take down the application during the situations of low compute resources. Aneka allows for the monitoring of resources to allow Optimization and billing for respective on demand resource usage. The proposed Orchestrator flow algorithm allows the application to load balance computing resources between the private and public cloud.
Monitoring functional status using a wearable real-time locating technology

Sensor technologies enable real-time, continuous, and objective monitoring of activity and functioning in later life. In long-term care, timely assessment of functional status is needed to prevent falls and other acute events. However, the electronic forms and paper and pencil tools currently used are time-consuming and conducted too infrequently (e.g., every 6 months) to provide the sensitivity and specificity required. Staff are also unable to detect subtle changes in functioning through observation alone. The purpose of this paper is to discuss the use of a wearable real-time locating system that utilizes ultra wideband radio technology to continuously and objectively measure activity and aspects of functional status. This paper discusses the associated conceptualization and development of the scoring algorithms, raw data transformation, use of traditional paper and pencil tools and electronic health record data to validate sensor data, and other tips for those interested in this type of wearable sensor technology.
Streaming statistical models via Merge &amp; Reduce

Abstract Merge &amp; Reduce is a general algorithmic scheme in the theory of data structures. Its main purpose is to transform static data structures—that support only queries—into dynamic data structures—that allow insertions of new elements—with as little overhead as possible. This can be used to turn classic offline algorithms for summarizing and analyzing data into streaming algorithms. We transfer these ideas to the setting of statistical data analysis in streaming environments. Our approach is conceptually different from previous settings where Merge &amp; Reduce has been employed. Instead of summarizing the data, we combine the Merge &amp; Reduce framework directly with statistical models. This enables performing computationally demanding data analysis tasks on massive data sets. The computations are divided into small tractable batches whose size is independent of the total number of observations n . The results are combined in a structured way at the cost of a bounded $$O(\log n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mo>log</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> factor in their memory requirements. It is only necessary, though nontrivial, to choose an appropriate statistical model and design merge and reduce operations on a casewise basis for the specific type of model. We illustrate our Merge &amp; Reduce schemes on simulated and real-world data employing (Bayesian) linear regression models, Gaussian mixture models and generalized linear models.
Evaluating Low-Memory GEMMs for Convolutional Neural Network Inference on FPGAs

FPGAs are becoming significant for implementing low-latency convolutional neural networks, because of performance demands and power constraints. Conventional implementations of convolutional layers are usually direct convolution, involving nested loops over channels, feature maps, and filters. Explicit general matrix multiplications (GEMMs) cost extra memory space, and the limited on-chip RAMs prevent an efficient GEMM-based implementation. In this paper, we evaluate a low-memory method of GEMMs on FPGAs based systolic arrays. We design a novel accelerator to save the bandwidth and increase the parallelism. We evaluate our design on MobileNet V1 and Inception V4. Our implementation achieves a throughput of around 3.5 TOP/s for both models. We also reduce the memory usage by 21% compared to explicit GEMM implementation for MobileNet V1 and 44% for Inception V4.
A Word Embedding Model for Analyzing Patterns and Their Distributional Semantics

Recent advances in natural language processing have catalysed active research in designing algorithms to generate contextual vector representations of words, or word embedding, in the machine learning and computational linguistics community. Existing works pay little attention to patterns of words, which encode rich semantic information and impose semantic constraints on a word’s context. This paper explores the feasibility of incorporating word embedding with pattern grammar, a grammar model to describe the syntactic environment of lexical items. Specifically, this research develops a method to extract patterns with semantic information of word embedding and investigates the statistical regularities and distributional semantics of the extracted patterns. The major results of this paper are as follows. Experiments on the LCMC Chinese corpus reveal that the frequency of patterns follows Zipf’s hypothesis, and the frequency and pattern length are inversely related. Therefore, the proposed method enables the study of distributional properties of patterns in large-scale corpora. Furthermore, experiments illustrate that our extracted patterns impose semantic constraints on context, proving that patterns encode rich semantic and contextual information. This sheds light on the potential applications of pattern-based word embedding in a wide range of natural language processing tasks.
AGILE MEETS PLAN-DRIVEN – HYBRID APPROACHES IN PRODUCT DEVELOPMENT: A SYSTEMATIC LITERATURE REVIEW

Abstract More than 15 years after the publication of the agile manifesto of software development, agile development approaches have also reached the processes of physical product development. Because of the boundary conditions and requirements here, which differ strongly from those of pure software development, these approaches often reach their limits. However, research and practice have quickly recognized that hybrid approaches integrate the strengths of agile and plan-driven development. This paper presents 25 hybrid development approaches that have been identified in a Systematic Literature Review.
A new chaotic map development through the composition of the MS Map and the Dyadic Transformation Map

Abstract In this paper, a new chaotic map is proposed, that is obtained from the composition of two chaotic maps, that is, the MS Map and the Dyadic Transformation Map. The composition process starts from the MS Map, followed by the Dyadic Transformation Map. The resulting composition is a new chaotic function. This is shown by the bifurcation diagram analysis result, Lyapunov Exponents, and the NIST randomness test. The bifurcation diagram shows that the best densities occur at λ ∈ (0.3, 5) and r = 3.8. The Lyapunov Exponents has nonnegative values for r ∈ [1, 4]. The NIST randomness test with initial value and parameters x 0 = 0.6, r = 3.8, and λ = 3.5 shows that the new chaotic map passes 14 out of 16 NIST tests.
Hierarchical Dirichlet Multinomial Allocation Model for Multi-Source Document Clustering

Mining a document structure from multiple data sources in terms of their underlying topics has become an important task of document clustering. The traditional document clustering approach cannot be applied directly to the multi-source document clustering problem. There are three typical difficulties: 1) The topics of different data sources are related but not the same. 2) Usually, each data source has its own focus on topics. 3) The number of clusters of the data sources are not necessarily the same and are not known beforehand. In this paper, based on our previous research, we design a novel multi-source document clustering model, namely, the hierarchical Dirichlet multinomial allocation (HDMA) model, to solve all the above problems. The HDMA model is investigated with a two-step hierarchical topic generation process. Topics are learnt to share their general characteristics across data source, while at the same time preserve the local characteristics of the data source. Each data source is applied with an exclusive topic partition to learn the source-level topic emphasis. A Gibbs sampling algorithm is then used to learn the number of clusters for each data source as well as the parameters of the HDMA model at the same time. Experimental results demonstrate that the HDMA model is effective.
Artificial Object Images Synthesis in Underwater Robot Vision System

the article discusses the features of the synthesis of images of artificial objects with the aim of their search and recognition in the vision system of an underwater robot. The results of research and development to given: a database - a classifier of features that allows you to store information about the reference underwater objects; software module for generating images of reference objects and their insertion in a sonar image file. In the modeling program for the synthesis of underwater objects in the HBO image, the algorithms for introducing objects used by taking into account the peculiarities of the formation of HBO images by sonar. The program allows implementing several objects of spherical, cylindrical and cubic shapes with specified parameters, which allows simulating the situation of search for an artificial object according to given geometric features against the background of other objects. The developed software allows simulating underwater objects according to the characteristics close to real objects with the help of their preliminary processing and subsequent implementation in real HBO images
Error-tolerant label prior for interactive image segmentation

User interactions are generally utilized to impose the hard constraint and estimate the label prior probability in the interactive image segmentation methods. The conventional interactive approaches cannot work well when the user inputs contain incorrect marks. The existing error tolerance methods mainly focus on the interaction itself and try to eliminate the influence of incorrect hard constraint, which however ignore the label prior estimation. This paper mainly focuses on solving the label prior estimation problem when error marks appear. The prior probability is generally defined as the matching degree between pixels and the cluster centers of marked pixels. Incorrect interaction leads to the formation of incorrect clusters. Therefore, a reliability learning model is constructed in this paper by 1) assigning smaller weighting factors to incorrect clusters, 2) assigning larger weighting factors to correct clusters with higher matching degree. Accurate label prior probability can be obtained by the weighted averaging. Then referring to the existing methods, an error-tolerant segmentation model is designed as a ratio energy function, which can both overcome the hard constraint and the label prior limitation with error marks. Experiments on the challenging Berkeley segmentation data set and Microsoft GrabCut database demonstrate the effectiveness of the proposed method.
Safe dependency atoms and possibility operators in team semantics

I consider the question of which dependencies are safe for a Team Semantics-based logic FO(D), in the sense that they do not increase its expressive power over sentences when added to it. I show that some dependencies, like totality, non-constancy and non-emptiness, are safe for all logics FO(D), and that other dependencies, like constancy, are not safe for FO(D) for some choices of D despite being strongly first order (that is, safe for FO(∅)). I furthermore show that the possibility operator ⋄ϕ, which holds in a team if and only if ϕ holds in some nonempty subteam, can be added to any logic FO(D) without increasing its expressive power over sentences.
DISCERNING BEHAVIOURAL DESIGN: A CONCEPTUAL MODEL

Abstract Behavioural design has emerged as an important domain of design practice and research due to its ability to deliver the desired outcomes beyond technical designs. Research on behavioural design is not successful in discerning it from other design domains, which is important for theory building. This paper discerns the unique characters of behavioural design by tracing the emergence of behaviour in design. Twelve interviews from six behaviour design cases belonging to four firms has been used to further discern the unique characteristics resulting into the conceptual model of behavioural design
A sparsity-promoting image decomposition model for depth recovery

This paper proposes a novel image decomposition model for scene depth recovery from low-quality depth measurements and its corresponding high resolution color image. Through our observation, the depth map mainly contains smooth regions separated by additive step discontinuities, and can be simultaneously decomposed into a local smooth surface and an approximately piecewise constant component. Therefore, the proposed unified model combines the least square polynomial approximation (for smooth surface) and a sparsity-promoting prior (for piecewise constant) to better portray the 2D depth signal intrinsically. As we know, the representation of the piecewise constant signal in gradient domain is extremely sparse. Previous researches using total variation filter based on L1-norm or Lp-norm (0 < p < 1) are both sub-optimal when addressing the tradeoff between enhancing the sparsity and keeping the model convex. We propose a novel non-convex penalty based on Moreau envelope, which promotes the prior sparsity and simultaneously maintains the convexity of the whole model for each variable. We prove the convexity of the proposed model and give the convergence analysis of the algorithm. We also introduce an iterative reweighted strategy applied on the sparsity prior to deal with the depth-color inconsistent problem and to locate the depth boundaries. Moreover, we provide an accelerated algorithm to deal with the problem of non-uniform down-sampling when transforming the depth observation matrix into the Fourier domain for fast processing. Experimental results demonstrate that the proposed method can handle various types of depth degradation and achieve promising performance in terms of recovery accuracy and running time.