Reviewerly/exHarmony huggingface dataset:
Software Product Management Fundamentals

Software Product Management is one of the most important and rewarding, and yet challenging, domains in the software industry. The benefits associated with Software Product Management excellence are significant and the notion of being a product-led organizationOrganization is a hot topic and a prerequisite to attracting the best talent in the industry. This chapter introduces and defines my view of Software Product Management after 20 years in the industry, where I started as a business analyst, contributing individually, to my current position as Head of Product, where I lead a product organization at SAPSAP today. We’ll discover how Software Product Management can relate to a forest ranger’s job and why Software Product Management is actually required as a practice in every software company and corporate IT organization. We will examine all the stakeholders involved in the process and how they relate to the six dimensions of software product management: Product Viability, Product Development, Go-to-Market/Product Marketing, Software Demonstrations and Trainings, The Market/Your Customers, and Organizational Maturity. In this chapter, we will also define the following terms: (1) Management and its special form of Software Product Management. (2) Product and the specialties of software products, digital products and data products. (3) Software Product Managers and the T-shaped superhero capes they wear. (4) Product teams and the role of product managers. The definitions and groundwork will be followed by an interpretation of the latest Product Management Festival survey (Trends & Benchmarks in Product Management, 2018), which provides brilliant insights into the current state of Software Product Management, the role of Software Product Managers and the importance of their managers.
Visual Dialog with Targeted Objects

Visual Dialog aims to exchange information of the image between a questioner and an answerer through asking and answering questions alternately. To generate an accurate response to the target question requires to understand the visual information of the image according to the semantic information of the question and dialog history. However, existing methods paid attention to the visual feature of the whole image while ignoring that certain objects in the image always convey specific semantic. In this paper, we propose a novel visual dialog approach to focus on objects in the image which contain both visual information of attributes and semantic information of categories. Besides, we design several selection methods with different types of semantic guidance of the question and dialog history to select relevant objects. We evaluate the proposed approach on two datasets VisDial v0.9 and VisDial v1.0. And the performance improvement demonstrates the effectiveness of our approach.
Methods for detecting manipulations in 3D scan data

While interest in using 3D scanning technology for crime scene investigation (CSI) has grown in recent years, a number of barriers still remain that prevent its wide adoption in the criminal justice system. One such barrier comes from the lack of tools that can validate a 3D scan and verify that it has not been manipulated. While a great deal of research has gone into the detection of manipulations for 2D images, the detection of manipulations for 3D scenes has yet to be fully realized. This paper introduces a series of techniques to detect if a 3D point cloud generated from a LiDAR scan has been subsequently manipulated. These techniques exploit fundamental structures inherent in the collection and storage of these types of data. While the proposed techniques are able to detect a number of different types of manipulations, their limitations are also discussed. The goal of this work is to provide a foundation for the creation of a validation toolkit that can ensure 3D scan data is valid and unaltered.
Advancing the theory and practice of system evaluation: a case study in geovisual analytics of social media

This paper advances the state-of-the-art in methodology design for empirical evaluation of (geo)visual analytics software. Specifically, we describe the process of design, development and application of a prototypical user study tailored to the evaluation of complex geovisual analytics tools that focus on social media analysis. We fist perform a synthesis of existing theory and best practices for software evaluation of comparable systems. We then demonstrate how the product of said synthesis – a methodological ‘check list’ – can be used to inform a proof-of-concept user study of an actual geovisual analytics software system. The resulting user study design accommodates for the use of real geographic social media datasets, the complexity of the intended analytical process, and for the learning challenges faced by the participants working with a fully-functional and mature geovisual analytics application, and is likely representative of a wide range of evaluation scenarios in (geo)visual analytics. A complete summary of all the study instruments is included to encourage their scrutiny, reuse and modification by others. Finally, we have discovered that participants’ curiosity and desire for autonomy played a noticeable role in the evaluation process – something not previously reported.
An Online Variational Inference and Ensemble Based Multi-label Classifier for Data Streams

Recently, multi-label classification algorithms have been increasingly required by a diversity of applications, such as text categorization, web, and social media mining. In particular, these applications often have streams of data coming continuously, and require learning and predicting done on-the-fly. In this paper, we introduce a scalable online variational inference based ensemble method for classifying multi-label data, where random projections are used to create the ensemble system. As a second-order generative method, the proposed classifier can effectively exploit the underlying structure of the data during learning. Experiments on several real-world datasets demonstrate the superior performance of our new method over several well-known methods in the literature.
Secure Cloud Data Using Hybrid Cryptographic Scheme

Storage and management of large user's data has become a key challenge in today's world. Cloud computing solves this problem by providing a large capacity storage area to store many user's data simultaneously at the same place. It provides the user with the facility to access its data at anytime, anywhere by just connecting to the network. This feature attracts many individual and organization to store their large data over cloud. It relieves the owner from its storage and management task as well as achieves great flexibility with economical cost. The data which owner wants to store over cloud may also contains some confidential data or information which requires extra security concern before storing it over un-trusted network. Security becomes a basic requirement before migrating any confidential data over cloud. In this paper, we proposed a hybrid symmetric encryption approach to provide more security for owner's data other than any single symmetric encryption algorithm. This use of hybrid approach makes data more secure and protect it from any malicious activity attended by intruder. Brute force attack also becomes impossible after using this proposed approach. Use of symmetric encryption also increases the processing ability as it has fast processing speed and efficient than asymmetric approach.
Building Tangible Augmented Reality Models for Use in Product Development

Abstract This paper discusses the issues involved with creating a Tangible Augmented Reality representation of a product so that it can be effectively and validly used to support the product design process. The development of a TAR representation entails building the physical model of the product, a digital model of the product, and developing interactions between the digital and physical representations. Different products have different user interactions, which makes developing TAR representation for products challenging since a new interaction needs to be mimicked. Challenges of developing TAR representations are discussed by investigating TAR representations of four consumer products: a space heater, a MP3 player, an electric can opener, and a seat assist cushion. The goal of TAR representations is to not only communicate the functionality of a product but also to improve the time efficiency of prototyping of physical products. Hence, having a good understanding of the user interactions that need to be developed and planning the representation for construction in the modeling software and AR software is crucial to making the process of developing a TAR representation efficient.
A novel method for Convolutional Neural Architecture Generation with memory limitation

A novel algorithm-based method for Neural Architecture Generation is introduced in this paper. Unlike NASNet or AutoML, which outputs one optimal trained model based on a Recurrent Neural Network, the presented method outputs multiple possible architectures, without training. This method is not meant for automatic model generation based on inputs, it is designed for analyzing input preprocessing methods. The classical convolutional design patterns are analyzed and the properties and validation steps for evaluation of the generated architectures are defined, including the estimated size of the model based on the parameter numbers and training batch sizes.
Energy Efficient Motion Design and Task Scheduling for an Autonomous Vehicle

Abstract This paper describes an approach for designing an energy efficient motion and task scheduling for an autonomous vehicle which is moving in complicated environments in industrial sector or in large warehouses. The vehicle is requested to serve a number of workstations while moving safely and efficiently in the environment. In the proposed approach, the overall problem is formulated as a constraint optimization problem by using the Bump-Surface concept. Then, a Pareto-based multi- objective optimization strategy is adopted, and a modified genetic algorithm is developed to determine the Pareto optimum solution. The efficiency of the developed method is investigated and discussed through simulated experiments.
Jacob's Ladder: The User Implications of Leveraging Graph Pivots

This paper reports on a simple visual technique that boils extracting a subgraph down to two operations-pivots and filters-that is agnostic to both the data abstraction, and its visual complexity scales independent of the size of the graph. The system's design, as well as its qualitative evaluation with users, clarifies exactly when and how the user's intent in a series of pivots is ambiguous-and, more usefully, when it is not. Reflections on our results show how, in the event of an ambiguous case, this innately practical operation could be further extended into "smart pivots" that anticipate the user's intent beyond the current step. They also reveal ways that a series of graph pivots can expose the semantics of the data from the user's perspective, and how this information could be leveraged to create adaptive data abstractions that do not rely as heavily on a system designer to create a comprehensive abstraction that anticipates all the user's tasks.
LUCK- Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function

LUCK allows to use any distance-based clustering algorithm to find linear correlated data. For that a novel distance function is introduced, which takes the distribution of the kNN of points into account and corresponds to the probability of two points being part of the same linear correlation. In this work in progress we tested the distance measure with DBSCAN and k-Means comparing it to the well-known linear correlation clustering algorithms ORCLUS, 4C, COPAC, LMCLUS, and CASH, receiving good results for difficult synthetic data sets containing crossing or non-continuous correlations.
Privacy Preservation Based on Key Attribute and Structure Generalization of Social Network for Medical Data Publication

Protection of privacy data published in health care field is the research to prevent the problem of disclosing sensitive data in healthcare. The Health Insurance Portability and Accountability Act (HIPPA) in the USA is the current practice regulation of privacy protection. Recently, however, the Institute of Medicine Committee on Health Research and the Privacy of Health Information concluded that HIPPA cannot adequately safeguard privacy and allow researchers to effectively use them for discoveries at the same time. Privacy protection method based on clustering is the process of developing methods and algorithm to ensure that the published data remains useful and protected. In this paper, we purposed an algorithm based on greedy clustering to group the data points according to the attributes and the connective information of the nodes in the published social network. During the procedure of clustering, we handled the loss of information, and evaluated the proposed approach in terms of classification accuracy and information loss rates on real medical datasets. The experimental results in our proposed approach show that the data privacy can be protected with less information loss. Finally we show a visualization process for the clustering.
Fractal Image Compression of an Atomic Image using Quadtree Decomposition

Researchers have made several efforts towards reduction of Compression ratio and improvement of PSNR of an image compressing algorithm. However little attention has been given to reduce the time complexity of the same except for few hardware approaches. In this particular work, a mystical atomic image was created, through the process of splitting the main image into two different blocks. Atomic image was formed by strategically calculating the significant bits from even and odd portion of the original image in spatial domain. Furthermore the most popular methods were implemented on atomic image for getting a lower time complexity, as well as, increased compression ratio and acceptable PSNR. As a result of application of the proposed algorithm we obtained maximum PSNR of 30.12dB for Lena Image and maximum compression ratio of 25.96 for MRI image.
Cryptographic Coprocessor with Modular Architecture for Research and Development of Countermeasures Against Power-Based Side-Channel Attacks

In modern information processing systems, ensuring confidentiality and secure data transmission is an extremely important issue. However, even the most secure cryptographic algorithm may be insufficient, if its implementation does not take into account the threat of information leak through side-channels. Performing an attack on an unsecured device can be very simple and may not require expensive hardware. For this reason research and development of countermeasures against such attacks is very important. This paper presents an environment designed to facilitate research, development and evaluation of countermeasures protecting hardware implementation of cryptographic primitives against side-channel attacks based on power consumption analysis. The environment is composed of hardware cryptographic coprocessor with modular architecture, data transfer and configuration layers and software testbench for design verification. Different crypto primitives (block ciphers, hash functions, checksum) were implemented along with basic countermeasures. Utilized methods of power consumption approximation are also described. Evaluation of capabilities of the entire environment prove that both, hardware architecture and testbench provide easily extensible and versatile framework for research and development of countermeasures against power-based side-channel attacks.
Innovating Blended Synchronous Learning Environment to Improve Quality of Education in Small School in Rural Area

The study proposed a blended synchronous learning environment (BSLE) to address the problems including incomplete subjects, insufficient class, and low-quality teaching in small schools in rural area(SSRA). Two Synchronous interactive classrooms are designed in BSLE, which delivers quality classrooms in urban schools to rural schools via the Internet. The instructor synchronizes classes for students in local and remote classrooms. At the same time, students in SSRAs can join classroom interactions in such an environment. The results of the study show that BSLE can extend some of the functions of face-to-face classroom teaching to remote students, and SSRAs' students like to take classes through two-way video conferencing at remote sites because it is very flexible and convenient. The study further confirmed that BSLE can promote the growth of rural students and the professional development of teachers in SSRA, as well as facilitate the convenient communication between left-behind children and parents.
Interpretable Knowledge Discovery Reinforced by Visual Methods

This tutorial covers the state-of-the-art research, development, and applications in the KDD area of interpretable knowledge discovery reinforced by visual methods to stimulate and facilitate future work. It serves the KDD mission and objectives of gaining insight from the data. The topic is interdisciplinary bridging of scientific research and applied communities in KDD, Visual Analytics, Information Visualization, and HCI. This is a novel and fast growing area with significant applications, and potential. First, in KDD, these studies have grown under the name of visual data mining. The recent growth under the names of deep visualization, and visual knowledge discovery, is motivated considerably by deep learning success in accuracy of prediction and its failure in explanation of the produced models without special interpretation efforts. In the areas of Visual Analytics, Information Visualization, and HCI, the increasing trend toward machine learning tasks, including deep learning, is also apparent. This tutorial reviews progress in these areas with a comparative analysis of what each area brings to the joint table. The comparison includes the approaches: (1) to visualize Machine Learning (ML) models produced by the analytical ML methods, (2) to discover ML models by visual means, (3) to explain deep and other ML models by visual means, (4) to discover visual ML models assisted by analytical ML algorithms, (5) to discover analytical ML models assisted by visual means. The presenter will use multiple relevant publications including his books: "Visual and Spatial Analysis: Advances in Visual Data Mining, Reasoning, and Problem Solving" (Springer, 2005), and "Visual Knowledge Discovery and Machine Learning" (Springer, 2018). The target audience of this tutorial consists of KDD researchers, graduate students, and practitioners with the basic knowledge of machine learning.
Feasibility Of Wearable-Based Collective Sensing To Detect Environmental Barriers For Facilitating The Elderly’s Mobility

In our aging society, the elderly's mobility has become critical for our collective prosperity.However, the elderly's mobility is limited in the current built environment due to various types of environmental barriers.Manual surveys have been conducted to detect such environmental barriers, but they are discontinuous, invasive to the elderly's daily lives, and labour-intensive.As such, these methods are not ideal for wider adoption.To continuously, less-invasively, and less-laboriously detect the environmental barriers and advance the elderly's mobility, this study proposes a wearable-based collective sensing approach.This approach measures collective stress, the stress commonly sensed from multiple people on a location, as an indicator of environmental barriers based on people's physiological and location data collected by wearable sensors.To test the feasibility of the proposed approach, a "collective stress metric" is suggested.Then, the values of the collective stress metric on locations of the test site were calculated based on the physiological and location data collected from 10 elderly subjects' daily trips for 2 weeks.Then, every location on the test site was categorized into locations "with environmental barriers" and "without environmental barriers" through site survey.Based on the collected data and results of site survey, the collective stress was statistically compared between locations with environmental barriers and without barriers.The result showed that the collective stress was statistically higher on locations with environmental barriers than without barriers.The results demonstrated that the collective stress has indication of environmental barriers, therefore, the proposed approach is feasible to detect the elderly's environmental barriers.
The Study of Wheel Driving Torque Optimization of Mars Rover with Active Suspension in Obstacle Crossing

Aiming at the problem of excessive driving torque of Mars rover in the process of obstacle crossing, a wheel driving torque optimization algorithm of rover wheels obstacle crossing ability of Mars rover is proposed. By using the redundant degrees of freedom of Mars rover to obtain the optimal configuration in the process of obstacle crossing, and the torque can be optimized. Based on the hybrid coordinate system method, the kinematics model of the Mars rover is established. Quasi-static method is used to calculate and analyze the torques of the front wheels, the middle wheels and the rear wheels of the Mars rover respectively in the process of obstacle crossing. And on this basis, the constraints of obstacles crossing are obtained. The result shows that the driving torques are reduced by 10.26%, 43.43% and 9.77% respectively for the front wheels, middle wheels and rear wheels, by using the wheel torque optimization algorithm.
Low Complexity Object Detection with Background Subtraction for Intelligent Remote Monitoring

Advancements in digital technologies have enabled cost-effective deployments of visual sensor nodes that can detect a motion event in the coverage area. The real world field remote monitoring image capture conditions, for example, in security and ecological studies are affected by wind, rain, snow, sunlight etc. and are seldom ideal. Motion detection is a precursor to subsequent intelligent processing on the image to extract information. Less complex object detection techniques often rely on maintaining a background image and subtracting foreground image, purporting to have an object in it, to create a difference image to determine the presence of a moving object. Correct object detection is critical as otherwise resulting false positive (without a moving object) images needlessly invoke further processing, storage and analysis. In this paper, we review background subtraction techniques and propose an image differencing technique that can significantly reduce the algorithm complexity along with other associated advantages. The results of proposed reduced image subset are provided to highlight the benefits.
Development of Corporate Digital Training

The authors consider digitalization of corporate training, the prospects for the development of corporate training using immersive technologies - virtual reality, augmented reality and mixed reality, summarize the main advantages of using immersive learning technologies, highlight the factors hindering their use, present examples of successful application of immersive technologies in corporate training of modern organizations. The authors use both general scientific (systemic and functional approaches) and special research methods – sociological (method of expert assessments, observation and analysis) and comparative. The conclusions substantiate the need for further research and solving the problems of intra-corporate digital staff training as a condition for improving the effectiveness and efficiency of business organizations.