Reviewerly/exHarmony huggingface dataset:
Role of Sensing in Building Business Models, why and how

In a crisp, Business Modeling is an alphanumeric archetypal to represent and evaluate an existing or anticipated business. The modeling process consists of resolving the business manifolds under specific and defined dimensions. However, the present business modeling fails to position the end products under users/ customers' acceptability scale. As a result, a business goes several iterations before it reaches a certain confidence level, compounding to a significant waste of time and resources. Through this paper, we shall attempt to address this problem by introducing a new dimension, “SENSING,” and present the framework for Sensing in Business Model Innovation (SENSEBMI) to predict the end-user's perception and discuss the approach of realizing this new paradigm shift.
Sampling-Based Planning for Retrieving Near-Cylindrical Objects in Cluttered Scenes Using Hierarchical Graphs

We present an incremental sampling-based task and motion planner for retrieving near-cylindrical objects, like bottle, in cluttered scenes, which computes a plan for removing obstacles to generate a collision-free motion of a robot to retrieve the target object. Our proposed planner uses a two-level hierarchy, including the first-level roadmap for the target object motion and the second-level retrieval graph for the entire robot motion, to aid in deciding the order and trajectory of object removal. We use an incremental expansion strategy to update the roadmap and retrieval graph from the collisions between the target object, the robot, and the obstacles, in order to optimize the object removal sequence. The performance of our method is highlighted in several benchmark scenes, including a fixed robotic arm in a cluttered scene with known obstacle locations and a scene, where locations of some objects or even the target object are unknown due to occlusions. Our method can also efficiently solve the high-dimensional planning problem of object retrieval using a mobile manipulator and be combined with the symbolic planner to plan complex multistep tasks. We deploy our method to a physical robot and integrate it with nonprehensile actions to improve operational efficiency. Compared to the state-of-the-art approaches, our method reduces task and motion planning time up to 24.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> with a higher success rate, and still provides a near-optimal plan.
Unsupervised Embedding Learning With Mutual-Information Graph Convolutional Networks

Recently, methods for unsupervised embedding learning have exhibited promising results for extracting desirable representations from unlabeled samples. In general, most methods learn the feature embeddings by handling each sample individually while the structural and semantic relationships between samples are not fully exploited. As a result, the learned embeddings are not sufficiently discriminative. To make use of such inter-sample information for deep embedding learning, this paper proposes an unsupervised method based on the graph convolutional network (GCN). On one hand, our method encodes structural information between the samples corresponding to the nodes in a local neighbourhood of the GCN graph. On the other hand, it leverages the mutual information between the original samples and the augmented ones to ensure that they are globally consistent with each other. Extensive experiments show that our method is not just robust to augmentation perturbations, but also learns discriminative embeddings. Consequently, it achieves the state-of-the-art performance on several challenging datasets.
A Theory of Organizational Structures for Development and Infrastructure Professionals

DevOps and continuous delivery have impacted the organizational structures of development and infrastructure groups in software-producing organizations. Our research aims at revealing the different options adopted by the software industry to organize such groups, understanding why different organizations adopt distinct structures, and discovering how organizations handle the drawbacks of each structure. We interviewed 68 carefully-selected IT professionals, 45 working in Brazil, 10 in the USA, 8 in Europe, 1 in Canada, and 4 in globally distributed teams. By analyzing these conversations through a Grounded Theory process, we identified conditions, causes, reasons to avoid, consequences, and contingencies related to each discovered structure (segregated departments, collaborative departments, API-mediated departments, and single department). In this way, we offer a theory to explain organizational structures for development and infrastructure professionals. This theory can support practitioners and researchers in comprehending and discussing the DevOps phenomenon and its related issues, and also provides valuable input to practitioners’ decision-making.
OrcBench: A Representative Serverless Benchmark

Serverless computing is rapidly growing area of research. No standardized benchmark currently exists for evaluating orchestration level decisions or executing large serverless workloads because of the limited data provided by cloud providers. Current benchmarks focus on other aspects, such as the cost of running general types of functions and their runtimes.We introduce OrcBench, the first orchestration benchmark based on the recently published Microsoft Azure serverless data set. OrcBench categorizes 8622 serverless functions into 17 distinct models, which represent 5.6 million invocations from the original trace.OrcBench also incorporates a time-series analysis to identify function chains within the dataset. OrcBench can use these to create workloads that mimic complete serverless applications, which includes simulating CPU and memory usage. The modeling allows these workloads to be scaled according to the target hardware configuration.
Towards complete dis-aggregation of data center rack power using light-weight mechanisms

Enterprises world-wide are increasingly prioritizing sustainability due to the growing focus on carbon neutrality as well as the requirement to adhere to emerging strict regulations from governments across the globe. With many enterprise workloads deployed on cloud and data centers, to fulfill the mandatory carbon reporting requirements of their clients, it is becoming inevitable for cloud providers and data center operators to quantify each client’s share of the total carbon emission from their facility. Accurate carbon quantification requires power measurements to be available at the lowest level of the hardware infrastructure such as physical servers and network switches. However, power sensing is quite limited in many data centers, with measurements normally available only at an aggregated level such as the rack level. To drill down to the level of a workload to capture the correct power usage per workload, it is very important to dis-aggregate this power across servers. In this paper, we propose a software based non-linear model using the Newton-Raphson method to estimate the power model parameters of individual servers using server utilizations when the overall rack level power measurements are given. The methodology is applicable to data centers with multiple types of servers in a rack and is light-weight in the sense that it does not require mechanisms such as shutting down individual servers in order to estimate idle power. The method is also generalized to account for the real world scenario where the time granularity of rack power and server utilization measurements may not match. We have conducted detailed evaluations of the methods proposed and find good convergence for parameter estimation even when tested with multiple different initial conditions.
A Novel Skill Model Supporting Complex and Reliable Interactions between Users, VPA and External Services

With the development of AI and dialog technologies, Virtual Personal Assistants (VPAs) have been used on various types of mobile and edge devices for convenient man-machine interaction. VPA's capabilities are commonly called "skills". VPA can use skills to get users intent and provide corresponding services such as playing music. However, the skills supported by existing VPAs have many shortcomings. First of all, skills cannot support complex interactions between users and VPAs. The interaction process is often just a simple process. Secondly, many skills are unreliable because they frequently terminate execution when they receive unexpected input. Moreover, the services supported by current skills are very limited that cannot meet the diverse needs of users. In order to solve these problems, this paper proposes a novel skill model named CRSModel to support complex and reliable interactions between users, VPA and external services. In order to improve the complexity of interactions between users and VPA, CRSModel adds many configurations that are helpful to support complex interactions, and appends the dialogue flow processing mechanism to the execution process that can let users change the dialog flow. In terms of reliability, CRSModel's execution process can capture unexpected input and guide the user to input an expected value. Finally, in order to meet the diverse needs of users, CRSModel can support external services that satisfy the REST protocol, greatly increasing the variety of services.
Increasing Transparency for Novel and Existing Complex Systems Development - Modular Design in an Agile Context

This paper addresses the question of how Modular Design methods can support the development of complex products.The goal of selected Modular Design methods is to improve transparency in the development of modular systems to be able to structure these systems in a way that adds value to the observed company and to derive optimized product architectures from the conducted procedures.The problem addressed here is that existing methods can develop increasingly less transparency within the development of complex products, since the extent of these products rises increasingly.This increases the complexity of the work steps and scope of work, which results in a reduction of the immediate transparency in observing the underlying product architecture.To address this pain points, two selected methods in the context of Modular Design are exemplary adapted to agile approaches and principles.These adaptations are applied to two underlying case studies, each of which has to do with the development of complex products in various aspects.In the case studies, different process perspectives are depicted, which offer both a development process (further development of an existing system) of a laser cutting machine tool of a mechanical engineering company, as well as a development project (new design of a system with a high degree of novel development) of a smart industrial valve of a medium-sized engineering manufacturer with, in addition, an unknown proportion of new development as an investigation environment.The results show the integration of agile aspects in selected Modular Design methods as a successful support in generating transparency in the development of complex products and systems, in different process perspectives.At the same time, the research also shows that adaptations to the studied methods are necessary, such as continuous and centralized concept documentation, to apply them in an agile context.
Secure Cloud Storage with Joint Deduplication and Erasure Protection

This work proposes a novel design for secure cloud storage systems using a third party to meet three seemingly opposing demands: reduce storage requirements on the cloud, protect against erasures (data loss), and maintain confidentiality of the data. More specifically, we achieve storage cost reductions using data deduplication without requiring system users to trust that the cloud operates honestly. We analyze the security of our scheme against honest-but-curious and covert adversaries that may collude with multiple parties and show that no novel sensitive information can be inferred, assuming random oracles and a high min-entropy data source. We also provide a mathematical analysis to characterize its potential for compression given the popularity of individual chunks of data and its overall erasure protection capabilities. In fact, we show that the storage cost of our scheme for a chunk with r replicas is O(log(r)/r), while deduplication without security or reliability considerations is O(1/r), i.e., our added cost for providing reliability and security is only O(log(r)). We provide a proof of concept implementation to simulate performance and verify our analytical results.
Contactless Authentication for Wearable Devices Using RFID

Technological advancements are strongly integrated into our daily lives and an increasing trend prompts the usage of smart healthcare devices for health management. Health providers are beginning to use wearable devices as equipment that can support remote care services. As a result, an accurate, robust, lightweight, and convenient authentication system for smart healthcare devices is urgently required. Considering RFID technology, we present a contactless authentication mechanism that secures a smartwatch at the hardware level. When the authorized person uses the smartwatch, then power is “ON” otherwise “OFF”. Thanks to our method the answer to the question “Am I authorized to use the wearable medical sensor?” and “Am I really the person who is proceeding?” is directly enforced by the system. This study significantly improved the usability and security of the authentication process.
Community Detection and Disease identification using Meta-heuristic based Clustering methods

Community detection in the field of medical science is an important field of research for disease diagnosis. A popular data mining technique for community detection is data clustering which partitions huge volume of data items based on some similarity measures. This paper presents a rigorous data clustering application with its practical implications towards medical field of research for disease diagnosis. The comparison study involves six benchmark UCI machine learning medical datasets and four meta-heuristic based partitional clustering algorithms such as Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), JAYA Algorithm and Particle Swarm Optimization (PSO) based partitional clustering method. To statistically verify the statistical significance of each model, the performance of each model, is accessed using Duncan’s multiple range test on each individual dataset separately considering four performance measures such as Accuracy, F-score, MCC and Kappa. In addition, we have conducted Nymenyi hypothesis test to obtain the rank of each model considering the results of all datasets altogether. The results obtained from the statistical test signifies that the SCA algorithm obtains statistically superior performance among the remaining algorithms considered in this study.
Covid-19 Impact on Cyber Crimes in India: A Systematic Study

In this digital era of online processing, most information is accessible electronically and is prone to cyber threats. There is a vast range of cyber threats whose behavior is hard to understand in the early phases. These attacks may have some motivation behind them that have significant societal impacts in the form of economic damage, psychological disturbance, a threat to national security, and so on. With the worldwide spread of COVID-19, India experienced an astonishing 86% rise in cybercrimes. Nowadays, cybercrime has become an attractive strategy for hackers to create chaos and disruption. This paper is based on the quantitative analysis of cybercrime in India and its impacts on society, with preventive measures to handle them. In our study, we found that digital related offenses and online frauds are drastically increasing in India during COVID-19 pandemic. As a result, awareness campaigns and security solutions are needed to prevent or mitigate them.
Investigation of Deep Fake Images Using Pre-Trained CNN Frameworks

Deep learning technologies, such as neural networks, have tackled complicated issues from large-scale data processing to computer vision and human-level control. Editing photographs with software designed for commercial purposes makes it easy for anyone to manipulate images in order to create fictitious ones. Generative adversarial networks (GANs) are currently being utilised to generate superficially realistic photographs, as opposed to the old methods that were previously used to create phony images. The GANs images are referred to as deep fakes. To the unaided eye, they appear to be real photographs. As a result, it's impossible to spot a phony image produced with GANs. The social network is made less safe as a result of the uploading of these bogus photographs. It is therefore critical that the digital image's legitimacy be detected before it is uploaded. Thus, in this chapter, the authors suggest a few pre-trained deep learning frameworks that may be used to effectively detect deep fake images.
Cyberbullying on Chinese Social Media

The rapid development of information and communication technologies contributes to the growth of social media channels, which also bring some problems such as cyberbullying. Previous studies have analyzed the prevalence and consequences of cyberbullying and the detection and prevention of it. However, little research pays attention to cyberbullying on Chinese social media. This research uses the content analysis method to analyze cyberbullying on one of the biggest social media platforms in China, Weibo, focusing on language features and factors that influence the frequency of cyberbullying language in comments on key opinion leaders' (KOLs) posts. The findings reveal that most cyberbullying language on Weibo appears in the form of mildly offensive or ordinary words with special meanings and offensive references, rather than directly offensive words. In addition, this research found that KOL type and post content type interact to affect the frequency of cyberbullying language on Weibo. Overall, this research has made a valuable contribution to cyberbullying research.
Edge Computing for Wireless Sensor Network

Wireless sensor networks (WSNs) are being substantially popular in edge computing these days. In order to establish a reliable and sustainable edge AI system in the network, multiple conditions need to be considered. The performance, accuracy aspects are vital to a successful edge AI node in a WSN as more and more complicated tasks are being solved at the edge of the network. Energy management is also a concern as a great amount of edge devices are being deployed at rural, isolated areas with little to no common energy source. Therefore, energy harvesting is a great solution to the problem. In this paper, we will consider deeply into those categories and provide statistics about the topic.
Deep Learning-Based Automatic Student Authentication

Classroom management relies heavily on the ability to keep track of student attendance. Attendance checks by calling names or handing out a sign-in sheet are time-consuming and vulnerable to fraud, especially the latter. Data science and image processing are the focus of this regarding counting the number of people present at a gathering for various objectives, such as determining a person's duty status, determining a person's physical presence in a classroom, determining a person's security clearance to enter a meeting hall, etc. It takes a lot of time and effort to maintain a database for future use when generating attendance records using the standard technique. With the help of the latest technology, attendance can be entered automatically. It is a frequently used face recognition technique that generates a binary code for each cell and compares it to the reference image. With the use of deep learning, this LBP method has been reworked in order to automate attendance generation.
Towards Collaborative Vision Video Production: How to Integrate Different Stakeholders

Differing mental models of stakeholders can lead to conflicting requirements which can threaten the success of the software project if they are not resolved. Therefore, producing a shared understanding of the project vision among stakeholders is crucial. Vision videos are one possible method to produce such a shared understanding by allowing stakeholders to identify and resolve discrepancies between their mental models. However, stakeholders often mistakenly expect a high effort being required for the production of vision videos and therefore refrain from using them. In this paper we present concepts that are designed to aid stakeholders in a collaborative production of vision videos by enabling them to produce vision videos using their personal smartphones. The concepts reduce an individual stakeholder’s workload by distributing the necessary tasks amongst all relevant stakeholders. Preliminary sessions of an experiment comparing a prototype of our concepts with existing methods have resulted in the discovery of promising tendencies. Participants using the prototype were only required to actively work on the vision video for little over half the time. We expect these tendencies to be strengthened by further experiment sessions. Nevertheless, our results indicate that the concepts presented in this paper are a meaningful step towards a collaborative vision video production.
Meeting Strangers Online: Feature Models for Trustworthiness Assessment

Getting to know new people online to later meet them offline for neighbourhood help, carpooling, or online dating has never been as easy as nowadays by social media performing computer-mediated introductions (CMIs). Unfortunately, interacting with strangers poses high risks such as unfulfilled expectations, fraud, or assaults. People most often tolerate risks if they believe others are trustworthy. However, conducting an online trustworthiness assessment usually is a challenge. Online cues differ from offline ones and people are either lacking awareness for the assessment’s relevance or find it too complicated. On these grounds, this work aims to aid software engineers to develop CMI that supports users in their online trustworthiness assessment. We focus on trust-related software features and nudges to i) increase user awareness, ii) trigger the trustworthiness assessment and iii) enable the assessment online. For that reason, we extend feature models to provide software engineers the possibility to create and document software features or nudges for trustworthiness assessment. The extended feature models for trustworthiness assessments can serve as reusable catalogues for validating features in terms of their impact on the trustworthiness assessment and for configuring CMI software product lines. Moreover, this work provides an example of how the extended feature models can be applied to catfishing protection in online dating.
An FPGA-Based Solution for Convolution Operation Acceleration

Hardware-based acceleration is an extensive attempt to facilitate many computationally-intensive mathematics operations. This paper proposes an FPGA-based architecture to accelerate the convolution operation - a complex and expensive computing step that appears in many Convolutional Neural Network models. We target the design to the standard convolution operation, intending to launch the product as an edge-AI solution. The project's purpose is to produce an FPGA IP core that can process a convolutional layer at a time. System developers can deploy the IP core with various FPGA families by using Verilog HDL as the primary design language for the architecture. The experimental results show that our single computing core synthesized on a simple edge computing FPGA board can offer 0.224 GOPS. When the board is fully utilized, 4.48 GOPS can be achieved.