Reviewerly/exHarmony huggingface dataset:
Design Guidelines for Collaborative Industrial Robot User Interfaces

Collaborative industrial robot (cobot) systems are deployed to automate tasks or as a tool for Human-Robot Interaction (HRI) scenarios, especially for manufacturing applications. A large number of manufacturers of this technology have entered the cobot market in recent years. Manufacturers intend to offer easy control possibilities to make cobots suitable for different user groups, but there are few evaluation tools for assessing user interface (UI) design specifically for cobots. Therefore, we propose a set of design guidelines for cobots based on existing literature on heuristics and cobot UI design. The guidelines were further developed on the basis of modified heuristic evaluations by researchers with robotics expertise, as well as interviews with cobot UI/User Experience (UX) design experts. The resulting design guidelines are intended for identification of usability problems during heuristic evaluation of the UI design of cobot systems.
An FPGA Based Energy-Efficient Read Mapper With Parallel Filtering and In-Situ Verification

In the assembly pipeline of Whole Genome Sequencing (WGS), read mapping is a widely used method to re-assemble the genome. It employs approximate string matching and dynamic programming-based algorithms on a large volume of data and associated structures, making it a computationally intensive process. Currently, the state-of-the-art data centers for genome sequencing incur substantial setup and energy costs for maintaining hardware, data storage and cooling systems. To enable low-cost genomics, we propose an energy-efficient architectural methodology for read mapping using a single system-on-chip (SoC) platform. The proposed methodology is based on the q-gram lemma and designed using a novel architecture for filtering and verification. The filtering algorithm is designed using a parallel sorted q-gram lemma based method for the first time, and it is complemented by an in-situ verification routine using parallel Myers bit-vector algorithm. We have implemented our design on the Zynq Ultrascale+ XCZU9EG MPSoC platform. It is then extensively validated using real genomic data to demonstrate up to 7.8× energy reduction and up to 13.3× less resource utilization when compared with the state-of-the-art software and hardware approaches.
Application of Data Mining Based on Rough Set in the Evaluation of University Teachers’ Wisdom Teaching

In recent years, the country is in an environment of rapid development, vigorously promoting scientific and technological innovation, and continuously increasing investment in university research, and the university itself also attaches great importance to scientific research. In the current background that university teachers pay much attention to teaching evaluation, universities are using Rough set data mining technology to better establish the teaching evaluation system so as to better evaluate their own teaching. In the context of smart education, education big data has been generally valued, and college student classroom behavior data as teaching process data has become a research hotspot in modern education. This paper takes the basic concepts and related technologies of Rough set theory and data mining as the theoretical basis of the research, and integrates its important content to analyze and research the improvement of the evaluation of college teachers’ wisdom teaching. This paper takes the data mining of the classic Rough set as the research object, and optimizes and improves the decision number method of teaching evaluation. Data mining through Rough sets can be regarded as an adaptive sorting algorithm. Therefore, the application of data mining technology in smart campuses of colleges and universities can create many opportunities and provide many conveniences for the development of colleges and universities by improving the evaluation of smart teaching of the teacher team. It is also conducive to comprehensively improving the management capabilities of universities. The experimental results show that this research has a better effect on using Rough set and data mining to evaluate the wisdom of teachers in colleges and universities.
Distributed Top-k Pattern Mining

Frequent pattern mining (\(\mathsf {FPM}\)) on a single large graph has been receiving increasing attention since it is crucial to applications in a variety of domains including e.g., social network analysis. The \(\mathsf {FPM}\) problem is defined as finding all the subgraphs (a.k.a. patterns) that appear frequently in a large graph according to a user-defined frequency threshold. In recent years, a host of techniques have been developed, while most of them suffers from high computational cost and inconvenient result inspection. To tackle the issues, in this paper, we propose an approach to mining top-k patterns from a single graph G under the distributed scenario. We formalize the distributed top-k pattern mining problem by incorporating viable support and interestingness metrics. We then develop a parallel algorithm, that preserves early termination property, to efficiently discover top-k patterns. Using real-life and synthetic graphs, we experimentally verify that our algorithm is rather effective and outperforms traditional counterparts in both efficiency and scalability.
Understanding the hand gesture command to visual attention model for mobile robot navigation: service robots in domestic environment

The thought of integrating these robots into our daily lives now seems more comfortable with robots which seem to gain more capabilities than they do in a determinist way. In recent years, robotic systems and techniques have acquired the unprecedented capacity for perceiving and understanding their world not just in a low-level manner but even close to humanly understandable concepts. Human–robot interaction is used frequently in the care of the aged and the disabled population. Human behavior is expected to achieve natural interaction from these robots. Human activity is essential both before and after contact with a human user is initiated. Intelligent service robots in evolving robotic technologies, from entertainment to healthcare, are currently being built to satisfy demand. The service robots have controlled by nonexpert users, and direct contact with them and their human users will be their support activities. With these service robotics, human-friendly social features are typically favored. Individuals tend to use voice commands, responses, and suggestions to express their peers’ opinions. Nevertheless, the information conveyed by the conversation in natural languages is not reliable because it appears to include ambiguity and qualitative details rather than detailed quantitative data. Therefore, human-friendly service robots need to cope with ambiguous details in natural language orders. Along with introducing service robotics into domestic and public environments, these developments have contributed to the performance of areas concerned with their human presence reactions. We deliver a valuable human comfortable framework for a robot’s secure and social movement for mobile service in domestic environments. The system suggested includes the hand movements’ social status and social contextual knowledge relevant to domestic scenarios. The comfortable human framework focused on the domestic situation will approximate a relative target position by ignoring socially appropriate obstacles and approaches to the specific object. The domestic scenario is including the movement planning system of the robot, consisting of the Simultaneous Location and Mapping algorithm to produce mobile robot movement control commands.
An Insight into Handwritten Text Recognition Techniques

Digitization and storing of information from scanned documents and text pictures is a vital task today. The capability of computers to obtain and interpret intelligible handwritten text input from sources such as images, touchscreens, papers, and other devices is known as Handwritten Text Recognition (HTR). HTR has been a boon to every existing field that requires digitization as the conversion of any handwritten document namely government documents, answer sheets, handwritten notebooks, etc could be easily misplaced or mishandled and digital preservation can avoid any mishaps to the document. Compared to typed text, the high variation in handwriting types across individuals and low consistency of the handwritten text face major challenges in translating it to computer-readable text. To make data easy to store and easy to analyze, many text recognition systems have been suggested, developed, and tested by researchers. This paper provides a systematic literature review of the previous techniques used in image recognition systems. All the recent research of various text recognition techniques is presented in this paper.
Virtual Assembling Using Hand Tracking with Leap Motion Controller

The hand tracking device Leap Motion Controller has been applied in many areas since it was released. Two innovative interaction mechanisms were designed by the author for virtual object manipulation, in which collision detection between the virtual hands and the objects is not required. Instead, it controls the grasping and releasing with the gestures of one hand; and controls the direction and amount of the object's displacement with the other hand. After several tests, the mechanisms are proven to have reasonably high precision. They allow the users to adjust the parameters to create customized systems, leading to an optimal outcome.
Administrative accounting consulting: a new way of running a company through technology

Objective: To describe the impact from the obstacles and potentialities that technology has brought to accounting consulting. Question: How can administrative accounting consulting help a company using technologies? Methods: The study is a bibliographic review. A search was conducted with the descriptors: technology; innovation; accounting; accounting on Spell’s data platform (Scientific Periodicals Electronic Library) in June 2021. The search resulted in 10 articles. Results: the results brought as obstacles: The increasing technological advance, adapt to the use of these new technologies, Centrality of the role of the accountant in traditional models, Lack of software knowledge,Lack of specialized workforce, malfunction of the connection, Skilled labor. And as potentialities: Use of different digital technologies, Agility and time gain, Convenience, inseparable relationship between technology and consultative accounting promoting agile and quality support to its various users, Customer satisfaction, Speed and flexibility and Cost reduction. Conclusions: It is understood that Information Technologies positively influence the exercise of consultative accounting, although in addition to the advantages has its disadvantages. It is worth mentioning that the technologies enable the control of accounting information for the entry and exit of materials and inputs, as well as their origin and final destination, avoiding unnecessary losses, including deviations, agility, flexibility and security.
Location of Irregular Holes Based on Machine Vision

In the actual environment, the various deformations of the positioning holes of the mechanical parts and the different backgrounds will cause the circle to have irregular boundaries, which will cause the identification and positioning of the center of the circle to be inaccurate. Therefore, this paper proposes a centroid compensation algorithm based on the centroid of a circular hole. The algorithm first performs image preprocessing on the circular hole to enhance its contrast, and then performs binary and filtering processing to find its centroid. According to the binary image in the pixel The area occupied by the plane is used to identify, determine and compensate the center position of the circular hole. Experiments show that the algorithm can accurately locate the center of irregular holes with deformation, and has high accuracy and stability.
The Online English Extensive Reading Activities Using Google Classroom in Pandemic Covid-19

Abstract. The study purpose was to increase English extensive reading activities using google classroom in online learning process, especially in pandemic Covid-19 situation. This research is action research which used exploratory mixed method using Elliot model. Process of collecting data used observation, interview, diary note, and test. The technique of analysis data was reduction, presentation, and taking conclusion. The research finding show that google classroom has given significant impact on English extensive reading in the classroom. Some students think that it is easy to delivery assignment and correction of the task. They can do it out of class. It can be seen from the average score from reading test in pretest is 53 and posttest is 87. Therefore, integrating digital literacy with the reading learning is a big challenge for the learning and teaching process. Keywords: Extensive Reading, Google Classroom, Online Class, Covid-19
The Hybrid Cryptography for Enhancing the Data Security in Fog Computing

Cloud computing is taking as exciting technology because of its economic behavior. The major issue in the cloud is its security because it is far from end-user a new thing applied science is developed called fog computing. Fog computing is the standard partition infrastructure between edge and cloud computing. Security is the main concern or large issue in the cloud as well as fog computing. To overcome this security issues different types of encryption techniques is used but security problem is still there. Here, in this work, a hybrid encryption approach is used. It is a combination of symmetric algorithms that is Chacha20poly1305, Fernet, Multifernet, AESCM, and AESGCM. It offers a higher degree of security than the existing algorithm. It is applied all over the data, All datasets are encrypted by this technique as well as decrypt by this hybrid approach. Consider all the parameters like decryption time, storage, correlation, and encryption time. The result shows how it is a better approach than the existing algorithm.
Handwritten Documents Text Recognition with Novel Pre-processing and Deep Learning

Data being the most valuable resource on earth today, there is a pressing need to transform all data digitally. The world has been historically and still in multiple sectors operating based on handwritten text. For example, Handwritten taxation data and calculations, claim forms, doctor's prescription, legal documents, resumes, financial documents, accounts sheets, and many more. Handwritten data can be very easily misinterpreted even by human personnel. Hence, the most critical challenge that remains is the transformation of handwritten documents. Researches from different authors present an application wherein they take in word crop images generated from a document manually and extract the text from it. In our thesis, we present an end to end solution wherein a text image is uploaded, and handwritten text from the entire image is extracted as-is. The three main contributions of our thesis are 1. Improved text localizer trained on the various handwritten and printed dataset, 2. Novel classification model to segregate printed and handwritten words, 3. Effective image preprocessing techniques applied to handwritten word crops to make them eligible to be fed to the deep learning model for improved overall accuracy.
Single Image Atmospheric Veil Removal Using New Priors

From an analysis of the priors used in previous algorithms for single image defogging, a new prior is proposed to obtain a better atmospheric veil removal. The Naka-Rushton function is used to modulate the atmospheric veil according to empirical observations on synthetic foggy images. The parameters of this function are set from features of the input image. The algorithm is able to take into account different kinds of airborne particles and different illumination conditions. The proposed method is extended to nighttime and underwater images by computing the atmospheric veil on each color channel. Qualitative and quantitative evaluations show the benefit of the proposed algorithm.
Fault Diagnosis of GIS Disconnector Based on BP Neural Network

When a fault occurs, the current of the GIS isolating switch will change greatly compared with the normal operating state of the angle of rotation. The fault diagnosis method based on BP neural network is adopted. First, collect the current and rotation angle signals. Then extract relevant features for network training. Finally, the diagnosis model is used to diagnose the fault of the GIS isolating switch. Simulation and experiment show that this method can effectively improve the fault recognition rate.
Time-Penalty Impact on Effective Index of Difficulty and Throughputs in Pointing Tasks

In realistic graphical user interfaces, clicking outside a target would require recovery time from an error, e.g., selecting an unintended hyperlink requires reloading the previous webpage. Several studies on target-pointing tasks have examined the effects of a “penalty time” for mis-clicks on movement time and error rate, but the effects on throughput (i.e., a unified metric on pointing performance) have not been thoroughly investigated. We conducted a crowdsourcing study with 127 workers and a lab-based controlled study with 30 university students. The penalty times varied from 0 to 10 s, and the results consistently indicated that the throughput differences were less than 5%, although the error rates were remarkably different when the penalty time was 0 s. This demonstrated the potential of normalization capability of the effective width method of Fitts’ law, and the throughput is considered a valid metric when researchers would like to compare several task conditions in realistic user interfaces in which error operations induce different recovery times. However, because the model fitness using the effective width method was comparatively low when the penalty time was 0 s, comparing throughputs for different conditions is not recommended. We also discussed potential issues related to the effective width method of Fitts’ law, such as endpoints not following a linear relationship to the given target width only under the zero-penalty condition.
Optimizing the Performance of Containerized Cloud Software Systems Using Adaptive PID Controllers

Control theory has proven to be a practical approach for the design and implementation of controllers, which does not inherit the problems of non-control theoretic controllers due to its strong mathematical background. State-of-the-art auto-scaling controllers suffer from one or more of the following limitations: (1) lack of a reliable performance model, (2) using a performance model with low scalability, tractability, or fidelity, (3) being application- or architecture-specific leading to low extendability, and (4) no guarantee on their efficiency. Consequently, in this article, we strive to mitigate these problems by leveraging an adaptive controller, which is composed of a neural network as the performance model and a Proportional-Integral-Derivative (PID) controller as the scaling engine. More specifically, we design, implement, and analyze different flavours of these adaptive and non-adaptive controllers, and we compare and contrast them against each other to find the most suitable one for managing containerized cloud software systems at runtime. The controller’s objective is to maintain the response time of the controlled software system in a pre-defined range, and meeting the Service-level Agreements, while leading to efficient resource provisioning.
Designing Photography Guidance for Rapid In-Camera Iteration

Designers have long known the benefits of iteration and rapid prototyping. Many experienced photographers follow a similar process, in particular of iterating in camera: trying out different angles and compositions, varying lighting, adjusting a subject’s pose, etc. However, amateurs often do not realize the benefits of capturing variations of a single shot. Inspired by the parallels between the design thinking process and the photographic process, we design new interfaces that provide contextual in-camera feedback to aid users in learning visual elements of photography. We interactively visualize results of image processing algorithms as additional information for the user at capture time. In this chapter, we explore ways to encourage stages of the design thinking process, specifically through the design of guided photography interfaces that aid in iterating through the exploration of three different photographic concepts: lighting, composition, and decluttering.
Study of the Error Detection and Correction Scheme for Distributed Network Covert Channels

Information hiding in communication networks is gaining recently increased attention from the security community. This is because such techniques are a double-edged sword that, on the one hand, can be used, e.g., to enhance the privacy of Internet users while on the other can be utilized by malware developers to enable a covert communication feature in malicious software. This means that to understand the risks that data hiding poses, it is of utmost importance to study the inner workings of potential information hiding methods and accompanying mechanisms (e.g., those that provide reliability of such communications) as well as to develop effective and efficient countermeasures. That is why, in this paper we perform a systematic experimental evaluation of the error detection and correcting scheme, which is suitable for complex network data hiding approaches, i.e., distributed network covert channels (DNCCs). The obtained results prove that the proposed solution guarantees secret communication reliability even when faced with severe networking conditions up to 20% of data corruption while maintaining a stable covert data rate.
Web User’s Access Pattern Identification Using Clustering Algorithms: A Survey

Users of web site usually perform their interest oriented actions by clicking or visiting web pages, which are traced in access log files. The clustering web client's access pattern may catch common client interest in a website. The conventional web usage mining techniques for clustering web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users' navigational behaviour. One of the best tasks of internet usage mining, web client grouping, which sets up clusters of clients displaying comparative browsing designs gives beneficial information to customized web benefits. This paper presents a survey covering the strategies and techniques of clustering algorithms and demanding situations appear within the area.
Recognition of Foggy Image for Surveillance Application

Poor weather conditions like fog and haze are major concern in many computer and vision applications. Images captured in poor weather are degraded in its various features like edge, contrast, saturation and color. These images cannot be used directly in applications like object detection, tracking and surveillance (security). It requires pre-processing so that degraded features can be restored to improve quality of foggy image. In this chapter, white balancing approach is used as a pre-processor to extract illuminant color of foggy image. Guided filter is used in intermediate stages to preserve features like edges, textures and fine details. In the final stage, partially overlapped sub-block histogram equalization has been used to enhance contrast of restored image. Qualitative and quantitative analyses have been done on both natural and synthetic data sets with state-of-the-art method.