Reviewerly/exHarmony huggingface dataset:
Chatbot on COVID-19 for Sustaining Good Health During the Pandemic

This paper throws light on the JAVA-based platform whereby the user converses with the machine based on artificial intelligence. The machine behaves so smartly that the user is unable to detect his/her conversations with the machine and feels to be communicating with human itself. The paper also reflects the evolution of chatbot from ELIZA to WOEBOT and emphasizes the essential attributes that any chatbot should possess. This chatbot tackles the queries related to the current pandemic COVID-19. This chatbot is developed such that it is user friendly and is aesthetically appealing to the user.
Upper Bounds on Distinct Maximal (Sub-)Repetitions in Compressed Strings

For $$\delta \in \mathbb {R}^+$$ , maximal $$\delta $$ -repetitions ( $$\delta $$ -subrepetitions) are fractional powers with exponent of at least $$2+\delta $$ (and $$1+\delta $$ , respectively) which are non-extendable with respect to their minimum period. In this paper, we prove that the number of distinct (unpositioned) maximal $$\delta $$ -repetitions in a string S with z LZ77-factors is bounded from above by $$z\left\lfloor 3 + \frac{6}{\delta }\right\rfloor \cdot \left\lfloor \log _{1+\frac{\delta }{4}}(|S|) \right\rfloor $$ . Also, the number of distinct (unpositioned) maximal q-th power-free $$\delta $$ -subrepetitions in a string S with z LZ77-factors is bounded from above by $$z\left\lfloor 3 + \frac{4}{\delta }\right\rfloor \cdot \left\lfloor \log _{1+\frac{\delta }{2q}}(|S|)\right\rfloor $$ . We further prove that for fixed $$\delta $$ and q, both upper bounds are asymptotically tight.
Low light Image Enhancement using DCE-Net aided by Pack and Unpack operations

The main objective of Low Light Image enhancement is to process an image so that the result is more suitable for practical application. Images captured in dark circumstances have very low contrast, due to this people can't clearly identify the objects, which increases the difficulty of applied computer vision tasks considerably. Generally, this enhancement is done using image processing techniques like Histogram Equalization and its variants (CLAHE). But recent advances in deep learning enabled us to restore dark images which were hard to enhance with normal techniques. But often these techniques are limited by speed and hardware. This paper combines the DCE-net, Pack and UnPack algorithms to improve the performance of low light image enhancement, which aim to reduce the runtime.
Full-field laser heterodyne imaging vibrometry using a CMOS–DVR system

A laser heterodyne imaging vibrometry is proposed for full-field vibration measurement. The vibration responses are imaged and recorded using a CMOS camera and a digital video recorder. A digital demodulation method based on a cumulative distribution function and autocorrelation is designed to demodulate signals affected by speckle noise. The experimental investigations confirm the viability of the proposed method for vibration measurement. Meanwhile, a comparison with laser Doppler vibrometry is performed to further validate the method. The results prove the proposed vibrometry is an effective and precise option for full-field vibration measurement.
Blockchain based framework for Software Development using DevOps

The Varity and complexity of software development methods is getting increased from mid-1960, to accelerate product's time to market it is more and more pervasive so it becomes crucial to ensure the quality, performance, and security of software product in untrustworthy environment. DevOps focuses on automation and inter departmental integration to create an environment and culture that will increase software development speed as there are frequent conflicts occurs between development and operations. This paper focusses on architecture that redefines the DevOps pipeline by positioning Blockchain technology in this environment. It stores all project artifacts on decentralized and secure Blockchain environment so that it can create trust between people with dishonest and stubbornness mindset that would make DevOps delivery to follow control requirements in technology and business environments of different sizes. Separately, blockchain and DevOps provides tangible and quantifiable value for just about any type of business. If they are used together, they can form a highly advanced business solution that can revolutionize services, operations and collaborations.
Privacy Preserving Data Mining

Data mining techniques provide benefits in many areas such as medicine, sports, marketing, signal processing as well as data and network security. However, although data mining techniques used in security subjects such as intrusion detection, biometric authentication, fraud and malware classification, “privacy” has become a serious problem, especially in data mining applications that involve the collection and sharing of personal data. For these reasons, the problem of protecting privacy in the context of data mining differs from traditional data privacy protection, as data mining can act as both a friend and foe. Chapter covers the previously developed privacy preserving data mining techniques in two parts: (i) techniques proposed for input data that will be subject to data mining and (ii) techniques suggested for processed data (output of the data mining algorithms). Also presents attacks against the privacy of data mining applications. The chapter conclude with a discussion of next-generation privacy-preserving data mining applications at both the individual and organizational levels.
Joint Limit Avoidance of a Mobile manipulator for Prevention of Rapid Velocity Increase

A mobile manipulator can perform tasks while moving in a large workspace. However, a combination of a mobile platform and a manipuator creates redundancy, which requires some form of redundancy control. Joint limit avoidance is a representative method of redundancy control. When the conventional method for joint limit avoidance is used, the joint velocity tends to increase rapidly near the joint limits, which may cause velocity divergence. In this study, the method for avoiding the joint limits of a mobile manipulator is proposed to prevent a rapid increase in velocity. A series of experiments show that the proposed method can effectively work in most cases.
Exploring CNN features in the context of adversarial robustness and human perception

Recent studies in the field of adversarial machine learning have highlighted the poor robustness of convolutional neural networks (CNNs) to small, carefully crafted variations of the inputs. Previous work in this area has largely been focused on very small image perturbations and how these completely throw off the classifier output and cause CNNs to make high-confidence misclassifications while leaving the image visually unchanged for a human observer. These attacks modify individual pixels of each image and are unlikely to exist in a natural environment. More recent work has demonstrated that CNNs are also vulnerable to simple transformations of the input image, such as rotations and translations. These ‘natural’ transformations are much more likely to occur, either accidentally or intentionally, in a real-world scenario. In fact, humans experience and successfully recognize countless objects under these types of transformations every day. In this paper, we study the effect of these transformations on CNN accuracy when classifying 3D face-like objects (Greebles). Furthermore, we visualize the learned feature representations by CNNs and analyze how robust these learned representations are and how they compare to the human visual system. This work serves as a basis for future research into understanding the differences between CNN and human object recognition, particularly in the context of adversarial examples.
FIBER: enabling flexible retrieval of electronic health records data for clinical predictive modeling

The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames.FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models.Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case.FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process.
VR SuperGun: Interfacing 1980s Arcade Hardware with Online Virtual Reality

VR SuperGun is a custom hardware and software prototype allowing play of original arcade platforms through a network connection, reconstituting the material form of the arcade cabinet in digital space. It extends the format of the SuperGun, a device that contains the wiring of an arcade cabinet in consolised form.
Handwritten Character Recognition Using Neural Network

In this paper, an enterprise is made to perceive manually written characters for English letters so as. The precept point of this mission is to plan a master framework for, "HCR(English) utilizing neural community". That could viably understand a particular individual-of-kind layout making use of the artificial neural community approach. The manually written man or woman acknowledgment trouble has grown to be the maximum famous trouble in ai. Handwritten man or woman acknowledgment has been a difficult space of exam, with the execution of gadgets getting to know we suggest a neural network-based methodology. The development is based totally on neural network, that is a subject of look at in artificial intelligence. Distinct strategies and methods are used to broaden a handwriting person recognition system. Acknowledgment, precision fee, execution, and execution time are massive versions on the way to be met through the technique being applied. The purpose is to illustrate the effectiveness of neural networks for handwriting character recognition.
Efficient FHE Radix-2 Arithmetic Operations Based on Redundant Encoding

Fully homomorphic encryption (FHE) is a novel encryption method that can perform operations on encrypted data. The performance of applications based on FHE is still low due to the high computational complexity of operations on the ciphertext. This article combines the characteristics of FHE and the redundant encoding method to achieve faster radix-2 arithmetic operations in BGV-like schemes. First, a ciphertext integer addition with multiplicative depth two, namely, redundant carry-free addition (RCFA), is proposed by applying the carry-free feature of the redundant encoding method. This addition is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.207\times $ </tex-math></inline-formula> faster and uses 15.0% of the occupied memory at 512 bits compared with the nonredundant method. After utilizing the single-instruction–multiple-data (SIMD) technique, RCFA with SIMD further improves the efficiency by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3903\times $ </tex-math></inline-formula> at 512 bits compared with the nonredundant SIMD method. Its ciphertext size and occupied memory are only 34.59% and 1.6% those of the nonredundant SIMD method. Second, to achieve efficient ciphertext multiplications, redundant multiplication (RM) of dual SIMD data (RMDS) and RM of SIMD and non-SIMD data (RMSNS) methods are proposed; they obtain speedups of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$65.6\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$631.0\times $ </tex-math></inline-formula> , respectively, at 32 bits compared with the nonredundant implementations. Finally, SHA-256 is implemented to validate the efficiency of the proposed arithmetic operations. Compared with the nonredundant SIMD method, this article obtains a speedup of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.34\times $ </tex-math></inline-formula> .
PKT: fast color-based spatial model for human skin detection

Abstract We present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit rate, very high precision, and accuracy rate) than most state-of-the-art models.
Orientation-Aware Planning for Parallel Task Execution of Omni-Directional Mobile Robot

Omni-directional mobile robot (OMR) systems have been very popular in academia and industry for their superb maneuverability and flexibility. Yet their potential has not been fully exploited, where the extra degree of freedom in OMR can potentially enable the robot to carry out extra tasks. For instance, gimbals or sensors on robots may suffer from a limited field of view or be constrained by the inherent mechanical design, which will require the chassis to be orientation-aware and respond in time. To solve this problem and further develop the OMR systems, in this paper, we categorize the tasks related to OMR chassis into orientation transition tasks and position transition tasks, where the two tasks can be carried out at the same time. By integrating the parallel task goals in a single planning problem, we proposed an orientation-aware planning architecture for OMR systems to execute the orientation transition and position transition in a unified and efficient way. A modified trajectory optimization method called orientation-aware timed-elastic-band (OATEB) is introduced to generate the trajectory that satisfies the requirements of both tasks. Experiments in both 2D simulated environments and real scenes are carried out. A four-wheeled OMR is deployed to conduct the real scene experiment and the results demonstrate that the proposed method is capable of simultaneously executing parallel tasks and is applicable to real-life scenarios.
Regression-Aware Classification Feature for Pedestrian Detection and Tracking in Video Surveillance Systems

Pedestrian detection and tracking in video surveillance systems is a complex task in computer vision research, which has widely used in many applications such as abnormal action detection, human pose, crowded scenes, fall detection in elderly humans, social distancing detection in the Covid-19 pandemic. This task is categorized into two sub-tasks: detection, and re-identification task. Previous methods independently treat two sub-tasks, only focusing on the re-identification task without employing re-detection. Since the performance of pedestrian detection directly affects the results of tracking, leveraging the detection task is crucial for improving the re-identification task. The total inference time is computed in both the detection and re-identification process, quite far from real-time speed. This paper joins both sub-tasks in a single end-to-end network based on Convolutional Neural Networks (CNNs). Moreover, the detection includes the classification and regression task. As both tasks have a positive correlation, separately learning classification and regression hurts the overall performance. Hence, this work introduces the Regression-Aware Classification Feature (RACF) module to improve feature representation. The convolutional layer is the core component of CNNs, which extracts local features without modeling global features. Therefore, the Cross-Global Context (CGC) is proposed to form long-range dependencies for learning appearance embedding of re-identification features. The proposed model is conducted on the challenging benchmark datasets, MOT17, which surpasses the state-of-the-art online trackers.
Cloud and mobile computing: the age of virtual internet space

Abstract This century is described as Cloud Computing Technology over the Information Technology Business. Cloud computing advancement mostly relies on the Internet possesses the most advanced planning of computation. It records as piles of correlated and schmoozed hardware, software, and internet infrastructure. It blends grid computing and other computing as a community. A short overview of cloud computing is obtained from this research paper by consulting several papers on cloud technologies. The substance conveyed by this represents the perspective of shaping IT businesses and the idea of cloud computing.
Landmark Detection in 3D Medical Images Using Reinforcement Learning

Computer vision tasks like key point localization for face recognition and pose estimation uses Deep Learning (DL) and Reinforcement Learning (RL). Similarly for medical image analysis, anatomical landmark detection is beneficial for automatic scan planning which reduces technician's time and error. This paper compares DL and RL models and variants of Q-learning methods like Deep Q-Network in a single and multi-agent setting. Results show that RL approaches are performing significantly better as compared to DL approaches. All the metrics are in mm. For five landmarks, with single-agent RL, Mean absolute error and Mean euclidean error are 1.66 mm and 3.87 mm, whereas, with multi-agent RL, errors are 2.12 mm and 4.33 mm respectively on the test set (n=60). While with DL approach, errors are 6.54 mm and 14.62 mm which is nearly 3 to 4 times more than RL based approach. The performance of all variants of the DQN approach is excellent, where Double DQN is performing best with errors as 2.10 mm and 4.34 mm.
CooperAR: promoting educational inclusion through augmented reality

Augmented reality (AR) is an emerging technology that has been successfully integrated into education. However, the use of AR as a tool to achieve educational inclusion has not been deeply explored. This study introduces CooperAR, a methodology aimed at attending educational inclusion, understanding inclusion as a process where the needs of all students are taken into consideration. CooperAR focuses on the creation of AR-based learning scenarios, in which teachers and students become co-creators of content. The methodology is based on the principles of Universal Design for learning and the Cooperative Learning approach. The effectiveness of CooperAR was validated in the context of a case study that involved 66 students. The results showed promising results, suggesting that CooperAR is an efficient methodology to promote educational inclusion through cooperation.
Quality-aware CNN-based in-loop filter for video coding

The state‐of‐the‐art video coding standard, Versatile Video Coding (VVC) or H.266, has demonstrated its superior coding efficiency over its predecessor HEVC/H.265. In this paper, a novel in‐loop filter based on convolutional neural network (CNN) is illustrated to further improve the coding efficiency over VVC. In this filter, one single NN model is used to process multiple video components simultaneously. In addition, with a quality map generated for each video component as network input, the same single NN model is capable of processing videos in different qualities and resolutions while maintaining coding efficiency, which reduces the overall network complexity significantly. Simulation results show that the proposed approach provides average BD‐rate savings of 6.27%, 18.78% and 20.42% under AI configuration, and average BD-rate savings of 5.18%, 21.95% and 22.13% under RA configuration, respectively for Y, Cb and Cr components.
A New Process Model of Incremental Asset Building for Software Project Management

Although global software industry is growing up rapidly, however surviving in software industry has not become remarkable as earning revenue and profit is below satisfactory in most of cases. Software companies are getting failed to make business by implementing typical software project management. The reasons of failing to earn profit is developing customized software of each client. In this paper, we have proposed a process model which will be followed additionally with typical software project management. By adopting this proposed process model will help the software companies to make profit of software business. This process model will build core and reusable software components based on targeted market segment and will integrate those components while developing software. Thus, software can be built with minimal customization and software companies can save their resources.
Publicly Traceable Attribute-Based Anonymous Authentication and Its Application to Voting

Numerous anonymous authentication schemes are designed to provide efficient authentication services while preserving privacy. Such schemes may easily neglect access control and accountability, which are two requirements that play an important role in some particular environments and applications. Prior designs of attribute-based anonymous authentication schemes did not concentrate on providing full anonymity while at the same time holding public traceability. To address this problem, we formally define and present a new primitive called traceable attribute-based anonymous authentication (TABAA) which achieves (i) full anonymity, i.e., both registration and authentication cannot reveal user’s privacy; (ii) reusable credential, i.e., a registered credential can be repeatedly used without being linked; (iii) access control, i.e., only when the user’s attribute satisfies the access policy can the user be involved in authentication; and (iv) public traceability, i.e., anyone, without help from the trusted third party, can trace a misbehaving user who has authenticated two messages corresponding to a common address. Then, we formally define the security requirements of TABAA, including unforgeability, anonymity, and accountability, and give a generic construction satisfying the security requirements. Furthermore, based on TABAA, we propose the first attribute-based, decentralized, fully anonymous, publicly traceable e-voting, which enables voters to engage in a number of different voting activities without repeated registration.