Reviewerly/exHarmony huggingface dataset:
Moving Object Detection System Based on the Modified Temporal Difference and OTSU algorithm

In this paper, we have proposed a robust and computationally inexpensive threefold algorithm used for the detection of moving objects in a video sequence. The adopted algorithm starts with computation of difference between images using modified temporal difference. Images differencing are calculated by subtracting two input frames, at each position of the pixel. Instead of generating image differencing using the traditional continuous approach, we have proposed to use a fixed and modified number of alternate frames centered on the current frame. This approach aims to reduce the computational complexity without compromising on image differencing quality. Object segmentation is finally performed on the refined difference image by the Otsu's algorithm. The evaluation tests show that the proposed approach reaches the better performance of detection compared with other approaches.
A Dynamic Load Scheduling Using Binary Self-adaptive JAYA (BSAJAYA) Algorithm in Cloud-Based Computing

The load scheduling is a paramount concern in the cloud-based computing due to the involvement of conflicting parameters and fluctuating demands of users. Though the problem of load scheduling comes in the category of NP-hard problem, it is utmost essential to design a load scheduling approach to tackle the challenges in cloud computing. These challenges could be elevated by using metaheuristic approaches that offer an optimal solution to an NP-hard problem. In this research, authors propose a binary self-adaptive JAYA-based load scheduling algorithm to solve the dynamically independent load scheduling problem in cloud-based computing. The proposed algorithm is compared with the lately invented metaheuristic-based task scheduling algorithms such as bird swarm optimization (BSO), modified particle swarm optimization (MPSO), and the standard JAYA. The proposed algorithm is evaluated using a real-world dataset for the various QoS scheduling parameters to advocate the effectiveness of the algorithm. The simulated results show notable improvements over other compared algorithms for makespan, average resource utilization, and load-balancing.
Exploiting Real-time Search Engine Queries for Earthquake Detection: A Summary of Results

Online search engine has been widely regarded as the most convenient approach for information acquisition. Indeed, the intensive information-seeking behaviors of search engine users make it possible to exploit search engine queries as effective “crowd sensors” for event monitoring. While some researchers have investigated the feasibility of using search engine queries for coarse-grained event analysis, the capability of search engine queries for real-time event detection has been largely neglected. To this end, in this article, we introduce a large-scale and systematic study on exploiting real-time search engine queries for outbreak event detection, with a focus on earthquake rapid reporting. In particular, we propose a realistic system of real-time earthquake detection through monitoring millions of queries related to earthquakes from a dominant online search engine in China. Specifically, we first investigate a large set of queries for selecting the representative queries that are highly correlated with the outbreak of earthquakes. Then, based on the real-time streams of selected queries, we design a novel machine learning–enhanced two-stage burst detection approach for detecting earthquake events. Meanwhile, the location of an earthquake epicenter can be accurately estimated based on the spatial-temporal distribution of search engine queries. Finally, through the extensive comparison with earthquake catalogs from China Earthquake Networks Center, 2015, the detection precision of our system can achieve 87.9%, and the accuracy of location estimation (province level) is 95.7%. In particular, 50% of successfully detected results can be found within 62 s after earthquake, and 50% of successful locations can be found within 25.5 km of seismic epicenter. Our system also found more than 23.3% extra earthquakes that were felt by people but not publicly released, 12.1% earthquake-like special outbreaks, and meanwhile, revealed many interesting findings, such as the typical query patterns of earthquake rumor and regular memorial events. Based on these results, our system can timely feed back information to the search engine users according to various cases and accelerate the information release of felt earthquakes.
Histopathology Image Registration By Integrated Texture And Spatial Proximity Based Landmark Selection And Modification

Three-dimensional (3D) digital pathology has been emerging for next-generation tissue based cancer research. To enable such histopathology image volume analysis, serial histopathology slides need to be well aligned. In this paper, we propose a histopathology image registration fine tuning method with integrated landmark evaluations by texture and spatial proximity measures. Representative anatomical structures and image corner features are first detected as landmark candidates. Next, we identify strong and modify weak matched landmarks by leveraging image texture features and landmark spatial proximity measures. Both qualitative and quantitative results of extensive experiments demonstrate that our proposed method is robust and can further enhance registration accuracy of our previously registered image set by 31.15% (correlation), 4.88% (mutual information), and 41.02% (mean squared error), respectively. The promising experimental results suggest that our method can be used as a fine tuning module to further boost registration accuracy, a premise of histology spatial and morphology analysis in an information-lossless 3D tissue space for cancer research.
Geometrical Bounds for Variance and Recentered Moments

We bound the variance and other moments of a random vector based on the range of its realizations, thus generalizing inequalities of Popoviciu and of Bhatia and Davis concerning measures on the line to several dimensions. This is done using convex duality and (infinite-dimensional) linear programming. The following consequence of our bounds exhibits symmetry breaking, provides a new proof of Jung’s theorem, and turns out to have applications to the aggregation dynamics modelling attractive–repulsive interactions: among probability measures on [Formula: see text] whose support has diameter at most [Formula: see text], we show that the variance around the mean is maximized precisely by those measures that assign mass [Formula: see text] to each vertex of a standard simplex. For [Formula: see text], the [Formula: see text] th moment—optimally centered—is maximized by the same measures among those satisfying the diameter constraint.
Cloud Computing, Exigency, Issues, Solutions: A survey

Abstract In the era of digitalization all over the world, cloud computing is the ubiquitous supreme technology that provides many computer resources and data storage network to the consumers smoothly on-demand. Many researchers of both academics as well as industries are proactively indulge in providing the benefits of cloud computing to the society. As per ray of IT industries is increases drastically also leads in the need of network of dynamic nature. This dynamic and scalable nature of cloud computing creates hurdles in security of data appears in the network. Mainly, three services are the centre of study in cloud computing i.e. Saas, Paas, Iaas. Several security aspects namely multitancy, variability, trouble in data and hackers attacks also focused on priority basis. This paper demonstrate the conceve outlook of latest issues related with cloud computing and the solution of issues. Many authors also convey several methodologies for cloud computing.It is especially recommended to newcomer ensuing their study in field of cloud computing to find the solutions related the issues of cloud computing.
A modelling &amp; Simulation via CloudSim for Live Migration in Virtual Machines

Abstract In growing era of cloud computing, one major utility is better and improved performance. For which Cloud Computing is a model which provides various resources available in a pool from where the users can get available the same when required. In the overall process there can be an important issue that is Fault tolerance to provide the high availability for the process which is being required by the user. And simultaneously if any fault occurs then user might not found any server downtime and other kind of delay in getting the response. There are many compute nodes such as Virtual machines further controlled by the Hypervisor. For transferring the load among compute nodes can be done by Cold Migration earlier there was a technique Non-Live migration. But at the same time there were lot many issues and challenges with the same that user might get the server downtime for infinite delay. For overcoming that there is an on-going technique which is known as Hot Migration or Live migration. It allows system administrators to shuffle the load available on an operating system instance to other Guest Operating system instance without any intervention on hosted service. This approach benefits to provide and efficient online system where no downfall in server maintenance. It also cures the load balancing, reconfiguration of data centers. The simulation of above aforesaid concept has been done via Cloudsim a tool for modelling and simulation in Cloud computing environment for live migration of virtual machines without intervention of cloud services. This paper simulated the work and gives the efficient results in comparison to the existing techniques.
An Infrastructure for Faithful Execution of Remote Attestation Protocols

Remote attestation is a technology for establishing trust in a remote computing system. Copland is a domain-specific language for specifying attestation protocols that operate in diverse, layered measurement topologies. An accompanying reference semantics characterizes attestation-relevant system events and bundling of cryptographic evidence. In this work we formally define and verify the Copland Compiler and Copland Virtual Machine for executing Copland protocols. The compiler and vm are implemented as monadic, functional programs in the Coq proof assistant and verified with respect to the Copland reference semantics. In addition we introduce the Attestation Manager Monad as an environment for managing attestation freshness, binding results of Copland protocols to variables, and appraising evidence results. These components lay the foundation for a verified attestation stack.
Accessibility and Historical Change: An Emergent Cluster Led Uncles and Aunts to Become Aunts and Uncles

There are times when a curiously odd relic of language presents us with a thread, which when pulled, reveals deep and general facts about human language. This paper unspools such a case. Prior to 1930, English speakers uniformly preferred male-before-female word order in conjoined nouns such as uncles and aunts; nephews and nieces; men and women . Since then, at least a half dozen items have systematically reversed their preferred order (e.g., aunts and uncles, nieces and nephews ) while others have not ( men and women ). We review evidence that the unusual reversals began with mother and dad(dy) and spread to semantically and morphologically related binomials over a period of decades. The present work proposes that three aspects of cognitive accessibility combine to quantify the probability of A&amp;amp;B order: (1) the relative accessibility of the A&amp;amp;B terms individually, (2) competition from B&amp;amp;A order, and critically, (3) cluster strength (i.e., similarity to related A'&amp;amp;B' cases). The emergent cluster of female-first binomials highlights the influence of semantic neighborhoods in memory retrieval. We suggest that cognitive accessibility can be used to predict the word order of both familiar and novel binomials generally, as well as the diachronic change focused on here.
Facial Emotion Detection Using Deep Learning and Haar Cascade Face Identification Algorithm

Emotions are the determinant of what makes us human. They influence each and every aspect of our everyday life, including concentration, perception, memory, decision making and contact with society. Human beings are exceptionally skilled and competent at reading and interpreting faces through evolution. In addition, facial recognition is so essential to human survival that an area called the Fusiform Face Area (FFA) had been developed for it by the human brain. In this paper, the key proposal is implementing multilevel Haar wavelet-based technique also known as Haar cascade face identification algorithm, which pulls out several expression attributes from major face regions at different levels. The translation of the structure and the arrangement of 43 facial muscles into emotions can be a difficult task to execute. So, we will Train FER2013 Dataset in Google collab for 25 epochs, which will churn out training accuracy to more than 90%. Many biometric technologies are available in our day-to-day activities to identify people, such as eye recognition, fingerprint recognition, face recognition. Face is a prime portion of the human being and needs detection for various applications such as forensic investigation, protection. The accuracy of the given technique is trialed on popular FER2013 facial expression dataset, and it achieves 91.85% accuracy for the given dataset.
Multi-Channel Augmented Reality Interactive Framework Design for Ship Outfitting Guidance

Industrial Augmented Reality (IAR) technique has been bloomed in recent years, but the interaction schemes of existing applications are relatively sole and inconvenient. To improve the interaction efficiency for complex scenes like the ship outfitting guidance, this paper proposes a natural multi-channel fusion interaction framework that integrates four information sensing channels including bar code scanning, free gesture, eye movement, and voice control to catch the user’s interaction intention comprehensively. A communication storage architecture and an asynchronous mechanism are introduced to address the multi-channel sensing signals in real-time. A prototype application was developed on the Microsoft HoloLens 2 to facilitate the product designers to browse the parts assembly sequences for process validation. Experimental results verify that the proposed method can realize the interaction intention of the subjects more intuitively and takes less responding time than the single-channel interaction scheme.
Extended Cyclic Codes Sandwiched Between Reed–Muller Codes

The famous Barnes-Wall lattices can be obtained by applying Construction D to a chain of Reed-Muller codes. By applying Construction ${{D}}^{{(cyc)}}$ to a chain of extended cyclic codes sandwiched between Reed-Muller codes, Hu and Nebe (J. London Math. Soc. (2) 101 (2020) 1068-1089) constructed new series of universally strongly perfect lattices sandwiched between Barnes-Wall lattices. In this paper, we first extend their construction to generalized Reed-Muller codes, and then explicitly determine the minimum vectors of those new sandwiched Reed-Muller codes for some special cases.
Power Amplifier enabled RF Fingerprint Identification

The development of radio frequency fingerprint (RFF) identification technique aims to identify and classify wireless devices by exploiting their unique radio frequency (RF) features. This paper proposes a wireless device RFF identification scheme, which relies on the non-linear memory effect resulting from the concatenation of two matched root-raised cosine (RRC) filters with a non-linear power amplifier (PA) inserted in-between. This unique feature can be extracted from the distorted constellation diagrams, which is processed into density trace figure. Classification algorithm based on image recognition method is developed to exploit the non-linear memory effect from the density trace figure. Simulation setup is carefully designed, and the results validated the effectiveness of our proposed RFF feature classification approach. In the range of SNR 1 dB to 25 dB, the overall identification accuracy rate exceeds 99%.
Background Filtering and Object Detection with Roadside LiDAR Data

The roadside LiDAR(light detection and ranging) sensors can obtain relevant information about all roadway users by collecting real-time 3D point cloud data of surrounding objects. In order to analyze the point cloud data of roadway users, background filtering and object detection are essential steps. This paper proposes a background filtering method based on point correlation by KDTree neighborhood searching and an adaptive threshold Euclidean clustering method. In order to improve the detection speed, background filtering will be performed on the original point cloud data before Euclidean clustering, so as to delete a large number of useless background points, and the clustering results will be distinguished by cuboid frame markers. The experimental results show that the single-frame point cloud clustering speed has increased by 31.3%, the clustering speed has been significantly improved.
Characterization and mechanical properties of offensive language taxonomy and detection techniques

Machine learning is the sub field of Artificial Intelligence which is responsible for developing a system that has the capability to learn and improve from past experience. There is an increasing demand for the offensive language detection because there is a large code-mixed text. Sometimes there is a possibility of code-mixed texts which are written using native scripts. If the system is getting trained on the data which is created using single language then it will leads to failure because of complexity. Using online communities and social media platforms has become increasing now. Recently many of the researchers have been investigating different ways of identifying the abusive content and developing systems to detect its different types like aggression identification, cyber bullying detection, hate speech identification, offensive language identification and toxic comments identification. Developing mechanisms to detect this is a challenging task. In the last few years, machine learning techniques and deep learning concept has been playing a significant role in the development of solutions for offensive language detection. So, considerable efforts have also been devoted to the development of machine learning techniques and deep learning models which is used to identify toxic content in recent years. First, this survey provides offensive language taxonomy and detection approaches. Then, the article focuses on the offensive language identification and toxic comments identification approaches. This article reviews various models used, type of learning model, languages for which the developed model is applied, other methods or approaches that were compared against this model, dataset used, offensive language detection type and performance metrics. The findings from the review are as well summarized and they urge that further effort is required to get improvement in the current state-of-the art approaches. This article also focuses on future work to improve the offensive language classification and toxic comments identification.
ROSES: Remote Online Sessions for Emerging Seismologists

Abstract In response to a pandemic causing the cancellation of numerous professional development programs for emerging seismologists, we successfully planned, promoted, and executed an 11 week online school for advanced graduate students worldwide during the summer of 2020. Remote Online Sessions for Emerging Seismologists included 11 distinct lessons focused on different topics in seismology. We highlight the course content, structure, technical requirements, and participation statistics. We additionally provide a series of “lessons learned” for those in the community wishing to establish similar programs.
Toward Deep Adaptive Hinging Hyperplanes

The adaptive hinging hyperplane (AHH) model is a popular piecewise linear representation with a generalized tree structure and has been successfully applied in dynamic system identification. In this article, we aim to construct the deep AHH (DAHH) model to extend and generalize the networking of AHH model for high-dimensional problems. The network structure of DAHH is determined through a forward growth, in which the activity ratio is introduced to select effective neurons and no connecting weights are involved between the layers. Then, all neurons in the DAHH network can be flexibly connected to the output in a skip-layer format, and only the corresponding weights are the parameters to optimize. With such a network framework, the backpropagation algorithm can be implemented in DAHH to efficiently tackle large-scale problems and the gradient vanishing problem is not encountered in the training of DAHH. In fact, the optimization problem of DAHH can maintain convexity with convex loss in the output layer, which brings natural advantages in optimization. Different from the existing neural networks, DAHH is easier to interpret, where neurons are connected sparsely and analysis of variance (ANOVA) decomposition can be applied, facilitating to revealing the interactions between variables. A theoretical analysis toward universal approximation ability and explicit domain partitions are also derived. Numerical experiments verify the effectiveness of the proposed DAHH.
Task Modelling for Interactive System Design

Task models have been used for decades in interactive system design and there are several mature modelling approaches with corresponding tool support. However, in our own work, we have also experienced their limitations, especially in situations where task models are partial ancillary models and not primary artifacts. This was one of the motivations for this paper, which presents a systematic examination of literature to better understand the current place of task models in the continual evolution of user-centred software development practices. While overview work in this domain typically focuses on the analysis of representative task modelling notations and/or tools and relies on foundation papers, we apply a mixed top-down and bottom-up approach to identify relevant themes and trends in the use of task models over the last twenty-years. The paper identifies and discusses dominant patterns of use as well as gaps. It provides a comprehensive framing of both past and present trends in task modelling and supports those who want to incorporate task modelling in their own work. From this we identify areas of research that should receive greater attention in order to address future considerations.
Shell Theory: A Statistical Model of Reality

The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically. We believe this problem is rooted in the mismatch between existing statistical techniques and commonly encountered data; object representations are typically high dimensional but statistical techniques tend to treat high dimensions a degenerate case. To address this problem, we develop a dedicated statistical framework for machine learning in high dimensions. The framework derives from the observation that object relations form a natural hierarchy; this leads us to model objects as instances of a high dimensional, hierarchal generative processes. Using a distance based statistical technique, also developed in this paper, we show that in such generative processes, instances of each process in the hierarchy, are almost-always encapsulated by a distinctive-shell that excludes almost-all other instances. The result is shell theory, a statistical machine learning framework in which separability constraints (distinctive-shells) are formally derived from the assumed generative process.