Reviewerly/exHarmony huggingface dataset:
Skeleton Moving Pose-based Human Fall Detection with Sparse Coding and Temporal Pyramid Pooling

This paper presents an efficient, yet high-accurate skeleton-based approach for human fall detection, which combines the sparse coding and temporal pyramid pooling techniques. The new method first separates the skeleton joints into five different parts, for each of which a moving pose descriptor is extracted to represent the human sub-actions. The principal component analysis is then employed to reduce the dimensions of the descriptors. Afterwards, sparse coding is invoked to encode each descriptor separately. Finally, these encoded descriptors are treated as a set of time series and then aggregated into the final video descriptors by temporal pyramid pooling, which can acquire temporal tendency to further boost the performance. Experimental results show that the new approach outperforms the state-of-the-art works on some commonly used datasets.
Consistency Hierarchy of Reservoir Computers

We study the propagation and distribution of information-carrying signals injected in dynamical systems serving as reservoir computers. Through different combinations of repeated input signals, a multivariate correlation analysis reveals measures known as the consistency spectrum and consistency capacity. These are high-dimensional portraits of the nonlinear functional dependence between input and reservoir state. For multiple inputs, a hierarchy of capacities characterizes the interference of signals from each source. For an individual input, the time-resolved capacities form a profile of the reservoir's nonlinear fading memory. We illustrate this methodology for a range of echo state networks.
Generalized Regression Neural Network Based Fast Fading Channel Tracking Using Frequency-Domain CSI Smoothing

In the high mobility environment, the channel state information (CSI) in the last part of the packet is different from the beginning part’s actual channel. This phenomenon degrades channel estimation accuracy, and hence it is necessary to be compensated to realize reliable communications. Decision feedback channel estimation (DFCE) has been widely considered as the channel tracking approach. It still causes estimation errors due to the decision-making process in the presence of time and frequency selective fading environments. To address these issues, this paper newly proposes a generalized regression neural network (GRNN) based channel tracking scheme incorporated with frequency-domain CSI smoothing. The latter part is the key to improve the dependability of the training data sets. Computer simulation results confirm that the proposed scheme can achieve superior BER performance and the lower root mean square error (RMSE) value of estimated CSI better than the conventional ones.
Introduce Floor Vibration to Virtual Reality

Floor vibration, a type of whole-body tactile stimulation, could mitigate cybersickness during virtual reality (VR) exposure. This study aims to further investigate its effects on cybersickness, as well as presence and emotional arousal by introducing floor vibration as a proxy for representing different virtual ground surfaces. For the investigation, a realistic walking-on-the-beach scenario was implemented, and floor vibrations were introduced in synchrony with the footsteps. Three conditions were designed based on the same scenario with different floor vibrations. The user study involving 26 participants found that there was no significant difference in presence and cybersickness across the three conditions, but the introduction of floor vibration (regardless the vibration type) had a mixed impact on the emotional arousal, as measured by changes in pupil sizes and skin conductance. Also, participants generally most preferred the matched vibration.
Linking Cybersecurity and Accounting: An Event, Impact, Response Framework

SYNOPSIS Due to recent high-profile cybersecurity breaches and increased practitioner and regulatory attention, organizations are under pressure to consider the accounting implications of these attacks and develop appropriate responses. Specifically, cybersecurity events may affect organizations' operations, financial and non-financial performance, and ultimately its stakeholders. To address how cybersecurity issues may affect accounting, this paper presents an Event, Impact, Response Framework to discuss current research and consider implications for both practitioners and researchers. The Framework highlights how practitioners may rely on research findings to better assess cybersecurity threats, understand their impact, and develop response strategies. Results encourage additional research examining how (1) organizations identify cybersecurity threats, incidents, and breaches, (2) cybersecurity affects different risks, and (3) management responses to cybersecurity risks and events. Further, the Framework suggests the need for cybersecurity research to extend beyond the AIS community to areas such as financial accounting, managerial accounting, and auditing.
Assesment of Traditional and Hybrid Controller for Controlling Robotic Manipulator System

Controlling of robot has always became an exigent work for engineers these controllers were came into existence since 1950, when a PID controller was used to control complex systems the structure of this controller was very easy to design and has always remain a most popular controller in the industry because of its lucidity and low cost. it is sufficient enough to handle linear system but was unfit for nonlinear system, researchers and scientist has tried to resolve the drawbacks of PID by fuzzy logic controller which was effective for nonlinear system further as the year passes by new controlling techniques has arrived which were more powerful in terms of controlling in comparison to the previous ones. neuro logic controller came to handle those systems which are complex, highly nonlinear and poorly dynamically analyzed and further this controller was combine with the Traditional controllers and give rise to neuro PID controller and neuro Fuzzy PID controller. This paper presents a detailed review on various controlling techniques which are being used in the controlling of robotic manipulator starting from the development of the first controller till now.
A Scalable Gesture Interaction System based on mm-Wave Radar

This paper proposes a novel gesture interaction system based on mm-wave radar. The overall structure, key algorithms and the implementation of the system will be introduced. Compared with the existing solutions, this system first ever provides a radar-based static hand shape recognition method, while supporting more scalable gesture interaction applications. At present, this system can support multiple noncontact gesture interactive applications, such as hand shape recognition, 3D hand trajectory tracking, basic gesture detection and handwritten recognition with high accuracy.
The Influence of Digital Transformations on Learners’ and Educators’ Creativity

The usage of information technology has transformed education systems with significant impacts on academic life and the reorganization of the educational environment. Therefore, it is essential to understand the benefits arising from the adoption of new effective teaching practices connected to the digital world. Among all benefits, we would like to emphasize the influence of digital transformation on learners’ and educators’ creativity. In our paper, we consider the concepts of “creative thinking” and “creative thinking skills” and analyze the possibilities of student-teacher creative collaboration. Also, we discuss an experiment designed whether corpus technologies influence students’ creativity development. The experiment included students (N = 50) who learned English idioms in the framework of the corpus approach. The results showed the positive impact of corpus technologies on creative-thinking skills and their efficiency for English learning purposes. In conclusion we recommend such technology as an effective digital tool in the educational process.
Augmented/Mixed Reality Technologies Supporting Digital Surgery

The operating room of the future implements a broad set of digital technologies, aimed to improve the quality of care, optimize patient outcome and enhance usability of existing surgical tools. While analyzing and visualizing surgical data and available digital information anytime on demand become a key piece of the modern surgical workflow, their integration remained difficult until the recent advancements of augmented/mixed reality technologies, applications and devices. This systematic review aims to collect and compare outcomes of the most recent (2019–21) articles on the topic of Augmented Reality (AR) and Mixed Reality (MR) used in the medical domain, focusing on surgical practice. The usability of heads-up holographic technology is getting more and more attention through the years on this field too, but comparisons of used devices and benchmarking are still missing. As further technological improvements are still ongoing, possible standards are required to guide future research and applications of AR/MR from surgical trainee assessment to situation awareness (SA). With examining existing recent studies this review could take a possible first step towards the intention of unified standardisation.
A formal method for privacy‐preservation in cognitive smart cities

Abstract The Internet of things (IoT) and communication technologies are enabling the consumer to use the smart devices. The explosion of smart devices is shifting the IoT into a framework, which we call the cognitive IoT. The cognitive IoT can enhance many sectors such as smart cities, healthcare, industry 4.0, transportation, just to name a few. Most of the data produced in smart cities are wasted because the important information is not extracted due to lack of standard mechanism for knowledge extraction and archiving methods. This has attracted the attention of researcher to design new approaches of machine and cognitive learning that can handle vast amount of dynamic data. The cognitive smart city is the integration of IoT, smart city technology, real‐time big data analytics and artificial intelligence (AI) strategies for proactive actions. The services in smart cities relies on the collection and analysis of the data which are provided by the use themselves or accessed by the services providers. The citizen engagement is the key for success of smart city; however, the engagement may get reduced due to privacy concerns arising from data collection. Therefore, privacy‐preservation shall be achieved in a manner where valuable data is exchanged with service provider, and other third party while protecting the citizens' privacy, upholding data laws and enforcement. Therefore, there is a need to control the anonymization and mix some more techniques to preserve the quality of the data. The proposed formal method for privacy‐preservation in smart cities is based on pseudonymization, clustering, anonymization and differential privacy methods. The modified clustering algorithm selects the initial cluster based on the concept of dissimilarity between the data sequences. We have assessed the functional correctness and preformation of the proposed model for privacy‐preservation in smart cities. The proposed method has lower discriminating rate as compared to other existing methods.
Towards a Taxonomy of Bug Tracking Process Smells: A Quantitative Analysis

Bug tracking is the process of monitoring and reporting malfunctions or issues found in software. While there is no consensus on a formally specified bug tracking process, some certain rules and best practices for an optimal bug tracking process are accepted by many companies and open-source software (OSS) projects. Despite slight variations between different platforms, the primary aim of all these rules and practices is to perform a more efficient bug tracking process. Practitioners’ noncompliance with the best practices not only impedes the benefits of the bug tracking process but also negatively affects the other phases of the life cycle of software development.In this study, based on the results of a multivocal literature review, we analyzed 60 sources in academic and gray literature and propose a taxonomy of 12 bad practices in the bug tracking process, that is bug tracking process smells. To quantitatively analyze these process smells, we inspect bug reports collected from six projects. Among these projects, four of them are Jira-based (MongoDB Core Server, Evergreen, Confluence Server & Data Center, Jira Server & Data Center) and the other two are Bugzilla-based (GCC and Wireshark). We observed that a considerable amount of bug tracking process smells exist in all projects with varying ratios.
<scp>OrangeMusic</scp>: An orange computing‐inspired recommender framework in internet of music things

Abstract Recent computational analytics in the domain of the Internet of Things provides crowd‐sourced reviews for decision assistance for innumerable aspects of our living standards and socio‐entertainments. However, one of the most significant tasks for obtainable online music libraries and websites is the demand for personalized and professionalized courses of action for music listeners and composers to elect suitable musical performances. In this paper, we illustrate a hybrid matrix factorization‐based content‐sensitive music recommender schema on the Internet of Music Things. Emerging orange computing technology offers a harmonic fusion framework for psychological care and happiness‐concerned computing. We elucidate the projected music recommender paradigm in the domain of Internet of Music Things, titled as OrangeMusic. The OrangeMusic schema differs from the earlier contributions in the following aspects: (a) Orange computing‐based information fusion framework is applied on the Internet of Music Things; (b) Provided musical content revisions can be exposed by listeners' rating metrics and be exploited to amend original listener‐provided ratings; (c) Music listeners' preferences and musical items are incorporated into the standard matrix factorization mechanism. The performance metrics flourish that our proposed OrangeMusic presents a proficient rating prediction and intensifies the accuracy of content‐sensitive music recommendation expressively.
Fertilization optimization algorithm on CEC2015 and large scale problems

Abstract This work, presents a novel optimizer called fertilization optimization algorithm, which is based on levy flight and random search within a search space. It is a biologically inspired algorithm by the fertilization of the egg in reproduction of mammals. The performance of the algorithm was compared with other optimization algorithms on CEC2015 time expensive benchmarks and large scale optimization problems. Remarkably, the fertilization optimization algorithm has overcome other optimizers in many cases and the examination and comparison results are encouraging to use the fertilization optimization algorithm in other possible applications.
Efficient Multiplier and FPGA Implementation for NTRU Prime

As quantum computing age is emerging on horizon, many of the current cryptography standards, e.g., RSA and Elliptic Curve Cryptography, are shown to be compromised under quantum attacks according to Shor's algorithm. A multiple-round competition has been launched by NIST to decide the next generation post-quantum cryptography (PQC) standards since 2017. Entering the final round, NTRU Prime system, proposed in 2016, remains one of a few that have a chance to be part of the future PQC standard. In this work, efficient multiplication architecture is proposed for Streamlined NTRU Prime system. To the best of our knowledge, this work is the first attempt at hardware architecture and implementation of NTRU prime system. Our FPGA implementation results have also shown the proposed multiplier compares favorably to the similar work on the original NTRU system.
Extensional equality preservation and verified generic programming

In verified generic programming, one cannot exploit the structure of concrete data types but has to rely on well chosen sets of specifications or abstract data types (ADTs). Functors and monads are at the core of many applications of functional programming. This raises the question of what useful ADTs for verified functors and monads could look like. The functorial map of many important monads preserves extensional equality. For instance, if $f, g : A \rightarrow B$ are extensionally equal, that is, $\forall x \in A, \ f \ x = g \ x$, then $map \ f : List \ A \rightarrow List \ B$ and $map \ g$ are also extensionally equal. This suggests that preservation of extensional equality could be a useful principle in verified generic programming. We explore this possibility with a minimalist approach: we deal with (the lack of) extensional equality in Martin-L\"of's intensional type theories without extending the theories or using full-fledged setoids. Perhaps surprisingly, this minimal approach turns out to be extremely useful. It allows one to derive simple generic proofs of monadic laws but also verified, generic results in dynamical systems and control theory. In turn, these results avoid tedious code duplication and ad-hoc proofs. Thus, our work is a contribution towards pragmatic, verified generic programming.
Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning

In order to apply Optical Character Recognition (OCR) to historical printings of Latin script fully automatically, we report on our efforts to construct a widely-applicable polyfont recognition model yielding text with a Character Error Rate (CER) around 2% when applied out-of-the-box. Moreover, we show how this model can be further finetuned to specific classes of printings with little manual and computational effort. The mixed or polyfont model is trained on a wide variety of materials, in terms of age (from the 15th to the 19th century), typography (various types of Fraktur and Antiqua), and languages (among others, German, Latin, and French). To optimize the results we combined established techniques of OCR training like pretraining, data augmentation, and voting. In addition, we used various preprocessing methods to enrich the training data and obtain more robust models. We also implemented a two-stage approach which first trains on all available, considerably unbalanced data and then refines the output by training on a selected more balanced subset. Evaluations on 29 previously unseen books resulted in a CER of 1.73%, outperforming a widely used standard model with a CER of 2.84% by almost 40%. Training a more specialized model for some unseen Early Modern Latin books starting from our mixed model led to a CER of 1.47%, an improvement of up to 50% compared to training from scratch and up to 30% compared to training from the aforementioned standard model. Our new mixed model is made openly available to the community1.
Lorien

Modern deep learning systems embrace the compilation idea to self generate code of a deep learning model to catch up the rapidly changed deep learning operators and newly emerged hardware platforms. The performance of the self-generated code is guaranteed via auto-tuning frameworks which normally take a long time to find proper execution schedules for the given operators, which hurts both user experiences and time-to-the-market in terms of model developments and deployments.
Attention Aware Debiasing for Unbiased Model Prediction

Due to the large applicability of AI systems in various applications, fairness in model predictions is extremely important to ensure that the systems work equally well for everyone. Biased feature representations might often lead to unfair model predictions. To address the concern, in this research, a novel method, termed as Attention Aware Debiasing (AAD) method, is proposed to learn unbiased feature representations. The proposed method uses an attention mechanism to focus on the features important for the main task while suppressing the features related to the sensitive attributes. This minimizes the model's dependency on the sensitive attribute while performing the main task. Multiple experiments are performed on two publicly available datasets, MORPH and UTKFace, to showcase the effectiveness of the proposed AAD method for bias mitigation. The proposed AAD method enhances the overall model performance and reduces the disparity in model prediction across different subgroups.
Residual Quantization for Low Bit-Width Neural Networks

Neural network quantization has shown to be an effective way for network compression and acceleration. However, existing binary or ternary quantization methods suffer from two major issues. First, low bit-width input/activation quantization easily results in severe prediction accuracy degradation. Second, network training and quantization are always treated as two non-related tasks, leading to accumulated parameter training error and quantization error. In this work, we introduce a novel scheme, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Residual Quantization</i> , to train a neural network with both weights and inputs constrained to low bit-width, e.g., binary or ternary values. On one hand, by recursively performing residual quantization, the resulting binary/ternary network is guaranteed to approximate the full-precision network with much smaller errors. On the other hand, we mathematically re-formulate the network training scheme in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>EM-like</b></i> manner, which iteratively performs network quantization and parameter optimization. During <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">expectation</b> , the low bit-width network is encouraged to approximate the full-precision network. During <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maximization</b> , the low bit-width network is further tuned to gain better representation capability. Extensive experiments well demonstrate that the proposed quantization scheme outperforms previous low bit-width methods and achieves much closer performance to the full-precision counterpart.
FEASIBILITY OF VISIBLE SHORT-WAVE INFRARED REFLECTANCE SPECTROSCOPY TO CHARACTERIZE REGOLITH-HOSTED RARE EARTH ELEMENT MINERALIZATION

Abstract Regolith-hosted rare earth element (REE) deposits predominate global resources of heavy REEs. Regoliths are underlain by various types of igneous rocks and do not always host economically valuable deposits. Thus a feasible and convenient method is desired to identify REE mineralization in a particular regolith. This study presents a detailed visible short-wave infrared reflectance (VSWIR) spectroscopic study of the Renju regolith-hosted REE deposit, South China, to provide diagnostic parameters for targeting REE orebodies in regoliths. The results show that the spectral parameters, M794_2nd and M800_2nd, derived from the VSWIR absorption of Nd3+ at approximately 800 nm, can be effectively used to estimate the total REE concentrations in regolith profiles. M1396_2nd/M1910_2nd ratios can serve as proxies to evaluate weathering intensities in a regolith. Abrupt changes of specific spectral features related to mineral abundances, chemical compositions, and weathering intensities can be correlated with variations of protolith that formed a regolith. These VSWIR proxies are robust and can be used for exploration of regolith-hosted REE deposits.