Reviewerly/exHarmony huggingface dataset:
Action Localization Using 2D-CNN and 3D-CNN Collaboration

Detecting human actions in videos is crucial for human-computer interaction, intelligent security, etc. Temporal and spatial information is very important for action localization, as how we utilize them determines the performance of detection. In order to address the problems of poor real-time and complex models of human action localization, a real-time detection architecture with two branches is presented in this paper, which predicts bounding boxes and action probabilities directly from video clips in one evaluation. We adopt the similar guidelines of YOLO (You Only Look Once) for bounding box regression and classification. The difference is that our model completes two tasks through two branches, one branch is used to extract spatial information from a key frame for bounding box regression, and another branch extracts spatiotemporal information from consecutive video frames for classification. In this paper, our model provides 65 frames-per-second on 16-frames input clips. Results on UCF-Sports and JHMDB-21 datasets present comparable accuracy to State-of-the-Art approaches. We obtain F-mAP of 90.39% and 76.29% with gains of 2.19% and 1.89%, respectively. On the JHMDB-21dataset, V-mAP reaches 90.7%, 90.0% and 69.5% with gains of 2.9%, 4.3% and 11.4% at IoU thresholds of 0.2, 0.5, and 0.75.
A Model for Information Security Culture with Innovation and Creativity as Enablers

This research aims to elicit a conceptual understanding of creativity and innovation to enable a totally aligned information security culture. Stimulating the creativity and innovation of employees in an organisation can help to solve information security problems and to create a culture where information security issues are addressed and resolved, as opposed to being introduced by end-users. The study applied a theoretical approach with a scoping literature review using the PRISMA method to derive traits and programmes that organisations can implement to stimulate creativity and innovation as part of the organisational culture. A model for engendering employee creativity and innovation as part of the information security culture is proposed, through the lens of the three levels of organisational culture. This study both offers novel insights for managerial practice and serves as a point of reference for further academic research about the influence of creativity and innovation in information security culture.
Policy Components - A Conceptual Model for Tailoring Information Security Policies

Today, many business processes are propelled by critical information that needs safeguarding. Procedures on how to achieve this end are found in information security policies (ISPs) that are rarely tailored to different target groups in organizations. The purpose of this paper is therefore to propose a conceptual model of policy components for software that supports modularizing and tailoring of ISPs. We employed design science research to this end. The conceptual model was developed as a Unified Modeling Language class diagram using existing ISPs from public agencies in Sweden. The conceptual model can act as a foundation for developing software to tailor ISPs.
Media Pembelajaran Mengenai Kosakata Bahasa Jawa Krama Untuk Anak SD Berbasis Animasi Motion Graphic

Indonesia has various languages, one of which is Javanese. Javanese is the language used to communicate by people from the Javanese tribe. In the world of education, Javanese is included in elementary school learning in areas such as East Java. But not a few children who have difficulty in learning about learning the Java language so that it can trigger a lack of understanding of the Java language properly and correctly, especially the Java language Krama. Therefore, the need for a media that is useful for helping children in learning about Java language Krama. This paper focuses on designing a motion graphic animation instructional media that is effective and attractive to help children in learning about Krama Java language vocabulary using qualitative methods such as interviews and observation.
ON THE BERLEKAMP — MASSEY ALGORITHM AND ITS APPLICATION FOR DECODING ALGORITHMS

The paper is devoted to the Berlekamp Masssey algorithm and its equivalent version based on the extended Euclidean algorithm. An optimized Berlekamp Massey algorithm is also given for the case ofa field of characteristic 2. The Berlekamp Massey algorithm has a quadratic complexity and is used, for example, to solve systems of linear equations in which the matrix of the system is the Toeplitz matrix. In particular, such systems of equations appear in algorithms for the syndrome decoding of BCH codes, Reed Solomon codes, generalized Reed Solomon codes, and Goppa codes. Algorithms for decoding the listed codes based on the Berlekamp Massey algorithm are given.
Human Activity Recognition

Abstract: Human Activity Recognition (HAR) is one of the active research areas in computer vision as well as human computer interaction. However, it remains a very complex task, due to unresolvable challenges such as sensor motion, sensor placement, cluttered background, and inherent variability in the way activities are conducted by different humans. Human activity recognition is an ability to interpret human body gesture or motion via sensors and determine human activity or action. Most of the human daily tasks can be simplified or automated if they can be recognized via HAR system. Typically, HAR system can be either supervised or unsupervised. A supervised HAR system required some prior training with dedicated datasets while unsupervised HAR system is being configured with a set of rules during development. HAR is considered as an important component in various scientific research contexts i.e. surveillance, healthcare and human computer interaction.
E-Voting System Using Homomorphic Encryption

Abstract: A protected e-voting system using Blockchain technology is proposed in this paper. It is using homomorphic encryption casting a vote electronically. It is represented that in spite of the fact that blend-based casting a ballot is an extremely straightforward answer for special e-casting a ballot it is helpless against an intimidation threat from politically contesting individuals. The intimidation assault particularly goes after special e-casting a ballot plot just results the political decision result and uncovers no vote, so is safe to the assault. Homomorphic encryption calculation is taken advantage of not just to count the votes without uncovering them yet in addition to change the votes when another round of counting is required. Additionally, it accomplishes all the security properties normally wanted in e-casting a ballot. Since Blockchain technology is used to store the votes any sign of tampering will be easily detected with the discrepancy of the time signatures and the hash values. The proposed will enable the citizen or a voter entity to vote from the confines of their home thereby negating their travel expenses which would otherwise be too extreme if the voter entity resided in a place where there is no adequate infrastructure to support traditional voting schemes. The voter entity can first verify their identity and receive an OTP which then enable them to cast a vote online in a dedicated web application.
Connecting Domains—Ecological Momentary Assessment in a Mobile Sensing Framework

Ecological Momentary Assessment (EMA) is the state-of-the-art methodology to capture an individual’s experiences (e.g., feelings, thoughts, behaviors) in daily life in an ecologically valid way. In this chapter, we outline the prominent role of EMA within a broader mobile sensing framework connecting domains of data acquisition from a range of sensing sources. We particularly highlight the advantages of context-aware assessment strategies that link the assessment of experiences to specific sensing events or patterns. Finally, we discuss strategies that go beyond assessment to implement innovative Ecological Momentary Interventions in real-life.
Towards verifiable web-based code review systems

Although code review is an essential step for ensuring the quality of software, it is surprising that current code review systems do not have mechanisms to protect the integrity of the code review process. We uncover multiple attacks against the code review infrastructure which are easy to execute, stealthy in nature, and can have a significant impact, such as allowing malicious or buggy code to be merged and propagated to future releases. To improve this status quo, in this work we lay the foundations for securing the code review process. Towards this end, we first identify a set of key design principles necessary to secure the code review process. We then use these principles to propose SecureReview, a security mechanism that can be applied on top of a Git-based code review system to ensure the integrity of the code review process and provide verifiable guarantees that the code review process followed the intended review policy. We implement SecureReview as a Chrome browser extension for GitHub and Gerrit. Our security analysis shows that SecureReview is effective in mitigating the aforementioned attacks. An experimental evaluation shows that the SecureReview implementation only adds a slight storage overhead (i.e., less than 0.0006 of the repository size).
GearWheels: A Software Tool to Support User Experiments on Gesture Input with Wearable Devices

We introduce GearWheels, a software tool for studies about gesture input with wearables, including smartwatches, rings, and glasses. GearWheels features an event-based asynchronous software architecture design implemented exclusively with web standards, communications protocols, and data formats, which makes it flexible to support many wearables via HTTP and WebSocket communications. GearWheels differentiates from prior software tools for gesture acquisition, elicitation, recognition, and analysis with its web-based, wearable-oriented, experiment-centered architecture design. We demonstrate GearWheels with a device affixed to the index finger, wrist, and the temple of a pair of glasses to illustrate touch stroke-gesture and motion-gesture input acquisition. We also perform a technical evaluation of GearWheels in the form of a simulation experiment, and report the request-response time performance of the software components of GearWheels with off-the-shelf wearables. We release GearWheels as open source software to assist researchers and practitioners in implementing studies about gesture input with wearables.
Big data clustering using fractional sail fish-sparse fuzzy C-means and particle whale optimization based MapReduce framework

The process of retrieving essential information from the dataset is a significant data mining approach, which is specifically termed as data clustering. However, nature-inspired optimizations are designed in recent decades to solve optimization problems, particularly for data clustering complexities. However, the existing methods are not feasible to process with a large amount of data, as the execution time taken by the traditional approaches is larger. Hence, an efficient and optimal data clustering scheme is designed using the devised Fractional Sail Fish-Sparse Fuzzy C-Means + Particle Whale optimization (FSF-Sparse FCM + PWO) based MapReduce Framework (MRF) to process high dimensional data. Theproposed FSF-Sparse FCM is designed by the integration of Sail Fish Optimization (SFO) with fractional concept and Sparse FCM. The proposed MRF poses two functions, such as the mapper function and reducer function to perform the process of data clustering. Moreover, the proposed FSF-Sparse FCM is employed in the mapper phase to compute the cluster centroids, and thereby the intermediate data is generated. The intermediate data is tuned in the reducer phase using Particle Whale Optimization (PWO), which is the integration of Particle Swarm Optimization (PSO) and Whale optimization algorithm (WOA). Accordingly, the optimal cluster centroid is computed at the reducer phase using the objective function based on DB-Index. The proposed FSF-Sparse FM + PWO obtained the highest accuracy of 0.903 and lowest DB-Index of 39.07.
Improving Image Interpretation in Digital Forensics

Visual media has grown in importance as a means of communication in the digital space in recent years. The dependability of digital visual information has recently been called into question due to the ease of duplication in both content and origin. Digital image forensics is a new field of study that seeks to validate the authenticity of images by recovering information about their past. Identifying the imaging devices that captured the digital image and detecting forgery are two of the issues addressed. With the publication of study results and an increasing number of applications, digital image interpretation in digital forensics has emerged as an intriguing area for future research. Keywords: Images, Forensic, Capturing, Visual Information, Interpretation. BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Jonathan Gasokpo Adjorlolo (2022). Improving Image Interpretation in Digital Forensics . SMART-IEEE-Creative Research Publications Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 23-28. www.isteams.net/ITlawbookchapter2022. dx.doi.org/
Applications of Hierarchical Bayesian Methods to Answer Multilayer Questions with Limited Data

There are many types of problems that include variables that are not well defined. Seeking answers to complex problems that involve many variables becomes mathematically challenging. Instead, many investigators use methods like principal component analysis to reduce the number of variables, or linear or logistic regression to rank the impact of the variables and eliminating those with the limited impact. However, eliminating variables can create a loss of integrity, especially for variables that might be associated with low likelihood but have high impact events. The use of hierarchical Bayesian methods resolves this issue by utilizing the benefits of information theory to help answer questions by incorporating a series of prior distributions for a number of variables used to solve an equation. The concept is to create distributions for the range and likelihood for each variable, and then create additional distributions to define the mean and shape values. At least three levels of analysis are required, but the hierarchical solution can include added levels beyond the initial variables (i.e., distributions related to the priors for the shape parameters). The results incorporate uncertainty, variability, and the ability to update the confidence in the values of the variables based on the receipt of new data.
Analysis for weak-value-amplification measurement in trapped ion systems

In this work, we carry out a comprehensive analysis for the weak-value-amplification (WVA) measurement in a single trapped Ca + 40 ion system, for arbitrary measurement strengths. In addition to carry out a compact expression for the meter's shift, we pay particular attention to the changes of the distribution profile of the output data and its variance width. With them, we are able to characterize the quality of the WVA measurement by means of the signal-to-noise ratio criterion and the Fisher information. For both types of characterizations, we compare the quality of the WVA scheme with that of the conventional measurement in detail. Viewing the versatility of phase tuning of the driving field, we also investigate the imaginary WVA measurement. All the extended analysis and results in this work can be examined by the state-of-the-art experiments in the trapped ion systems, which may shed light on possible applications in quantum technologies. • We analyze the weak-value-amplification (WVA) scheme in a single trapped 40 Ca + ion system for arbitrary measurement strengths. • The compact expressions for both the variance width of the distribution profile and the meter’s shift are obtained. • The signal-to-noise ratio (SNR) and the Fisher information (FI) are calculated to characterize the quality of the WVA scheme. • We compare the quality of the WVA scheme with that of the conventional measurement for both the SNR criterion and the FI. • All the analyses and results in this work can be examined by the state-of-the-art experiments in the trapped ion systems.
Local Optimal-Oriented Pattern and Exponential Weighed-Jaya Optimization-Based Deep Convolutional Networks for Video Summarization

Video summarization is used to generate a short summary video for providing the users a very useful visual and synthetic abstract of the video content. There are various methods are developed for video summarization in existing, still an effective method is required due to some drawbacks, like cost and time. The ultimate goal of the research is to concentrate on an effective video summarization methodology that represents the development of short summary from the entire video stream in an effective manner. At first, the input cricket video consisting of number of frames is given to the keyframe generation phase, which is performed based on Discrete Cosine Transform (DCT) and Euclidean distance for obtaining the keyframes. Then, the residual keyframe generation is carried out based on Deep Convolutional Neural Network (DCNN), which is trained optimally using the proposed Exponential weighed moving average-Jaya (EWMA-Jaya) optimization.
Digitalization for Sustainable Agriculture Production in Marginalized and Remote Communities

Digitalization is an important agriculture tool for achieving the Sustainable Development Goals (SDGs) in marginalized and remote communities. There is a scarcity of data linking digitalization, sustainable agriculture production, marginalized and remote communities in Sub-Saharan Africa. This study reviews digitalization for sustainable agriculture production in marginalized and remote communities of Sub-Saharan Africa. A total of 150 published papers from 2000-2022 were retrieved, and all those outside Africa were removed to leave a total of 125 papers. Results obtained indicate that digital technology transforms agriculture by promoting precision agriculture in marginalized rural communities in a more efficient approach by integrating different processes. Econet Wireless Zimbabwe is offering various bundle options which come through small messages (SMS) and call centers such as Eco-Farmer. Governments should invest in and make policies to improve digitalization in marginalized communities as it ultimately solves challenges such as nutrition, food insecurity, and climate change.
Digital Inclusion in Education Using Cloud Computing and Augmented and Virtual Reality

Information communication technologies (ICTs) to support teaching and learning in marginalized communities is a major drawback that alienates developing countries from the digital space. This chapter proffers digital inclusion in education for marginalized communities by using and merging cloud computing, augmented, and virtual reality (AVR) technologies. A systematic review of the literature was conducted. Diana Laurilliard's six learning types in the conversational framework was used to establish how digital technology is utilized and widely adopted in education in the context of marginalized communities. AVR technology embedded in the cloud supports all the six learning types and promote digital inclusion in education. A model that marginalized communities can utilize was developed. Future research can develop a model that captures the university-wide curriculum needs of higher institutions of learning in marginalized communities and the training of the educators to use ICTs to reduce the digital divide and improve digital literacy.
The Global Digital Divide and Digital Transformation

The use of digital gadgets and services is pervasive. Digitalization in data, information, and technologies is driving the Fourth Industrial Revolution (also known as 4IR or Industry 4.0), resulting in a global digital divide. The chapter proposes a strategy in collaboration with a variety of service sector partners that would allow governments to capitalize on the possibility of closing the technology gap between nations in a manner that benefits inclusive and sustainable growth in middle and low countries. Emerging revolution and internet access can strengthen socio-economic development and improve people's way of living, but they also have the potential to widen political divides, undermine democracy, and increase inequality. In response to COVID-19, the chapter also proposes digital technologies as tools for achieving sustainable development goals and digitalization response measures in marginalized societies.
A Joint Particle Filter for Quaternion-Valued $\alpha$-Stable Signals via the Characteristic Function

The filtering paradigm is revisited through the perspective of characteristic functions. This results in the derivation of a novel particle filtering technique for sequential estimation/tracking of quaternion-valued <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> -stable random signals. Importantly, the derived particle filter incorporates an efficient information fusion format and collaborative/distributed estimation framework to accommodate the push toward use of sensor networks. The distributed setting provides for the distribution of computational complexity among agents of a sensor network, while allowing each agent to retain an estimate of the state. Furthermore, the quaternion-valued structure allows for the derivation of a rigorous algorithm that is advantageous when dealing with signals of a multidimensional nature commonly encountered in sensor arrays.
Learning Minimum Variance Unbiased Estimators

The Gauss-Markov theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non-linear settings via deep learning with bias constraints. The classical approach to designing non-linear MVUEs is through maximum likelihood estimation (MLE) which often involves real-time computationally challenging optimizations. On the other hand, deep learning methods allow for non-linear estimators with fixed computational complexity. Learning based estimators perform optimally on average with respect to their training set but may suffer from significant bias in other parameters. To avoid this, we propose to add a simple bias constraint to the loss function, resulting in an estimator we refer to as Bias Constrained Estimator (BCE). We prove that this yields asymptotic MVUEs that behave similarly to the classical MLEs and asymptotically attain the Cramer Rao bound. We demonstrate the advantages of our approach in the context of signal to noise ratio estimation as well as covariance estimation.
Implementation of Federated Learning on Resource-constrained devices: Lessons learned

In this paper, we implement and deploy the most widely used algorithm in Federated Learning (FL), i.e. Federated Averaging (FedAvg), on an experimental testbed. The testbed consists of Raspberry Pi devices (RPis) connected to a wireless network. We perform extensive evaluations in various real-world scenarios and provide insightful results and lessons learned. Specifically, we evaluate FedAvg on our testbed to investigate the effect of the following parameters on training: (i) number of users selected at each round, (ii) number of local gradient steps before communicating with the server, (iii) clients disconnecting from the server, (iv) data distribution heterogeneity across clients, and (v) mobility of users. Finally, we examine the impact of the wireless environment on the learning performance under varying network parameters.