Reviewerly/exHarmony huggingface dataset:
Simulation Framework on Developing Neural Network Model of C. Elegans

The nervous system is a complex yet efficient structure – with superior information processing capabilities that surely surpass any man-made high-performance computer. However, without having a complete architectural blueprint of this “technology,” understanding its underlying mechanisms is a challenging task. Current research has focused on investigating the interactions between neurons. However, this is not sufficient to explain each neuron’s functionality – as they might be physiologically different. One of the approaches to understanding the role of a specific neuron is to observe how its behaviour changes as a network develops. Notwithstanding, observations of biological changes in behaviour accompanying network developmental are experimentally challenging. This therefore creates a new possibility for research exploration: using a network developmental model that can simulate the behaviour of the biological network during the development process. In this research, the biological network of C. Elegans is used as the foundation for the design of the model. Although composed of only with 302 neurons, the nematode's network has the capability of handling complex biological processes, for instance, locomotory and sensory behaviour. In addition, the invariant developmental cell lineage and available network connectivity information provide advantages in terms of analysing the developmental pattern. This creates the possibility of defining a mathematical description of the developmental trajectory. This work aim to design a network trajectory model that is governed by specific rules – a model that has the ability to self-generate a single cell for the multicellular network. For this purpose, a high-processing event-based neural simulator with a 2D virtual environment is developed – to be used for the simulation of a complex multi-stimulus environment. This paper describes the approach and initial design of the network developmental trajectory model.
An Image Based Encryption Algorithm for Multimedia Applications

Multimedia is the most popular domain in recent era, where it handles distinct information such as text, image, music, video and etc. The security and channel capacity are the challenging parameters in real time multimedia applications. In this research work, an image based double encryption scheme has proposed along with DCT compression technique. The input image is encrypted by Chaotic Baker map and Advanced Encryption Standard (AES) algorithms. The encrypted image is compressed by DCT compression technique. The PSNR and MSE of proposed double encryption method attains 77.1617 and 0.0013 respectively. The experimental results are compared with the individual encryption methods shows that the performance of Double encryption is superior to the other existing methods.
Hand-Held Object with Action Recognition Based On Convolutional Neural Network in Spatio Temporal Domain

Several applications such as object recognition and face recognition are established with the progress of smart devices and computer technology, to assist human-computer interaction (HCI). In HCI, Hand-held object recognition hasamain role. This approach helps the computer to realise the user’s intentions and also meetsthe user requirements. Hand as an organ which is considered as a direct and natural way of communication for humans. The Hand-held Object Recognition (HHOR) assigns a label for the object which is heldin hand this could help machines in understanding the environment and the intention of the people. However, it has not been well studied in the community.So, in this paper, we proposed system for recognizing such activities happening between hands and faces in real time. The interaction events (e.g. eating, phoning and smoking) between hands and faces are analysed using the event analysis approach. Ratio histogram is used for obtaining the essential colour bins for detecting the desired objects via re-projection method. For object tracking and feature extraction, a code book method is used. To recognize various human-object interaction events, the dynamic and multiplicity contexts of event are modelled together. Finally, atwo stage cascaded CNN classifiers for the recognition is implemented as this technology improves the performance of object recognition.To make fair comparisons, six methods were compared in this paper based on the HMDB dataset. This system is effective and can be performed in real time because an exhaustive search process to find possible interaction pairs in the huge space of all possible event parameters is not involved. Experimental results have proved the superiority of our proposed system to analyse different human behaviours and events between hands and a face.
Risks to the Public

Mini-editorial (PGN) 2020 was a crazy year, with all kinds of risks on display. As usual, many of the lessons noted in past issues of SEN and RISKS have been largely ignored, and failures continue to mirror events from the past that have long been discussed here. Issues such as safety, security, and reliability always seem to need more foresight than they receive. Y2K con- tinues to hit somewhere each New Year's Day, when short- term remediations that demanded periodic upgrading have been forgotten. (I suppose old COBOL code will still ex- ist in year 2100, when there may be ambiguities relating to dates that could be 21xx or 20xx (although 19xx is unlikely), and the narrow windowing xes will fail even more dramati- cally.) Election integrity continues to be a real concern, where we are caught in the crosshairs between computer systems and networks that are not meaningfully trustworthy or au- ditable, and the nontechnological risks are still pervasive from unbalanced redistricting, creative dysinformation, poli- tics, Citzens United, and foreign interference. We need non- partisan scrutiny and defense against would-be subverters to overcome potential attacks and inadvertent mistakes. In pres- ence of potential risks in every part of the process, a strong sense of risk-awareness is required by voters, election officials, and the media (both proactively and remedially, as needed).
Differential Privacy Preservation in Adaptive K-Nets Clustering

K-Nets is a deterministic clustering algorithm based on the network structure. It can automatically detect the sym-metric structure in the data and can be used to process clusters of different sizes, shapes or a specific number. However, K-Nets has the following shortcomings: (1) the clustering result is more sensitive to the manually input parameter K, so the accuracy will be affected; (2) the algorithm only considers the average distance of K-nearest neighbors, which may lead to some wrong distribution center points in the dataset with large density difference or the same score values during calculation; (3) it does not consider the privacy leakage during the clustering process. To solve the above problems, we propose a differential privacy protection method in adaptive K-Nets clustering, called ADP-K-Nets. Firstly, for reducing the influence of the parameters on the result, the natural eigenvalues are adaptively obtained through the characteristic of the natural neighbors and used as parameter values to find data points. Then we define a new method for calculating the score, which can solve the problem of incorrectly selecting cluster centers when there are large density differences or conflicts in the calculation process. Also, the Laplace noise is added in calculating the local density of every data point to protect data privacy. Experimental results show that our method ensures the performance of clustering compared with some existing algorithms.
Make Member Testimonials Part of Recruitment Strategy

The Membership Management ReportVolume 17, Issue 7 p. 2-2 Articles Make Member Testimonials Part of Recruitment Strategy First published: 03 June 2021 https://doi.org/10.1002/mmr.31728Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume17, Issue7July 2021Pages 2-2 RelatedInformation
Lean education

Lean technologies are widely used in various industries. Currently, the development of quality management of higher education is facing new challenges, the key of which is to ensure competitiveness and attractiveness for potential students, employers and research customers. In the context of the «struggle» for financing, it is necessary to find internal reserves for development, which is the main result of reducing losses. The application of lean manufacturing methods in education is possible taking into account the adaptation of tools and their consistent implementation, taking into account scientific and practical experience. Activities to optimize the educational process should be carried out taking into account the views of interested parties, such as: teachers, students and employers. The article discusses the application of the principles of lean method and 5S in higher education. The main difficulties in implementation are reflected.
Canadian Library Association (CLA)

Jerome Hill, is a residential center offering programming in the Arts and Humanities.It offers time and space in a contemplative environment to think, create, and connect.The Foundation encourages the visionary work of artists, scholars, and thinkers in the Arts and Humanities.The Camargo Core Program is the historical and flagship
PLN: Das Técnicas Tradicionais aos Modelos de Deep Learning

With the massive amount of data generated daily on the Web, researchers in the field of Natural Language Processing have focused on extracting useful information from unstructured data.This volume of data makes it impractical for anyone to manually process them in order to extract meaningful information, i.e., feelings, opinions, irony, hate speech, fake news, and others.The main objective of this short course is to introduce principles, traditional techniques, and tools in the field of NLP, developing models for binary classification tasks.The course focuses on practical activities using the Python language and libraries such as: NLTK, SpaCy, and Scikit-Learn.In the final part, some topics about Deep Learning will be discussed, including the BERT language model. ResumoCom a imensa quantidade de dados gerados diariamente na Web, pesquisadores da área de Processamento de Linguagem Natural (PLN) têm buscado extrair
High Density Video Impulse Noise Reduction Scheme Based on Spatially Growing Modified Median Filter

In this paper a spatially growing modified median filter method has been proposed for the restoration of digital videos which are adulterated by the saturated impulse noise i.e., salt-and pepper noise. The proposed denoising process executes filtering action only to the corrupted pixels in the video keeping noise free pixels in the video unharmed. The current study has used a spatially growing window method for the exclusion of high density noise present in the digital videos. It has used sliding window of increasing dimension centered at any pixel and sequentially swapped the noise corrupted pixels by the median value of the trimmed window. However, if the whole pixels in the window are noise corrupted then the dimension of sliding window is enlarged in order to obtained the noise free pixels for median computing. At the same time, this process has been found to be capable to removing the high density salt and pepper noise and also well preserved the fine details of the videos. Experimentally, it has been found that the proposed method generate enhanced PSNR and SSIM results.
H<sub>2</sub>O Innovation's MBR/RO water reuse project reaches major milestone

H 2 O Innovation Inc's water reuse project Virginia WaterHub® – carried out in partnership with Sustainable Water, (Membrane Technology, May 2019, page 6) has reached 378 541 m 3 (100 million gallons) of recycled water since it was first commissioned during the summer of 2019.
Correction

This article refers to:Differentiating Text-Based and Knowledge-Based Validation Processes during Reading: Evidence from Eye Movements
Design and Development of Cybersecurity Concentration Courses and Laboratory Experiences for Undergraduate Students

Abstract Information and Communication Technologies (ICT) have become increasingly important for US citizens, who are becoming dependent on the use of information networks and services in their daily lives. Yet, while uptake of new technology among citizens is high, a large portion of the population remains unaware of their exposure to risks from security breaches and “cyber-abuse” in the form of network disruptions, malicious code, criminality and hackings, as well as hardware and software failures. There is an urgent need for the development and implementation of awareness-raising campaigns targeted at the safe and responsible use of ICT. The demand for cybersecurity experts in both the public and private sectors is far outpacing the development of the talent pool, making for a hyper-competitive labor market. Against ever evolving cyber-threats the need to graduate students skilled in the concepts and technologies of cybersecurity is becoming a critical responsibility of academic institutions in order to help preserve the sovereignty of the US and her allies. Roughly two-thirds of security practitioners indicate that their organization does not invest enough in security awareness training. Professionals in the field consider it imperative for academic institutions to increase course development in computer security to make students both knowledgeable and technologically prepared for future challenges in this field. Knowledge about ongoing and planned activities will help the institutions to design and develop effective programs individually and in collaboration with others. Universities are only beginning to catch up. The objective of this paper is to describe our experiences in the design and development of cybersecurity courses and laboratory exercises for a cybersecurity concentration for our computer science majors. The findings presented in this paper may be used by interested parties in cybersecurity curriculum and course development.
Region Based 3D Face Recognition using a Convolutional Neural Network

In this paper we are proposing a compact CNN model for expression insensitive 3D face recognition. 3D face recognition is a trendy interested area in computer vision and is applied in different real time applications. Lots of research work is going on in industry and academia in this area. Traditional machine learning approaches for 3D face recognition is now superimposed by deep neural networks and are trained using large amount of data. We are applying a region based 3D face recognition approach along with a fusion CNN. 15 sub regions are generated from the frontal face region and features are extracted from it. The features extracted from each region are fused using the fusion CNN. These facial features are rich in identification information and they are not represented using single features. Fusions of multiple features extracted from the 15 regions are combined. The set of values extracted from the features after preprocessing are given as input to the CNN. The fusion CNN uses the features from different layers and fuses them together for a prediction as shown in figure1. The lower layer and higher layer features are fused. The computation time of the proposed system is 3.24 s for preprocessing and 0.09 s for matching. The overall computation time is 3.33 s. The running time of our previous region based approaches [13][14] is around 6 .48 seconds and 12 seconds. It is evident that the computation time of our proposed approach stands good and can be applied in time critical security applications. The three major steps are preprocessing, deep feature learning and deep feature classification.
Weighted Principal Component Analysis based on statistical properties of features for Spike Sorting

Spike Sorting is a challenging problem in Computational Neuroscience because of the complexity of neural data. One of the greatest issues are overlapping clusters. This paper focuses on the feature extraction step in the Spike Sorting pipeline and proposes an adaptation of Principal Component Analysis (PCA) to increase the separability between clusters. This is achieved by weighting the features before applying PCA, taking into consideration the multimodality and the distance between probability distributions. The information extracted from the characteristics of a multimodal distribution is the number of modes (peaks). The distance between the probability distributions is quantified using Jensen-Shannon divergence. The computed information, number of modes and distance, is aggregated into a coefficient representing the weight of the features. The new approach has been validated on a synthetic dataset and shows improvements compared with the state-of-the-art PCA.
Enterprise Risk Management in Projects

Project management inherently involves high levels of risk, because projects by definition are being done for the first time. There are a number of classical project domain types, each with their own characteristics. Some are more predictable, such as those encountered in civil engineering. Highly unpredictable projects are encountered in software engineering, and projects involving massive undertakings or emergency response typically faced by government bureaucracies. The chapter gives a framework for project risk analysis, based on PMBOK. This included a number of qualitative elements which can be extremely valuable in project management. But they are less concrete, and therefore we found it easier to focus on quantitative tools. We want to point out that qualitative tools are also very important. The qualitative tools presented start with the deterministic critical path method, which assumes no risk in duration nor in resource availability. We present simulation as a very useful means to quantify project duration risk. Simulation allows any kind of assumption, and could also incorporate some aspects of resource availability risk through spreadsheet models.
Measurement of coaxiality of rectangular spline shaft based on image processing

The existing measurement methods of coaxiality of rectangular spline shaft include rotation axis method, coordinate method and V-frame method. These methods belong to the contact measurement, which are low measurement efficiency and can not realize the real-time on-line measurement of coaxiality of rectangular spline shaft. In this paper, a visual measurement method of coaxiality of rectangular spline shaft based on image processing is proposed, and it is an active non-contact measurement method. The on-line measurement of coaxiality of rectangular spline shaft is achieved, and its feasibility is verified by experiments, therefore the robustness of coaxiality measurement of rectangular spline shaft is improved.
Uganda – Revenue for FY21/22

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 58, Issue 6 p. 23465B-23465C Full Issue Uganda – Revenue for FY21/22 First published: 17 August 2021 https://doi.org/10.1111/j.1467-6346.2021.10098.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume58, Issue6August 2021Pages 23465B-23465C RelatedInformation
Implementasi K-Means Clustering Analysis untuk Mengelompokkan Kelurahan-Kelurahan Di DKI Jakarta Berdasarkan Jumlah Positif Covid-19.

Covid 19 is a disease caused by the Corona Virus, attacking the respiratory system, and worsening the condition of a person with congenital disease. Until now, the government has still determined the case of the COVID-19 outbreak as an emergency and established health protocols for its citizens. DKI Jakarta is one of the areas with the highest cases of covid 19. Therefore, this study is grouping or clustering urban villages in DKI Jakarta province into groups of urban villages with very high, medium, or low cases using the K-Means Clustering method . This grouping aims to provide information to the government and as one of the considerations in dealing with COVID-19 cases as well as prioritizing the provision of medical facilities and infrastructure. The K-Means Clustering study with standardized data using Euclidean distance succeeded in grouping urban villages in DKI Jakarta into 3 groups.
Discrete Geometry

A number of important recent developments in various branches of discrete geometry were presented at the workshop, which took place in hybrid format due to a pandemic situation. The presentations illustrated both the diversity of the area and its strong connections to other fields of mathematics such as topology, combinatorics, algebraic geometry or functional analysis. The open questions abound and many of the results presented were obtained by young researchers, confirming the great vitality of discrete geometry.
Statement of Retraction: Visualization and Integration of Images of Radioactive Substances as Point Cloud Data in 3-D Environment Models

This article refers to:RETRACTED ARTICLE: Visualization and Integration of Images of Radioactive Substances as Point Cloud Data in 3-D Environment Models