Reviewerly/exHarmony huggingface dataset:
Development of an Inclusive Participatory Democracy System

Barriers such as unequal access, lack of digital skill, low income and disability constitute limiting factors for technology-mediated citizens and government interaction in developing countries. It is against this backdrop that this work explores the integration of Voice, Web and SMS technologies, in Nigeria's democratic process. The proposed system takes advantage of the ubiquitous nature of mobile devices to explore the plausibility of increasing the level of citizens' participation in democratic practices, particularly, those in rural areas with no Internet access and the physically challenged electorates. The server module for the e-democracy system was developed in PHP. Ozeki SMS server and Voxeo Voice server were used for SMS transaction code and VoiceXML code respectively. The prototype e-democracy system shows that developing nations can take advantage of their present level of technological development to give voice to the voiceless and improve their democratic system.
Addressing Digital Competencies, Curriculum Development, and Instructional Design in Science Teacher Education

The author reports on a mixed-methods study focusing on teacher candidates' (TCs') digital competencies as they integrated digital literacies in a science methods course. The emphasis is on course assignments which incorporated digital literacies on a variety of levels as TCs developed 1) digital case studies, 2) scientific timelines, 3) concept presentations, and 4) science resource websites focusing on multimedia interactive activities. Results indicate that the explicit integration of digital literacies created and engaged learning communities while improving technological and scientific literacies in a purposeful manner. Findings include enhanced technological literacy in terms of learning about technology, awareness of the process of knowledge construction, personalized learning pedagogy, and heightened self-efficacy. There was also evidence that TCs utilized digital literacies learned in the course during their practicum.
Vegetables and Fruits

Vegetables are the edible portion of plants eaten with (or as) the main course. They are prevalent in salads and soups. Vegetables may be processed into beverages or vegetable starches, eaten fresh or lightly processed, dried, pickled, or frozen. They impart their own characteristic flavor, color, and texture to diets, and undergo changes during storage and cooking. Ranked next to the cereal crops wheat, rice, and corn, potatoes are the most prolific vegetable crop grown for human consumption.
Trust in Corporations in the Age of AI

Trust in Corporations in the Age of Artificial IntelligenceAmanda CarriganHumans have participated in trade for millennia and today there are trillions of dollars invested in many types of business organisations. As the activities of business have become more complex we have unfortunately witnessed a staggering increase in instances of misbehaviour by corporations, through the human actors who manage these vast sums of money. It seems not a month goes by without there being reports of questionable activities and breaches of law, particularly by financial organisations. Commonly we are seeing misbehaviour where managers are using artificial intelligence (AI) and they blame AI for this. The types of AI used in business ranges from algorithms in smart phones, communication systems, and customer service systems, to on-line sales sites and stock trading. In our current economic climate where AI miscalculates interest payments and overcharges fees it is no stretch to see why a significant proportion of the public do not trust business organisations nor AI which causes individuals loss of jobs and money. In the absence of deliberate criminal conduct these problems tend to go unnoticed.There is light at the end of the tunnel however as researchers project laptops will match the power of a human brain in the early 2020â€™s. Organisational management could be replaced by robo-managers with the potential to do a far superior job with humanistic algorithms and access to all organisational information. Trust in business might thus be restored, possibly, within the coming decade.
Fully Homomorphic Encryption Without Noise

In this paper, the authors present a novel fully homomorphic encryption scheme operating between ZN and capable of arbitrarily performing additions and multiplications. The new scheme is compact and each operation (addition or multiplication) performed on any two ciphertexts produces a fresh ciphertext without any associated noise. Thus, the scheme does not need any bootstrapping procedure or noise reduction technique to refresh ciphertexts. In the absence, to the best of the knowledge of the authors, of any existing fully, partially or leveled homomorphic encryption scheme using ZN as the set of plaintexts, the new cryptosystem has been implemented and has had its performance compared to the identity encoding.
Cost Function Analysis in Generative Adversarial Neural Network

Deep learning is the most popular method in the field of artificial intelligence, and a faster way to study deep learning is to generate admissible neural network. The generative adversarial network is a learning network which is composed of the generative model and the discriminant model, and the corresponding generative and adversarial strategies are established at the cost of producing the preset ideal output. Crucial to the learning model is the cost function. This paper expounds the generation against the theoretical foundation of the network model, and then optimize the parameters of the cost function for generating stable against the network training, analyzes the mathematical optimization model in the course of the generated against the training of the network, from the perspective of construct the punish function to improve the corresponding generated against the cost of network function, to create a new generation network optimization model theory basis.
Optimizing Global Representation Using Convolutional Bidirectional Recurrent Network Model for Text Categorization

Deep learning is capable of achieving remarkable performance in sentence and document modelling. Recurrent Neural Network (RNN) is the mainstream architecture for text categorization. But RNN is a biased model of which later inputs are more dominant than earlier inputs. To optimizing global representation in RNN for document modelling, the convolutional bidirectional recurrent network (CBI-RNN) is introduced to text categorization. One convolutional layer and one max pooling layer are utilized to extract phrase level local information from word embedding. The BI-LSTM with global pooling is adopted to extract the global information for selecting features which are most favorable for classification. Depending upon the global pooling scheme utilized in the model, model variants are named CBI-RNN-Max and CBI-RNN-Att. Advanced sub-sequence representation learning is also introduced in the proposed model and its performance is reported in comparison with different model variations. The proposed model is applied to the text classification data set Reuters21578-R8, WebKB. Experimental results indicate that the proposed model captures more contextual information and achieves state-of -the-art performance on both datasets.
Research Progress of Off-Grid DOA Estimation Algorithm Based on OMP Algorithm

research-article Research Progress of Off-Grid DOA Estimation Algorithm Based on OMP Algorithm Share on Authors: Song Cai Hohai University, China Hohai University, ChinaSearch about this author , Haiyuan Wang Hohai University, China Hohai University, ChinaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 3043–3046https://doi.org/10.1145/3495018.3501231Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
GUI-Based DL-Network Designer for KISTI’s Supercomputer Users

With the increase in research on AI (Artificial Intelligence), the importance of DL (Deep Learning) in various fields, such as materials, biotechnology, genomes, and new drugs, is increasing significantly, thereby increasing the number of deep-learning framework users.However, to design a deep neural network, a considerable understanding of the framework is required.To solve this problem, a GUI (Graphical User Interface)-based DNN (Deep Neural Network) design tool is being actively researched and developed.The GUI-based DNN design tool can design DNNs quickly and easily.However, the existing GUI-based DNN design tool has certain limitations such as poor usability, framework dependency, and difficulty encountered in changing GUI components.In this study, a deep learning algorithm that solves the problem of poor usability was developed using a template to increase the accessibility for users.Moreover, the proposed tool was developed to save and share only the necessary parts for quick operation.To solve the framework dependency, we applied ONNX (Open Neural Network Exchange), which is an exchange standard for neural networks, and configured it such that DNNs designed with the existing deep-learning framework can be imported.Finally, to address the difficulty encountered in changing GUI components, we defined and developed the JSON format to quickly respond to version updates.The developed DL neural network designer was validated by running it with KISTI's supercomputer-based AI Studio.
Hazing or Dehazing: the big dilemma for object detection

One of the biggest adversaries to the computer vision pipeline is bad weather which can deteriorate the visual quality of the captured images and lead to a decreased performance of tasks. Examples of such tasks can include image classification, object detection and semantic segmentation. To ameliorate this acquisition bottleneck, vision experts have developed restoration approaches which aim to recover the lost visual information due to the presence of poor climactic conditions such as atmospheric haze. The technique of single image dehazing has achieved great strides in producing aesthetically pleasing restorations to the human perception. However, it is important to establish whether these approaches bring the same merits to high-level vision tasks. To this end, we formulate a study around the task of object detection that aims to uncover the underlying relationship between this high-level task and atmospheric haze as well as examine the ability of the current dehazing process to enhance the detection performance. From our experiments we find that while there is a clear negative relationship between hazy conditions and detection performance, there is little help from the dehazing process to achieve the desired haze-free results.
Defining Security’s Role

Security/loss-prevention functions, while diverse, have a common goal. As noted earlier the definition of security has been debated, but the bottom line is clear. Security services protect both private and public places. Law enforcement protects both public and private property. The difference is found in their primary goals. Law enforcement agencies are charged with the protection of government interests, representing the people. Private security is charged with protecting a specific interest, whether public or private. The old distinction between public law enforcement and private security will continue to exist. However, the story is different when considering the relationship between contract and proprietary security. As business continues to evolve, it appears that the hybrid systems will become a dominant organizational scheme for many businesses establishing security operations. Finally, all people concerned with security, whether federal government agencies, state law enforcement organizations, local law enforcement, or private security, will need to learn to work together to focus resources needed to successfully combat threats, particularly those threats created by the potential of terrorist attacks and cybercrimes.
Enterprise Interoperability

Many interoperable frameworks have been suggested, developed, and implemented in the past. Most of them are based on XML, web services, and semantic web technologies. In the meantime, globalization and the further development of internet and web have raised new challenges and opportunities. New form of networked organizations, known as virtual enterprises, seeks for knowledge sharing around common goals. The rise of the social and ubiquitous world, where everything is going to be connected, gives a lot of technical opportunities for electronic business, but at the same time makes new barriers due to increasing heterogeneity issues. This chapter gives an overview of main challenges, obstacles, approaches, recent research efforts, and forecasts in order to overcome recent interoperability problems.
The Development and Practice of Online and Offline Hybrid Golden Course Using the “Intelligent Hardware Experimental Course” as an Example

The golden course is based on the standard of “Two characters One Degree” and was proposed at the New Era Chinese Undergraduate Education Work Conference of National Colleges and Universities. For example, the “Intelligent Hardware Experimental Course” explores an online and offline hybrid teaching model. The study proposes online teaching based on a massive open online course (MOOC) model. Furthermore, this study designs a portable offline version (POV) of the experimental teaching instrument with an outline of the implementation of its teaching process. The application of the developed curriculum has significant effects on learning, namely memorizing core knowledge, improving practical ability, and cultivating the spirit of patriotism.
Camouflaged target tracking based on a scaleadaptive correlation filter algorithm

Considering the continuous development of intelligent technology and the wide application prospects of unmanned aerial vehicles (UAVs), the target tracking technology will be applied in future combat. In order to deal with the complex battlefield environment and the scale change of the target during tracking, an improved scale-adaptive correlation filter algorithm based on fusion features is proposed to realize the tracking of military personnel. First, the fast histogram of oriented gradient (FHOG) features and the local binary pattern (LBP) features are adaptively weighted and fused to achieve the target location. Then the translation filter of kernelized correlation filters(KCF) and the scale filter of discriminative scale space tracking(DSST) are combined to comprehensively improve the calculation speed and scale adaptation performance of the algorithm and track the target in time. The experimental results prove the robustness and practicability of the proposed method, which can accurately track military targets in real time.
Coordinate transformation and connection feature for Skeleton-based action recognition

Graph structure is an important part of Graph convolutional networks (GCNs), which can reflect the connection between each nodes of non-Euclidean data. A connection feature between nodes is hidden in graph structure, which can provide additional spatial features that represent the relationship between human joints. However many GCNs-based methods ignore these spatial features. We put forward a connection feature extraction module, which can obtain implicit connection between human joints, and extract the implicit spatial features from the structural connection and implicit connection of human joints. In order to enhance the temporal representation, we propose a long-range frame-difference feature extraction module. Furthermore, we also propose a coordinate transformation module, which can map joint from Cartesian coordinates to spherical coordinates to extract more representative features. Experiments show that our method outperforms several advanced methods.
Efficient Key Frame Selection Approach for Object Detection in Wide Area Surveillance Applications

This paper presents an efficient preprocessing algorithm for object detection in wide area surveillance video analysis. The proposed key-frame selection method utilizes the pixel intensity differences among subsequent frames to automatically select only the frames that contain the desired contextual information and discard the rest of the insignificant frames. For improving effectiveness and efficiency, a batch updating based on a modular representation strategy is also incorporated. Experimental results show that the proposed key frame selection technique has a significant positive performance impact on wide area surveillance applications such as automatic object detection and recognition in aerial imagery.
An Improved Substation Equipment Recognition Algorithm by KNN Classification of Subspace Feature Vector

With the development of intelligent substation construction technology, 3D recognition of substation equipment is becoming more and more important. This paper proposes an improved substation equipment recognition algorithm based on KNN classification of subspace feature vector, in which the number of dividing subspaces is unchanged to maximize keeping the shape features of equipment point cloud, and solves the problem of distorting shape feature of point cloud caused by normalization. At the same time, this paper preselects the standard devices from the template library by size screening of standard devices to reduce the comparison range of recognition and shorten average recognition time. The experiment results show that recognition accuracy of the improved recognition algorithm is improved by 2%-8% and the average recognition time is reduced by an order of magnitude, which shows the effectiveness of the recognition method of this paper.
Socio-Technical Determinants of Information Security Perceptions in US Local Governments

Concerns about electronic information security in government have increased alongside increased use of online media. However, to date, few studies have examined the social mechanisms influencing electronic information security. This article applies a socio-technical framework to model how technical, organizational and environmental complexities limit electronic information security perceived by local government managers. Furthermore, it examines to what extent organizational design buffers security risks. Using data from a 2010 national survey of local government managers, this article empirically tests the proposed model in the context of U.S. local government's online media use. Findings show that, in addition to technical complexity, organizational and environmental complexities are negatively associated with local managers' awareness of and confidence in electronic information security. On the other hand, internal security policy and decentralized decision-making appear to buffer security risks and enhance perceived information security.
Learners and Mobile

Studies about mobile phones, the learning process, and educational institutions have grown in recent years though research has mostly focused in the United States and in specific groups. This research contributes to the literature by looking at three educational levels in Singapore and by taking a two-pronged approach to the relationship among these three variables. It answers the following reflexivity: How do students learn to use mobile phone functions? How do they use mobile phones for learning functions? This chapter considers learning as the reflexive process where attitude and aptitude are acquired and shared for curricular and extra-curricular activities. Findings indicate two themes: how students 1) develop the skills to use the expanding array of mobile phone technologies, and integrate these in their daily life and 2) use mobile phones in school and in their schoolwork. Data for the study came from focus interviews with 36 informants who were selected through maximum variation sampling according to their age, educational level, and household income.
Point-wise gated restricted Boltzmann machines using clean data

The point-wise gated restricted Boltzmann machine (pgRBM), an RBM variant, finds the task-relevant patterns from data containing irrelevant patterns and thus achieves satisfied classification results. Given that the training data composes of noisy data and clean data, how the clean data is applied to promote the performance of the pgRBM is a problem. To address the problem, this paper proposes a method, named as pgRBM based on random noisy data and clean data (pgrncRBM). The pgrncRBM makes use of RBM and the clean data to obtain the initial values of the task-relevant weights. However, if the noise is an image, the pgrncRBM cannot learn the task-relevant patterns from the noisy data. Therefore, this paper combines the Spike-and-Slab RBM with the pgRBM and proposes a method, named as pgRBM based on image noisy data and clean data (pgincRBM). The pgincRBM uses the spike-and-slab RBM to model the noise, so it can learn the 'clean' data from the data containing image noisy. Finally, we discuss the validities of these methods on MNIST variation datasets.
The Adoption of Mobile Devices as Digital Tools for Seamless Learning

This study examines the views of undergraduate students in Education Faculty related to mobile learning and reveals their mobile usage behaviors. Mobile usage behaviors include students' view about effectiveness of mobile learning, their mobile design preferences, use of mobile device for purpose of learning, the activity types conducted with mobile devices and their mobile usage frequency. It comes out that university students have very positive attitudes towards mobile learning and they think that m-learning is a really effective learning method. However, mobile devices are used mostly for two purposes: socialization and entertainment. University students agree that mobile learning removes constraints like time and space dependency. They view simplicity and fluency as the prerequisites for a mobile application. Their behaviors are infrequent when it comes to the use of mobile devices for accessing library, reading article, doing homework and note-taking. Their readiness for m-learning is considerably high and they have necessary skills for this learning form.
Blockchain in India

Blockchain has been the recent talk of the town in the sphere of digital technology which attempts to bring transparency in the wake of an Indian Digital Economy by inducing transparency and cracking on the rampant corruption in Public and Private Sectors by making use of Cryptographic Techniques to make the system hack-proof which is aimed at bringing an upheaval not only in the field of Finance but also other industries like Agriculture, Telecom, Mass Media and Public Sector Undertakings where coupled with contemporary technologies like Internet of Things, Blockchain can do wonders. This paper aims to give a brief view of about Blockchain, the basic ideas that revolve around it and how it can be implemented in a nation like India with over a billion population.
Conceptual Models

A crucial step in designing a user interface for a software application is to design a coherent, task-focused conceptual model (CM). With a CM, designers design better, developers develop better, and users learn and use better. Unfortunately, this step is often skipped, resulting in incoherent, arbitrary, inconsistent, overly-complex applications that impede design, development, learning, understanding, and use. This course covers what CMs are, how they help, how to develop them, and provides hands-on experience.