Reviewerly/exHarmony huggingface dataset:
Video on Mobile Phones as an Effective Way to Promote Sustainable Practices by Facilitating Innovation Uptake in Mali

A challenge for researchers and other developers of new technologies in agriculture is to find ways of communicating their results and recommendations.This challenge is particularly acute in regions in which farmers have limited access to education and where illiteracy is widespread, such as in the rural areas of Mali.One approach that shows potential, yet remains largely unused by extension services, is the dissemination of educational videos on mobile phones with video and Bluetooth technology (3G mobile phones).It has been shown that the conditions are suitable for this approach to be used in Mali, with 3G mobile phones widely available in the region but there is little empirical evidence of its acceptance and potential impact.This article aims to explore the potential of video on mobile phones as a tool to facilitate the diffusion of new agricultural technologies in Western Africa.Three videos showing agricultural innovations for sustainable production were shown and shared with 200 farmers in twelve villages in Mali.The villages were revisited 10 months later and farmers were asked about their experiences with the videos that had been shared and their previous knowledge of the innovations shown in them.Of the farmers who had watched one of the videos (N=148), 60.1% had adopted at least one of the videos' innovations.Mobile-phone videos could be accessed by people who had previously received limited access to information sources, such as younger women, and video based information was found to be understandable for illiterate farmers.These results show that mobile-phone videos could enhance information transfer and thereby expand outreach.The use of video on mobile phones is a novel approach to farmer-to-farmer exchange and has tremendous potential for enhancing dissemination programs or specific research and development projects to enable more resilient, inclusive and democratic systems.
A short-term photovoltaic power forecasting model based on a radial basis function neural network and similar days

Intermittence and fluctuation natures of photovoltaic (PV) solar energy pose great challenge on the grid stability and power scheduling. PV power forecasting is an effective measure to alleviate the issue. This study presents an improved model for forecasting one-day-ahead hourly PV power generation using Numerical Weather Prediction (NWP) and historical data, which is based on Radial Basis Function (RBF) neural network and similar day method. Firstly, historical similar days of the same weather type are selected according to the correlation of meteorological data. Secondly, the RBF neural network based forecasting model is trained using the historical data of similar days. Finally, the model is used to forecast the power generation using the NWP data of the forecast day. Experimental results show that the proposed method is accurate and reliable.
An Automatic Modulation Recognition Method with Low Parameter Estimation Dependence Based on Spatial Transformer Networks

Recently, automatic modulation recognition has been an important research topic in wireless communication. Due to the application of deep learning, it is prospective of using convolution neural networks on raw in-phase and quadrature signals in developing automatic modulation recognition methods. However, the errors introduced during signal reception and processing will greatly deteriorate the classification performance, which affects the practical application of such methods. Therefore, we first analyze and quantify the errors introduced by signal detection and isolation in noncooperative communication through a baseline convolution neural network. In response to these errors, we then design a signal spatial transformer module based on the attention model to eliminate errors by a priori learning of signal structure. By cascading a signal spatial transformer module in front of the baseline classification network, we propose a method that can adaptively resample the signal capture to adjust time drift, symbol rate, and clock recovery. Besides, it can also automatically add a perturbation on the signal carrier to correct frequency offset. By applying this improved model to automatic modulation recognition, we obtain a significant improvement in classification performance compared with several existing methods. Our method significantly improves the prospect of the application of automatic modulation recognition based on deep learning under nonideal synchronization.
A new encrypted method in image steganography

&lt;p&gt;Steganography is data hiding technique in internet. Here we send CAPTCHA codes within a cover image using Image steganography. CAPTCHA are the crazy codes. They are used in human response test. The word is actually an acronym for: "&lt;strong&gt;C&lt;/strong&gt;ompletely &lt;strong&gt;A&lt;/strong&gt;utomated &lt;strong&gt;P&lt;/strong&gt;ublic &lt;strong&gt;T&lt;/strong&gt;uring test to tell &lt;strong&gt;C&lt;/strong&gt;omputers and &lt;strong&gt;H&lt;/strong&gt;umans &lt;strong&gt;A&lt;/strong&gt;part". It is a type of challenge–response test used in computing to determine whether or not the user is a human. Websites implement CAPTCHA codes into their registration processes due to spam. This is the utility of CAPTCHA codes. Here we generate CAPTCHA codes and later send them in an encrypted version. So, actually CAPTCHA codes are embedded into cover image with an encrypted form resulting stego image and thus attackers cannot fetch the actual CAPTCHA resulting in a secured transmission of confidential data using image steganography.&lt;/p&gt;
Spimes Not Things. Creating A Design Manifesto For A Sustainable Internet of Things

(2019). Spimes Not Things. Creating A Design Manifesto For A Sustainable Internet of Things. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 2133-2152.
Bidirectional controlled quantum teleportation by a genuine entangled 9-qubit state

Since the first protocol of quantum teleportation based on an EPR pair was presented by Bennett et al. in 1993, it has become a hot topic in quantum communication.It is known that entanglement plays a key role in quantum teleportation.Various schemes of controlled quantum teleportation and bidirectional controlled quantum teleportation have been developed by using all kinds of multi-parties entangled states as a quantum channel, e.g.GHZ states, W states, 5-qubit cluster states, 6-qubit cluster states, 7-qubit cluster states, maximally entangled 5-qubit states, maximally entangled 6qubit states, genuine entangled 6-qubit states, genuine entangled 7-qubit states, genuine entangled 8-qubit states.In this paper, a genuine entangled 9-qubit state is used as channel of quantum teleportation, and two schemes of bidirectional controlled quantum teleportation are proposed.One scheme is that with the control of Charlie Alice can transmit an arbitrary unknown qubit state to Bob and Bob can transmit an arbitrary unknown 3-qubit state to Alice in the same time.Another scheme is that with the control of Charlie Alice and Bob can transmit an arbitrary unknown 2-qubit to each other simultaneously.Compared with the existed schemes, our scheme can transmit more qubits of arbitrary state in total by an entangled state, and the intrinsic efficiency of our scheme is maybe the best.Moreover, the genuine entangled 9-qubit state used in our scheme can be produced through some simple quantum unitary gates.
Eyes on the Road: Surveillance Logics in the Autonomous Vehicle Economy

As emblematic products of a previous industrial era, cars are often tied to a sphere outside the digital economy and its ruling logics and are, therefore, governed by different ideas of privacy, agency, and sovereignty. However, as self-driving cars gain momentum, computerization, and automation, their platformization contributes to transforming the automobile as technology, industry, and cultural artifact. This creates new spaces of consumption within cars, around them, and between them—all of which are premised on highly detailed contextual information that is collected, processed, and transmitted in the process of navigation. Thus, the ads and entertainment delivered to passengers, the landscapes surrounding the vehicles, and the proprietary data networks that link self-driving car fleets are all redefined as revenue streams in the new auto economy. This paper explores the transformation of the existing auto economy in the context of the emergence of self-driving cars by following the transposition of surveillance logics that are central to the digital economy and by considering the implications of their adaptation to the arena of automated mobility.
Inverse kinematics-based motion planning for dual-arm robot with orientation constraints

This article proposes an efficient and probabilistic complete planning algorithm to address motion planning problem involving orientation constraints for decoupled dual-arm robots. The algorithm is to combine sampling-based planning method with analytical inverse kinematic calculation, which randomly samples constraint-satisfying configurations on the constraint manifold using the analytical inverse kinematic solver and incrementally connects them to the motion paths in joint space. As the analytical inverse kinematic solver is applied to calculate constraint-satisfying joint configurations, the proposed algorithm is characterized by its efficiency and accuracy. We have demonstrated the effectiveness of our approach on the Willow Garage’s PR2 simulation platform by generating trajectory across a wide range of orientation-constrained scenarios for dual-arm manipulation.
A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network

In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neural network. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. This model is formed by two levels of nested parallelism of neurons and connections. Thus, this solution provides a distributed set of independent computations between the processing units called neuroprocessors (NPs) which define the SSOM architecture. The NP modules have an innovative architecture compared to those proposed in the literature. Indeed, each NP performs three different tasks without requiring additional external modules. To validate our approach, we evaluate the performance of several SOM network architectures after their integration on an FPGA support. This architecture has achieved a performance almost twice as fast as that obtained in the recent literature.
Applying Eye Tracking in Information Security

In this paper, we are considering the possibility of using eye tracking technology in the context of information security. We expect that this novel technique will have a very effective impact in this field. Eye tracking technology is widely used to investigate user behavior when working with a computer. This technology allows to obtain analysis in the form fixation point (location of a user’s eye gaze), scan-path (gaze trajectory), heat maps (areas of interest), salient picture components. Eye tracking research has been widely used to improve the design and usability of web pages, as well as to explore an understanding how users are guided by them. Moreover, this technology is widely used in the protection of information.
Applications of Algebraic Geometry in Cryptography

One of the most important applications of algebraic geometry, known as linguistics, has been used in linguistics, military and diplomatic. It was said that the first to mobilize a comment between the army is the Pharaohs. He also mentioned that the Arabs have old attempts at encryption. The Chinese used many methods in conveying messages during war. Their intention was to get the wrong signals. This research is a general study on science (word) searching for several methods in English linguistics, a word of thanks and a message.
The Road to Artificial General Intelligence

Artificial general intelligence (AGI) is a holy grail for many scientists of artificial intelligence. In recent years, a large community of researchers has emerged, focusing on the original ambitio...
ANIMAL DETECTION USING A SERIES OF IMAGES UNDER COMPLEX SHOOTING CONDITIONS

Abstract. Camera traps providing enormous number of images during a season help to observe remotely animals in the wild. However, analysis of such image collection manually is impossible. In this research, we develop a method for automatic animal detection based on background modeling of scene under complex shooting. First, we design a fast algorithm for image selection without motions. Second, the images are processed by modified Multi-Scale Retinex algorithm in order to align uneven illumination. Finally, background is subtracted from incoming image using adaptive threshold. A threshold value is adjusted by saliency map, which is calculated using pyramid consisting of the original image and images modified by MSR algorithm. Proposed method allows to achieve high estimators of animals detection.
Neural Network Based Uncertainty Prediction for Autonomous Vehicle Application

This paper proposes a framework for uncertainty prediction in complex fusion networks, where signals become available sporadically. Assuming no information of sensor characteristics is available, a surrogated model of uncertainty is yielded directly from data through artificial neural networks. The strategy developed is applied to autonomous vehicle localization through odometry sensors (speed and orientation), so as to determine the location uncertainty in the trajectory. The results obtained allow for fusion of autonomous vehicle location measurements, and effective correction of the accumulated odometry error in most scenarios. The neural networks applicability and generalization capacity are proven, evidencing the suitability of the presented methodology for uncertainty estimation in non-linear and intractable processes.
Process Design of Cooperative Education Management System by Cloud-based Blockchain E-portfolio

&lt;p class="0abstract"&gt;The digital competence is necessary for 21&lt;sup&gt;st&lt;/sup&gt; century living, but from several countries survey, a large number of people still have insufficient digital competence. Moreover, from literature study found three problems of today cooperative education information system, (1) lack of information sharing among university community, (2) lack of connect information to public, and (3) lack of enhancing 21&lt;sup&gt;st&lt;/sup&gt; century learning skill. This paper proposes a process design of cooperative education management system by using the integration of cooperative education, E-Portfolio, digital competence framework 2.0, cloud computing technology, and blockchain technology. These processes make result of digital competence assessment credible and share to open digital labor market finally.&lt;/p&gt;
Document-Level Event Factuality Identification via Adversarial Neural Network

Zhong Qian, Peifeng Li, Qiaoming Zhu, Guodong Zhou. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A Comparative Analysis and Discussion of Email Spam Classification Methods Using Machine Learning Techniques

Email communication is by far the most easy, ubiquitous, and convenient medium ever used by humankind for communicating with people. Free email service providers such as Gmail, Yahoo! Mail, Hotmail, and Outlook are used by millions of people worldwide. Emails as a form of communication became popular when Internet service providers such as AOL, Prodigy, and CompuServe provided each user with an email account by default in the 1990s. Machine learning is currently one of the most-discussed fields in computer science. With the rise in the adoption of data analysis and machine learning fueled by Google open sourcing, most email classifiers started using some variation of this method. Machine learning is one of the most-discussed fields in computer science. Multiple machine learning models, supervised or unsupervised, can be trained and tested on the proposed model. The proposed system architecture focuses on retrieving emails through available third-party APIs and processes them appropriately for machine learning.
A data‐driven particle filter for terrain based navigation of sensor‐limited autonomous underwater vehicles

Abstract In this article a new Data‐Driven formulation of the Particle Filter framework is proposed. The new formulation is able to learn an approximate proposal distribution from previous data. By doing so, the need to explicitly model all the disturbances that might affect the system is relaxed. Such characteristics are particularly suited for Terrain Based Navigation for sensor‐limited AUVs, where typical scenarios often include non‐negligible sources of noise affecting the system, which are unknown and hard to model. Numerical results are presented that demonstrate the superior accuracy, robustness and efficiency of the proposed Data‐Driven approach.
Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification

Use symptoms to stratify temporal disease trajectories. We use data from the Danish National Patient Registry to stratify temporal disease pairs by the symptom distributions they associate to. The underlying data comprise of 6.6 million patients collectively assigned with 7.5 million symptoms from chapter XVIII in the WHO International Classification of Disease version 10 terminology. We stratify 33 disease pairs into 67 temporal disease-symptom-disease trajectories from three main diagnoses (two diabetes subtypes and COPD), where the symptom significantly changes the risk of developing the subsequent diseases. We combine these trajectories into three temporal disease networks, one for each main diagnosis. We confirm apparent relations between diseases and symptoms and discovered that multiple symptoms decrease the risk for diabetes progression. Symptoms can be used to stratify disease trajectories, and we suggest that this approach can be applied to temporal disease trajectories systematically using structured claims data. The method can be extended to also use text-mined symptoms from unstructured data in health records.
Conceptualizing and qualifying disruptive business models

Purpose This paper aims to elaborate a set of characteristics that conceptualize and qualify a disruptive business model. Design/methodology/approach The literature on disruptive business models will be analyzed using the latent semantic analysis (LSA) technique, complemented by content analysis, to obtain a more precise qualification and conceptualization regarding disruptive business models. Findings The results found described concepts already described in the theory. However, such findings, highlighted by the LSA, bring new perspectives to the analysis of the disruptive business models, little discussed in the literature and which reveal important considerations to be made on this subject. Research limitations/implications It should be noted, about the technique used, a limitation on the choice of the number of singular values. For this to be a problem in the open literature, the authors tried to work not just with the cost-benefit ratio given the addition of each new dimension in the analysis, as well as a criterion of saturation of the terms presented. Practical implications The presentation of this set of characteristics can be used as a validation tool to identify if a business is or is not a disruptive business model by managers. Originality/value The originality of this paper is the achievement of a consolidated set of characteristics that conceptualize and qualify the disruptive business models by conducting an in-depth analysis of the literature on disruptive business models through the LSA technique, considering the difficulty of obtaining precise concepts on this subject in the literature.
LANA-I: An Arabic Conversational Intelligent Tutoring System for Children with ASD

Children with Autism Spectrum Disorder (ASD) share certain difficulties but being autistic will affect them in different ways in terms of their level of intellectual ability. Children with high functioning autism or Asperger syndrome are very intelligent academically but they still have difficulties in social and communication skills. Many of these children are taught within mainstream schools but there is a shortage of specialised teachers to deal with their specific needs. One solution is to use a virtual tutor to supplement the education of children with ASD in mainstream schools. This paper describes research to develop a novel Arabic Conversational Intelligent Tutoring System, called LANA-I, for children with ASD that adapts to the Visual, Auditory and Kinaesthetic learning styles model (VAK) to enhance learning. This paper also proposes an evaluation methodology and describes an experimental evaluation of LANA-I. The evaluation was conducted with neurotypical children and indicated promising results with a statistically significant difference between user’s scores with and without adapting to learning style. Moreover, the results show that LANA-I is effective as an Arabic Conversational Agent (CA) with the majority of conversations leading to the goal of completing the tutorial and the majority of the correct responses (89%).
Enhanced VIRET Tool for Lifelog Data

We present an enhanced version of the VIRET tool, an interactive video retrieval system for known-item and ad-hoc search tasks. The new features for the Lifelog Search Challenge comprise attribute-based filtering using lifelog specific modalities like time, biometric data, and GPS locations. The interface and models for content-based retrieval in image sequences are the same as for the Video Browser Showdown 2019. Hence, the performance of temporal multi-modal queries implemented for video data can be compared with other lifelog systems.
Theory of sampling and sampling practice

The unique feature of this well written comprehensive book is its step-by-step guidance in all thirty-eight chapters under twelve parts. Basic knowledge of calculus, matrix theory and mathematical ...
Blockchain: An Empirical Investigation of Its Scope for Improvement

General Purpose Technologies, or GPTs are defined in the economic literature as the key technologies that shape the economy. Despite the large conceptual literature base on Blockchain potential to revolutionize the current economic system, there is a lack of empirical research on its economic nature and the course of technological development. The paper at hand covers this research gap by providing the quantitative approach aimed at understanding the evolutionary path of Blockchain and its scope for improvement – an acknowledged feature of a GPT - in line with the industrial dynamics and GPT literature. The longitudinal analysis of Blockchain-related patents from PATSTAT and their rule-based classification both from technological and application perspectives is complemented by the study of Blockchain media landscape to provide insights into the social context in which it emerges. The increasing amount of patents addressing essential technical issues, such as security, scalability, and usability contribute to wider adoption of the technology, whereas the positive sentiment in the media associated with Blockchain creates beneficial social context for its development. The empirical results advance the claim that Blockchain does show a positive scope for improvement peculiar to the GPTs in the making and, therefore, deserves attention as a technology that will define macroeconomic dynamics in a long term.