Reviewerly/exHarmony huggingface dataset:
A Scale-adaptive Color Preservation Neural Style Transfer Method

Since the feature maps of deep neural networks were adopted to compute the representation of style and content information, neural style transfer (NST) methods have sprung up like mushrooms. But the existing methods ignore a fundamental fact that a style or an artistic image not only contains style information but also contains content information. And we find that there may be a conflict between style and content. Motivated by this idea, we propose a novel method, which only adopts the detail layer of the style image to compute the style loss. To avoid the potential conflicts between the style loss and the content loss, we just abandon the latter. The smooth base layer of the content image will be added to the intermediate results to keep the semantic content invariant. Our ablation studies show that this strategy can make the results scale-adaptive to the style image. Furthermore, we use an interpolation method so that the overall color of our results remains unchanged and our results have a colorful stroke. The qualitative and quantitative analyses show that our results have a better visual effect than the existing methods.
Disruptabottle: Encouraging Hydration with an Overflowing Bottle

We present a prototype for a targeted behavioural intervention, Disruptabottle, which explores what happens when a 'nudge' technology becomes a 'shove' technology. If you do not drink water at a fast enough rate, the bottle will overflow and spill. This reminds the user that they haven't drunk enough, aggressively nudging them to drink in order to prevent further spillage. This persuasive technology attempts to motivate conscious decision making by drawing attention to the user's drinking habits. Furthermore, we evaluated the emotions and opinions of potential users towards Disruptabottle, finding that participants generally received the device positively; with 59% reporting that they would use the device and 92% believing it to be an effective way of encouraging healthy drinking habits.
RETRACTED: Arabic Command Based Human Computer Interaction

Abstract This paper means to build up a strategy for helping debilitated individuals old and amputee, by using unimodal human computer interaction; real time voice command and interaction between patient and computer where these mixes give a promising answer for help the incapacitated individuals. The fundamental goal of the work is to design a project that deals easily with a patient that uses Arabic commands. The project is based on training a dataset that collected from different persons that have ages from 12-58 years old, unknown word and background noise. The propose system can works with different direction in Arabi language (“اذهب“, ” يمين“, ”يسار“, ”قف”) and with a multispeed that based on pulse width modulation (PWM). These functions based on code written in MATLAB environment. The project uses ATmega328microcontroller.
A Cognitive Method for Automatically Retrieving Complex Information on a Large Scale

Modern retrieval systems tend to deteriorate because of their large output of useless and even misleading information, especially for complex search requests on a large scale. Complex information retrieval (IR) tasks requiring multi-hop reasoning need to fuse multiple scattered text across two or more documents. However, there are two challenges for multi-hop retrieval. To be specific, the first challenge is that since some important supporting facts have little lexical or semantic relationship with the retrieval query, the retriever often omits them; the second challenge is that once a retriever chooses misinformation related to the query as the entities of cognitive graphs, the retriever will fail. In this study, in order to improve the performance of retrievers in complex tasks, an intelligent sensor technique was proposed based on a sub-scope with cognitive reasoning (2SCR-IR), a novel method of retrieving reasoning paths over the cognitive graph to provide users with verified multi-hop reasoning chains. Inspired by the users' process of step-by-step searching online, 2SCR-IR includes a dynamic fusion layer that starts from the entities mentioned in the given query, explores the cognitive graph dynamically built from the query and contexts, gradually finds relevant supporting entities mentioned in the given documents, and verifies the rationality of the retrieval facts. Our experimental results show that 2SCR-IR achieves competitive results on the HotpotQA full wiki and distractor settings, and outperforms the previous state-of-the-art methods by a more than two points absolute gain on the full wiki setting.
A Novel Image Description With the Stretched Natural Vector Method: Application to Face Recognition

Nowadays there is growing research interest in designing high performance algorithms for automatic facial recognition systems, and an efficient computational approach is required. Accurate face recognition, however, is difficult due to facial complexity. In this paper, we propose a novel and efficient facial image representation named the Stretched Natural Vector (SNV) method which is defined on the intensity values in a grayscale image matrix, where each entry in an intensity matrix records the level of gray at a single pixel in a m×n array. We prove that the SNV defined in this context can distinguish photo matrices in strict one-to-one fashion. This is to say it is theoretically possible to fully recover a grayscale image matrix from the corresponding complete SNV. Experiments on a number of datasets demonstrate that our truncated SNV method compares favorably both in recognition accuracy and efficiency (measured in wall-clock time) against “Full-Pixel” algorithm, Principal Component Analysis (PCA) method, and even its widely used variants - two dimensional PCA (2DPCA) method and two dimensional Euler PCA (2D-EPCA) method.
Cloud Computing as a Strategy for Preservation of Digital Resources in Academic Libraries in South Africa

Academic libraries around the world have been struggling with how to preserve their intellectual output. Of all the preservation challenges facing academic libraries, none is more pressing than developing strategies for digital preservation. The chapter thus aimed at investigating the adoption of cloud computing as a strategy to preserve digital resources in academic libraries. Data collection was largely based on a critical review of literature relating to the adoption of cloud computing in academic libraries. The findings revealed that although cloud computing has been seen as one of suitable preservation strategies by many institutions, the level of its adoption is low in academic libraries in South Africa, especially in the area of preservation of digital resources. Several recommendations were also made, and among others were the enactment of preservation policies and improving technology infrastructure. The study further proposed a roadmap that will serve as a guide in the adoption of cloud computing in academic libraries.
Whirlwind Classification with Imbalanced Upper Air Data Handling using SMOTE Algorithm and SVM Classifier

Abstract Whirlwind is a natural disaster that often occurs and is difficult to predict from some time before. Early identification is needed to prevent a lot of casualties and losses. Whirlwind caused by instability in the atmosphere. Instability in the atmosphere usually occurs at the beginning of the day and the whirlwind can be identified based on the upper air parameter which can represent atmospheric instability. The purpose of this research is to optimize SVM classification with SMOTE algorithm to handling problems in imbalanced data and this research can minimize casualties and losses or also be a breakthrough for disaster-prone areas to be given early warning. The process for identifying whirlwind has several stages, namely pre-processing, imbalanced data handling, and classification. Pre-processing is normalized data. Whirlwind data has a classification problem, namely inter-class data that is not balanced so it needs to be corrected using the SMOTE Algorithm. Research on the identification of whirlwind using a combination of SMOTE-SVM produces the best accuracy is 98.8 %. When compared to data without the SMOTE algorithm the results obtained are better if the SMOTE method is applied. The specificity value is also better when given the SMOTE method. Based on these results it can be concluded that SMOTE can overcome the problem of imbalanced data in the upper air data by increasing the value of the classification of the whirlwind.
Key Advantages and Risks of Implementing Artificial Intelligence in the Activities of Professional Communicators

Nowadays we observe a wide penetration of digital technologies into communication experts practice. Literature review demonstrates a great interest of both technical and humanitarian scholars to this subject. The research results, obtained by opinion survey (young adults of 19-24 years old), illustrate possible pragmatic and ethical effects of AI applications as well as their advantages and risks.
Simulate a first-order Bézier curve in image encoding

Abstract Bézier curve of the first rank is a simple equation in terms of form, but it is characterized by the nature of private transactions making it difficult to use in image encryption because the dispersion of color values is not enough, this results in an encrypted image that gives clear references to the original image. This weakness in the equation does not exist in the case of text encryption where enough to change the numerical values of the components of the text to get a digital matrix representing the encrypted text.Through this algorithm we have used the Bézier curve technique from the first order of image coding we used a new method to generate the coefficients of the equation where we simulated the Bazier equation where it became as follows: • y=x_1*(t-1)+x_2*t • Where 0&lt;t&lt;1 To illustrate the work of this technology in image encoding the core of our work is in choosing a vector (1 × 4) with four numerical components )k_1, k_2, k_3, k_4 ( So that k_1&lt;k_2 and k_3 &lt;k_4, k_1 and k_2 have the same signal, as well as k_3 and k_4 also have the same reference to give them t_1 and t_2 Where t_1=k_1/k_2 and t_2=k_3/k_4 which will ensure that both have t_1 and t_2 have positive values less than 1. We have thus designed the equation of Bézier curve suitable for a scattering of color values of the image process, as well we’ll see it by explaining the way in detail below and tables of readings and global standards that have been inferred by the application of the algorithm
An Evaluation of the Power Consumption of Coauthentication as a Continuous User Authentication Method in Mobile Systems

Methods for continuous user authentication have become important with the proliferation of mobile devices in m-Health and human-centered systems. These methods must guarantee user identity with high assurance, authenticate without explicit intervention, and be power-aware. We present an evaluation of the power consumption of collaborative authentication (coauthentication) as a continuous authentication method. Coauthentication is a single-factor method in which multiple registered devices work together to authenticate a user, minimizing obtrusiveness while providing high user authentication assurance. To evaluate coauthentication's power consumption, we conducted experiments using two Bluetooth-enabled mobile devices and a stand-alone server in a local area network and running coauthentication continuously for eight hours. We found that the protocol uses approximately between 1.19% and 4.0% of the total power used by the devices. These results give evidence of the feasibility of using coauthentication as a continuous authentication method in mobile devices from the power consumption perspective.
Improving Person Re-identification by Mask Guiding and Part Pooling

Person re-identification (re-ID) is a promising computer vision task. State-of-the-art methods mainly utilize deep learning based approaches to learn visual features for describing person appearances. Due to occlusion, complex background, different postures and light intensity, the technology faces many challenges. In this paper, a person re-identification method is proposed combining mask guiding and part pooling. First, person mask is generated by a segmentation sub-net, combining Macro-Micro Adversarial Network (MMAN) with Fully Convolution Networks (FCN). To alleviate the influence of background, a mask guiding strategy is designed integrating the mask with person feature map. Then a part pooling strategy is employed to extract local features of the person. The final loss function of the network is defined as the combination of global loss and local loss, which can describe the person in a comprehensive manner. Four public datasets are employed to test the performance of the proposed method. Our method achieves rank-1/mAP of 89.05%/72.83% on the Market-1501, 79.03%/62.06% on the DukeMTMC-reID, 46.79%/29.61% on the MSMT-17, 49.50%/45.03% on the CUHK03-NP. Experimental results show that the designed mask guiding and part pooling strategies can improve person re-ID performance.
A fast hybrid data encryption for FPGA based edge computing

Recently, the Internet of Things (IoT) has been widely employed in industrial field for data collection and control application based on edge computing. The data security during storage and communication is one of the most concern in industrial system for safety. While the single security strategy and low data throughput of present data security system in embedded platform is hard to meet the increasing requirements, especially in high performance edge computing platform, such as FPGA based embedded platform. Therefore, in this paper, a fast hybrid data encryption is designed and implementation in an FPGA to enhance the data security and throughput, by combining Advanced Encryption Standard (AES) encryption and Message Digest (MD5) encryption with a customized high parallel computation. Experimental results with the National Laboratory for Information Science and Technology dataset in a heterogeneous FPGA show that the proposed mixed encryption implementation can realized the data encryption with a high throughput for the security in edge computing application.
ClusterLens: A Focus+Context Approach to Multi-Resolution Spatial Clustering

Spatial clustering can reduce visual clutter on maps and facilitate pattern recognition. However, interactive map exploration needs the spatial clustering to be a dynamic generation and representation process. Users may change derivative representations of clusters during the exploration process. To address this issue, we present ClusterLens, a new interaction technique that brings a focus+context approach into multi-resolution spatial clustering process. A lens is laid on a base map to avoid occlusion with the original and actual point locations. The lens can aggregate the data points at various spatial resolutions as map zoom level changes. We propose three primitives of resolution for spatial clustering: heatmap, circle, and grid, to generate and represent clusters in a separate mapping system. The lens and the base map are linked at all times. We also incorporate coordinated views into the ClusterLens system to facilitate context switching and comparison. We discuss the applicability of our technique and present a use case where ClusterLens can be useful to explore data distributions and reveal spatial patterns.
In-Vehicle Device Control System by Hand Posture Recognition with Movement Detection Using Infrared Array Sensor

Nowadays, with the development of automotive driving technologies, more and more functions and devices with control systems based on tactile, optical, and acoustic sensors are assembled into cars. However, these systems are faced with environmental limitations such as environmental noise and illumination conditions. Moreover, operations of these systems will cause lack of concentration on driving, which is a major cause of car accidents. In order to overcome these limitations, in this paper, an infrared array sensor is applied to construct a hand posture recognition system for in-vehicle device control. In the system, 10 kinds of target hand postures and posture movements toward four directions are combined to achieve the aim of the device selection and operations. The input images are separated into images with objects and without objects. Then, images in which object appears in boundary areas as well as blurred images are removed to improve the accuracy of the system. A convolutional neural network is applied as a classifier in order to realize the recognition of the 10 target hand postures and non-target postures for the in-vehicle device selection. After that, a detection method of the posture movement directions is applied for the device operations. Both indoor and in-vehicle experiments are conducted to verify the robustness of this system, and the results show that the proposed system can overcome the disadvantages of other systems and has a wide application with high accuracy.
MIPOSE

Giving computers the ability to learn from demonstrations is important for users to perform complex tasks. In this paper, we present an intelligent self-learning interface for dynamic human pose recognition. We capture 20 samples for an unknown pose to train a stable generative adversarial networks (GAN) system which aims to conduct data enhancement, then we adopt a threshold isolation method to distinguish relatively similar poses. A few minutes of learning time is sufficient to train a GAN system to successfully generate qualified pose samples. Our platform provides a feasible scheme for micro-intelligent interface, which can benefit to human-robot interaction greatly.
Improving Reliability for Provisioning of Virtual Machines in Desktop Clouds

Desktop clouds (DC) provide services in non-stationary environments that face reliability and performance threats not found in traditional clusters and datacenters. The idle resources available on computers can be claimed by users, turned off and faulted any time. For instance, platforms such as CernVM and UnaCloud harvest idle resources on computer labs to run virtual machines and support scientific applications. These platforms deal with interruptions and interferences caused by both users and applications. This non-stationarity is one of the main sources of issues in the design of reliable desktop cloud infrastructures that are capable of mitigating their own faults and errors. Based on a fault analysis that we have been carrying out and refining for a couple of years, we have found that reliability problems begin as the number of virtual machines that are going to be executed increases; these virtual machines must first be provisioned in the physical machines where they will be hosted. On the one hand, the main factors that can affect the provisioning of virtual machines in a DC are: the use of disk space, and the transmission of virtual images over the network. On the other hand, the applications and actions performed by users in the desktops may cause the virtual machine malfunction. In this paper, we propose an strategy based on known techniques applied to a particular environment: the scalable provisioning of virtual machines in desktop clouds. In addition, we describe the implementation and analyze its effectiveness.
Spam Detection in Social Network Using Machine Learning Approach

Social network helps people to continue communication with their links. The rapidly growing network’s popularity permits the users for gathering huge amount of personal details for the users. The social network offers a system through which the users usually preserve the contact with the friends. With the increment in the popularity of social networking, the users integrate huge amount of information for the users. Though, the amount of information and the ease of accessing user information can become the cause to attract malicious groups. Therefore, the networks are influenced by the spammers and lot of work has been done for identification and fixing. In research work, we have used SVM as a classification technique for detecting spam in the social network. To determine performance of the proposed work, different parameters are computed. To determine the efficiency of the proposed work, the comparison between proposed and existing work has been performed.
Knowledge-Based Management and Reasoning on Cultural and Natural Touristic Routes

There is great potential in interdisciplinary traveling platforms mingling knowledge about cultural heritage aspects, such as places with schedules providing visits or even containing augmented reality features also, along with environmental concerns to enhance personalized tourist experience and tripping avocation. For an ontological framework to support and nominate trip detours of targeted interests according to end-users, it should incorporate and unify as much heterogeneous information, deriving either from web sources or wherever there are ubiquitously available such as sensors or open databases. A plethora of qualitatively diverse data along with adequate quantities of them escalate the contingent results in terms of conferring a plurality of relevant options which can be utterly manifested through involving axioms with rule-based reasoning functionalities upon properties considered to be irrelevant to each other at first glance. Thus, managing to import predefined concepts from other ontologies, such as temporality or spatiality, and combine them with new defined concepts to tourist assets, such as points of interest, results in novel meaningful relationships never established before. Apart from the utilization of pre-existent resources and logic towards automatic detouring suggestions, a wide-spectrum modeling enables a suitable problem statement relevant to the e-Tracer framework and comprehension of the issues, providing the opportunity of statistical analysis of knowledge when adequate amounts amassed.
Performance Degradation in Parallel-Server Systems with Shared Resources

Parallel server systems are ubiquitous. Multicore CPUs are in practically every personal device from mobile handsets to high-end desktop PCs. At larger scale, data centers consist of a huge number of physical servers often shared by multiple users (for economic reasons). Moreover, the simultaneous users are typically unaware of each other due to reasons that can be technical (cf. security & privacy), practical (coordination layer would add complexity) and business related (usage can be business sensitive information). This results in server-side variability in terms of unpredictable response times. We study means for tackling these challenges. In particular, we consider a model where multiple users (dispatchers) route their jobs to a pool of servers using different (dispatching) policies. The goal is to determine how different policies interact: whether users' decisions support each other, or if some decisions are simply counterproductive. The lack of coordination is shown to increase, e.g., the mean response times, with two common and robust dispatching policies: the static Size-Interval-Task Assignment (SITA) and the dynamic Round-Robin (RR). We refer to this phenomenon as the price of ignorance.