Reviewerly/exHarmony huggingface dataset:
Deadlock-Free and Collision-Free Liver Surgical Navigation by Switching Potential-Based and Sensor-Based Functions

In this study, we developed a deadlock-free and collision-free liver surgical navigation method by switching potential-based and sensor-based approaches. The potential-based approach selects a near-optimal route from a scalpel tip to an arbitrary neighbor position around a tumor in a 3D organ map converted from digital imaging and communications in medicine (DICOM) data captured by magnetic resonance imaging or computed tomography. However, among complex-shaped blood vessels, the approach sometimes loses the route. To overcome this drawback, we switch to the sensor-based approach. This approach always finds a route near a tumor. However, the path becomes longer. Therefore, when the potential-based approach recovers to find another path, we switch the sensor-based approach back to the potential-based approach. The usefulness of this switching method was carefully ascertained in several kinds of allocations of tumor and blood vessels.
Pedagogy for Engineering and Digital Pedagogy

While universities and schools have been forced to shut down around the globe due to the COVID-19, there has been an increasing move towards teaching online. In these unprecedented times, it is imperative to support the digital transformation in education to operate effectively. The demand for online teaching has reached new heights. However, many educators are rushing to select a system such as Blackboard, Moodle or Pearson Catalytics and are endeavouring to move classes online as quickly and painlessly as possible. In this document, the author highlights the need for educators in engineering to innovate and contribute to the Technology Education Pedagogy. Online teaching is not only recording a lecture, sharing contents, screen, documents, and presentations, and cannot be seen as a low-cost alternative to face-to-face learning especially during this crisis which will expose many education institutions' and educators' flaws or strengths.
MLV-Viewer: Data Mining Visualizations in Decision Support System

Information visualization is defined as interactive visual representations supported by the computer, in order to increase cognition. The tools and methods applied can help to accelerate the understanding of a large volume of data, improving understanding, especially with multidimensional data sets. Visual analysis methods allow decision makers to combine flexibility, creativity and human knowledge with the enormous storage and processing resources of today's computers to gain insight into complex problems. If we add data mining to advanced visual interfaces, decision makers will be able to interact directly with data analysis, adapting to their tastes and needs. The MLV-Viewer prototype, in its data mining aspect, will be described in this article.
Research on Face Recognition Based on Improved Dropout Algorithm

Aiming at the problem of overfitting caused by deep convolutional neural networks in small and medium face databases, based on the idea of Dropout, this paper proposes a method of scaling the activation values of its training and testing phases, which is conducive to defining the model at one time and the training and testing process can be carried out on the same model only by changing its hold/abandon probability. Experiments show that this method can effectively alleviate the problem of overfitting and achieve regularization to a certain extent. At the same time, using a specific face image database for training, after repeated training, the test set accuracy was improved by 1.5%.
Shadow detection in SAR images based on greyscale distribution, a saliency model, and geometrical matching

Shadow information has been widely used in synthetic aperture radar interpretation, but corresponding shadow detection technology has not been given much attention in past studies. In this paper, we propose a hierarchical architecture based on greyscale distribution, a saliency model, and geometrical matching for shadow detection in SAR images. We find that the greyscale distribution of one Gaussian filtered image might contain a ‘distortion’ in its uphill part, which is the effect of shadow existence. Based on this distortion, a global threshold can be determined and then be used to segment candidate shadows. If there is no obvious distortion, a shadow saliency model is proposed as a substitute to extract such candidate areas. Usually, these candidate areas may contain some non-shadow components. According to the geometric relationships between shadow and object, we design a matching strategy to eliminate non-shadow parts from candidate regions. The remained areas are final shadow detection results. Experiments on two real datasets, Moving and Stationary Target Acquisition Recognition and MiniSAR, show that our method performs much better than two other published methods. The results demonstrate the effectiveness and feasibility of our proposed algorithm in practical SAR shadow detection tasks.
Multi-object Graph-based Segmentation with Non-overlapping Surfaces

For 3D images, segmentation via fitting surface meshes to object boundaries provides an efficient way to handle large images and enforce geometric prior knowledge. Furthermore, fitting such meshes with graph cuts has proven to be a versatile and robust framework. However, when segmenting multiple distinct objects in one image, current methods do not allow the natural constraint that objects should not overlap. In this paper, we present an extension to graph cut based methods which can provide a globally optimal segmentation of thousands of objects while guaranteeing no overlap. Our method works by separating objects with planes whose positions are determined as part of the graph cut. To demonstrate the general applicability of our method, we apply it to several 3D microscopy data sets from both biology and materials science. Our results show both quantitative and qualitative improvements.
Performance of mobile users with text-only and text-and-icon menus in seated and walking situations

We investigated mobile users’ performance on text-only and text-and-icon menu interfaces of short and medium lengths. We conducted two experiments, with 18 participants each, on four mobile menus in two common usage situations, seated and walking. In Experiment 1, seated participants conducted known-item searches of word menus on short and medium text-only menus and short and medium text-and-icon menus. In Experiment 2, we replicated the first experiment in a walking context of use. We used selection time and error as performance measures in both experiments, plus walking speed in the second. Results showed that, when seated, participants recorded faster times in selecting targets with text-and-icon medium menu than text-only medium menu. Participants, when walking, had significantly faster selection times with text-and-icon short menu than text-only short menu, and with text-and-icon medium menu than text-only medium menu. Text-and-icon medium menu also resulted in fewer incorrect selections by users than text-only medium menu in the walking situation. Further, when comparing menus of the same length in the walking situation, text-and-icon menu had a more efficient learning curve than text-only menu. Walking speed was better with text-and-icon medium menu than text-only medium menu. We discuss results and highlight theoretical and practical implications.
The scope of audience design in child-directed speech: Parents’ tailoring of word lengths for adult versus child listeners.

When communicating with other people, adults reduce or lengthen words based on their predictability, frequency, and discourse status. But younger listeners have less experience than older listeners in processing speech variation across time. In 2 experiments, we tested whether English-speaking parents reduce word durations differently across utterances in child-directed speech (CDS) versus adult-directed speech (ADS). In a child-friendly game with an array of objects and destinations, adult participants (N = 48) read instructions to an experimenter (adult-directed) and then to their own 2- to 3-year-old children (child-directed). In Experiment 1, speakers produced sentences containing high-frequency target nouns, and in Experiment 2, they produced sentences containing low-frequency target nouns. In both CDS and ADS in both experiments, speakers reduced repeated mentions of target nouns across successive utterances. However, speakers reduced less in CDS than in ADS, and low-frequency nouns in CDS were overall longer than low-frequency nouns in ADS. Together, the results suggest that repetition reduction may be beyond speaker control, but that speakers still engage in audience design when producing words for relatively inexperienced listeners. We conclude that language production involves nested audience-driven and speaker-driven processes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Computational Methods for Semantic Analysis of Historical Texts

This chapter presents an overview of the state of the art in the analysis of semantics phenomena in historical texts at scale, highlighting its critical aspects and proposing a new approach which joins together the expertise of computational specialists with that of humanities scholars. Semantic phenomena are grounded in linguistic, cognitive, social, cultural, and contextual factors, occur due to specific linguistic contexts or language-external events, and their dynamic mechanisms are often led by innovative language users and communities. Manual semantic annotation is very time-consuming and cannot usually be extended from one set of texts to another. The basic idea behind computational methods in historical semantics consists in building semantic spaces from text data to reflect the historical period of the corpus in question, with its conceptual and cultural frame of reference. Truly cutting-edge computational research in historical semantics should involve the development of innovative and impactful methods, which are built to answer questions relevant to humanists.
IRON-MAN: An Approach to Perform Temporal Motionless Analysis of Video Using CNN in MPSoC

This paper proposes a novel human-inspired methodology called IRON-MAN (Integrated RatiONal prediction and Motionless ANalysis) for mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video (TMAV). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image (ECTI) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption and lifespan reliability of the device.
HOW STRATEGY TO PUBLISH ARTICLES IN INTERNATIONAL REPUTABLE JOURNALS SUCCESFULL? AN EXPLORATORY STUDY FROM INDONESIA

This study aims to explore strategies and ways for articles to be accepted in reputable international journals in Indonesia. The method in this study is a qualitative exploratory case study that is to explore and look for factors that influence the success of publishing articles in reputable international journals. Participants in this study were 7 people in Indonesia who had succeeded in publishing many articles. For confidentiality of information, all participants were given the initial names P1, P2, P3, P4, P5, P6 and P7. Determination of participants with purposive sampling method determined to have competence and experience in making articles and successfully publishing articles in reputable international journals. Data was collected by in-depth interviews of all participants in a separate place and time and the results of the interview were recorded with permission from all participants. Based on the analysis results of this study it was concluded that key to success articles was accepted and could be published in reputable international journals, namely articles must be well written, the title of the article that attracted readers and editorial team, using language with good grammar. The level of plagiarism and similiarity is small, articles use latest references, use supporting software to reference citations and languages, submit articles in journals that are relevant to the topic, arrange articles by following journal templates, submit articles to high impact journal and the high number of publications, do collaboration. between author different countries is a good point and the writer must have patience and finally the article must have a novelty. This research novelty is the first research in Indonesia to analyze success strategies and tips for publication in reputable international journals
Minimum Expected Relative Entropy Principle

Stochastic filtering estimates a time-varying (multivariate) parameter (a hidden variable) from noisy observations. It needs both observation and parameter evolution models. The latter is often missing or makes the estimation too complex. Then, the axiomatic minimum relative entropy (MRE) principle completes the posterior probability density (pd) of the parameter. The MRE principle recommends to modify a prior guess of the constructed pd to the smallest extent enforced by new observations. The MRE principle does not deal with a generic uncertain prior guess. Such uncertainty arises, for instance, when the MRE principle is used recursively. The paper fills this gap. The proposed minimum expected relative entropy (MeRE) principle: (a) makes Bayesian estimation less sensitive to the choice of the prior pd; (b) provides a stabilised parameter tracking with a data-dependent forgetting that copes with abrupt parameter changes; (c) applies in all cases exploiting MRE, for instance, in stochastic modelling.
Computational discrimination between natural images based on gaze during mental imagery

Abstract When retrieving image from memory, humans usually move their eyes spontaneously as if the image were in front of them. Such eye movements correlate strongly with the spatial layout of the recalled image content and function as memory cues facilitating the retrieval procedure. However, how close the correlation is between imagery eye movements and the eye movements while looking at the original image is unclear so far. In this work we first quantify the similarity of eye movements between recalling an image and encoding the same image, followed by the investigation on whether comparing such pairs of eye movements can be used for computational image retrieval. Our results show that computational image retrieval based on eye movements during spontaneous imagery is feasible. Furthermore, we show that such a retrieval approach can be generalized to unseen images.
Encrypt-to-Self: Securely Outsourcing Storage

We put forward a symmetric encryption primitive tailored towards a specific application: outsourced storage. The setting assumes a memory-bounded computing device that inflates the amount of volatile or permanent memory available to it by letting other (untrusted) devices hold encryptions of information that they return on request. For instance, web servers typically hold for each of the client connections they manage a multitude of data, ranging from user preferences to technical information like database credentials. If the amount of data per session is considerable, busy servers sooner or later run out of memory. One admissible solution to this is to let the server encrypt the session data to itself and to let the client store the ciphertext, with the agreement that the client reproduce the ciphertext in each subsequent request (e.g., via a cookie) so that the session data can be recovered when required. In this article we develop the cryptographic mechanism that should be used to achieve confidential and authentic data storage in the encrypt-to-self setting, i.e., where encryptor and decryptor coincide and constitute the only entity holding keys. We argue that standard authenticated encryption represents only a suboptimal solution for preserving confidentiality, as much as message authentication codes are suboptimal for preserving authenticity. The crucial observation is that such schemes instantaneously give up on all security promises in the moment the key is compromised. In contrast, data protected with our new primitive remains fully integrity protected and unmalleable. In the course of this paper we develop a formal model for encrypt-to-self systems, show that it solves the outsourced storage problem, propose surprisingly efficient provably secure constructions, and report on our implementations.
A new knowledgeable encapsulation method of steel production scheduling model

The steel production scheduling is a typical continuous/discrete hybrid process; it is dynamic and difficult to predict. The scheduling model is the core object of steel production scheduling, and its modeling methods directly affect the precise decision-making and execution efficiency of scheduling. However, the current linear program and simulation model do not yet realize the scheduling model quick reuse and dynamic construction. Therefore, a new model knowledgeable encapsulation method is proposed, which consists of a knowledgeable encapsulation framework and knowledgeable mapping method. The knowledgeable encapsulation framework includes the model knowledge description interface, model knowledge publication interface, model knowledge behavior interface, and a web platform. The interfaces and the platform are designed to help model providers to encapsulate the scheduling model in an open network environment. The mapping method is constructed to strengthen the relationship between the model knowledge. Finally, a knowledge encapsulation platform is established to verify the effectiveness of the model knowledge encapsulation method.
Cloud Computing Framework for e-Health Security Requirements and Security Policy Rules Case Study: A European Cloud-Based Health System

The final few years, Information and Communication Technology (ICT) have delivered the concept of central enterprise model in e-health. Health-care is increasingly being supported via IT functions and new technologies, such as Cloud Computing. But sharing sensitive private data in Cloud Computing can be risky, when an unauthorized person gets access to this information and uses this in a different way than those supposed by the Providers. Numerous nations are sharp to go their typical health care services to the modern innovation of Cloud Computing, in order to move forward the first-class of care and to limit the cost. In any case, these possibilities introduce new safety risks and require a special treatment of safety issues, which cannot be ignored. Our work focuses on analyzing the challenges when using Cloud Computing in e-health systems and on moderation of these risks. In this paper, we present a list of the main security requirements that have to be viewed when migrating an e-health system to a SaaS Cloud Computing environment by means of each Health-care Providers and Cloud Service Providers and at the same time we propose some basic provisions to mitigate the significant risks.
Exploiting Self-Similarity for Under-Determined MIMO Modulation Recognition

Modulation recognition (modrec) is an essential functional component of future wireless networks with critical applications in dynamic spectrum access. While predominantly studied in single-input single-output (SISO) systems, practical modrec for multiple-input multiple-output (MIMO) communications requires more research attention. Existing MIMO modrec impose stringent requirements of fully- or over-determined sensing front-end, i.e. the number of sensor antennas should exceed that at the transmitter. This poses a prohibitive sensor cost even for simple 2x2 MIMO systems and will severely hamper progress in flexible spectrum access with advanced higher-order MIMO.We design a MIMO modrec framework that enables efficient and cost-effective modulation classification for under-determined settings characterized by fewer sensor antennas than those used for transmission. Our key idea is to exploit the inherent multi-scale self-similarity of MIMO modulation IQ constellations, which persists in under-determined settings. Our framework called SYMMeTRy (Self-similarit Y for MIMO ModulaTion Recognition) designs domain-aware classification features with high discriminative potential by summarizing regularities of symbol co-location in the MIMO constellation. To this end, we summarize the fractal geometry of observed samples to extract discriminative features for supervised MIMO modrec. We evaluate SYMMeTRy in a realistic simulation and in a small-scale MIMO testbed. We demonstrate that it maintains high and consistent performance across various noise regimes, channel fading conditions and with increasing MIMO transmitter complexity. Our efforts highlight SYMMeTRy's high potential to enable efficient and practical MIMO modrec.
Research on Computer Forensics Technology Based on Data Recovery

Abstract With the rapid development of information technology, fundamental changes have taken place in the way people work. However, computer crime has also become the main type of cases in the Internet era. Therefore, computer forensics technology has become an important research content of computer crime evidence collection. Firstly, this paper analyzes the relationship between computer forensics and data recovery. Then, this paper analyzes the steps of computer forensics. Finally, this paper analyzes the application of anti-forensics technology and computer forensics technology.
Extrinsic Plagiarism Detection for French Language with Word Embeddings

Plagiarism is the process of using the ideas of another without naming the source. Plagiarism is unacceptable and could be viewed as cheating and stealing. Plagiarism detection is necessary but complicated as it is often facing significant challenges given the large amount of material on the World-wide-web and the limited access to a substantial part of them. This paper presents an investigation of the so-called word embeddings, a recent machine learning paradigm, with the aim of detecting plagiarism in French documents. The proposed model performs better than state of the art methods and achieves a plagiarism detection accuracy of 62% using Gutenberg project novels.
Object Recognition Software Using RGBD Kinect Images and the YOLO Algorithm for Mobile Robot Navigation

This work presents a vision system based on the YOLO algorithm to identify static objects that could be obstacles in the path of a mobile robot. In order to identify the objects and its distances a Microsoft Kinect sensor was used. In addition, a Nvidia Jetson TX2 GPU was used to increase the image processing algorithm performance. Our experimental results indicate that the YOLO network has detected all the predefined obstacles for which it has been trained with good reliability and the calculus of the distance using the depth information returned by Microsoft Kinect had an error below of 3,64%.