Reviewerly/exHarmony huggingface dataset:
Multi-Granular Semantic Analysis Based on Nasal Endoscopic Video

The semantic analysis of nasal endoscopic video is a challenging task since lots of irrelevant and insignificant information exists in the untrimmed surgical video, i.e. background, blur, judder or blood-stained video fragments. It is important to identify the start and end point of the valid surgical fragments automatically and remove the invalid fragments of endoscopic surgery videos for medical education & research. However, the performance of deep-learning based methods, which use a fixed time interval and a sliding window, are severely affected when the interference information appears randomly in the nasal endoscopic video. Specifically, the surgical video is a continuous process globally, while many local discontinuity fragments are brought when endoscope enters and exits the cavity frequently. Hence, we propose a multi-granularity semantic analysis framework that can simultaneously meet the accuracy and timeliness required for endoscopic surgery video semantic analysis. Our approach is an end-to-end solution. First, a joint model is created to extract the temporal-spatial features of the surgical video on a coarse-grained scale. Meanwhile, an attention mechanism is used to automatically select the informative spatial features of endoscopic video. Second, a hierarchical self-correction module is proposed to correct the boundaries of the surgical operation iteratively on a fine-grained scale. Finally, we justify the proposed network through extensive experiments and quantitative comparisons against other state-of-the-art approaches. We achieve a good performance in terms of accuracy and efficiency.
Web-Based Classification for Safer Browsing

In the cyber world, attack (phishing) is a big problem and it is clear from the reports produced by the Anti-Phishing Working Group (APWG) every year. Phishing is a crime that can be committed over the internet by the attacker to fetches the personal credentials of a user by asking their details like login/credit or debit card credentials, etc. for financial gains. Since 1996 phishing is a well-known threat and internet crime. Research community is still working on phishing detection and prevention. There is no such model/solution existed that can prevent this threat completely. One useful practice is to make the users aware of possible phishing attacking sites. The other is to detect the phishing site. The objective of this paper is to analyze and study existing solutions for phishing detection. The proposed technique uses logistic regression to correctly classify whether the given site is malicious or not.
An Intelligent Information Hiding Scheme Based on Histogram Translation in Frequency Domain

In view of overflow control and security, this paper proposes a reversible information hiding scheme based on histogram translation in frequency domain, in which a universal overflow control algorithm is presented. It differs from the traditional method, and it conducts the overflow control independently. The proposed overflow control scheme allows multi-level overlapping hiding, i.e. secret main and auxiliary information are hidden in multiple layers, and auxiliary information can also be hidden in multiple layers. In addition, security against intentional or accidental modification is another focus. Password and chaotic mechanism are put forward. Experimental results demonstrate that our solution has better recoverability, robustness and security. Moreover, our solution offers higher PSNR and SSIM than other approaches. Our solution is operability and suitable for multimedia data integrity with high requirements.
Students’ Attitudes Towards the Mobile App Used in English Classes

Mobile devices have penetrated all spheres of human activities, including education. Especially the mobile phones/smartphones are becoming widely used in learning thanks to their proved benefits such as improved knowledge retention and increased student engagement. The purpose of this article is to discuss students’ attitudes towards the mobile app used for learning vocabulary and phrases in English classes at a university level. The methods included a literature review and a questionnaire survey. The results showed that the positive aspects of the mobile app prevailed over the negative ones. The mobile app helped students prepare for the final achievement test, learning was less stressful, interacting with the app helped students retain the English vocabulary better, as well as become more confident in their learning. In addition, using the app gave students confidence knowing that they had all necessary resources at hand and could access them at any time when moving around. They also enjoyed using the app and would opt for its implementation in other courses taught at the faculty. Thus, the findings of this study revealed that students have positive attitudes towards the use of mobile apps in classes and they consider mobile apps suitable additional learning tools, which can thanks to their interactivity, ubiquity, or portability, motivate them to learn.
Open Innovation in Mature Industries an Example from Maritime Industry in Norway

Open innovation has received increasingly attention in scientific research (Chesbrough 2002; van de Vrande et al. 2009). It showed to be a fruitful strategy to employ in highly dynamic and changing environment (Chesbrough 2003a, b). Since its emergence, evidence to support open innovation model was taken mainly from the so-called ‘high technology’ industries, such as computers, information technology, and pharmaceuticals. However, more resent work Chesbrough and Growther (2006) confirmed the relevance of this approach in more mature industries. However, little is known so far under which conditions open innovation serves as appropriate strategy in such mature industries. This paper investigates innovation process in maritime industry, which is characterised by long-term trust-based relationships between involved partners, and substantial financial investment. Using example from a shipping company in Norway we show how the innovative action of the firm may depend on the combined influence of entrepreneurial orientation (Lumpkin and Dess 1996; Rauch et al. 2009) within the firm and cooperative links towards knowledge providers. We investigate innovation process that led to construction of a ship that was specifically designed to operate under the harsh Arctic conditions. Findings indicate that open innovation approach is particularly suitable for innovation process in mature industries when product requirements are dictated by extra demanding conditions. We contribute to the domain of open innovation by specifying that firm entrepreneurial orientation, collaborative links with customer and partner are particularly suitable when the level of uncertainty is high.
A Novel Hybrid Consensus-based High-degree Cubature Information Filter

In this note, the problem of distributed nonlinear state estimation with networked sensors is considered. Based on statistical linear regression, a more suitable strategy to compute the measurement information contribution of the fifth-degree cubature information filter is presented. Based on the new strategy for measurement update and hybrid consensus on both information and measurements, a novel hybrid consensus-based high-degree cubature information filter (HCHCIF) is proposed. The effectiveness and superiority of the proposed HCHCIF is validated by tracing a maneuvering target in a sparse sensor network with naive nodes. Simulation results show that the proposed HCHCIF outperforms the existing distributed algorithms in the aspects of estimation accuracy and consensus on estimates from different nodes.
Learning the Front-End Speech Feature with Raw Waveform for End-to-End Speaker Recognition

State-of-the-art deep neural network-based speaker recognition systems tend to follow the paradigm of speech feature extraction and then the speaker classifier training, namely "divide and conquer" approaches. These methods usually rely on fixed, handcrafted features such as Mel frequency cepstral coefficients (MFCCs) to preprocess the waveform before the classification pipeline. In this paper, inspired by the success and promising work to model a system directly from the raw speech signal for applications such as audio speech recognition, anti-spoofing and emotion recognition, we present an end-to-end speaker recognition system, combining front-end raw waveform feature extractor, back-end speaker embedding classifier and angle-based loss optimizer. Specifically, this means that the proposed frontend raw waveform feature extractor builds on a trainable alternative for MFCCs without modification of the acoustic model. And we will detail the superiority of the raw waveform feature extractor, namely utilizing the time convolution layer to reduce temporal variations aiming to adaptively learn a front-end speech feature representation by supervised training together with the rest of classification model. Our experiments, conducted on CSTR VCTK Corpus dataset, demonstrate that the proposed end-to-end speaker recognition system can achieve state-of-the-art performance compared to baseline models.
Personal Meaning Organization (PMO): A Hermeneutic Approach to Design

Contemporary design exhibits a transition from the design of objects (as things) towards the design of interactions between subjects and objects (as processes). Furthermore, subjects are no longer considered users, but rather persons (personal identities). These persons perceive what we design from an autonomous self-referred meaning organization, which allows them to make embodied value judgments during the coupling process of interacting with material culture. Hence, for design research, it is essential to interpret these judgements as generative meanings capable of driving design decisions. Personal Meaning Organization (PMO) is a suitable framework to set out interpretative clues and a plausible explanatory model. It is based on early attachment processes understood as nuclear affective interactions that predetermine the organization of meanings within a coherent narrative structure. As we illustrate through a validation process, incorporating PMO into design is a convenient epistemological approach to solve the problem of creating interactions for others.
Classifying Candidate Axioms via Dimensionality Reduction Techniques

We assess the role of similarity measures and learning methods in classifying candidate axioms for automated schema induction through kernel-based learning algorithms. The evaluation is based on (i) three different similarity measures between axioms, and (ii) two alternative dimensionality reduction techniques to check the extent to which the considered similarities allow to separate true axioms from false axioms. The result of the dimensionality reduction process is subsequently fed to several learning algorithms, comparing the accuracy of all combinations of similarity, dimensionality reduction technique, and classification method. As a result, it is observed that it is not necessary to use sophisticated semantics-based similarity measures to obtain accurate predictions, and furthermore that classification performance only marginally depends on the choice of the learning method. Our results open the way to implementing efficient surrogate models for axiom scoring to speed up ontology learning and schema induction methods.
The Evolution of Privacy in the Blockchain: A Historical Survey

In order to store information in a decentralized context and without the presence of a guarantor authority, it is necessary to replicate the information on multiple nodes. This is the underlying idea of the blockchain, that is generating increasing interest nowadays as one of the most-promising disruptive technologies. However, the ledger is accessible to all participants and if adequate precautions are not taken, this may lead to serious privacy issues. Present paper retraces the history of blockchain with particular attention to the evolution of privacy and anonymity concerns, starting from bitcoin. Furthermore, this work presents the most popular solutions to ensure privacy in the blockchain, as well as the main cryptocurrencies that have been proposed after bitcoin to overcome this problem. A critical survey is presented classifying the approaches in mixing protocols and knowledge limitation protocols. Open challenges and future directions of research in this field are proposed.
A Globally Adaptive Region Growing Method for Cervical Tumor Segmentation Based on MR Images

During the diagnosis and treatment of cervical cancer, the relative position between cervical tumor boundary and cervical wall is an essential standard for cervical cancer staging. To obtain an accurate tumor boundary, it is of great clinical significance to design an algorithm which can accurately extract cervical tumor. Based on Magnetic Resonance Imaging (MRI), an algorithm combining the global information of images with Region Growing criteria is proposed in this paper, named Globally Adaptive Region Growing (GARG). First, preprocess the original images to reduce the interference of surrounding tissues and smooth the noise. Second, combining the global information into traditional Region Growing method, an automatic threshold initialization segmentation is realized. Third, apply the morphological operations to acquire more accurate target regions. The results obtained by the algorithm are contrasted with the tumor regions manually painted by doctors. The highest overlap rate reaches 93.8%, with 85.4% on average, which are superior to the results obtained by classic Region Growing, C-V Level Set and Threshold method. It has been proved that the algorithm can provide effective reference for the identification of tumor boundaries, which improves work efficiency of doctors to a large extent. It also reduces the influence of subjectivity and uncertainty on segmentation results at the same time. The proposed GARG method greatly shows the innovation on cervical tumor segmentation and its clinical significance.
Distributed AI for Special-Purpose Vehicles

In this paper, we elaborate on two issues that are crucial to consider when exploiting data across a fleet of industrial assets deployed in the field: 1) reliable storage and efficient communication of large quantities of data in the absence of continuous connectivity, and 2) the traditional centralized data analytics model which is challenged by the inherently distributed context when considering a fleet of distributed assets. We illustrate how advanced machine learning techniques can run locally at the edge, in the context of two industry-relevant use cases related to special-purpose vehicles: data compression and vehicle overload detection. These techniques exploit real-world usage data captured in the field using the I-HUMS platform provided by our industrial partner ILIAS solutions Inc.
Life less normal

column Life less normal Share on Author: Alex Taylor City, University of London City, University of LondonView Profile Authors Info & Claims InteractionsVolume 27Issue 4July - August 2020 pp 79–82https://doi.org/10.1145/3406296Online:09 July 2020Publication History 0citation532DownloadsMetricsTotal Citations0Total Downloads532Last 12 Months342Last 6 weeks29 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
Gray Scale Image Coloring Method Based on GAN

In order to solve the problem that the existing gray-scale image coloring model is not rich in color expression and the edge distinction between different objects is not obvious, this paper proposed a gray-scale image coloring method based on the generated adversarial network (GAN), which adopts convolution neural network with the same size of the feature image as the generator and the cost function, based on its "end-to-end" feature, we trained the GAN model, takes the gray image as the model input, and colorizes the color image at the output of the generator. The method proposed in this paper is applied to the remote sensing image data set UCMerced_LandUse for experimental verification. According to the experimental results, it can be observed that the gray-scale image coloring method based on GAN proposed in this paper has better evaluation index than other methods with good coloring performance, the color expression of the image after coloring by the model is more abundant.
A fixed point algorithm for improving fidelity of quantum gates

This work considers the problem of quantum gate generation for controllable quantum systems with drift. It is assumed that an approximate solution called seed is pre-computed by some known algorithm. This work presents a method, called Fixed-Point Algorithm (FPA) that is able to improve arbitrarily the fidelity of the given seed. When the infidelity of the seed is small enough and the approximate solution is attractive in the context of a tracking control problem (that is verified with probability one, in some sense), the Banach Fixed-Point Theorem allows to prove the exponential convergence of the FPA. Even when the FPA does not converge, several iterated applications of the FPA may produce the desired fidelity. The FPA produces only small corrections in the control pulses and preserves the original bandwidth of the seed. The computational effort of each step of the FPA corresponds to the one of the numerical integration of a stabilized closed loop system. A piecewise-constant and a smooth numerical implementations are developed. Several numerical experiments with a N -qubit system illustrates the effectiveness of the method in several different applications including the conversion of piecewise-constant control pulses into smooth ones and the reduction of their bandwidth.
Bitcoin Hype Analysis and Perspectives in the South Asian Market

After demonetization, the emphasis was given to a cashless economy by the Government of India. Keeping in view the concept of cashless economy, the importance of Crypto currency cannot be denied. Crypto currency (CC) is a virtual currency and it works as a medium of exchange by using cryptography for security. It comprises diverse currencies such as Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC), Cardano (ADA), Neo (NEO), Stellar Lumens (XLM), and so on. Many countries like Canada, Australia, Bulgaria, Chile, Denmark, Estonia, Finland, Germany, and Luxembourg have adopted Bitcoin in order to moving towards a digital eco-system. The research was conducted to find out the awareness, perception and understanding about the functioning of bitcoin among individuals. This article is all about awareness of bitcoin amongst individuals and prospective if allowed by the Government of India.
An Improved Current Statistical Model with Maneuver Detection

Current statistical model (CSM) performs well in maneuvering target tracking, however it suffers low accuracy when the target does not or weakly maneuver. To improve the performance of CSM in target tracking, we propose a new improved current statistical model with maneuver detection (ICSM-MD). The new model reduces the tracking error in weak or non-maneuvering situations by employing a specifically designed arctangent function to adaptively adjust the maximum acceleration, but it worsens the tracking performance for strong maneuvering targets. To overcome this deficiency, strong tracking filter (STF) is employed in ICSM. Moreover, to track more accurate, a maneuver detection scheme is adopted. The state estimate and covariance matrix are properly modified if a maneuver is detected, which reduces the variance of acceleration estimate. Consequently, ICSM-MD has a higher tracking precision and converges more quickly when the target acceleration jumps. Simulation results demonstrate the high performance of ICSM-MD in both strongly and weakly maneuvering target tracking.
Methodological aspects of improvement of Graphic disciplines teaching in higher education institutions

The gradual process of reforming educational activities lays new demands on education in the higher education system of Ukraine, and therefore there is a need to improve the methodology of teaching any discipline, to continuously monitor the quality of applicants’ educational success, as well as to apply the competence-based approach to education, in general. The article determines the relevance of the study of the educational process optimization in higher education institutions (HEI), including mastering modern principles of teaching Graphic disciplines the creation of which contributes to the active formation of professional competence of students majoring in artistic and architectural specialties. The process of education in HEI is known to be a significant factor in the socialization of a personality, since in addition to acquiring new knowledge and professional competences, it also promotes self-education and self-realization in future life. The authors reveal the peculiarities, technological means and methods of teaching Graphic disciplines and organization of the educational process for freshman and sophomore students of art-related HEI. There is a need to increase the efficiency not only of classroom contact hours, but also of the extracurricular work. This requires that teachers should constantly search for new ways and approaches that could facilitate mastering of educational material. The model of the educational process for near future is one of the directions determining the improvement of the whole education system and involves this type of training, which is characterized by a considerable amount of independent work, which also requires a sufficiently high level of stable skills of individual graphic competence. In this regard, the formation of students’ motivation and interest throughout the educational process in HEI, the recognition of the necessity to obtain a quality graphic education, which is the key to success in their future professional activity, are of great importance.
Automatic Digital Recognition of Multiple Electricity Dashboards

Intelligent detection of electric transmissions and distribution equipment is impeded by factors such as the potential diversity of displays, the vulnerability of meter digits reading, and the slow speed of training and predicting. To deal with the problem, this paper aims to use the technology of pattern recognition to automatically recognize various kinds of digital displays by analyzing images collected by a CCD camera. To be more specific, firstly, we design a multi-layer convolutional module to classify different types of dashboards. Secondly, to deal with different types of dashboards, we presents an improved Adaboost algorithm in the training process that can quickly identify and cut out the display area of the meters. Lastly, we implement several digital image processing algorithms to recognize the dashboard readings accurately. The experimental results show that our method performs well in improving recognition precision.
Proof-Theoretic Results of Common Sense Modal Predicate Calculi

The paper presents Hilbert-style systems and sequent calculi for some weaker versions of common sense modal predicate calculus. The main results are the strong completeness results for the Hilbert-style systems and cut elimination theorems for the sequent calculi.