Reviewerly/exHarmony huggingface dataset:
Semantics Beyond Language

Critics often view linguistics as the cognitive science that lags behind all the others (Prinz ). Consider a paradoxical claim; perhaps the main problem with linguistics comes from its strong attachment to language. Maybe the problem lies even deeper, in the structure of propositional logic based on the language and its grammar (Chihara , Barker ), which permeates even logical deep structure (if such exists). Standard linguistics has its role to play; yet, it does not cover enough ground. Processes developed in AI, such as deep computing algorithms, and by neuroscience, such as mind-maps, place much of informational processing (thinking) beyond the language. The main stream of communication may happen at the level of Gestalts and images, while language statements may be seen as only simplified reports of what happens, not the engine of thinking (Boltuc ). Till recently, linguistics did not pay much attention to the Freudian revolution (the role of unconscious thought-processes, Floridi ()), or to Libet’s experiment (indicating that we decide before we become conscious of the issue at hand). This affects semantic AI. In this paper we do not propose a conceptual revolution; AI pioneers, like Goertzel, Sowa, Thaler, Wang, have been in this revolution for decades. The point is to mainstream its philosophical, theoretical and strategic implications. The language of deep learning algorithms, used in semantic AI, should recreate gestalts developed at the mind-maps level not just grammatical structure.
Maximally Asymmetric Multiple-Valued Functions

The asymmetry of a function f(x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , ..., x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) is the fewest elements of the range of f that must be changed so that f becomes a symmetric function. The functions with maximal asymmetry for the case of r-valued n-variable functions have been characterized and counted for r=2 in two previous papers. In this paper, we extend these results to . We do this for two types of symmetry, functions whose value is unchanged by 1) any permutation of the variable labels and by 2) any permutation of variable labels and variable values. We also derive the maximum possible asymmetry. We show that, as n→∞ and r is fixed, the maximum asymmetry approaches (r-1)r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-1</sup> .
Achieving Inclusion with Contextualized User-Sensitive Design

Users and contexts in human-computer interaction systems often have great diversity, which limits the inclusion provided by a single design. Existing user-sensitive design and contextual design methodologies have made useful attempts to pay attention to diversities, but they still cannot provide sufficient design basis for adapting to dynamically changing user capabilities, needs and usage contexts. Based on the analysis of the interaction between the behavior model and various elements in the human-computer system, this paper constructs a contextualized user-sensitive design framework, and studies the diversity and changing factors in the human-computer system from the two basic dimensions of the user and the context. In order to reflect the multi-dimensional dynamic characteristics of users, the authors propose a new user research and analysis tool, the generalized user balance sheet, for user-sensitive design, and takes a questionnaire case to reveal the huge differences of users’ needs in different contexts. Based on the contextualized user-sensitive design framework, this paper shows the basic methods and essential system elements of user-sensitive design and contextualized design, as well as the possibility of combining the two for inclusive design.
Efficient Video Indexing for Monitoring Disease Activity and Progression in the Upper Gastrointestinal Tract

Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. While the endoscopy video contains a wealth of information, tools to capture this information for the purpose of clinical reporting are rather poor. In date, endoscopists do not have any access to tools that enable them to browse the video data in an efficient and user friendly manner. Fast and reliable video retrieval methods could for example, allow them to review data from previous exams and therefore improve their ability to monitor disease progression. Deep learning provides new avenues of compressing and indexing video in an extremely efficient manner. In this study, we propose to use an autoencoder for efficient video compression and fast retrieval of video images. To boost the accuracy of video image retrieval and to address data variability like multi-modality and view-point changes, we propose the integration of a Siamese network. We demonstrate that our approach is competitive in retrieving images from 3 large scale videos of 3 different patients obtained against the query samples of their previous diagnosis. Quantitative validation shows that the combined approach yield an overall improvement of 5% and 8% over classical and variational autoencoders, respectively.
Anomaly Detection of ICS based on EB-OCSVM

Abstract Industrial process anomaly detection mechanisms have been proposed to protect industrial control system to minimize the risk of damage or loss of resources. In this paper, an one-class Support Vector Machine based extended boundary (EB-OSCVM) is used to detect anomalies in industrial multivariate time series data from a simulated Tennessee Eastman Process (TEP) with many cyber attacks. In detail, determine the change points of each process variable and capture the causality relationship between the variables based on the location and time delay of the change points. Then, by monitoring the leaf nodes in the causality graph, we can know whether the system is abnormal, it can effectively reduce the dimension of process data. The EB-OSCVM extend classification boundary of OCSVM in order to reduce the error of noise, if data is outside the boundary of EB-OCSVM, there is an anomaly. Finally, tracing the anomaly source according to causal direction. An experiment is used to verify the effectiveness of the proposed approach, the results demonstrate that the approach presents a high-accuracy solution and traces the source of anomaly correctly.
Predictably Deterrable? The Case of System Trespassers

Can computing environments deter system trespassers and increase intruders’ likelihood to cover their tracks during the progression of a system trespassing event? To generate sufficient empirical evidence to answer this question, we designed a series of randomized field trials using a large set of target computers built for the sole purpose of being infiltrated. We configured these computers to present varying levels of ambiguity regarding the presence of surveillance in the system, and investigated how this ambiguity influenced system trespassers’ likelihood to issue clean tracks commands. Findings indicate that the presence of unambiguous signs of surveillance increases the probability of clean tracks commands being entered on the system. Nevertheless, even when given clear signs of detection, we find that intruders are less likely to use clean tracks commands in the absence of subsequent presentations of sanction threats. These results indicate that the implementation of deterring policies and tools in cyber space could nudge system trespassers to exhibit more cautiousness during their engagement in system trespassing events. Our findings also emphasize the relevance of social-science models in guiding cyber security experts’ continuing efforts to predict and respond to system trespassers’ illegitimate online activities.
IVIFCM-TOPSIS for Bank Credit Risk Assessment

Bank credit risk assessment is performed by credit rating agencies in order to reduce information asymmetry in financial markets. This costly process has been automated in earlier studies by using systems based on machine learning methods. However, such systems suffer from interpretability issues and do not utilize expert knowledge effectively. To overcome those problems, multi-criteria group decision-making (MCGDM) methods have recently been used to simulate the assessment process performed by the committee of multiple credit risk experts. However, standard MCGDM methods fail to consider high uncertainty inherently associated with the assessment and do not work effectively when the assessed credit risk criteria interact with each other. To address these issues, we propose MCGDM model for bank credit risk assessment that has two advantages: (1) The imprecise assessment criteria are represented by interval-valued intuitionistic fuzzy sets, and (2) the interactions among the criteria are modeled using fuzzy cognitive maps. When combined with traditional TOPSIS approach to ranking alternatives, we show that the proposed model can be effectively applied to assess bank credit risk.
Communication over Cloud Computing: A Security Survey

Cloud computing is a new model that gives appropriate, on-demand access to a shared pool configurable computing resources. In cloud computing, IT-related abilities are given as services, available without requiring a deep knowledge of the underlying technologies, also, with insignificant administration exertion. The security issue turns out to be more complex under the cloud model as new measurements have gone into the issue scope related to the model architecture, multi-tenancy, elasticity, and layers dependency stack. In this paper, a survey on communication security issues over cloud computing is presented along with their solution.
Syndrome-Generalized Belief Propagation Decoding for Quantum Memories

Quantum low-density parity check (QLDPC) codes are promising in realization of scalable, fault tolerant quantum memory for computation. Many of the QLDPC codes constructions suffer from unavoidable short cycles in their Tanner graph which degrade the decoding performance of the belief propagation (BP) algorithm. In this paper, we propose a syndrome based generalized belief propagation (GBP) algorithm for decoding of quantum LDPC codes and analyze how the proposed algorithm escapes from short cycle trapping sets effectively compared to the BP algorithm. Simulation results show improved decoding performance of the GBP algorithm over BP for the dual containing Calderbank, Shor and Steane (CSS) codes when cycles of length 4 are considered in the region based approach.
User-Centered Gestures for Mobile Phones: Exploring a Method to Evaluate User Gestures for UX Designers

The objective of this paper is to explore how users react to certain gestures (e.g., swipe, scroll, or tap) for certain use cases. More specifically, the goal is to explore and suggest guidelines to user experience (UX) designers when choosing gestures for specific use cases on a smartphone application. Building on the Task-technology fit theory, we are specifically interested in the degree of alignment between gestures and each mobile use case. We hypothesize that some gestures are better aligned with certain use cases because they require less cognitive effort than others. In other words, certain gestures are likely to become so natural for users that they do not have to consciously invest effort to accomplish these gestures. Likewise, we hypothesize that the emotional valence of gestures will be affected by the use case. To attain this objective, a lab-experiment was conducted with 20 participants, where cognitive load and emotional valence were measured. Results suggest that the combination of gestures and use cases have an impact on the user cognitive load and valence. These findings contribute to human-computer interaction (HCI) research by providing insights to help user experience (UX) designers select appropriate gestures.
A new decision support system for knowledge management in archaeological activities”

The use of Information Technologies (IT) has today become an added value for appropriate decision making. This has contributed to improving the companies’ strategies in the market. However, the full potential of these technologies in the relevant field of Archaeology has yet to be fully exploited. To contribute to reducing this gap, this paper presents a new and original design of a Process Maturity Framework for archaeological knowledge and data management which may be applied for high-level timely decision making, supported by an ‘IT Governance’ reference frame, in order to improve the quality and efficiency of the services provided by the Diagnostic, Prospecting, Monitoring and Excavation processes of the Preemptive Archaeology Program. This new Process Maturity Model (PMM) takes the processes which are currently established in each phase of archaeological projects as its reference to improve information analysis, reports generation and support decision-making processes, as well as to manage and control the materials and context found in the field. This is achieved by emphasizing the use of the information required for future queries and projections, ensuring its’ quality and integrity in order to generate reports more efficiently, whilst also allowing a more agile and timely decision-making process. Said information has been collected during the field and laboratory processes by analysing the proper application and management of the technology from an ‘IT Governance’ framework in companies which offer archaeological services. The different phases of the implementation of the model designed, based in ITIL, since it is the most holistic of the current benchmarks in Technology Services Management, are shown by means of a hypothetical, yet real, application of the PMM in an Archaeology Consultancy firm. Thus, a set of basic parameters is initially established in order to implement a PMM. Then, a diagnostic on the processes and IT Service Management applied to each archaeological phase is performed. Afterwards, an evaluation of the current maturity level of the processes is carried out and, finally, the continuous improvement plan is described.
New Research on Transfer Learning Model of Named Entity Recognition

Abstract This paper integrates the current Google’s most powerful NLP transfer learning model BERT with the traditional state-of-the-art BiLSTM-CRF model to solve the problem of named entity recognition. A bi-directional LSTM model can consider an effectively infinite amount of context on both sides of a word and eliminates the problem of limited context that applies to any feed-forward models. Google’s model applied a feedforward neural network, causing its performance to weaken. We seek to solve these issues by proposing a more powerful neural network model named BT-BiLSTM. The new neural network model has obtained F1 scores on three Chinese datasets exceeds the previous BiLSTM-CRF model, especially on the value of recall. It shows the great value of the combination of large scale none-labelled data pre-trained language model with named entity recognition, which inspire new ideas on other future work.
Social Preferences in Decision Making Under Cybersecurity Risks and Uncertainties

The most costly cybersecurity incidents for organizations result from the failures of their third parties. This means that organizations should not only invest in their own protection and cybersecurity measures, but also pay attention to that of their business and operational partners. While economic impact and real extent of third parties cybersecurity risks is hard to quantify, decision makers inevitably compare their decisions with other entities in their network. This paper presents a theoretically derived model to analyze the impact of social preferences and other factors on the willingness to cooperate in third party ecosystems. We hypothesize that willingness to cooperate among the organizations in the context of cybersecurity increases following the experience of cybersecurity attacks and increased perceived cybersecurity risks. The effects are mediated by perceived cybersecurity value and moderated by social preferences. These hypotheses are tested using a variance-based structural equation modeling analysis based on feedback from a sample of Norwegian organizations. Our empirical results confirm the strong positive impact of social preferences and cybersecurity attack experience on the willingness to cooperate, and support the reciprocal behavior of cybersecurity decision makers. We further show that more perception of cybersecurity risk and value deter the decision makers to cooperate with other organizations.
Development of 2D Game with Construct 2

Developing a computer game presents a big challenge, since creating a single product requires much more knowledge and experience in different areas than just programming. However, the question is how to bring your work to life in hours and days instead of weeks and months. Using the Construct 2 drag and drop method, we are able to implement visual materials into our game, and the principle of programming is reduced to the events option, which helps us to assign actions through selected graphics icons. Application instant preview allows fast prototyping and iterative development which enables testing during the entire creation process, making it much easier to detect and resolve problems. It gave us flexibility and the ability to see how the prototype of each version actually responds to certain actions, conditions, and events. As a final point, with a good organization and understanding of powerful event system, flexible behaviours, stunning visual effects and capabilities provided by the Construct application package, it is possible to do any conceivable 2D project.
LoGSRN: Deep Super Resolution Network for Digital Elevation Model

The spatial resolution of a Digital Elevation Model (DEM) plays a crucial role in many practical remote sensing applications.However, it is normally limited by the spatial resolution of the raw input imagery, from which a DEM is derived.One solution to enhance the limited resolution of a DEM during the post-processing, is fusing previously obtained high resolution DEM data.This data-driven approach appears particularly promising, considering the recent success of a deep convolutional network in single image super resolution (SISR).In this paper, we propose a new SISR network that can recover a high resolution DEM.Instead of configuring a single network directly mapping low resolution depth values to high resolution depth values, we propose a new model consisting of 3 subnetworks, i.e. a) extracting feature maps; b) inferring the high frequency details; c) refining the result combining the low resolution input with the details from b).This is similar to LapSRN [1] in that both adopt a Laplacian image pyramid to model the scaling process in SISR.However, the proposed method implements a much deeper subnetworks efficiently with multiple recursive feedback and feedforward connections, and an additional Laplacian of Gaussian (LoG)based loss function help to produce more effective training results.In this research, we also produce a high quality DEM dataset obtained from optical and lidar sensors, from satellites and aircraft respectively, covering different scenes found in remote sensing applications.Our experiments demonstrate that the proposed model performs better than other standard deep SISR models in terms of the training convergence and the Peak Signal to Noise Ratio (PSNR) of a reconstructed DEM.
Direct Error-Driven Learning for Deep Neural Networks With Applications to Big Data

In this brief, heterogeneity and noise in big data are shown to increase the generalization error for a traditional learning regime utilized for deep neural networks (deep NNs). To reduce this error, while overcoming the issue of vanishing gradients, a direct error-driven learning (EDL) scheme is proposed. First, to reduce the impact of heterogeneity and data noise, the concept of a neighborhood is introduced. Using this neighborhood, an approximation of generalization error is obtained and an overall error, comprised of learning and the approximate generalization errors, is defined. A novel NN weight-tuning law is obtained through a layer-wise performance measure enabling the direct use of overall error for learning. Additional constraints are introduced into the layer-wise performance measure to guide and improve the learning process in the presence of noisy dimensions. The proposed direct EDL scheme effectively addresses the issue of heterogeneity and noise while mitigating vanishing gradients and noisy dimensions. A comprehensive simulation study is presented where the proposed approach is shown to mitigate the vanishing gradient problem while improving generalization by 6%.
The complexity of comments from Senegalese online presses face with opinion mining methods

At a time when comments are growing in social networks, e-commerce sites, sites dedicated to information, etc., the technic of opinion mining has finally emerged as the essential tool for processing these data. In the literature, approaches and many methods have been proposed to analyze the texts of official languages (French, English, Portuguese...). Recent solutions increasingly provide results close to reality. However, the complexity of comments from Senegalese online presses question the performance of current methods of opinion mining. The purpose of this article is to highlight the obstacles that these comments may cause with regard to current methods. In perspective, we will try to propose a possible solution to analyze the comments of Senegalese in order to predict their opinion.
Silent Battles: Towards Unmasking Hidden Cyber Attack

When looking at the media, it can easily be seen that new cyber attacks are reported on a regular basis. The corresponding effects of these attacks can be manifold, ranging from downtime of popular services affected by a rather trivial Denial-of-Service attack, to physical destruction based on sophisticated cyber weapons. This can also range from single affected systems up to an entire nation (e.g., when the cyber incident has major influence on a democratic election). Some of these attacks have gained broader public attention only by chance. This raises the fundamental question: do some cyber activities remain hidden, even though they have a significant impact on our everyday lives, and how can such unknown cyber involvements be unmasked? The authors investigate this question in depth in this paper. The first part of the paper analyzes the characteristics of silent battles and hidden cyber attacks - what needs to be considered on the way towards a better detection of hidden cyber attacks? After that, an evaluation of the current cyber security landscape is provided, summarizing what developments we can see and what we can expect. Based on this, the complexity of detecting hidden cyber attacks is discussed and we ask the question: why does detection fail and how can it be improved? To investigate this question, the capabilities of the attackers are examined and are reflected in a 3-Layer Vulnerability Model. It is shown that a traditional Cyber Kill Chain is not sufficient to detect complex cyber attacks. Therefore, to improve the detection of hidden cyber attacks, a new detection model based on combining the 3-Layer Vulnerability Model and the Cyber Kill Chain is proposed.