Reviewerly/exHarmony huggingface dataset:
MESRS: Models Ensemble Speech Recognition System

Speech recognition (SR) technology is used to recognize spoken words and phonemes recorded in audio and video files. This paper presents a novel method for SR, based on our implementation for an ensemble of multiple deep learning (DL) models with different architectures. Contrary to standard SR systems, we ensemble the most commonly used DL architectures followed by dynamic weighted averages, in order to classify audio clips correctly. Models’ training is performed using conversion of audio signals from the audio space into the image space. We used the converted images as training input for the models. This way, most of the default parameters originally used for training models using images, can also be used for training our models. We show that the combination of space conversion and models ensemble can achieve high accuracy results. This paper has 2 main objectives. The first - represent a new trend of definition by expanding the DL process for the audio space. The second - present a new platform for deep learning models ensemble using weighted averages. Previous works in this field tend to stay in the comfort zone of a single DL architecture, fine-tuned to capture all edge cases. We show that applying dynamic weighted average over multiple architectures can improve the final classification results significantly. Since models that classify high pitch audio might not be as good in classifying low pitch audio and vice versa, we harness the capabilities of multiple architectures in order to handle the various edge cases.
Fast Generation of High-Fidelity RGB-D Images by Deep Learning With Adaptive Convolution

Using the raw data from consumer-level RGB-D cameras as input, we propose a deep-learning based approach to efficiently generate RGB-D images with completed information in high resolution.To process the input images in low resolution with missing regions, new operators for adaptive convolution are introduced in our deep-learning network that consists of three cascaded modules -the completion module, the refinement module and the super-resolution module.The completion module is based on an architecture of encoder-decoder, where the features of input raw RGB-D will be automatically extracted by the encoding layers of a deep neural-network.The decoding layers are applied to reconstruct the completed depth map, which is followed by a refinement module to sharpen the boundary of different regions.For the super-resolution module, we generate RGB-D images in high resolution by multiple layers for feature extraction and a layer for up-sampling.Benefited from the adaptive convolution operators proposed in this paper, our results outperform the existing deep-learning based approaches for RGB-D image complete and super-resolution.As an endto-end approach, high fidelity RGB-D images can be generated efficiently at the rate of 22 frames per second.
Geometry accuracy analysis of the three-dimensional model using the close-range photogrammetry method for conservation object of culture (case study: Cow Statue, Faculty Of Livestock, Diponegoro University)

Abstract Conservation of cultural objects in the form of maintenance and protection to keep objects from damage and authenticity important. Documentation using a three-dimensional model (3D model) is a cheap, effective and relatively fast solution. The documentation results in the form of a three-dimensional model, which itself must have a good fit of visuals and geometry. The purpose of this study was to analyze the geometry accuracy level and the suitability of the three-dimensional model of physical objects using the close-range photogrammetric method (CRP). This study, using a non-metric camera, namely a DSLR camera to take photos of objects simultaneously and thoroughly according to the CRP concept. The results of the photos are processed based on near-range photogrammetric methods using the VisualSfM technique in point cloud creation followed by Meshlab in making three-dimensional models. The results showed that the shape and color of the model were quite good but still had some rough areas due to a lack of point cloud data. The level of color suitability of the digital model is quite good with several colors in coarse-textured areas that look less suitable for physical objects. The suitability of the geometry of the model through the difference in distance between the test points has an average of 3.524 mm and a standard deviation of 2.989 mm.
Experimental Analysis of Cooling Fan Noise by Wavelet-Based Beamforming and Proper Orthogonal Decomposition

We use the proper orthogonal decomposition (POD) method to decompose acoustic imaging from wavelet-based beamforming results into their corresponding modes, and demonstrate the method to analyze the noise characteristics of small-dimensional components, where research literature is still scarce.In particular, we consider the noise from a central processing unit (CPU) cooling fan, though the developed method could find realistic applications in a wider scheme of electronic industry.In addition to developing the new analysis method, we also endeavor to prepare a realistic experimental setup with a CPU cooling fan in an anechoic room.The results show that the combination of POD and wavelet-based beamforming methods is suitable for CPU cooling fan acoustic imaging, which can enhance the analysis ability of acoustic imaging test.The results offered in this work are helpful in cooling fan design and evaluation, which would open doors to a wide range of industrial applications.
Chinese cursive character detection method

Chinese is a widely used language in the world. Chinese cursive script is one of the most distinctive calligraphy art and traditional cultures of China. However, for its connected writing, there is a lack of research on text recognition for cursive images. Here, the authors construct a small cursive image dataset named as Chinese Cursive and there are 523 images in this dataset. It contains continuous strokes text, recognises difficulty etc. Each cursive character is corresponded to a label. The authors proposed a cursive detection method named as SE-seglink for the dataset. The SE-seglink further enhances the image feature extraction. Compared to the existing methods, the SE-seglink performs better in recognising cursive scripts and improves the precision of text detection in cursive images. After multiple sets of comparative experiments, the effectiveness of the SE-seglink method was evaluated by the experiment on the benchmark image dataset ICDAR2015.
The Shoutcasters, the Game Enthusiasts, and the AI: Foraging for Explanations of Real-time Strategy Players

Assessing and understanding intelligent agents is a difficult task for users who lack an AI background. “Explainable AI” (XAI) aims to address this problem, but what should be in an explanation? One route toward answering this question is to turn to theories of how humans try to obtain information they seek. Information Foraging Theory (IFT) is one such theory. In this article, we present a series of studies 1 using IFT: the first investigates how expert explainers supply explanations in the RTS domain, the second investigates what explanations domain experts demand from agents in the RTS domain, and the last focuses on how both populations try to explain a state-of-the-art AI. Our results show that RTS environments like StarCraft offer so many options that change so rapidly, foraging tends to be very costly. Ways foragers attempted to manage such costs included “satisficing” approaches to reduce their cognitive load, such as focusing more on What information than on Why information, strategic use of language to communicate a lot of nuanced information in a few words, and optimizing their environment when possible to make their most valuable information patches readily available. Further, when a real AI entered the picture, even very experienced domain experts had difficulty understanding and judging some of the AI’s unconventional behaviors. Finally, our results reveal ways Information Foraging Theory can inform future XAI interactive explanation environments, and also how XAI can inform IFT.
A Novel System for Multi-level Crohn’s Disease Classification and Grading Based on a Multiclass Support Vector Machine

Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient’s quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI examinations of 800 patients from the University of Palermo Policlinico Hospital. For each E-MRI image, a team of radiologists has extracted 20 features associated with CD, calculated a disease activity index and classified patients into three classes (no activity, mild activity and severe activity). The 20 features have been used as the input variables to the SVM classifier, while the activity index has been adopted as the response variable. Different feature reduction techniques have been applied to improve the classifier performance, while a Bayesian optimization technique has been used to find the optimal hyperparameters of the RBF kernel. K-fold cross-validation has been used to enhance the evaluation reliability. The proposed SVM classifier achieved a better performance when compared with other standard classification methods. Experimental results show an accuracy index of 91.45% with an error of 8.55% that outperform the operator-based reference values reported in literature.
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In this paper, a set of generators (in a unique from) called the distinguished set of generators, of a cyclic code C of length n=2k (where k is a natural number) over Z2m is obtained. This set of generators is used to find the rank of the cyclic code C. It is proved that the rank of a cyclic code C of length n=2k over Z2m is equal to n−v, where v is the degree of a minimal degree polynomial in C. Then a description of all MHDR (maximum hamming distance with respect to rank) cyclic codes of length n=2k over Z2m is given. An example of the best cyclic codes over Z8 of length 4 having largest minimum Hamming, Lee and Euclidean distances among all cyclic codes of the same rank is also given. Further, using an isomorphism between cyclic and negacyclic codes of odd length over finite chain rings given by Dinh, Lopez and Szabo, the above results are extended to cyclic and negacyclic codes of length pk over Zpm for an odd prime p.
Anonymity Effects

Today online social media sites function as the medium of expression for billions of users. As a result, aside from conventional social media sites like Facebook and Twitter, platform designers introduced many alternative social media platforms (e.g., 4chan, Whisper, Snapchat, Mastodon) to serve specific userbases. Among these platforms, anonymous social media sites like Whisper and 4chan hold a special place for researchers. Unlike conventional social media sites, posts on anonymous social media sites are not associated with persistent user identities or profiles. Thus, these anonymous social media sites can provide an extremely interesting data-driven lens into the effects of anonymity on online user behavior. However, to the best of our knowledge, currently there are no publicly available datasets to facilitate research efforts on these anonymity effects.
Towards an Information Security Awareness Maturity Model

In order to achieve continuous improvement Maturity Models (MM) are often used to assess the abilities of employees. Moreover, the continuous improvement is also required in the field of Information Security Awareness (ISA). This is due to the fact, that ISA trainings have to be repeated frequently in order to keep the level of awareness of the employees up and to stay in their mind. Within our research project, we are using the Integrated Behavorial Model (IBM) as definition of ISA. The IBM includes many different aspects like knowledge, attitude, and habit. We carried out a systematic literature review to determine if a MM based on the IBM can be defined to assess the maturity of ISA. Since the IBM covers aspects of psychology, we did not only search for MM for information security, since the human factor is often neglected. Moreover, the awareness is often only assessed via the knowledge of employees. However, knowledge is only one aspect of the IBM. At the end, none of the uncovered MMs considers all aspects of the IBM. In contrast to MM for information security, MM of other fields of research are considering psychological aspects if they are dealing with human factors. Therefore, it is possible to create a MM based on the IBM for ISA. Moreover, we can easily derive some of the used assessments for our MM.
Fitts’ Evaluation of a Developed Human-in-the-Loop Assistive Device

In this work, a new human-computer assistive technology gadget designed for people with impairments is evaluated. The developed human-in-the-loop interface device has an embedded assistance controller and can replace the traditional mouse, gamepad and keyboard, enabling human-computer hands-free full access. This work is concerned with the assistive device performance characterization aspects. Based on the experiments carried out, the human-computer performance improvement with the embedded controller is analysed in detail. Results show that adding the human-in-the-loop assistance controller improves human-computer hands-free skills, which is an innovative contribution for the replacement of computer interfaces that depend on the human hands.
Incorporating word embeddings in unsupervised morphological segmentation

Abstract We investigate the usage of semantic information for morphological segmentation since words that are derived from each other will remain semantically related. We use mathematical models such as maximum likelihood estimate (MLE) and maximum a posteriori estimate (MAP) by incorporating semantic information obtained from dense word vector representations. Our approach does not require any annotated data which make it fully unsupervised and require only a small amount of raw data together with pretrained word embeddings for training purposes. The results show that using dense vector representations helps in morphological segmentation especially for low-resource languages. We present results for Turkish, English, and German. Our semantic MLE model outperforms other unsupervised models for Turkish language. Our proposed models could be also used for any other low-resource language with concatenative morphology.
Put That Needle There

Informed by modern imaging techniques, current medical navigation systems support physicians during a variety of interventions, such as needle-based operations. During these, an abundance of information is often displayed on monitors placed in positions that are uncomfortable for the operator to view. In this article, we address these issues with the concept and prototype of a customized flexible display that is placed on the patient’s body to provide the essential information at just the right location. We present an empirical evaluation comparing the flexible display against a control condition using a standard interventional monitor setup and an additional condition that combines both. Our results show that the flexible display significantly reduces task load while improving overall usability. Furthermore, we found indications that the flexible display reduces task completion time while also observing a negative effect on accuracy, which needs to be balanced carefully.
Characterizing scalable measures of quantum resources

The question of how quantities, like entanglement and coherence, depend on the number of copies of a given state $\rho$ is addressed. This is a hard problem, often involving optimizations over Hilbert spaces of large dimensions. Here, we propose a way to circumvent the direct evaluation of such quantities, provided that the employed measures satisfy a self-similarity property. We say that a quantity ${\cal E}(\rho^{\otimes N})$ is {\it scalable} if it can be described as a function of the variables $\{{\cal E}(\rho^{\otimes i_1}),\dots,{\cal E}(\rho^{\otimes i_q}); N\}$ for $N>i_j$, while, preserving the tensor-product structure. If analyticity is assumed, recursive relations can be derived for the Maclaurin series of ${\cal E}(\rho^{\otimes N})$, which enable us to determine its possible functional forms (in terms of the mentioned variables). In particular, we find that if ${\cal E}(\rho^{\otimes 2^n})$ depends only on ${\cal E}(\rho)$, ${\cal E}(\rho^{\otimes 2})$, and $n$, then it is completely determined by Fibonacci polynomials, to leading order. We show that the one-shot distillable (OSD) entanglement is well described as a scalable measure for several families of states. For a particular two-qutrit state $\varrho$, we determine the OSD entanglement for $\varrho^{\otimes 96}$ from smaller tensorings, with an accuracy of $97 \%$ and no extra computational effort. Finally, we show that superactivation of non-additivity may occur in this context.
TensorFlow for Generating Edge Detection Dataset

Solving any problem using machine learning requires datasets. Most datasets are labeled manually for supervised learning tasks. This paper describes a method for generating labeled data for edge detection tasks on an image. This eliminates a number of problems associated with manual data labeling.
The Impact of Using Virtual-Augmented Reality on Some Design Careers (Product, Multimedia, Graphic)

The Product, Multimedia and Graphic design represent vital areas of the design, which human need in all daily life, as in many other areas of design, such as accessories, jewelry, clothes, fabrics, furniture, ceramics, sculpture, architecture, urban planning... etc. The Product, multimedia, and graphic design careers were greatly influenced by the emergence of new information's technologies, including virtual and augmented reality (50% of mobile users now make electronically shopping using augmented reality techniques), which contributed to the emergence of new areas of design practices and 3D modeling in the labor market, these new changes have led to the urgent need for new generation and one teamwork of product, multimedia, and graphic designers who have the new innovative capabilities and familiarity with many of modern sciences like cinematic tricks, 3D motion graphics, application programming, 3D modeling, virtual models... etc., which all are associated with such new technologies. The virtual and augmented reality makes such design careers closer to each other, so this paper discusses the expected and increased relation of product, multimedia, and graphic design professions in the light of using these new technologies in the product design, advertising, marketing... etc., and its effect on the labor market and the design process practicing..
A Study on ĐApps Characteristics

Repositories are important indicators for liveness and maturity in software development communities. They host user-facing applications or re-usable artefacts to build such applications. While rarely decentralised themselves, they are important for hosting code for decentralized applications. In this study, we investigate public repositories dedicated to decentralised applications, or ĐApps, executing on heterogeneous blockchain platforms. The study is the first to report aggregated metrics on the repository-level and application-level characteristics including ĐApps metadata, associated smart contracts composition and inconsistencies between repositories in both schema and content. The main contributions are data acquisition tools and an evolving public dataset along with an initial analysis to derive key metrics in a reproducible way. Insights provided encompass the dominance of Ethereum, the absence of smart contracts for a significant portion of applications, and unused application advertisement potential by absence from popular repositories. The insights can be exploited by developers to build high-quality and highly popular applications and set up corresponding quality checks.
A Novel Similarity Measure for Group Recommender Systems with Optimal Time Complexity

Once we subscribe to an e-commerce portal, or to a social media website, we interact with multiple brands and with content from numerous providers. However, a unique user profile is created, containing all our preferences. Suppose that a company wants to understand who are its customers. It wants to treat costumers as a target, and understand what campaigns the company should run on them. On the one hand, an approach that clusters the users and performs group recommendations would be useful, while on the other hand, a generic user profile would not be helpful, since the preferences in it are not specific for a brand. Hence, we have to determine multiple user clusterings (one for each brand). This task makes the problem of producing group recommendation challenging, since little and very sparse information about the users is available, and for each pair of users we have to detect as many similarities as the brands existing in the system. To tackle this problem, in this paper, we introduce a novel and optimal measure to compute the similarity between users, based on Kolmogorov complexity. Further, we test it in the group recommendation scenario. The results show that our similarity measure can provide similar accuracy when compared to classical measures, but with significant performance gains, having a strictly lower time complexity than the state-of-the-art similarity measure.
AI Is the New UX: Emerging Research Innovations in AI, User Experience, and Design as They Apply to Industry, Business, and Education, and Ethics

Emerging applications of artificial intelligence (AI) such as: predictive software integrated into websites like Amazon Prime, autonomous features integrated into automobiles, or smart home technologies such as those found in Alexa or Siri, have an ever-increasing impact on business, industry, research, and higher education. New trends and innovations in the use of AI technology in design, user experience, and behavioral psychology will change how we design for user experiences, how we interact with technology and it will fundamentally change us as humans. Steven Pinker describes the 21st century as the “Conceptual Age.” He says that we have progressed from the agriculture Age to the Industrial Age to the Information Age and now to the Conceptual Age. It is characterized by technology and globalization with an emphasis on creators, empathizers, pattern recognizers and meaning makers. This transition leads to the EQ-Based AI areas where those skills are a focus (Pink 2006).