Reviewerly/exHarmony huggingface dataset:
The <i>K</i>-Means Algorithm Evolution

Clustering is one of the main methods for getting insight on the underlying nature and structure of data.The purpose of clustering is organizing a set of data into clusters, such that the elements in each cluster are similar and different from those in other clusters.One of the most used clustering algorithms presently is K-means, because of its easiness for interpreting its results and implementation.The solution to the K-means clustering problem is NP-hard, which justifies the use of heuristic methods for its solution.To date, a large number of improvements to the algorithm have been proposed, of which the most relevant were selected using systematic review methodology.As a result, 1125 documents on improvements were retrieved, and 79 were left after applying inclusion and exclusion criteria.The improvements selected were classified and summarized according to the algorithm steps: initialization, classification, centroid calculation, and convergence.It is remarkable that some of the most successful algorithm variants were found.Some articles on trends in recent years were included, concerning K-means improvements and its use in other areas.Finally, it is considered that the main improvements may inspire the development of new heuristics for K-means or other clustering algorithms.
Compressing Deep Neural Networks Using Toeplitz Matrix: Algorithm Design and Fpga Implementation

Deep neural networks (DNNs) have emerged as an important artificial intelligence technique. However, the computation-intensive and storage-intensive DNNs pose severe challenges on efficient execution over the underlying hardware platform. In this paper we propose to impose Toeplitz structure on DNN models to achieve high compression ratio with negligible performance loss. Accordingly, the hardware performance can be significantly improved after performing model compression. We evaluate the proposed approach on speech recognition and implement the corresponding compressed model on FPGA. Experimental results show that our approach enables high hardware performance while retaining high task performance.
Digitising Swiss German: how to process and study a polycentric spoken language

Swiss dialects of German are, unlike many dialects of other standardised languages, widely used in everyday communication. Despite this fact, automatic processing of Swiss German is still a considerable challenge due to the fact that it is mostly a spoken variety and that it is subject to considerable regional variation. This paper presents the ArchiMob corpus, a freely available general-purpose corpus of spoken Swiss German based on oral history interviews. The corpus is a result of a long design process, intensive manual work and specially adapted computational processing. We first present the modalities of access of the corpus for linguistic, historic and computational research. We then describe how the documents were transcribed, segmented and aligned with the sound source. This work involved a series of experiments that have led to automatically annotated normalisation and part-of-speech tagging layers. Finally, we present several case studies to motivate the use of the corpus for digital humanities in general and for dialectology in particular.
A Two-class Hyper-spherical Autoencoder for Supervised Anomaly Detection

Supervised anomaly detection has been a tough problem due to its necessity of special handling of unseen anomalies. In this paper, we present a heuristic implementation of variational auto-encoder with von-Mises Fisher prior applied to a supervised anomaly detector. The closed latent space like sphere is suitable for detecting unseen anomalies because we have a possibility to "fill" the space with seen training samples. If it ideally works, the reconstruction error will be high for all unseen anomalies. Experiments show that our model can separate normal and anomaly samples in the spherical latent space. It is also shown that he proposed model improves the performance for seen anomalies without degrading the performance for unseen anomalies.
Shot Type Feasibility in Autonomous UAV Cinematography

Aerial cinematography relying on camera-equipped umanned aerial vehicles (UAVs), or drones, has revolutionized media production during the past years. Autonomous UAV function-alities are already being employed to a degree, in a manner structured mainly around visual target tracking. From a cinematographic point of view, the desired shot type (i.e., Close-Up, Long Shot, etc.) is the most important factor affecting the artistic result. Achieving a specific shot type depends on the target-to-camera distance and the camera focal length. However, the interaction between UAV/camera motion trajectory (e.g., Orbit, Chase, etc.) and the visual tracker requirements constrains the range of feasible shot types at each time instance. In this paper, which extends previous work, these constraints are explored for a number of standard UAV/camera motion types, UAV shot types are classified and rules regarding shot feasibility over time are analytically derived. The proposed rules are evaluated in a realistic UAV simulation environment and achieve high performance, indicating possible benefits from their integration into an intelligent shooting system.
Effective Media Traffic Classification Using Deep Learning

Traffic classification (TC) is very important as it can provide useful information which can be used in the flexible management of the network. However, TC has become more and more complicated because of the emergence of various network applications and techniques. In this paper, we apply deep learning based method to the classification of four different kinds of media traffic, i.e., audio, picture, text and video. We collect traffic data from the real network environment. Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) based traffic classification methods are designed to accurately classify the target traffic into different categories. We found that MLP has very good performance in most scenarios. Moreover, specific architecture can reduce the training time of the neural network in the classification.
An improved partitioning algorithm based on FCM algorithm for extended target tracking in PHD filter

Extended target Gaussian mixture probability hypothesis density (ET-GM-PHD) filter is a promising approach for multi-extended target tracking problem. Correct partitions of measurements are significant to the GM-PHD filter. In order to partition the measurement sets accurately and rapidly, an improved Fuzzy C-Means (FCM) approach is proposed. The proposed approach consists of two stages. Firstly, elliptical gating and Gaussian kernel density analysis technique are used to remove the clutter. Secondly, an improved FCM approach which takes the full merits of the prior information of targets is implemented. The predictive state of targets is utilized in the FCM initialization. And the shape of targets is considered in the iteration of FCM. Both real data and simulated data are performed in this work. Experimentation infers that the proposed method outperforms the others on both effectiveness and computation, especially when multiple closely targets are involved.
Multi-script text versus non-text classification of regions in scene images

Text versus non-text region classification is an essential but difficult step in scene-image analysis due to the considerable shape complexity of text and background patterns. There exists a high probability of confusion between background elements and letter parts. This paper proposes a feature-based classification of image blocks using the color autocorrelation histogram (CAH) and the scale-invariant feature transform (SIFT) algorithm, yielding a combined scale and color-invariant feature suitable for scene-text classification. For the evaluation, features were extracted from different color spaces, applying color-histogram autocorrelation. The color features are adjoined with a SIFT descriptor. Parameter tuning is performed and evaluated. For the classification, a standard nearest-neighbor (1NN) and a support-vector machine (SVM) were compared. The proposed method appears to perform robustly and is especially suitable for Asian scripts such as Kannada and Thai, where urban scene-text fonts are characterized by a high curvature and salient color variations.
A novel image watermarking method based on center symmetric local binary pattern with minimum distortion

In this study, a novel semi-fragile image-watermarking method based on Center-Symmetric Local Binary Patterns (CSLBP) in integer wavelet domain is proposed. Integer Wavelet Transform (IWT), block division, Center Symmetric Local Binary Pattern (CSLBP) based watermark embedding and extraction, watermarking key construction, watermarking key encryption using logistic map based Advanced Encryption Standard (AES), and watermarking key decryption techniques have been used in phases of the proposed method. Firstly, High-High (HH) sub-band is obtained by using Integer Wavelet Transform (IWT) with lifting scheme. HH sub-band is used to embed watermark for providing high visual quality. HH sub-band is divided into 4 x 4 size of non-overlapping blocks. Then CSLBP based watermarking embedding is implemented to the blocks. Watermarking key is composed of CSLBP, and defined rules. These rules are defined to achieve high visual quality in the wavelet domain. In this work, logistic map based PRNG is used to generate encryption key, and AES to provide confidentiality. In order, visual quality, robustness and confidentiality are considered for obtaining experimental results. The results clearly illustrated that the presented method is secure, semi-fragile watermarking method with high visual quality.
Theoretical and Experimental Evaluation of Hybrid ACO-k-means Image Segmentation Algorithm for MRI Images Using Drift-analysis

The hybrid Ant Colony Optimization (ACO) – k-means image segmentation algorithm for MRI images segmentation is considered. The proposed algorithm and sub-system for the medical image segmentation have been implemented. The time complexity of proposed algorithm is investigated using consequences from Drift theorem. It is established that the proposed algorithm has a polynomial estimation of complexity. Images from the Ossirix image dataset and real medical images were used for testing. Comparison of segmentation accuracy have been performed between proposed algorithm and competing algorithms C-means and Magic Wand.
Transitioning international software engineering standards to academia: Analyzing the results of the adoption of ISO/IEC 29110 in four Mexican universities

Software standards, targeted for the software industry, were developed to contribute to the development of quality products within budget and schedule, by optimizing efforts and resources. For small companies, the largest percentage of software companies in Mexico, they are fundamental for their growth and survival. However, academic programs do not always match industry requirements. In previous studies, the curricula in Computer Science and Informatics, and Software Engineering, of 4 Mexican universities, were compared with two software industry standards: the MoProSoft standard, a Mexican standard designed for organizations having up to 50 people and the Basic profile of the ISO/IEC 29110 developed specifically for organizations having up to 25 people. The analysis of the academic programs showed a better coverage of ISO/IEC 29110 than MoProSoft. In this paper, these two standards are mapped to understand the results of the analysis in detail and provide recommendations regarding academic programs. The analysis provides an evidence that the processes of the Basic profile of ISO/IEC 29110 are better covered by the universities curricula because the processes provides the minimal set of practices to be performed while a project is executed from the beginning until the delivery of a software. In addition, this mapping presents a clear differentiation between these two standards that might help Software Development Centers to understand where to start in the implementation of one of them.
Does online anonymity boost illegal market trading?

Anonymity allows the online trade in illegal products and services on cryptomarkets to flourish in spite of being enacted in a public location. Bolstered by the extensive media coverage of the cryptomarket trade in drugs, fraud and weapons, these platforms may function as a kind of criminal ‘gateway’, and in so doing facilitate – or indeed amplify – criminality. I argue, however, that researchers must establish – not assume – that the criminality facilitated by online anonymity will exclusively and uniformly produce more harmful outcomes. I consider here two possibilities in connection to the cryptomarket trade in illegal drugs: reduced drug market violence and reduced drug harms to users. Whether these potential ‘benefits’ are viewed as valuable will vary and depend substantially on the perspective and interests of the observer, including drug sellers, drug buyers, law enforcement officials, and academic researchers.
Human action recognition using MHI and SHI based GLAC features and Collaborative Representation Classifier

This paper introduces a method for identifying human actions in depth action videos. We first generate the corresponding Motion History Images (MHIs) and Static History Images (SHIs) to an action video by utilizing the so-called 3D Motion Trail Model (3DMTM). We then extract the Gradient Local Auto -Correlations (GLAC) features from the MHIs as well as SHIs to characterize the action video. Next, we concatenate the set of MHIs based GLAC features with the set of SHIs based GLAC features to gain a single action representation vector. Thus, the computed feature vectors in all action samples are passed to the l2-regularized Collaborative Representation Classifier (l2-CRC) for recognizing multiple human actions effectively. Experimental evaluations on three action datasets, MSR-Action3D, DHA and UTD-MHAD, reveal that the proposed recognition system attains superiority over the state-of-the-art approaches considerably. In addition, the computational efficiency test indicates the real-time compatibility of the system.
Universal Bounding Box Regression and Its Applications

Bounding-box regression is a popular technique to refine or predict localization boxes in recent object detection approaches. Typically, bounding-box regressors are trained to regress from either region proposals or fixed anchor boxes to nearby bounding boxes of a pre-defined target object classes. This paper investigates whether the technique is generalizable to unseen classes and is transferable to other tasks beyond supervised object detection. To this end, we propose a class-agnostic and anchor-free box regressor, dubbed Universal Bounding-Box Regressor (UBBR), which predicts a bounding box of the nearest object from any given box. Trained on a relatively small set of annotated images, UBBR successfully generalizes to unseen classes, and can be used to improve localization in many vision problems. We demonstrate its effectiveness on weakly supervised object detection and object discovery.
IITG-HingCoS corpus: A Hinglish code-switching database for automatic speech recognition

Code-switching is a phenomenon in linguistics which refers to the use of two or more languages, especially within the same discourse. This phenomenon has been observed in many multilingual communities across the globe. In the recent past, there have been increasing demand for automatic speech recognition (ASR) systems to deal with code-switching. However, for training such systems, very limited code-switching resources are available as yet. Thus, the development of code-switching resources is highly desirable. In this work, we describe the collection of a Hinglish (Hindi-English) code-switching database at the Indian Institute of Technology Guwahati (IITG) which is referred to as the IITG-HingCoS corpus. This corpus consists of code-switching text data having 25,988 sentences with a total of 0.58 million words. In addition to that, the corpus also contains 25 h of matching speech data corresponding to 9251 code-switching sentences covering a vocabulary of 6542 words. This paper elaborates the sources and the protocol used for collecting the corpus. The baseline experimental results on the collected corpus for language modeling and ASR tasks are also presented.
Analyzing Performance of Different Machine Learning Approaches With Doc2vec for Classifying Sentiment of Bengali Natural Language

Vector or numeric representation of text documents has been a revolution in natural language processing as it represents similar parts of text in such a way that they are very close to each other, making it very easy to classify or find similarities among them. These vectors also represent the way we use the words or parts of documents as well which helps finding similarity even between pair of words. While word2vec is such a technique that represents each word as a vector, doc2vec takes it to another level by representing a whole sentence or document as a vector. Being able to represent an entire document as a vector allows comparing a substantial number of words or sentences at a time which can save computational power as well as bandwidth. This relatively newer doc2vec technology has not yet been implemented for Bengali sentiment analysis and its feasibility is also unknown. In this study, we have trained a doc2vec model using a corpus constructed with 7,000 Bengali sentences. The model consists of two types of data differentiated by their polarity i.e. positive and negative. Later, we have employed several machine learning algorithms for comparing the accuracy of classification among which Bi-Directional Long Short-Term Memory (BLSTM) has obtained the highest accuracy of 77.85% along with precision, recall and F-1 score of 78.06%,77.39% and 77.72% respectively.
Improving Children Speech Recognition through Feature Learning from Raw Speech Signal

Children speech recognition based on short-term spectral features is a challenging task.One of the reasons is that children speech has high fundamental frequency that is comparable to formant frequency values.Furthermore, as children grow, their vocal apparatus also undergoes changes.This presents difficulties in extracting standard short-term spectral-based features reliably for speech recognition.In recent years, novel acoustic modeling methods have emerged that learn both the feature and phone classifier in an end-to-end manner from the raw speech signal.Through an investigation on PF-STAR corpus we show that children speech recognition can be improved using end-to-end acoustic modeling methods.
Measuring User Experience in Chatbots: An Approach to Interpersonal Communication Competence

The increasing usage of chatbots is fundamentally changing the way people interact with new technology. Instead of clicking buttons to functionally navigate on a web page, people can access content and services by the use of natural language in interaction with an artificial agent (e.g., chatbot). This change toward human–chatbot interaction is typically manifested through a social and natural conversational style. This shift of how to interact with data and services has major repercussions for how to explore and measure conversational user experience with chatbots. Hence, in this paper we suggest the importance of measuring the social aspects in human–chatbot interaction through a focus on interpersonal communicational competence (ICC). We build our suggested framework on previous knowledge about communicational competence in successful human–human interaction. Based on this we have developed factors that might be of importance for chatbots' ICC and how these can be measured.
Multi-level magnification correlation hashing for scalable histopathological image retrieval

Content-based Medical Image Retrieval (CBMIR) presents promising results in computer-aided diagnosis which provides the clinicians with interpretative guidance based on the visual similarity. In this paper, we focus on the tasks of histopathological image retrieval for breast cancer diagnosis. Instead of the holistic-based and cell-based methods proposed in previous works, we propose a multi-magnification correlation hashing (MMCH) framework that learns the discriminative binary codes for histopathological images. The patch-link graph is constructed with both low and high magnification images where the semantic links are propagated from the pre-labeled patches to unlabeled data. We then learn the binary hashing codes based on the global labels from the low-magnification images with structure-regularization. The experiments on BreakHis dataset and PLOSONE dataset demonstrate that MMCH outperforms the previous hashing methods on histopathological image retrieval.
Equivalence between Bayes and the maximum likelihood estimator in M/M/1 queue

Let θ be the unknown parameter for the arrival process in a single M/M/1 queue. The paper discusses the bound for the equivalence of Bayes and maximum likelihood estimator for the parameter θ. Also the bound on the difference of Bayes estimator from its true value is found out.
Multi Q-Table Q-Learning

Q-learning is a popular reinforcement learning technique for solving shortest path (STP) problem. In a maze with multiple sub-tasks such as collecting treasures and avoiding traps, it has been observed that the Q-learning converges to the optimal path. However, the sum of obtained rewards along the path in average is moderate. This paper proposes Multi-Q-Table Q-learning to address a problem of low average sum of rewards. The proposed method constructs a new Q-table whenever a sub-goal is reached. This modification let an agent to learn that the sub-reward is already collect and it can be obtained only once. Our experimental results show that a modified algorithm can achieve an optimal answer to collect all treasures (positive rewards), avoid pit and reach goal with the shortest path. With a small size of maze, the proposed algorithm uses the larger amount of time to achieved optimal solution compared to the conventional Q-learning.
Gehna

Jewelry weaves into our everyday lives as no other wearable does. It comes in many wearable forms, is fashionable, and can adorn any part of the body. In this paper, through an exploratory, Research through Design (RtD) process, we tap into this vast potential space of input interaction that jewelry can enable. We do so by first identifying a small set of fundamental structural elements --- called Jewelements --- that any jewelry is composed of, and then defining their properties that enable the interaction. We leverage this synthesis along with observational data and literature to formulate a design space of jewelry-enabled input techniques. This work encapsulates both the extensions of common existing input methods (e.g., touch) as well as new ones inspired by jewelry. Furthermore, we discuss our prototypical sensor-based implementations. Through this work, we invite the community to engage in the conversation on how jewelry as a material can help shape wearable-based input.