Reviewerly/exHarmony huggingface dataset:
MULTI-LEVEL WAVELET BASED IMAGE CODING OVER LIFTING SCHEME FOR EMBEDDED WIRELESS DEVICES

This paper provides a concise synopsis in the application of Lifting Scheme (LS) in wavelet image coding. Lifting theorem was applied via LS 5/3 wavelet transform to develop a design wherein multipliers are replaced with shifters, thus lowering the volume of operations entailed in the process of computing a DWT to approximately one-half of the requisite volumes of a convolution approach. Consequently, the computations required for image coding are reduced and less intricate. In addition, the lifting scheme can be modified to meet the demands of in-place computation, for DWT to be implemented in low memory systems, which presents a unique solution to the issues associated with existing time consuming software algorithms. This LS filter consists of integer adder units and binary shifter instead of multiplier and divider units found in convolution based filters; thus it has been modified to provide energy efficient hardware performance. The low power 5/3 LS based wavelet transform involves solutions developed to resolve the energy and bandwidth communication problems related to image data transmission. These solutions entail constructing 2D-DWT image codec architecture to save the computational and communication energy erstwhile dissipated in existing architectures.
THE RISE OF ROBOT PETS AND DISCOURSES OF TECHNO-COMPANIONSHIP IN LATER LIFE

Abstract Robot pets of varying degrees of sophistication are advertised as ideal companions for older adults, with claims that they support their emotional and cognitive needs. A new generation of robot pets (e.g., Lovot) is emerging equipped with internet connections, recognition software, surveillance capacity, and AI platforms to mimic desired aspects of animal-human relationships. This paper draws upon promotional materials gathered at the Consumer Electronics Show 2019 and interviews with robot designers, to probe what the robotization of human-animal relationships tells us about shifting notions of companionship through the life course. The argument is that robot pets are inscribed within discourses that instrumentalize human-animal-technology entanglements and position digital technologies as idealized solutions to perceived age-related problems. Theoretical ideas from animals studies add a creative dimension to aging studies to explore what the societal enthusiasm for robot pets reveals about our understanding of the lived material and relational worlds of older adults.
A New Causal Direction Reasoning Method for Decision Making on Noisy Data.

A major trend in self-driving technology is to introduce new causal reasoning mechanisms for decision-making. This paper mainly discusses the problem of causal direction reasoning for nonlinear noisy data. The paper presents the errors-in-variables (EIV) system to construct a causality model and the Hilbert-Schmidt independence criterion (HSIC) to compute the dependence between variables. Then, the paper proposes a new method that is based on the EIV model and HSIC. In the proposed method, noise at both the input and output is considered simultaneously. The proposed method has strong robustness and maintains a relatively stable inference accuracy when the observational noise is considerable. Experiments on simulated and real-world data are presented to demonstrate the performance of the proposed method.
The impact of language transfer on native speaker recognition of native and non-native speech

There are many identifiable acoustic differences between native and non-native English, including patterns of prosody (Ramirez Verdugo, 2005), vowel quality (Flege et al., 1997) and extent of reduction in speech (Baker et al., 2011), many of which arise due to transfer from the native language (L1 transfer). This study examines the effect that differences generated through language transfer have upon the recognition of the Mandarin-accented English compared to American English by native speakers of American English. Listeners were presented with a set of 40 different TIMIT sentences (Garofolo et al., 1993) spoken by 5 native speakers of American English and 5 native Mandarin speakers, and were asked to transcribe what they heard. These transcriptions will be evaluated for accuracy, with errors identified and classified according to the type(s) of acoustic deviations from native English speech attested in any misheard words, as well as whether these errors are typical of Mandarin-speaking learners of English. Results will be used to evaluate the relative importance of these differences between native and non-native English speech in recognition and to identify potentially problematic speech patterns for use with multi-dialectal systems of automatic speech recognition.
“Who”: Perception of talker properties

Spoken language simultaneously provides listeners with linguistic content and information about the person speaking. Listeners can identify specific individuals from auditory-only speech signals. Furthermore, listeners can identify a range of physical and socio-cultural talker characteristics (indexical characteristics) with above chance accuracy. Listeners are sensitive to relatively stable aspects of a talker's identity, such as their age, height, gender, race/ethnicity, sexual orientation, region of origin, native, and language status. Talkers also transmit more transitory information (e.g., current health status or emotional state). The mental representations that listeners build for individual talkers and speaker groups interact with the processing of linguistic information, such that the same physical acoustic signal can be interpreted in different ways depending on the talker's perceived indexical characteristics. In this talk, I will focus on how these mental models can allow for more efficient and accurate processing of speech signals. For example, listeners are more accurate at identifying words from familiar than unfamiliar talkers. Likewise, listeners more accurately understand speech from non-native speakers after a period of exposure. Research on the “who” of speech perception demonstrates the need for considering the interaction between talker characteristics and listener knowledge when making decisions about speech privacy.
An Improved Bisecting K-Means Text Clustering Method

Bisecting K-means clustering method belongs to the hierarchical algorithm in text clustering, in which the selection of K value and initial center of mass will affect the final result of clustering. Chinese word segmentation has the characteristics of vague word and word boundary, etc. We transformed the corpus into word vector by word2vec, reduced the dimension of data by ontology modeling, and cleaned the data by jieba word segmentation and TF-IDF to improve the accuracy of the data. We propose an improved algorithm based on hierarchical clustering and Bisecting K-means clustering to cluster the data many times until it converges. Through experiments, it is proved that the clustering result of this method is better than that of K-means clustering algorithm and Bisecting K-means clustering algorithm.
Algorithmic Aspects on the Construction of Separating Codes

In this paper, we discuss algorithmic aspects of separating codes, that is, codes where any two subsets (of a specified size) of their code words have at least one position with distinct elements. More precisely we focus on the (non trivial) case of binary 2-separating codes. Firstly, we use the Lovász Local Lemma to obtain a lower bound on the existence of such codes that matches the previously best known lower bound. Then, we use the algorithmic version of the Lovász Local Lemma to construct such codes and discuss its implications regarding computational complexity. Finally, we obtain explicit separating codes, with computational complexity polynomial in the length of the code and with rate larger than the well-known Simplex code.
Modeling acoustic cues to distinctive features in a lexical speech analysis system

Acoustic cues are robust elements that can be used to infer information contained in the speech signal, such as underlying linguistic distinctive features and the words intended by the speaker (Stevens JASA 2002). Yet, most current automatic speech recognition systems do not take advantage of a feature-cue-based framework for signal analysis. In this project, a set of common acoustic cues has been explicitly modeled by Gaussian mixture models. This set of acoustic cues can provide evidence for the overall phoneme and word sequences of an utterance. The extracted cues and their values can also determine a speaker’s linguistic production pattern, i.e., the systematic context-governed modifications in surface-phonetic form that occur pervasively in conversational speech. The simple Gaussian mixture model representation structure reduces the need for extensive amounts of training data, in contrast to conventional schemes based on large neural networks.
Nomi d’arte e soprannomi di cantautori, rapper e band della scena musicale siciliana

Nowadays, the names and bynames of pop-groups and musicians are more and more characterized by multiple, differentiated onomaturgical solutions which allow us to explore and comprehend current linguistic and cultural trends as well as the metalinguistic awareness/competence of authors and «users» of the music industry. On the one hand, as far as the national and international scenario is concerned, many names are based on a sort of game of mirrors: from personal names to place names and street names, from sign names to brand names, a lot of linguistic material “rebounds” on the names of singers and bands. On the other hand, as far as the Sicilian dialect song context is concerned, most names derive and develop both from (inter)national models and, above all, from the geolinguistic and geocultural background of the musicians themselves. In the latter case, place identity , a typical feature of dialect songs in Sicily, reverberates meaningfully on the artistic names and nicknames chosen by the authors to represent themselves.
Modeling a piezoelectric cantilever beam with Simscape

Previous work developed a lumped parameter model in Simscape for the transverse vibration of a thin bar [C. J. Childs and S. C. Thompson, J. Acoust. Soc. Am. 145, 1895 (2019)]. The current presentation expands that to include a layered bar, perhaps of different materials, and includes the electromechanical coupling for the case where the layers may be piezoelectric materials. This allows modeling such devices as the crystal or ceramic microphone that uses a piezoelectric bimorph element, and thin film piezoelectric cantilevers under consideration for vibration energy harvesting applications. While this paper uses Simscape for its examples, modeling these features in Modelica is equally possible.
A Study on the Teaching Method of Character Education for Foreign Students Using Visual Media in Korean Language Class

This study aims to suggest a teaching method for character education to foreign students of Korean, using visual media in Korean language classes. This class is a fusion of Korean language education and Character education. It consists of four parts: public advertisement, competition, functions and role of school, plagiarism dilemma discussion. The class is enacted in three steps. First, students watch video clips related to character education, then they study the content including Korean vocabulary, grammar and expressions. Second, they do activities, discuss and share their opinions about the topic. Third, they write down their ideas, internal changes, and enlightenment related to the subject. This method was applied to the Korean language class in the undergraduate liberal arts class. As a result, the students
A Tight Coupling of Vision-Lidar Measurements for an Effective Odometry.

We propose a new way of fusing visual and lidar measurements for an effective odometry. When to combine visual measurements with lidar measurements, it is typical to assign the depth information from lidar measurements to seemingly corresponding, visual features or to utilize the results of visual odometry as the motion initial guess for a lidar odometry. Instead, our method 1) builds two maps in different modalities: a lidar voxel map and a visual map with map-points and 2) use them together when to solve the residuals for an odometry. And the results of the odometry residual optimization are used to keep the lidar voxel map to be globally consistent with the operating environment. By using visual-lidar measurements in such a tightly-coupled way, our method estimates the odometry more accurately as incorporating more geometric constraints into the odometry residuals, and removes any chances of errors in assigning the depths of lidars to non-corresponding visual features. Experimental results showed that our method outperforms the visual (i.e., the front-end of the ORB-SLAM2), and lidar odometry, and a depth-enhanced visual odometry.
A Study on the Effect of Government Support on Innovation Efficiency of Logistics Firms

With the development of various technologies for the 4th industrial revolution, such as IoT, Big Data, CPS, and AI, innovation is taking place in many areas of the logistics industry and its importance is emphasized even more. However, since the efforts made to innovate do not directly lead to outcomes, the analysis should be made from the perspective of innovation efficiency which takes into account a variety of inputs and outputs. In this study, Data Envelopment Analysis is applied to 98 logistics firms(input factors: costs of innovation, number of innovators / output factors: total sales) to derive innovation efficiency, and the impact of government support policies (in 7 areas: taxation, funding, finance, human resources, technology, certification, purchase) is verified by Tobit regression and Kruskal-Wallis one-way ANOVA. The results show that the effects of the current policy for innovation in the logistics industry are concentrated on financial support, such as taxation and finance, and that the efficiency of firms with significantly high or low dependency is low.
Fuzzy Based Latent Dirichlet Allocation in Spatio-Temporal and Randomized Least Angle Regression

Due to the emerging growth of Social Networks and web blogs, several news providers share their news articles on different web sites and web blogs. It is also used to get public opinion about the articles. Twitter is one of the popular microblogs which act as intermediate for publics to distribute their thoughts. Our intention is to find Twitter data related to the news in the web news articles are also used to enhance the performance of Evolving Fuzzy System-Penguins Search Optimization Algorithm (EFS-PeSOA) based web news mining. In this paper, Latent Dirichlet Allocation (LDA) is used to model the topics within the text of tweets. Twitter-specific tokenizer, part-of-speech tagger, and snowball stemmer are used to generate terms from the twitter data. Term-Frequency and Inverse-Document-Frequency (tf-idf) of each term in tweets are calculated along with the terms in web news articles for the creation of evolving fuzzy rules. Based on the evolving fuzzy rules, web news articles are categorized. In order to enhance the efficiency of categorization of web news articles, a Spatio-Temporal Generalized Additive Model (STGM) is developed where the spatial and temporal information of the tweets are considered for categorization. However, it generates different terms in tweets. So a Randomized Least Angle Regression (RLAR) is used to choose the most significant terms in tweets and only the selected term’s tf-idf values are used in EFS for categorization of web news articles.
Adversarial Language Games for Advanced Natural Language Intelligence

We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial language games are ubiquitous in human activities, little attention has been devoted to this field in natural language processing. In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word. The attacker is tasked with inducing the defender to utter the target word invisible to the defender, while the defender is tasked with detecting the target word before being induced by the attacker. In Adversarial Taboo, a successful attacker and defender need to hide or infer the intention, and induce or defend during conversations. This requires several advanced language abilities, such as adversarial pragmatic reasoning and goal-oriented language interactions in open domain, which will facilitate many downstream NLP tasks. To instantiate the game, we create a game environment and a competition platform. Comprehensive experiments on several baseline attack and defense strategies show promising and interesting results, based on which we discuss some directions for future research.
Reversible data hiding using lagrange’s interpolation in encrypted domain

Data security has become a major concern in the recent years. Error free reversible data hiding has become more tedious. In this paper we proposed a very special data security method which provides a very powerful technique by which secret data can be distributed into two or more images using Lagrange’s Interpolation. Specifically, our algorithm splits the alpha channel into ‘n’ number of sub alpha channels depending upon the user requirement. We divide secret data into segments of 8 bits, each segment of the data is embedded into the pixels of alpha channel. The alpha channel is then split into n sub alpha channels using Lagrange’s interpolation. For more security we encrypted the alpha channel with well-known logistic map. Later n Encrypted alpha channels are merged with equal number of Encrypted PNG images and transmitted. On the receiver end Secret data can be retrieved only if the receiver has all the n shares of images and Data hiding keys. Even the original images can be retrieved with high visual quality using the encryption keys. In our algorithm with the help of alpha channel we are able to achieve high embedding capacity when compared to other algorithms. For an image of size MxN we can embed 8mn size of data with use of alpha channel.
Pengenalan Ras Kucing Scottish Fold Menggunakan Metode Histogram of Oriented Gradients dan Jaringan Saraf Tiruan

Abstrak Kucing merupakan salah satu hewan peliharaan yang cukup populer dikalangan manusia. Menurut The Cat Fanciers Association (CFA), terdapat 42 ras kucing di dunia. Dari 42 ras kucing tersebut, hanya terdapat 1% ras asli kucing dari jumlah keseluruhan populasi kucing di dunia, dikarenakan banyaknya perkawinan silang antar ras kucing. Setiap ras memiliki penanganan yang berbeda, salah satunya yaitu kucing Scottish Fold merupakan ras kucing yang dikenal rentan. Dengan metode pengolahan citra dan machine learning, dibangun sistem untuk mengenali ras kucing Scottish Fold dengan ekstraksi fitur HOG dan proses klasifi- kasi Jaringan Saraf Tiruan. Pengenalan ras kucing dibedakan menjadi pengenalan ras kucing Scottish Fold dan Non-Scottish Fold. Evaluasi dari model yang telah dibangun menghasilkan nilai akurasi sebesar 97,5% untuk telinga kucing, 96% untuk wajah kucing tanpa background dan 90,5% untuk wajah kucing dengan background menggunakan parameter HOG 8x8 pixel per cells, 1x1 block per cells, bins 9, dan parameter JST dengan jumlah neuron 50 pada 1 hidden layer. Kata kunci : Kucing, Pengenalan Ras, Pengenalan Wajah, Histogram of Orieneted Gradients, Jaringan Saraf Tiruan Abstract Cat is one of the pets that quite popular around humans. According to The Cat Fanciers Association (CFA), there are 42 cat breed in this world. From 42 cat breeds, there are only 1% pure-bred cat in this world, because of cross-breed between cats. Every cat-breed has different way to take care of. Scottish Fold is one of cat-breed that pretty vulnerable. With image processing and machine learning, a system to recognize Scottish Fold cat-breed using HOG for extraction feature and Artificial Neural Network for classification is built. Cat-breed recognition divided by two as Scottish Fold and Non-Scottish Fold. The accuracy of this system is 97,5% for catâ€™s ears image, 96% for catâ€™s face image without background, and 90,5% for catâ€™s face image with background using HOG parameter 8x8 pixel per cells, 1x1 cells per block, 9 bins, and ANN parameter 50 neuron in 1 hidden layer. Keywords: Cats, Race Recognition, Face Recognition, Histogram of Oriented Gradients, Artificial Neural Network
Demosaicing Using MFIT (Matrix Factorization Iterative Tunable) Based on Convolution Neural Network

Demosaicing is introduced for estimation of the other missing color for producing the absolute color image. Enormous research has been done in past to develop the Demosaicing algorithm, however all these methods have shortcomings, hence in this paper, we propose an algorithm called MF-IT (Matrix Factorization Iterative Tunable), this methodology is based on the CNN (Convolution Neural Network), the main aim of this research is to improvise the reconstruction quality of the given image. This is achieved by using the image-block adjustments based transform, which helps in utilizing the image block based transform. One of the main advantage of MFIT is shift invariance, which can be easily obtained without any support of striding image blocks. Henceforth, In order to evaluate the performance of our methodology it is compared against the various state-of-art method. The result analysis shows that proposed methodology simply outperforms the other method.
Give Me Two Points and I'll Tell You Who You Are.

Vehicular mobility traces are datasets of vehicle location records in a region with high spatio-temporal precision. The access to this sensitive information can threaten the safety and privacy of drivers, given that the analysis of this data makes it possible to discover other contextual and latent information, such as daily home routes or workplace's address. To prevent this, many obfuscation and anonymization techniques have been proposed to mitigate the problem of user location privacy. In this work, we analyze an anonymization technique called mix-zone, which selects urban regions that promote the simultaneous anonymization of vehicles by changing their current pseudonym. We show how information about drivers' behavior in a city, such as their road preferences, can be used to reidentify their trajectories. To do this, we present a simple and efficient re-identification technique that uses only two georeferenced points as input data. We validate our technique with a real dataset of taxicabs, being able to re-identify up to 100% of the anonymized trajectories.
Concoction of Steganography and Cryptography

In recent year, the seuciryt of digital applications require significant research attention, When the usage and number of users increases. This in turn makes it easy to steal the sensitive information. This paper describes how the data is encrypted and hidden in an image. Initially the data is taken and encrypted it with Burrows Wheeler Transform Algorithm and then modified Double Advanced Encryption Standard encryption is applied and the encrypted data is hidden in image. Each character of the data is represented as 8-bit binary format obtained from its ASCII value out of which first three bits is replaced with last three bits of pixel value in R Matrix and the next three bits are replaced with last three bits of pixel value (in binary format) in the G matrix and the last two bits are replaced with last two bits of pixel value in the B matrix of the image. Here hiding is achieved by process of replacing bits in image matrix. After hiding the data in the image, Mean Square Error (MSE), Peak signal to noise ratio (PSNR) values of original image and the stego image (data hidden image) are tabulated and the graph is plotted for them.