Reviewerly/exHarmony huggingface dataset:
The LDC-IL Speech Corpora

This paper introduces the first set of speech corpora released in 2019 by the Linguistic Data Consortium for Indian Languages (LDC-IL), a scheme under the Department of Higher Education, Ministry of Human Resource Development, Government of India. The datasets include a total of 13 scheduled languages of India, collected in various environments across length and breadth of the vast country, from a total of 5662 speakers of different age-groups with a total size of more than 1552 hours. The dataset is still growing as we prune them and make them ready for release. Unique language corpus is usually the largest available at present for these languages. Established in 2008, on the lines of the LDC of University of Pennsylvania, the LDC-IL has worked for over 10 years on various types language resources, including building the speech corpora. LDC-IL is a fully government funded project implemented by CIIL, Mysuru. Due to some restraints in the government business such as cost analysis and copyright issues, it took rather a long time to release the LDC-IL dataset for the public use. This paper gives a brief of the raw speech corpora now released and ready for public use (both commercial and non-commercial purposes). It also discusses how the two major bottlenecks of copyright and costing was addressed which held up the release of these datasets for several years.
A Flexible Personalized Topic Query Scheme

Topic modeling is often used to visually analyze data in unstructured text corpora. However, in terms of the flexibility of topic modeling, the existing methods of topic modeling can only provide a single visual topic model, which cannot meet the needs of different users individually. Therefore, based on the existing research, this paper proposes a flexible personalized topic query scheme. Given a topic, the goal is to find multiple topics most relevant to the topic in a specific area. We use the real data to carry on the experiment, and the experiment shows that, compared with the existing relevant topic query scheme, the topic query scheme proposed in this paper is more advantageous.
Registration for Infrared Image Sequence via Distance Transform of Binary Features

This paper presents a fast geometric registration method for sequential infrared images of complex terrain backgrounds. In order to make use of the continuity of the scene in image sequence, we aim at building an optimization model to formulate the registration problem by simple features. Three key elements are involved in our proposal: matching criterion between pair images, optimization model for the registration and iterative solution to the mathematic model. Firstly, because the brightness of neighboring images is generally unchanged, the sum of squared differences can be used to evaluate the match between two images without consideration of image noises. Secondly, in order to build a convex optimization model for the image registration problem, binarization and distance transformation is introduced. The transformation facilitates the optimization convergence; and it simplifies the computation of matching criterion to sum of squared distance values. Thirdly, the Levenberg-Marquart algorithm is utilized to solve the convex optimization model and quadratic convergence can be obtained. Real data experimental results show that our method is fast and robust, which means it is a potential solution for low resolution infrared image sequence registration.
Proposal of Quantized Tree-LSTM and Its Experimental Evaluation

Recently, Tree-LSTM, which takes into account the input of the tree structure, has emerged and surpassed the accuracy of the conventional linear LSTM. Also, there is a method called quantization that represents the information of a neural net in fewer bits. This enables applications such as compressing model size, speeding up computation, and putting the model on a small device. In this work, we applied quantization of weights to the Tree-LSTM to reduce the parameter size and experimentally evaluated how it affects the accuracy. Also, We confirmed that Layer Normalization is valid for our proposal.
Deep Learning

Deep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities, security, automated machines. In this chapter, brief information about DL theory is given, advantages and disadvantages of deep learning are discussed, most used types of DNN are mentioned, popular DL architectures and frameworks are glanced and aimed to build smart systems for the finance and real estate domains. Finally, a case study about image recognition using transfer learning is developed.
A Compiler Design for a Programmable CNN Accelerator

Convolutional Neural Networks (CNNs) are widely used in many AI applications, such as image classification, target detection, and target tracking. Due to the increase of CNN computational complexity, hardware acceleration is necessary for inference. Programmable accelerators are promising because of their support for different CNN models. To program an existing programmable accelerator, dedicated instructions need to be generated. In this paper, a compiler is designed to generate the instructions. The compiler explores the best partition of CNN models, schedules the sequence of computing, and generates the instructions automatically. With the proposed compiler, the instruction-driven CNN accelerator achieves the throughput varied from 114 FPS (ResNet152) to 1130 FPS (AlexNet).
An Interpretable Measurement for Playing Archetypes of Driving Agents in Video Games

Analysis and evaluating the behavior of different none-player characters (NPCs) plays a significant role in improving the playing experience of a video game. Furthermore, different playing archetypes can be acquired by changing the configuration of NPCs. Hence, by having an interpretable measurement for playing archetypes of NPCs, a wide range of behavior can be attained. Additionally, the aforementioned measurement gives insight of how the changes in the configuration of the NPC changes its behavior. The proposed method can significantly increase the interpretability of the NPCs and ease the designing of desired NPCs with appropriate behavior. In this paper, by comparing the behavior of different NPCs with that of the previously gathered data, cross-correlation as a similarity measurement is used to interpret the changes in the behavior of the NPCs, given their configurations. The proposed method can help game designers evaluate the performance of their NPCs. It is shown that by comparing the gathered data from the state of NPCs with that of users, useful information for determining the driving style of a NPC can be acquired. Afterwards, by setting references for behavior patterns, new NPCs can be evaluated and classified into different playing archetypes. Moreover, in this paper the importance of the cross-correlation values and also the fitted Gaussian model on this signal, in the behavior of different NPCs is discussed in detail. The test bed for the proposed method is a driving game in Unity game engine.
Data-efficient Alignment of Multimodal Sequences by Aligning Gradient Updates and Internal Feature Distributions

The task of video and text sequence alignment is a pre-requisite step toward joint understanding of movie videos and screenplays. However, supervised methods face the obstacle of limited realistic training data. With this pa-per, we attempt to enhance data efficiency of the end-to-end alignment network NeuMATCH [15]. Recent research [56] suggests that network components dealing with different modalities may overfit and generalize at different speeds, creating difficulties for training. We propose to employ (1) layer-wise adaptive rate scaling (LARS) to align the magnitudes of gradient updates in different layers and balance the pace of learning and (2) sequence-wise batch normalization (SBN) to align the internal feature distributions from different modalities. Finally, we leverage random projection to reduce the dimensionality of input features. On the YouTube Movie Summary dataset, the combined use of these technique closes the performance gap when the pretraining on the LSMDC dataset is omitted and achieves the state-of-the-art result. Extensive empirical comparisons and analysis reveal that these techniques improve optimization and regularize the network more effectively than two different setups of layer normalization.
Convolutional neural network-based restoration method of basketball contour image

Basketball image restoration is the process of taking damaged / noise images and predicting clean, original images. The vulnerability can take many forms such as motion blur, noise and camera misfocusing. Image Reconstruction Performed by this imaging point source, which is activated by converting blurred image, the so-called point diffusion function (including line) using the dot source image to recover the lost blurring process image information. The traditional outline tracking algorithm for basketball shooting dynamic hand image is vague, has poor stability and takes a long time. Recurrence nest tracking algorithm based on the dynamic boundary. The motion that the camera arm monitors are used to determine the target of the curve. The effective stiffness matrix is ​​obtained by initial calculation, as well as by using the characteristic curve recurrence calculation. The system image will then be applied to the dynamic boundary, where the energy is reduced to the target boundary. The purpose of basketball image restoration technology is to reduce noise and restore image processing technology's resolution loss in one of the image domain or frequency domains. Image restoration for basketball is performed on the frequency field except for the most direct previous art. It is computed by Fourier image and PSF, and the presence of convolution transforms the resolution loss caused by the blur factor. The probability sample is representing the entire population of sub-normal distribution with a Gaussian mixture model. The hybrid system, under normal conditions, which belongs to a subset of the data point seems obvious that this is a graded without learning is a subfield
Face Recognition Based Attendance System and Emotion Classification

Attendance monitoring system is basic in all associations for checking the attendance of student or employee. In every association, attendance is taken physically by calling their register numbers or names and noted in their registers given by the association heads, and it consumes more time and effort. Face recognition is an example of recognition strategy and one of the most significant biometrics; it is utilized in an expansive range of utilizations. In our project, monitoring the attendance and classification of emotion is simplified by face recognition mechanism by using MATLAB and sending SMS to absentees by using GSM mechanism. Attendance is recorded in the proposed work with the aid of using a digital camera that is connected in the study room or working environment. This constantly shoots the pictures of student or employee and detects his/her face. It classifies the faces in pixels by PCA algorithm and examines the detected faces with the database and marks the attendance. Without physical connection of individual, it gives the participation of present student or employee in the association. For face recognition, the eigenface algorithm is utilized and classification of emotions is finished by Viola–Jones algorithm. In our work, when a face of an understudy matches with the dataset, it denotes the understudy as present and afterward at last it will send the SMS to the versatile which understudies is missing, via GPRS/GSM modem and Arduino. This system also detects the faces and classifies emotion whether interested or uninterested and loads the data in to the excel sheet.
Secure Management of Patient Medical Data Using QR Code and CP-ABE

QR codes are widely used in various services provided by agencies such as banks, credit card companies, and airlines. Among these, QR code based data management for the medical data of hospital patients is a representative area of QR code applications. As medical data usually contain sensitive patient information, preventing their leakage is a serious concern. Therefore, in this study, we present a method for the secure management of patient medical data by leveraging QR code and ciphertext-policy attribute-based encryption (CP-ABE). Especially, the proposed method can be used to securely store sensitive patient medical data in a QR code, which allows access only to authorized persons.
Deep Learning for Moving Object Detection and Tracking

Moving object detection and tracking is the process of identifying and locating the class objects such as people, vehicle, toy, and human faces in the video sequences more precisely without background disturbances. It is the first and foremost step in any kind of video analytics applications, and it is greatly influencing the high-level abstractions such as classification and tracking. Traditional methods are easily affected by the background disturbances and achieve poor results. With the advent of deep learning, it is possible to improve the results with high level features. The deep learning model helps to get more useful insights about the events in the real world. This chapter introduces the deep convolutional neural network and reviews the deep learning models used for moving object detection. This chapter also discusses the parameters involved and metrics used to assess the performance of moving object detection in deep learning model. Finally, the chapter is concluded with possible recommendations for the benefit of research community.
The Effect of Neurofeedback Training in CAVE-VR for Enhancing Working Memory

In recent years, increasing evidence of the positive impact of Virtual Reality (VR) on neurofeedback training has emerged. The immersive properties of VR training scenarios have been shown to facilitate neurofeedback learning while leading to cognitive enhancements such as increased working memory performance. However, in the design of an immersive VR environment, there are several covariates that can influence the level of immersion. To date, the specific factors which contribute to the improvement of neurofeedback performance have not yet been clarified. This research aims to investigate the effects of vividness in a Cave automatic virtual environment (CAVE-VR) on neurofeedback training outcome, and to assess the effect on working memory performance. To achieve this, we recruited 21 participants, exposed to neurofeedback training inside a CAVE-VR environment. Participants were divided into three experimental groups, each of which received feedback in a different neurofeedback training scenario with increasing level of vividness (i.e., low, medium, high) while also assessing the effect of neurofeedback on working memory performance. Current findings show that highly vivid feedback in CAVE-VR results in increased neurofeedback performance. In addition, highly vivid training scenarios had a positive effect on user’s motivation, concentration, and reduced boredom. Finally, current results corroborate the efficacy of the neurofeedback enhancement protocol in CAVE-VR for improving working memory performance.
Knowledge Representation for Causal Calculi on Internet of Things

The chapter introduces knowledge representation for expressing causal calculus in Internet of Things (IoT). This research discusses how IoT devices and sensors harness causal inference. Causal calculi are the mathematical foundations for expressing and computing causation. Causation describes how one event may cause another. IoT devices and systems may collect, parse, process, calculate, and send causal implications. There are several approaches to causal calculi. Independently Pearl and Shafer give causal systems. In addition, Halpern and Pearl give a causal system. Causal systems are founded in logic and probability theories—Pearl’s method uses Bayesian networks on acyclic directed graphs. Shafer’s method works on the dynamics of probability trees. And, the Halpern–Pearl model builds on Pearl’s model yielding two causal views. One of these views attribute causes of past while the other view focuses on causal reasoning giving events.
Protecting Multi-Level Memories From Jamming Using Q-ary Expurgated Robust Codes

Robust codes can effectively detect jamming in q-ary memories for q = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> . When q is not a power of two, a binary information word has to be converted into a q-ary word prior to the encoding. This conversion expurgates the code since some of the q-ary codewords are never used. Unless properly designed, expurgation can significantly degrade the effectiveness of the code in terms of its error detection capability. This article analyzes the error masking probability of an expurgated robust code when applied to q-ary memories. It is shown that by wisely designing the converter, this degradation can be minimized, and in some cases, the code's efficiency can be superior to the original code.
Not Just Twitter

Twitter monitors 500 million tweets a day to enforce its “Twitter Rules” and Terms of Service. Its policies seek to reduce threats, trolls, harassment, and hate speech on the platform. Policing content on a website is censorship. Whether done to protect the platform or its users, the result is the same: it stifles voices and undermines the marketplace for ideas. Social media giants have tremendous resources, large content moderation teams, and sophisticated lawyers, yet they still struggle to create and enforce rules that users perceive to be fair and protective of their speech. Online public spheres are numerous and heterogeneous. Invitation to discussion and argument is a necessary component of their existence. Local communities consist of those whose members share a common identity, interest, or purpose. The existence and vitality of a multitude of platforms is essential for the survival of local communities online, yet speech regulation poses a severe threat.
A Game of Double Agents: Repeated Stackelberg Games with Role Switch

We introduce a novel variation of the widely used 2-player Stackelberg game formalism. In our variation, a master player can decide to act as a leader or as a follower across the iterations of the game. This model naturally arises in many real-world applications and particularly in cyber-security scenarios, where an analyzer agent can arbitrarily decide which role to play in each iteration. We propose a first solution approach for this model assuming bounded rationality for the players and adopting a Monte Carlo Tree Search approach to devise the analyzer’s strategy. We empirically show the effectiveness of our method in two experimental domains, i.e. synthetic game instances (using randomly generated games) and malware analysis (using real malware samples).
A bilingual cognitive robot that learns like a toddler

We propose an embodied architecture featuring a developmental agent and a social robot for human-robot verbal engagement at preschool level. Initially, we modelled bilingual acquisition and demonstrated the agent’s skill to appropriately detect the spoken content and automatically match the human user’s language. We aim to contribute at designing multilingual robot agents able to understand and communicate contexts in multiple tongues without loss of meaning in translations, which can greatly affect human cognitive and interaction capabilities. Furthermore, we design a novel methodology to teach the robot how to learn gradually from experience new knowledge not specified at design time and directly apply it for task solving, without being trained anew. The demonstrated system shows promising potential to becoming a low-cost social companion that can be easily integrated in the human world.
MPB: Multi-Peak Binarization for Pupil Detection

Automatic pupil detection is a fundamental part of eye-related tasks like eye tracking, gaze estimation and eye movement identification. Especially, in ophthalmology, to provide assistance and fulfil the demand of diagnosis and treatment, an accurate and real-time algorithm is required. In this paper, we propose a fast and robust Multi-Peak Binarization (MPB) based method for pupil detection in ophthalmology scenarios. A novel strategy for region of interest and candidate connected area detection is presented. Constraints for pruning the irregular shapes and accelerating the MPB algorithm are defined. The proposed method is evaluated on an open-dataset and the experimental results demonstrate the high performance of our approach.
Wasserstein Embeddings for Nonnegative Matrix Factorization

In the field of document clustering (or dictionary learning), the fitting error called the Wasserstein (In this paper, we use “Wasserstein”, “Earth Mover’s”, “Kantorovich–Rubinstein” interchangeably) distance showed some advantages for measuring the approximation of the original data. Further, It is able to capture redundant information, for instance synonyms in bag-of-words, which in practice cannot be retrieved using classical metrics. However, despite the use of smoothed approximation allowing faster computations, this distance suffers from its high computational cost and remains uneasy to handle with a substantial amount of data. To circumvent this issue, we propose a different scheme of NMF relying on the Kullback-Leibler divergence for the term approximating the original data and a regularization term consisting in the approximation of the Wasserstein embeddings in order to leverage more semantic relations. With experiments on benchmark datasets, the results show that our proposal achieves good clustering and support for visualizing the clusters.
Content-Based Image Retrieval in Cloud Image Repositories

Cloud computing is a brand new kind of Internet-based computing which offers discussed laptop processing energy as well as information to other products and computers on need. It is the delivery of hosted products with the Web. Cloud computing services might be public, private, or even hybrid. The growing market of cloud has provide a service paradigm of storage/computation outsourcing will help to decrease user’s burden of IT infrastructure maintenance and lower the price for both specific users and the enterprises. The three major advantages of cloud computing are actually self service provisioning, elasticity, and pay per usage. The main purpose of this paper is to propose a secure frame work for outsourced privacy preserving storage and retrieval in large shared image repositories. In this paper, we had achieved the communication level when comparing with existing system and also achieved the accuracy level to the best.