Reviewerly/exHarmony huggingface dataset:
Computing with Words Through Interval Type-2 Fuzzy Sets for Decision Making Environment

Interval Type-2 fuzzy sets (IT2FSs) are used for modeling uncertainty and imprecision in a better way. In a conversation, the information given by humans are mostly words. IT2FSs can be used to provide a suitable mathematical representation of a word. The IT2FSs can be further processed using Computing with the words (CWW) engine to return the IT2FS output representation that can be decoded to give the output word. In this paper, an attempt has been made to develop a system that will help in decision making by considering person’s subjective importance for various factors for selection. For demonstration we have taken an example of restaurant recommender system that suggests the suitability of a restaurant depending on person’s subjective importance given to selection criteria (i.e.,cost, time and food quality). Firstly, a codebook is constructed to capture the vocabulary words. IT2FSs membership functions are used to represent these vocabulary words. The linguistic ratings corresponding to selection criteria are taken from experts for restaurants. The linguistic weights are person’s subjective importance given to the selection criteria. Finally, the CWW engine uses linguistic weights and linguistic ratings to obtain the suitability of the restaurant. The output is the recommended word which is also represented using IT2FS. The output word is more effective for human understanding in conversation where the precise information is not very useful and sometimes deceptive .
Learning Recurrent Neural Network Language Models With Context-Sensitive Label Smoothing for Automatic Speech Recognition

Recurrent neural network language models (RNNLMs) have become very successful in many natural language processing tasks. However, RNNLMs trained with a cross entropy loss function and hard output targets are prone to overfitting, which weakens the language models’ generalization power. In the current work, we investigate a new strategy of label smoothing in place of hard output targets to regularize RNNLM training. We propose an approach of context-sensitive candidate label smoothing that has two advantages. First, it not only helps prevent overfitted model but also distinguishes plausible words from implausible ones. Second, it helps alleviate the problems of data sparsity and unbalanced word occurrence in training data. We evaluate our proposed candidate label smoothing method on RNNLM training for two speech recognition tasks, and demonstrate its positive impacts on test set word error rate and perplexity.
Stanza: Layer Separation for Distributed Training in Deep Learning

The parameter server architecture is prevalently used for distributed deep learning. Each worker machine in a such system trains the complete model, which leads to a large amount of network data transfer between workers and servers. We empirically observe that the data transfer has a major impact on training time. We present a new distributed training system called Stanza to tackle this problem. Stanza exploits the fact that in many models such as convolution neural networks, most data exchange is attributed to the fully connected layers, while most computation is carried out in convolutional layers. Thus, we propose layer separation in distributed training: most nodes of the cluster train only the convolutional layers, while the rest train the fully connected layers. Gradients and parameters of the fully connected layers no longer need to be exchanged across the entire cluster, thereby substantially reducing the data transfer volume. We implement Stanza on PyTorch and evaluate its performance on Azure and EC2. Results show that Stanza accelerates training significantly over current parameter server systems: on EC2 instances with Tesla V100 GPU and 10Gb bandwidth for example, Stanza is 1.34x–13.9x faster for common deep learning models.
AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

The large capacity of neural networks enables them to learn complex functions. To avoid overfitting, networks however require a lot of training data that can be expensive and time-consuming to collect. A common practical approach to attenuate overfitting is the use of network regularization techniques.We propose a novel regularization method that progressively penalizes the magnitude of activations during training. The combined activation signals produced by all neurons in a given layer form the representation of the input image in that feature space. We propose to regularize this representation in the last feature layer before classification layers. Our method's effect on generalization is analyzed with label randomization tests and cumulative ablations. Experimental results show the advantages of our approach in comparison with commonly-used regularizers on standard benchmark datasets.
Benefit and Cost of Cloud Computing Security

Running information security technologies and policies in cloud computing systems will create negative impact to business organizations. Chief information security officers, as potential users, need a solution to analyze the cost and benefit of implementing information security technologies and policies in cloud computing systems. We propose that Petri nets can be used to analyze the work-flow security in cloud computing systems, diagnose possible malicious behaviour, and analyze invisible executions and failures in such systems.
Diabetes Diagnosis and Treatment Research Based on Machine Learning

The remarkable progress of biotechnology and medical science has led to the significant production of biomedical data. Diabetes mellitus (DM), a common chronic disease, has also been generated a large number of medical data in the process of diagnosis and treatment. So the exploration of medical data has became a hotpot. Nowadays, researchers are using machine learning to discover potentially valuable knowledge in medical data more than ever before. The purpose of this study is to using machine learning algorithms and improved deep learning algorithms to explore the potential medical value of diabetic electronic medical records, and then apply to track the patient's condition based on the patient's diagnostic records, and help diabetic patients in areas with poor medical conditions to track their condition and further help them improve their happiness in life.
A Comparative Study of Different CNN Encoders for Monocular Depth Prediction

Depth estimation of an observed scene is an important task for many domains such as mobile robotics, autonomous driving, and augmented reality. Traditionally, specialized sensors such as stereo cameras and structured light (RGB-D) ones are used to obtain depth along with color information of the environment. However, extending typical monocular cameras with the ability to infer depth information is an attractive solution. In this paper, we will demonstrate a Convolutional Neural Network (CNN) in an encoder-decoder architecture to perform monocular depth prediction. Additionally, we will evaluate and compare different CNN encoders' performance.
Measuring the maturity of Indian small and medium enterprises for unofficial readiness for capability maturity model integration‐based software process improvement

Abstract Establishing the maturity levels of ‐ Small and Medium Enterprises (SMEs) without Capability Maturity Model Integration (CMMI) certification has always been regarded as an extremely challenging task. Software process improvement (SPI) has targeted to monitor and improve software processes, thereby improving the software business. Although there is scientific interest in SPI, little attention has been specifically given to the exploration of maturity levels for non‐CMMI SMEs. The goal is to explore the effect of time on process maturity and maturity levels achieved informally or unofficially by SMEs that are not otherwise CMMI certified. To find out the maturity levels achieved informally, a CMMI‐DEV v1.3 based survey questionnaire is administered to Indian software SMEs. Time of establishment of SMEs and follow‐up of CMMI‐based processes and practices unofficially are used as two important parameters to decide upon process maturity and achievement of specific CMMI level informally. This paper has been successful in ascertaining the effect of time of establishment of SMEs and follow‐up of CMMI‐based processes on process maturity using proposed RuleML that advocates adoption of more than 70% of CMMI‐DEV v1.3 process area‐specific practices for an SME to be unofficially ready for CMMI‐based SPI initiatives. The findings manifest multidimensional aspects of unofficial readiness of SMEs for CMMI‐based SPI that can be used by relevant authorities to select SMEs for funding for SPI initiatives. Finally, the proposed work has been validated statistically using t ‐test for CMMI Level II and Level III.
Crypto-governance in Stock Exchanges: Towards Efficient and Self-regulated Trading System

Crypto-governance is gaining traction in stocks, commodities, and other derivatives trading in the exchange market, especially when products in the market move towards hyper liquidity. The governance enables an opportunity to share distributed and cryptographic-protected trade platform among trade parties. The crypto-governance perspective is trying to converge various innovations in blockchain applications. When designing crypto-governance, the scheme needs to provide cohesive decision making derived from diverse parties. This research targets to determine how crypto-governance perspective could benefit the stock exchange in regulating and providing a self-regulated trading system efficiently.
Investigating Target Set Reduction for End-to-End Speech Recognition of Hindi-English Code-Switching Data

End-to-end (E2E) systems are fast replacing the conventional systems in the domain of automatic speech recognition. As the target labels are learned directly from speech data, the E2E systems need a bigger corpus for effective training. In the context of code-switching task, the E2E systems face two challenges: (i) the expansion of the target set due to multiple languages involved, and (ii) the lack of availability of sufficiently large domain-specific corpus. Towards addressing those challenges, we propose an approach for reducing the number of target labels for reliable training of the E2E systems on limited data. The efficacy of the proposed approach has been demonstrated on two prominent architectures, namely CTC-based and attention-based E2E networks. The experimental validations are performed on a recently created Hindi-English code-switching corpus. For contrast purpose, the results for the full target set based E2E system and a hybrid DNN-HMM system are also reported.
Crime Analysis using Text Mining

Crime analysis is a technique for discerning and breaking down instances and sequences or patterns in crime. Our approach can anticipate locals which have high possibilities for crime and we are able to imagine crime induces zones. The broaden development of this mechanized technique, crime information savants (police departments, CBI, CID etc.) can help the Law imposition officers to expedites the pave the way for comprehending infringements. Using text mining, the already opaque can be segregated from the useful data in unstructured information. The technique, we have between software engineering and criminal equity for constructing an information mining system that can help unravel the crime in India. This approach will help solve the crime faster and efficiently.
Reinforcement learning applied to games

With the continuous evolution of computational power, especially in the computer graphics area, reinforcement learning has been gaining traction in the community as many novel methods are being created and older ones revamped. Many of these employ artificial neural networks, yet treat them as a black box system. In this paper, we start by introducing classic and recent developments in the area of machine learning, followed by an overview of the most relevant work done with deep neural networks applied to games. Our objective is to give the reader insights on how neural networks learn and clarify the decisions made in the development of modern systems.
Generative Adversarial Networks for Graph Data Imputation from Signed Observations

We study the problem of missing data imputation for graph signals from signed one-bit quantized observations. More precisely, we consider that the true graph data is drawn from a distribution of signals that are smooth or bandlimited on a known graph. However, instead of observing these signals, we observe a signed version of them and only at a subset of the nodes on the graph. Our goal is to estimate the true underlying graph signals from our observations. To achieve this, we propose a generative adversarial network (GAN) where the key is to incorporate graph-aware losses in the associated minimax optimization problem. We illustrate the benefits of the proposed method via numerical experiments on hand-written digits from the MNIST dataset.
Feature Drift Resilient Tracking of The Carotid Artery Wall Using Unscented Kalman Filtering With Data Fusion

An analysis of the motion of the common carotid artery (CCA) provides effective indicators for cardiovascular diseases. Here, we propose a method for tracking CCA wall motion from a B-mode ultrasound video sequence. An unscented Kalman filter based on a suitably devised state-space model fuses measurements produced by an optical flow algorithm and a CCA wall localization algorithm. This approach compensates for feature drift, which is a detrimental effect in optical flow algorithms. The proposed method is demonstrated to outperform a state-of-the-art tracking method based on optical flow.
OOV Recovery with Efficient 2nd Pass Decoding and Open-vocabulary Word-level RNNLM Rescoring for Hybrid ASR

In this paper, we investigate out-of-vocabulary (OOV) word recovery in hybrid automatic speech recognition (ASR) systems, with emphasis on dynamic vocabulary expansion for both Weight Finite State Transducer (WFST)-based decoding and word-level RNNLM rescoring. We first describe our OOV candidate generation method based on a hybrid lexical model (HLM) with phoneme-sequence constraints. Next, we introduce a framework for efficient second pass OOV recovery with a dynamically expanded vocabulary, showing that, by calibrating OOV candidates’ language model (LM) scores, it significantly improves OOV recovery and overall decoding performance compared to HLM-based first pass decoding. Finally we propose an open-vocabulary word-level recurrent neural network language model (RNNLM) re-scoring framework, making it possible to re-score ASR hypotheses containing recovered OOVs, using a single word-level RNNLM ignorant of OOVs when it was trained. By evaluating OOV recovery and overall decoding performance on Spanish/English ASR ‘tasks, we show the proposed OOV recovery pipeline has the potential of an efficient open-vocab word-based ASR decoding framework, with minimal extra computation versus a standard WFST based decoding and RNNLM rescoring pipeline.
Video Deblurring Via 3d CNN and Fourier Accumulation Learning

Camera shake and target movement often leads to undesirable image blurring in videos. How to exploit spatial-temporal information of adjacent frames and reduce the processing time of deblurring are two major issues in video deblurring. In this paper, we propose a simple yet effective Fourier accumulation embedded 3D convolutional encoder-decoder network for video deblurring. Firstly, a 3D convolutional encoder-decoder module is constructed to extract multiscale spatial-temporal deep features and generate intermediate deblurred frames with complementary information which is beneficial for the deblurring of each frame. Then we embed a Fourier accumulation module following the 3D convolutional encoder-decoder, the Fourier accumulation module could fuse intermediate deblurred frames with learned weights in Fourier domain and then produce shaper deblurred frames. Experimental results show that our method has competitive performance compared with other state-of-the-art methods.
Incorporating Written Domain Numeric Grammars into End-To-End Contextual Speech Recognition Systems for Improved Recognition of Numeric Sequences

Accurate recognition of numeric sequences is crucial for many contextual speech recognition applications. For example, a user might create a calendar event and be prompted by a virtual assistant for the time, date, and duration of the event. We propose a modular and scalable solution for improved recognition of numeric sequences. We use finite state transducers built from written domain numeric grammars to increase the likelihood of hypotheses containing matching numeric entities during beam search in an end-to-end speech recognition system. Using our technique results in relative reduction in word error rate of up to 59% on a variety of numeric sequence recognition tasks (times, percentages, digit sequences, ...).
MIC-GAN: Multi-view Assisted Image Completion Using Conditional Generative Adversarial Networks

Consider a set of images of a scene captured from multiple views with some missing regions in each image. In this work, we propose a convolutional neural network (CNN) architecture which fills the missing regions in one image using the information present in the remaining images. The network takes the set of images and their corresponding binary maps as inputs and generates an image with the completed missing regions. The binary map indicates the missing regions present in the corresponding image. The network is trained using an adversarial approach and is observed to generate sharp output images qualitatively. We evaluate the performance of the proposed approach on the dataset extracted from the standard dataset, MVS-Synth.
Adversarial Text Image Super-Resolution using Sinkhorn Distance

Convolutional neural network-based methods have demonstrated promising results for single image super-resolution. However, existing methods usually approach the problem on natural scenes rather than texts, whereas the latter can provide more informative messages to viewers. In this paper, instead of using the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm as the supervision metric, we propose a novel one for better preserving semantic information in text images. Our new metric combines optimal transport in a primal form with Sinkhorn distance defined in an adversarially learned feature space. Since the Sinkhorn distance measures the similarity between two features in terms of both feature components and spatial locations, our metric can maintain the spatial structure of texts during network optimization. Experimental results on text datasets show that our method performs favorably against state-of-the-art approaches in both quantitative and qualitative evaluations. We will publish the code, datasets, and models upon acceptance.
Semi-Supervised Sentence Classification Based on User Polarity in the Social Scenarios

The data sparsity is the main challenge in sentence classification in social scenarios, the recent methods incorporate user information by encoding user node in the user-relation network to alleviate this issue. However, the connection between users is not always available due to privacy protection or other commercial reasons. Thus, in this paper, a concept called user polarity is proposed to quantify the tendency of sentences published by a user which are categorized into the same class. Then a self-training framework based on user polarity is proposed, which incorporates user information without connection between users, to alleviate the data sparsity in sentence classification. A regularization term is used to strengthen the prediction of the model in some special points, and a sample selector is designed to reduce the noise in the pseudo-labeled data generated in self-training process. Besides, some hard samples are selected to improve the retraining process. The experimental results conducted on SemEval 2019 task 8 indicate that our method performs significantly better than other three semi-supervised methods and achieves state-of-the-art performance on this benchmark.
A Study on Digital Image Forgery Techniques and its Detection

With the huge trending advancements and wide spread usage of digital image manipulating software tools has led users a motivation to manipulate and modify the digital images easily. A digital image sometimes plays a vital rule and forms basic essential evidence in various fields such as forensics investigation, crime detection and legal proceedings, medical imaging, journalism. This tends to the question on the Originality, legitimacy and security of digital images. This paper presents a survey firstly on recent types of image tampering techniques, tools used for tampering, and discussion on identification schemes used for numerous types of image tamper detection techniques.
Deep Meta-Relation Network for Visual Few-Shot Learning

This paper proposes a novel metric-based deep learning method to solve the few-shot learning problem. It models the relation between images as high dimensional vector, and trains a network module to judge, when given two relational features, which one indicates a stronger connection between the image objects. By training such a network module, we introduce a comparative mechanism into the metric space, i.e., the similarity score of any two images is computed after seeing other images in the same task. Further more, we propose to incorporate a batch classification loss into episodic training to mitigate the hard training problem that occurs when embedding network is going deeper. Experiments demonstrate that the proposed network can achieve promising performance.
Speaker Embeddings Incorporating Acoustic Conditions for Diarization

We present our work on training speaker embeddings, especially effective for speaker diarization. For various speaker recognition tasks, extracting speaker embeddings using Deep Neural Networks (DNNs) has become major methods. These embeddings are generally trained to be discriminate speakers and be robust with respect to different acoustic conditions. In speaker diarization, however, the acoustic conditions can be used as consistent information for discriminating speakers. Such information can include the distances to a microphone in a meeting, or the channels for each speaker in telephone conversation recorded in monaural. Hence, the proposed speaker-embedding network leverages differences in acoustic conditions to train effective speaker embeddings for speaker diarization. The information on acoustic conditions can be anything that contributes to distinguishing between recording environments; for example, we explore using i-vectors. Experiments conducted on a practical diarization system demonstrated that the proposed embeddings significantly improve performance over embeddings without information on acoustic conditions.
A Hierarchical Tracker for Multi-Domain Dialogue State Tracking

The goal of Dialogue State Tracking (DST) is to estimate the current dialogue state given all the preceding conversation. Due to the increased number of state candidates, data sparsity problem is still a major hurdle for multi-domain DST. Existing methods generally choose to predict a value for each possible slot over all domains with quite low efficiency. In this paper, we propose a hierarchical dialogue state tracker which consists of three sequential modules: domain classification, slot detection and value extraction. It predicts domains, slots and values dynamically by given the dialogue history and outputs of the preceding module, which can dramatically improve the model efficiency. Experimental results on MultiWOZ2.1 also show that our approach achieves state-of-the-art joint goal accuracy, and confirm that the hierarchical structure can enhance existing DST models significantly.