Reviewerly/exHarmony huggingface dataset:
Towards Enhancing Information Retrieval Systems: A Brief Survey of Strategies and Challenges

The exponentially increasing of resources generated and stored in various forms and made available within the internet has posed numerous challenges inside information retrieval (IR) operations and consequently accelerated a myriad of studies in the realm of IR technologies. In the IR system, the large part of the researches is centred on how to satisfy an information requirement from a query to voluminous document sets. In this regard, the importance is on improving the relevance quality of the results (i.e. retrieved documents). The effectiveness of the IR system depends on the efficacy of the respective adopted IR model and strategy. Understanding of the IR models, strategies and their challenges is important in choosing an appropriate and viable strategy toward the development of effective IR systems for a specific/predefined role. This paper surveys the basic IR models, challenges and adopted strategies to enhance the IR systems are also highlighted.
Real-time detection of the interaction between an upper-limb power-assist robot user and another person for perception-assist

Assisting aged population or a population with disabilities is a critical problem in today’s world. To compensate for their declined motor function, power-assist wearable robots have been proposed. In some cases, however, the cognitive function of these populations may have declined as well, and it may be insufficient to compensate only for their motor function deficiency. Perception-assist wearable robots, which can perceive environmental information using visual sensors attached to them, have been proposed to address this problem. This study addresses the problem of identifying motion intentions of the user of an upper-limb power-assist wearable robot, while the user engages in desired interactions with others. It is important to consider both the interacting parties in order to accurately predict proper interaction. Therefore, this paper presents an interaction recognition methodology by combining both the user’s motion intention and the other party’s motion intention with environmental information. A fuzzy reasoning model is proposed to semantically combine the motion intentions of both parties and environmental information. In this method, the motion intentions of both the user and the other party are simultaneously estimated using kinematic information and visual information, respectively, and they are employed for predicting the interactions between both parties. The effectiveness of the proposed approach is experimentally evaluated.
Counterfactual Attention Supervision

Neural attention mechanism has been used as a form of explanation for model behavior. Users can either passively consume explanation, or actively disagree with explanation then supervise attention into more proper values (attention supervision). Though attention supervision was shown to be effective in some tasks, we find the existing attention supervision is biased, for which we propose to augment counterfactual observations to debias and contribute to accuracy gains. To this end, we propose a counterfactual method to estimate such missing observations and debias the existing supervisions. We validate the effectiveness of our counterfactual supervision on widely adopted image benchmark datasets: CUFED and PEC.
Detecting Extended Contexts in Tweets using DBpedia

In an era of extensive social media usage, we have enormous amount of user data generated everyday. Finding relevant information from Twitter amongst a constant stream of messages is a herculean task considering the complex sentimental nature of data. Multiple applications across the world now rely on Twitter for various domain sensitive and analytical use-cases. This paper proposes a scalable technique which can be used for a better context modeling of tweets generating primary and extended contexts for a set of tweets. We use ontologies from DBPedia as the resource for generating the contexts and subsequently find relevant extended contexts. DBpedia Spotlight in conjunction with DBpedia Ontology is the backbone for this proposed model. Using tweets from a twitter trend as the input, the framework output is validated against the similarity scores between generated contexts. Assuming the trend is confined to a bounded context, similarity scores calculated shows the advantages of this new approach.
Exploiting Bias for Cooperative Planning in Multi-Agent Tree Search

Graph search over states and actions is a valuable tool for robotic planning and navigation. However, the required computation is sensitive to the size of the state and action spaces, a fact which is further exacerbated in multi-agent planning by the number of agents and the presence of sparse reward signals dependent on the cooperation of agents. To tackle these problems, we introduce an algorithm that is pre-trained in a centralized fashion but implemented on robots in a distributed way at runtime. The centralized portion uses imitation learning to iteratively construct policies that help guide an individual agent`s own runtime search as well as predict other agents' future actions by exploiting previously discovered joint actions. Our algorithm includes a novel method of tree search based on a mixture of the individual and joint action space, which can be interpreted as a cascading effect where agents are biased by exploration of new actions, exploitation of previously profitable ones, and recommendation provided by deep neural nets. Simulations show the efficacy of the proposed method in cooperative scenarios with sparse rewards.
Robust Algorithms for Counting and Detection of Moving Vehicles using Deep Learning

Automatic detection and counting of moving objects in a video is a challenging task and has become key application area of deep learning algorithms as far as overwhelming number of vehicles on road and crime detection problems are concerned. In this paper, details of an intelligent agent are discussed which is developed for detection and counting of moving vehicle in a video stream captured through surveillance camera. The agent that we have developed works in two phases; first is object detection and second is the counting of moving vehicles. For object detection, we have applied state-of-the-art Deep Learning object detection algorithm Single Shot Detector (SSD). For counting of vehicles, we have devised two different algorithms which take input from object detection part. The first counting algorithm is an add-on for robustness on naive approach while the later one is an improvement regarding removal of dependency on capture environment parameters such as imaginary lines, direction of moving vehicles etc. Input to the agent is fed through surveillance camera in the form of video stream. We have used transfer learning by adopting standard Convolutional Neural Network models trained over COCO dataset. The algorithms that we developed for counting of moving vehicles are discussed and empirical results are presented which show their efficacy. We have also discussed the challenges that were faced in counting of moving vehicles in the video stream.
Policy Based Synthesis: Data Generation and Augmentation Methods For RF Machine Learning

The current dataset generation methods for RF Machine Learning (RFML) tasks consist of either completely synthetically generated data or completely raw digitized data from an RF front end. The synthetic datasets are often unrealistic in terms of waveforms or protocols, and the raw captures are typically unlabeled (or often mislabeled), and can skew machine learning algorithms to focus on non-salient features. Further, the associated storage and processing requirements are quite large. In this work, a novel dataset generation and augmentation method called policy-based synthesis is presented that aims to address the short-comings of either approach by combining basic protocol knowledge with simulated channel and device impairments to supplement over-the-air captures made in a controlled environment. This method permits the learning of salient features and regularizes radio and device anomalies that are not of interest. Practical considerations for collecting and processing data for this hybridized approach are also detailed and examples are provided on a dataset that includes protocols commonly used in the 2.4 GHz ISM band such as Bluetooth and Wi-Fi.
Engagement Index for Classroom Lecture using Computer Vision

Effectiveness of any interaction is determined on the basis of interaction level and understanding developed between the interacting parties. In this paper, communication between two parties: teacher and students in a classroom lecture is being considered. This classroom session can be considered as highly interactive or less interactive by testing the communication skills of the teacher, how he conveys his ideas to the students, and how well the students interpret his ideas. The main objective of the project is to find out the engagement index of teacher & student interaction in a classroom lecture. This is achieved by computer vision and machine learning techniques. Analysis of the dataset is divided into verbal and non-verbal sections, namely, audio and video recordings. The videos are used to do analysis of the students. The audios are used to analysis of teachers. Based on certain parameters like attentiveness of students during lectures, the number of questions asked by both, the students' and the teachers', the engagement index is calculated.
TendencyRL: Multi-stage Discriminative Hints for Efficient Goal-Oriented Reverse Curriculum Learning

Deep reinforcement learning algorithms have been proven successful in a variety of simulation tasks with dense reward feedback. However, real-world RL applications, e.g. robotic manipulation, remain challenging as most of them are multi-stage and a positive reward can only be received when the final goal is accomplished. In this work, we propose a potential solution to such problems with the introduction of an experience-based tendency reward shaping mechanism, which provides the robot with additional hints based on a discriminative learning on past experience. The reward along with a stage-awareness network help accelerate solving a multistage task split into shorter phases in a reverse curriculum learning manner. We extensively study the advantages of TRL on the standard long-term goal-oriented robotics domains such as pick-and-place, and show that TRL performs more efficiently and robustly than prior approaches in tasks with large state space. In addition, we demonstrate that TRL can solve difficult robot manipulation challenges directly from perception.
Selective Super-Resolution for Scene Text Images

In this paper, we realize the enhancement of super-resolution using images with scene text. Specifically, this paper proposes the use of Super-Resolution Convolutional Neural Networks (SRCNN) which are constructed to tackle issues associated with characters and text. We demonstrate that standard SRCNNs trained for general object super-resolution is not sufficient and that the proposed method is a viable method in creating a robust model for text. To do so, we analyze the characteristics of SRCNNs through quantitative and qualitative evaluations with scene text data. In addition, analysis using the correlation between layers by Singular Vector Canonical Correlation Analysis (SVCCA) and comparison of filters of each SRCNN using t-SNE is performed. Furthermore, in order to create a unified super-resolution model specialized for both text and objects, a model using SRCNNs trained with the different data types and Content-wise Network Fusion (CNF) is used. We integrate the SRCNN trained for character images and then SRCNN trained for general object images, and verify the accuracy improvement of scene images which include text. We also examine how each SRCNN affects super-resolution images after fusion.
Fourier Descriptors Based Hand Gesture Recognition Using Neural Networks

With the advanced computing and efficient memory utilization, vision-based learning models are being developed on a large scale. Sign language recognition is one such application where the use of Artificial Neural Networks (ANN) is being explored. In this article, the use of Fourier Descriptors for hand gesture recognition is discussed. The system model was set up as follows: Images of 24 hand gestures of American Sign Language (ASL) were subjected to edge detection algorithms, the contours were extracted and Complex Fourier descriptors were obtained as features for classification using a 2 layer feed-forward neural network The effects of subsampling of the contour, number of hidden neurons and training functions on the performance of the network were observed. Maximum accuracy of 87.5% was achieved.
Voice Conversion and Spoofed Voice Detection from Parallel English and Urdu Corpus using Cyclic GANs

With the advent of Generative Adversarial Networks (GANs), the fake news epidemic is booming; which not only encompasses pictures and videos but also audio. This is a big issue in an automatic speech verification (ASV) devices allowing anyone to steal an identity from a database of users. We aim to address this issue for a database of speaker utterances in the Urdu language by a two-fold solution. First, we will describe a Cyclic GAN based one-to-one conversion method that can generate speech from given speaker to a target voice bi-directionally. Cyclic GANs have much more strong mapping capabilities than ordinary GANs due to the property of Cyclic consistency loss. This framework ensures that given sufficient training data, generated output is very similar to the input. Furthermore, adversarial examples generated by the model are used for spoofed voice detection. We will use a Gradient Boosting method to learn to distinguish the voice utterances of various speakers that are stored in a database from the adversarial examples. For the testing of English language, we used the VCTK dataset and for the Urdu language, we used Urdu speech recordings containing a single word utterance from each speaker. This is tested for male → male, male → female, female → male and female → female voice conversions. The results obtained from learning from the adversarial examples are optimistic but more data and efforts are needed to make it usable into practical systems that can support speech verification at large scale.
Effect of Iterative Variations on Despeckling Filters in Ultrasound Imaging

The ultrasound medical field are more favored in clinical diagnosis than further imaging mode, because it is conveyable, suitable, harmless and economic nature of it. The images are acquired and per-processed as this step plays a critical role in clinical diagnosis. The acquisition of the image may sometimes include various types of noise. In this paper we will work upon the speckle noise that may leads to suppression of the pixel quality and dissimilarity of the image as it is intrinsic in nature. The lower quality of the image, the more is the hurdle for the better extrication of features, identification, study and resolution of edges[1]. Because if the image is not properly captured due to the noise, that may result in inappropriate diagnosis by doctor. Under this paper, the work has been done by imposing various filters have been over the pictures, with different speckle values. And thereafter the image quality parameters are taken into considerations to compare the values of imposing filters over the image. The main aim of this paper is to have a comparison of some basic filters on various images.
A Multi-resident Activity Recognition Approach based on Frequent Itemset Mining Features

Activity recognition is one of the most important supporting technologies for smart-home applications, and most existing works conducted on this topic assume that there is only one resident in the smart home. However, there are often more than one resident at home in reality, which makes it ever more challenging to recognize their activities. In this work, a knowledge-driven approach for multi-resident activity recognition based on the features constructed by frequent itemset is proposed. Unlike traditional single-label based methods, we address this problem as learning multiple activity labels from the sensor-data sequence. Based on the binary ambient sensor data, our approach utilizes both features generated from single sensor and frequent itemset features of each class, i.e. activity. After that, five multi-label approaches are applied to recognize different activities in a short period of time. Finally, we conduct several experiments on a multi-resident activity dataset, and the experimental result demonstrates that the proposed approach with frequent itemset features is better than those without frequent itemset features added.
A QoE-Based Alarm Model for Terminal Video Quality

With the advent of the 5G era, quality of experience (QoE) has become one of the most important indicators to measure mobile video quality. If QoE degradation events can be accurately predicted, it will be beneficial for operators to provide better services. However, many factors could affect QoE, including users' subjective factors, network and users' terminal parameters. This paper proposes a model for QoE degradation alarm in the mobile video stream. Specifically, a large-scale dataset is collected in the practical environment by a self-developed application. The dataset is cleaned to reduce the influence of redundancy and outliers firstly. Then, a QoE degradation alarm model is proposed based on data-driven approaches. The random forest is chosen as the core of this model which integrates the advantages of several classification tree and can deal with the high-dimensional data. In order to have a better prediction performance, Synthetic Minority Oversampling Technique (SMOTE) is used for solving the imbalance problem of the train set. As is shown by the experimental results, this alarm model can strike a balance between precision and false alarm rate and performance outperforms the state-of-the-art methods. This result means the proposed model can save the operational cost while increasing users' satisfaction with the mobile video quality.
Dual-precision fixed-point arithmetic for low-power ray-triangle intersections

Ray-Triangle intersection is a fundamental computation in most ray tracing algorithms. The prohibitive cost of the ray-triangle test algorithms limits the utilization of these algorithms in settings with low power budgets, such as mobile systems. In this work, we analyze the precision requirements for ray-triangle intersection and observe that for most of the rays a low fixed-point precision is sufficient and only for a small fraction of rays a high precision is required. Accordingly, we propose a dual-precision fixed-point hardware accelerator for ray-triangle intersection targeting low-power systems, where the higher precision is only activated for tests deemed critical by our algorithm. Towards this goal, we develop a thresholding technique that autonomously switches between the lower and higher precisions, where the lower precision unit is used for the majority of the tests resulting in significant benefits in power consumption. We evaluate our methodology on a representative set of scenes and our proposed methodology in hardware. Our methodology introduces negligible accuracy errors in ray-triangle tests with an average of 0.25% misclassifications, while offering 87% savings in energy consumption compared to a baseline floating-point design and 27% savings compared to a high-precision fixed-point design.
Operability Evaluation System and Comparison Experiment of Gesture Operation and Button Operation of Robot Manipulator

Robot manipulators are now widely used in manufacturing industries. When programmed to perform required movements via a process called teaching, they can accomplish a wide variety of complex tasks. In conventional teaching processes, a teaching pendant (TP), which is a tethered controller with which the operator inputs specific movement commands, is used. However, such devices normally have so many buttons that operators often experience difficulty in programming operations intuitively, so the teaching process consumes significant amounts of time and effort. To solve this problem, intuitive operation methods in which human body motions (such as hand gestures) are used as input to the robot have been studied. However, no quantitative comparisons between those new gesture-based and conventional teaching methods have yet been made. With that point in mind, the present paper proposes a virtual space-based evaluation system in which operators control the position and orientation of a six-degree-of-freedom virtual robot hand in order to evaluate and compare TP- and gesture-based operation methods. The experimental results show that when compared to TP-based methods, gesture-based operation methods achieved higher evaluations in terms of time and psychological aspects. In addition, four types of gesture-based operating methods were proposed in order to consider different ways in which position changes and orientation changes could be handled. The operability levels of the four gesture types were also compared quantitatively using the proposed evaluation system, and the obtained experimental results suggest that such differences can affect the cognitive burden imposed on the operator.
Secret Data Hiding in Image Using AMBTC Technique

Proposed paper discloses a technique for secret data hiding in an image using AMBTC coding scheme. Different from the conventional techniques, proposed technique discloses a novel way of secret data hiding technique in both smooth and complex blocks of the cover image. It suggests different data hiding approach for smooth block and complex block. In case of smooth block, bit-plane is simply modified by secret data. However, in case of complex block, bit-plane is divided in to fixed number of bit sections. The granular division of bit-plane corresponding to only complex blocks helps in to increasing embedding capacity. Each section of the bit-plane is processed for redundancy check. If predetermined redundancy is determined for a predetermined number of sections, then only corresponding sections are used for secret data hiding otherwise bit-plane is not changed. Selective secret data hiding for different sections increasing embedding capacity while preserve image quality. Performance of the proposed technique are compared with the existing techniques, which show significant improvement in data embedding capacity as well as a good quality stego image.
Sub-Word Embeddings for OCR Corrections in Highly Fusional Indic Languages

Texts in Indic Languages contain a large proportion of out-of-vocabulary (OOV) words due to frequent fusion using conjoining rules (of which there are around 4000 in Sanskrit). OCR errors further accentuate this complexity for the error correction systems. Variations of sub-word units such as n-grams, possibly encapsulating the context, can be extracted from the OCR text as well as the language text individually. Some of the sub-word units that are derived from the texts in such languages highly correlate to the word conjoining rules. Signals such as frequency values (on a corpus) associated with such sub-word units have been used previously with log-linear classifiers for detecting errors in Indic OCR texts. We explore two different encodings to capture such signals and augment the input to Long Short Term Memory (LSTM) based OCR correction models, that have proven useful in the past for jointly learning the language as well as OCR-specific confusions. The first type of encoding makes direct use of sub-word unit frequency values, derived from the training data. The formulation results in faster convergence and better accuracy values of the error correction model on four different languages with varying complexities. The second type of encoding makes use of trainable sub-word embeddings. We introduce a new procedure for training fastText embeddings on the sub-word units and further observe a large gain in F-Scores, as well as word-level accuracy values.