Reviewerly/exHarmony huggingface dataset:
Periodic Q-Learning

The use of target networks is a common practice in deep reinforcement learning for stabilizing the training; however, theoretical understanding of this technique is still limited. In this paper, we study the so-called periodic Q-learning algorithm (PQ-learning for short), which resembles the technique used in deep Q-learning for solving infinite-horizon discounted Markov decision processes (DMDP) in the tabular setting. PQ-learning maintains two separate Q-value estimates - the online estimate and target estimate. The online estimate follows the standard Q-learning update, while the target estimate is updated periodically. In contrast to the standard Q-learning, PQ-learning enjoys a simple finite time analysis and achieves better sample complexity for finding an epsilon-optimal policy. Our result provides a preliminary justification of the effectiveness of utilizing target estimates or networks in Q-learning algorithms.
Comparative Analysis of Texture Features and Deep Learning Method for Real-time Indoor Object Recognition

Object recognition and classification are considered as major tasks in the field of computer vision. They are well suited for applications such as a real-time system for people counting, object recognition system for people with visual impairments, surveillance systems, etc. The deployment of computer vision, machine learning, and deep learning algorithms enable to recognize the objects from an image or video frame. This paper proposes a real-time system for indoor object recognition. Moreover, the proposed work mainly focuses on analyzing the performance of various texture features, machine learning classifiers and deep learning methodologies to recognize the objects in indoor areas. The proposed methodology is validated in a publically available indoor object dataset “MCindoor20000”. The dataset consists of three categories of objects including doors, stairs, and sign. Our developed deep learning model using transfer learning approach yielded 100 % accuracy and texture features such as LPQ and BSIF have yielded an accuracy of more than 98% with SVM and KNN classifiers.
Optical reservoir computer using speckle in a multimode waveguide

Reservoir computing (RC) is a class of recurrent neural network that expands the dimensionality of a time-domain signal by mapping it into a higher-dimension space to capture and predict features of complex, non-linear temporal dynamics. Hardware level implementation of RC requires a reservoir with a large number of fixed nodes and the ability to activate and read the output weights of the neurons. As training is performed at a single output layer using simple linear regression techniques, RC is significantly simpler than other recurrent neural networks and thus provides a potentially faster learning framework with low training cost. Here, we report on an optical implementation of a reservoir computer using speckle generated in a multimode fiber (MMF). Neurons are activated by driving pixels of a spatial light modulator (SLM) with time domain waveforms and the output of the SLM is imaged onto the MMF. The MMF output is imaged onto a camera whose image is digitally processed and fed back into the fiber through the SLM. We demonstrate recovery of Mackey- Glass waveforms and classification of multi-frequency sinusoids using the speckle-based optical reservoir computer. As all the components used in the experiment can be readily mapped into an integrated photonic circuit our result demonstrates a framework for building a scalable, chip-scale, optical reservoir computer.
Large-Scale Semantic Concept Detection Based On Visual Contents

Indexing video by the concept is one of the most appropriate solutions for such problem. It's based on an association between a concept and its corresponding visual, sound or textual features. This kind of association is not a trivial task. It requires knowledge about the concept and its context. In this paper, we investigate a new concept detection approach to improve the performance of content-based multimedia documents retrieval systems. To achieve this goal, we tackle the problem from different plans and make four contributions at various stages of the indexing process. We first propose a new weakly supervised semi-automatic method based on the genetic algorithm to extract and annotate the video plans for training set. Subsequently, we develop a method to detect the basic concepts. We also deal with the issue of noise reduction when generating visual dictionary (BoVS). The different contributions are tested and evaluated on a big dataset (TRECVID 2015).
Integer Wavelet Transform-Based ECG Steganography for Hiding Patients’ Confidential Information in e-Healthcare Systems

A novel ECG steganography technique to hide patients’ confidential information using integer wavelet transform (IWT) is proposed. Modified least significant bit (m-LSB) method has been applied to embed the secret bits into the transform coefficients of an ECG signal. To strengthen the security of the embedded information, chaotic maps are employed which randomly select the ECG coefficients to embed the secret information. The formulated approach outperformed the other state-of-the-art techniques with high embedding capacity (EC) and excellent peak signal-to-noise Ratio (PSNR) at minimal percentage residual difference (PRD) and Kullback–Leibler divergence (KL-divergence). The proficiency of the proposed technique is also evaluated in terms of the clinical diagnostic distortion measures like wavelet-based weighted PRD (WWPRD) and wavelet energy-based diagnostic distortion (WEDD). The proposed method is competent to hide the confidential information while keeping the diagnostic features intact.
Neural Differential Equations for Face Recognition

Recently, Convolutional Neural Networks(CNNs) have been very efficient in many computer vision areas, especially in facial recognition where most CNN models showed near-perfect performance on standard benchmarks. One of the main problems faced by these CNN architectures today is that they need a large amount of processing power and memory to deliver their exceptional performance. This paper aims on comparing and contrasting these CNNs with a new advancement in deep learning called neural differential equations, where the whole architecture is thought of as a differential equation to be solved. This paper aims to show that this method can achieve comparable performance to a residual network with much less parameters. As a part of this project we will show that the models trained over the ODENet, that is neural net using the ordinary differential equations, takes very less memory and time to train when compared to that of CNNs. As to run these models on the benchmark datasets, these models have been run on the standard face datasets named Faces95 and Faces96 respectively.
Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings

We propose unifying techniques from probabilistic databases and relational embedding models with the goal of performing complex queries on incomplete and uncertain data. We formalize a probabilistic database model with respect to which all queries are done. This allows us to leverage the rich literature of theory and algorithms from probabilistic databases for solving problems. While this formalization can be used with any relational embedding model, the lack of a well-defined joint probability distribution causes simple query problems to become provably hard. With this in mind, we introduce \TO, a relational embedding model designed to be a tractable probabilistic database, by exploiting typical embedding assumptions within the probabilistic framework. Using a principled, efficient inference algorithm that can be derived from its definition, we empirically demonstrate that \TOs is an effective and general model for these querying tasks.
Performance Analysis of the State-of-the-Art Neural Named Entity Recognition Model on Judicial Domain

Extracting named entities out of unstructured text data is an important problem in natural language processing, with applications in tasks such as sentiment analysis, information retrieval and answer selection in question answering. For identifying named entities, many methods have been developed ranging from knowledge-based methods to supervised machine learning methods. Recently, deep learning models have shown better performance than conventional methods in Named Entity Recognition (NER) tasks. However, it remains unclear how the state-of-the-art neural NER models that have shown superior performance on benchmark datasets perform on other domains. This paper aims to analyze the performance of a state-of-the-art neural NER model in identifying the named entities from judicial text data. The experimentally obtained results show that the model achieved very good results in identifying person names, location names and average result in organization names.
Pose Estimation of Complex Human Motion

In this paper, a human body pose estimation method based on skeleton matching is proposed. In the process of human body pose skeleton matching, we obtain the key point feature information of the human body by performing CNN operation on the input image: the joint point heat map, and the joint point according to the specific connection rules are connected to form a human skeleton. This method is not only suitable for image data, but the accuracy of human skeleton matching after inputting video data is also verified.
High-Performance Fuzzy C-Means Image Clustering Based on Adaptive Frequency-Domain Filtering and Morphological Reconstruction

Fuzzy C-means is a universally admired technique used in image segmentation. Morphological reconstruction is employed as image reconstruction at a lowered observation resolution to remove noise from images, so that space complexity of FCM algorithm is reduced, image is morphologically reconstructed, and noise present in original image is also avoided. Secondly, filtering of membership function is employed to trim the membership function to reduce the complexity of FCM function. Adaptive frequency-domain filtering of membership function allows for FCM function to be simpler and faster than existing algorithm.
Real-Time Multi-class Instance Segmentation with One-Time Deep Embedding Clustering

In recent years, instance segmentation research has been considered as an extension of object detection and semantic segmentation, which can provide pixel-level annotations on detected objects. Several approaches for instance segmentation exploit object detection network to generate bounding box and segment each bounding box with segmentation network. However, these approaches need more time consumption due to two independent networks as their framework. On the other hand, some approaches based on clustering transform each pixel into unique representation and produce instance mask by postprocessing. Nevertheless, most clustering approaches have to cluster all instances of each class individually, which contribute to additional time consumption. In this research, we propose a fast clustering method called one-time clustering with single network aiming at reducing time consumption on multi-class instance segmentation. Moreover, we present a class-sensitive loss function that allows the network to generate unique embedding which contains class and instance information. With the informative embeddings, we can cluster them only once instead of clustering for each class in other clustering approaches. Our approach is up to 6x faster than the state-of-the-art UPSNet [1], which appeared in CVPR 2019, and get about 25% lower AP performance on Cityscape dataset. It achieves significantly faster speed and great segmentation quality while having an acceptable AP performance.
The Influence of Knowledge Sharing on Academic Performance

This research aims to understand the effect of knowledge sharing on academic performance.Two processes of knowledge sharing are distinguished: donating and collecting.This research is intended to contribute to academic institutions in improving academic performance.The sample in this study amounted to 346 samples consisting of students at the Indonesian Economy Building School (STIE INABA) by using probability sampling techniques.The analysis technique used is multiple linear regression.The variables studied are knowledge sharing (knowledge donating and knowledge collecting) and academic performance.There search findings indicate that knowledge donating and knowledge collecting are positively significant related to academic performance.Based on these results, a number of theoretical and practical implications are discussed, and suggestions for further research are presented.
Client-Side Monitoring of HTTP Clusters Using Machine Learning Techniques

Large online web sites are supported in the back-end by a cluster of servers behind a load balancer. Ensuring proper operation of the cluster with minimal monitoring efforts from the load balancer is necessary to ensure performance. Previous monitoring efforts require extensive data from the system and fail to include the client perspective. We monitor the cluster using machine learning techniques that process data collected and uploaded by web clients, an approach that might complement system-side information. To experiment our solution, we trained the machine learning algorithms in a cluster of 10 machines with a load balancer and evaluated the results of these algorithms when one of the machines is overloaded. While a fine-grained view of the state of the machines, may require much effort to accomplish, given the compensation effect of the remaining healthy machines, the results show that we can achieve a coarse grained view of the entire system, to produce relevant insight about the cluster.
Error detection in Knowledge Graphs: Path Ranking, Embeddings or both?

This paper attempts to compare and combine different approaches for de-tecting errors in Knowledge Graphs. Knowledge Graphs constitute a mainstreamapproach for the representation of relational information on big heterogeneous data,however, they may contain a big amount of imputed noise when constructed auto-matically. To address this problem, different error detection methodologies have beenproposed, mainly focusing on path ranking and representation learning. This workpresents various mainstream approaches and proposes a novel hybrid and modularmethodology for the task. We compare these methods on two benchmarks and one real-world biomedical publications dataset, showcasing the potential of our approach anddrawing insights regarding the state-of-art in error detection in Knowledge Graphs
PAPRIKA: Private Online False Discovery Rate Control

In hypothesis testing, a false discovery occurs when a hypothesis is incorrectly rejected due to noise in the sample. When adaptively testing multiple hypotheses, the probability of a false discovery increases as more tests are performed. Thus the problem of False Discovery Rate (FDR) control is to find a procedure for testing multiple hypotheses that accounts for this effect in determining the set of hypotheses to reject. The goal is to minimize the number (or fraction) of false discoveries, while maintaining a high true positive rate (i.e., correct discoveries). In this work, we study False Discovery Rate (FDR) control in multiple hypothesis testing under the constraint of differential privacy for the sample. Unlike previous work in this direction, we focus on the online setting, meaning that a decision about each hypothesis must be made immediately after the test is performed, rather than waiting for the output of all tests as in the offline setting. We provide new private algorithms based on state-of-the-art results in non-private online FDR control. Our algorithms have strong provable guarantees for privacy and statistical performance as measured by FDR and power. We also provide experimental results to demonstrate the efficacy of our algorithms in a variety of data environments.
A Study of Stimulating Factors in Self-Motivated Professional Development among Teachers at University Level

The purpose of this study is to find out the factors which stimulate university teachers towards self-motivated professional development at university level. It was a descriptive study based on survey design; a mixed methods design was selected to collect the data through mixed method techniques. 200 questionnaires were distributed with the help of the snowball sampling technique, which helped in reaching 57 teachers for interview. Thematic Analysis, Descriptive Statistics and Chi- Square Test were applied to data. The result shows the intrinsic factors of stimulation were self-thrust towards excellence, respect, acknowledgment and inner satisfaction. On the contrary, extrinsic factors were a good salary package, promotion, better social and professional status and ability to cope with the advanced educational requirements. The study recommends that action through teachers’ consideration, reflection and shared vision may include extrinsic and intrinsic factors of motivation.
Digital Image Forensics Using Local Optimal-Oriented Pattern and ELM

Due to the advances in computer graphic technology, it has become very difficult to classify computer graphics from photographic images. It is also very difficult to distinguish tampered images from authentic images. In the chapter, an algorithm has been proposed to solve both the problems simultaneously. A texture descriptor local optimal-oriented pattern has been used to extract features from computer-generated and photographic images. The extreme learning machine has been used to classify the images. Experiment results demonstrate that it has high accuracy and is also robust against post-processing operations.
Cloud-Based RDF Data Management

Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible Universal Resource Identifiers as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation, and Web technologies. As a consequence, the RDF data model is used in a variety of applications today for integrating knowledge and information: in open Web or government data via the Linked Open Data initiative, in scientific domains such as bioinformatics, and more recently in search engines and personal assistants of enterprises in the form of knowledge graphs.
Short Paper: User Identification across Online Social Networks Based on Similarities among Distributions of Friends’ Locations

This paper describes a user identification method conducted across online social networks (OSNs) using information regarding friends’ locations, in contrast to a conventional method based on the similarity of two display names. This latter method encounters the problem of decreased identification accuracy if a user registers different display names across OSNs. Our proposed method aims at addressing this issue by utilizing information on friends’ locations. We convert location information extracted from an OSN to that of such geographic administrative units as country, state and city, and calculate the weighted occurrence frequency of a location pair on the basis of distance between the pair. This is based on the hypothesis that a friend list relatively rarely includes an account pair whose locations are extremely distant from one another. We calculate a similarity score between two accounts’ weighted occurrence frequencies for each geographic administrative unit. Finally, we identify a user on the basis of individual similarity scores or weighted average scores for a given geographic administrative unit. Evaluation experiments show that our proposed method improves performance over that of a conventional method (accuracy: 83.1%$\rightarrow$91.2%).
Construction of information design theory

Designing, testing and manufacturing new technologies is a complex process that often requires many redesigns and rethinks. There are multiple criteria a new technological product must meet. Examples include the fact that it must provide a useful function, it must execute that function efficiently and effectively and it must be user friendly. In addition, cost of production and of sale are key considerations. Generally, once the manufacturer is satisfied with the results in all the criteria, the product can be released and sold. This iterative process whereby designs for manufacturing are gradually improved is the standard engineering approach. It is an extremely reliable way to ensure your product satisfies the basic criteria you set for it at the beginning. However, this is where the intellectual effort behind the design process stops. A product is made and, potentially, sells well and follow ups designed, but often little thought is put into unintended side effects of it. Dr Nariaki Nishino, who is an Associate Professor at the Department of Technology Management for Innovation, School of Engineering in The University of Tokyo, explains that new technology often receives little scrutiny of its unplanned effects. 'This is particularly true of modern technologies involving the digitisation of previously analogue products,' he outlines. Over the last 10 years, it has been generally seen as a positive and desirable to digitise as much as possible. Particularly, it has been seen as important to integrate digitised products extensively so that data from one is shared with another, etc. 'Such data sharing and integrated technology can provide many benefits for the user, however, there are side effects and important consequences to the sharing of so much personal data,' confirms Nishino. 'These unintended consequences are known as artefacts of the product and after ignored in the design process.' More broadly, such artefacts of engineering can include a wide range of the negatives of modern life – environmental problems, loneliness, urban decay. Nishino is a key team member of the Research into Artefacts, Centre for Engineering (RACE) division at The University of Tokyo, which was set up to examine how engineering could start considering these artefacts in the design process.
Effects of Mobile Learning in Blended Learning Environments

Mobile learning readiness for promoting student attitude and knowledge construction is an emerging trend to integrate technology into educational settings. The successful implementation of mobile devices has the potential to enhance learning. In this sense, blended learning environments combining the strengths of traditional learning and the strengths of mobile learning can be considered as a promising trend. This paper aims to examine the effective integration of mobile learning into blended learning environments in order to determine the impacts of these learning environments on students’ learning acquisition, academic achievements and attitude. This literature review selected twenty-two peer-reviewed journal articles according to the pre-determined criteria (E. g., studies using a quantitative research design such as quasi-experimental and experimental, etc. or types of research designs such as mixed-method, case study, etc. including at least a quantitative aspect in their data collection procedure) and carefully analyzed these articles to extract reliable and meaningful information from them. The findings demonstrate that the integration of mobile technologies into a blended learning environment significantly influences students’ learning acquisition in a positive way although there are few empirical findings from the reviewed articles which highlight no significant effect on their learning acquisition. With regard to student attitude, students were satisfied and motivated to use mobile technologies in blended learning environments for academic purposes according to the majority of the reviewed articles.