Reviewerly/exHarmony huggingface dataset:
The Importance of Object-Based Seed Sampling for Superpixel Segmentation

Superpixel segmentation can be defined as an image partition into connected regions, such that image objects may be represented by the union of their superpixels. In this context, multiple iterations of superpixel segmentation from improved seed sets is a strategy exploited by several algorithms. The Iterative Spanning Forest (ISF) framework divides this strategy into three independent components: a seed sampling method, a superpixel delineation algorithm based on strength of connectedness between seeds and pixels, and a seed recomputation procedure. A recent work shows that object information can be added to each component of ISF such that the user can control the number of seeds inside the objects and so improve superpixel segmentation. However, it is uncertain how the added information impacts each component of the pipeline. Therefore, in this work, a study is conducted to evaluate such inclusion in the seed sampling procedure, partially elucidating its benefits. Additionally, we introduce a novel object-based sampling approach, named Object Saliency Map sampling by Ordered Extraction (OSMOX), and demonstrate the results for supervised and unsupervised object information. The experiments show considerable improvements in under-segmentation error, specially with a low number of superpixels.
Securing multi-tenancy systems through multi DB instances and multiple databases on different physical servers

&lt;p&gt;Use of the same application by multiple users through internet as a service is supported by cloud computing system. Both the user and attacker stay in the same machine as both of them are users of the same application creating an in-secure environment. Service must ensure secrecy both at the application and data layer level. Data isolation and Application isolation are two basic aspects that must be ensured to cater for security as desired by the clients that accesses the service. In this paper a more secured mechanism has been presented that help ensuring data isolation and security when Multi-tenancy of the users to the same service has been implemented.&lt;/p&gt;
An Un-realized Algorithm for Effective Privacy Preservation Using Classification and Regression Trees

In this digital age, privacy preservation has attracted much attention, as a huge amount of data are generated from multiple sources and transmitted across the Internet.Several perturbation algorithms have emerged to keep sensitive data hidden behind additive noises.In this paper, a novel un-realization algorithm is developed based on a classification and regression tree (CART).First, the sample dataset was distorted, and the duplicate elements were removed, creating a perturbed dataset and an un-realized dataset.Then, a decision tree was set up by the modified CART algorithm and another by the traditional CART based on the un-realized dataset.Finally, the Gini values of the two trees were compared.If the results are the same, then the privacy of the data is preserved.The proposed algorithm was compared with several traditional un-realization algorithms through experiments.The results show that our algorithm achieved excellent results in Gini value, time complexity and output accuracy.
VR 3D Education for Vocational Training

The progress and development in the fields of technologies in the past few decades are impressive. With so many innovations that have had an impact on human lives and have changed them so drastically, living in a time where new technologies are still making massive changes, and, unquestionably, it wil continue with that trend. As the techniques are continuously evolving, people are forced to prepare ourselves and our descendants to the new and upcoming technologies so that they would be able to understand them, use them, teach others about them and also make some improvements in the specific fields of applications. This article presents an overview of the principal results of research on the impact of Virtual Reality (VR) 3D education for students in industrial vocational training. They were introducedto the generated model and had to explore all elements to have personal experience in the virtual environment. After the training, a user experience survey has been conducted, and the results obtained after the use of the system were presented.
Generation of Hip-Hop Lyrics with Hierarchical Modeling and Conditional Templates

This paper addresses Hip-Hop lyric generation with conditional Neural Language Models.We develop a simple yet effective mechanism to extract and apply conditional templates from text snippets, and show-on the basis of a large-scale crowd-sourced manual evaluation-that these templates significantly improve the quality and realism of the generated snippets.Importantly, the proposed approach enables end-to-end training, targeting formal properties of text such as rhythm and rhyme, which are central characteristics of rap texts.Additionally, we explore how generating text at different scales (e.g.character-level or word-level) affects the quality of the output.We find that a hybrid form-a hierarchical model that aims to integrate Language Modeling at both word and character-level scalesyields significant improvements in text quality, yet surprisingly, cannot exploit conditional templates to their fullest extent.Our findings highlight that text generation models based on Recurrent Neural Networks (RNN) are sensitive to the modeling scale and call for further research on the observed differences in effectiveness of the conditioning mechanism at different scales.
Scalable Model Compression by Entropy Penalized Reparameterization

We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a “latent” space, amounting to a reparameterization. This space is equipped with a learned probability model, which is used to impose an entropy penalty on the parameter representation during training, and to compress the representation using a simple arithmetic coder after training. Classification accuracy and model compressibility is maximized jointly, with the bitrate--accuracy trade-off specified by a hyperparameter. We evaluate the method on the MNIST, CIFAR-10 and ImageNet classification benchmarks using six distinct model architectures. Our results show that state-of-the-art model compression can be achieved in a scalable and general way without requiring complex procedures such as multi-stage training.
Towards an Integrative Approach for Automated Literature Reviews Using Machine Learning

Due to a huge amount of scientific publications which are mostly stored as unstructured data, complexity and workload of the fundamental process of literature reviews increase constantly.Based on previous literature, we develop an artifact that partially automates the literature review process from collecting articles up to their evaluation.This artifact uses a custom crawler, the word2vec algorithm, LDA topic modeling, rapid automatic keyword extraction, and agglomerative hierarchical clustering to enable the automatic acquisition, processing, and clustering of relevant literature and subsequent graphical presentation of the results using illustrations such as dendrograms.Moreover, the artifact provides information on which topics each cluster addresses and which keywords they contain.We evaluate our artifact based on an exemplary set of 308 publications.Our findings indicate that the developed artifact delivers better results than known previous approaches and can be a helpful tool to support researchers in conducting literature reviews.
Mining the Dark Web: A Novel Approach for Placing a Dark Website under Investigation

In the last two decades, illicit activities have dramatically increased on the Dark Web.Every year, Dark Web witnesses establishing new markets, in which administrators, vendors, and consumers aim to illegal acquisition and consumption.On the other hand, this rapid growth makes it quite difficult for law and security agencies to detect and investigate all those activities with manual analyses.In this paper, we introduce our approach of utilizing data mining techniques to produce useful patterns from a dark web market contents.We start from a brief description of the methodology on which the research stands, then we present the system modules that perform three basic missions: crawling and extracting the entire market data, data pre-processing, and data mining.The data mining methods include generating Association Rules from products' titles, and from the generated rules, we infer conceptual compositions vendors use when promoting their products.Clustering is the second mining aspect, where the system clusters vendors and products.From the generated clusters, we discuss the common characteristics among clustered objects, find the Top Vendors, and analyze products promoted by the latter, in addition to the most viewed and sold items on the market.Overall, this approach helps in placing a dark website under investigation.
An Algorithm for Mining High Utility Sequential Patterns with Time Interval

Abstract Mining High Utility Sequential Patterns (HUSP) is an emerging topic in data mining which attracts many researchers. The HUSP mining algorithms can extract sequential patterns having high utility (importance) in a quantitative sequence database. In real world applications, the time intervals between elements are also very important. However, recent HUSP mining algorithms cannot extract sequential patterns with time intervals between elements. Thus, in this paper, we propose an algorithm for mining high utility sequential patterns with the time interval problem. We consider not only sequential patterns’ utilities, but also their time intervals. The sequence weight utility value is used to ensure the important downward closure property. Besides that, we use four time constraints for dealing with time interval in the sequence to extract more meaningful patterns. Experimental results show that our proposed method is efficient and effective in mining high utility sequential pattern with time intervals.
Smart Attendance System using Face Recognition

To maintain a discipline and let students grasp utmost knowledge in schools, colleges and universities the attendance system was introduced.There are two conventional techniques to mark attendance of students in a particular class.One of them is by calling the roll number and the second is to take students sign on a piece of paper against their roll number.Hence there was a need to evolve this system in such a way that it could become user friendly, less time consuming and efficient.This is an automated system to assist the faculty in taking attendance of the whole class without any disturbance or time waste.The idea can encompass a large number application one of which include face identification, it will help save time and efficiently identifies and eliminates the chances of proxy attendance.The main purpose of this project is to built automated attendance system using Raspberry pi 3B+ with OpenCV/Python libraries and recognizer algorithm have been implemented.The proposed system can be implemented in any field where attendance system is present and plays a vital role.In addition, as the project objectives and the design criteria all met, it's greatest to say this project is an engineering solution for all university and colleges to track and manage the attendance.
Contextual teaching and learning approach to supplementary reading materials based on 2013 Curriculum

The objectives of this study are to (1) develop supplementary reading materials using Contextual Teaching and Learning Approach for students of SMP N 1 Moyudan and (2) describe the efficacy of the supplementary reading materials. This research belongs to Educational Research and Development (R&amp;D) using a simplified six steps of (Gall, Gall, &amp; Borg, 2003) model. The finding shows that the students learn English to achieve the 2013 curriculum learning objectives. The necessities of the teaching and learning English is to improve students' English skills so that they gain good score for the national examinations though students have less motivation to learn reading and they cannot learn independently. They cannot differentiate or understand WH-questions that is important in learning reading. The efficacy of this product helps students to improve their reading skill especially to understand the topic, the generic structure, the main idea, the grammar and the vocabulary used in the text. The supplementary reading materials as the product of this study provide additional learning resources for students.
A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection

Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword – referred to as the expectation of the distribution – and use it as a posterior regularization parameter during model training. Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training. This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. Our approach iteratively leverages the crowd to estimate both keyword-specific expectation and the disagreement between the crowd and the model in order to discover new keywords that are most beneficial for model training. These keywords and their expectation not only improve the resulting performance but also make the model training process more transparent. We empirically demonstrate the merits of our approach, both in terms of accuracy and interpretability, on multiple real-world datasets and show that our approach improves the state of the art by 24.3%.
Low Complexity Pruned DCT Approximation for Image Compression in Wireless Multimedia Sensor Networks

In this paper we propose a novel efficient low-complexity Discrete Cosine Transform (DCT) approximation using only 14 additions. The proposed transformation is based upon a pruned version of the Signed DCT (SDCT). Associated with a JPEG compression chain, this DCT approximation ensures a very good rate-distortion compromise, a very low computational complexity and a significant compatibility with the exact DCT. The proposed transformation is very suitable for the limited energy resource wireless multimedia sensor networks (WMSNs) requiring very low bitrates and other embedded systems. Simulation results show the superiority of the proposed fast algorithm compared to any existing pruned DCT approximations.
Enhancing Graph-Based Semisupervised Learning via Knowledge-Aware Data Embedding

Semisupervised learning (SSL) is a family of classification methods conceived to reduce the amount of required labeled information in the training phase. Graph-based methods are among the most popular semisupervised strategies: the nearest neighbor graph is built in such a way that the manifold of the data is captured and the labeled information is propagated to target samples along the structure of the manifold. Research in graph-based SSL has mainly focused on two aspects: 1) the construction of the k-nearest neighbors graph and/or 2) the propagation algorithm providing the classification. Differently from the previous literature, in this article, we focus on the data representation with the aim of incorporating semisupervision earlier in the process. To this end, we propose an algorithm that learns a new knowledge-aware data embedding via an ensemble of semisupervised autoencoders to enhance a graph-based semisupervised classification. The experiments carried out on different classification tasks demonstrate the benefit of our approach.
Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision

Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requiring a distinct model and training data. While relation extraction can often be trained with readily available weak or distant supervision, entity linkers typically require expensive mention-level supervision – which is not available in many domains. Instead, we propose a model which is trained to simultaneously produce entity linking and relation decisions while requiring no mention-level annotations. This approach avoids cascading errors that arise from pipelined methods and more accurately predicts entity relationships from text. We show that our model outperforms a state-of-the art entity linking and relation extraction pipeline on two biomedical datasets and can drastically improve the overall recall of the system.
mistr: A Computational Framework for Mixture and Composite Distributions

Finite mixtures and composite distributions allow to model the probabilistic representation of data with more generality than simple distributions and are useful to consider in a wide range of applications. The R package mistr provides an extensible computational framework for creating, transforming, and evaluating these models, together with multiple methods for their visualization and description. In this paper we present the main computational framework of the package and illustrate its application. In addition, we provide and show functions for data modeling using two specific composite distributions as well as a numerical example where a composite distribution is estimated to describe the log-returns of selected stocks.
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip

Although Shannon theory states that it is asymptotically optimal to separate the source and channel coding as two independent processes, in many practical communication scenarios this decomposition is limited by the finite bit-length and computational power for decoding. Recently, neural joint source-channel coding (NECST) (Choi et al. 2018) is proposed to sidestep this problem. While it leverages the advancements of amortized inference and deep learning (Kingma and Welling 2013; Grover and Ermon 2018) to improve the encoding and decoding process, it still cannot always achieve compelling results in terms of compression and error correction performance due to the limited robustness of its learned coding networks. In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme. More specifically, on the encoder side, we propose to explicitly maximize the mutual information between the codeword and data; while on the decoder side, the amortized reconstruction is regularized within an adversarial framework. Extensive experiments conducted on various real-world datasets evidence that our IABF can achieve state-of-the-art performances on both compression and error correction benchmarks and outperform the baselines by a significant margin.
Visual Reasoning on Complex Events in Soccer Videos Using Answer Set Programming

In the context of computer vision, most of the traditional action recognition techniques assign a single label to a video after analyzing the whole video. We believe that under- standing of the visual world is not limited to recognizing a specific action class or individual object instances, but also extends to how those objects interact in the scene, which im- plies recognizing events happening in the scene. In this paper we present an approach for identifying complex events in videos, starting from detection of objects and simple events using a state-of-the-art object detector (YOLO). We provide a logic based representation of events by using a realization of the Event calculus that allows us to define complex events in terms of logical rules. Axioms of the calculus are encoded in a logic program under Answer Set semantics in order to reason and formulate queries over the extracted events. The applicability of the framework is demonstrated over the scenario of recognizing different kinds of kick events in soccer videos.
Decentralized Data Marketplace to Enable Trusted Machine Economy

Transacting IoT data must be different in many from traditional approaches in order to build much-needed trust in data marketplaces, trust that will be the key to their sustainability. Data generated internally to an organization is usually not enough to remain competitive, enhance customer experiences, or improve strategic decision-making. In this paper, we propose a decentralized and trustless architecture through the posting of trade records while including the transaction process on distributed ledgers. This approach can efficiently enhance the degree of transparency, as all contract-oriented interactions will be written on-chain. Storage via an end-to-end encrypted message channel allows transmitting and accessing trusted data streams over distributed ledgers regardless of the size or cost of the device, while simultaneously making a verifiable Auth-compliant request to the platform. Furthermore, the platform will complete matching, trading and refunding processes with-out human intervention, and it also protects the rights of data providers and consumers through trading policies which apply revolutionary game theory to the machine economy.
HLHLp: Quantized Neural Networks Training for Reaching Flat Minima in Loss Surface

Quantization of deep neural networks is extremely essential for efficient implementations. Low-precision networks are typically designed to represent original floating-point counterparts with high fidelity, and several elaborate quantization algorithms have been developed. We propose a novel training scheme for quantized neural networks to reach flat minima in the loss surface with the aid of quantization noise. The proposed training scheme employs high-low-high-low precision in an alternating manner for network training. The learning rate is also abruptly changed at each stage for coarse- or fine-tuning. With the proposed training technique, we show quite good performance improvements for convolutional neural networks when compared to the previous fine-tuning based quantization scheme. We achieve the state-of-the-art results for recurrent neural network based language modeling with 2-bit weight and activation.
XRaSE: Towards Virtually Tangible Software using Augmented Reality

Software engineering has seen much progress in recent past including introduction of new methodologies, new paradigms for software teams, and from smaller monolithic applications to complex, intricate, and distributed software applications. However, the way we represent, discuss, and collaborate on software applications throughout the software development life cycle is still primarily using the source code, textual representations, or charts on 2D computer screens - the confines of which have long limited how we visualize and comprehend software systems. In this paper, we present XRaSE, a novel prototype implementation that leverages augmented reality to visualize a software application as a virtually tangible entity. This immersive approach is aimed at making activities like application comprehension, architecture analysis, knowledge communication, and analysis of a software's dynamic aspects, more intuitive, richer and collaborative.