Reviewerly/exHarmony huggingface dataset:
Constellation diagram processing with convolutional neural networks for channel phase response estimation

A novel constellation diagram processing is proposed to implement channel phase response estimation by using convolutional neural networks (CNN). The constellation diagram images of asymmetric 4-QAM signals are presented as graphical patterns designed for CNN rotation angle prediction. The convolutional neural network is configured as a six-layer neural network in which constellation diagrams are feeding the input layer and rotation angle prediction only takes one convolutional layer. For rotation angle prediction, the highest accuracy is achieved at epochs of 20 (85%) for image with pixel size 28 × 28. We experimentally verified this channel phase response estimation method in a 1.2 m BNC coaxial cable as a transmission line using 6 sub-bands located between 150 kHz and 276 kHz. For experimental validation, the proposed methodology allows recovering between 64% and 97% of the transmitted symbols obtained after constellation diagram processing, compared to 20% recovering without phase precompensation.
CuRL: Coupled Representation Learning of Cards and Merchants to Detect Transaction Frauds

Payment networks like Mastercard or Visa process billions of transactions every year. A significant number of these transactions are fraudulent that cause huge losses to financial institutions. Conventional fraud detection methods fail to capture higher-order interactions between payment entities i.e., cards and merchants, which could be crucial to detect out-of-pattern, possibly fraudulent transactions. Several works have focused on capturing these interactions by representing the transaction data either as a bipartite graph or homogeneous graph projections of the payment entities. In a homogeneous graph, higher-order cross-interactions between the entities are lost and hence the representations learned are sub-optimal. In a bipartite graph, the sequences generated through random walk are stochastic, computationally expensive to generate, and sometimes drift away to include uncorrelated nodes. Moreover, scaling graph-learning algorithms and using them for real-time fraud scoring is an open challenge.
Natural Language Inference Using Evidence from Knowledge Graphs

Knowledge plays an essential role in inference, but is less explored by previous works in the Natural Language Inference (NLI) task. Although traditional neural models obtained impressive performance on standard benchmarks, they often encounter performance degradation when being applied to knowledge-intensive domains like medicine and science. To address this problem and further fill the knowledge gap, we present a simple Evidence-Based Inference Model (EBIM) to integrate clues collected from knowledge graphs as evidence for inference. To effectively incorporate the knowledge, we propose an efficient approach to retrieve paths in knowledge graphs as clues and then prune them to avoid involving too much irrelevant noise. In addition, we design a specialized CNN-based encoder according to the structure of clues to better model them. Experiments show that the proposed encoder outperforms strong baselines, and our EBIM model outperforms other knowledge-based approaches on the SciTail benchmark and establishes a new state-of-the-art performance on the MedNLI dataset.
Enhancing High-Quality User Stories with AQUSA: An Overview Study of Data Cleaning Process

Agile software development has ended up profoundly prevalent over the final two decades. In conjunction with the increment of these methodology, amount of scientific research on this topic has also increased. This research concentrates on one component from Agile software development which is User Stories (US). In a recent paper, quality framework for User Stories was proposed together with a tool implementing which is still minimal finding to achieve high quality of user story document. It is closely related to the data requirement as part of the success factor in development project. The main goal is to analyze US requirement written and identification of potential errors in datasets that bring the effectiveness in forecasting the quality of User Stories for monitoring purpose. One of the steps require to identify the quality User Stories is by the route passing through data cleaning process. The research analysis considers performing data cleaning and pre-processing towards existing dataset of user story requirement from open-source agile software projects. Results of analyzing the User Stories will reveal the possibility step of data cleaning as initial step to extend the AQUSA tools and forecast the quality of User Stories. Thus, it can be related to the requirements quality based on the number of issues report in the dataset where highest number of issues report can be categorized as poor requirement statement.
Generalized Rules of Probabilistic Independence

Probabilistic independence, as a fundamental concept of probability, enables probabilistic inference to become computationally feasible for increasing numbers of variables. By adding five more rules to an existing sound, yet incomplete, system of rules of independence, Studený completed it for the class of structural semi-graphoid independence relations over four variables. In this paper, we generalize Studený’s rules to larger numbers of variables. We thereby contribute enhanced insights in the structural properties of probabilistic independence. In addition, we are further closing in on the class of probabilistic independence relations, as the class of relations closed under the generalized rules is a proper subclass of the class closed under the previously existing rules.
Enhancing the Capabilities of Approaching to Service Scenarios and Settling for Intelligent Wheelchair Robots

Wheelchairs are the evaded partner in the lives of many differently-abled people to support their day-to-day activities. Manual and powered wheelchairs fail to meet the docking behavior for a specific task on the table. As the main aim of this research, a human study was performed to identify the docking behavior of a wheelchair to the table or desk in four different scenarios such as writing, reading, eating, and using a Laptop. With 3D point cloud data, this paper introduces an algorithm for automatically detecting secure docking locations at rectangular docking structures (tables, desks) with proper alignment knowledge. Within the sense of delivering advanced driving assistance for powered wheelchair users, the secure docking areas will then be given as priorities to an autonomous route designer. The experimental findings show that the proposed wheelchair docking system is capable of maintaining user comfort during approaches.
Quick Browser: A Unified Model to Detect and Read Simple Object in Real-time

There are many real-life use cases such as barcode scanning or billboard reading where people need to detect objects and read the object contents. Commonly existing methods are first trying to localize object regions, then determine layout and lastly classify content units. However, for simple fixed structured objects like license plates, this approach becomes surplus and heavy to run. This work aims to solve this detect-and-read problem in a lightweight way by integrating multi-digit recognition into a one-stage object detection model. Our unified method not only eliminates the duplication in feature extraction (one for localizing, one again for classifying) but also provides useful contextual information around object regions for classification. Additionally, our choice of backbones and modifications in architecture, loss function, data augmentation and training make the method robust, efficient and speedy. Secondly, we made a public benchmark dataset of diverse real-life 1D barcodes for a reliable evaluation, which we collected, annotated and checked carefully. Eventually, experimental results prove the method's efficiency on the barcode problem by outperforming industrial tools in both detecting and decoding rates with a real-time fps at a VGA-similar resolution. It also did a great job expectedly on the license-plate recognition task (on the AOLP dataset) by outperforming the current state-of-the-art method significantly in terms of recognition rate and inference time.
A Policy Study on Variations of Management Concept of College Physical Education in Age of Internet

Physical education course management in China, as a major part of overall college education management, in the information technology age, should reflect the obvious characteristics of the contemporary social changes. In a similar sense, management concepts have evolved significant variations in the internet age with numerous online and multimedia applications for many aspects of social life, especially in the last two decades. Information technology should certainly enlighten college administrators and their physical education managers with new management ideas. This essay studies the variations of management concept and their inspirations, in terms of fresh course management concept, teaching method innovation, and interpersonal approaches in the interaction with college students, as well as to college physical education managers. Therefore this essay provides suggestions to policy makers and college teachers of physical education aiming at keeping pace with society in adopting flexibly and effectively the management concept of the new age of internet and multimedia in physical education.
Latent shared representation for multi-view subspace clustering

Currently, multi-view subspace clustering has obtained wide attention in the field of machine learning and pattern recognition. Since different views contain different view-specific information of the same object, how to utilize these views to recover a latent shared representation is important for subsequent clustering. In this paper, we propose a latent shared representation model for multi-view subspace clustering. Our model uses the view-specific generated matrices to recover a latent shared representation from the multi-view data. To consider the correlation information of multi-view data, we adopt an empirical Hilbert-Schmidt independence criterion to constrain these view-specific generated matrices. Based on the inexact augmented Lagrangian method, we also develop an alternating optimization algorithm to solve our model. Experimental results on the real-world multi-view data sets have validated the effectiveness of our model.
Practical High-order Entropy-compressed Text Self-indexing

Compressed self-indexes are used widely in string processing applications, such as information retrieval, genome analysis, data mining, and web searching. The index not only indexes the data, but also encodes the data, and it is in compressed form. Moreover, the index and the data it encodes can be operated upon directly, without need to uncompress the entire index, thus saving time while maintaining small storage space. In some applications, such as in genome analysis, existing methods do not exploit the full possibilities of compressed self-indexes, and thus we seek faster and more space-efficient indexes. In this paper, we propose a practical high-order entropy-compressed self-index for efficient pattern matching in a text. We give practical implementations of compressed suffix arrays using a hybrid encoding in the representation of the neighbor function . We analyze the performance in theory and practice of our recommended indexing method, called GeCSA. We can improve retrieval time further using an iterated version of the neighbor function. Experimental results on the tested data demonstrate that the proposed index GeCSA has good overall advantages in space usage and retrieval time over the state-of-the-art indexing methods, especially on the repetitive data.
A Strategic Weight Refinement Maneuver for Convolutional Neural Networks

Stochastic Gradient Descent algorithms (SGD) remain a popular optimizer for deep learning networks and have been increasingly used in applications involving large datasets producing promising results. SGD approximates the gradient on a small subset of training examples, randomly selected in every iteration during network training. This randomness leads to the selection of an inconsistent order of training examples resulting in ambiguous values to solve the cost function. This paper applies Guided Stochastic Gradient Descent (GSGD) - a variant of SGD in deep learning neural networks. GSGD minimizes the training loss and maximizes the classification accuracy by overcoming the inconsistent order of data examples in SGDs. It temporarily bypasses the inconsistent data instances during gradient computation and weight update, leading to better convergence at the rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\frac{1}{\rho T-})$</tex> . Previously, GSGD has only been used in the shallow learning networks like the logistic regression. We try to incorporate GSGD in deep learning neural networks like the Convolutional Neural Networks (CNNs) and evaluate the classification accuracy in comparison with the same networks trained with SGDs. We test our approach on benchmark image datasets. Our baseline results show GSGD leads to a better convergence rate and improves classification accuracy by up to 3% of standard CNNs.
Curriculum Learning Revisited: Incremental Batch Learning with Instance Typicality Ranking

The technique of curriculum learning mimics cognitive mechanisms observed in human learning, where simpler concepts are presented prior to gradual introduction of more difficult concepts. Until now, the major obstacle for curriculum methods was the lack of a reliable method for estimating the difficulty of training instances. In this paper we show that, instead of trying to assess the difficulty of learning instances, a simple graph-based method of computing the typicality of instances can be used in conjunction with curriculum methods. We design new batch schedulers which organize ordered instances into batches of varying size and learning difficulty. Our method does not require any changes to the architecture of trained models, we improve the training merely by manipulating the order and frequency of instance presentation to the model.
A Low Area Random Number Generator Based on Stability Changes of Ring Oscillators

This paper presents a new random number generator based on stability changes of ring oscillators. Multiplexer and inverter are added to each ring to be able to turn on and off the oscillations. Fourteen ring oscillators are combined with the XOR gate after each ring’s output is sampled with a D type flip-flop. The multiplexers are controlled with a control unit. The designed TRNG is subjected to NIST SP800-22 test suite that contains statistical tests for randomness. 320 million consecutive bits are collected as 320 bitstreams with a data acquisition card. The collected data passed all the randomness requirements of the NIST SP800-22 test suite. Comparisons with wake-up and shut-down methods are performed to show the improvement provided by the proposed new method. Up to 44% improvement in the area, up to 41% improvement in resource usage, and 100% improvement in throughput are obtained. All TRNG designs are implemented in Xilinx Virtex V xc5vlx50t FPGA.
A Novel Approach for Extraction of Knowledge in Data Analysis Using Meta Heuristic Models

Now days, enormous amount of the data are generated and collected from different domains and analyze of all these data manually is tedious task. Therefore, Knowledge Discovery in Databases (KDD) process have to used to find potentially, valid, novel, and explicable patterns in data. Lately, delicate registering turned out to be increasingly more alluring for the specialists, who work in the related research field of data mining. The main objective of this paper is about how to use soft computing model to pull out knowledge from database. The created knowledge base will predict /inference the data for the new data element of same data set. The concepts of data mining pre processing techniques have applied on the data to make the data in suitable form for knowledge extraction. Thereafter, statistical analysis and soft computing techniques have been applied on the information to pick the ideal model for the data set. The choice of the optimal model decided based on the residual analysis and average error of the model. The preferable model has been used to pull out the knowledge from the data mining later on. The objective of the paper is to develop an integrated system to extracting exact information (knowledge) from the data. The quality of extracted information based on the particular model has also been reviewed. Here Iris flowers data have been used for getting knowledge related to quality of Iris flowers based on certain primary information.
ActivityExplorer: A semi-supervised approach to discover unknown activity classes in HAR systems

A semi-supervised activity recognition system is here proposed to deal with partially labeled video-sequences, where the uncertainty in the data comes from two different factors: only a subset of the data has a class label assigned and only part of the activity classes are known. In particular, the paper presents ActivityExplorer, an approach able to identify clusters of similar activity patterns within the dataset and to identify those clusters that might correspond to new activity classes, still unknown to the recognition system. These capabilities are realized thanks to a combination of metric learning, used to determine a suitable subspace for pattern classification, an advanced clustering technique and ad hoc indicators defined to estimate the membership of each pattern to known classes and possibly identify new activities.
A Multi-level Semantic Fusion Approach for News Reprint Pattern Detection

News reprint analysis is gradually becoming a hot research topic. Most existing studies in news reprint analysis mainly focus on mining news reprint relations, there is little study exploring the detection of the patterns of news reprint yet. To fill this gap, we aim to identify news reprint patterns such as word variant, sentence conversion, and topic restatement, which can provide deep insights into the news propagation mechanism. The challenge of this question lies in how to dig domain information and obtain the deep semantic representation of news to discover the writing styles of various reprint patterns. This paper proposes a media domain information-driven multi-level semantic fusion (MDID-MLSF) approach. It simultaneously considers news media information and extracts word-sentence-paragraph-level news semantic information by the interactive matching mechanism. Specifically, Word Mover's Distance (WMD) algorithm and Multilayer Perceptron (MLP) are employed to obtain semantic information at the word level and sentence level. Then, the model utilizes attention-based hierarchical Bi-LSTM to obtain the paragraph level semantic information. Finally, news media information and different levels' semantic information are jointly modeled to detect the patterns of news reprint. We empirically evaluate the performance of the proposed model on a real-world dataset, the experimental results demonstrate the efficacy of the model.
VisDG: Towards Spontaneous Visual Dialogue Generation

Current vision and language understanding tasks can be generally divided into two categories: image (video) description and visual question answering. Both of them aim to train a model that either straightforwardly generates a description or answers the predefined questions based on an image or a sequence of images from a spectators perspective. However, a large proportion of real-world human interactions also involve spontaneous dialogue exchanges among multiple speakers as well as dynamic visual-textual context, which requires an AI agent to hold a natural and open-ended dialog with humans in a first-person manner based on both visual and textual context. To move closer towards achieving such a spontaneous multimodal conversation, we introduce a new visual dialogue generation dataset (VisDG) based on keyframes and corresponding subtitles extracted from Friends - an American sitcom television series. Specifically, given a start frame and its corresponding dialogue text, the agent has to generate both a meaningful textual response as well as a correct image candidate for the latter part of the dialogue turn. Furthermore, we also propose an end-to-end image-text synergistic network (ITSN) for the task, which outperforms several sophisticated baselines on the proposed VisDG.
Hybrid Collaborative Fusion Based Product Recommendation Exploiting Sentiments from Implicit and Explicit Reviews

Product recommendation is an important functionality in online ecommerce systems. The goal of the recommendation system is to recommend products with has higher purchase success ratio. User profile, product purchase history etc. have been used in many works to provide high quality recommendations. Product reviews is one of the important source for personalized recommendation. Typical collaborative recommendation systems are built upon user rating on products. But in many cases, these rating information are inaccurate or not available. There is also a problem of biased reviews decreasing the accuracy of recommendation systems. This work proposes a aspect mining collaborative fusion based recommendation system considering both the implicit and explicit reviews. The sentiments about different aspects mined from reviews are translated to multi-dimensional ratings. These ratings are then fused with user profile and demographic attributes to improve the quality of recommendation. The proposed recommendation system has 3.79% lower RMSE, 4.51% lower MAE and 22% lower MRE compared to most recent collaborative filtering based recommendation system.
Compensation of the position errors of optical elements by adapting their additively manufactured mounting structure

Additive Manufacturing (AM) is often not considered as a manufacturing technique for mounting structures, because the assembly of optical systems usually requires small manufacturing tolerances, that are currently hard to achieve. But in comparison to conventional manufacturing techniques, AM has the advantage of being able to easily individualize each manufactured structure. Hence, mounting structures can be designed and adapted such, that the measured inner decentrations of conventionally manufactured optical elements to be mounted can be compensated. With this, the error budget contributions from optical elements can be minimized, either to relax their or their mounting structure tolerances or to reduce the overall system tolerance. To prove this concept, we designed an optical system to quantify small displacements of optical elements within a simple to replace additive manufactured mounting structure. From analysing the recorded intensity distribution, one can accurately quantify the lateral decentration of one of the optical elements in respect to the other one. The first experimental results show, that it is feasible to control and adjust the decentration of an optical element mounted in an additively manufactured structure.