Reviewerly/exHarmony huggingface dataset:
Applying a Probabilistic Network Method to Solve Business-Related Few-Shot Classification Problems

It can be challenging to learn algorithms due to the research of business-related few-shot classification problems. Therefore, in this paper, we evaluate the classification of few-shot learning in the commercial field. To accurately identify the categories of few-shot learning problems, we proposed a probabilistic network (PN) method based on few-shot and one-shot learning problems. The enhancement of the original data was followed by the subsequent development of the PN method based on feature extraction, category comparison, and loss function analysis. The effectiveness of the method was validated using two examples (absenteeism at work and Las Vegas Strip hotels). Experimental results demonstrate the ability of the PN method to effectively identify the categories of commercial few-shot learning problems. Therefore, the proposed method can be applied to business-related few-shot classification problems.
Towards a semantic model for IoT-based seismic event detection and classification

In the seismic domain, collecting seismic signal and alerting movements of earth crust is crucial for monitoring and forecasting seismic activities. At the same time, with the advent of the Internet of Things (IoT) paradigm, the device interoperability is the minimum requirement for communication among any available sensing device. Semantic web technologies promote this interoperability, by enhancing the quality of data that become ontology-annotated. The paper introduces an ontology model for describing the seismic domain, through the data collection from sensors, to gather seismic signals aimed at the seismic event recognition. The ontology has been built on the well-known SOSA and SSN ontologies, modeled to describe systems of sensors, actuators, and observations. The ontology, namely VEO (Volcano Event Ontology), has been modeled on actual data sensors, collected by a monitoring network at Mt. Vesuvius (Naples, Italy). Along with the ontology model of the seismic domain, a machine learning-based classification has been accomplished to identify seismic events (underwater explosions, quarry blasts, and thunders). A VEO-driven knowledge-base collects raw seismic data and detects events, accessible by SPARQL queries.
Head Gesture Recognition based on 6DOF Inertial sensor using Artificial Neural Network

This paper proposed and investigated the head gesture recognition idea based on a single inertial sensor and deep learning approach. During the experiments presented in this paper, the main aim was to evaluate and determine the best deep learning models that can accurately recognize the head movements. The developed system can identify eight head gestures activities. The data collected in this work was done using a 6DOF inertial sensor placed on a headphone pair from 9 volunteers with ages between 20 and 40 years old. The results show that the best-proposed model's classification accuracy did reach a value equal to 93.64 % using raw data. In this experiment, each head gesture command's accuracy was in the range of 71.11 % to 100 %.
Text classification by untrained sentence embeddings

Recurrent Neural Networks (RNNs) represent a natural paradigm for modeling sequential data like text written in natural language. In fact, RNNs and their variations have long been the architecture of choice in many applications, however in practice they require the use of labored architectures (such as gating mechanisms) and computationally heavy training processes. In this paper we address the question of whether it is possible to generate sentence embeddings via completely untrained recurrent dynamics, on top of which to apply a simple learning algorithm for text classification. This would allow to obtain extremely efficient models in terms of training time. Our work investigates the extent to which this approach can be used, by analyzing the results on different tasks. Finally, we show that, within certain limits, it is possible to build extremely efficient models for text classification that remain competitive in accuracy with reference models in the state-of-the-art.
Image Compression Using Chain Coding for Electronic Shelf Labels (ESL) Systems

One of the most important and recurring tasks in managing a store is to provide accurate, up-to-date price information to customers on the shelves.Manual updating of price tags has been a timeconsuming, error-prone task with high labor costs.An Electronic shelf labels (ESL) system is becoming an attractive alternative for this task because of the dynamic-price-updating and customer's productevaluation-display features.A common ESL system configuration in a retail store includes thousands of battery-powered ESL tags that are mostly connected wirelessly in a dense indoor environment.Raising the success ratio of wireless communication is essential for the system's viability due to its limited battery life.Most of the ESL traffic is the image data of goods that appear on the tags, and reducing the amount of the data is one of the most effective ways to enhance communication performance and reduce retransmission.This paper proposes an ESL image compression mechanism based on chain coding that utilizes ESL images' characteristics.The performance results show that the proposed mechanism could compress the ESL images smaller and decompress faster.
Quantizaiton for Deep Neural Network Training with 8-bit Dynamic Fixed Point

Recent advances in deep neural networks have achieved higher accuracy with more complex models. Nevertheless, they require much longer training time. To reduce the training time, training methods using quantized weight, activation, and gradient have been proposed. Because neural network calculation by integer format improves the energy efficiency of hardware for deep learning models, training methods for deep neural networks with fixed point format have been proposed. However, the narrow representation range of the fixed point degrades the accuracy achieved when using fixed points. In this work, we propose quantization method without accuracy degradation for deep neural networks training. The proposed quantization method can change the fixed point representation range to preserve accuracy by adding bias to the exponent of fixed point. We investigated the effectiveness of proposed quantization method on the ImageNet task using ResNet-50. Using the proposed method, the evaluated model can be trained using 8-bit dynamic fixed point without accuracy degradation.
A Brazilian Sign Language Video Database for Automatic Recognition

Communication is a basic human necessity, but the deaf community is still facing some barriers. In Brazil, there are about 9.7 million people with some degree of hearing impairment but most Brazilians do not know the Brazilian Sign Language (Libras). Wearable gadgets like gloves or bracelets with sensors are a good tool to help in communication, but these solutions are pricey or intrusive. On the other hand, computer vision solutions are cheaper but challenging mainly due to the absence of reliable video databases. In this work, we propose a video database compounded by different signs, with various backgrounds, and different speakers. We also developed a system to perform computer vision-based sign recognition using the k-nearest neighbors (K-NN) and random forest (RF). In our tests, we achieved an average accuracy of 65.81 % in multiclass classification.
Identifying religious extremism-based threats in SriLanka using bilingual social media intelligence

Religion is one’s relation to what he or she regards as holy, sacred, spiritual, or worthy of especial reverence. Religious extremism is the advocacy of extreme measures over a religion whereas religious extremists are even willing to murder as they provide sanctions for violence in the service of God. Sri Lanka has a tragic history of religious and ethnic extremism and the Easter Sunday attack coordinated by a radical Islamic group that killed over 300 and injured another several hundred can be identified as the recent climax of these events. In this modern information age, it is evident that these radical extremist groups utilize social media for spreading their extreme ideologies due to its free and unregulated nature. If there were a mechanism to even slightly identify the possibility of tragic incidents like Easter Sunday bombing, the 300 souls who had to sacrifice their lives for an unreasonable cause would be still alive happily. In this research, we propose a predictive methodology for identifying any upcoming religious extremism-based threats in Sri Lanka using social media intelligence. We aim to specifically address Sri Lanka’s multi-lingual culture by analyzing all the bilingual social media posts in Sinhala and Tamil languages. A hybrid sentiment analysis methodology consisting of a Machine Learning model and a sentiment lexicon was trained on carefully chosen labelled social media text data and each text was classified as either religious-extreme or not, using Naïve Bayes, SVM, and Random Forest algorithms. When comparing their results, we were able to achieve the best results with the Naïve Bayes algorithm resulting in an accuracy of 81% for Sinhala tweets while Random Forest algorithm resulted in an accuracy of 73% for Tamil tweets proving that social media intelligence can be used to predict religious extremism-based threats.
How to build a constructicon in five years

Abstract We provide a practical step-by-step methodology of how to build a full-scale constructicon resource for a natural language, sharing our experience from the nearly completed project of the Russian Constructicon, an open-access searchable database of over 2,200 Russian constructions ( https://site.uit.no/russian-constructicon/ ). The constructions are organized in families, clusters, and networks based on their semantic and syntactic properties, illustrated with corpus examples, and tagged for the CEFR level of language proficiency. The resource is designed for both researchers and L2 learners of Russian and offers the largest electronic database of constructions built for any language. We explain what makes the Russian Constructicon different from other constructicons, report on the major stages of our work, and share the methods used to systematically expand the inventory of constructions. Our objective is to encourage colleagues to build constructicon resources for additional natural languages, thus taking Construction Grammar to a new quantitative and qualitative level, facilitating cross-linguistic comparison.
Parameterized Luenberger-Type<i>H</i><sub>∞</sub>State Estimator for Delayed Static Neural Networks

This article proposes a new Luenberger-type state estimator that has parameterized observer gains dependent on the activation function, to improve the H∞ state estimation performance of the static neural networks with time-varying delay. The nonlinearity of the activation function has a significant impact on stability analysis and robustness/performance. In the proposed state estimator, a parameter-dependent estimator gain is reconstructed by using the properties of the sector nonlinearity of the activation functions that are represented as linear combinations of weighting parameters. In the reformulated form, the constraints of the parameters for the activation function are considered in terms of linear matrix inequalities. Based on the Lyapunov-Krasovskii function and the improved reciprocally convex inequality, enhanced conditions for designing a new state estimator that guarantees H∞ performance are derived through a parameterization technique. The compared results with recent studies demonstrate the superiority and effectiveness of the presented method.
Explaining the Influence of Prior Knowledge on POMCP Policies

Partially Observable Monte Carlo Planning is a recently proposed online planning algorithm which makes use of Monte Carlo Tree Search to solve Partially Observable Monte Carlo Decision Processes. This solver is very successful because of its capability to scale to large uncertain environments, a very important property for current real-world planning problems. In this work we propose three main contributions related to POMCP usage and interpretability. First, we introduce a new planning problem related to mobile robot collision avoidance in paths with uncertain segment difficulties, and we show how POMCP performance in this context can take advantage of prior knowledge about segment difficulty relationships. This problem has direct real-world applications, such as, safety management in industrial environments where human-robot interaction is a crucial issue. Then, we present an experimental analysis about the relationships between prior knowledge provided to the algorithm and performance improvement, showing that in our case study prior knowledge affects two main properties, namely, the distance between the belief and the real state, and the mutual information between segment difficulty and action taken in the segment. This analysis aims to improve POMCP explainability, following the line of recently proposed eXplainable AI and, in particular, eXplainable planning. Finally, we analyze results on a synthetic case study and show how the proposed measures can improve the understanding about internal planning mechanisms.
Diversity Blocks for De-biasing Classification Models

Recent studies have highlighted a major caveat in various high performing automated systems for tasks such as facial analysis (e.g. gender prediction), object classification, and image to caption generation. Several of the existing systems have been shown to yield biased results towards or against a particular subgroup. The biased behavior exhibited by these models when deployed and used in a real world scenario presents with the challenge of automated systems being unfair. In this research, we propose a novel technique, diversity block, for de-biasing existing models without re-training them. The proposed technique requires small amount of training data and can be incorporated with an existing model for addressing the challenge of biased predictions. This is done by adding a diversity block and computing the prediction based on the scores of the original model and the diversity block in order to get a more confident and de-biased prediction. The efficacy of the proposed technique has been demonstrated on the task of gender prediction, along with an auxiliary case study on object classification.
Topic-Guided RNN Model for Vietnamese Text Generation

Text generation is one of the most important tasks in NLP and has been applied in many applications such as machine translation, question answering and text summarization. Most of recent studies on text generation use only the input for output generation. In this research we suggest that topic information of an input document is an important factor for generating the destination text. We will propose a deep neural network model in which we use topic information together with the input text for generating summarized texts. The experiment on Vietnamese news corpus shows that our model outperforms a baseline model at least 23% in BLEU score.
Algorithm Study of New Association Rules and Classification Rules in Data Mining

Abstract In order to further improve the efficiency of data mining, it proposes a kind of data mining algorithm based on new association rules and classification rules. Specific research process is as follows. Firstly, MDML-PP algorithm is analyzed and applied to the multi-dimensional multi-level association rules in data mining, and then selects the test data sets for performance evaluation, meanwhile, it also studies the multi-support rate cut and classification rule mining algorithm, which is applied to multi-support rate classification rules in data mining, finally, using the compare experiment to verify the effectiveness of multi-support rate classification rules. The results show that the proposed algorithm in this paper can be better used in data mining with multi-dimensional and multi-layer association rules, which can improve the efficiency and quality of data mining.
TranstextNet: Transducing Text for Recognizing Unseen Visual Relationships

An important challenge in visual scene understanding is the recognition of interactions between objects in an image. This task - often called visual relationship detection (VRD) - must be solved to enable higher understanding of the semantic content in images. VRD can become particularly hard where there is severe statistical sparsity of some potentially involved objects, and the number of many relationships in standard training sets is limited. In this paper we show how to transduce auxiliary text so as to enable recognition of relationships absent in the visual training data. This transduction is performed by learning a shared relationship representation for both the textual and visual information. The proposed approach is model-agnostic and can be used as a plug-in module in existing VRD and scene graph generation (SGG) recognition systems to improve their performance and extend their capabilities. We consider the application of our technique using three widely accepted SGG models [20], [24], [16], and different auxiliary text sources: image captions, text generated by a deep text generation model (GPT-2), and ebooks from the Gutenberg Project. We conduct an extensive empirical study of both the VRD and SGG tasks over large-scale benchmark datasets. Our method is the first to enable recognition of visual relationships missing in the visual training data and appearing only in the auxiliary text. We conclusively show that text ingestion enables recognition of unseen visual relationships, and moreover, advances the state-of-the-art in all SGG tasks.
South Indian Character Recognition Using Statistical Feature Extraction and Distance Classifier

This paper approached a set of associated south Indian characters using two systems: statistical methodology and a model-based approach. These two strategies have constraints. The principle failure of the measurable methodology is that it relies on single-scale statistical components. The model-based arrangement of techniques is that the codebook is tending to worsen much time. The endeavor is made to enhance programmed essayist recognizable by limitations and the constraints of both procedures. For the statistical way to deal with the multi-scale statistical components and discover one of them to build up the evidence of character to the execution of single scale elements. For the model-based methodology the utilization of Kohen maps for codebook era is preventable, also, that a haphazardly produced codebook is more efficient. Exploration in programmed character recognition mainly focuses on statistical methodology. This has figured out the detailed and occurrence of extricating statistical elements, for example, run-length appropriations, incline dissemination, entropy, and edge-pivot dispersion. All statistical-features are accomplished by the distribution-features. Edge-pivot dissemination is a component that depicts variations in the method for a character's stroke in composition. The edge-pivot transport is to reason by technique for a window that falls logically into a predetermined state over an edge-recognized parallel handwritten text. The two edge sections developing from this pixel are reflected, when the pixel of the window is on. These points are ascertained and written assets. A joint likelihood scattering is picked up since from a huge specimen of such matches.
Application of NNARX in Modeling a Solar Radiation Prediction

Solar energy defined as a radiant energy which emitted by the sun. Recent trend shows that solar energy had been an alternative power source to generate electricity using photovoltaic system. Solar radiation is the key measurement for this potential solar energy source but with the high cost to build and maintained the infrastructure had embark a new chapter with the implementation of prediction model. Many prediction models had been developed using various method including machine learning method and Artificial Neural Network (ANN) approach. Solar radiation prediction involves various non-linear parameter. That is why, a non-linear ANN method had been used. Non-linear Neural Network Autoregressive Model with Exogenous Input (NNARX) is a dynamic ANN method and had been widely applies to solve a non-linear dynamic time series prediction model. This paper will develop a NNARX to performed solar radiation prediction using both meteorological and measured data parameter for Malaysia. Performance analysis will be carried out using Mean Square Error (MSE) calculation based on actual and predicted data gain from the approach. Based on the result, it is shown that NNARX had given a significant prediction values on solar radiation with lowest MSE value of 0.0116.
WITHDRAWN: Advanced patterns of predictions and cavernous data analytics using quantum machine learning

Machine Learning is one of the prominent areas of research in knowledge discovery and in identification of hidden patterns from the datasets. Machine Learning focuses on the training of intelligent models so that the prediction can be accurate and fast. Machine Learning learns from the data and then gets trained for prediction. For example, the preferences of the customers are fetched from the existing records based on interests of similar customers. Quantum Machine Learning is a scientific area in which quantum computation and machine learning interplay concepts are explored. For instance, we may want to figure out if quantum computers can speed up the time it takes for a machine learning model to train or test. In the other hand, to help us discover quantum error correction codes, approximate the properties of quantum systems, or develop new quantum algorithms, we will use techniques from machine learning. This manuscript is focusing on the advanced segments of knowledge discovery using Quantum Computing and associated Quantum Machine Learning with real-world scenarios.
PV-Pix: Slum Community Co-design of Self-Powered Deformable Smart Messaging Materials

Working with emergent users in two of Mumbai's slums, we explored the value and uses of photovoltaic (PV) self-powering digital materials. Through a series of co-design workshops, a diary study and responses by artists and craftspeople, we developed the PV-Pix concept for inter-home connections. Each PV-Pix element consists of a deformable energy harvesting material that, when actuated by a person in one home, changes its physical state both there and in a connected home. To explore the concept we considered two forms of PV-Pix: one uses rigid materials and the other flexible ones. We deployed two low-fidelity prototypes, each constructed of a grid of one PV-Pix type, in four slum homes over a four week period to further understand the usability and uses of the materials, eliciting interesting inter-family communication practices. Encouraged by these results we report on a first-step towards working prototypes and demonstrate the technical viability of the approach.
Benchmarking the Natural Gradient in Policy Gradient Methods and Evolution Strategies

Reinforcement learning offers various gradient-based algorithms to tackle model-free optimization problems. However, a major drawback of such techniques is premature convergence due to a lack of exploration. This paper reviews several Evolution Strategies and policy gradient algorithms using the natural gradient in order to reach better convergence. By incorporating knowledge of second-order derivatives, the natural gradient prevents premature convergence. We will present benchmarks on the Trust Region Policy Optimization (TRPO) as a natural policy gradient algorithm and on Separable Natural Evolution Strategies (sNES) and compare results and implementations of these two algorithms. Our experience has shown that sNES performs better in most benchmarks while being easier to handle due to having less hyperparameters.