Reviewerly/exHarmony huggingface dataset:
Correlation filter via random-projection based CNNs features combination for visual tracking

Object tracking based on the Convolutional Neural Networks (CNNs) with multiple feature correlation filter (CF) has become one of the best object tracking frameworks. In this paper, we propose a novel approach of CNNs based CF, which combines deep features from CNNs into low-dimensional features. To achieve the dimensionality reduction, random-projection is used due to its data-independence and superior computational efficiency over other widely used. In our proposed approach, the spectral graph theory is applied to generate a random projection matrix. This method bypasses the time-consuming Gram–Schmidt orthogonalization, where the dimension of the feature is high. The combined features have very low dimensions, less than one tenth of the dimensions of the original deep features from CNNs, offering an improvement of tracking speed and without loss of performance simultaneously. Extensive experiments are conducted on large-scale benchmark datasets. The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods.
Real-World Person Re-Identification via Super-Resolution and Semi-Supervised Methods

Person re-identification has made great progress over the years. However, due to the problem of super-resolution and few labeled samples, it is difficult to apply in practice. In this paper, we propose a semi-supervised super-resolution person re-identification method based on soft multi-labels. Firstly, a Mixed-Space Super-Resolution model (MSSR) is constructed based on Generative Adversarial Networks (GAN), which aims to convert low-resolution person images into high-resolution images. Secondly, a Part-based Graph Convolutional Network (PGCN) is proposed to extract discriminative feature by exploring the relationship of local features within person. Finally, to solve the problem of label limitation, we use the PGCN trained with a small amount of labeled samples to predict the soft multi-labels of unlabeled samples, and further train PGCN with unlabeled samples based on a novel multi-label similarity loss. Experiments have been conducted on the Market1501, CUHK03, and MSMT17 datasets to evaluate this method, which show that it outperforms other semi-supervised methods.
Human Activity Recognition Using Smartphone Sensor Based on Selective Classifiers

HAR, elaborated as Human Activity Recognition, is a motile procedure of recognizing different physical activities performed by the human being in different environments. Walking, jogging, running, stair-up, stair-down, etc. are some of the examples of such kinds of actions. Our primary goal is to reckon different actions performed by a human subject. Although several techniques exist to identify different activities, we use Smartphone accelerometers because of the availability and ease of use and at the same time state-of-the-art technology. Here, we are approaches to using machine learning and deep learning techniques on the publicly available datasets. Random forest, Support vector machine, and CNN have been used to analyze and compare the performance. Although there is a lot of work to be done so far, we want to show that deep learning has better results than machine learning. A comparative study is done on these three classifiers using different accuracy measurements like accuracy rate, performance, and so on. Compared to all these three classifiers, experiment results show the CNN model can identify human activity with a good accuracy rate of 99% and with high performance.
Using Machine Learning in Admissions

Diverse classrooms are linked to enhanced intellectual engagement and understanding of different perspectives. College admissions decisions have traditionally relied heavily on academic characteristics like GPA and standardized testing. Universities started to adopt holistic strategies while attempting to increase diversity. Yet, increasing subjective assessment may increase risk of human bias. Machine Learning (ML) could assist in admitting a more diverse student body, but algorithmic bias could be introduced. Our goal is to develop software tools to minimize human bias in admissions while actively eliminating algorithmic bias. We will examine past admissions data and identify risks of use of possible privileged values, which have historically put certain groups at a disadvantage. This tool may help reduce bias risk and select a more diverse and academically prepared group for admission.
The Importance of Discretization Methods in Machine Learning Applications: A Case Study of Predicting ICU Mortality

Data preprocessing represents one of the most crucial stages in data analysis. While developing novel machine learning techniques has been the main focus of research in the data science field, less attention has been given to data preprocessing. This paper examines the importance of discretization as a preprocessing step and helps achieve better classification performance compared to using continuous attributes. We examine the performance of multiple parametric and non-parametric discretization methods in conjunction with several machine learning classifiers for the problem of predicting the Intensive Care Unit (ICU) mortality. Our results demonstrate the significance of discretizing the input attributes in this problem. Using discretized data achieved a classification accuracy and F1 score of 89.19% and 0.38, respectively, while using continuous attributes achieved a classification accuracy F1 score of 86.19% and 0.08, respectively. These results demonstrate that discretizing continuous attributes prior to applying machine learning models could significantly enhance performance enhancement.
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning

In this paper, we identify a new phenomenon called activation-divergence which occurs in Federated Learning (FL) due to data heterogeneity (i.e., data being non-IID) across multiple users. Specifically, we argue that the activation vectors in FL can diverge, even if subsets of users share a few common classes with data residing on different devices. To address the activation-divergence issue, we introduce a prior based on the principle of maximum entropy; this prior assumes minimal information about the per-device activation vectors and aims at making the activation vectors of same classes as similar as possible across multiple devices. Our results show that, for both IID and non-IID settings, our proposed approach results in better accuracy (due to the significantly more similar activation vectors across multiple devices), and is more communication-efficient than state-of-the-art approaches in FL. Finally, we illustrate the effectiveness of our approach on a few common benchmarks and two large medical datasets (The code is available at https://github.com/weichennone/FedMAX).
A Formal Model for Emulating the Generation of Human Knowledge in Semantic Memory

Abstract The transfer of information processed by human beings from their short-term memory (STM) to their semantic memory creates two kinds of knowledge: a semantic network of associations and a structured set of rules to govern human deliberate behaviour under explicit attention. This paper focuses on the memory processes that create the first of these two kinds of knowledge. Human memory storage and processing are modeled using the Real-time Maude rewrite language. Maude’s capability of specifying complex data structures as many sorted algebras and the time features of Real-Time Maude are exploited for (1) providing a means for formalising alternative memory models, (2) modelling in silico experiments to compare and validate such models. We aim at using our model for the comparison of alternative cognitive hypothesis and theories and the analysis of interactive systems.
A Legal Question Answering Ontology-Based System

Question-answering systems (QASs) aim to provide a relevant and concise answer to questions asked in natural language by a user. In this article, we describe our method of developing a question-answering system, operating in the legal domain in Morocco, which mostly uses the French and Arabic languages, and sometimes English. Its purpose is to give relevant and concise answers to questions in the legal domain, stated in natural language by a user, without him having to go through the legal documents to find an answer to his question. The implementation of the proposed system is based on three processes: the first process consists of modeling the legal domain knowledge by an ontology, both (i) independent of the language, and (ii) capable of supporting several languages. The second process consists of extracting the RDF triplet components from the user's question. The third process consists of reformulating the question by a SPARQL query(s) with which we can query the ontology and thus retrieve the appropriate answer to the question asked by the user.
Deep Learning Based Smart Garbage Monitoring System

India has witnessed an unprecedented increase in garbage levels in the past 20 years. Massive quantities of waste, particularly solid waste, are generated daily and seldom picked up. Consequently, garbage is being dumped in landfills and water bodies, hence not managed effectively. This mismanagement has detrimental consequences on our environment. Thus, there is a need to develop an efficient system to manage waste. In this paper, an IoT-based, automated smart bin monitoring system is proposed. Moreover, a deep learning model was used to forecast future garbage levels from the data collected. The proposed neural network model was able to predict garbage levels with an accuracy of 80.33%. Results verify the accurate prognosis of garbage levels. Additionally, data were analysed using bar charts. The amalgamation of IoT and Deep learning can bring a revolutionary change in technology and be applied to waste management. Consequently, prediction and examination of garbage levels may help municipal authorities incorporate an efficient garbage management system and reduce the overflow of garbagebins.
Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition

Multi-view human action recognition remains a challenging problem due to large view changes. In this article, we propose a transfer learning-based framework called transferable dictionary learning and view adaptation (TDVA) model for multi-view human action recognition. In the transferable dictionary learning phase, TDVA learns a set of view-specific transferable dictionaries enabling the same actions from different views to share the same sparse representations, which can transfer features of actions from different views to an intermediate domain. In the view adaptation phase, TDVA comprehensively analyzes global, local, and individual characteristics of samples, and jointly learns balanced distribution adaptation, locality preservation, and discrimination preservation, aiming at transferring sparse features of actions of different views from the intermediate domain to a common domain. In other words, TDVA progressively bridges the distribution gap among actions from various views by these two phases. Experimental results on IXMAS, ACT4 2 , and NUCLA action datasets demonstrate that TDVA outperforms state-of-the-art methods.
SpaML: a Bimodal Ensemble Learning Spam Detector based on NLP Techniques

In this paper, we put forward a new tool, called SpaML, for spam detection using a set of supervised and unsupervised classifiers, and two techniques imbued with Natural Language Processing (NLP), namely Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). We first present the NLP techniques used. Then, we present our classifiers and their performance on each of these techniques. Then, we present our overall Ensemble Learning classifier and the strategy we are using to combine them. Finally, we present the interesting results shown by SpaML in terms of accuracy and precision.
Bayesian calibration of computer models based on Takagi–Sugeno fuzzy models

Computer models have been playing a vital role in scientific and engineering problems. However, before using the models, we usually need to calibrate them by utilizing some experimental data from real tests. When calibrating computer models, it is necessary to consider the model structure error/discrepancy and formulate a realistic prior for the model discrepancy function. In this paper, we use a novel approach, i.e., a Takagi–Sugeno (T–S) fuzzy model, to formulate the model discrepancy function, which can be described as a collection of 0-order T–S type fuzzy if-then rules. The premises of the fuzzy rules are determined based on fuzzy c-means (FCM) clustering, while the consequence parameters are estimated simultaneously with the calibration parameters. The advantages of this modeling approach can be summarized as: (1) strong ability to exploit expert knowledge, (2) low computational costs, and (3) easy to interpret. Further, based on the T–S fuzzy model prior for the model discrepancy function, we construct the Bayesian model for the calibration problem and design a sampler which combines Gibbs and Metropolis–Hastings steps, forming the framework of Bayesian calibration method based on Takagi–Sugeno fuzzy model (BC-TS). To our best knowledge, it is the first attempt to bring a fuzzy model into the Bayesian model calibration. The issue of identifiability is also addressed with the help of Fisher information matrix and analysis results show that the proposed BC-TS method has a good identifiability property. Finally, the effectiveness of the proposed BC-TS is verified by a numerical example and a practical example, i.e., the Sandia 2014 verification and validation (V&V) challenge problem.
FUSE: Multi-faceted Set Expansion by Coherent Clustering of Skip-Grams

Set expansion aims to expand a small set of seed entities into a complete set of relevant entities. Most existing approaches assume the input seed set is unambiguous and completely ignore the multi-faceted semantics of seed entities. As a result, given the seed set {"Canon", "Sony", "Nikon"}, previous models return one mixed set of entities that are either Camera Brands or Japanese Companies. In this paper, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and return multiple sets of entities, one for each semantic facet. We propose an unsupervised framework, FUSE, which consists of three major components: (1) facet discovery module: identifies all semantic facets of each seed entity by extracting and clustering its skip-grams, and (2) facet fusion module: discovers shared semantic facets of the entire seed set by an optimization formulation, and (3) entity expansion module: expands each semantic facet by utilizing a masked language model with pre-trained BERT models. Extensive experiments demonstrate that FUSE can accurately identify multiple semantic facets of the seed set and generate quality entities for each facet.
Augmented Deep Learning Architecture to Effectively Segment the Cancerous Regions in Biomedical Images

The segmentation of cancerous region is most crucial task in biomedical image processing. The detection of cancerous tissues in early stage is essential for planning the treatment of cancer. In this manuscript, we proposes a modified deep learning architecture for efficient segmentation of biomedical images. Our proposed network effectively Figure out the boundaries of the cancerous region. The network once trained produced exceedingly good results on other datasets without retraining. The evaluation metrics mIoU and BF score shows an improvement of 2.5% and 3% for brain tumour segmentation when trained network is tested on other dataset. Improvement of 2% in both the metrics was achieved for skin lesion segmentation when trained network was tested on other dataset for 100 epochs of training.
Reduced complexity hard‐ and soft‐input BCH decoding with applications in concatenated codes

Error correction coding for optical communication and storage requires high rate codes that enable high data throughput and low residual errors. Recently, different concatenated coding schemes were proposed that are based on binary BCH codes with low error correcting capabilities. In this work, low-complexity hard- and soft-input decoding methods for such codes are investigated. We propose three concepts to reduce the complexity of the decoder. For the algebraic decoding we demonstrate that Peterson's algorithm can be more efficient than the Berlekamp–Massey algorithm for single, double, and triple error correcting BCH codes. We propose an inversion-less version of Peterson's algorithm and a corresponding decoding architecture. Furthermore, we propose a decoding approach that combines algebraic hard-input decoding with soft-input bit-flipping decoding. An acceptance criterion is utilised to determine the reliability of the estimated codewords. For many received codewords the stopping criterion indicates that the hard-decoding result is sufficiently reliable, and the costly soft-input decoding can be omitted. To reduce the memory size for the soft-values, we propose a bit-flipping decoder that stores only the positions and soft values of a small number of code symbols. This method significantly reduces the memory requirements and has little adverse effect on the decoding performance.
Deep capsule network for recognition and separation of fully overlapping handwritten digits

The recognition and separation of fully overlapping handwritten digits is an effective means to test the recognition ability of the network. It is also the basis of separating overlapping complex handwritten characters. This paper constructs a deep capsule network, FOD_DCNet, for the recognition and separation of fully overlapping handwritten digits. Firstly, we used small sized convolution kernels to extract features, which is conducive to extracting fine-grained features while reducing training parameter; secondly, we expanded the capsules dimension to express the extracted features to avoid the loss and omission of features; thirdly, we proposed "series dual dynamic routing collocation" to optimize the routing classification function. Compared with CapsNet, our FOD_DCNet reduces the number of iterations of each route, and increases the classification efficiency. The classification accuracy of FOD_DCNet can reach 93.53%, which is 5.43% higher than CapsNet and its parameter amount is only 55.61% of CapsNet.
Predicting Software Defects with Explainable Machine Learning

Most software systems must evolve to cope with stakeholders’ requirements and fix existing defects. Hence, software defect prediction represents an area of interest in both academia and the software industry. As a result, predicting software defects can help the development team to maintain substantial levels of software quality. For this reason, machine learning models have increased in popularity for software defect prediction and have demonstrated effectiveness in many scenarios. In this paper, we evaluate a machine learning approach for selecting features to predict software module defects. We use a tree boosting algorithm that receives as input a training set comprising records of software features encoding characteristics of each module and outputs whether the corresponding module is defective prone. For nine projects within the widely known NASA data program, we build prediction models from a set of easy-to-compute module features. We then sample this sizable model space by randomly selecting software features to compose each model. This significant number of models allows us to structure our work along model understandability and predictive accuracy. We argue that explaining model predictions is meaningful to provide information to developers on features related to each module defective-prone. We show that (i) features that contribute most to finding the best models may vary depending on the project, and (ii) effective models are highly understandable based on a survey with 40 developers.
End-to-end secure group communication for the Internet of Things

The remarkable growth of the Internet of Things (IoT) has given rise to application areas where it is crucial to secure the endpoints, to guarantee users privacy and even safety. In this context, secure group communication among low-cost devices is a fundamental and challenging research question. Namely, the limited computational resources of nodes, uncertain periods of connectivity, and potentially large number of connected devices make it impractical to apply established solutions to IoT settings. This paper specifies a security protocol, named Sharelock, that provides end-to-end security and confidentiality of messages exchanged by groups of communicating nodes. The protocol establishes long-term security for nodes communicating through untrusted edge servers and scales to large groups of nodes by using cryptographic primitives that are not only applicable to low-cost devices, but also suitable for post-quantum cryptography. The protocol is designed to support the exchange and storage of generic messages among nodes, thereby making it appropriate for a wide range of IoT applications.
Image Overlays on a video frame Using HOG algorithm

Snap chat is popularly known application where some cool and awesome face filters can be added on our pictures. This paper put forwards a technique for applying face filters on the detected facial region in the in a video frame and apply filters by taking input from user to select the desired filter that could applied on face region. Applying face filters on a captured video frame will be done by tracking and detecting faces using face detection algorithm in dlib based Histograms of Oriented Gradients (HOG) feature-based classifier and Linear Support vector Machine (LSVM) model is a commonly used model for detecting face. Results are obtained using Python code with Open CV and dlib library.
An Artificial Bee Colony and Pigeon Inspired Optimization Hybrid Feature Selection Algorithm for Twitter Sentiment Analysis

Twitter Sentiment Study is a difficult task that comprises the various kind of preprocessing phases, including reduction in dimensionality. The reduction in dimensionality ensures minimum computational complexity and improved performance in the classification course. In Twitter data, each tweet has functionality values that may or may not reflect an individual’s response. As a result, when tweets are signified as feature matrices, many sparse data points are created and possibly overhead and error rates increase in sentiment analysis on Twitter. This paper proposes a novel kind of algorithm as Artificial Bee Colony and Pigeon Inspired Optimization Hybrid Feature Selection Algorithm. The ABC-PIO combines with the characteristics that ABC can produce various samples, PIO can reach the best value rapidly and Cauchy perturbation strategy can improve optimal solution. The proposed technique archive Accuracy of 99.01% for Decision tree, 77.34% for Navy Bias and 60.89% Random Forest. The comparative analysis show that the proposed ABC-PIO with Decision tree archive much better results compared to other existing techniques.
Efficient microwave-to-optical single-photon conversion with a single flying circular Rydberg atom

We propose a scheme for converting a microwave (mw) single photon in a mw cavity to a flying optical photon. The conversion is realized by using a flying circular Rydberg atom, which plays a role of the “data bus” as an excellent memory to connect the mw and optical cavities. To link the energy levels of atom in optical domain and mw domain, we use fast decircularization method and three-photon Raman transition method. Thank to these low loss processes and the super long lifetime of circular Rydberg states, this scheme can efficiently convert single mw photons into the optical domain. Based on existing experiments and data, the conversion efficiency is simulated as 60%. The theoretical limit of the conversion efficiency is about 87%.