Reviewerly/exHarmony huggingface dataset:
Implementation of Artificial Neural network into the copper and cobalt leaching process

In this paper, the leaching laboratory experiment is described using the artificial neural network (ANN) to predict the copper and cobalt recovery. The ANN Multi-layer, feedforward, and back-propagation learning algorithm is trained to optimize the leaching process parameters such as acid concentration, leaching time, temperature, pulp density, and sodium metabisulfite. These parameters are responsible for the high recovery of cobalt by reducing sulphuric acid leaching process. The ANN algorithm was built with two neurons as output layers corresponding to copper and cobalt leaching recovery, 15 hidden layers, and 5 input variables defining the leaching parameters. The optimized trained neural network depicts the testing and training step with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> about 0.8429 and 1, respectively, and corresponding to 94.98 % of copper recovery and 98.43 % or cobalt recovery.
Simple Baseline Machine Learning Text Classifiers for Small Datasets

Abstract Text classification is important to better understand online media. A major problem for creating accurate text classifiers using machine learning is small training sets due to the cost of annotating them. On this basis, we investigated how SVM and NBSVM text classifiers should be designed to achieve high accuracy and how the training sets should be sized to efficiently use annotation labor. We used a four-way repeated-measures full-factorial design of 32 design factor combinations. For each design factor combination 22 training set sizes were examined. These training sets were subsets of seven public text datasets. We study the statistical variance of accuracy estimates by randomly drawing new training sets, resulting in accuracy estimates for 98,560 different experimental runs. Our major contribution is a set of empirically evaluated guidelines for creating online media text classifiers using small training sets. We recommend uni- and bi-gram features as text representation, btc term weighting and a linear-kernel NBSVM. Our results suggest that high classification accuracy can be achieved using a manually annotated dataset of only 300 examples.
A Comparative Study of Spark on the bare metal and Kubernetes

Kubernetes makes it easier to automate deployment and scale containerized applications to achieve near-native performance in cloud environment. However, there still lacks a systematic comparison study on how Spark applications perform between on the bare metal and on Kubernetes. In this paper, we focus on the performance evaluation of these applications running on the two environments by a series of experiments. Based on these experiments, we locate what stages cause their performance gap and reveal out root causes to the gap by analysing work-flows of these Spark applications and their resource costs. Through extensive measurements, we find out that Spark on the bare metal almost always contribute to better performance when compared with Spark on Kubernetes. More CPU usage of executors and better data locality on the bare metal are the root causes to the gap. By contrast, Spark on Kubernetes also has some its advantages over Spark on the bare metal in terms of disk W-IOPs. The research work in this paper can help practitioners and researchers to make more informed decisions on tuning their cloud environment and configuring the big data applications, so as to achieve better performance and higher resources utilization.
High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis

Summary In microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. We introduce a surprisingly simple, interpretable and efficient method for the estimation of compositional data regression through the lens of a novel high-dimensional log-error-in-variable regression model. The proposed method provides corrections on sequencing data with possible overdispersion and simultaneously avoids any subjective imputation of zero read counts. We provide theoretical justifications with matching upper and lower bounds for the estimation error. The merit of the procedure is illustrated through real data analysis and simulation studies.
Semantic role labeling for knowledge graph extraction from text

Abstract This paper introduces , a new semantic role labeling method that transforms a text into a frame-oriented knowledge graph. It performs dependency parsing, identifies the words that evoke lexical frames, locates the roles and fillers for each frame, runs coercion techniques, and formalizes the results as a knowledge graph. This formal representation complies with the frame semantics used in Framester, a factual-linguistic linked data resource. We tested our method on the WSJ section of the Peen Treebank annotated with VerbNet and PropBank labels and on the Brown corpus. The evaluation has been performed according to the CoNLL Shared Task on Joint Parsing of Syntactic and Semantic Dependencies. The obtained precision, recall, and F1 values indicate that TakeFive is competitive with other existing methods such as SEMAFOR, Pikes, PathLSTM, and FRED. We finally discuss how to combine TakeFive and FRED, obtaining higher values of precision, recall, and F1 measure.
Binary Precision Neural Network Manycore Accelerator

This article presents a low-power, programmable, domain-specific manycore accelerator, Binarized neural Network Manycore Accelerator (BiNMAC), which adopts and efficiently executes binary precision weight/activation neural network models. Such networks have compact models in which weights are constrained to only 1 bit and can be packed several in one memory entry that minimizes memory footprint to its finest. Packing weights also facilitates executing single instruction, multiple data with simple circuitry that allows maximizing performance and efficiency. The proposed BiNMAC has light-weight cores that support domain-specific instructions, and a router-based memory access architecture that helps with efficient implementation of layers in binary precision weight/activation neural networks of proper size. With only 3.73% and 1.98% area and average power overhead, respectively, novel instructions such as Combined Population-Count-XNOR , Patch-Select , and Bit-based Accumulation are added to the instruction set architecture of the BiNMAC, each of which replaces execution cycles of frequently used functions with 1 clock cycle that otherwise would have taken 54, 4, and 3 clock cycles, respectively. Additionally, customized logic is added to every core to transpose 16×16-bit blocks of memory on a bit-level basis, that expedites reshaping intermediate data to be well-aligned for bitwise operations. A 64-cluster architecture of the BiNMAC is fully placed and routed in 65-nm TSMC CMOS technology, where a single cluster occupies an area of 0.53 mm 2 with an average power of 232 mW at 1-GHz clock frequency and 1.1 V. The 64-cluster architecture takes 36.5 mm 2 area and, if fully exploited, consumes a total power of 16.4 W and can perform 1,360 Giga Operations Per Second (GOPS) while providing full programmability. To demonstrate its scalability, four binarized case studies including ResNet-20 and LeNet-5 for high-performance image classification, as well as a ConvNet and a multilayer perceptron for low-power physiological applications were implemented on BiNMAC. The implementation results indicate that the population-count instruction alone can expedite the performance by approximately 5×. When other new instructions are added to a RISC machine with existing population-count instruction, the performance is increased by 58% on average. To compare the performance of the BiNMAC with other commercial-off-the-shelf platforms, the case studies with their double-precision floating-point models are also implemented on the NVIDIA Jetson TX2 SoC (CPU+GPU). The results indicate that, within a margin of ∼2.1%--9.5% accuracy loss, BiNMAC on average outperforms the TX2 GPU by approximately 1.9× (or 7.5× with fabrication technology scaled) in energy consumption for image classification applications. On low power settings and within a margin of ∼3.7%--5.5% accuracy loss compared to ARM Cortex-A57 CPU implementation, BiNMAC is roughly ∼9.7×--17.2× (or 38.8×--68.8× with fabrication technology scaled) more energy efficient for physiological applications while meeting the application deadline.
Personality Traits Identification Through Handwriting Analysis

Personality traits are of paramount importance in identifying the human's behavior. They represent a useful information source for forensic control, recruitment profiling, medical symptoms, and other applications. Personality traits are identified through various physical aspects, including sense, honesty, and other emotions. These aspects can be revealed through handwritten features. Since handwriting is unique for everyone, its identification process is not as straight forward as it appears; rather it involves efficient tools for extraction and classification of features. The process has been the subject of various research works. However, results reported remain unsatisfactory due to mainly dissimilarities in handwriting. In this paper, we present an approach of recognition of personality traits based on textural features extracted from handwritten samples. Experiments are carried out using artificial neural networks and the TxPI-u database. Results deliver a significant recognition rate which endorses its effectiveness against similar works.
CLEF eHealth Evaluation Lab 2021

Motivated by the ever increasing difficulties faced by laypeople in retrieving and digesting valid and relevant information to make health-centred decisions, the CLEF eHealth lab series has offered shared tasks to the community in the fields of Information Extraction (IE), management, and Information Retrieval (IR) since 2013. These tasks have attracted large participation and led to statistically significant improvements in processing quality. In 2021, CLEF eHealth is calling for participants to contribute to the following two tasks: Task 1 on IE focuses on IE from noisy text. Participants will identify and classify Named Entities in written ultrasonography reports, containing misspellings and inconsistencies, from a major public hospital in Argentina. Identified entities will then have to be classified, which can be very challenging as it requires to handle lexical variations. Task 2 is a novel extension of the most popular and established task on consumer health search (CHS), aiming at retrieving relevant, understandable, and credible information for patients and their next-of-kins. In this paper we describe recent advances in the fields of IE and IR, and the subsequent offerings of this years CLEF eHealth lab challenges.
Resume Screening Using Natural Language Processing and Machine Learning: A Systematic Review

Curriculum vitae or resume screening is a time-consuming procedure. Natural language processing and machine learning have the capability to understand and parse the unstructured written language, and extract the desired information. The idea is to train the machine to analyze the written documents like a human being. This paper presents a systematic review on resume screening and enlightens the comparison of recognized works. Several techniques and approaches of machine learning for evaluating and analyzing the unstructured data have been discussed. Existing resume parsers use semantic search to understand the context of the language in order to find the reliable and comprehensive results. A review on the use of semantic search for context-based searching has been explained. In addition, this paper also shows the research challenges and future scope of resume parsing in terms of writing style, word choice and syntax of unstructured written language.
On Predicting Behavioral Deterioration in Online Discussion Forums

Early detection of behavioral deterioration can be of great importance in preventing individuals' misbehavior from escalating in severity. This paper addresses the problem of behavioral deterioration in the context of online discussion forums. We propose a novel method that builds behavioral sequences from temporal information to gain a better understanding of behaviors exhibited by forum members, and then explores n-gram features to predict behavioral deterioration from consecutive combinations of sequential patterns corresponding to misbehavior. We conduct extensive experiments using real-world datasets and demonstrate the ability of our method to predict behavioral deterioration with a high degree of accuracy, as evaluated by F-1 scores. Our quantitative analysis of the model's performance yields F-1 scores of over 0.7. Specifically, we find that the best-performing model is linear SVM, with an average F-1 score of 0.74. Some future research avenues are proposed.
Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.
Reliable Camera Model Identification Using Sparse Gaussian Processes

Identifying the model of a camera that has captured an image can be an important task in criminal investigations. Many methods assume that the image under analysis originates from a given set of known camera models. In practice, however, a photo can come from an unknown camera model, or its appearance could have been altered by unknown post-processing. In such a case, forensic detectors are prone to fail silently. One way to mitigate silent failures is to use a rejection mechanism for unknown examples. In this work, we propose Gaussian processes (GPs), which intrinsically provide such a rejection mechanism. This makes GPs a potentially powerful tool in multimedia forensics, where forensic analysts regularly work on images from unknown origins. We demonstrate that GPs scale well to the task of camera model identification. Probabilistic predictions from a GP classifier achieve high classification accuracy for known camera models while providing reliable uncertainty estimates. The built-in uncertainty estimates effectively tackle open-set camera model identification, outperforming two state-of-the-art methods.
Personalized Activity Recognition Using Partially Available Target Data

Recent years have witnessed a growing body of research on autonomous activity recognition models for use in deployment of mobile systems in new settings such as when a wearable system is adopted by a new user. Current research, however, lacks comprehensive frameworks for transfer learning. Specifically, it lacks the ability to deal with partially available data in new settings. To address these limitations, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OptiMapper</i> , a novel uninformed cross-subject transfer learning framework for activity recognition. OptiMapper is a combinatorial optimization framework that extracts abstract knowledge across subjects and utilizes this knowledge for developing a personalized and accurate activity recognition model in new subjects. To this end, a novel community-detection-based clustering of unlabeled data is proposed that uses the target user data to construct a network of unannotated sensor observations. The clusters of these target observations are then mapped onto the source clusters using a complete bipartite graph model. In the next step, the mapped labels are conditionally fused with the prediction of a base learner to create a personalized and labeled training dataset for the target user. We present two instantiations of OptiMapper. The first instantiation, which is applicable for transfer learning across domains with identical activity labels, performs a one-to-one bipartite mapping between clusters of the source and target users. The second instantiation performs optimal many-to-one mapping between the source clusters and those of the target. The many-to-one mapping allows us to find an optimal mapping even when the target dataset does not contain sufficient instances of all activity classes. We show that this type of cross-domain mapping can be formulated as a transportation problem and solved optimally. We evaluate our transfer learning techniques on several activity recognition datasets. Our results show that the proposed community detection approach can achieve, on average, 69 percent utilization of the datasets for clustering with an overall clustering accuracy of 87.5 percent. Our results also suggest that the proposed transfer learning algorithms can achieve up to 22.5 percent improvement in the activity recognition accuracy, compared to the state-of-the-art techniques. The experimental results also demonstrate high and sustained performance even in presence of partial data.
Designing Explainable Text Classification Pipelines: Insights from IT Ticket Complexity Prediction Case Study

Nowadays, enterprises need to handle a continually growing amount of text data generated internally by their employees and externally by current or potential customers. Accordingly, the attention of managers shifts to an efficient usage of this data to address related business challenges. However, it is usually hard to extract the meaning out of unstructured text data in an automatic way. There are multiple discussions and no general opinion in the research and practitioners’ community on the design of text classificationText classification tasks, specifically the choice of text representation techniques and classification algorithms. One essential point in this discussion is about building solutions that are both accurate and understandable for humans. Being able to evaluate the classification decision is a critical success factor of a text classificationText classification task in an enterprise setting, be it legal documents, medical records, or IT ticketsIT tickets. Hence, our study aims to investigate the core design elements of a typical text classificationText classification pipeline and their contribution to the overall performance of the system. In particular, we consider text representation techniques and classification algorithms, in the context of their explainabilityExplainability, providing ultimate insights from our IT ticket complexity prediction case study. We compare the performance of a highly explainable text representation technique based on the case study tailored linguistic features with a common TF-IDFTF-IDF approach. We apply interpretableMachine learning machine learningInterpretable machine learning algorithms such as kNN, its enhanced versions, decision trees, naïve Bayes, logistic regression, as well as semi-supervised techniques to predict the ticket class label of low, medium, or high complexity. As our study shows, simple, explainable algorithms, such as decision trees and naïve Bayes, demonstrate remarkable performance results when applied with our linguistic features-based text representation. Furthermore, we note that text classificationText classification is inherently related to Granular ComputingGranular computing.
Driver Activity Recognition Through Deep Learning

Distracted drivers contribute to a significant proportion of road accidents all over the world. Activities such as texting on cellphones, eating, and reaching for something at the back of the vehicle lead to drivers not paying attention to the road and may result in traffic accidents. This paper proposes the use of residual neural networks (ResNet) with spatio-temporal three-dimensional (3D) kernels to perform distracted driver behaviour recognition. Recently, convolutional neural networks (CNN) with 3D kernels have become an effective tool for action recognition. The 3D kernels extract spatio-temporal features from videos to perform tasks such as human activity recognition. ResNets are a variant of CNNs that utilise skip-connections to realise the training of very deep networks. The large number of parameters in 3D ResNets exposes the possibility of overfitting. Using a large video dataset is thus essential, as it avoids the occurrence of overfitting. This paper examines how different datasets and network depths influence the performance of 3D ResNets. The results are overwhelmingly positive. The findings present a significant positive correlation between the accuracy of a model and the network depth. Furthermore, the quality of the dataset greatly determines the model's ability to generalise effectively.
Electronic Wallet Technology and the Enabling Environment of Smallholder Farmers in Nigeria

Purpose – The purpose of this paper is to critically examine the impact of growth enhancement support scheme (GESS) on the enabling environment of smallholder farmers in sub-Saharan Africa. Its special focus is to investigate the GESS impact on access to rural farm credit and transport cost of smallholder farmers in the agricultural transformation agenda (ATA) in Nigeria.Design/methodology/approach – This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional that describes and interprets what exist at present. A total of one thousand, two hundred farmers were sampled across the six geopolitical zones of Nigeria.Findings – Results from the use of a double-hurdle model indicate that the GESS has a significant impact on farmers’ access to credit, but does not significantly affect rural farm transport cost, which subsequently influence the price of food in the country.Practical implication – This implies that if the federal government of Nigeria is to work towards an ideal agricultural transformation agenda, transport networks should be closely aligned with the GESS priorities to provide connectivity to rural areas that provide most of the country’s agricultural output.Originality/value – This research adds to the literature on agricultural and rural development debate in developing countries. It concludes that embracing rural finance and transportation infrastructure should form the foundation of the ATA in Nigeria, which in turn would provide the enabling environment for more widespread rural economy in sub-Saharan Africa.