Reviewerly/exHarmony huggingface dataset:
Exploring the Structure of IoT Data: A Symbolic Analysis Perspective

With the development of different kinds of techniques, especially the Internet of Things (IoT), a large amount of quantitative (either numeric or categorical) data have been generated, transmitted, and stored in the modern society. People hope to understand the interested phenomenon from the collected quantitative data by utilizing different data analysis methods. Exploring the structure of data (e.g., the cluster centers or prototypes) has always been a hot spot in the domain of data mining and knowledge discovery, yet it seems that the modeling and analyzing process still focus on a low-level abstraction of the data because normally, the structure found is only represented by some numeric data points. In this study, we highlight that a low-level abstraction may not be a user-friendly way for people to grasp the knowledge contained in the data. Instead, we explore the structure of the data from a perspective of symbolic analysis. Specifically, two modes of abstraction are proposed. In the vertical mode (i.e., values of each feature are abstracted), the numeric prototypes are characterized by the symbolic prototypes such that people could get rid of being stuck in minor details of each feature. In the horizontal mode (i.e., values of each prototype are abstracted), the linguistic summarization is used to describe all the features of each symbolic prototype such that people could immediately grasp the essential information conveyed in the symbolic prototype. We conduct comprehensive experimental studies on the publicly available data to illustrate the feasibility and validity of the proposed symbolic analysis process.
A Reconfigurable Spatial Architecture for Energy-Efficient Inception Neural Networks

Convolutional neural networks (CNNs) have been widely utilized in modern artificial intelligent (AI) systems. In particular, GoogLeNet, one of the most popular CNNs, consisting of a number of inception layers and max-pooling layers, has been intensively studied for mobile and embedded scenarios. However, the energy efficiency of GoogLeNet in hardware is still limited as the huge data movement between the processor and the memory. Therefore, designing a dataflow and the corresponding hardware architecture to achieve parallel processing with minimal data movement is rather critical to achieve high energy efficiency and throughput. In this paper, we propose a novel column stationary (CS) dataflow that maximally exploits the local data reuse of both the filter weights and feature maps. Moreover, a reconfigurable spatial architecture was proposed to map multiple convolution kernels (with different types and dimensions) in parallel to the processing engines (PEs) array. In this case, multiple convolution kernels can share the same input feature maps (activations) in computing process. In our hardware design, we utilize three typical convolution kernels (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5 \times 5$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \times 3$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1 \times 1$ </tex-math></inline-formula> , corresponding to the inception layers of GoogLeNet) as an example to test the efficiency of our proposed dataflow and hardware architecture. The accelerator was implemented for one inception layer of the GoogLeNet in a 55-nm foundry’s CMOS process. The test results show that our CS dataflow can reduce ~85% energy consumption for memory access and save area of 13% and power of 12% for computing. In summary, our CS dataflow is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> more energy-efficient compared to state-of-the-art dataflows.
Multi-Person Pose Estimation in the Wild: Using Adversarial Method to Train a Top-Down Pose Estimation Network

Recent studies estimate human anatomical key points through the single monocular image, in which multichannel heatmaps are the key factor in determining the quality of human pose estimation. Multichannel heatmaps can efficiently handle the image-to-coordinate mapping task and the processing of semantic features. Most methods ignore physical constraints and internal relationships of human body parts, which easily misclassify left and right symmetrical parts as similar features. Some studies use RNNs on the top to incorporate priors about the structure of pose components and body configuration. Therefore, a novel top-down convolutional network is proposed to consider these priors during training, which can improve the robustness under complex field conditions in the wild. In order to learn the prior knowledge of human pose configuration, the hierarchy of fully convolutional networks (discriminator) is used to distinguish real poses from fake ones. Consequently, the pose network is inclined to make a pose estimation that the discriminator misjudges as true, which is reasonable in complex situations. The performance of the method is experimentally validated by pose estimation on the MS COCO human key point detection task. The proposed approach outperforms the original method and generates robust pose predictions, demonstrating efficiency by using adversarial learning.
Improving the Flexibility of Shape-Constrained Symbolic Regression with Extended Constraints

We describe an approach to utilize a broader spectrum of domain knowledge to model magnetization curves for high magnetic field strengths $$0 \le H \le 10^{6}$$ with access to data points far below the saturation polarization. Thereby, we extend the implementation of Shape-Constrained Symbolic Regression. The extension allows the modification of model estimates by a given expression to apply additional sets of constraints. We apply the given expression of an Extended Constraint row-by-row and compare the minimum and maximum outputs with the target interval. Furthermore, we introduce regions and thresholds as additional tools for constraint description and soft constraint evaluation. Our achieved results demonstrate the positive impact of such additional knowledge. The logical downside is the dependence on that knowledge to describe applicable constraints. Nevertheless, the approach is a promising way to reduce the human calculation effort for extrapolating magnetization curves. For future work, we plan to combine soft and hard constraint evaluation as well as the utilization of structure template GP.
Analyzing Sentiment Towards a Product using DistilBERT and LSTM

For improving customer experience towards a product, analyzing the customer’s review is the best possible solution, where customers’ positive and negative opinions can be identified. In our study, we have developed two different supervised natural language task models using (1) deep neural Long Short-Term Memory (LSTM) model and (2) advanced pretrained Distil Bidirectional Encoder Representations from Transformers (DistilBERT), where in the first model, we have analyzed how each of the words in a sentence contribute to the sentiment of that sentence, by training LSTM with our 50,000 Amazon reviews corpora, publicly available in the Amazon website. In the second model, we have examined how the positional embeddings of the words in a sentence and multi-headed self-attention help in faster with better sentence representation for sentiment analysis by fine-tuning DistilBERT, which contains 66 million parameters and 6 layers of encoders. The main contribution of our research includes- (1) a comparative analysis of the efficacy between these above mention models, where the Transformer based model outperforms the LSTM model in all metrics of evaluation, and (2) the establishment of a much faster and lighter sentiment analyzer model, DistilBERT.
Hate Speech Detection in Hindi language using BERT and Convolution Neural Network

Social media has become crucial in our lives; it inculcates our opinions by providing untreated information. Whether we might be not participating actively but indirectly everyone became part of its coverage. Wide spread of information over the internet without any validation made it hard to analyze the impact of misleading information. Cyber hate, which is used as a tool to incite violence against a group of people based on ethnicity, nationality, language, sexual orientation, religious faiths, etc., poses a disgraceful utilization of social media. Previous apposite studies reported hate speech mainly in the English language. Less effort has been made for the resource-constraint language such as Hindi, Marathi, Kannada, etc. This work entitles hate speech detection in low-resource Hindi language using BERT and Deep Convolution Neural Network. The proposed Hindi Hate Speech BERT Convolution Neural Network model intends to detect hate speech in real-time so that any harmful incidence can be avoided as early as possible. This model presents a two-stage architecture: In the first stage, we have applied a pre-trained BERT encoder to generate encodings. In the second stage, a convolution neural network followed by a sigmoid layer is used to detect text as hatred or non-hatred. Our model achieved 0.84 & 0.77 f1-score for Hasoc 2020 and Hasoc 2021 dataset respectively.
An Approach to Criminal Suspect Prediction Software using Machine Learning Classifiers

The goal and the objective of this project is to predict the suspect of criminal and its location by analyzing criminal records and finding criminal hotspots. Investigations in India regarding larceny are very futile in nature and seldom result in apprehension of the criminal. Our software is for police officials to lead their investigation effectively and work towards capturing the unrighteous using data analytics thereby uncovering theft and criminal trends. Such trends lead to uncovering dimensions for an investigation which are difficult to recognize through manual inspection techniques. Such software is not only helpful for the police for effective and smart work in case of low-profile crimes but also the increase in arrests for larceny would ultimately lead to low crime rate and a lawful society. The inclusion of machine learning technique such as KNN technique is used to suspect the criminal by analyzing its data. Through the use of such software for police work in the hope of reduction in crime rate is a progressive step forward for Digital India.
FTX, Cryptocurrencies, and Anarchism Ignored

The aim of this paper is to lay due emphasis on the stark contrast between the philosophy of cryptoanarchism that inspired cryptocurrencies on the one hand, and the oligarchic corporatism of FTX, the now defunct cryptoexchange. The paper highlights that the mismatch between both has led to the reputational damage to cryptocurrencies as a whole, and has also caused widespread economic damage. The findings of the paper recommend a return for cryptocurrencies’ investing hordes to the precepts of cryptoanarchism, lest further reputational and monetary damage to the cryptocurrency occur.
Policy components – a conceptual model for modularizing and tailoring of information security policies

Purpose This paper aims to propose a conceptual model of policy components for software that supports modularizing and tailoring of information security policies (ISPs). Design/methodology/approach This study used a design science research approach, drawing on design knowledge from the field of situational method engineering. The conceptual model was developed as a unified modeling language class diagram using existing ISPs from public agencies in Sweden. Findings This study’s demonstration as proof of concept indicates that the conceptual model can be used to create free-standing modules that provide guidance about information security in relation to a specific work task and that these modules can be used across multiple tailored ISPs. Thus, the model can be considered as a step toward developing software to tailor ISPs. Research limitations/implications The proposed conceptual model bears several short- and long-term implications for research. In the short term, the model can act as a foundation for developing software to design tailored ISPs. In the long term, having software that enables tailorable ISPs will allow researchers to do new types of studies, such as evaluating the software's effectiveness in the ISP development process. Practical implications Practitioners can use the model to develop software that assist information security managers in designing tailored ISPs. Such a tool can offer the opportunity for information security managers to design more purposeful ISPs. Originality/value The proposed model offers a detailed and well-elaborated starting point for developing software that supports modularizing and tailoring of ISPs.
Getting a Grip on the Ever‐Changing Software in Cyber‐Physical Systems

ABSTRACT As industrial cyber‐physical systems grow ever more complex, their software grows naturally and changes continuously. In order to make risk‐free changes to their software, it is crucial to understand how the system behaves, and how software changes have an impact on system behavior. We propose a generic two‐fold approach to infer state machine models capturing system behavior, and to compare these models to determine and visualize the impact of software changes on system behavior, in a way to make them easily understandable for engineers. Our approach has been applied in the industry at ASML to help prevent software regression problems during critical software redesigns. In that, our approach has been shown to reduce risk and to be valuable.
Reasoning about Quality and Fuzziness of Strategic Behaviors

Temporal logics are extensively used for the specification of on-going behaviors of computer systems. Two significant developments in this area are the extension of traditional temporal logics with modalities that enable the specification of on-going strategic behaviors in multi-agent systems, and the transition of temporal logics to a quantitative setting, where different satisfaction values enable the specifier to formalize concepts such as certainty or quality. In the first class, SL ( Strategy Logic ) is one of the most natural and expressive logics describing strategic behaviors. In the second class, a notable logic is LTL[ℱ] , which extends LTL with quality operators . In this work, we introduce and study SL[ℱ] , which enables the specification of quantitative strategic behaviors. The satisfaction value of an SL[ℱ] formula is a real value in [0,1], reflecting “how much” or “how well” the strategic on-going objectives of the underlying agents are satisfied. We demonstrate the applications of SL[ℱ] in quantitative reasoning about multi-agent systems, showing how it can express and measure concepts like stability in multi-agent systems, and how it generalizes some fuzzy temporal logics. We also provide a model-checking algorithm for SL[ℱ] , based on a quantitative extension of Quantified CTL ⋆ . Our algorithm provides the first decidability result for a quantitative extension of Strategy Logic. In addition, it can be used for synthesizing strategies that maximize the quality of the systems’ behavior.
The Impact of African Firms’ Utilization of Financial and Technology Resource on Innovation: A Simple Mediation

This study aimed at analyzing the influence of the use of technology on the use of financial services and innovation of SMEs in Africa. The study further analyzes the mediating role of the use of financial services on firm use of technology-innovation relationships among SMEs in Africa. The study utilizes data retrieved from the enterprise survey portal. The analysis employs multiple regression to test the models using SPSS, and the Sobel test further examines mediation. The results reveal a significant positive association of firm use of technology on the use of financial services and innovation. Also, there is a positive association between the use of financial services and SME innovations in Africa. The study found partial mediation of the use of financial services on the use of technology-innovation relationships. The results have implications on African SMEs need to amplify the sensitization and emphasis on fostering the use of technology in innovating new products and processes. While promoting innovation, the use of technology can enhance financial inclusion resulting in wider and deeper financing options for enterprises in Africa, which are deeply hampered by financial and technological vulnerability.
Deep consistency-preserving hash auto-encoders for neuroimage cross-modal retrieval

Abstract Cross-modal hashing is an efficient method to embed high-dimensional heterogeneous modal feature descriptors into a consistency-preserving Hamming space with low-dimensional. Most existing cross-modal hashing methods have been able to bridge the heterogeneous modality gap, but there are still two challenges resulting in limited retrieval accuracy: (1) ignoring the continuous similarity of samples on manifold; (2) lack of discriminability of hash codes with the same semantics. To cope with these problems, we propose a Deep Consistency-Preserving Hash Auto-encoders model, called DCPHA, based on the multi-manifold property of the feature distribution. Specifically, DCPHA consists of a pair of asymmetric auto-encoders and two semantics-preserving attention branches working in the encoding and decoding stages, respectively. When the number of input medical image modalities is greater than 2, the encoder is a multiple pseudo-Siamese network designed to extract specific modality features of different medical image modalities. In addition, we define the continuous similarity of heterogeneous and homogeneous samples on Riemann manifold from the perspective of multiple sub-manifolds, respectively, and the two constraints, i.e., multi-semantic consistency and multi-manifold similarity-preserving, are embedded in the learning of hash codes to obtain high-quality hash codes with consistency-preserving. The extensive experiments show that the proposed DCPHA has the most stable and state-of-the-art performance. We make code and models publicly available: https://github.com/Socrates023/DCPHA .
Predicting Model Training Time to Optimize Distributed Machine Learning Applications

Despite major advances in recent years, the field of Machine Learning continues to face research and technical challenges. Mostly, these stem from big data and streaming data, which require models to be frequently updated or re-trained, at the expense of significant computational resources. One solution is the use of distributed learning algorithms, which can learn in a distributed manner, from distributed datasets. In this paper, we describe CEDEs—a distributed learning system in which models are heterogeneous distributed Ensembles, i.e., complex models constituted by different base models, trained with different and distributed subsets of data. Specifically, we address the issue of predicting the training time of a given model, given its characteristics and the characteristics of the data. Given that the creation of an Ensemble may imply the training of hundreds of base models, information about the predicted duration of each of these individual tasks is paramount for an efficient management of the cluster’s computational resources and for minimizing makespan, i.e., the time it takes to train the whole Ensemble. Results show that the proposed approach is able to predict the training time of Decision Trees with an average error of 0.103 s, and the training time of Neural Networks with an average error of 21.263 s. We also show how results depend significantly on the hyperparameters of the model and on the characteristics of the input data.
Predicting length of stay ranges by using novel deep neural networks

Accurately predicting length of stay (LOS) is considered a challenging task for health care systems globally. In previous studies on LOS range prediction, researchers commonly pre-classified the LOS ranges, which were the same for all patients in the same classification, and then utilized a classifier for prediction. In this study, we innovatively aimed to predict the specific LOS range for each patient (the LOS range was different for each patient).In the modified deep neural network (DNN), the overall sample error (root mean square error (RMSE) method), the estimated sample error (ERRpred method), the probability distribution with different loss functions (Dispred_Loss1, Dispred_Loss2, and Dispred_Loss3 method), and the generative adversarial networks (WGAN-GP for LOS method) are used for LOS range prediction. The Medical Information Mart for Intensive Care III (MIMIC-III) database is used to validate these methods.The RMSE method is convenient for LOS range prediction, but the predicted ranges are all consistent in the same batch of samples. The ERRpred method can achieve better prediction results in samples with low errors. However, the prediction effect is worse in samples with larger errors. The Dispred_Loss1 method encounters a training instability problem. The Dispred_Loss2 and Dispred_Loss3 methods perform well in making predictions. Although WGAN-GP for LOS method does not show a substantial advantage over other methods, this method might have the potential to improve the predictive performance.The results show that it is possible to achieve an acceptable accurate LOS range prediction through a reasonable model design, which may help physicians in the clinic.
COVID‐19 clinical medical relationship extraction based on MPNet

Abstract With the rapid development of biomedical research and information technology, the number of clinical medical literature has increased exponentially. At present, COVID‐19 clinical text research has some problems, such as lack of corpus and poor annotation quality. In clinical medical literature, there are many medical related semantic relationships between entities. After the task of entity recognition, how to further extract the relationships between entities efficiently and accurately becomes very critical. In this study, a COVID‐19 clinical trial data relationship extraction model based on deep learning method is proposed. The model adopts MPNet model, bidirectional‐GRU (BiGRU) network, MAtt mechanism and Conditional Random Field inference layer integration architecture and improves the problem that static word vector cannot represent ambiguity through pre‐trained language model. BiGRU network is used to replace the current Bi directional long short term memory structure and simplify the network structure of Long Short Term Memory to improve the training efficiency of the model. Through comparative experiments, the proposed method performs well in the COVID‐19 clinical text entity relation extraction task.
A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance

In this paper, a novel state estimation approach based on the variational Bayesian adaptive Kalman filter (VBAKF) and road classification is proposed for a suspension system with time-varying and unknown noise covariance. Using the VB approach, the time-varying noise covariance can be inferred from the inverse-Wishart distribution and then optimized state estimation by the finite sampling posterior probability distribution function (PDF) of noise covariance and backward Kalman smoothing. In addition, a new road classification algorithm based on multi-objective optimization and the linear classifier is proposed to identify the unknown noise covariance. Simulation results for a suspension model with time-varying and unknown noise covariance show that the proposed approach has a higher performance in state estimation accuracy than other filters.