Reviewerly/exHarmony huggingface dataset:
Causal Relationship Between Financial Sector Development in SMEs &amp; Economic Growth in Southern Africa Region

This study explores causal relationship between financial sector development in SMEs and economic growth in Zimbabwe using annual time series and the Error Correction Model (ECM) framework. Monetary sector improvement and financial development stayed a controversial issue in Southern African nations. Market analysts have distinctive hypothetical and exact perspectives on the causal connection between monetary sector improvement and financial development. support supply driving speculation that monetary sector improvement prompts financial development &amp; credit to request pulling speculation which proposes that monetary improvement results from financial development. Study made use of Unit Root Tests, Cointegration, ECM and Granger Causality Tests. Empirical findings revealed a bidirectional relationship between financial sector development in SMEs, economic &amp; business growth. Business &amp; Economic Growth enhance a strong and flexible legal system allowing banks to allocate resources (credit) more efficiently to SMEs. Credit should be accessed by all enterprise fairly to encourage the development of indigenous businesses through SMEs.
Health Record Chain (HRC): Implementation of Mobile Healthcare system using Blockchain to enhance Privacy of Electronic Health Record EHR

Mobile health applications connect to wearable devices with inbuilt sensors to monitor critical human body parameters such as heart rate, pulse rate, body temperatures ,and others. This paper will introduce our system, Health Record Chain HRC, to investigate how to design and implement secure mobile health systems that leverage the smart mobile devices' new authentication and geo-location mechanism. HRC introduces an enhancing framework to ensure the users' security and privacy and conform to the related regulations ( General Data Protection Regulation GDPR, Health Insurance Portability and Accountability Act HIPPA) using blockchain. The immutability of blockchain conflicts with GDPR’ "Right to be forgotten" when the user can delete all of his/her data. This work implements a blockchain system with a mobile application and a web interface to address this conflict by hashing the Electronic health record EHR to Ethereum based blockchain.
Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting

In this study, we describe a text processing pipeline that transforms user-generated text into structured data. To do this, we train neural and transformer-based models for aspect-based sentiment analysis. As most research deals with explicit aspects from product or service data, we extract and classify implicit and explicit aspect phrases from German-language physician review texts. Patients often rate on the basis of perceived friendliness or competence. The vocabulary is difficult, the topic sensitive, and the data user-generated. The aspect phrases come with various wordings using insertions and are not noun-based, which makes the presented case equally relevant and reality-based. To find complex, indirect aspect phrases, up-to-date deep learning approaches must be combined with supervised training data. We describe three aspect phrase datasets, one of them new, as well as a newly annotated aspect polarity dataset. Alongside this, we build an algorithm to rate the aspect phrase importance. All in all, we train eight transformers on the new raw data domain, compare 54 neural aspect extraction models and, based on this, create eight aspect polarity models for our pipeline. These models are evaluated by using Precision, Recall, and F-Score measures. Finally, we evaluate our aspect phrase importance measure algorithm.
Meteorological Satellite Operation Prediction Using a BiLSTM Deep Learning Model

The current satellite management system mainly relies on manual work. If small faults cannot be found in time, it may cause systematic fault problems and then affect the accuracy of satellite data and the service quality of meteorological satellite. If the operation trend of satellite will be predicted, the fault can be avoided. However, the satellite system is complex, and the telemetry signal is unstable, nonlinear, and time-related. It is difficult to predict through a certain model. Based on these, this paper proposes a bidirectional long short-term memory (BiLSTM) deep leaning model to predict the operation trend of meteorological satellite. In the method, the layer number of the model is designed to be two, and the prediction results, which are forecasted by LSTM network as the future trend data and historical data, are both taken as the input of BiLSTM model. The dataset for the research is generated and transmitted from Advanced Geostationary Radiation Imager (AGRI), which is the load of FY4A meteorological satellite. In order to demonstrate the superiority of the BiLSTM prediction model, it is compared with LSTM based on the same dataset in the experiment. The result shows that the BiLSTM method reports a state-of-the-art performance on satellite telemetry data.
Comparison of HOG and Fisherfaces Based Face Recognition System Using MATLAB

Face recognition and validation is not an easy task due to barriers in between like variation in pose, facial expressions and illumination. There are many algorithms available to build a face recognition system. One such popular method of approach is the Histogram of Oriented Gradients (HOG). It is a simple but effective algorithm. Even though it gives satisfactory results, it sometimes mismatches query image with irrelevant images, especially in poor lighting conditions. This paper presents a more accurate technique called Fisherfaces. It is a more reliable method for face recognition and validation. Fisherface algorithm is utilized primarily for reducing the dimensionality of the feature space. Fisherface method for image recognition involves a series of steps. Firstly, the face space dimension is reduced using Principal Component Analysis (PCA) method, then the Linear Discriminant Analysis (LDA) method is used for feature extraction. Fisherface method produced good results even under complex situations like varying illumination conditions and images with different poses and expressions which is a major drawback of HOG. Fisherface algorithm can reach a maximum accuracy of 96.87%. Error Correcting Output Code (ECOC) is the classifier used for both HOG and Fisherfaces.
Application of geostatistical hierarchical clustering for geochemical population identification in Bondar Hanza copper porphyry deposit

Several machine learning approaches have been developed for the identification of geochemical populations. In these approaches, the geochemical elements are usually the sole quantitative variables used as inputs for geochemical population recognition. This means that the presence of other qualitative variables, such as geological information, is overlooked in the analysis. Hierarchical clustering, as an unsupervised machine learning method, is a common approach for dimensional reduction in the analysis of geochemical data. In this study, an alternative to this technique, known as geostatistical hierarchical clustering (GHC), is applied to identify geochemical populations in 3D in the Bondar Hanza copper porphyry deposit, Iran. In this paradigm, the qualitative geological variables can also be incorporated for geochemical population identification, in addition to qualitative geochemical elements. In this study, an innovative solution is presented to tune the weighting parameters of each variable in GHC, based on the associations that the clusters (i.e., geochemical populations) should have with the geological information. The results are compared with k-means and number–size fractal/multifractal (N–S) methods. As a result, GHC showed better agreement with alterations, rock types, and mineralization zones in this deposit. Finally, some important instructions are provided for further mineral exploration.
On Implicit Assumptions Underlying Software Engineering Research

Background: Software engineering research articles should make precise claims regarding their contribution, so that practitioners can decide when they might be interested and researchers can better recognize (1) whether the given research is valid, (2) which published works to use as stepping stones for their own research (and which not), and (3) where additional research is required. In particular, articles should spell out what assumptions were made at each research step. Question: Can we identify recurring patterns of assumptions that are not spelled out? Method: This is a position paper. It formulates impressions, but does not present concrete evidence. Results: Assumptions that are wrong or assumptions that are risky and not explicit threaten the integrity of the scientific record. There are several recurring types of such assumptions. The frequency of these problems is currently unknown. Conclusion: The software engineering research community should become more conscious and more explicit with respect to the assumptions that underlie individual research works.
Unsupervised Super Resolution Reconstruction of Traffic Surveillance Vehicle Images

The surveillance of public transportation is of great significance to improve public safety. However, the low resolution of vehicle images becomes a bottleneck in real scenarios. Since high-low resolution vehicle images pairs are not available in traffic surveillance scenarios, this paper aims to study the problem of unsupervised super-resolution to reconstruct the high quality vehicle image. Most of the existing super-resolution algorithms adopt pre-defined down-sampling methods for paired training, however, the models trained in this pattern cannot achieve the expected results in traffic surveillance scenarios. Therefore, we propose a super-resolution method that does not require paired data, and raise a novel down-sampling network to generate low-resolution images of vehicles close to the real-world data, and then utilize the synthesized pairs for pair-wise training. Our extensive experiments on private real-world dataset Vehicle5k demonstrate the advantages of the proposed approach over baseline approaches.
A Novel Naive Bayesian Approach to Inference with Applications to the MNIST Handwritten Digit Classification

Naive Bayesian approach is an effective method for many data analysis problems such as pattern classification and machine learning. However, it often suffers from the underflow problem when the input data has a high dimension. Such a problem is often addressed by taking logarithms and working in the transformed domain. In this paper we propose a novel approach to this problem based on geometric means and apply it to the classical MNIST handwritten digit classification problem. The results show that it not only achieves satisfactory accuracy but also demonstrates its power of presenting "second best" guesses that are meaningful and useful in the pattern classification domain.
An 176.3 GOPs Object Detection CNN Accelerator Emulated in a 28nm CMOS Technology

Object Detection methods are an important subject in the implementations of Artificial Intelligent systems. Many attempts to build up a real-time object detection hardware/software in an SoC are presented in recent research. However, due to the demanding in both memory bandwidth and parallel computing resources, only few designs can fulfill the real-time requirement, which is important for applications or payloads such as Drones, UAVs, and autonomous vehicles. In this paper, issues in design of an SoC based object-detection will be discussed, and an on-going hardware design base on YOLO algorithm and ARC Platform will be presented. With the optimization in both numbers of Processing Element and Memory Bandwidth, An 176.3 GOPs CNN accelerator with 30 fps performance at 400MHz is presented. In addition to the Object-Detection engine, a ZCA image preprocessor and NMS postprocessing are also proposed to simplify the corresponding CNN model and enhance the real-time performance of Object-Detection. The emulation results and demonstration videos in the ARC platform will be presented. The post-layout simulation of current designverified the targeting real-time performance in a 28nm CMOS technology.
Adversarial Learning with Domain-Adaptive Pretraining for Few-Shot Relation Classification across Domains

The existing methods for domain-adaptive few-shot relation classification based on word embeddings or pretraining models trained on massive corpora, are not strong enough to cover the wide disparity of text and relation definitions to the specific target domain, leading to the inferior performance. To fill in this gap, here we propose an enhanced adversarial approach utilizing domain-adaptive pretraining model to obtain semantic features of relations, which continues unsupervised pretraining on corpus in target domain. We also construct a classification enhancer module to emphasize the class differentiation by making greater use of the supporting and query data, which not only helps to deal with few-shot problem, but also diminishes the negative effect of domain alignment caused by adversarial learning. Experimental results on FewRel2.0-DA dataset demonstrate that our proposed method achieves strong performance, which can improve the best reported result by up to 5.3 % on average accuracy for few-shot relation classification across domains.
Multiclass Weapon Detection using Multi Contrast Convolutional Neural Networks and Faster Region-Based Convolutional Neural Networks

In this day and age, increasingly easy access to firearms and other hand-held weapons has stirred up public violence concerns. Many of these weapons are comfortably concealed. The tremendous developments in technology can assure more security in public places by detecting such weapons in real-time and alerting the concerned authorities before any damage. In this paper, we propose using two state-of-the-art algorithms for performing real-time detection of concealed hand-held weapons. The algorithms used are Multi Contrast Convolutional Neural Networks (MC-CNN) and Faster Region-Based Convolutional Neural Networks (Faster R-CNN). Additionally, we present a comprehensive comparative analysis and evaluation of the Faster R-CNN and MC-CNN in detecting the weapons. This study has diverse industrial applications in real-time bank surveillance cameras and other public places, provided that they are under CCTV surveillance.
Vision-Based Elderly Fall Detection Algorithm for Mobile Robot

Fall ranks first among the elderly aged 65 and above. In order to detect falls of the elderly living alone, we use a vision-based detection method to complete fall detection for the elderly on a low-cost small mobile robot. We propose a deep learning fall detection framework for mobile robot. In this framework, Raspberry Pi 4 Model B is selected as hardware platform for mobile robot, and lightweight NanoDet-Lite is used for fall detection. The mAP of our method is 0.912 and model size is only 2.17MB. Our method works more than 3 times faster than YOLOv3-tiny on Raspberry Pi without any hardware accelerator. In ncnn framework, NanoDet-Lite works at 22.03 FPS on Raspberry Pi and mAP reaches 0.902. The results show that our method not only can be applied to the low-cost mobile robot, but also has a good detection performance.
Meta and Media Data Stream Forensics in the Encrypted Domain of Video Conferences

Our paper presents a systematic approach to investigate whether and how events can be identified and extracted during the use of video conferencing software. Our approach is based on the encrypted meta and multimedia data exchanged during video conference sessions. It relies on the network data stream which contains data interpretable without decryption (plain data) and encrypted data (encrypted content) some of which is decrypted using our approach (decrypted content). This systematic approach uses a forensic process model and the fission of network data streams before applying methods on the specific individual data types. Our approach is applied exemplary to the Zoom Videoconferencing Service with Client Version 5.4.57862.0110 [4], the mobile Android App Client Version 5.5.2 (1328) [4], the webbased client and the servers (accessed between Jan 21st and Feb 4th). The investigation includes over 50 different configurations. For the heuristic speaker identification, two series of nine sets for eight different speakers are collected. The results show that various user data can be derived from characteristics of encrypted media streams, even if end-to-end encryption is used. The findings suggest user privacy risks. Our approach offers the identification of various events, which enable activity tracking (e.g. camera on/off, increased activity in front of camera) by evaluating heuristic features of the network streams. Further research into user identification within the encrypted audio stream based on pattern recognition using heuristic features of the corresponding network data stream is conducted and suggests the possibility to identify users within a specific set.
Development of a Dynamic Information System for Safety Management of Construction Workers Based on IoT and BIM Tools

Abstract In this paper, according to the current situation of safety management of construction workers, BIM and the Internet of Things are jointly applied to the safety management of construction workers, and a safety management system for construction workers is constructed. Solutions are put forward for accident-prone areas, construction personnel’s physical condition, construction personnel’s operation and safety training, hoping to fundamentally and effectively reduce construction personnel safety accidents.
The archaeology of overburden: Method within the madness at Švédův Stůl, Czech Republic

In the 19th and early to mid-20th centuries, a rush to better understand the European Palaeolithic led to the substantive removal of deposits from limestone caves. In the 21st century the situation has changed. Many caves are now excavated, leaving behind a human-made environment of diminished cave sediments and large spoil heaps, with the latter now targeted by those searching for artefacts missed during the original excavations. In an age in which archaeologists are increasingly attempting to balance their roles as cultural heritage educators and destroyers, the question remains - how much do we know about the taphonomy of these features? In this paper we report results from the excavation of a large spoil heap outside Švédův Stůl Cave, in the Moravian Karst region of Czech Republic. Results show heterogeneous sediment formation (revealed primarily through their field characteristics and ED-XRF and Itrax geochemical analyses) and patterns in artefact distributions (evident through assessment of Iron Age, Neolithic and modern artefacts) and faunal remains. This allows partial context to be provided for some artefacts and a methodology to be developed for excavation of overburden.
Visualization Analysis of Library Research in the Context of Big Data Based on Knowledge Map

The development of big data technology has brought a series of new content, opportunities and challenges to the library, and scholars have conducted many studies around this. This study obtained 98 related papers from the core collection of Web of Science, using the knowledge map research method, and using the CiteSpace software to analyze the number of annual papers, journals, authors, institutions, keywords and topic changes. The results show that scholars' attention to this field has gradually increased, and the number of annual papers has increased year by year. China is the country with the highest contribution to the research, and the contribution of Chinese scholars is higher than that of other countries. Big data, university library, data management and information service are the key research contents of this field. In the end, this paper makes a research prospect, and scholars should further strengthen the research on user behavior, user portrait and intellectual property risk.
Big Data Analysis System Based on Cloudera Distribution Hadoop

With the development and popularization of big data and related technologies, people's demand for big data ecosystem consisting of data resources, API, open source platform, data infrastructure, big data analysis and application components is increasingly significant. In this paper, the application of big data ecosystem based on Cloudera Distribution Hadoop (CDH) is discussed. This article first describes the development trend of data scale in recent years and the state-of-the-art work on big data analysis systems. Then, it focuses on the technical architecture of the big data ecosystem based on CDH, which analyzes the hierarchical structure of the system. Finally, a detailed analysis is made on the performance tuning settings of the system.
Ada-LLD: Adaptive Node Similarity Using Multi-Scale Local Label Distributions

In many applications, data is represented as a network connecting nodes of various types. While types might be known for some nodes in the network, the type of a newly added node is typically unknown. In this paper, we focus on predicting the types of these new nodes based on their connectivity to the already labeled nodes. To tackle this problem, we propose Adaptive Node Similarity Using Multi-Scale Local Label Distributions (Ada-LLD) which learns the dependency of a node's class label from the distribution of class labels in this node's local neighborhood. In contrast to previous approaches, our approach is able to learn how class labels correlate with labels in variously sized neighborhoods. We propose a neural network architecture that combines information from differently sized neighborhoods allowing for the detection of correlations on multiple scales. Our evaluations demonstrate that our method significantly improves prediction quality on real world data sets. In the spirit of reproducible research we make our code available.
Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices

Low-power wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints concerning time and location, on one hand, and fulfilling long-term tracking, on the other hand. In the case of epileptic seizures, as the attacks infrequently occur, using an anomaly detection approach reduces the need to record long hours of data for each patient before detecting the successive coming seizures. In this work, by combining the concepts of self-aware system and anomaly detection, we propose an energy-efficient system to detect epileptic seizures on single-lead electrocardiographic signals, which is personalized after analyzing the first seizure of the patient. This system, then, uses a simple anomaly-detection model, whenever the model is deemed reliable, and uses a more complex model otherwise. We show that after the personalization, the number of patients, for which the method provides high sensitivity, can reach 26 out of 43 patients with the false alarm rate (FAR) of 4 alarms/day. Thus, the number of responders to the system is increased by 24%, while the FAR is only increased by one alarm/day, compared to the system that just uses the simple model. This benefit occurs while the system complexity decreases by 27.7% compared to the complex model. After adding the two-level (simple and complex) anomaly-detection, the complexity is tuned between 72.3% and 37.6% of the complex model. Similarly, the sensitivity is tuned between 66.5% and 60.3%.