Reviewerly/exHarmony huggingface dataset:
A Hemolysis Image Detection Method Based on GAN-CNN-ELM

Since manual hemolysis test methods are given priority with practical experience and its cost is high, the characteristics of hemolysis images are studied. A hemolysis image detection method based on generative adversarial networks (GANs) and convolutional neural networks (CNNs) with extreme learning machine (ELM) is proposed. First, the image enhancement and data enhancement are performed on a sample set, and GAN is used to expand the sample data volume. Second, CNN is used to extract the feature vectors of the processed images and label eigenvectors with one-hot encoding. Third, the feature matrix is input to the map in the ELM network to minimize the error and obtain the optimal weight by training. Finally, the image to be detected is input to the trained model, and the image with the greatest probability is selected as the final category. Through model comparison experiments, the results show that the hemolysis image detection method based on the GAN-CNN-ELM model is better than GAN-CNN, GAN-ELM, GAN-ELM-L1, GAN-SVM, GAN-CNN-SVM, and CNN-ELM in accuracy and speed, and the accuracy rate is 98.91%.
A Cloud Microservices Architecture for Data Integrity Verifiability Based on Blockchain

The current digital age, mainly characterized by an economy based upon information technology, demands a data integrity service, even more so because organizations and companies are migrating their services over the cloud. This is not a simple task; it is cumbersome since traditional schemes in databases could be subject to modifications. However, it can be solved using blockchain technology. This paper provides a data integrity verifiability architecture for cloud systems based on blockchain. The architecture provides a mechanism to store events (as logs) within a blockchain platform from any cloud system. Users can then consult data integrity through a microservice, acting as an intermediate server that carries out a set of verification steps within the blockchain, which confirms the integrity of a previously stored log. Our architecture takes advantage of the blockchain strength concerning integrity, providing a traceability track of the stored logs. A prototype system and a case study were implemented based on the proposed architecture. Our experimental results show that the proposed decentralized architecture can be adapted to cloud existing systems that were born without blockchain technology and require a modular and scalable audit characteristic.
Knock, knock, log: Threat analysis, detection &amp; mitigation of covert channels in syslog using port scans as cover

In this paper we perform a threat analysis for a covert Command and Control (C2) channel using port scans as cover and syslog as carrier for data infiltration. We describe a theoretical threat scenario in which an adversary makes use of known covert channels in TCP and DNS, and propose a novel method for hiding information in TCP ports scans and the resulting (sys)logs as a carrier for hidden messages. For forensic purposes, we provide details on Indicators-of-Compromise (IoC) as well as mitigating measures aiming at preventing the covert channel apriori. Moreover, we propose a novel detection scheme in order to identify and prevent such threats hidden in port scans and evaluate its effectiveness using datasets generated by a proof of concept implementation of the proposed covert channel based threat scenario.
An efficient differential fault attack against SIMON key schedule

SIMON, a family of lightweight block ciphers, has received much attention from the cryptology community due to its improved performance for hardware compared with that of other ciphers. A popular physical attack method, differential fault attack (DFA), works for SIMON. With the aim of improving the efficiency of attacks applied to the SIMON family, in this work, an efficient DFA against SIMON key schedule is presented under a random bit fault model. First, we analyze in detail how to identify the fault induction position. Then, on the basis of the position, we show in detail how to recover 4 bits, 7.5 bits, and 2 bits of K T -1 (the ( T -1) th round key), K T -2 (the ( T -2) th round key) and K T -3 (the ( T -3) th round key), respectively, by inducing a one-bit fault on average in K T -5 (the ( T -5) th round key). As a result, by inducing one round of faults, our attack can recover three rounds of keys. Compared with previously developed DFAs on SIMON under the random bit fault model, to recover the full keys of the SIMON family, our attack requires the fewest fault inductions and locations of the induced fault. Moreover, when the value of key words m is 2 or 3, our attack can reduce the number of required rounds of fault induction to one. Finally, simulation experiments and comparisons are carried out to demonstrate the correctness and effectiveness of our attack.
A Study on the Application of Computer-Aided Dual-Coding Theory in English Vocabulary Teaching

Learning English needs a huge amount of vocabulary recalls as well as multiple grammatical structures and practices. With the development and improvement of the humanization and quality of English teaching, the multidimensional teaching of English has formed a powerful learning model and teaching method. The teaching of dual-coding theory assisted by computer teaching software mainly focuses on the dual activation representation of language combined with nonverbal graphic recognition to complete the recognition of information. According to the dual-coding hypothesis, in Gilford’s intelligence theory, the capacity of spatial and information recognition and reading is highly significant. With the large amount of visual recognition and information acquisition, images involve spatial domain information reading, which is a learning method based on the principle of information feature recognition. Based on the principle of dual-coding theory, it is a very effective way to present information and recall specific information in the form of vision and language. In this study, the application research for the purpose of English vocabulary teaching is adopted that has strong innovative and practical characteristics. According to the steps and contents of English vocabulary learning, the study is carried out step by step from the context in detail, so it continuously improves the learning quality and efficiency of English vocabulary teaching. Vocabulary teaching can help the students better improve their cognitive level of vocabulary and meet the vocabulary test requirements of the new institution entrance examination. Further, how to improve the effect of senior high school English vocabulary teaching is a major topic in the field of senior high school English teaching. In order to sort out this issue, I proposed a computer-aided dual-coding theory to help quickly understand vocabulary usage in order to increase learning efficiency. The Paivio–Desrochers model is used in this study. In addition, this study also attempts to explore the application of dual-coding theory in senior high school English vocabulary teaching.
Study on 3-dimensional stereoscopic image model in intelligent coal mine

This paper briefly describes the image analysis technology needed in the construction of intelligent coal mine, mainly for the reconstruction process of 3-dimensional (3D) image model in coal mine. If all the pixel points are concerned to calculate in whole picture, it should ask for higher computing processing equipment and large amount of computation. Therefore, this paper proposes a method which is firstly carried out target detection or image segmentation to get object to be recognized on the image, then taken image registration of target area according to the detected or segmented pixels, and the dense point matrix is diffused and fused according to the sparse point matrix, and then the 3D model is generated. In the 3D model, the length, depth and angle are easier to get accurate results, which plays a very important role in the subsequent scene analysis. In image 3D reconstruction, the region of registration has been greatly reduced due to image recognition or segmentation, and the number of registration points found by the improved registration using swarm intelligence algorithm is 6 times than algorithm without improvement, and the depth data obtained in the subsequent 3D reconstruction is basically consistent with that in the actual scene. In the future production monitoring, it is the judgement that can meet the corresponding business logic and production requirements.
HFOD: A hardware-friendly quantization method for object detection on embedded FPGAs

There are two research hotspots for improving performance and energy efficiency of the inference phase of Convolutional neural networks (CNNs). The first one is model compression techniques while the second is hardware accelerator implementation. To overcome the incompatibility of algorithm optimization and hardware design, this paper proposes HFOD, a hardware-friendly quantization method for object detection on embedded FPGAs. We adopt a channel-wise, uniform quantization method to compress YOLOv3-Tiny model. Weights are quantized to 2-bit while activations are quantized to 8-bit for all convolutional layers. To achieve highly-efficient implementations on FPGA, we add batch normalization (BN) layer fusion in quantization process. A flexible, efficient convolutional unit structure is designed to utilize hardware-friendly quantization, and the accelerator is developed based on an automatic synthesis template. Experimental results show that the resources of FPGA in the proposed accelerator design contribute more computing performance compared with regular 8-bit/16-bit fixed point quantization. The model size and the activation size of the proposed network with 2-bit weights and 8-bit activations can be effectively reduced by 16× and 4× with a small amount of accuracy loss, respectively. Our HFOD method can achieve 90.6 GOPS on PYNQ-Z2 at 150MHz, which is 1.4× faster and 2× better in power efficiency than peer FPGA implementation on the same platform.
An analysis of the COVID-19 contact tracing App in South Africa: Challenges experienced by users

South Africa is one of the countries in Africa that has a high COVID-19 virus infection rate. The government of South Africa introduced the COVID Alert SA App, a contact tracing mobile application (App), to reduce the spread of the COVID-19 virus. This paper reports on the challenges users experienced when using the COVID Alert SA App. Using the design-reality gap model, the study analyzed online user reviews of the App and government reports. The qualitative data were analyzed using thematic analysis to highlight insights on the challenges of using the COVID Alert SA App. The findings indicate that public awareness was a major limitation of the App, amongst others, including a lack of trust relating to privacy and security when using the App, and a lack of technical support. The insights can be useful for developers, researchers, policymakers, and other stakeholders to improve the adoption and use of the contact tracing App to reduce the spread of the COVID-19 virus.
Summary Generation Using Natural Language Processing Techniques and Cosine Similarity

The COVID-19 pandemic has led to an unprecedented challenge to public health. It resulted in global efforts to understand, record, and alleviate the disease. This research serves the purpose of generating a relevant summary related to Coronavirus. The research uses the COVID-19 Open Research Dataset (CORD-19) provided by Allen Institute for AI. The dataset contains 236,336 academic full-text articles as of July 19, 2021. This paper introduces a web-based system to handle user questions over the Coronavirus full-text scholarly articles. The system periodically runs backend services to process such large amount article with basic Natural Language Processing (NLP) techniques that include tokenization, N-Grams extraction, and part-of-speech (PoS) tagging. It automatically identifies the keywords from the question and uses cosine similarity to summarize the associated content and present to the user. This research will possibly benefit researchers, health workers as well as other individuals. Moreover, the same service can be used to train with the datasets of different domains (e.g., education) to generate a relevant summary for other user groups (e.g., students).
Static hand gesture recognition based on hierarchical decision and classification of finger features

AbstractConsidering that the distinctions among static hand gestures are the difference between fingers sticking out, a method of grouping and classifying hand gestures step by step by using the information of the quantity, direction, position and shape of the outstretched fingers was proposed this paper. Firstly, the gesture region was segmented by using the skin color information of the hand, and the gesture direction was normalized by using the direction information of the gesture contour lines. Secondly, the finger was segmented one by one by using convex decomposition in the hand gesture image based on the convex characteristic of the gesture shape. Thirdly, the features of quantity, direction, position and shape of the segmented fingers were extracted. Lastly, a hierarchical decision classifier embedded with deep sparse autoencoders was constructed. The quantity of fingers was used to divide the gesture images into groups first, then the direction, position and shape features of the fingers were used to subdivide and recognize gestures within each group. The experimental results show that the proposed method is robust as lighting, direction and scale changes and significantly superior to the traditional method both in the recognition rate and the recognition stability.
Key Requirements for the Detection and Sharing of Behavioral Indicators of Compromise

Cyber threat intelligence feeds the focus on atomic and computed indicators of compromise. These indicators are the main source of tactical cyber intelligence most organizations benefit from. They are expressed in machine-readable formats, and they are easily loaded into security devices in order to protect infrastructures. However, their usefulness is very limited, specially in terms of time of life. These indicators can be useful when dealing with non-advanced actors, but they are easily avoided by advanced ones. To detect advanced actor’s activities, an analyst must deal with behavioral indicators of compromise, which represent tactics, techniques and procedures that are not as common as the atomic and computed ones. In this paper, we analyze why these indicators are not widely used, and we identify key requirements for successful behavioral IOC detection, specification and sharing. We follow the intelligence cycle as the arranged sequence of steps for a defensive team to work, thereby providing a common reference for these teams to identify gaps in their capabilities.
Augmented Reality SDK’s: A Comparative Study

Augmented Reality (AR) is one of the most exciting and rapidly developing technology in recent years. AR technology is utilized by a variety of devices and platforms, each with its own set of specifications and software development kits (SDKs). The major benefit of SDKs is that they allow developers to apply typical and time-tested techniques and shortcuts in development, rather than tackling all generic and typical problems individually and wasting time on them. This paper provides a study of various SDKs, including their advantages and disadvantages. Four different types of AR SDK’S were considered: Vuforia, Wikitude, ARToolKit and Kudan. The study compares the AR SDK’S in terms of licence, supported platforms, cloud recognition, geolocation, SLAM (Simultaneous Localization and Mapping), and other additional features. The comparative study has shown that none of the discussed AR SDK’s can satisfy all the criteria because each framework is suitable for a particular application as well as its own area of implementation.
A SOM-Based Customer Stratification Model

This paper used the SOM in combination of the optimized RFM for customer stratification, to develop targeted marketing strategies for enterprises. In this paper, customers were grouped into four categories, including core customers, opportunistic customers, service drain customers, and marginal customers, using the customer consumption data of a retail enterprise by SOM, a clustering algorithm based on neural networks, in combination with the optimized RFM from the perspective of machine learning. The value of customers in different categories was determined based on their typical features for a visualized analysis, to develop targeted marketing strategies for enterprises.
The mediating effect of IT governance between corporate governance mechanisms, business continuity, and transparency &amp; disclosure: An empirical study of Covid-19 Pandemic in Jordan

The present study aims to assess the mediating role of IT governance between corporate governance mechanisms and business continuity, and transparency & disclosure during the pandemic of Covid-19 in Jordan. The study uses a sample of 232 questionnaires retrieved from different firms of different sectors. The sample includes a survey response from board members, senior executives, auditors, IT experts, and other practitioners. Factor analysis and structural equation modeling were conducted to estimate the results. The results indicate that IT governance exhibits a statistically significant positive effect on business continuity and transparency & disclosure. Further, the results reveal that corporate governance mechanisms are less efficient in their influence on business continuity and transparency & disclosure in the absence of the mediating effect of IT governance during the pandemic. The current study’s findings provide insight and empirical evidence to the importance of IT governance and its role in business continuity and transparency & disclosure in constraining the negative effects during any crisis. The current study provides a novel contribution as it links corporate governance, transparency, and IT governance in the context of Covid-19 in an emerging country.
The Meta-Analytic Rain Cloud Plot: A New Approach to Visualizing Clearinghouse Data

As the body of scientific evidence about effective policies and practices grows, so does the need to effectively communicate that evidence to policy-makers and practitioners. Clearinghouses have emerged to facilitate the evidence-based decision-making process for education practitioners. While the results and methods for developing and analyzing the data in clearinghouses are based upon rigorous and scientific study, there has been little rigor or empirical effort to determine effective ways of presenting that evidence to practitioners. In this paper, we present a new visualization for clearinghouse data, called a Meta-Analytic Rain Cloud (MARC) Plot, designed based on evidence from the data visualization and statistical cognition literatures. We evaluate the efficacy of this visualization in a statistical cognition experiment and find that compared to three other visualizations used in practice, the MARC Plot is more effective in helping participants correctly interpret evidence (0.76, 0.43, and 0.43 standard deviation improvements respectively; each p < 0.05, corrected for multiple comparisons). To our knowledge, this is one of the first studies providing evidence regarding how to best present the type of information found in clearinghouses.
MUSAK: A Multi-Scale Space Kinematic Method for Drone Detection

Accurate and robust drone detection is an important and challenging task. However, on this issue, previous research, whether based on appearance or motion features, has not yet provided a satisfactory solution, especially under a complex background. To this end, the present work proposes a motion-based method termed the Multi-Scale Space Kinematic detection method (MUSAK). It fully leverages the motion patterns by extracting 3D, pseudo 3D and 2D kinematic parameters at three scale spaces according to the keypoints quality and builds three Gated Recurrent Unit (GRU)-based detection branches for drone recognition. The MUSAK method is evaluated on a hybrid dataset named multiscale UAV dataset (MUD), consisting of public datasets and self-collected data with motion labels. The experimental results show that MUSAK improves the performance by a large margin, a 95% increase in average precision (AP), compared with the previous state-of-the-art (SOTA) motion-based methods, and the hybrid MUSAK method, which integrates with the appearance-based method Faster Region-based Convolutional Neural Network (Faster R-CNN), achieves a new SOTA performance on AP metrics (AP, APM, and APS).
Studying the Effect of Syntactic Simplification on Text Summarization

The need for automatic text summarization (ATS) is increased manifold in recent times due to the overwhelming growth of textual data available in electronic form. However, existing ATS systems suffer from two major shortcomings. Summarizers of extractive type, that is, the ones which select important sentences of the documents in their original form as the output, tend to copy some irrelevant or unimportant parts of the input text in the output summary. On the other hand, abstractive summarizers, that is, the ones that produce a gist of the limited size of the original document, often fail to include important contents in the generated summary. Simplification of the input texts before submitting them to the ATS system(s) may obliterate the above difficulties. The present work examines the effectiveness of simplification of input for five different known ATS systems. In this work, DEPSYM++ simplifier has been used for the above purpose, which carries out four different kinds of simplification on sentences of the input text corresponding to the presence of appositive clause, relative clause, conjoint clause, and passive voice. The results obtained are found to be very encouraging when experiments were carried out on three different gold data sets and under different evaluation metrics commonly used for performance evaluation for summarizers.
Feature extraction of auto insurance size of loss data using functional principal component analysis

To better understand the major pattern and natural variation of statistical reporting data, visualization and interpretability have been an ongoing challenging issue, particularly in complex statistical data analysis. In auto insurance rate regulation, the study of the size-of-loss distribution is critical because it serves as a benchmark for rate filings conducted by insurance companies. In this work, we propose dimension reduction and feature exaction approaches via functional principal component analysis to analyse auto insurance statistical reporting data. Dimension reduction aims to estimate the overall functional pattern of the size-of-loss, while feature extraction is to study the similarity and variability of extracted features in the feature subspace. We also investigate the functionality of relative claim frequency, size-of-loss, and the pattern and variability of extracted features, using loss data at the industry level. The proposed method helps improve data explainability and understanding of the overall auto insurance size-of-loss distributions pattern. Also, we found that the proposed method can capture the tail behaviour of the size-of-loss distribution. It is also applicable to similar data analysis in many applications, including economics and finance. The impact of this research is to help develop strategies to control and manage the proposed rate changes by insurance companies for rate regulation purposes in the aspect related to large loss loading for reinsurance pricing.