Reviewerly/exHarmony huggingface dataset:
Application Profiling From Encrypted Traffic

Everyday millions of people use Internet for various purposes including information access, communication, business, education, entertainment and more. As a result, huge amount of information is exchanged between billions of connected devices. This information can be encapsulated in different types of data packets. This information is also referred to as network traffic. The traffic analysis is a challenging task when the traffic is encrypted and the contents are not readable. So complex algorithms required to deduce the information and form patterns for traffic analysis. Many of currently available techniques rely on application specific attribute analysis, deep packet inspection (DPI) or content-based analysis that become ineffective on encrypted traffic. The article will focused on analysis techniques for encrypted traffic that are adaptive to address the evolving nature and increasing volume of network traffic. The proposed solution solution is less dependent on application and protocol specific parameters so that it can adapt to new types of applications and protocols. Our results shows that processing required for traffic analysis need to be in acceptable limits to ensure applicability in real-time applications without compromising performance.
An algorithm to identify and authenticate students writing electronic exams in supervised environment

The use of internet technology in teaching and learning is continuously expanding. The submission of student assessments and the administration of them are two of the most important aspects of online learning. The most difficult aspect of administering electronic assessments is verifying the authenticity of test-takers. Various researchers have proposed and implemented solutions that authenticate users mostly at the beginning of the session, and a few at the beginning and at the end of the sessions of electronic exams, which include a bio and multi-biometric systems. Many of such are still trying to achieve systems that cater for continuous authentication and are ideal for university centers. Thus, the purpose of the study is to develop a solution that will improve the level of security for identity and continuous authenticating students during electronic exams in a supervised environment.
A Study on Securing Data in Smart Healthcare Applications

As the human population is growing drastically fast, the need for medical attention is also increasing proportionally. New methods and ways to reduce visits to a doctor or hospital is becoming a priority. Healthcare is an important aspect of one&#x2019;s daily life. With the evolving technology and advancement in science, smart healthcare applications are being implemented. Big data and IoT brought the smartness in healthcare system. The data can be accessed by anyone, anytime and from anywhere, which gives a flexibility to use the online platform for multiple users at the same time. It is faster, cheaper, and easily accessible. Although, the applications are cost effective and impressive to a certain extent, a major issue arises in the form of security of data which is being shared online. Fraudsters make use of the patient or doctor details to claim insurance and buy drugs. This paper presents a study on the development of smart healthcare applications and discuses the security measures to protect user&#x2019;s ID and their details.
Early Identification of Active Developers Based on their Past Contributions in OSS Projects

Open Source Software (OSS) developers are free to contribute and free to leave a project, if the project is (not) suitable for them. On the one hand, OSS projects need to manage the human resource to continuously maintain OSS in the future. Some existing studies proposed an approach that estimates how long developers contribute to OSS projects. Using developers&#x2019; contributions during the first few months in the target project, the proposed model identified long-term contributors or core developers. However, the approach frequently miss to find capable developers because many developers leave the project soon after participating. To avoid the loss of capable developers, this study build a prediction model to identify future active developers based on their past contributions to any OSS projects. Using dataset from four large-scale OSS projects as a case study, we evaluated our proposed model to identify future active developers based on their past contributions to any OSS projects before participating in a future target project. Our proposed approach contributes to manage human resource in OSS development process.
Blockchain Technology Used in the Mid-Day Meal Scheme Program Supply Chain Management

This study aims to apply blockchain technology in the supply chain through a mid-day meal scheme run in India to provide better nutritional value to the children in primary and upper primary schools. This technology has gained a lot of attention from researchers, engineers, and practitioners in different sectors around the world. Current mid-day meal scheme in India has lot of unchecked processes which can lead to low quality and unhealthy meals for the students. There is a lot of corruption too in this supply chain of mid-day meal, and it is difficult to keep track at which point the discrepancy might have occurred. Having previous applications of the blockchain technology in the supply chain, the exploration of this technology in mid-day meal schemes has a lot of potential. What does it do? This digital technology can make the system immutable, fully tolerant, transparent, and fully traceable. It should keep in check the movement of the meals over the supply chain and the data for which will abstain from tampering. The leveraging of this technology should redefine the supply chain operations giving better performance, distributed governance, and process automation. The trust and performance issues with the previous intermediaries will be resolved. The smart contracts, Web portals, and blockchain frameworks in the supply chain would make the transactions smooth, efficient, and trustworthy. The future application for this can include integrating IoT to help keep the quality of the meal under check and AI for better analysis and automation.
PancreaSys: An Automated Cloud-Based Pancreatic Cancer Grading System

Pancreatic cancer is one of the deadliest diseases which has taken millions of lives over the past 20 years. Due to challenges in grading pancreatic cancer, this study presents an automated cloud-based system, utilizing a convolutional neural network deep learning (DL) approach to classifying four classes of pancreatic cancer grade from pathology image into Normal, Grade I, Grade II, and Grade III. This cloud-based system, named PancreaSys, takes an input of high power field images from the web user interface, slices them into smaller patches, makes predictions, and stitches back the patches before returning the final result to the pathologist. Anvil and Google Colab are used as the backbone of the system to build a web user interface for deploying the DL model in the classification of the cancer grade. This work employs the transfer learning approach on a pre-trained DenseNet201 model with data augmentation to alleviate the small dataset’s challenges. A 5-fold cross-validation (CV) was employed to ensure all samples in a dataset were used to evaluate and mitigate selection bias during splitting the dataset into 80% training and 20% validation sets. The experiments were done on three different datasets (May Grunwald-Giemsa (MGG), hematoxylin and eosin (H&amp;amp;E), and a mixture of both, called the Mixed dataset) to observe the model performance on two different pathology stains (MGG and H&amp;amp;E). Promising performances are reported in predicting the pancreatic cancer grade from pathology images, with a mean f1-score of 0.88, 0.96, and 0.89 for the MGG, H&amp;amp;E, and Mixed datasets, respectively. The outcome from this research is expected to serve as a prognosis system for the pathologist in providing accurate grading for pancreatic cancer in pathological images.
Extracting football players video sprites from broadcast video

The main focus of this paper is to explore an alternative method in video sprite extraction. For the case of this work, the priority is creating a dataset of football player&#x0027;s video sprites - directly from footage of broadcast football matches. The quality of the generated dataset is essential, considering that the obtained dataset can be used for further accurate 2D and 3D reconstructions. At its core, our approach relies on Mask R-CNN to get the player&#x0027;s image masks. The process is prone to multiple errors, depending on the player&#x0027;s pose and position. If we have a semi-supervised dataset collecting process, we can leverage the curated images, and then we use a variational autoencoder (VAE). In the reconstruction phase, many uncommon image masks will be converted to more generic ones, thus, improving the process of creating the dataset.
Buy/Hold/Trade or Sell/Divest/Disengage

An electronic hive mind (EHM) can be a distributed virtual community and a mental space for information-gathering, analysis, and ultimately, decision making; it can play the role of executive functioning (in the same way a frontal lobe does for a human brain) and inform real-world actions. To see how this might function, the EHM around cryptocurrencies was explored from multiple social media platforms. This topic addresses an issue that is not fully defined and is of broad-scale mainstream interests. Cryptocurrencies may be everything from virtual ephemera and hot promises to a life-changing innovation. As a phenomenon, it has instantiated in different ways around the world, with cryptocurrency “farming” centers, nation-state-issued cryptocurrencies, government efforts at regulating such exchanges, and volatile gains and losses for cryptocurrency speculators and investors. How people engage with cryptocurrencies can affect their real-world net worth as well as other aspects of their lives, so this is not merely a theoretical issue but one with real-world impacts. This work explores three hypotheses around social messaging, the general membership of the target electronic hive mind, and mass virtual executive functioning and discovers a mind hyped on seductive promise.
Decentralizing Certificates Issuance Through Blockchain

When the monetary or financial value is not take into account in blockchain, there is still room for a digital ledger system for universities. As in any institution, universities have processes defined in a unique way, involving many verification steps. Issuance of end-of-course certificates requires fulfillment of multiple requirements, i.e. signatures from authorities, instructors and in some cases administrative staff. This work presents an initial work for certificates issuance using blockchain for education purposes. We generate a prototype including no signature but registered nodes to record transactions over the infrastructure generated for the Smart Ecosystem for Learning and Inclusion or SELI project. This prototype records certificates in a private non-monetary blockchain network, and is provided as an open source project. Countries like Ecuador, Turkey, Uruguay, and Finland can share certificates from the SELI platform through local nodes. This article provides relevant details about the implementation of the system, always with the aim of reusing existing software to reduce implementation time. The main result is that the project has generated a simple international university network with three countries for the exchange of certificates.
A New Technique for Reducing the Segmentation Error of Left Ventricle Contours using Magnetic Resonance Images

The number of Cardiac Magnetic Resonance Images produced for a patient is overwhelming, and this leads to several issues such as labour intensive, time-consuming and detected contours error. The current practice to evaluate the Cardiac Magnetic Resonance Images by the experts is either manual or semi-automatic. Ideally, an automatic evaluation is preferred to assist the cardiac experts in their clinical evaluation. The automatic segmentation for the left ventricle that is the endocardium (Endo) and epicardium (Epi) is currently lacking. The two most usable segmentation model approaches namely Level Set Model (LSM) and Variational LSM (VLSM) are very popular because of their fast iterating of the contours shape objects. Due to the unstructured LV shape and papillary muscles of the left ventricle, both models issue was re-initialisation on detected contours. In this paper presented, the combined Sign-Euclidean distance function takes the distance measurement, from centre to endocardium contour towards Epicontour. The distance measurement function using the distance mapping technique is guided by the curves line using energy function to reduce segmentation error. The experiments were conducted utilizing the Sunnybrook and Pusat Jantung Sarawak (PJS) cardiac datasets. The results shows that the Sign-Euclidean distance function reduces segmentation error between segmented contours, the highest error identified in Endo-contour is; HF-I05 (Endo-14.74); HF-NI-11 (Endo-8.79); P-A004 (Endo-8.04); in Epi-contours; HF-I-08 (Epi-3.08); HF-NI-07 (Epi-2.81); P-AOOI (Epi-3.34). This paper aims to develop a combined Sign-Euclidean distance function that measures among segmented contours and reduces segmentation error against ground-truth contour.
Progressive and compressive learning

The expressive power of deep neural networks has enabled us to successfully tackle several modeling problems in computer vision, natural language processing, and financial forecasting in the last few years. Nowadays, neural networks achieving state-of-the-art (SoTA) performance in any field can be formed by hundreds of layers with millions of parameters. While achieving impressive performances, it is often required several days with high-end hardware in order to optimize a single SoTA neural network. But more importantly, it took several years of experiments for the community to gradually discover more and more efficient neural network architectures, going from VGGNet to ResNet, then DenseNet. In addition to the expensive and time-consuming experimentation process, SoTA neural networks, which require powerful processors to run, cannot be easily deployed to mobile or embedded devices. For these reasons, improving the training and deployment efficiency of deep neural networks has become an important area of research in the deep learning community. In this chapter, we will cover two topics, namely progressive neural network learning and compressive learning, which have been extensively developed recently to enhance the training and deployment of deep models.
Predicting Tamil Nadu Election 2021 Results Using Sentimental Analysis Before Counting

Sentiment analysis (SA) could be a way to examine people’s suppositions from composed dialects. These days, due to the development of social media, people’s conclusions are exceptionally pivotal for decision-making in each viewpoint. Apart from counting the individual votes, techniques such as sentimental machine learning models can be used to classify the text to find whether the given statement is in favor or not to the particular political party from collecting the data from social media like Facebook and Twitter. In this work, an attempt is made to apply different classifiers like Naive Bayes (NB) and support vector machine (SVM) in predicting the result of the Tamil Nadu election 2021 from Twitter and Facebook posts. The model was built using the tweets obtained before the counting of votes began on May 2nd, 2021. Using the model, the results in Tamil Nadu could be predicted easily.
Fine tuning a deep neural network to localize low magnitude earthquakes

A main challenge in microseismic monitoring is that the seismic signals recorded at the Earth&#x0027;s surface are weak and thus localization of those microseismic earthquakes becomes challenging. Diffraction stacking is a traditional method used to localize weak earthquakes, which involves stacking the waveforms along precomputed travel-time curves from different locations, where the maximum is used to determine the source location. In this work we aim to recover the source location of weak microseismic earthquakes using a deep neural network (DNN) that resembles the U-Net but uses fewer skip connections. However, the size of the field data is too small to train the DNN from scratch. Thus, we propose to pretrain a DNN using synthetic data that resembles the field data and that learns to map the source location in terms of a 3D Gaussian distribution directly from the seismic signals. This pretrained DNN is capable of localizing the higher magnitude earthquakes in the field data, but fails for the weaker earthquakes. To be able to localize the weaker magnitude earthquakes we therefore, fine tune the pretrained DNN using the higher magnitude field-data earthquakes. We observe that the updated model is able to extrapolate the information learned during the fine tuning step from higher magnitude earthquake data to lower magnitude earthquake data.
A Reputation Model for Aggregating Rating Levels Using Beta Distribution

With the speedy advancement in technology, modern electronic marketing has typically deployed the use of the World Wide Web for transactions online. The use of the web have significant challenges and decision making in retail for launching new products and services in an open environment of high risk. It is imperative to enhance and safeguard product's name and trust between the buyer and the seller to reduce such threats from the users' perceptive. This call for the development of rating clustering methods such as simple mean, naive, and normal distribution methods using the customers’ rating views to generate product or service reputation scores for administrative judgment. However, the existing models only considered rating level weights and average weights within the unbounded limit. This paper proposes a method to produce a more accurate and effective reputation score using the standard beta-distribution of a bounded interval. This method will take into account both sparse and dense datasets to generate a reputation score.
Do Predictive Models Always Deteriorate in Performance with Time? A Case Study in Elective Mortality Predictive Model Performance

It is generally observed and assumed that machine learning-based predictive models deteriorate in performance with increasing time passed subsequent to training. In this study we utilized a large, real-world dataset comprising approximately 1.8 million patient admissions to hospitals in the US state of Maryland for the years 2016, 2017 and 2018 to consider model performance maintenance. In particular we developed predictive models to predict the in-hospital mortality of elective admission patients, predicted at the time of scheduling an elective admission. These models were trained on data from the first calendar half of 2016 and were then evaluated on admissions from each subsequent half year period until the end of 2018. The performance maintenance observed is not what is typically anticipated and we consider possible factors explaining this performance behavior and given the large real-world datasets involved, discuss what may be the implications for other real-world predictive model applications.
Applying Efforts for Behavior-Based Authentication for Mobile Cloud Security

Mobile devices have become smart for their ability to access different and complex applications. Now, these devices can also sense human behavior and differentiate among the two subjects’ habits. The security concerns become more critical when the unauthorized user accessing the network services, using a paired network device (i.e., smart phone or laptop). Therefore, an authentication system is required that continuously observe the activities of the end device user, using a continuous authentication system. The proposed authentication model involves the machine learning-based techniques to analyze the application usages patterns to discover the misleading user behavior. In addition, the correlation coefficient is used for feature ranking. In this work, a data mining model is developed to provide a behavior-based authentication system for mobile devices. The proposed data model includes the PSO for feature selection or dimensionality reduction of the behavioral dataset. Additionally for learning and classification of behaviors, C4.5 or J48 decision tree is used. The experimentations are conducted on a keystroke dataset, and the classification accuracy achieved is 90.01%.
Learning Latent Variable Models with Regularization

Learning from data needs a good representation of the data. In many applications, data are often represented by many features, which makes learning more difficult. This becomes particularly challenging if there are more features than available training examples, which can cause over-fitting. Many techniques have been proposed in the literature for dimensional-ity reduction by learning latent variable models. However, many of these techniques generally require a large amount of data to avoid overfitting. In this paper, we introduce a regularization technique for learning latent variable models to address the overfitting problem. The proposed regularization technique has the potential to take into account the local curvatures described by row and column variance matrices. Furthermore, when the precision matrices are identity, the proposed technique is related to learning factor models with trace penalization. We provide empirical evidence that validates the proposed technique.
Computing Movie Script Similarity with Neural Word Embeddings

Typical applications of machine learning in the movie domain are box office revenue prediction and recommender systems using features derived from viewers&#x0027; behavioral data, casting, budget, etc. None of these models have used a movie&#x0027;s script as their key feature, which is one of the only artifacts available early in the production process. We examine film similarity taking into account only the film&#x0027;s script, with a view to support decision making early in the movie production process. We describe two models for calculating movie script similarity, one based on a one-hot vector encoding, and the second using neural word embeddings (namely, word2vec). In a quantitative evaluation, we asked 3 human expert annotators to rank the output of each algorithm, and a random baseline, on a test set of 45 films stratified across many genres. The model using word2vec embeddings ranked first in 61 &#x0025; of annotator judgments and ranked above the one-hot vector encoding in 70&#x0025; of rankings.
On the Naming Recommendations Derived from Expert Python Code

Python's naming convention provides suggestions on naming style, but these suggestions are not always clear for beginners who struggle to understand the usefulness of the conventions. In this research, we studied two properties that affect naming, namely, name-length and style. We analyzed python code from five professional project libraries and compared the name statistics with those obtained from student code. Our results show that, compared to students' work, expert developers use longer names (more descriptive) and are more closely compliant with the existing python naming convention. This research provides specific recommendations on the length and style of naming integers, floats, data structures, functions, function arguments and classes for students of Python.