Reviewerly/exHarmony huggingface dataset:
Discovering Relationships Among Software Artifacts

Software systems have become ubiquitous in today's world. Most software will evolve after initial deployment. Software changes that are a part of that evolution often are documented in a requirements change document. One of the challenges when changing software is understanding the portions of the existing requirements and the existing code that could be affected by the change in order to avoid or minimize unexpected side effects from the changes. Researchers have addressed the problem of minimizing the effect of changes by using different methods, including text mining and clustering. Some approaches to determine change impact are based on information retrieval (IR) techniques using both term frequency-inverse document frequency (TF-IDF) and latent semantic indexing (LSI) methods. Other approaches are based on visualization techniques using degree and betweenness centrality measures. In this research, we approach the problem by applying IR techniques along with data mining. We apply TF-IDF and LSI to investigate which changes have a high potential of modifying existing requirements. We also analyze similarities between changes that do not map to existing requirements. In both cases, our threshold for identifying similarity is 80%. We designed our approach to identify, for a given change, one or more requirements that have a high potential of being associated with the change as well as identifying intra-document requirements or changes that have a high potential for consolidation. We were able to identify requirements that had a similarity of at least 80% to a change request using TF-IDF and LSI. We were also able to isolate changes that did not show a high similarity to any requirement, thus indicating that the change request was likely a request for a new requirement. The results are encouraging for assessing the impact of software change requests on requirements of an existing system.
Video Review Analysis via Transformer-Based Sentiment Change Detection

With the popularization of video websites, huge amounts of reviews have been generated by the audience online. These reviews usually reflect people's sentiment changes which are valuable information for understanding the market and guiding social media operation. A number of studies analyzing sentiment changes have made great progress. However, most of them are not sensitive enough to detect sentiment changes thoroughly and subtly. To solve this problem, this paper proposes a transformer-based sentiment change detection model by using the Anomaly Clearing Cumulative SUM (ACCSUM) model to analyze review streams and distinguish sentiment change points therein. Our proposed model can detect sentiment change points precisely and thoroughly by excluding abnormal reviews from review streams. Experiment results show that our model can find sentiment change points with greater sentiment gap.
Self-Learning System for Child Development Using Conversational AI and Natural Language Processing (NLP)

Education and learning are the most important aspects of the evolution of societies. They have been a favorite subject for philosophers and psychologists to work upon. Same questions are now being re-dealt by computer scientists in current scenarios. Although evolution is a continuous process, the pace of evolution is not a linear graph. Children acquire a huge amount of knowledge with very little input from teachers, friends, parents, and surroundings. Understanding how human brain works and more precisely, how the child brain actually functions is opening the path of researches in artificial intelligence (AI).
Radiation Source Identification based on Box Dimension of PF and SIB Fusion

To solve the problem that single characteristic of radio transmitters in low-end receivers is not ideal for the individual identification of radiation sources, the algorithm that box dimension of Product Function (PF) decomposed by Local Mean Decomposition(LMD) fuses Rectangular Integral Bispectrum(SIB) is proposed. Firstly, the acquired time domain signal is decomposed by LMD to obtain a set of PFs, and the box dimension of the PF is used as the radiation source feature. Then, the SIB is estimated by the signal high-order spectrum, and the SIB after dimensionality reduction is taken as the feature. By Canonical Correlation Analysis (CCA), the two features are fused as the fusion feature, which is identified by Support Vector Machine (SVM). Finally, this algorithm is validated in experiments where USRP-2920 is regarded as the signal receiver. In the classification experiment of 6 walkie-talkies as signal source devices, the recognition rate of the fusion feature is 13%~28% higher than the single feature. This paper applies the LMD algorithm to the field of RF radiation source identification for the first time.
From Online to Non-i.i.d. Batch Learning

This paper initializes the study of online-to-batch conversion when the samples in batch learning are not i.i.d. Our motivation originated from two facts. First, sample sets in reality are seldom i.i.d., thus preventing the application of the existing conversions. Second, the online model of learning permits an adversarial stream of samples that almost for sure violates the i.i.d. assumption, raising the possibility of adapting an online algorithm effectively to learn from a non-i.i.d. sample set. We present a set of techniques to utilize an online algorithm as a black box to perform batch learning in the absence of the i.i.d. assumption. Our techniques are generic, and are applicable to virtually any online algorithms on classification. This provides strong evidence that the great variety of known algorithms in the online-learning literature can indeed be harnessed to learn from sufficiently-representative non-i.i.d. samples.
Performance On The Discrete Variable Based Satellite-to-Ground Quantum Key Distribution Links

This paper focuses on evaluating the available secure key lengths of the satellite-to-ground quantum key distribution (QKD) systems under multiple scenarios. The finite-length secure key rate calculation models are provided for the BB84 and BBM92 based systems, respectively, considering the practical optical link loss. The considered scenarios consist of the solar synchronous orbit satellite scenarios and the walker satellite constellation scenarios. For the BB84 protocol based QKD systems, it is shown that the amount of the secure key reduces slowly as the orbit height grows from 200km to 1100km. Increasing the size of the telescope improves the performance considerably when the deviation of the pointing error is small. However, if the pointing error becomes larger, the benefit from enlarging the telescope size shrinks heavily. On the contrary, the BBM92 protocol based QKD systems are much sensitive to the orbit height and gains much lower performance under the same configurations compared with those under the BB84 protocol. Moreover, the ground station located at higher latitude can achieve better performance than that at lower latitude in the walker satellite constellation with vertical orbital inclination.
Examining artifacts generated by setting Facebook Messenger as a default <scp>SMS</scp> application on Android: Implication for personal data privacy

Abstract The use of mobile devices and social media applications in organized crime is increasingly increasing. Facebook Messenger is the most popular social media applications used globally. Unprecedented time is spent by many interacting globally with known and unknown individuals using Facebook. During their interaction, personal information is uploaded. Thus, crafting a myriad of privacy trepidation to users. While there are researches performed on the forensic artifacts' extraction from Facebook, no research is conducted on setting Facebook Messenger applications as a default messaging application on Android. Two Android mobile devices were used for data generation and Facebook Messenger account was created. Disc imaging and data partition were examined and accessed to identify changes in the orca database of the application package using DB browser. The data were then generated using unique words which were used for conducting key searches. The research discovered that mqtt_log_event0.txt of the Com.Facebook.orca/Cache directory stores chat when messenger is set as a default messaging app. The research finding shows that chats are recorded under messages tab together with SMS of data/data/com.facebook.orca/databases/smstakeover_db and data/data/com.facebook.orca/databases/threads_db. This indicates that only smstakeover_db stores SMS messaging information when using messenger application. It is observed that once the user deletes a sent SMS message, the phone number and the deleted time stamp remained in the data/data/com.facebook.orca/databases/smstakeover_db database in the address_table are recoverable. The results suggest that anonymization of data is essential if Facebook chats are to be shared for further research into social media content.
A Comprehensive Methodology for Evaluating Conversation-Based Interfaces to Relational Databases (C-BIRDs)

Evaluation can be defined as a process of determining the significance of a research output. This is usually done by devising a well-structured study on this output using one or more evaluation measures in which a careful inspection is performed. This paper presents a review of evaluation techniques for Conversational Agents (CAs) and Natural Language Interfaces to Databases (NLIDBs). It then introduces the developed customized evaluation methodology for Conversation-Based Interface to Relational Databases (C-BIRDs). The evaluation methodology created has been divided into two groups of measures. The first is based on quantitative measures, including two measures: task success and dialogue length. The second group is based on a number of qualitative measures, including: prototype ease of use, naturalness of system responses, positive/negative emotion, appearance, text on screen, organization of information, and error message clarity. Then an elaboration is carried out on the devised methodology by adding a discussion and recommendations on the sample size, the experimental setup and the scaling in order to provide a comprehensive evaluation methodology for C-BIRDs. In conclusion the evaluation methodology created is better way for identifying the strengths and weaknesses of C-BIRDs in comparison to the usage of single measure evaluations.
Human-Machine Interaction for Improved Cybersecurity Named Entity Recognition Considering Semantic Similarity

The automated and timely conversion or extraction of cybersecurity information from unstructured text from online sources is important and required for many applications. Named Entity Recognition (NER) is used to detect the relevant domain entities such as product, attack name, malware name, hacker group name, etc. To train a new NER model for cybersecurity, traditional NER requires a training corpus annotated with cybersecurity entities and state-of-the-art methods require time-consuming and labor intensive feature engineering. We propose a Human-Machine Interaction method for semi-automatic labeling and corpus generation for cybersecurity entities. Our method evaluates the learned NER model with the sentences that we collected in the training process, and the user selects only the correct pair of the named entity and its category for next iteration training. Thus, each iteration gets better training corpora to train the NER model. Some entities are ambiguous since the word or phrase has multiple meanings. We introduce a new semantic similarity measure and determine which category the word belongs to based on this semantic similarity of the entire sentence. The experimental evaluation result shows that our method is better than existing methods in finding undiscovered keywords of given categories.
Online Memorization of Random Firing Sequences by a Recurrent Neural Network

This paper studies the capability of a recurrent neural network model to memorize random dynamical firing patterns by a simple local learning rule. Two modes of learning/memorization are considered: The first mode is strictly online, with a single pass through the data, while the second mode uses multiple passes through the data. In both modes, the learning is strictly local (quasi-Hebbian): At any given time step, only the weights between the neurons firing (or supposed to be firing) at the previous time step and those firing (or supposed to be firing) at the present time step are modified. The main result of the paper is an upper bound on the probability that the single-pass memorization is not perfect. It follows that the memorization capacity in this mode asymptotically scales like that of the classical Hopfield model (which, in contrast, memorizes static patterns). However, multiple-rounds memorization is shown to achieve a higher capacity (with a nonvanishing number of bits per connection/synapse). These mathematical findings may be helpful for understanding the functions of short-term memory and long-term memory in neuroscience.
Web Application Based on MVC Laravel Architecture for Online Shops

Working with traditional methods to develop a web application has great limitations, is very time-consuming and can lead to a number of unexpected errors. Therefore, a new technology, namely MVC pattern frameworks, was found by some companies to deal with such issues. In this paper, we present a design and implementation for a web-based application for e-commercial shops and third-parties to buy products from online shops using the Laravel framework. As result of our research, we were able to determine that the development was standardized and non-business logic relationships were automatically processed. Moreover, there was much scalability, which provided us with more efficiency through the implementations.
Digital Elevation Model Enhancement using CNN-Based Despeckled SAR Images

Synthetic Aperture Radar (SAR) imaging systems have the ability to acquire images of the terrain surface in all weather conditions and all day times. Digital Elevation Model (DEM) can be generated from two or more SAR images, and is considered essential in various recent applications. Acquired SAR images are often exposed to speckle noise, which has a negatively bad effect on the processing and interpretation of the SAR images, and hence on the DEM generation process. In this paper, a Convolution Neural Network (CNN) based preprocessing layer is suggested in the DEM generation process from SAR images. The main purpose of the suggested CNN based preprocessing layer is removing speckle noise from input SAR images, from which an enhanced DEM can be generated. Extensive experiments are carried out on SAR images, and different DEMs are generated from original SAR images and from despeckled ones. Comparative analysis is figured out, and results show significant enhancements in despeckled SAR images and in the subsequent generated DEMs.
An Integrated Blockchain Approach for Provenance of Rotorcraft Maintenance Data

The U.S. Army Engineer Research and Development Center's Information Technology Laboratory (ITL) is creating a Data Lake Ecosystem to support efficient storage, querying and analysis of terabyte-scale datasets within a High Performance Computing (HPC) environment. The datasets contain important current and historical data, and many datasets may be related to a single subject. The ecosystem must support the ability to perform holistic analysis across datasets and must ensure data integrity and security. For the latter, it is essential to keep a record of the creation and operations performed on a Data Lake object. This type of record-keeping is called data provenance. Assured data provenance can help detect the creation, manipulation, and deletion of objects within the Data Lake Ecosystem. However, developing assured data provenance remains a critical issue. There are challenges in secure collection and storage, verifiability, and privacy of the provenance data. Hence, there is a need for guaranteeing the security and integrity of data provenance for a Data Lake Ecosystem. In this paper, we propose a trusted data provenance application for rotorcraft maintenance data based on blockchain technology. Blockchain-based data provenance facilitates recording and tracking by treating the data as relevant assets in a transactional network. We designed and implemented a proof-of-concept application to collect and verify Data Lake provenance by embedding the data provenance on a private blockchain platform. This application allows the replication of data provenance on every node of a trusted closed network, ensuring high availability and fault tolerance. With the proposed blockchain model, data provenance for unique Data Lake objects can be stored securely and efficiently verified. Results from evaluations demonstrate that with blockchain technology, it is possible to provide secure, immutable, and reliable data provenance that is essential for maintaining the integrity of information in a Data Lake environment.
Clustering Ensembles Based on Probability Density Function Estimation

The traditional clustering algorithms rely excessively on the similarity of the geographic distance between objects, like DBSCAN, which is unlikely to handle uncertain objects that are geometrically indistinguishable. Attempting to prevent this reliance, a model that adopts a probability density estimation function for clustering ensembles is therefore proposed. In other words, the object would be assigned to its most appropriate cluster through mining the distribution of data in the base clusterings fully considering the relationship between the objects, and finding the probability density function that fits datasets. It is worth mentioning that the approach of generating base clusterings through data sampling not only relatively simple to extract potential relationships between objects due to random combinations but also is beneficial to cluster large data sets in the real world. It is first to extract a certain proportion of samples from the dataset, and subsequently to run the k-means algorithm to generate base clusterings repeatedly. Accordingly, an object-cluster association matrix set was conducted, which has summarized the base clusterings, namely the binary association matrix. Under the inspiration of the Bayesian classifier, the object is divided into its corresponding clusters determined by the prior probabilities and posterior probabilities of clusters. Secondly, we can easily obtain the discrete probability distributions of the data objects under the condition of each object’s label from the matrix, which is posterior probabilities of clusters, so one important point left in this paper is to estimate the probability distributions of object labels. To address this, a well-known kernel density estimation method was adopted to attain the cluster probability distributions. Finally, the data objects are assigned to their most likely clusters via calculating the Bayesian formula. Extensive experimental results of the proposed method on several datasets were compared with other clustering ensembles algorithms, demonstrating the effectiveness, efficiency, and scalability.
Javastylosis

To solve some critical issues raised by anastylosis techniques, such as the deterioration of the original fragments due to continuous handling, and to compare alternative matching solutions for choosing the best one for real restoration, the pattern recognition and computer vision communities are proposing approaches that work on virtual copies of the fragments, providing new methodologies that help in the matching task. In this paper, we present Javastylosis, a tool that can support restorers for reassembling 2D images of damaged frescoes. With this computer-assisted methodology the user can apply chromatic and semantics principles. Each virtual fragments can be moved and rotated, and the same operations can be applied on groups of fragments. During the matching task, spurious elements can be discarded, and the final result is a digital solution of the 'puzzles' made by the artwork fragments, providing a table of coordinates useful to the restorers to correctly reposition the real fragments. The graphical interface, designed to be easy and intuitive, has been studied for and tested by people belonging to the autism spectrum but with very acute visual skills, so that inclusion opportunities can be given to this group of disadvantaged people.
Constructive Spherical Codes by Hopf Foliations

We present a new systematic approach to constructing spherical codes in dimensions $2^k$, based on Hopf foliations. Using the fact that a sphere $S^{2n-1}$ is foliated by manifolds $S_{\cos\eta}^{n-1} \times S_{\sin\eta}^{n-1}$, $\eta\in[0,\pi/2]$, we distribute points in dimension $2^k$ via a recursive algorithm from a basic construction in $\mathbb{R}^4$. Our procedure outperforms some current constructive methods in several small-distance regimes and constitutes a compromise between achieving a large number of codewords for a minimum given distance and effective constructiveness with low encoding computational cost. Bounds for the asymptotic density are derived and compared with other constructions. The encoding process has storage complexity $O(n)$ and time complexity $O(n \log n)$. We also propose a sub-optimal decoding procedure, which does not require storing the codebook and has time complexity $O(n \log n)$.
Using Data Mining Methods to Detect Medical Fraud

Medical fraudulent activities have made medical insurance expenditures rise year by year. This not only increases the burden on the medical and financial system, but also makes it difficult for many people in need to obtain these resources. Therefore, how to solve this problem has become one of critical issues. Therefore, this study aims to establish a predictive model of medical insurance fraud through data mining methods, and attempts to discover important factors affecting fraud. In this work, we will use Decision Tree (DT), Support Vector Machines (SVM), and Back Propagation Neural Networks (BPN) to establish classification models. A comparison of these three methods will be done. And, we will use decision trees to extract important factors that could provide important information for effectively detect medical fraud. Hopefully, we can effectively reduce the negative impact of medical insurance fraud.
Exploring the Use of Machine Learning as Game Mechanic – Demonstrative Learning Multiplayer Game Prototype

Games and Machine Learning (ML) most commonly intersect in the areas: 1) ML testing in Games, 2) ML use for Game-AI and 3) ML use for game assets. These areas have seen strong interest in the past, but the use of ML as main game mechanic has been less explored so far. Most likely due to a lack of real-time learning capabilities and inaccessibility of algorithms to the gaming community in the past. While some game-like experiences like the "Game of life"-type simulations exist and versions of it use ML, these usually provide minimal interaction and confine the player to initial parameter setup and subsequent watching of a simulation. This paper describes a digital game prototype which explores using Demonstrative Learning in a multi-player setting as main game mechanic, to achieve interaction levels similar to Interactive Machine Learning. We describe the process the considerations that have led to using this type of ML among other decisions related to finding suitable compromises between capabilities and needs of ML and ideal player experience. The prototype is used to discuss game design choices and shows how a focus on the ML-mechanics can inspire game design and facilitate creative design and future research and design possibilities.