Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis Layer Implementation Method for a Streaming Data Processing System

Analysis is an important part of the widely used streaming data processing. The frequency of flow element occurrence and their values sum are calculated during analysis. The algorithms like Count-Min Sketch and others give a big error in restoring the aggregate with a large number of elements. The article proposes application of a vector matrix. Each vector has a length of 'n'. If the number of different elements approaches 'n', then the window size is automatically reduced. This allows accurate storage of the aggregate without element loss. The SELECT operator for searching in a vector array is also proposed. It allows getting various slices of the aggregated data accumulated over the window. The comparison of the developed method with the Count-Min Sketch data processing method in the Analysis Layer was performed. The experiment showed that the method based on the vector matrix more than twice reduces memory consumption. It also ensures the exact SELECT statement execution. An introduction of a floating window allows maintaining the calculation accuracy and avoiding losing records from the stream. The same query sketch-based execution
Agent-based Decentral Production Planning and Control: A New Approach for Multi-resource Scheduling

Manufacturing jobs commonly require more than one resource in order to equip machines with tools and process jobs. To achieve a feasible production plan and control its execution in an agent-based decentral production we developed a new approach presented in this paper. We introduce a negotiation procedure, based on the job priority and overlapping time slots across all resources. In addition, we provide simulative evidence that our approach is superior, in terms of time-based key performance indicators, to commonly used queuing procedures and the approach provides a more stable production under uncertain customer order arrivals and deviating processing times.
An Endogenous and Self-organizing Approach for the Federation of Autonomous MQTT Brokers

: Many applications for the Internet of Things (IoT) use the publish/subscribe (P/S) communication paradigm. Among the most representative protocols, there is MQTT. Its basic architecture relies on a single server/broker: publishers send data topics to the broker, and then it forwards the data to subscribers. Having a single server may make things easier for conﬁguration and management; however, there is room for a potential bottleneck, besides being a single point of failure. Clustering servers usually address scalability in MQTT broker deployment; however, most solutions are proprietary. Meanwhile, autonomous brokers could be federated together to scale and increase availability. A self-organizing federation proposal already available in the literature implies substantial changes to brokers’ inner implementation; furthermore, there has not been any implementation yet. This work explores an endogenous federation approach: design a supporting agent (called federator) that realizes the brokers’ federation based on the native P/S mechanism. There are no implied modiﬁcations to regular/standard brokers, but it requires changes to the client-side (i.e., publishers and subscribers). This work presents a primary architecture and an initial case study to grasp some fundamentals and beneﬁts of adopting the proposed solution.
Census TopDown: The Impacts of Differential Privacy on Redistricting

The 2020 Decennial Census will be released with a new disclosure avoidance system in place, putting differential privacy in the spotlight for a wide range of data users. We consider several key applications of Census data in redistricting, developing tools and demonstrations for practitioners who are concerned about the impacts of this new noising algorithm called TopDown. Based on a close look at reconstructed Texas data, we find reassuring evidence that TopDown will not threaten the ability to produce districts with tolerable population balance or to detect signals of racial polarization for Voting Rights Act enforcement.
Physicochemical mechanisms of plasma-liquid interactions within plasma channels in liquid

The goal of this study is to advance the fundamental understanding of the physical and chemical mechanisms by which excited radical species produced by electrical plasmas directly in water, OH radicals especially, induce chemical changes in aqueous organic compounds and to exploit this for the development and optimization of drinking and wastewater plasma-based treatment systems. To achieve this goal, this study measured and correlated the production rate of hydrogen peroxide (H2O2) with physicochemical properties of 11 organic compounds. The observed individual correlations between the investigated physicochemical properties and the resulting H2O2 concentrations were used to develop an equation that would allow predicting the measured H2O2 concentration from physicochemical properties of a compound. Results reveal that the production rate of H2O2 directly depends on the surface tension of the solution and compounds’ bulk liquid concentration, hydrophobicity (Kow value), and molecular volume. Other properties such as vapor pressure, Henry’s constant, enthalpy of vaporization, ionization energy, electron affinity, and molecular dipole moment do not affect the H2O2 chemistry. Kow value and surface tension of the solution determine the compound’s concentration at the plasma interface. Once at the interface, the molecular volume determines the rate at which the molecule will react with OH radicals.
FakeWhastApp.BR: NLP and Machine Learning Techniques for Misinformation Detection in Brazilian Portuguese WhatsApp Messages

In the past few years, the large-scale dissemination of misinformation through social media has become a critical issue, harming the trustworthiness of legit information, social stability, democracy and public health. Thus, developing automated misinformation detection methods has become a field of high interests both in academia and in industry. In many developing countries such as Brazil, India, and Mexico, one of the primary sources of misinformation is the messaging application WhatsApp. Despite this scenario, due to the private messaging nature of WhatsApp, there still few methods of misinformation detection developed specifically for this platform. In this work we present the FakeWhatsApp.BR, a dataset of WhatsApp messages in Brazilian Portuguese, collected from Brazilian public groups and manually labeled. Besides, we evaluated a series of misinformation classifiers combining Natural Language Processing-based techniques of feature extraction and a set of well-know machine learning algorithms, totaling 108 different scenarios. Our best result achieved a F1 score of 0.73, and the analysis of errors indicates that they occur mainly due to the predominance of short texts that accompany media files. When texts with less than 50 words are filtered, the F1 score rises to 0.87.
The VoIP PBX Honeypot Advance Persistent Threat Analysis

PBX hacking is a multi-billion dollar per year criminal and terrorism funding source. This paper follows on from a previous 10-day Honeypot experiment, to run a VoIP PBX Honeypot for a longer period of 103-day to not only validate any similarities, but to also analyse non-VoIP methods hackers use in an attempt to gain access to a VoIP System. Over the 103-day data collection period, the Honeypot recorded over 100 million SIP messages. Different techniques were used (including SQL injections in Invites) and hackers of the same IP subnet also attempted using web vulnerabilities in different telephony phone systems to gain access. Of specific interest, over the Christmas period of 2018, attack intensity decreased significantly. To validate these findings, the Honeypot experiment was also conducted for a short period over the Christmas period of 2019 which found that unlike Christmas 2018, attacks increased. The sophistication, scale and complexity of the fraud would suggest an Advance Persistent Threat exists with an aim to infiltrate a VoIP system (including a PBX) to conduct Toll Fraud and where possible to also add that system to a botnet of infected voice systems.
Challenges in Data Acquisition and Management in Big Data Environments

In the recent years, the term big data has attracted a lot of attention. It refers to the processing of data that is characterized mainly by 4Vs, namely volume, velocity, variety and veracity. The need for collecting and analysing big data has increased manifolds these days as organizations want to derive meaningful information out of any data that is available and create value for the business. A challenge that comes with big data is inferior data quality due to which a lot of time is spent on data cleaning. One prerequisite for solving data quality issues is to understand the reasons for their occurrence. In this paper, we discuss various issues that cause reduced quality of the data during the acquisition and management. Furthermore, we extend the research to categorize the quality of data with respect to the identified issues.
Citation Segmentation from Sparse & Noisy Data: An Unsupervised Joint Inference Approach with Markov Logic Networks

Bibliographische Daten sind das Rückgrat der wissenschaftlichen Forschung. Liegen sie nur in Druckform vor, können sie ihr Potential nicht voll entfalten. Erst ihre Speicherung in bibliographischen (Online-) Datenbanken öffnet effiziente Suchmöglichkeiten für den zeitgemäßen und breitgefächerten Einsatz in internationalen Forschungsgemeinschaften. Zu diesem Zweck ist es erforderlich, die Materialien zu digitalisieren und die den bibliographischen Referenzen innewohnende Struktur automatisch zu erkennen, indem einzelne Felder (z. B. Autor, Titel, Erscheinungsort) extrahiert werden.
Evaluating Random Input Generation Strategies for Accessibility Testing

Mobile accessibility testing is the process of checking whether a mobile app can be perceived, understood, and operated by a wide range of users. Accessibility testing tools can support this activity by automatically generating user inputs to navigate through the app under evaluation and run accessibility checks in each new discovered screen. The algorithm that determines which user input will be generated to simulate the user interaction plays a pivotal role in such an approach. In the state of the art approaches, a Uniform Random algorithm is usually employed. In this paper, we compared the results of the default algorithm implemented by a state of the art tool with four different biased random strategies taking into account the number of activities executed, screen states traversed, and accessibility violations revealed. Our results show that the default algorithm had the worst performance while the algorithm biased towards different weights assigned to specific actions and widgets had the best performance.
Entity Resolution in Large Patent Databases: An Optimization Approach

Entity resolution in databases focuses on detecting and merging entities that refer to the same real-world object. Collective resolution is among the most prominent mechanisms suggested to address this challenge since the resolution decisions are not made independently, but are based on the available relationships within the data. In this paper, we introduce a novel resolution approach that combines the essence of collective resolution with rules and transformations among entity attributes and values. We illustrate how the approach’s parameters are optimized based on a global optimization algorithm, i.e., simulated annealing, and explain how this optimization is performed using a small training set. The quality of the approach is verified through an extensive experimental evaluation with 40M real-world scientific entities from the Patstat database.
A Practical Guide to Support Predictive Tasks in Data Science

Currently, professionals from the most diverse areas of knowledge need to explore their data repositories in order to extract knowledge and create new products or services. Several tools have been proposed in order to facilitate the tasks involved in the Data Science lifecycle. However, such tools require their users to have specific (and deep) knowledge in different areas of Computing and Statistics, making their use practically unfeasible for non-specialist professionals in data science. In this paper, we propose a guideline to support predictive tasks in data science. In addition to being useful for non-experts in Data Science, the proposed guideline can support data scientists, data engineers or programmers which are starting to deal with predictive tasks. Besides, we present a tool, called DSAdvisor, which follows the stages of the proposed guideline. DSAdvisor aims to encourage non-expert users to build machine learning models to solve predictive tasks, extracting knowledge from their own data repositories. More specifically, DSAdvisor guides these professionals in predictive tasks involving regression and classification.
Recommendation of Academic Collaborators: A Methodology Incorporating Word Embedding and Network Embedding

: Fruitful academic collaborations have become increasingly more important for solving scientific problems, participating in research projects, and improving productivity. As such, frameworks for recommending suitable collaborators are attracting extensive attention from scholars. In an effort to improve on the current solutions, we developed an approach that produces recommendations with better precision, recall, and accuracy. Our strategy is to leverage the benefits of the two most common similarity indicators for collaborator recommendation – research interests and co-authorship network topology into one unified framework. A Word2Vec model creates word embeddings of research interests, which solves the problem of calculating similarity solely based on co-occurrence, not context, while a Node2Vec model automatically extracts and learns the topological features of a co-authorship network, moving beyond just local features to capture global network features as well. The two similarity measures are then fused with CombMNZ resulting in a ranked list of recommended collaborators for the target scholar(s). The workings of the framework and its benefits are demonstrated through a case study on academics in the field of intelligent driving and a comparison with the two most commonly used baselines: Random Walk with Restart (RWR) and Latent Dirichlet Allocation (LDA). Our framework should be of benefit for academics, research centers, and private-enterprise R&D managers to find partners, so as to achieve the ultimate goal of completing research projects, solving scientific problems, and promoting discipline development and progress.
Three Is Enough for Steiner Trees

In the Steiner tree problem, the input consists of an edge-weighted graph G together with a set S of terminal vertices. The goal is to find a minimum weight tree in G that spans all terminals. This fundamental NP-hard problem has direct applications in many subfields of combinatorial optimization, such as planning, scheduling, etc. In this work we introduce a new heuristic for the Steiner tree problem, based on a simple routine for improving the cost of sub-optimal Steiner trees: first, the sub-optimal tree is split into three connected components, and then these components are reconnected by using an algorithm that computes an optimal Steiner tree with 3-terminals (the roots of the three components). We have implemented our heuristic into a solver and compared it with several state-of-the-art solvers on well-known data sets. Our solver performs very well across all the data sets, and outperforms most of the other benchmarked solvers on very large graphs, which have been either obtained from real-world applications or from randomly generated data sets.
C*DynaConf: An Apache Cassandra Auto-tuning Tool for Internet of Things Data

Internet of Things environments may generate massive volumes of time series data, with specific characteristics that must be considered to facilitate its storage. The Apache Cassandra NoSQL database provides compaction strategies that improve data pages’ organization, benefiting the storage and query performance for time series data. This study exploits the temporal characteristics of IoT data, and proposes an engine called C*DynaConf based on the TWCS (Time Window Compaction Strategy), which dynamically changes its compaction parameters according to configurations previously defined as optimal, considering current metadata and metrics from the database. The results show that the engine’s use brought a 4.52% average gain in operations performed compared to a test case with optimal initial configuration that changes the scenario’s characteristics change over time.
A Principled Approach to Enriching Security-related Data for Running Processes through Statistics and Natural Language Processing

: We propose a principled method of enriching security related information for running processes. Our methodology applies to large organizational infrastructures, where information is properly collected and stored. The data we use is based on the Hubble Stack (an open-source project), but any alternative solution that provides the same type of information will sufﬁce. Using statistical and natural language processing (NLP) methods we enrich our data with tags and we provide an analysis on how these tags can be used in Machine Learning approaches for anomaly detection.
Adaptive Complex Data-Intensive Web Systems via Object-oriented Paradigms: A Real-life Case Study

This paper focuses the attention on the emerging class of Adaptive Complex Data-Intensive Web Systems (ACDIWS), which start from classical Adaptive Web Systems (AWS) and add innovative characteristics of complex application scenarios and big Web data. One among the state-of-the-art results is represented by the OO-XAHM framework, which makes use of an object-oriented approach for achieving the adaptation effect over complex data-intensive Web systems. Along this fortunate line of research, this paper contributes to the research context with a real-life case study that shows the potentialities of OO-XAHM on the Web portal of the well-known Italian archaeological site Pompeii.
Research on Optimization of 4G-LTE Wireless Network Cells Anomaly Diagnosis Algorithm based on Multidimensional Time Series Data

With the continuous increase of network terminal equipment, the operation scenarios of 4G-LTE wireless networks are becoming more and more complex. The traditional manual method of analysis and screening of network cell equipment can no longer meet the needs of production. Therefore, an efficient wireless network cell abnormality diagnosis algorithm is needed to screen abnormalities of equipment to improve operation and maintenance efficiency. In view of the fact that the existing single-dimensional anomaly diagnosis algorithm cannot achieve fully automated detection and the existing multidimensional anomaly diagnosis algorithm has low detection efficiency on multidimensional time series data, there are a large number of errors and omissions. This paper proposes a multidimensional time series data based on 4G-LTE wireless network cell anomaly diagnosis optimization algorithm uses small-sample supervised algorithms to assist the training of massive-sample unsupervised algorithms, thereby improving the detection performance of unsupervised learning algorithms. This paper verifies the effectiveness of the optimization algorithm through experiments, and has a great improvement in the four commonly used unsupervised algorithms, which can well improve the anomaly detection capabilities of the existing algorithms.
A Real-time and Energy-aware Framework for Data Stream Processing in the Internet of Things

: The Internet of things (IoT) has transformed the internet, enabling the communication between every kind of objects (things). The growing number of sensors and smart devices increased the possibilities of data generation and collection. This led to an explosion of data streams being produced which are challenging to be processed in real-time. Regarding the nature of the data, the huge volume, heterogeneity, continuity, disor-dering, noise and unpredictable rate are some challenging aspects to tackle. Regarding the data processing, the core activities from the data acquisition to the production of high-level knowledge also pose challenges related to limited computational and energy resources and high network latency. In this context, we propose a framework to support activities of a data stream processing workﬂow for IoT. It aims allowing real-time data processing with low power consumption. Edge computing is used to bring the data processing closer to the data sources and allow actions to be triggered quickly. An adaptive sampling strategy combined with a data prediction model are adopted to reduce the network trafﬁc, thus decreasing the power consumption of the network devices. Experiments show that the proposed framework is able to achieve up to 60.58% average energy consumption savings to sensor nodes and still meet a strict execution time threshold of 1s without compromising the accuracy of the output data on different scales of input streams.
An Interface Design Catalog for Interactive Labeling Systems

: Machine Learning (ML) systems have been widely used in recent years in different areas of human knowledge. However, to achieve highly accurate ML systems, it is necessary to train the ML model with data carefully labeled by specialists in the problem domain. In the context of ML and Human Computer Interaction (HCI), there are studies that propose interfaces that facilitate interactive labeling by domain specialists, in order to minimize effort and maximize productivity. This paper extends a previous secondary study that discusses some labeling systems. This paper proposes a catalog of design elements for the interface development of this type of system. We built the catalog based on the interface elements found in the studies analyzed in the previous secondary study. With this contribution, we expect to improve the development of better interfaces for interactive labeling systems and, thus, enhance the development of more accurate ML systems.
Facial Expression Recognition System for Stress Detection with Deep Learning

Stress is the body's natural reaction to external and internal stimuli. Despite being something natural, prolonged exposure to stressors can contribute to serious health problems. These reactions are reflected not only physiologically, but also psychologically, translating into emotions and facial expressions. Based on this, we developed a proof of concept for a stress detector. With a convolutional neural network capable of classifying facial expressions, and an application that uses this model to classify real-time images of the user's face and thereby assess the presence of signs of stress. For the creation of the classification model was used transfer learning together with fine-tuning. In this way, we took advantage of the pre-trained networks VGG16, VGG19, and Inception-ResNet V2 to solve the problem at hand. For the transfer learning process two classifier architectures were considered. After several experiments, it was determined that VGG16, together with a classifier based on a convolutional layer, was the candidate with the best performance at classifying stressful emotions. The results obtained are very promising and the proposed stress-detection system is noninvasive, only requiring a webcam to monitor the user's facial expressions.