Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Pattern and Preference-Aware Travel Route Recommendation over Location-Based Social Networks

Travel route recommendation for Location-based Social Networks (LBSNs) has been received much attention to research people’s activity patterns and personalized preferences. Existing travel route recommendation schemes in literature are confronted with three problems: 1) the location is limited in practical environment, and the data sparsity is always happened when they recommend travel route services based on the location information; 2) they fail to consider the order of mobile trajectory, which is valuable to reﬂect the interest and preference of users for travel route recommendation; 3) they can’t be adapted to different kinds of POI category, which causes the extendibility is low. In this paper, we propose PP-TRR , a pattern and preference-aware travel route recommendation scheme to tackle the above problems. First, we construct the system architecture of our proposed travel route recommendation. Then, we model the movement pattern of each user. Finally, we present the travel route recommendation scheme to recommend personalized services for targeted users. The experimental results show that our method outperforms the existing method.
The Safety and Therapeutic Effectiveness of Nonwashed Mediastinal Shed Blood Reinfused Into Patients Following Open Heart Surgery

Abstract : This prospective study was designed to determine if use of nonwashed shed mediastinal blood could exacerbate platelet and related hematologic dysfunctions following cardiopulmonary bypass (CPB) when compared to the alternative use of autologous and homologous liquid preserved blood for volume support. Thirty-two patients undergoing CPB for open heart surgery were randomized to receive either nonwashed shed mediastinal (NSM) blood (Group 1, n = 16) or liquid preserved packed red blood cells (Group 2, n = 16) for transfusion (TXN) therapy of postoperative bleeding. Patient blood samples and bleeding times were obtained preoperatively, postCPB-preTXN, 2 and 24 hours postTXN, and on postoperative days 2, 3, and 7. Group 1 patients received an average of 710 +/- 90 ml (range 300-1700 ml) of NSM blood containing significantly greater (p < 0.0001) amounts of fibrin degradation products and D-dimer protein. Of the hematologic, microaggregate, and plasma protein measurements performed, only Protein C was significantly greater (p < 0.05, 1 vs. 2) after transfusion. Patient bleeding times were not significantly different between the groups at all time points. Total postoperative blood loss was not different between groups. There was a trend toward less need for homologous transfusion in Group 1 (p < 0.1). This study documents the safety, ease, and cost-saving of using nonwashed shed mediastinal blood as primary blood volume support following open heart surgery.
Standardized container virtualization approach for collecting host intrusion detection data

Anomaly-based Intrusion Detection Systems (IDS) can be instrumental in detecting attacks on IT systems. For evaluation and training of IDS, data sets containing samples of common security-scenarios are essential. Existing data sets are not sufficient for training modern IDS. This work introduces a new methodology for recording data that is useful in the context of intrusion detection. The approach presented is comprised of a system architecture as well as a novel framework for simulating security-related scenarios.
Students Perception on the use of Project-Based Learning in Software Engineering Education

Balancing theory and practice is a recurring challenge in Software Engineering (SE) education. However, the curriculum guidelines of the ACM/IEEE and Brazilian Computer Society emphasize the need of providing students with sufficient practical experiences for the development of competences expected for SE professional. Student-centered and learn-by-doing approaches, such as Project-Based Learning (PBL), have been advocated as suited for the development of competences. These approaches aim to promote higher motivation for the learner, a more active role in the learning process, and better learning in the application level. The goal of this paper is to evaluate the students' perception on the adoption of PBL in SE education. To achieve this goal, we performed a long-term study spanning for about 2 years. This study includes a survey to collect responses of 32 undergraduate students enrolled in an introductory SE course that used PBL. We compare the results to the responses of 17 students who participated in a SE course with similar syllabus but with a traditional teacher-centered learning method (non-PBL). Our results show a positive reception of the PBL method, and an increased perception of the contribution of a practical software development assignment in learning specific SE topics in the context of the PBL course, in comparison to a non-PBL method.
Knowledge Extraction and Applications utilizing Context Data in Knowledge Graphs

Context is widely considered for NLP and knowledge discovery since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such context data, but also to store this data for further NLP approaches. Here, we propose a multiple step knowledge graphbased approach to utilize context data for NLP and knowledge expression and extraction. We introduce the graph-theoretic foundation for a general context concept within semantic networks and show a proof-of-concept-based on biomedical literature and text mining. We discuss the impact of this novel approach on text analysis, various forms of text recognition and knowledge extraction and retrieval.
An Adaptation of IoT to Improve Parcel Delivery System

Recently, IoT technology has been applied in various field. One of the possible fields of an application is logistics system. In current system, a delivery must go through the designated logistics hub, which doesn’t provide shortest distance. Such system costs time and inefficient expenses. In this paper, we propose an enhanced parcel delivery system based on IoT technology for reducing total delivery distance and seeking for much economy. First, we designed a sort of IoT devices which can be attached to parcels. This device has various functionalities including the ability to figure out current delivery route. Second, we addressed some difficulties such as: (i) issues linking IoT device into its platform; (ii) issues for designing IoT devices functionalities. Third, we propose ways to improve the efficiency of IoT based parcel delivery system. From these considerations, our system may improve total economics of parcel delivery system.
smAR2t: a Models at Runtime Architecture to Interact with the Web Of Things using Augmented Reality

The Internet of Things has a high heterogeneity of devices, cloud services, communication protocols and comprises different research and development areas. This implies in ease-of-use problems for users who interact with high-density smart environments using common interface solutions, demanding specific applications for each device. There are also situations where things change their locations and networks, bringing even more complexity to this scenery. A great number of recent researches point Augmented Reality(AR) as an interface solution that is capable of providing rich user experiences for high-density IoT environments. W3C's Web of Things standards(WoT) are proving to be a feasible solution for IoT's heterogeneity issues by using well-known web protocols and the Thing Description(TD) formal model. This paper's goal is to propose an architecture called smAR2t that integrates these solutions by managing mobility, heterogeneity and high-density of smart environments, allowing users to browse their surroundings with AR and to interact with things seamlessly according to each of their TDs. This could be done by: representing TDs in a higher level of abstraction with models; transforming these models at runtime to reflect real-time interactions and behaviors; updating an auto-generated AR interface according to these models.
Exact and approximation algorithms for joint routing and flow rate optimization

—This paper addresses the comparison of algorithms for a version of the Network Utility Maximization (NUM) problem. The joint formulation of routing and transmission rate control within the multi-user and single-path setting is assumed within the NUM. Since the problem is NP-hard, the efﬁcient heuristics are designed, implemented and compared experimentally with other existing heuristics and exact linear programming solver. The linear approximation is applied for a nonlinear utility function. The results of the experiments demonstrate a trade-off between computing time and precision of goal value.
A Framework for Network Intrusion Detection using Network Programmability and Data Stream Clustering Machine Learning Algorithms

Several operational security mechanisms have been developed to mitigate malicious activity in the Internet. However, the most these mechanisms require a signature basis and present the inability to predict new malicious activity. Other anomaly-based mechanisms are inefficient due to the possibility of an attacker simulates legitimate traffic, which causes many false alarms. Thus, to overcome that problem, in this paper we present an anomaly-based framework that uses network programmability and machine learning algorithms over continuous data stream. Our approach overcomes the main challenges that occur when develop an anomaly-based system using machine learning techniques. We have done an experimental evaluation to demonstrate the feasibility of the proposed framework. In the experiments, we use a DDoS attack as network intrusion and we show that the technique attains an Accuracy of 98.98%, a Recall of 60%, a Precision of 60% and an FPR of 0.48% for 1% DDoS attack on the real normal traffic. This shows the effectiveness of our technique.
The CNN-Corpus: A Large Textual Corpus for Single-Document Extractive Summarization

This paper details the features and the methodology adopted in the construction of the CNN-corpus, a test corpus for single document extractive text summarization of news articles. The current version of the CNN-corpus encompasses 3,000 texts in English, and each of them has an abstractive and an extractive summary. The corpus allows quantitative and qualitative assessments of extractive summarization strategies.
Customized Genetic Algorithm for Facility Allocation using p-median

The p-median problem is classified as a NP-hard problem, which demands a long time for solution. To increase the use of the method in public management, commercial, military and industrial applications, several heuristic methods has been proposed in literature. In this work, we propose a customized Genetic Algorithm for solving the p-median problem, and we present its evaluation using benchmark problems of OR-library. The customized method combines parameters used in previous studies and introduces the evolution of solutions in stationary mode for solving PMP problems. The proposed Genetic Algorithm found the optimum solution in 37 of 40 instances of p-median problem. The mean deviation from the optimal solution was 0.002% and the mean processing time using CPU core i7 was 17.7s.
Gamified Augmented Reality Training for An Assembly Task: A Study About User Engagement

Augmented Reality and Gamification are displaying beneficial effects to enhance user experience and performance in many domains. They are widespread across many areas like education, industrial training, marketing, and services. However, the idea of combining the two approaches for an innovative training instrument is fairly new, especially in assembly training. Moreover, learning about the effects of gamification on human, user engagement, in particular, is a complicated subject. There have been several efforts toward this direction, yet the overall situation is still nascent. In this work, we present a gamified augmented reality training for an industrial task and investigate user engagement effect while training with the gamified and the nongamified system. The result shows that people perform better and engage to a greater degree in the gamified design.
An Exploratory Analysis of Precedent Relevance in the Brazilian Supreme Court Rulings

The new Brazilian Code of Civil Procedure (CPC) has elevated the importance of precedents in the legal decision-making process. This increased the need to find relevant precedents for a given issue or dispute. Precedents play a central role in judicial thinking by providing information to judges about the legal relevance of particular facts and by establishing legal rules. Precedents are also an important argumentative tool, enabling lawyers to present arguments based on previous decisions. The automated search for relevant precedents is an unattended issue in the Brazilian scenario, partly due to the court's massive production of decisions -- only in 2018 the Brazilian Supreme Court (STF) produced more than 121.000 new rulings -- and partly due to the technical challenges arising from the unstructured nature of the court's practices. In this paper, we present a study of precedent relevance, taking into account the uniqueness of the Brazilian legal system and of STF. To do so, we conducted an exploratory investigation over the precedent network extracted from 1.152. 963 decisions published by the STF between 2008 and 2018. This exploratory analysis, although interesting in itself, reveals important challenges that need to be overcome by future research in order for the technology to have the kind of impact it can have on legal practice and academia. In our conclusion, we set out possible paths forward, briefly considering some of the most promising ways to sort out the signal from the noise.
Clash Royale Challenge: How to Select Training Decks for Win-rate Prediction

We summarize the sixth data mining competition organized at the Knowledge Pit platform in association with the Federated Conference on Computer Science and Information Systems series, titled Clash Royale Challenge: How to Select Training Decks for Win-rate Prediction. We outline the scope of this challenge and briefly present its results. We also discuss the problem of acquiring knowledge about new notions from video games through an active learning cycle. We explain how this task is related to the problem considered in the challenge and share results of experiments that we conducted to demonstrate usefulness of the active learning approach in practice.
FLOSS in Software Engineering Education: Supporting the Instructor in the Quest for Providing Real Experience for Students

Software engineering courses play an important role in computer science programs and are expected to provide the required basic knowledge and skills for professional practice in software industry. However, teaching software engineering principles, concepts and practices, and relating them to real-world scenarios are challenging tasks. The adoption of open source software projects may address such challenges. In this paper we report on an experience of the teaching object-oriented modeling with Unified Modeling Language (UML) Class Diagrams using open source projects. We conducted a case study with students of the software engineering discipline of the Computer Science course. We supported the instructor in some activities related to syllabus planning, including the selection of a Free/Libre/Open Source Software (FLOSS) projects and the creation of examples to be used in the classroom. The instructor selected and used a small FLOSS project to support the modeling activities. Then, the instructor applied an evaluation activity and a perception questionnaire about the methodology used. After the end of the classes, we conducted an interview with the instructor to present a brief report of his experience in the classroom. In the perception of students, the experience with FLOSS projects enhanced their ability to handle real projects and third-party code, and to deal with the job market. They also reported developing skills such as proactivity and communication. From the instructor's perspective, the group was enthusiastic and dynamic, and interacted more during practical activities.
On the Nature of Symbolic Execution

In the symbolic execution of a program real values are replaced by so-called symbolic values. Consequently, programming expressions cannot be evaluated, and thus the state, i.e., the assignment of values to program variables, in a symbolic execution is replaced by a substitution which assigns to each program variable an expression. The goal of symbolic execution is to generate a path condition that specifies concrete input values for which the actual execution of the program follows the same path (as generated by the symbolic execution). In the full version of this abstract we provide a formal definition of symbolic execution in terms of a symbolic transition system and prove its correctness with respect to an operational semantics which models the execution on concrete values. Our approach is modular, starting with a formal model for a basic programming language with a statically fixed number of programming variables. This model is extended to a programming language with recursive procedures which are called by a call-by-value parameter mechanism. Finally, we show how to extend this latter model of symbolic execution to arrays and object-oriented languages which feature dynamically allocated variables.
Best Response Dynamics for VLSI Physical Design Placement

The physical design placement problem is one of the hardest and most important problems in micro chips production. The placement defines how to place the electrical components on the chip. We consider the problem as a combinatorial optimization problem, whose instance is defined by a set of 2-dimensional rectangles, with various sizes and wire connectivity requirements. We focus on minimizing the placement area and the total wire-length.We propose a local-search method for coping with the problem, based on natural dynamics common in game theory. Specifically, we suggest to perform variants of Best-Response Dynamics (BRD). In our method, we assume that every component is controlled by a selfish agent, who aim at minimizing his individual cost, which depends on his own location and the wire-length of his connections.We suggest several BRD methods, based on selfish migrations of a single or a cooperative of components. We performed a comprehensive experimental study on various test-benches, and compared our results with commonly known algorithms, in particular, with simulated annealing. The results show that selfish local-search, especially when applied with cooperatives of components, may be beneficial for the placement problem.
Predictable and Consistent Information Extraction

Information extraction programs (extractors) can be applied to documents to isolate structured versions of some content, that is, to create tabular records corresponding to facts found in the documents. If the data in an extracted table needs to be updated for any reason (for example, as a result of data cleaning), the source document will no longer be synchronized with the data. But documents are the principal medium for sharing information among humans. We therefore wish to ensure that changes to extracted tables are reflected correctly in their source documents. In this work, we characterize extractors for which we are able to predict the effects that updates to source documents will have on extracted records. We introduce three general properties for extractors that, if satisfied, can guarantee that consistency will be maintained if the lineage of extracted records is respected when changing the documents. We propose a property verification process that uses static analysis for a substantial subset of JAPE, a well-established rule-based extraction language, and illustrate it through an example based on a freely-available extractor library.
Efficient Production Monitoring on the Basis of Domain Ontologies by Utilizing IoT

The Internet-of-Things (IoT) technologies and cyber-physical systems has facilitated production monitoring and control. However, researches and applications still lack a standardized framework and an integrated technological solution that can maximize the leverage of real-time monitoring. This can be achieved through enabling data transfer and exchange between all entities/organizations in supply chains and accordingly utilizing the monitored data. This paper introduces a framework for production monitoring that utilizes and integrates ontological model, which implements and integrates Semantic Sensor Network (SSN) ontology with production monitoring services. In addition, Complex Event Processing is integrated in the proposed model to enable event patterns identification and undertake the appropriate (proactive) action accordingly. The framework is constructed based on ISA-95 and SCOR standards. The utility, applicability and efficacy of the proposed framework is validated by its application on a real-life large-scale case study in the domain of laser cutting machines.
Urban Sound Classification using Long Short-Term Memory Neural Network

Environmental sound classification has received more attention in recent years. Analysis of environmental sounds is difficult because of its unstructured nature. However, the presence of strong spectro-temporal patterns makes the classification possible. Since LSTM neural networks are efficient at learning temporal dependencies we propose and examine a LSTM model for urban sound classification. The model is trained on magnitude mel-spectrograms extracted from UrbanSound8K dataset audio. The proposed network is evaluated using 5-fold cross-validation and compared with the baseline CNN. It is shown that the LSTM model outperforms a set of existing solutions and is more accurate and confident than the CNN.
British Sign Language Recognition In The Wild Based On Multi-Class SVM

Developing assistive, cost-effective, non-invasive technologies to aid communication of people with hearing impairments is of prime importance in our society, in order to widen accessibility and inclusiveness. For this purpose, we have developed an intelligent vision system embedded on a smartphone and deployed in the wild. In particular, it integrates both computer vision methods involving Histogram of Oriented Gradients (HOG) and machine learning techniques such as multi-class Support Vector Machine (SVM) to detect and recognize British Visual Language (BSL) signs automatically. Our system was successfully tested on a real-world dataset containing 13,066 samples and shown an accuracy of over 99% with an average processing time of 170ms, thus appropriate for real-time visual signing.
Object detection in the police surveillance scenario

Police and various security services use video analysis when investigating criminal activity. One typical scenario is the selection of object in image sequence and search for similar objects in other images. Algorithms supporting this scenario must reconcile several seemingly contradicting factors: training and detection speed, detection reliability and learning from sparse data. In the system that we propose a combined SVM/Cascade detector is used for both speed and detection reliability. In addition, object tracking and background-foreground separation algorithm together with sample synthesis is used to collect rich training data. Experiments show that the system is effective, useful and suitable for selected tasks of police surveillance.