Reviewerly/exHarmony huggingface dataset:
Lab-H: A Laboratory to Develop 3D Printable Inclusive Open Educational Resources

This work principally offers selected educational experiences, a set of case studies, about 3D printing skills to support the interchange of Media Education practices between the University of Salerno and educators, generally teachers, of different grades of the school system. Each 3D models is related to a case study, they are on the website ready to be free downloadable, all projects follow an Open Education policy. All of these resources become a set of inclusive open-source resources, ready for a free download, for 3D printers to develop teachers’ and students’ digital skills. The technologies that can be downloaded from the Lab-H website try to help teachers in their daily activities. Lab-H in collaboration with “G.Rodari” primary school of Perugia tested D-BOX and Ames Room, and it will test soon also Montessori didactical object and a 3D printable robot. D-Box is a 3D printing game to convert a decimal number into a binary number; to realize this it is based on a set of platforms related with powers of 2. The distorted room of Ames based on a well-known test of New Look psychology endorses the perspective-taking skills. Moreover, ASD-robot is designed to increase social skills in children affected by Autism Spectrum Disorder for better recognize emotions through the use of a display that acts as a human face with emoticons images tested in “Collodi” primary school of Pagani (Salerno).
Overview of Touché 2022: Argument Retrieval

The goal of the Touché lab on argument retrieval is to foster and support the development of technologies for argument mining and argument analysis. In the third edition of Touché, we organize three shared tasks: (a) argument retrieval for controversial topics, where participants retrieve a gist of arguments from a collection of online debates, (b) argument retrieval for comparative questions, where participants retrieve argumentative passages from a generic web crawl, and (c) image retrieval for arguments, where participants retrieve images from a focused web crawl that show support or opposition to some stance. In this paper, we briefly summarize the results of two years of organizing Touché and describe the planned setup for the third edition at CLEF 2022.
Underwater Object Tracking using Unscented Kalman Filter

Tracking an object is the major consideration in underwater warfare environment. To perform this task, researchers are more inclined towards the passive bearing-only tracking (BOT) method. In this paper, the estimation of object path provided noisy measurements is implemented with the help of Unscented Kalman Filter (UKF). It is assumed that the observer is executing the ‘S’ maneuver so that it can track the target, which is considered to be travelling in a straight path. The UKF is implemented to various scenarios in the MATLAB 2018a environment. Monte Carlo simulation is used to test the algorithm's efficiency.
An Adversarial Training Based Speech Emotion Classifier With Isolated Gaussian Regularization

Speaker individual bias may cause emotion-related features to form clusters with irregular borders (non-Gaussian distributions), making the model sensitive to local irregularities of pattern distributions, resulting in the model over-fit of the in-domain dataset. This problem may cause a decrease in the validation scores in cross-domain (i.e., speaker-independent, channel-variant) implementation. To mitigate this problem, in this paper, we propose an adversarial training-based classifier to regularize the distribution of latent representations to further smooth the boundaries among different categories. In the regularization phase, the representations are mapped into Gaussian distributions in an unsupervised manner to improve the discriminative ability of the latent representations. A single Gaussian distribution is used for mapping the latent representations in our previous study. In this presented work, we adopt a mixture of isolated Gaussian distributions. Moreover, multi-instance learning was adopted by dividing speech into a bag of segments to capture the most salient part of presenting an emotion. The model was evaluated on the IEMOCAP and MELD datasets with in-corpus speaker-independent sittings. In addition, we investigated the accuracy of cross-corpus sittings in simulating speaker-independent and channel-variants. In the experiment, the proposed model was compared not only with baseline models but also with different configurations of our model. The results show that the proposed model is competitive with respect to the baseline, as demonstrated both by in-corpus and cross-corpus validation.
A Two-Stage NER Method for Outstanding Papers in MCM

In this paper, we introduce a two-stage NER method to recognize the entities about papers occurring in MCM. Firstly, we pick out the NN-labeled and the NP-labeled words from the pre-labeled dataset to generate the preprocessed word frequency table and labeled the method-related words with “MET”. Secondly, [−3, 3] windows are applied to identify the capital words and words occurring in the above frequency table around the MET-labeled words which we recognize in step 1 and refine the tags with “T-MET”. After the above two steps, we use these recognized entities with “T-MET” tags to construct the document-by-method matrix for further clustering. We then have an exploratory data analysis of our dataset.
Deep Learning-Based Implicit Continuous Authentication of Smartphone User

Mobile devices especially smartphones are an unavoidable personal companion of the twenty-first-century human being. The data they are carrying and stored in the cloud through connected applications are private in nature and crucial. These are to be protected from unwanted usage to avoid both mental and financial distresses to its owner. The traditional security methods for authentication are the usage of PIN, fingerprint and face unlock. But the issue with these mechanisms is they are just an entry point authentication and once the user is authenticated it believes that the device is in the hands of its genuine user until it is locked. An intruder who has access to any of the traditional authentication credentials can steal the private data. Similarly, an unlocked device which falls at the hands of an intruder can also act as genuine owner. In addition to the entry point authentication, a continuous monitoring of the user throughout the interaction time is highly essential. Continuous authentication is the solution to have an absolute protection to the valuable data. In this paper, we are proposing an implicit continuous authentication mechanism using face recognition. Deep neural network-based classifiers were used for the model creation and subsequent binary classification of the current user of the device as genuine or intruder. Two CNN models were implemented and tested with unseen data. Model-01 gave an accuracy of 86% with specificity 0.91, whereas Model-02 returned an accuracy of 96% with specificity 1. Thus, Model-02 can be used in real time as it is robust in detecting the intruders correctly. Advantage of the proposed mechanism is that there is no need for any new sensors to be added to the smartphone. Our system would be using the inbuilt front camera to continuously monitor its owner.
On Testing Security Requirements in Industry – A Survey Study

[Context and motivation] Among all categories of non-functional requirements, requirements concerning security are those that are specified frequently and tackled with care. [Question/problem] Constant changes in technologies used to develop software products drive to new and changing security requirements, which requires adapting of the approaches used to investigate if the security requirements are satisfied. And, thus, the question arises if and how security requirements are tested. [Principal ideas/results] We conducted an online survey among software development practitioners. 190 respondents from a wide variety of countries shared with us their experience concerning testing security requirements. [Contribution] We learned that security requirements are tested in the majority of surveyed projects. However, in some having high impact (economic, human health, environment) the dedicated effort is small or none. There are different techniques used from automated ones like static code analysis, to manual ones like code reviews. Most developers, QAs and DevOps are testing security. The greatest challenges concern culture, knowledge, and difficulty in specifying tests.
SCAN:Syntactic Knowledge and Commonsense Knowledge Adapter Based Network for Aspect-level Sentiment Classification

Aspect-level sentiment classification is a most pronounced approach, which is defined as an automated technique to extract significant information from a large number of texts. However, current research still has limitations in ALSC tasks (e.g. accuracy of dependency parsing and overlook of commonsense knowledge). In this work, we propose a syntactic knowledge and commonsense knowledge adapter based network, which deals with the position information, syntactic structure and external knowledge, respectively. The performance of our model is evaluated on the three benchmark datasets. Experimental results demonstrate that our model is a best alternative in ALSC tasks compared with the state-of-the-art methods.
Recurrent Multi-task Graph Convolutional Networks for COVID-19 Knowledge Graph Link Prediction

Knowledge graphs (KGs) are a way to model data involving intricate relations between a number of entities. Understanding the information contained in KGs and predicting what hidden relations may be present can provide valuable domain-specific knowledge. Thus, we use data provided by the 5th Annual Oak Ridge National Laboratory Smoky Mountains Computational Sciences Data Challenge 2 as well as auxiliary textual data processed with natural language processing techniques to form and analyze a COVID-19 KG of biomedical concepts and research papers. Moreover, we propose a recurrent graph convolutional network model that predicts both the existence of novel links between concepts in this COVID-19 KG and the time at which the link will form. We demonstrate our model’s promising performance against several baseline models. The utilization of our work can give insights that are useful in COVID-19-related fields such as drug development and public health. All code for our paper is publicly available at https://github.com/RemingtonKim/SMCDC2021 .
Towards Tracking by 2D-target Registration for Surgical Optical Tracking System

Optical tracking system (OTS) is essential for surgical navigation and has been commonly employed in clinical practice due to high accuracy and precision. However, current OTS will be influenced by occlusion, which results in interruption of the surgical process. To address this question, we propose a tracking by 2D-target registration approach and utilize a multi-marker 2D-target and a stereo camera to carry out surgical instrument tracking. The proposed tracking approach tracks the multi-marker 2D-target in a 3D point cloud registration manner. As a result, OTS can track surgical instrument accurately and robustly by using the proposed tracking approach, even if a part of the markers are occluded. Simulations and experiments have been carried out to validate the feasibility and performance of the proposed approach. The results show that OTS can still track the instrument by using the proposed tracking approach even when 0 45.5% of the markers are occluded. The average ∼tracking error of the proposed approach is 0.35±0.21mm.
A Generative Approach to Enrich Arabic Story Text with Visual Aids

Enriching the script of a story with visual aids is an effective approach for promoting language learning and literacy development for young children and learners. In this paper, we propose a new system, that can generate short Arabic stories with generated images that accurately represent the story, scene and context of the given input. We use a text generation technique with a text-to-image synthesis network and minimize the human intervention. We build a corpus of Arabic stories with vocabulary and visualizations. The obtained results with various generative models to create text-image contents show the effectiveness of the proposed approach. The system can be used in education and assist the instructors to build stories on different domains. It can be used in distance learning to deliver online tutorials during COVID-19.
A Framework for Sentiment Analysis Classification based on Comparative Study

A number of Feature Selection and Ensemble Methods for Sentiment Analysis Classification had been introduced in many searches. This paper presents A frame work for sentiment analysis classification based on comparative study on different classification algorithms i.e., comparison between combinations of classification algorithms: Bayes, SVM, Decision Tree. We also examined the effect of using feature selection methods (statistical, wrapper, or embedded), ensemble methods (Bagging, Boosting, Stacking, or Vote), tuning parameters of methods (SVMAttributeEval, Stacking), and the effect of merging feature subsets selected by embedded method on the classification accuracy. Particularly, the results showed that accuracy depends on the feature selection method, ensemble methods, number of selected features, type of classifier, and tuning parameters of the algorithms used. A high accuracy of up to 99.85% was achieved by merging features of two embedded methods when using stacking ensemble method. Also, a high accuracy of 99.5% was achieved by tuning parameters in stacking method, and it reached 99.95% and 100% by tuning parameters in SVMAttributeEval method using statistical and machine learning approaches, respectively. Furthermore, tuning algorithms' parameters reduced the time needed to select feature subsets. Thus, these combinations of algorithms can be followed as a frame work for sentiment analyses.
Improvement of collision detection using Time Elastic Band algorithm

When TEB (Time Elastic Band) algorithm is applied to the dynamic obstacle avoidance of robots, it has the problem of too much calculation and easy to collide with dynamic obstacles. To solve this problem, an improved robot collision detection method is proposed. Firstly, A* algorithm is used for global path planning, and then TEB algorithm is selected as the core algorithm of the local path planning algorithm. Based on the geometric relationship between the robot and the obstacle, the mathematical model is established, the corresponding collision strategy is designed, the motion parameters of the robot and the obstacle are calculated, the collision risk is determined and whether to use the TEB algorithm to avoid the obstacle is determined. The simulation results show that the path planned by TEB algorithm is smoother than that planned by A* algorithm. And the path length, iteration times and running time after adding collision detection path planning algorithm are greatly shortened, which proves the real-time and effectiveness of the improved algorithm
UAV Based Vehicle Detection on Real and Synthetic Image Pairs: Performance Differences and Influence Analysis of Context and Simulation Parameters

The automated evaluation of the airborne acquired sensor data plays a decisive role in the use of UAV systems (Unmanned Aerial Vehicles). To generate or augment training data for respective algorithms, virtual environments are often used to replace complex and cost-intensive real flights. However deviation in data characteristics between these two domains can cause performance differences, leading to the so called “Reality Gap”. Our current research focuses on the comparison of detection performance between different real and synthetic training and test datasets and the analysis of the underlying influencing factors and parameters. For a targeted evaluation, dedicated UAV flights are performed and a dataset with over 3300 aerial images of vehicles under different conditions is recorded. Based on the telemetry data and a remodeling of the test area in the virtual environment, coupled real and synthetic image pairs are generated. Using the deep learning based detector network YOLOv3, the performance differences between this image pairs are evaluated for different training set compositions (real, synthetic-only and mixed training data). In addition, the impact of specific object, context, environmental, or simulation parameters on detection performance will be investigated to identify design guidelines that improve training data generation and lead to more robust detection models.
Designing an Interactive Indoor Delivery Robot and Its Implications

This research was carried out to provide an indoor service platform for employees with special needs. Disabled people have more energy/cost to adjust to the new work environment generated by COVID-19 than people without disabilities. Low job proficiency increases the number of customers waiting in the shop, which can reduce the café's recycling rate and raise the danger of group infection. To resolve these issues, innovative technology must be used to prevent COVID-19 and improve employee health security for persons with disabilities. As a response to these issues, this paper proposes how to design an interactive delivery robot. The robot interacts with the human user via verbal expressions, such as explaining the destination using a speaker and a touch sensor and providing delivery services using a Lidar sensor and two actuators. The robot's ability to go from the cafe to the target location, deliver the drink, and then return to the cafe to repeat the operation was proven as a consequence of the real design and implementation. We used ROS to bring those functions together on a single platform. This research is anticipated to revive the COVID-19 period, which has seen mutual communication go away, as well as contribute to economic restoration. Furthermore, I believe that given its aesthetic and practical benefits, it will be feasible to alleviate the job uncertainty that occurs in stores that employ individuals with impairments.
A Possibilistic Evolutionary Approach to Handle the Uncertainty of Software Metrics Thresholds in Code Smells Detection

A code smells detection rule is a combination of metrics with their corresponding crisp thresholds and labels. The goal of this paper is to deal with metrics' thresholds uncertainty; as usually such thresholds could not be exactly determined to judge the smelliness of a particular software class. To deal with this issue, we first propose to encode each metric value into a binary possibility distribution with respect to a threshold computed from a discretization technique; using the Possibilistic C-means classifier. Then, we propose ADIPOK-UMT as an evolutionary algorithm that evolves a population of PK-NN classifiers for the detection of smells under thresholds' uncertainty. The experimental results reveal that the possibility distribution-based encoding allows the implicit weighting of software metrics (features) with respect to their computed discretization thresholds. Moreover, ADIPOK-UMT is shown to outperform four relevant state-of-art approaches on a set of commonly adopted benchmark software systems.
Implementing SPARQL-Based Prefiltering on Jena Fuseki TDB Store to Reduce the Semantic Web Services Search Space

The increased number of services and their increased ontological complexity make the discovery a very time-consuming process as they were built on DL (Description Logic) reasoning. The proposed mechanism focused on providing a lightweight process by including preprocessing stage to reduce the search space before actual matchmaking process. In this approach, two SPARQL-based filters were applied to the OWL-S services published along with ontologies in the Jena Fuseki TDB store. These filters perform a different degree of semantic matching on functional parameters in the service description profile based on user requirements through queries. These queries were generated automatically from the user requests having different numbers of input and output. The paper also demonstrates a case scenario from OWL-S services test collection, and results obtained from these filters were analyzed to prove the efficiency of the proposal. From the analysis of results, we observed that both the filters completely discard the services that do not match any functional requirement requested by the user, thereby reducing the search space from 7.45 to 1.29% of the original repository. Consequently, this improved the performance with a penalty on precision of service discovery depending upon the degree of matching applied using filters.
TSCF: An Efficient Two-Stage Cuckoo Filter for Data Deduplication

The rapid growth of data on the Internet has brought huge challenges to storage systems. Data deduplication technology is proposed to solve the problem of data redundancy. As one of the data deduplication technologies, the memory-assisted method uses an approximate membership data structure to greatly reduce the space consumption of membership determination. The approximate membership data structures represented by the cuckoo filter have been widely used. However, there is a lack of efficient ways to solve the problem that the insertion time increases exponentially with the load rate of the cuckoo filter. In this paper, an efficient cuckoo filter named TSCF is proposed with a two-stage insertion algorithm. The TSCF balances the load of the filter through active relocations in the first stage, laying the foundation for the second stage. Through the experiments, the cumulative relocation times of the TSCF are reduced to 37% and 46% respectively compared with the SCF and the CFBF, indicating that the TSCF greatly reduces the relocation times and insertion time of the entire insertion process, and improves the performance of the cuckoo filter.
Efficient Blockchain Scheme for IoT Data Storage and Manipulation in Smart City Environment

We describe an implementation of a data storage and manipulation system for the Internet of Things (IoT) networks based on blockchain and using a Practical Byzantine Fault Tolerance (PBFT)-like protocol to achieve consensus. In our proposed model, blockchain ledger consistency is ensured by leaders contending for the next available spot(s) in which the newly accepted blocks will be appended to the blockchain. The contention takes place on a shared overlay accessible to all leaders nodes. We use a multiple-entry PBFT to eliminate the need for a dedicated leader node and the delays involved in changing leaders in different rounds. Our model allowed the multiple leaders to work in parallel to propose blocks using dedicated overlay networks. The performance of the proposed scheme is analyzed through an analytical model that employs a discrete-time Markov chain and M/G/1 queuing model with exhaustive limited service. The results obtained from the model confirm the efficiency of the proposed model in a wide range of parameter values and show that the proposed model outperforms the multiple entry points of the PBFT protocol with single block insertion.