Reviewerly/exHarmony huggingface dataset:
ARMPC - ARIMA based prediction model for Adaptive Bitrate Scheme in Streaming

With the boom of the internet, online streaming algorithms such as Adaptive bitrate streaming have gained prominence. The Adaptive Bitrate (ABR) scheme uses the Model Predictive Control (MPC) to determine the best possible bitrate for the given network conditions. Though this method works well, the major disadvantage is its heavy reliance on the throughput prediction error which makes it difficult to perform well in congested network conditions. Other methods such as DeepMPC have also been explored in this paper which use the Deep Learning algorithms to predict the bandwidth. These work better than the trivial harmonic predictor but demand high computational power. This paper proposes ARMPC which uses the Auto-Regressive Integrated Moving Average Technique (ARIMA) to predict the future bandwidth. Using trace-driven experiments, we have shown both mathematically and practically that the ARMPC can provide us with improvements in both the prediction and the computational points of view.
Public Administration Suppliers Classification Model Based on Supervised Machine Learning

Context: Public contracts are agreements made between the Public Administration and individuals to achieve public interest objectives. Within this relationship, some problems such as contractual breaches may occur. In the Piauí State, the Audit Court, environment in which this research was developed, is responsible for analyzing and judging the accountability of the Legislative, Executive and Judiciary Powers..
Face Mask Detection System using Convolutional Neural Network

According to the World Health Organization, the coronavirus also known as the COVID-19 pandemic, is causing a global health crisis, WHO and Other health organizations recommended wearing face masks as a protection measurement especially in work, school, university, malls, and any public places. The proposed system consists of a Convolutional Neural Network-based system that can recognize faces with masks and without a mask, The dataset used for this project consists of 3835 images of two types of images, people with masks and people without masks. By utilizing the TensorFlow framework we&#x2019;re able to get a 98.80% accuracy rate in training time while training the model with 200 epochs.
Comparative Study of latest CNN based Optical Flow Estimation

Deep learning is continuously evolving and making significant advances in several applications. Nevertheless, it has a remarkable influence in the field of image processing. Recently, deep learning has hit the motion estimation area well. Optical flow estimation is a mature and ever-growing field of research. It can be considered a multidisciplinary field. However, it is not easy to get dataset suitable for deep learning of its models. While they have made fundamental contributions, However, it is unclear how to generate more data and generalize it to live scene videos. In this paper, we have tried off extensive analyses and categorized various deep learning-based optical flow estimation techniques. Lately hybrid methods have been very successful. Despite their high performance, even they have performed the state of the art of certain datasets, most bibliographic studies haven’t taken into account these methods. For this, we have added a comparative section of these hybrid algorithms to this study. While describing the set of datasets commonly used by the scientific community, we’ve identified the differences and the correspondences between deep methods and conventional methods. We hope that this extensive research will be a fundamental resource for researchers in the field of image processing and help them better understand and use methods using motion estimation.
Personalized Recommendation Algorithm of Literary Works Based on Annotated Corpus

Literary work personalized recommendation service is a service that focuses on users’ needs, actively analyses users’ interests and hobbies, and intelligently and efficiently discovers users’ interesting information. Previous recommendation algorithms were unable to make effective and accurate recommendations in real time, resulting in poor recommendation outcomes. This study proposes a personalized recommendation algorithm for literary works based on the annotated corpus to address these issues. The dictionary is first used to mark the original text that was created by splitting words. The user’s reading behavior is then analyzed using a combination of individual personality characteristics and a set of factors that differ from the individual background, and personality characteristics and situations. Finally, we count the frequency of each modifier and word in the modifier vector in the corpus for each word, create the feature vector, and perform cluster analysis. The results show that this method’s MAE (mean absolute error) value is always lower than the traditional method, especially when the neighbor set size is 5, and that this method is clearly superior to the traditional method, with a maximum difference of 6.23%. Conclusions. The algorithm can produce satisfactory recommendation results and can be used to make personalized literary recommendations.
Human Activity and Posture Classification using Smartphone Sensors and Matlab Mobile

Human Activity Recognition (HAR) is significant, especially in the medical field. Activity recognition has been used in various ways as technology has advanced, particularly using a smartphone-based approach. This work aims to evaluate the accuracy of the triaxial accelerometer in the Matlab Mobile and examine the development and performance of the algorithms in identifying human motions on individuals of similar ages and physical appearances. Motion signals from three subjects are measured, data is preprocessed using a filtering technique, features are extracted, feature normalization is used to reduce bias in data measurement, and activities are classified. Confusion matrix, precision, recall, accuracy, F1-score, and Kappa score are performance indicators used to determine this classification approach. As a result, this research discovered that the Quadratic Support Vector Machine (SVM) produces the best results, with a 99.22 % accuracy rate, proving the efficacy of its activity identification method.
BERT-RF Fusion Model Based on Bayesian optimization for Sentiment Classification

Social media platforms have accumulated massive amounts of social text data. Mining people’s sentiment tendencies from these data is great significance. However, compared with ordinary text, social text data is shorter and more colloquial, and it is difficult to extract feature information, which affects the accuracy of sentiment classification. To improve the accuracy of sentiment classification, the BERT-RF fusion model based on Bayesian optimization for sentiment classification is proposed. Firstly, the key features in the social text are extracted through the deep structure of the BERT model, and the random forest model is used to replace the final output layer of the BERT, and the relevant social text is classified according to the key features. Hyperparameters of random forest are optimized using Bayesian method. The experimental results show that our model has better performance for sentiment classification.
Online Knowledge Distillation for Efficient Action Recognition

Existing skeleton-based action recognition methods require heavy computational resources for accurate predictions. One promising technique to obtain an accurate yet lightweight action recognition network is knowledge distillation (KD), which distills the knowledge from a powerful teacher model to a less-parameterized student model. However, existing distillation works in action recognition require a pre-trained teacher network and a two-stage learning procedure. In this work, we propose a novel Online Knowledge Distillation framework by distilling Action Recognition structure knowledge in a one-stage manner to improve the distillation efficiency, termed OKDAR. Specifically, OKDAR learns a single multi-branch network and acquires the predictions from each one, which is then assembled by a feature mix model as the implicit teacher network to teach each student in reverse. The effectiveness of our approach is demonstrated by extensive experiments on two common benchmarks, i.e., NTU-RGB+D 60 and NTU-RGB+D 120.
An Overview on the Use of Adversarial Learning Strategies to Ensure Fairness in Machine Learning Models

Context: The information age brought wide data availability, which allowed technological advances, especially when looking at machine learning (ML) algorithms that have achieved significant results for the most diverse tasks. Thus, information systems are now implementing and incorporating these algorithms, including in critical areas. Problem: Given this widespread use and already observed examples of misuse of its decisions, it is essential to consider the harm and social impacts that ML models can bring for society, for example, biased and discriminatory decisions coming from biased data or programmers. Solution: This article provides an overview of an eminent area of study on the use of adversarial learning to encode fairness constraints in ML models. IS Theory: This work is related to socio-technical theory since we consider one of the so-called socio-algorithmic problems, algorithmic discrimination. We consider a specific set of approaches to encoding fair behaviors. Method: We selected and analyzed the literature works on the use of adversarial learning for encoding fairness, aiming to answer defined research questions. Summary of Results: As main results, this work presents answers to the following research questions: What is the type of their approach? What fairness constraints did they encode into their models? What evaluation metrics did they use to assess their proposals? What datasets did they use? Contributions and Impact in the IS area: We expect to assist future research in the fairness area. Thus the article’s main contribution is to provide a reference for the community, summarizing the main topics about the adversarial learning approaches for achieving fairness.
Trustless AutoML for the Age of Internet of Things

This paper introduces the concept of Trustless AutoML, and proposes a framework that combines AutoML techniques with blockchain to fully decentralize the design and training process of machine learning models. The goal is to introduce full transparency and trust in the model design pipeline to establish a root-of-trust.
Iconic registration based on the local dissimilarity card

In this work, we focus on iconic registration as one of the fundamental tasks in medical imaging. We have proposed a new approach that consists of the registration of two mono-modal images based on the local dissimilarity card (LDC). Instead of maximizing the similarity between the two input images as in traditional registration techniques, the main contribution of this work is minimizing the dissimilarity between them. Experiments in RMI medical images have shown that the proposed approach is as reliable as an original method using mutual information-based registration as a reference.
Approximate weighted model integration on DNF structures

Weighted model counting consists of computing the weighted sum of all satisfying assignments of a propositional formula. Weighted model counting is well-known to be #P-hard for exact solving, but admits a fully polynomial randomized approximation scheme when restricted to DNF structures. In this work, we study weighted model integration, a generalization of weighted model counting which involves real variables in addition to propositional variables, and pose the following question: Does weighted model integration on DNF structures admit a fully polynomial randomized approximation scheme? Building on classical results from approximate weighted model counting and approximate volume computation, we show that weighted model integration on DNF structures can indeed be approximated for a class of weight functions. Our approximation algorithm is based on three subroutines, each of which can be a weak (i.e., approximate), or a strong (i.e., exact) oracle, and in all cases, comes along with accuracy guarantees. We experimentally verify our approach over randomly generated DNF instances of varying sizes, and show that our algorithm scales to large problem instances, involving up to 1K variables, which are currently out of reach for existing, general-purpose weighted model integration solvers.
W2V-ATT: research on text-dependent MDD method based on wav2vec2.0

Mispronunciation Detection and Diagnosis (MDD) is one of the key components of the Computer Assisted Pronunciation Training (CAPT) system. The construction of the current mainstream MDD system is an automatic speech recognition (ASR) system based on DNN-HMM, on which a large amount of labeled data is required for training. In this paper, the self-supervised pre-training model wav2vec2.0 is applied to the MDD task. Self-supervised pre-training uses a large amount of unlabeled data to learn common features, and only a small amount of labeled data is required for training in subsequent applications. In order to utilize the prior text information, the audio features are combined with the text features through the attention mechanism, and the information of both is used in the decoding process. The experiment is conducted on the publicly available L2-Aritic and TIMIT datasets, yielding satisfactory results.
Application of improved cuckoo algorithm to optimize generalized regression neural network in software quality prediction

Software quality prediction technology is the main method of early prediction and control of software quality. Generalized regression neural network (GRNN) can better map the nonlinear relationship between software metrics and software quality elements, but the prediction accuracy of the software quality prediction model based on GRNN is low. To improve the accuracy of the quality prediction model, we use the improved cuckoo search (CS) algorithm to optimize the smoothing factor of GRNN, solve the problems of insufficient population diversity and slow convergence speed in the later stage of the cuckoo algorithm, and propose a software quality prediction model based on the improved CS algorithm to optimize GRNN by introducing Gaussian disturbance function, to improve the accuracy of predicting the number of software defects. Finally, the paper uses the public promise data set for simulation experiments and verifies the model by comparing it with the GRNN model optimized by the CS algorithm and the standard GRNN model.
Evaluating social bots detection approaches in different domains

Context: Social bots are automated users who make use of social networks to publish and interact with network users, mimicking or attempting to alter user behaviors with purposes such as spreading spam, malicious content, or misleading information, with various negative effects. Problem: Detecting these bots is a major challenge since, as detection mechanisms evolve, they are also enhanced to avoid such mechanisms, either by improving strategies for emulating real users or by organizing groups of bots in networks with the same purpose (botnets). IS Theory: The paper was developed considering Social Network Theory and Social Information Processing Theory. Method: The paper evaluates bots detection techniques by comparing the classifiers trained against three distinct datasets, aiming to emulate the behavior of a social network through time, to verify the performance of the classifiers in distinct conditions and the resilience of those techniques. Contributions and Impact in the IS area: The objective is to evaluate the effectiveness of the most common techniques in the domain in a variety of conditions based on the datasets used, an important challenge in the development and deployment of information systems. Summary of Results: The performance of the classifiers, when confronted against other datasets, was poor, showing that the classifiers trained for this purpose require constant maintenance to remain effective, reinforcing the need for improved techniques that are more resilient to changes over time and subject of messages. Proposed Solution: To counter those weaknesses, techniques that explore other characteristics, such as the message content, could be explored to improve the resilience of the classifiers.
Designing and Implementing of an Online Management Information System for Exam Committee

In the era of technology and devices connection to Internet, developing management systems (MS), whether they are web based or mobile based, become very essential. Therefore, it becomes quite common to see a Management Information System (MIS) wherever the process of management is there, and it becomes important to design and develop an MIS for any management unit that is still processing their tasks classically. However, issues of the exam committee unit in the Faculty of Engineering (FENG) at Koya University (KoU) are still progressing without the concept of information system. This result has been obtained during a small survey that was conducted between 10th to 20th November 2021 inside the FENG among those academic staff who were nominated as members of the FENG exam committee. Therefore, authors of this work have proposed an online based information management system for facilitating the works of this committee. As a software development life cycle (SDLC), this work followed the waterfall methodology for designing and implementation. At end, a test has been conducted to check the system in three directions: achieving system requirements, style, and formatting for webpages, and dynamically of the system during updating data and records. According to the survey, the system has been agreed by the software testers.
Does the social distancing measures impact collaboration in software processes?

Context: Social distancing measures to reduce the impacts of the COVID-19 pandemic bring challenges for several organizations and their Information Systems (IS). Problem: Social distancing were suddenly implemented, with no adequate planning. Furthermore, there is not a clear understanding of the impacts of social distancing measures on the software development teams’ work. Solution: This study aims to identify the presence and impacts of social distancing on collaboration between people, as well as on activities and technologies used in software development, from the perspective of professionals who work in software development teams. IS Theory: The research was developed under the aegis of the Team Resilience Theory. Method: A field study was conducted based on structured interviews and qualitative analysis. Results: In general, it was observed no impact on the software processes activities, although their performance was affected. It was also observed that the impact on the collaboration dynamics in software development was perceived in different ways, by several team members. This study identified the most common tools used by organizations, as well as the benefits and challenges of collaboration in software development after social distancing measures. Contribution: The impacts of social distancing on collaboration in software development were identified, reinforcing the Team Resilience Theory and similar study conducted in the beginning of the COVID-19 pandemic. This study contributes to the Grand Research Challenge ”Systemic and Socially Aware Perspective for Information Systems”.
Do Patients Tend to Find Positive or Negative Feedback on Social Networks? A Study of The Main Aspects of Modelling Patient Understanding Based on Emotional Variants

Information on social networks can have an immediate influence on the public health status regarding a given medication or treatment. That means such positive or negative information about a given drug or service is learned, which frequently affects the emotional state of a patient. Here we draw upon the main question “Do Patients tend to Find Positive or Negative Feedback on Social Networks?”. Using sentiment analysis to comprehend the emotional state of patients is of critical importance, not only analysis of the text is required, a deep study of the main aspects of modeling patient understanding based on the emotional variants approach becomes apparent. Existing feature learning algorithms fail to define a relevant feature that promises the understanding of affect conveyed towards a given target. In this study, the goal is to define a based-emotional embedded-concepts spectrum that consists of defining affective information from selected affect seeds to other biomedical concepts. We develop concept-level emotional classification according to three categorical emotional variants and moral basics: PAIN or AFFECT, MOOD, and POSITIONALITY, where the objective is to predict the predominant concept-level affects. The experimental results on Twitter data show that the proposed strategy achieved significant performance improvements, thus, it might have an impact in real-world scenarios and helps provide situational awareness.
EasyBand2.0: A Framework with Context-Aware Recommendation Mechanism for Safety-Aware Mobility during Pandemic Outbreaks

COVID-19 (Corona Virus Disease 2019) is a pandemic which has been spreading exponentially around the globe. Many countries had adopted stay-at-home or lockdown policies to control its spreading. However, prolonged stay-at-home can cause worse effects like economic crises, unemployment, food scarcity, and mental health problems of individuals. EasyBand2.0 is a wearable personal safety device that helps in social distancing and also helps in safe mobility. Under the IoMT (Internet of Medical Things) framework the wearable EasyBand2.0 device helps in social distancing, it avoids human-to-human contact and helps maintain a safer distance. EasyBand2.0 uses the Low Power BLE technology to sense distance between two user devices and alert them based on the distance and time spent in proximity. Safe mobility of people is also important as travel is resumed in all forms. This paper proposes a software application along with the easy band to further be integrated with a system that works based on GPS (Global Positioning System) or GIS (Geographic Information System) to provide travel logging for contact tracing without exposing personal data. A CARS (Context Aware Recommendation System) based safe zone recommender system is proposed in this paper to aid safe mobility.
Robust approach linking cryptography,3D watermarking using RSA algorithm and spherical harmonics transform to secure multiresolution meshes transmission

In order to contribute to safe sharing of 3D mul-tiresolution meshes, a new approach of crypto-watermarking is proposed. This approach is based on the use of RSA algorithm and spherical harmonics transform. The aim being is to increase the integration rate while maintaining mesh quality on the one hand and extract correctly inserted image on the other hand. To embed data, host mesh is decomposed using spherical harmonics transform. Resulting coefficients are watermarked twice to insert the grayscale image already encrypted using RSA algorithm. Finally, watermarked mesh is reconstructed through the use on 10% of coefficients already calculated. Found results prove that our approach is able to insert a high amount of data without influencing the mesh quality. The application of the most popular attacks does not prevent a correct extraction of data already inserted. Our algorithm is,then,robust against these attacks.