Reviewerly/exHarmony huggingface dataset:
Ocean Protocol: Tools for the Web3 Data Economy

This chapter describes a toolset to enable Web3 data economy. Ocean Protocol is an on-ramp for data services into crypto ecosystems, using datatokens. Each datatoken is a fungible ERC20 token to access a given data service. Ocean smart contracts and libraries make it easy to publish data services (deploy and mint datatokens) and consume data services (spend datatokens). Ocean contracts run on Ethereum mainnet to start, with other deployments to follow. Ethereum composability enables crypto wallets as data wallets, crypto exchanges as data marketplaces, data DAOs as data co-ops, and more. Ocean Market is an open-source community marketplace for data. It supports automatic determination of price using an “automated market maker” (AMM). Each datatoken has its own AMM pool. Anyone can add liquidity, aka stake (equivalent in AMMs). This is curation, as stake is a proxy to dataset quality. We envision thousands of data marketplaces, where Ocean Market is just one. In addition to Ocean Market being open-source (and therefore forkable), Ocean includes tools to help developers build their own marketplaces and other apps. Ocean’s “Compute-to-Data” feature gives compute access on privately held data, which never leaves the data owner’s premises. Ocean-based marketplaces enable monetization of private data while preserving privacy. These tools are part of a system designed for long-term growth of a permissionless Web3 Data Economy. The Ocean Data Farming program incentivizes a supply of data. The community-driven OceanDAO funds software development, outreach, and more.
Lognormal Features for Early Diagnosis of Alzheimer’s Disease Through Handwriting Analysis

Alzheimer's disease causes most of dementia cases. Although currently there is no cure for this disease, predicting the cognitive decline of people at the first stage of the disease allows clinicians to alleviate its burden. Clinicians evaluate individuals' cognitive decline by using neuropsychological tests consisting of different sections, each devoted to test a specific set of cognitive skills. The sigma-lognormal model allows complex movements to be represented as a summation of simple time-overlapped movements, and has been used in several fields to model numerous human movements such as, for example, handwriting and speech. Recently, this theory has been also used for detecting and monitoring neurodegenerative disorders. In this paper, we present the results of a preliminary study aimed at exploring the use of lognormal features to classify patients affected by Alzheimer's disease. The promising results achieved confirms that lognormal features can be used to support Alzheimer's diagnosis.
Locally Varying Distance Transform for Unsupervised Visual Anomaly Detection

Unsupervised anomaly detection on image data is notoriously unstable. We believe this is because many classical anomaly detectors implicitly assume data is low dimensional. However, image data is always high dimensional. Images can be projected to a low dimensional embedding but such projections rely on global transformations that truncate minor variations. As anomalies are rare, the final embedding often lacks the key variations needed to distinguish anomalies from normal instances. This paper proposes a new embedding using a set of locally varying data projections, with each projection responsible for persevering the variations that distinguish a local cluster of instances from all other instances. The locally varying embedding ensures the variations that distinguish anomalies are preserved, while simultaneously allowing the probability that an instance belongs to a cluster, to be statistically inferred from the one-dimensional, local projection associated with the cluster. Statistical agglomeration of an instance's cluster membership probabilities, creates a global measure of its affinity to the dataset and causes anomalies to emerge, as instances whose affinity scores are surprisingly low.
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checking

This paper presents COOL-MC, a tool that integrates state-of-the-art reinforcement learning (RL) and model checking. Specifically, the tool builds upon the OpenAI gym and the probabilistic model checker Storm. COOL-MC provides the following features: (1) a simulator to train RL policies in the OpenAI gym for Markov decision processes (MDPs) that are defined as input for Storm, (2) a new model builder for Storm, which uses callback functions to verify (neural network) RL policies, (3) formal abstractions that relate models and policies specified in OpenAI gym or Storm, and (4) algorithms to obtain bounds on the performance of so-called permissive policies. We describe the components and architecture of COOL-MC and demonstrate its features on multiple benchmark environments.
Cross-Project Defect Prediction: A Literature Review

Background: Software defect prediction models aim at identifying the potential faulty modules of a software project based on historical data collected from previous versions of the same project. Due to the lack of availability of software engineering data from the same project, the researchers proposed cross-project defect prediction (CPDP) models where the data collected from one or more projects are used to predict faults in other project. There are a number of approaches proposed with different levels of success and very limited repeatability. Goals: The purpose of this paper is to investigate the existing studies of cross-project models for defect prediction. It synthesizes the literature focusing on characteristics such as project type, software metrics, data preprocessing techniques, features selection approaches, classifiers, and performance measures used. Method: This paper follows the well-known Systematic Literature Review (SLR) approach proposed by Barbara Kitchenham in 2007. Results: Our finding shows that most of the article was published between 2015 and 2021. Moreover, the studies are mostly based on open-source datasets and the software metrics used to create the models are mainly product metrics. We also found out that most studies attempted to improve their models improving data preprocessing and feature selection approaches. Furthermore, logistic regression followed by naive bayes and random forest are the most adopted classifier techniques in such models. Finally, the f-measure followed by recall and AUC are the most preferred evaluation measure used to evaluate the performance of the models. Conclusions: This study provides an overview of the different approaches used to improve the CPDP models analyzing the different techniques used for data preprocessing, feature selection, and the selection of the classifiers. Moreover, we identified some aspects that need further investigation.
Face recognition using Deep Neural Network and Support Based Anchor Boxes Methods in video frames

Face recognition using deep learning algorithms has gained a lot of interest in recent decades in the area of computer vision. The most up-to-date and accurate face detection algorithms are built using deep learning. Partially detecting faces, on the other hand, is still a work in progress. Hair, hats, eyeglasses, hands, cell phones, and side-angle-captured photographs obstruct the view of certain people’s faces. Such photos reveal fewer face features. The revised support-based anchor boxes section strategy was used to offer a unique Chi-Square-based deep neural network face detection approach in this paper. The range of anchor boxes and scales was limited, and only those that were essential to the face form were chosen. The suggested research uses a video dataset and performs Image Pre-Processing, Face Area Estimation, Feature Extraction, and Face Detection on it. Finally, the outcome face targets can be identified clearly. Face Detection Dataset and Benchmark (FDDB) were used to train and evaluate the suggested model, which can recognize partially covered faces with greater accuracy and precision. Extensive tests were carried out, with accuracy, precision, recall, F1 score, inference time, and FPS being used as evaluation metrics. The results reveal that the suggested model is more accurate and exact than existing state-of-the-art approaches at detecting the face in the image, incorporating occluded features, with 99.32 Percentage
Recent Works for Sentiment Analysis using Machine Learning and Lexicon Based Approaches

Sentiment analysis has got attentions for its guidance significance since the influence of online public reviews dramatically increased these years. It aims to perceive people’s attitude from their online reviews about a topic by classifying the words or the document’s polarity into distinct opposite feelings. Given that the massive information and data source online are unstructured and unprocessed where we need to deal with a large amount of information containing slang and expression with distinct language, particular methods and techniques with high accuracy and precision that help us do the processing are needed. In order to assist the future investigators comprehending the influence of online reviews with heterogeneous polarity and the possible improvement of existing works, this paper shows several approaches and applications of SA to analyze and compare the different outcomes and the performance of each. It turns out that existing works conclude that lexicon and machine learning based approach are the two main categories of sentiment analysis generally. The result shows that machine learning based methods can provide an effective and satisfactory assessment of general sentiment classification in processing massive online comments, but manual coding is still dominant in high-precision sentiment analysis. It suggests that when analysis of each comment needs to be performed accurately (especially if the data set is important), a second check through manual coding is necessary.
Flow-Based Visual Quality Enhancer for Super-Resolution Magnetic Resonance Spectroscopic Imaging

Magnetic Resonance Spectroscopic Imaging (MRSI) is an essential tool for quantifying metabolites in the body, but the low spatial resolution limits its clinical applications. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but the super-resolved images are often blurry compared to the experimentally-acquired high-resolution images. Attempts have been made with the generative adversarial networks to improve the image visual quality. In this work, we consider another type of generative model, the flow-based model, of which the training is more stable and interpretable compared to the adversarial networks. Specifically, we propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI. Different from previous flow-based models, our enhancer network incorporates anatomical information from additional image modalities (MRI) and uses a learnable base distribution. In addition, we impose a guide loss and a data-consistency loss to encourage the network to generate images with high visual quality while maintaining high fidelity. Experiments on a 1H-MRSI dataset acquired from 25 high-grade glioma patients indicate that our enhancer network outperforms the adversarial networks and the baseline flow-based methods. Our method also allows visual quality adjustment and uncertainty estimation. Our code is available at https://github.com/dsy199610/Flow-Enhancer-SR-MRSI .
Game-theoretic Designs for Blockchain-based IoT: Taxonomy and Research Directions

In the last few years, the combination of the Internet of Things (IoT) and blockchain technologies has widely attracted significant research attention from academia and the industry due to the salient characteristics of blockchain and its natural compatibility with the functional requirements of IoT architectures. Despite the advantages of utilizing the blockchain in IoT systems, several issues have been raised regarding the mitigation of security attacks on blockchain-based IoT systems, management of blockchain operations, node communications, energy consumption, efficient consensus algorithm design, data sharing, and edge/fog computing. Many of the solutions proposed in the literature tried to address these issues using game-theoretic models. This paper surveys and investigates state-of the-art blockchain-based IoT solutions leveraging game theory to address common issues in blockchain design. The developed taxonomy enabled us to highlight existing challenges to the integration of game theory models into the design of blockchain-driven IoT and bring forward some critical research questions.
Proximity-based Eating Event Detection in Smart Eyeglasses with Expert and Data Models

We compare performances of an expert model-based approach and a data-based baseline for eating event detection using proximity sensor data of smart eyeglasses. Proximity sensors in smart eyeglasses can provide dynamic distance estimates of cyclic temporalis muscle contraction during chewing without skin contact. Our expert model is based on proximity signal preprocessing and two-threshold grid search. In contrast, baseline data models were based on One-class Support-Vector-Machines. We evaluate both models with in-lab and free-living data from 15 participants. Free-living data were obtained across one day of wearing smart eyeglasses with temple-integrated proximity sensors in unconstrained settings. Overall, the retrieval performance F1 score of the two-threshold-based algorithm for free-living data ranged between 0.6 and 0.7, and outperformed all tested SVM model configurations. While SVM models achieved maximum recall, precision was often below 0.5. We report head-side specific performances for a bilateral arrangement of the proximity sensors and detail performance characteristics in model parameter sweeps. We conclude that eating detection using proximity sensors in smart eyeglasses is a promising approach for unconstrained automated dietary monitoring. Nevertheless, further investigations are needed to deal with the proximity signal characteristics in everyday life monitoring.
Multidisciplinary Dimension of E-Learning in the Innovative Eco-System of a Modern University

The article presents multidisciplinary scale of e-learning in the context of digital transformations, caused by the range of modern crises. A proposed innovative approach considers e-learning as an integral component of the holistic process of students' cognitive, professional and personality self-development. Student's synergetic interaction with e-developmental environment as innovative ecosystem involves a big variety of e-training activities based on the model of multidisciplinary project development. Ecosystem complexity determines the diversity of interaction modes. Student's profile as agent of positive changes in the target professional field and society presents system-forming factor.
Facial recognition based workplace security system using LBPH algorithm

Workplace security is a major issue in today's working environment. To address the security concerns computer vision and its applications provide facial recognition-based security solutions. Every day, humans execute facial recognition effortlessly and almost without any effort. The goal of this paper is to create a reliable facial recognition system. This article presents a real-time face recognition-based security system for home and offices. It has been developed using the Local Binary Pattern Histogram (LBPH) algorithm. LBPH based Facial Recognition model has been developed and tested for security at workplaces. Variations in features, poses and angles of human faces as well as illumination have been selected as key parameters during testing of recognition algorithms. This model successfully passed over the two conditions and achieved more than 85% of accuracy. This kind of system can be used for video surveillance for streets, crowded areas and airports. Due to its resilience in varied lighting situations, the result suggests that LBPH FR is the best as compared to Eigenface, Fisherface.
A Mobile Cloud-based Access Control with Efficiently Outsourced Decryption

Most cryptographic-based access control schemes implemented in IoT-based and mobile cloud computing generally provide secure and lightweight privacy-preserving data sharing and access for data users. Ciphertext policy attribute-based encryption (CP-ABE) is one of the suitable techniques used to support fine-grained and secure data sharing in data outsourcing environment. However, its cryptographic construct is based on pairing and exponentiation which are expensive operations that are not practical to be run in the resource-constraint devices. In this paper, we propose a secure and efficient mobile-cloud based access control based on the fully outsourced CP-ABE decryption. Essentially, we introduce a transformation key technique to allow a mobile user to compute the secret key upon the decryption. Our proposed scheme outperforms the existing works in the way that there is no secret key retained at the mobile device and no cost of CPABE decryption at the mobile client side. Finally, we conducted performance evaluation to substantiate that our proposed scheme is efficient in practice.
A Notion of Equivalence for Refactorings with Abstract Execution

Relational verification through dynamic logic is a promising approach for verifying object oriented programs. Recent advances from symbolic to abstract executions have enabled reasoning about incomplete/abstract versions of such programs. This has proven fruitful in the exploration of correctness of refactorings primarily related to code blocks in Java. In this paper we explore further types of equivalent transformations and refactorings and discuss the challenges that still need to be overcome for full round-trip correctness of refactorings in object-oriented languages.
FedQNN: A Computation–Communication-Efficient Federated Learning Framework for IoT With Low-Bitwidth Neural Network Quantization

Federated learning (FL) allows participants to train deep learning models collaboratively without disclosing their data to the server or any other participants, providing excellent value in the field of privacy-sensitive IoT. However, this distributed training paradigm requires clients to perform intensive computation for many iterations, which may exceed the capability of a typical IoT terminal with limited processing power, storage capacity, and energy budget. Heavy communication between the server and clients may also result in intolerant bandwidth requirements and energy consumption for many IoT systems. In this article, we introduce the FedQNN, a computation–communication-efficient FL framework for IoT scenarios. It is the first work that integrates ultralow-bitwidth quantization into the FL environment, allowing clients to perform lightweight fix-point computation efficiently with less power. Furthermore, both upstream and downstream data are significantly compressed for more efficient communication using a combination of sparsification and quantization strategies. We performed extensive experiments on a variety of data sets and models while comparing with other frameworks, and the results demonstrate that the proposed method can save up to 90% of our clients’ computational energy, reduce model sizes by 30+ times, and significantly compress both communication bandwidth and transmitted data size while maintaining reasonable accuracy. The robustness against the non-independent and identically distributed (I.I.D.) condition is also validated.
Clustering of Human Activities from Wearables by Adopting Nearest Neighbors

The ubiquitous availability of wearable sensing platforms renders recording activity data a straightforward endeavor. For many scenarios, however, obtaining accurate annotations for these sensor data can be infeasible due to practical constraints on logistics and costs, as well as concerns regarding privacy and ethics. Thus, we turn to unsupervised clustering which explores techniques which are capable of learning to group data in the absence of ground truth annotations. Our work focuses on learning to cluster movement data by exploiting the consistent patterns which we expect them to contain. This could allow vast amounts of unlabelled sensor data, which can be collected at scale, to be leveraged to bootstrap powerful classifiers. We adapt the Semantic Clustering by Adopting Nearest Neighbors (SCAN) framework to human activity recognition, which allows for the grouping of unlabelled windows of multi-sensor recordings based on the similarity of underlying movements. Using SCAN, we outperform the existing state-of-the-art unsupervised clustering technique for human activity recognition by clear margins on four diverse benchmark datasets.
Industrial project-based course on service oriented design

An increasing number of academic courses adopt a project-based evaluation of the students' knowledge and skills. In such courses, students gather development and management experiences similar to those present in industry. In this paper, we present an overview on the Service Oriented Design Master course where an industrial project proposed in collaboration with an IT company is developed by students in teams. We share our experience concerning organizational issues to be addressed before the beginning of the course, during the course, and after the end of the course. Further, we provide some information about the current edition of the course and the shareable course material.
Anomaly and activity recognition in a video surveillance using Masked Autoencoder

Video cameras are used to monitor many of the public locations in today's globe, including supermarkets, public gardens, malls, university campuses, and so on. There is a requirement to provide crucial security and monitor aberrant anomaly activity at such locations. The fundamental disadvantage of the previous method is that manual procedures must be conducted 24 hours a day, seven days a week, and human error is a possibility. The goal of this study is to detect anomalies and recognise human activity in films. The masked spatiotemporal autoencoder (MAE) is used to detect video anomalies. The suggested model design has three basic elements: a spatial encoder, a temporal encoder-decoder, and a spatial decoder.
Bi-LSTM-CRF Network for Clinical Event Extraction With Medical Knowledge Features

Extracting clinical event expressions and their types from clinical text is a fundamental task for many applications in clinical NLP.State-of-the-art systems need handcraft features and do not take into account the representation of the low-frequency words.To address these issues, a Bi-LSTM-CRF neural network architecture based on medical knowledge features is proposed.First, we employ convolutional neural networks (CNNs) to encode character-level information of a word and extract medical knowledge features from an open-source clinical knowledge system.Then, we concatenate character-level and wordlevel embedding and the medical knowledge features of words together, and feed them into bi-directional long short-term memory (Bi-LSTM) to build context information of each word.Finally, we jointly use a conditional random field (CRF) to decode labels for the whole sentence.We evaluate our model on two publicly available clinical datasets, namely THYME corpus and 2012 i2b2 dataset.Experimental results show that our model outperforms previous state-of-the-art systems with different methodologies, including machine learning-based methods, deep learning-based methods, and Bert-based methods.