Reviewerly/exHarmony huggingface dataset:
Evaluating fMRI-Based Estimation of Eye Gaze During Naturalistic Viewing

Abstract The collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eye’s orbit using a 1.5-min calibration scan. Here, we provide confirmatory validation of the PEER method’s ability to infer eye gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation &amp;lt; 2°). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n = 448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately classify which of the two movies is being watched based on the predicted eye gaze patterns (area under the curve = 0.90 ± 0.02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.
Poster

There is heightened concern over deliberately inaccurate news. Recently, so-called deepfake videos and images that are modified by or generated by artificial intelligence techniques have become more realistic and easier to create. These techniques could be used to create fake announcements from public figures or videos of events that did not happen, misleading mass audiences in dangerous ways. Although some recent research has examined accurate detection of deepfakes, those methodologies do not generalize well to real-world scenarios and are not available to the public in a usable form. In this project, we propose a system that will robustly and efficiently enable users to determine whether or not a video posted online is a deepfake. We approach the problem from the journalists' perspective and work towards developing a tool to fit seamlessly into their workflow. Results demonstrate accurate detection on both within and mismatched datasets.
Domain Adaptation Problem

In general, the concept of learning can be divided into two broad settings based on the presence or absence of a teacher or relevant prior knowledge that can replace it. In the first case, learning mainly concerns the acquisition of knowledge from scratch, for which full permanent supervision is obviously necessary and cannot be replaced. This particular case is reminiscent of a human being who learns without any prior knowledge and therefore needs a teacher to provide them with all the necessary information. This information usually takes the form of oral instructions, annotations or written labels. Obviously, this type of learning takes place mainly among very young children who learn by following direct instructions from their parents or other family members. From a certain age, the human being generally acquires a sufficient basis to learn new things without supervision or under partial supervision. This type of learning often defines a person’s general intelligence and intuition because it reflects their ability to generalize to different applications and thus their ability to operate at a higher abstract level.
Formalizing GDPR Provisions in Reified I/O Logic: The DAPRECO Knowledge Base

The DAPRECO knowledge base is the main outcome of the interdisciplinary project bearing the same name ( https://www.fnr.lu/projects/data-protection-regulation-compliance ). It is a repository of rules written in LegalRuleML, an XML formalism designed to be a standard for representing the semantic and logical content of legal documents. The rules represent the provisions of the General Data Protection Regulation (GDPR), the new Regulation that is significantly affecting the digital market in the European Union and beyond. The DAPRECO knowledge base builds upon the Privacy Ontology (PrOnto) (Palmirani et al in Proceedings of the 7th international conference on electronic government and the information systems perspective: technology-enabled innovation for democracy, government and governance, 2018c), which provides a model for the legal concepts involved in the GDPR, by adding a further layer of constraints in the form of if-then rules, referring either to standard first order logic implications or to deontic statements. If-then rules are formalized in reified Input/Output logic (Robaldo and Sun in J Log Comput 7, 2017) and then codified in LegalRuleML. Reified Input/Output logic is an application of standard Input/Output logic for legal reasoning, in which Input/Output logic is combined with the reification-based approach in Hobbs and Gordon (A formal theory of commonsense psychology, how people think people think. Cambridge University Press, Cambridge, 2017). The DAPRECO knowledge base is then a case study for reified Input/Output logic, and it shows that the formalism indeed appears to be a good candidate to effectively formalize, via uniform and simple (flat) representations, complex linguistic/deontic phenomena that may be found in legal texts. To date, the DAPRECO knowledge base is the biggest knowledge base in LegalRuleML and Input/Output logic freely available online ( https://github.com/dapreco/daprecokb/blob/master/gdpr/rioKB_GDPR.xml ).
On Exponential Stabilization of $N$-Level Quantum Angular Momentum Systems

In this paper, we consider the feedback stabilization problem for $N$-level quantum angular momentum systems undergoing continuous-time measurements. By using stochastic and geometric control tools, we provide sufficient conditions on the feedback control law ensuring almost sure exponential convergence to a predetermined eigenstate of the measurement operator. In order to achieve these results, we establish general features of quantum trajectories which are of interest by themselves. We illustrate the results by designing a class of feedback control laws satisfying the above-mentioned conditions and finally we demonstrate the effectiveness of our methodology through numerical simulations for three-level quantum angular momentum systems.
The Application of Data Mining in Determining Patterns of Interest of High School Graduates

Abstract This study proposes an application of Data Mining in determining patterns of Interest of high school Graduates to attend AMIK Tunas Bangsa Pematangsiantar and the dominant factors which affect the interest of high school graduates. The data was picked up from the application forms completed by those enrolled in the AMIK . In this study, algorithm C 4.5 decision tree was applied in order to obtain the pattern of data classification, i.e. school, status, school location, and year of graduation.. The interest pattern of these graduates demonstrated that the best variable of the predictor used is the school status which gives as much as 81, 71% performance accuracy to registration.
Speaker's Voice Characteristics and Similarity Measurement using Euclidean Distances

The Euclidean distance and MFCC feature extraction technique are the composite way and useful for successfully measure the similarity between speakers. This type of approaches is used in the forensic statics for similarity of voice measurement. The speaker voice similarity is measured by using Euclidean distance (ED) through a different set of speakers as same-same speakers (SS) and difference speakers (DS). This approaches suggested that the feature coefficient of speech maximum dependent on speakers voice. The ED is calculated from the mean of Mel-frequency cepstral coefficient (MFCC) for speaker similarity measurement. There is ED difference method is proposed for finding the similarity index between SS and DS.
Content based image retrieval by ensembles of deep learning object classifiers

Ensemble learning has demonstrated its efficiency in many computer vision tasks. In this paper, we address this paradigm within content based image retrieval (CBIR). We propose to build an ensemble of convolutional neural networks (CNNs), either by training the CNNs on different bags of images, or by using CNNs trained on the same dataset, but having different architectures. Each network is used to extract the class probability vectors from images to use them as representations. The final image representation is then generated by combining the extracted class probability vectors from the built ensemble. We show that the use of CNN ensembles is very efficient in generating a powerful image representation compared to individual CNNs. Moreover, we propose an Averarge Query Expansion technique for our proposal to enhance the retrieval results. Several experiments were conducted to extensively evaluate the application of ensemble learning in CBIR. Results in terms of precision, recall, and mean average precision show the outperformance of our proposal compared to the state of the art.
Two-Sided Malicious Security for Private Intersection-Sum with Cardinality

Private intersection-sum with cardinality allows two parties, where each party holds a private set and one of the parties additionally holds a private integer value associated with each element in her set, to jointly compute the cardinality of the intersection of the two sets as well as the sum of the associated integer values for all the elements in the intersection, and nothing beyond that. We present a new construction for private intersection sum with cardinality that provides malicious security with abort and guarantees that both parties receive the output upon successful completion of the protocol. A central building block for our constructions is a primitive called shuffled distributed oblivious PRF (DOPRF), which is a PRF that offers oblivious evaluation using a secret key shared between two parties, and in addition to this allows obliviously permuting the PRF outputs of several parallel oblivious evaluations. We present the first construction for shuffled DOPRF with malicious security. We further present several new sigma proof protocols for relations across Pedersen commitments, ElGamal encryptions, and Camenisch-Shoup encryptions that we use in our main construction, for which we develop new batching techniques to reduce communication. We implement and evaluate the efficiency of our protocol and show that we can achieve communication cost that is only $$4{-}5{\times }$$ greater than the most efficient semi-honest protocol. When measuring monetary cost of executing the protocol in the cloud, our protocol is $$25{\times }$$ more expensive than the semi-honest protocol. Our construction also allows for different parameter regimes that enable trade-offs between communication and computation.
Accurate and efficient sequential ensemble learning for highly imbalanced multi-class data

Multi-class classification for highly imbalanced data is a challenging task in which multiple issues must be resolved simultaneously, including (i) accuracy on classifying highly imbalanced multi-class data; (ii) training efficiency for large data; and (iii) sensitivity to high imbalance ratio (IR). In this paper, a novel sequential ensemble learning (SEL) framework is designed to simultaneously resolve these issues. SEL framework provides a significant property over traditional AdaBoost, in which the majority samples can be divided into multiple small and disjoint subsets for training multiple weak learners without compromising accuracy (while AdaBoost cannot). To ensure the class balance and majority-disjoint property of subsets, a learning strategy called balanced and majority-disjoint subsets division (BMSD) is developed. Unfortunately it is difficult to derive a general learner combination method (LCM) for any kind of weak learner. In this work, LCM is specifically designed for extreme learning machine, called LCM-ELM. The proposed SEL framework with BMSD and LCM-ELM has been compared with state-of-the-art methods over 16 benchmark datasets. In the experiments, under highly imbalanced multi-class data (IR up to 14K; data size up to 493K), (i) the proposed works improve the performance in different measures including G-mean, macro-F, micro-F, MAUC; (ii) training time is significantly reduced.
Maximum Correntropy Criterion-Based Hierarchical One-Class Classification

Due to the effectiveness of anomaly/outlier detection, one-class algorithms have been extensively studied in the past. The representatives include the shallow-structure methods and deep networks, such as the one-class support vector machine (OC-SVM), one-class extreme learning machine (OC-ELM), deep support vector data description (Deep SVDD), and multilayer OC-ELM (ML-OCELM/MK-OCELM). However, existing algorithms are generally built on the minimum mean-square-error (mse) criterion, which is robust to the Gaussian noises but less effective in dealing with large outliers. To alleviate this deficiency, a robust maximum correntropy criterion (MCC)-based OC-ELM (MC-OCELM) is first proposed and then further extended to a hierarchical network to enhance its capability in characterizing complex and large data (named HC-OCELM). The gradient derivation combining with a fixed-point iterative updation scheme is adopted for the output weight optimization. Experiments on many benchmark data sets are conducted for effectiveness validation. Comparisons to many state-of-the-art approaches are provided for the superiority demonstration.
An Empirical Study of TextRank for Keyword Extraction

As a typical keyword extraction technology, TextRank has been used in a wide variety of commercial applications, including text classification, information retrieval and clustering. In these applications, the parameters of TextRank, including the co-occurrence window size, iteration number and decay factor, are set roughly, which might affect the effectiveness of returned results. In this work, we conduct an empirical study on TextRank, towards finding optimal parameter settings for keyword extraction. The experiments are done in Hulth2003 and Krapivin2009 datasets, which are two real datasets. We first remove the stop word by an open published English stop word list XPO6. And then, we extract the word stems by Porter Stemmer. Porter Stemmer is a tool which can find the stems of words with multiple variants, discard redundant information, strengthen the filtering effect, and extract the effective features of the text fully. We carry out extensive experiments to evaluate the effects of the parameters to keywords extraction, and evaluate the effectiveness of corresponding results by Precision, Recall and Accuracy. Experimental results show that TextRank shows the best performance when setting co-occurrence window size w = 3, iteration number t = 20, decay factor c = 0.9 and rank k = 10 respectively, and the results are independent of the text length.
Comparison of Landsat OLI, ASTER, and Sentinel 2A data in lithological mapping : A Case study of Rich area (Central High Atlas, Morocco)

The eastern part of the Rich area consists of the massive Paleozoic and Meso-Cenozoic cover formations that present the geodynamic development of the study area, where is characterized by various carbonate facies of Jurassic age. The geographical characteristic of the study area leaves the zone difficult to map by conventional methods. The objective of this work focuses on the mapping of the constituent lithological units of the study area using multispectral data of Landsat OLI, ASTER, and Sentinel 2A MSI. The processing of these data is based on a precise methodology that distinguishs and highlights the limits of the different lithological units that have an approximate similarity of spectral signature. Three techniques were used to enhance the image including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). Lithological mapping was performed using two types of supervised classification : Maximum likelihood classifier (MLC) and Support Vector Machine (SVM). The results of processing data show the effectiveness of Sentinel 2A data in mapping of lithological units than the ASTER and Landsat OLI data. The classification evaluation of two methods of the Sentinel 2A MSI image showed that the SVM method give a better classification with an overall accuracy of 93,93% and a Kappa coefficient of 0.93, while the MLC method present an overall accuracy of 82,86% and a Kappa coefficient of 0.80. The results of mapping obtained show a good correlation with the geological map of the study area as well as the efficiency of remote sensing in identification of different lithological units in the Central High Atlas.
Model Extraction Attacks and Defenses on Cloud-Based Machine Learning Models

Machine learning models have achieved state-of-the-art performance in various fields, from image classification to speech recognition. However, such models are trained with a large amount of sensitive training data, and are typically computationally expensive to build. As a result, many cloud providers (e.g., Google) have launched machine-learning-as-a-service, which helps clients benefit from the sophisticated cloud-based machine learning models via accessing public APIs. Such a business paradigm significantly expedites and simplifies the development circles. Unfortunately, the commercial value of such cloud-based machine learning models motivates attackers to conduct model extraction attacks for free use or as a springboard to conduct other attacks (e.g., craft adversarial examples in black-box settings). In this article, we conduct a thorough investigation of existing approaches to model extraction attacks and defenses on cloud-based models. We classify the state-of-the-art attack schemes into two categories based on whether the attacker aims to steal the property (i.e., parameters, hyperparameters, and architecture) or the functionality of the model. We also categorize defending schemes into two groups based on whether the scheme relies on output disturbance or query observation. We not only present a detailed survey of each method, but also demonstrate the comparison of both attack and defense approaches via experiments. We highlight several future directions in both model extraction attacks and its defenses, which shed light on possible avenues for further studies.
Manifesto for children’s genuine participation in digital technology design and making

We articulate in this paper what participation at its best entails in the context of digital technology design with children, forming a theoretical framework for genuine participation of children in digital technology design and making. We integrate in the framework a set of conditions for the meaningful and effective participation of children and the nexus analytic concepts of historical body, interaction order and discourses in place, and complement that with the lenses of empowerment, values and value. In addition to these theoretical lenses, we rely on the insights gained during our empirical work with children for more than a decade. We contribute to research on Child Computer Interaction (CCI) by explaining what ‘participation at its best’ entails in practice and how it can be studied in research. Thus, CCI researchers and practitioners advocating participation, empowerment and inclusion of children can benefit from this framework when planning, analysing and evaluating their projects with children.
A critical review of blockchain applications to banking and finance: a qualitative thematic analysis approach

Blockchain technology is a disruptive technology which has revolutionised the conventional business models, traditional business transaction work flows and opening the door to huge opportunities of business value co-creations. Blockchain holds the promise to disrupt various industries and organisations, and one of the most well-known areas is its applications to the banking and finance sector. This qualitative study has analysed state-of-the-art blockchain research and technology in the banking and finance sector. Firstly, we selected and reviewed 76 literature in the banking and finance sector from 2016 to 2020 and provided a critical bibliometric review of existing literature. Secondly, we applied the well-known thematic analysis methodology to systematically identify the hot research topics, five potential business benefits and three types of potential challenges of applying blockchain technology to the banking and finance sector. Finally, we concluded by summarising the limitations of our study and subsequently highlighted future research directions. Despite several studies in other fields, no systematic review has thus far synthesised the research on blockchain application in this field to the best of the authors’ knowledge. Hence, the current study provides a starting point for future research and facilitates the work of both academic scholars and industry practitioners.
Measuring the impact of blockchain and smart contracts on construction supply chain visibility

This work assesses the impact of blockchain and smart contracts on the visibility of construction supply chain and in the context of payments (intersection of cash and product flows). It uses comparative empirical experiments (Charrette Test Method) to draw comparisons between the visibility of state-of-practice and blockchain-enabled payment systems in a commercial construction project. Comparisons were drawn across four levels of granularity. The findings are threefold: 1) blockchain improved information completeness and information accuracy compared with the digital state-of-practice solution; 2) the improvements were significantly more pronounced for information queries that had higher product, trade, and temporal granularity; and 3) blockchain-enabled solution was robust when faced with more granular information queries, while the conventional solution experienced 50% and 66.7% decline respectively in completeness and accuracy of information. The paper concludes with a discussion of mechanisms contributing to visibility and the implications for the construction industry.
Swing-Up of Quantum Emitter Population Using Detuned Pulses

The swing up of quantum emitter population (SUPER) scheme, taking a radically different off-resonant approach to coherently excite a two-level system, is explained and proposed for single-photon generation from quantum dots.
TensorRT-Based Framework and Optimization Methodology for Deep Learning Inference on Jetson Boards

As deep learning inference applications are increasing in embedded devices, an embedded device tends to equip neural processing units (NPUs) in addition to a multi-core CPU and a GPU. NVIDIA Jetson AGX Xavier is an example. For fast and efficient development of deep learning applications, TensorRT is provided as the SDK for high-performance inference, including an optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. Like most deep learning frameworks, TensorRT assumes that the inference is executed on a single processing element, GPU or NPU, not both. In this article, we present a TensorRT-based framework supporting various optimization parameters to accelerate a deep learning application targeted on an NVIDIA Jetson embedded platform with heterogeneous processors, including multi-threading, pipelining, buffer assignment, and network duplication. Since the design space of allocating layers to diverse processing elements and optimizing other parameters is huge, we devise a parameter optimization methodology that consists of a heuristic for balancing pipeline stages among heterogeneous processors and fine-tuning the process for optimizing parameters. With nine real-life benchmarks, we could achieve 101%~680% performance improvement and up to 55% energy reduction over the baseline inference using a GPU only.