Reviewerly/exHarmony huggingface dataset:
An unsupervised method for word sense disambiguation

Word sense disambiguation (WSD) finds the actual meaning of a word according to its context. This paper presents a novel WSD method to find the correct sense of a word present in a sentence. The proposed method uses both the WordNet lexical dictionary and the Wikipedia corpus. Initially, we find all the probable senses of the target word using WordNet. For each of the words present in a sense, we calculate the collocation extraction score with the other words in the sentence. The collocation extraction score finds the probability of the occurrence of two words together in the Wikipedia corpus. The maximum collocation extraction score assigns the proper sense for that context of the sentence. Our method is not limited to the bi-grams that are made up of only two consecutive words. Our method can find the probability of having two words together in a sentence when other words separate these two words. To compare our WSD method with current knowledge-based unsupervised and supervised systems, we use different Senseval and SemEval datasets for doing WSD on English words. Finally, the experimental analysis illustrates the significance of the proposed approach over many baseline and current systems.
E-textbook technology: Are instructors using it and what is the impact on student learning?

Purpose Today’s digital and mobile learning environment has contributed to the increased availability of and interest in e-textbooks, and many school systems are conducting trials to evaluate their effectiveness. The purpose of this paper is to identify and analyze instructors’ levels of use (LoU) of e-textbook features and innovations at a southeastern US community college. This study also evaluated the effectiveness of e-textbooks compared to paper textbooks on student achievement during a pilot period of e-textbook implementation. Design/methodology/approach Using a survey research design, the LoU of the Innovation framework was applied to identify and analyze instructors’ LoU rankings for eight e-textbook features. The study also used historical data on student demographics and final course grades to evaluate student achievement between text formats. Descriptive and inferential statistics were used to answer the research questions. Findings Results showed that e-textbook features were used at a low to non-existent level by instructors and that there was no significant difference in grade average between text formats among students. However, interactions between text format, age and gender were found. Originality/value This study added to the body of knowledge regarding e-textbook efficacy. While many studies stop with the conclusion that there is no difference in student outcomes between text formats, this study addressed a gap in literature on how to improve student performance with e-textbook technology by using the LoU of an innovation framework.
Hierarchical Graph Pooling With Self-Adaptive Cluster Aggregation

Graph neural network (GNN) introduces deep neural networks into graph structure data. It has achieved advanced performance in many fields, such as traffic prediction, recommendation systems, and computer vision, which has received extensive attention from the academic community. Most of the existing research on GNNs focuses on graph convolution, while graph pooling is usually ignored. Although there are also some graph pooling methods, most of the current pooling methods are based on top-k node selection. In the top-k-based pooling method, unselected nodes will be directly discarded, which will cause the loss of feature information during the pooling process. In this article, we propose a novel graph pooling operator, called hierarchical graph pooling with self-adaptive cluster aggregation (HGP-SACA), which uses a sparse and differentiable method to capture the graph structure. Before using top-k for cluster selection, the unselected clusters and the selected clusters in the neighbor perform an n-hop feature information aggregation. The merged clusters which contain neighborhood clusters are used for top-k selection, which can enhance the function of the unselected clusters. Through extensive theoretical analysis and experimental verification on multiple data sets, our experimental results show that combining the existing GNN architecture with HGP-SACA can achieve state-of-the-art results on multiple graph classification benchmarks, which proves the effectiveness of our proposed model.
A hybrid of deep and textural features to differentiate glomerulosclerosis and minimal change disease from glomerulus biopsy images

The minimal change disease (MCD) and glomerulosclerosis (GS) are two common kidney diseases. Unless adequately treated, these diseases leads to chronic kidney diseases. Accurate differentiation of these two diseases is of paramount importance as their methods of treatment and prognoses are different. Thus, this article propose a method capable of differentiating MCD from GS in glomerulus biopsies images based on a new hybrid deep and texture feature space. We conducted an extensive study to determine the best set of features for image representation. Our feature extraction methodology, which includes Haraliks and geostatistics texture descriptors and pre-trained CNNs, resulted in 13,476 characteristics. We then used mutual information to order the elements by importance and select the best set for differentiating MCD from GS using the random forest classifier. The proposed method achieved an accuracy of 90.3% and a Kappa index of 80.5%. Representation of glomerulus biopsy images with a hybrid of deep and textural features facilitates the accurate differentiation of GS and MCD.
Three-Round Secure Multiparty Computation from Black-Box Two-Round Oblivious Transfer

We give constructions of three-round secure multiparty computation (MPC) protocols for general functions that make black-box use of a two-round oblivious transfer (OT). For the case of semi-honest adversaries, we make use of a two-round, semi-honest secure OT in the plain model. This resolves the round-complexity of black-box (semi-honest) MPC protocols from minimal assumptions and answers an open question of Applebaum et al. (ITCS 2020). For the case of malicious adversaries, we make use of a two-round maliciously-secure OT in the common random/reference string model that satisfies a (mild) variant of adaptive security for the receiver.
Arabic Light Stemmer Based on ISRI Stemmer

The process of stemming is considered as one of the most essential steps in natural language processing and retrieving information. Nevertheless, in Arabic language, the task of stemming remains a major challenge due to the fact that Arabic language has a particular morphology, thereby making it different from other languages. Majority of existing algorithms are limited to a given number of words, create ambiguity between original letters and affixes, and often make use of dictionary patterns or words. We therefore, for the first time, present a design and implementation of Arabic light stemmer based on Information Science Research Institute algorithm. The algorithm is evaluated empirically using a newly created Arabic dataset which was created using data from different Arabic websites with contents that have been written in modern Arabic language. The experimental results indicated that the proposed method outperforms when benchmarked with existing methods.
Semi-supervised Selective Clustering Ensemble based on constraint information

Clustering is an important research direction in data mining. However, there is no one clustering algorithm that can be applied efficiently in all situation. Clustering ensemble is the best way to solve the above-mentioned problems. It combines the results of multiple clustering algorithms, and the final result is significantly better than a single clustering algorithm. Although there is a lot of constraint information, the existing clustering ensemble algorithm does not utilize it. This paper uses constraint information in consensus function and proposes a Semi-supervised Selective Clustering Ensemble based on Chameleon (SSCEC) and Semi-supervised Selective Clustering Ensemble based on Ncut (SSCEN) to solve the above problem. SSCEC uses the chameleon algorithm as consensus function, and processes constraint information in subgraph partition and subgraph combining. SSCEN uses the Normalized cut algorithm as consensus function, and processes constraint information in the process of graph dichotomy. The experiment results show that our proposed two semi-supervised member selection clustering ensemble algorithms are better than other semi-supervised algorithms.
Communication-Efficient Federated Learning via Predictive Coding

Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless mobile devices, the communication overhead is a critical bottleneck due to limited power and bandwidth. Prior work has utilized various data compression tools such as quantization and sparsification to reduce the overhead. In this paper, we propose a predictive coding based compression scheme for federated learning. The scheme has shared prediction functions among all devices and allows each worker to transmit a compressed residual vector derived from the reference. In each communication round, we select the predictor and quantizer based on the rate–distortion cost, and further reduce the redundancy with entropy coding. Extensive simulations reveal that the communication cost can be reduced up to 99% with even better learning performance when compared with other baseline methods.
An Interactive Robot Platform for Introducing Reinforcement Learning to K-12 Students

As artificial intelligence (AI) plays a more prominent role in our everyday lives, it becomes increasingly important to introduce basic AI concepts to K-12 students. To help do this, we combined the physical (LEGO® robotics) and the virtual (web-based GUI) worlds for helping students learn some of the fundamental concepts of reinforcement learning (RL). We chose RL because it is conceptually easy to understand but received the least attention in previous research on teaching AI to K-12 students. Our initial pilot study of 6 high school students in an urban city consisted of three separate activities, run remotely on three consecutive Friday afternoons. Students’ engagement and learning were measured through a qualitative assessment of students’ discussions and their answers to our evaluation questions. Even with only three sessions, students were optimizing learning strategies, and understanding key RL concepts and the value of human inputs in RL training.
Fundamental relationships between subjective quality, user acceptance, and the VMAF metric for a quality-based bit-rate ladder design for over-the-top video streaming services

A quality-based bit rate ladder design for over-the-top video streaming services is presented. Following the design criterion of maximizing subjective quality under the constraint of minimizing storage costs, the bit rate ladder is defined by three parameters. The first parameter is the lowest VMAF score at which a video signal is on average subjectively indistinguishable from the original video signal. Following the international recommendation ITU-R BT.500, extensive subjective tests were carried out to evaluate the fundamental relationships between the subjective quality and the VMAF score using a 4K OLED TV environment. Based on the test results, this VMAF score is set to 95. The second parameter is the lowest VMAF score being accepted on average by more than 50 % of the users for watching video signals of free streaming services. Additional tests yield in setting this VMAF score to 55. The third parameter is the maximum difference of two VMAF scores, for which the associated subjective qualities are approximately the same on average. In a third test, this difference is determined to be 2. This results in an ideal bit rate ladder providing each video signal in 21 qualities associated to the VMAF scores 95, 93, …, 57, 55. This bit rate ladder design can be applied to complete video signals occurring in per-title encoding strategies or to individual scenes of video signals occurring in per scene or shot-based encoding strategies. Applications using less than 21 renditions for this range, may suffer from impaired subjective quality
A Preliminary Geothermal Prospectivity Mapping Based on Integrated GIS, Remote-Sensing, and Geophysical Techniques around Northeastern Nigeria

Spatial mapping of potential geothermal areas is an effective tool for a preliminary investigation and the development of a clean and renewable energy source around the globe. Specific locations within the Earth’s crust display some manifestations of sub-surface geothermal occurrences, such as hot springs, a volcanic plug, mud volcanoes, and hydrothermal alterations, that need to be investigated further. The present area of investigations also reveals some of these manifestations. However, no attempt was made to examine the prospectivity of this terrain using the efficient GIS-based multicriteria evaluation (MCE) within the scope of the Analytic hierarchy process (AHP). The integration of remote sensing, Geographic information system (GIS), and other geophysical methods (Magnetic and gravity) was performed to map the promising geothermal areas. Multiple input data sets such as aero-magnetic, aero-gravity, aero-radiometric, digital elevation model (DEM), geological map, and Landsat-8 Operational Land Imager (OLI) data were selected, processed, and use to generate five thematic layers, which include heat flow, temperature gradients, integrated lineaments, residual gravity, and lithology maps. The five thematic layers were standardized and synthesized into a geothermal prospectivity map. The respective ranks and weight of the thematic layers and their classes were assigned based on expert opinion and knowledge of the local geology. This research aims to apply an efficient method to evaluate the factors influencing the geothermal energy prospects, identify and map prospective geothermal regions, and, finally, create a geothermal prospectivity model.
IoT-Based Activities of Daily Living for Abnormal Behavior Detection: Privacy Issues and Potential Countermeasures

Activities of daily living (ADL) systems have been playing an important role in assessing and monitoring the quality of life of elderly people for many years. With the recent advancement and integration of Internet of Things (IoT) devices within ADL systems, the number and quality of services offered has increased significantly. One of these vital services is abnormal behaviour detection based on the data collected from IoT devices within smart homes. However, the IoT data collected could have enormous privacy implications for smart home users if the data is not handled properly. We address this issue by analyzing a generic ADL system for abnormal behaviour detection, including its entities and their interactions. We highlight three major privacy issues: (i) identity privacy, (ii) data confidentiality, and (iii) metadata data leakage. These issues are particularly relevant to ADL systems, and we propose potential countermeasures to tackle them. Finally, we sketch a privacy-preserving version of an example ADL system to demonstrate the effectiveness of our proposed countermeasures, before suggesting future research directions.
Artificial spiking neuron based on a single-photon avalanche diode and a microcavity laser

We present an experimental realization and characterization of artificial spiking neuron based on an optoelectronic pair “microcavity laser-single photon avalanche diode” operating in few photon regime. We show that basic properties of biological neurons, such as an existence of the threshold and the refractory period, the insensitivity to the effect of the stimuli strength above the threshold, and the dependence of the neuron fire rate of the stimuli strength, can be realized with such a type of artificial neuron. To compare, we present corresponding results of the numerical simulation in the framework of the FitzHugh–Nagumo neuron model.
Single-shot 3D tracking based on polarization multiplexed Fourier-phase camera

For moving objects, 3D mapping and tracking has found important applications in the 3D reconstruction for vision odometry or simultaneous localization and mapping. This paper presents a novel camera architecture to locate the fast-moving objects in four-dimensional (4D) space ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m2"> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m3"> <mml:mrow> <mml:mi>z</mml:mi> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m4"> <mml:mrow> <mml:mi>t</mml:mi> </mml:mrow> </mml:math> ) through a single-shot image. Our 3D tracking system records two orthogonal fields-of-view (FoVs) with different polarization states on one polarization sensor. An optical spatial modulator is applied to build up temporal Fourier-phase coding channels, and the integration is performed in the corresponding CMOS pixels during the exposure time. With the 8 bit grayscale modulation, each coding channel can achieve 256 times temporal resolution improvement. A fast single-shot 3D tracking system with 0.78 ms temporal resolution in 200 ms exposure is experimentally demonstrated. Furthermore, it provides a new image format, Fourier-phase map, which has a compact data volume. The latent spatio-temporal information in one 2D image can be efficiently reconstructed at relatively low computation cost through the straightforward phase matching algorithm. Cooperated with scene-driven exposure as well as reasonable Fourier-phase prediction, one could acquire 4D data ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m5"> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m6"> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m7"> <mml:mrow> <mml:mi>z</mml:mi> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m8"> <mml:mrow> <mml:mi>t</mml:mi> </mml:mrow> </mml:math> ) of the moving objects, segment 3D motion based on temporal cues, and track targets in a complicated environment.
Video saliency prediction via spatio-temporal reasoning

Video saliency detection often suffers from two issues: hard to disentangle the temporal motion patterns and spatial layout patterns, and hard to capture the temporal motion patterns. Thus a novel deep learning network architecture is proposed for video saliency in this paper. The proposed network consists of three parts: high-level representation module, attention module, and memory and reasoning module. The high-level representation module and attention module are used for capturing spatial saliency that is mainly learned from static images. The memory and reasoning module is used to infer the saliency from the information about spatial layout in frames and temporal motion between frames. Because high-level representation module and attention module could concentrate on high-level representation of spatial patterns, and the memory and reasoning module could concentrate on spatial and temporal saliency reasoning, the temporal patterns and spatial patterns could be disentangled efficiently. The quantitative and qualitative results show the proposed method achieves a promising results across a wide of metrics.
High-Threshold AVSS with Optimal Communication Complexity

Asynchronous verifiable secret sharing (AVSS) protocols protect a secret that is distributed among n parties. Dual-threshold AVSS protocols guarantee consensus in the presence of t Byzantine failures and privacy if fewer than p parties attempt to reconstruct the secret. In this work, we construct a dual-threshold AVSS protocol called Haven that is optimal along several dimensions. First, it is a high-threshold AVSS scheme, meaning that it is a dual-threshold AVSS with optimal parameters $$t < n/3$$ and $$p < n - t$$ . Second, it has $$O(n^2)$$ message complexity, and for large secrets it achieves the optimal O(n) communication overhead, without the need for a public key infrastructure or trusted setup. While these properties have been achieved individually before, to our knowledge this is the first protocol that achieves all of the above simultaneously. The core component of Haven is a high-threshold AVSS scheme for small secrets based on polynomial commitments that achieves $$O(n^2 \log (n))$$ communication overhead, as compared to prior schemes that require $$O(n^3)$$ overhead with $$t < n/4$$ Byzantine failures or $$O(n^4)$$ overhead for the recent high-threshold protocol of Kokoris-Kogias et al. (CCS 2020). Using standard amortization methods based on erasure coding, we can reduce the communication complexity to $$O(n \vert s \vert )$$ for a large secret $$s $$ .
An Unsupervised Learning Approach for Detecting Relapses from Spontaneous Speech in Patients with Psychosis

In this work, we aim to explore and develop a speech analysis system that identifies relapses in patients with psychotic disorders (i.e., bipolar disorder and schizophrenia) with the long-term goal of monitoring and detecting relapse indicators, in order to aid in timely diagnoses of psychotic relapses. To this end, we utilize an unsupervised learning approach, employing convolutional autoencoders to build personalized speech models for patients. We use data from interviews between patients and clinicians to train and evaluate our models. The models are trained, learning to reconstruct spectrograms of speech segments corresponding to non-relapsing periods; then, the reconstruction error of the model is used to determine whether unseen speech data correspond to an anomalous (relapsing or pre-relapsing) state, or a stable one. A preliminary study using data from 5 patients and 95 interviews in total yielded encouraging results, indicating the potential usability of such models in real-time health monitoring.