Reviewerly/exHarmony huggingface dataset:
Locally Differentially Private Personal Data Markets Using Contextual Dynamic Pricing Mechanism

Data is becoming the world's most valuable asset and the ultimate renewable resource. This phenomenon has led to online personal data markets where data owners and collectors engage in the data sale and purchase. From the collector's standpoint, a key question is how to set a proper pricing rule that brings profitable tradings. One feasible solution is to set the price slightly above the owner's data cost. Nonetheless, data cost is generally unknown by the collector as being the owner's private information. To bridge this gap, we propose a novel learning algorithm, modified stochastic gradient descent (MSGD) that infers the owner's cost model from her interactions with the collector. To protect owners’ data privacy during trading, we employ the framework of local differential privacy (LDP) that allows owners to perturb their genuine data and trading behaviors. The vital challenge is how the collector can derive the accurate cost model from noisy knowledge gathered from owners. For this, MSGD relies on auxiliary parameters to correct biased gradients caused by noise. We formally prove that the proposed MSGD algorithm produces a sublinear regret of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(T^{\frac{5}{6}}\sqrt{\log (T^{\frac{1}{3}})})$</tex-math></inline-formula> . The effectiveness of our design is further validated via a series of in-person experiments that involve 30 volunteers.
Spin-Variable Reduction Method for Handling Linear Equality Constraints in Ising Machines

We propose a spin-variable reduction method for Ising machines to handle linear equality constraints in a combinatorial optimization problem. Ising machines including quantum-annealing machines can effectively solve combinatorial optimization problems. They are designed to find the lowest-energy solution of a quadratic unconstrained binary optimization (QUBO), which is mapped from the combinatorial optimization problem. The proposed method reduces the number of binary variables to formulate the QUBO compared to the conventional penalty method. We demonstrate a sufficient condition to obtain the optimum of the combinatorial optimization problem in the spin-variable reduction method and its general applicability. We apply it to typical combinatorial optimization problems, such as the graph κ-partitioning problem and the quadratic assignment problem. Experiments using simulated-annealing and quantum-annealing based Ising machines demonstrate that the spin-variable reduction method outperforms the penalty method. The proposed method extends the application of Ising machines to larger-size combinatorial optimization problems with linear equality constraints.
Speaker-specificity in speech production: The contribution of source and filter

This study examines the extent to which speaker-specific information is encoded in different features of vocal output and the relationships between those features. A range of acoustic features, grouped as source (laryngeal voice quality measures and fundamental frequency) and filter features (formants and Mel-frequency cepstral coefficients; MFCCs), were extracted from the vocalic portion of the hesitation marker um for 90 male speakers of Standard Southern British English. Little overall correlation between the sets of features was observed, suggesting no strong interdependence between source and filter in our data. Although filter features were consistently better at discriminating between same- and different-speaker pairs compared with source features, combining source and filter has the potential of producing the lowest error rates and the strongest speaker discrimination scores. Taken together, results show that source and filter provide complementary speaker-specific information. However, the extent of the improvements in speaker discrimination performance when combining source and filter varied across speakers. We explore potential explanations for this finding and discuss the implications for source-filter theory, and for applied fields such as speaker recognition and forensic speech science.
A novel algorithm for mining maximal frequent gradual patterns

The problem of mining frequent gradual patterns has received important attention within the data mining community, because it has many applications in many domains, such as economy, health, education, market, bio-informatics and web mining. Algorithms to extract frequent gradual patterns in the large databases are greedy in CPU time and memory space and the number of frequent patterns generated by these algorithms is sometimes too large to be fully exploited within a reasonable timeframe. This raises the problem of improving the performances of these algorithms and the problem of exploiting concise representations of frequent gradual patterns. This paper presents a new maximal frequent gradual pattern mining approach that relies on an in-depth traversing of the search space in the lexicographical order and a reduction of the search space and the computational load of fundamental operations. This approach leads to a new, more efficient algorithm called MSGriteMiner. Complexity analysis, in terms of CPU time and memory usage, and experiments carried out on various well-known databases show that MSGriteMiner is better than the previous algorithms and confirm the interest of the proposed approach.
Single image deraining via a recurrent multi-attention enhancement network

Single image deraining has attracted considerable attention because rain streaks can severely degrade image quality and affect performance in computer vision tasks. While recurrent networks have achieved promising results, they still have limitations such as their receptive field size and feature representation ability, increasing the difficulty in removing long rain streaks while preserving details. In this paper, we present a recurrent multi-attention enhancement network for single image deraining that uses multiple attention mechanisms to effectively enhance feature representation in two stages. In the first stage, we utilize a non-local block to enhance the attention of the location information, effectively expanding the receptive field and helping to remove long rain streaks. In the second stage, a feature-enhanced residual block (FERB) is used to effectively acquire useful channel and spatial information to preserve more details. The proposed network utilizes the results of the current cycle in the next cycle and reuses the original rainy image at each cycle to produce more details in the derained image. Extensive experiments show that our method outperforms state-of-the-art methods in terms of visual effects and quantitative results.
DETECTING SUSPICIOUS ACTIVITIES IN SURVEILLANCE VIDEOS USING DEEP LEARNING METHODS

Video surveillance plays an important role in today's world.Technology has evolved tremendously as artificial intelligence, machine learning and deep learning become mainstream.Using a combination of the above, there are various systems that helps in distinguishing different types of suspicious behavior from live videos.The most unpredictable thing is the behavior of a person and it is very difficult to find out whether it is suspicious or normal.A deep learning approach is used to detect suspicious or unusual activity in the academic environment and send alert messages to the appropriate authorities if suspicious activity is detected.Surveillance is often done using a series of frames captured from a video.All frames are divided into two parts.In the first part, the features are calculated from the video frames, and in the second part, based on the extracted features, the classifier predicts the class as suspicious or normal.
UMRFormer-net: a three-dimensional U-shaped pancreas segmentation method based on a double-layer bridged transformer network

Background: Methods based on the combination of transformer and convolutional neural networks (CNNs) have achieved impressive results in the field of medical image segmentation. However, most of the recently proposed combination segmentation approaches simply treat transformers as auxiliary modules which help to extract long-range information and encode global context into convolutional representations, and there is a lack of investigation on how to optimally combine self-attention with convolution. Methods: We designed a novel transformer block (MRFormer) that combines a multi-head self-attention layer and a residual depthwise convolutional block as the basic unit to deeply integrate both long-range and local spatial information. The MRFormer block was embedded between the encoder and decoder in U-Net at the last two layers. This framework (UMRFormer-Net) was applied to the segmentation of three-dimensional (3D) pancreas, and its ability to effectively capture the characteristic contextual information of the pancreas and surrounding tissues was investigated. Results: Experimental results show that the proposed UMRFormer-Net achieved accuracy in pancreas segmentation that was comparable or superior to that of existing state-of-the-art 3D methods in both the Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma (CPTAC-PDA) dataset and the public Medical Segmentation Decathlon dataset (self-division). UMRFormer-Net statistically significantly outperformed existing transformer-related methods and state-of-the-art 3D methods (P<0.05, P<0.01, or P<0.001), with a higher Dice coefficient (85.54% and 77.36%, respectively) or a lower 95% Hausdorff distance (4.05 and 8.34 mm, respectively). Conclusions: UMRFormer-Net can obtain more matched and accurate segmentation boundary and region information in pancreas segmentation, thus improving the accuracy of pancreas segmentation. The code is available at https://github.com/supersunshinefk/UMRFormer-Net.
Source-o-grapher: A tool towards the investigation of software resilience in Open Source Software projects

This work presents Source-o-grapher, a tool built with the aim to investigate software resilience aspects of Open Source Software (OSS) projects. The tool uses several metrics from the literature to evaluate an OSS project on four major dimensions: structural (source code), business and legal, integration and social (community of the project). Many of these metrics are automatically acquired by the tool using the Github repository of the project whereas some others are manually input by the expert who performs the analysis.
Rifle Detection and Performance Evaluation Using Deep Learning Frameworks

Deep learning models being used to improve human life has been an ongoing domain of research. Violence, especially with the proliferation of arms, has been on the increase worldwide. Many tragedies have occurred right across the globe, leading to people losing their lives as a result of being shot at with guns. This research sought to use deep learning frameworks to detect rifles in images and assess their performance based on the metrics of accuracy and F1 score. The study used a combination of images from Google open images and other sources to form a dataset of 2105 images; 1857 of those was used to train YOLOv3 and RetinaNet models to detect rifles, using Darknet-53 and ResNet50 respectively as the backbone networks. The models were evaluated after training using a test dataset containing 248 images, both the training and evaluation of the models were carried out using scripts written in Python. The results obtained showed that YOLOv3 had better output in terms of accuracy, precision, recall, and, consequently, the F1 scored better than RetinaNet
Using Graph Neural Network to Enhance Quality of Service Prediction

Quality of service (QoS) prediction has great importance in today's web services computing. Several researchers proposed methods to enhance the quality of service prediction. The most used one is collaborative filtering (CF), which can be categorized into three main categories: memory-based algorithms, model-based algorithms, and context-based CF algorithms. This paper proposes a model-based algorithm using the graph neural network (GNN) to predict the QoS values. To evaluate the performance of the proposed method, an experiment was conducted. The WS-dream dataset used in the experiment and the proposed method performance were compared with three baseline methods (User item-based Pearson correlation coefficient for QoS prediction-UIPCC, reputation-aware network embedding-based QoS Prediction-RANEP, and trust-aware approach TAP for personalized QoS prediction). The experiment results show that the proposed method, the GNN-based QoS prediction algorithm, performs better than memory-based and other model-based methods in terms of RMSE and MAE in most cases.
Comparative Study of Convolutional Neural Network Architecture in Lymphoma Detection

In this study, we propose an automatic classification of three common types of lymphoma: (1) lymphoma, (2) benign lesion, and (3) carcinoma using lymphoma cell images magnified by 100x and by 400x.A comparative study was performed to find the best architecture to classify lymphoma cell images using the Keras library in Tensorflow.The architectures used in this study are ResNet50, MobileNetV1, and VGG16.Based on the accuracy of lymphoma classification for each architecture, the MobileNet model had the highest accuracy in all three classes at both 100x and 400x magnification levels, which suggests that MobileNet is the best model for lymphoma cell classification.This study can be later used as the base argument in modifying the MobileNet architecture further to get more accurate results in future similar studies.
Style over substance: A psychologically informed approach to feature selection and generalisability for author classification

Author profiling, or classifying user generated content based on demographic or other personal attributes, is a key task in social media-based research. Whilst high-accuracy has been achieved on many attributes, most studies tend to train and test models on a single domain only, ignoring cross-domain performance and research shows that models often transfer poorly into new domains as they tend to depend heavily on topic-specific (i.e., lexical) features. Knowledge specific to the field (e.g., Psychology, Political Science) is often ignored, with a reliance on data driven algorithms for feature development and selection. Focusing on political affiliation, we evaluate an approach that selects stylistic features according to known psychological correlates (personality traits) of this attribute. Training data was collected from Reddit posts made by regular users of the political subreddits of r/republican and r/democrat. A second, non-political dataset, was created by collecting posts by the same users but in different subreddits. Our results show that introducing domain specific knowledge in the form of psychologically informed stylistic features resulted in better out of training domain performance than lexical or more commonly used stylistic features.
An Empirical Study on the Effects of Online and Offline Blended Curriculum Practice

To explore the necessity and function of online and offline blended curriculum teaching in universities, this research used the "Social Security Theory" course for postgraduates as an example to carry out blended teaching practice.In the research, the implementation processes of blended curriculum teaching was explored and SPSS and other tools were used to evaluate the teaching effect.Results showed that online and offline blended curriculum teaching significantly promoted the innovation ability of postgraduate students, and the variance analysis of students' scores showed that there were significant differences between the blended teaching group (M = 84.8)and the traditional one (M = 74.7)(F = 31.978,P < 0.001).The blended teaching also reduced teachers' repetitive teaching stuffs and teachers' offline teaching hours had been reduced greatly from 36 h to 18 h.Meanwhile, the satisfaction of students also showed that there was a significant difference between the blended teaching group (M = 4.26) and the traditional one (M = 3.58) (F = 228.244,P < 0.001).
The Exploration of O2O Teaching Mode in College English Classroom

With the wide application of the multimedia and Internet-based technology in college English class, college-teaching mode has become the core issue of teaching reform and practice.With the help of mosoteach app teaching platform, based on the theory production-oriented approach and combined with the questionnaire and the characteristics of private undergraduate students, this research puts forward O2O with "one body and two wings" College English teaching model to improve students' autonomous learning competence.Meanwhile, through comparing and analyzing the traditional college English teaching mode and the O2O with "one body and two wings" college English teaching mode, this research attempts to enlighten the future online and offline hybrid English teaching.
Time–frequency fusion for enhancement of deep learning-based physical layer identification

Existing studies on deep learning-based physical layer identification have mainly exploited raw in-phase/ quadrature (IQ) samples or power spectral density (PSD) samples as inputs independently. The raw IQ and PSD samples represent the information in the time and frequency domains, respectively. It has been observed from the results of existing studies that identification using raw IQ samples outperforms that using PSD in low signal-to-noise ratio (SNR) regimes, and that identification using PSD outperforms that using raw IQ in high SNR regimes. In this paper, we propose to use the fusion of raw IQ and PSD samples to enhance deep learning-based physical layer identification. In particular, we design three general fusion frameworks, i.e., input, feature, and decision fusions, and integrate them with three typical deep neural network architectures of fully connected neural network, convolutional neural network, and recurrent neural network to form fusion identification schemes. We conduct experiments using 50 off-the-shelf Wi-Fi devices to validate the concerned fusion schemes and investigate their performance gains in identification and model training. Our experimental results verify that the proposed fusion identification schemes can achieve comparable or superior identification performances to the state-of-the-art schemes in the entire SNR regime. Moreover, for the considered fusion schemes, we further investigate the impacts of fusion strategies, deep-learning networks, and SNR conditions on the identification performance and training time.
Technological Prospects of Cloud Computing in Web Mining: Recent Trends and Opportunities

Web has immense potentials to grow with new technologies and flourish with new opportunities in almost every sphere of human life. The internet grew by WWW and explored with e-business and social network. The Web is known and participating in mining of various kinds of data ranging from users views and patterns to Bitcoin applications. Web mining includes different set and types of data and extract useful information and from various sources of web and gain knowledge using applicable data mining technique on dataset. Web mining types broadly categorised in three main areas, web uses mining, web content mining, and web structured mining. Each category of web mining is involved to handle issues of heterogeneous behaviour of web data. All three technique and applications are highly required of high end architectures which can facilitate infrastructures and support for all required criteria. Cloud computing is an emerging technology and blowing with intensive support for varieties of applications. The techniques and applications of Web usage mining are extremely demanded in cloud computing. Cloud computing allow these technique to retrieve relevant and useful data through virtually integrated mode of data warehouse. It helps the users to reduce cost and infrastructure for implementation. This paper presents methodologies of web mining using Cloud Computing technology and its prospects.
Majorization ladder in bosonic Gaussian channels

We show the existence of a majorization ladder in bosonic Gaussian channels, that is, we prove that the channel output resulting from the nth energy eigenstate (Fock state) majorizes the channel output resulting from the (n+1)th energy eigenstate (Fock state). This reflects a remarkable link between the energy at the input of the channel and a disorder relation at its output as captured by majorization theory. This result was previously known in the special cases of a pure-loss channel and quantum-limited amplifier, and we achieve here its non-trivial generalization to any single-mode phase-covariant (or -contravariant) bosonic Gaussian channel. The key to our proof is the explicit construction of a column-stochastic matrix that relates the outputs of the channel for any two subsequent Fock states at its input. This is made possible by exploiting a recently found recurrence relation on multiphoton transition probabilities for Gaussian unitaries [Jabbour and Cerf, Phys. Rev. Res. 3, 043065 (2021)]. Possible generalizations and implications of these results are then discussed.
Future Teachers Seek to Avoid Climate Emergency With a Virtual Escape Room

Students often show negative attitudes and emotions towards the study of science. It is important to design learning situations that achieve positive emotions among students, since it has been shown that these favour the learning process. The aim of this study was to implement a virtual escape room in the subject of Knowledge of Natural Sciences in the Degree of Primary Education at the Universidad Internacional de La Rioja (UNIR) in an entirely online environment and to analyse the self-perceived emotions of the students and the degree of satisfaction with the learning experience. The results show that the experience has a positive impact on learning, the emotions experienced, and the satisfaction of the students. It is concluded that virtual escape rooms are presented as a way of approaching learning through different creative challenges, which allow for the creation of a positive emotional environment in which the teaching and learning process is more attractive.
Escape Room as a Tool for the Study of Special Needs in University Education

For a society where progress and constant development are essential and that faced with the changes experienced in society (pandemics, wars) as well as in education (lack of motivation of students, increase in dropouts, and poor results in studies), the educational system suggests the need for change, proposing the use of more innovative and active methodologies that give the student a leading role and make them the protagonists of the teaching-learning process. This chapter presents an activity with escape room developed with students of the Master's Degree in Special Education that aims to facilitate the learning of practical cases on the subject of developmental disorders.
Assessment of indium prospecting potential in national-scale geochemical perspective, Papua New Guinea

The spatial distribution and statistics of indium in stream sediments from Papua New Guinea (PNG) were studied based on the results of Papua New Guinea National-Scale Geochemical Baseline (PNG-NGB) project (2015–2018). 1399 samples were collected covering approximately 128,000 km2 with sample density of approximately 1.1 site/100 km2. Indium concentration in the stream sediments of PNG ranged from 0.003 μg/g to 0.24 μg/g with a baseline (median) value of 0.067 μg/g, a value of slightly higher than the upper crustal abundance of indium and Europe, North America, Australia and China. 64 indium anomalies are decided by cumulative – frequency method and contrast value, 39 anomalies are associated with porphyry, VMS, skarn, bauxite, and epithermal deposits and occurrences while porphyry and VMS mineralization is the most important type for indium prospecting and exploration. Parent rock type is also an important factor that controls the geochemical pattern of the indium distribution in stream sediments of PNG.