Reviewerly/exHarmony huggingface dataset:
Siamese Bi-Directional Gated Recurrent Units Network for Generating Reciprocal Recommendations in Online Job Recommendation

Unlike conventional Recommender Systems (RS) techniques where items are recommended to users, in Reciprocal Recommender Systems users become the items that are recommended to other users. Hence the generated recommendations should be accepted by both the service user (receiver of the recommendation) and the recommended user. In this paper, we propose SBiGRU: Siamese Bidirectional Gated Recurrent Units-based model to generate reciprocal recommendations in the online Job Recommendation by computing the semantic similarity between service users and recommended users. It can recommend reciprocal recommendations to any recruiter and job seeker even in the absence of interaction data that may not be available due to data privacy being exercised by concerned parties. The performance of SBiGRU is compared with existing state-of-the-art approaches and two Recurrent Neural Network variations viz. Bidirectional LSTM (SBiLSTM) and unidirectional LSTM (SLSTM).
Explainability Metrics and Properties for Counterfactual Explanation Methods

The increasing application of Explainable AI (XAI) methods to enhance the transparency and trustworthiness of AI systems designates the need to quantitatively assess and analyze the theoretical and behavioral characteristics of explanations generated by these methods. A fair amount of metrics and properties exist, however these metrics are method-specific, complex and at times hard to interpret. This work focuses on (i) identification of these metrics and properties applicable to the selected post-hoc counterfactual explanation methods (a mechanism for generating explanations), (ii) assessing the applicability of the identified metrics and properties to compare counterfactual examples across explanation methods, and (iii) analyzing the properties of those counterfactual explanation methods. A pipeline is designed to implement the proof-of-concept tool, comprising of the following steps-selecting a data set, training some suitable classifier, deploying counterfactual generation method(s), and implementing defined XAI metrics to infer properties satisfied by explanation methods. The outcome of the experiments reflects that desirable properties for counterfactual explanations are more or less satisfied as measured by different metrics. Certain inconsistencies were identified in the counterfactual explanation methods such as the resulting counterfactual instances failed to be pushed to the desired class, defeating one of the main purposes of obtaining counterfactual explanations. Besides, several other properties have been discussed to analyze counterfactual explanation methods.
Diagnosis of Breast Cancer with Strongly Supervised Deep Learning Neural Network

The strongly supervised deep convolutional neural network (DCNN) has better performance in assessing breast cancer (BC) because of the more accurate features from the slice-level precise labeling compared with the image-level labeling weakly supervised DCNN. However, manual slice-level precise labeling is time consuming and expensive. In addition, the slice-level diagnosis adopted in the DCNN system is incomplete and defective because of the lack of other slices’ information. In this paper, we studied the impact of the region of interest (ROI) and lesion-level multi-slice diagnosis in the DCNN auxiliary diagnosis system. Firstly, we proposed an improved region-growing algorithm to generate slice-level precise ROI. Secondly, we adopted the average weighting method as the lesion-level diagnosis criteria after exploring four different weighting methods. Finally, we proposed our complete system, which combined the densely connected convolutional network (DenseNet) with the slice-level ROI and the average weighting lesion-level diagnosis after evaluating the performance of five DCNNs. The proposed system achieved an AUC of 0.958, an accuracy of 92.5%, a sensitivity of 95.0%, and a specificity of 90.0%. The experimental results showed that our proposed system had a better performance in BC diagnosis because of the more precise ROI and more complete information of multi-slices.
A two‐stage hue‐preserving and saturation improvement color image enhancement algorithm without gamut problem

Abstract A novel hue-preserving and color enrichment color image enhancement algorithm without gamut problem is proposed in this paper. First, the pixel values of the input image are stretched to the edge of RGB color space to improve the saturation. Then, the pixel values are converted to the complementary color space for saturation enhancement, so that the whole method has no gamut problem and keeps the contrast intensity of the output picture unchanged. Experimental results demonstrate that the proposed algorithm can overcome the poor color effect of traditional methods in the condition of low exposure and can restore the picture color better. Besides, higher saturation can be achieved through the proposed algorithm than up-to-date color image enhancement methods.
GAME: Generative-Based Adaptive Model Extraction Attack

The outstanding performance of deep learning has prompted the rise of Machine Learning as a Service (MLaaS), which significantly reduces the difficulty for users to train and deploy models. For privacy and security considerations, most models in the MLaaS scenario only provide users with black-box access. However, previous works have shown that this defense mechanism still faces potential threats, such as model extraction attacks, which aim at stealing the function or parameters of a black-box victim model. To further study the vulnerability of publicly deployed models, we propose a novel model extraction attack named Generative-Based Adaptive Model Extraction (GAME), which augments query data adaptively in a sample limited scenario using auxiliary classifier GANs (AC-GAN). Compared with the previous work, our attack has the following advantages: adaptive data generation without original datasets, high fidelity, high accuracy, and high stability under different data distributions. According to extensive experiments, we observe that: (1) GAME poses a threat to victim models despite the model architectures and the training sets; (2) synthetic samples closed to decision boundary without deviating from the center of the target distribution can accelerate the extraction process; (3) compared to state-of-the-art work, GAME improves relative accuracy by 12% at much lower data and query costs without the reliance on domain relevance of proxy datasets.
High performance of privacy-preserving acute myocardial infarction auxiliary diagnosis based on federated learning: a multicenter retrospective study

Multicenter medical research is becoming a new trend. However, interagency data privacy security protection has become a major bottleneck of multicenter research. Therefore, to overcome this privacy protection issue, the aim of the present study was to apply a self-developed privacy-preserving machine learning framework for researchers who can build models on medical data from multiple sources, while providing privacy protection for both sensitive data and the learned model.Based on Arya, a novel privacy computing platform developed by Healink, we constructed a privacy-preserving federated learning (FL) model using the fully connected neural network with datasets from 2-3 individual medical institutions. In the dataset, 80% of records were used for joint modeling on acute myocardial infarction (AMI) diagnosis. Modeling efficacy was evaluated with the remaining 20% of records. As the control, 1,500 medical records from 1 medical institution were used for single-center modeling and efficacy evaluation. During the process, the original data were still kept in individual hospital without moving or transferring out of the hospitial. The diagnostic efficacy (sensitivity, positive predictive value, and accuracy) was evaluated.Our privacy-preserving FL model gives reliable AMI diagnostic efficacy. Three-center modeling (79% sensitivity, 88% positive predictive value, and 82.3% accuracy) and two-center modeling (77.8% or 77.6% sensitivity, 86.7% or 85.30% positive predictive value, and 81% or 79.7% accuracy) achieved relative high diagnostic efficacy; and single-center modeling achieved relative low diagnostic efficacy (76% sensitivity, 84.7% positive predictive value, and 79% accuracy).The Arya privacy computing platform is efficient and practical for the FL model, which could promote multicenter medical research securely without sacrificing diagnostic efficacy.
Privacy-Preserving Techniques for Trustworthy Data Sharing: Opportunities and Challenges for Future Research

Abstract One of the foundations of data sharing in the European Union (EU) is trust, especially in view of the advancing digitalization and recent developments with respect to European Data Spaces. In this chapter, we argue that privacy-preserving techniques, such as multi-party computation and fully homomorphic encryption, can play a positive role in enhancing trust in data sharing transactions. We therefore focus on an interdisciplinary perspective on how privacy-preserving techniques can facilitate trustworthy data sharing. We start with introducing the legal landscape of data sharing in the EU. Then, we discuss the different functions of third-party intermediaries, namely, data marketplaces. Before giving a legal perspective on privacy-preserving techniques for enhancing trust in data sharing, we briefly touch upon the Data Governance Act (DGA) proposal with relation to trust and its intersection with the General Data Protection Regulation (GDPR). We continue with an overview on the technical aspects of privacy-preserving methods in the later part, where we focus on methods based on cryptography (such as homomorphic encryption, multi-party computation, private set intersection) and link them to smart contracts. We discuss the main principles behind these methods and highlight the open challenges with respect to privacy, performance bottlenecks, and a more widespread application of privacy-preserving analytics. Finally, we suggest directions for future research by highlighting that the mutual understanding of legal frameworks and technical capabilities will form an essential building block of sustainable and secure data sharing in the future
Federated Search Using Query Log Evidence

In this work, we targeted the search engine of a sports-related website that presented an opportunity for search result quality improvement. We reframed the engine as a Federated Search instance, where each collection represented a searchable entity type within the system, using Apache Solr for querying each resource and a Python Flask server to merge results. We extend previous work on individual search term weighing, making use of past search terms as a relevance indicator for user selected documents. To incorporate term weights we define four strategies combining two binary variables: integration with default relevance (linear scaling or linear combination) and search term frequency (raw value or log-smoothed). To evaluate our solution, we extracted two query sets from search logs: one with frequently submitted queries, and another with ambiguous result access patterns. We used click-through information as a relevance proxy and tried to mitigate its limitations by evaluating under distinct IR metrics, including MRR, MAP and NDCG. Moreover, we also measured Spearman rank correlation coefficients to test similarities between produced rankings and reference orderings according to user access patterns. Results show consistency across all metrics in both sets. Previous search terms were key to obtaining a higher effectiveness, with runs that used pure search term frequency performing best. Compared to the baseline, our best strategies were able to maintain quality on frequent queries and improve retrieval effectiveness on ambiguous queries, with up to $$\sim $$ six percentage points better performance on most metrics.
A Generic Approach to Extend Interpretability of Deep Networks

The recent advent of machine learning as a transforming technology has sparked fears about human inability to comprehend the rational of gradually more complex approaches. Interpretable Machine Learning (IML) was triggered by such concerns, with the purpose of enabling different actors to grasp the application scenarios, including trustworthiness and decision support in highly regulated sectors as those related to health and public services. YOLO (You Only Look Once) models, as other deep Convolutional Neural Network (CNN) approaches, have recently shown remarkable performance in several tasks dealing with object detection. However, interpretability of these models is still an open issue. Therefore, in this work we extend the LIME (Local Interpretable Model-agnostic Explanations) framework to be used with YOLO models. The main contribution is a public add-on to LIME that can effectively improve YOLO interpretability. Results on complex images show the potential improvement.
Efficiency Online Learning During Covid-19 Pandemic

COVID-19 pandemic has had a profound effect on campuses, compelling learning systems to shift away from face-to-face engagement and toward online instruction. The purpose of this research is to investigate the effectiveness of remote learning in the event of a COVID-19 pandemic. To obtain responses, a self-administered online survey was used. After data screening, 315 data were proceeded to the next analysis. Additionally, the data were devoid of missing cases and outliers. The measuring model was evaluated for indicator and internal consistency reliability, convergent and discriminant validity. Meanwhile, the structural model was evaluated by SmartPLS version 3.2.9 utilising the bootstrapping approach. Findings revealed self-discipline and flexibility were significant towards effectiveness of online learning among students at the UiTM Kelantan, Kota Bharu Campus. The findings argue that, in the modern era, internet technologies have increased the flexibility of post secondary education in a variety of ways. Institutions should make strategic use of these new flexibility, concentrating on chances to engage students more deeply in learning that results in practical outcomes.
Agile Estimation and Planning

In Chapter 6 , we covered the difference between multiple delivery frameworks. We also learned that Scrum is by far the most popular Agile framework, and one of the reasons for this is that Scrum allows for the most accurate planning, short term and long term, thus establishing predictability that far surpasses traditional project management. This statement may not seem immediately intuitive. In traditional (Waterfall) project management, a lot of time and effort goes into planning. Opinions of senior leaders in each area are collected, the experience of prior relevant projects is studied and analyzed in depth, and a lot of time is spent creating work breakdown structures, which list all tasks that need to be done to complete the project. All those efforts create a sense of thoughtful, reliable planning, which can be relied upon.
Investigating (re)current state-of-the-art in human activity recognition datasets

Many human activities consist of physical gestures that tend to be performed in certain sequences. Wearable inertial sensor data have as a consequence been employed to automatically detect human activities, lately predominantly with deep learning methods. This article focuses on the necessity of recurrent layers—more specifically Long Short-Term Memory (LSTM) layers—in common Deep Learning architectures for Human Activity Recognition (HAR). Our experimental pipeline investigates the effects of employing none, one, or two LSTM layers, as well as different layers' sizes, within the popular DeepConvLSTM architecture. We evaluate the architecture's performance on five well-known activity recognition datasets and provide an in-depth analysis of the per-class results, showing trends which type of activities or datasets profit the most from the removal of LSTM layers. For 4 out of 5 datasets, an altered architecture with one LSTM layer produces the best prediction results. In our previous work we already investigated the impact of a 2-layered LSTM when dealing with sequential activity data. Extending upon this, we now propose a metric, r GP , which aims to measure the effectiveness of learned temporal patterns for a dataset and can be used as a decision metric whether to include recurrent layers into a network at all. Even for datasets including activities without explicit temporal processes, the r GP can be high, suggesting that temporal patterns were learned, and consequently convolutional networks are being outperformed by networks including recurrent layers. We conclude this article by putting forward the question to what degree popular HAR datasets contain unwanted temporal dependencies, which if not taken care of, can benefit networks in achieving high benchmark scores and give a false sense of overall generability to a real-world setting.
Quantum scissors for noiseless linear amplification of polarization frequency hyper-encoded coherent state

Abstract Quantum scissor (QS) is a powerful tool to realize the optical truncation and noiseless linear amplification (NLA) of the Fock state. The hyper-encoding technology which encodes messages in two or more degrees of freedom of a photon is a promising tool for increasing the channel capacity of photons and has been widely used in quantum computing and quantum communication fields. Here, we propose the one-photon and three-photon QSs for the frequency-encoded and polarization frequency hyper-encoded coherent states, which can realize the one-order and three-order truncation and NLA of the coherent state, and preserve the encoded features of the photons. The quantum scissors for the hyper-encoded coherent state would introduce some unwanted disturb items with small probability. Our QSs can be extended to distill the hyper-encoded multi-spatial-mode entanglement. Our QSs have application potential in the future quantum information processing field.
Prediction of Breast Cancer Through Random Forest

8% of women are diagnosed with breast cancer. (BC) BC is the second most common cause of death in both developed and undeveloped countries. BC is characterized by the mutation of genes, constant pain, changes in the size, color (redness), and skin texture of breasts. Classification of breast cancer leads pathologists to find a systematic and objective prognostic; generally, the most frequent classification is binary (benign/malignant).Machine Learning (ML) techniques are broadly used in breast cancer classification. They provide high classification accuracy and effective diagnostic capabilities. Breast cancer remains one of the top diseases that lead to thousands of deaths in women yearly. Artificial intelligence (AI) has been utilized to rapidly and accurately identify breast tumors and for early diagnosis. This paper aims to research, determine and classify these tumors.Machine learning algorithm such as Random Forest (RF) is used to classify medical images into malignant and benign. Moreover, Machine learning has been employed recently for the same purpose.The results showed that Random Forest achieved high accuracy; therefore, the researchers utilized various functions for this algorithm and added more features such as bagging and boosting to increase its efficacy.The random Forest algorithm achieved an enhanced accuracy of 98%.
E-learning issues and solutions for students with disabilities during COVID-19 pandemic: Al-Zaytoonah University of Jordan case study

&lt;span lang="EN-US"&gt;In this research, approaches to making e-learning accessible to students with impairments were studied based on the authors’ experience at Jordan’s Al-Zaytoonah University. The educational, organizational, and instructional concerns are underlined when analyzing the university’s experience in giving access to online content. Furthermore, these difficulties must be addressed in order to create the necessary organizational change to effectively solve accessibility concerns. This study looked into the e-learning issues that 56 university students with disabilities had reported. Students reported issues with: accessibility to websites and learning course management systems; accessibility to digital audio and video; time management in completing online tasks; trouble handling data during lectures; and a lack of appropriate adaptive strategies in online questionnaires. Students also noted technical issues with using e-learning, instructors’ communication with e-learning, and their treatment of all students equally, overlooking the necessity of considering the needs of students with impairments. In this study, we present the problems raised by students with disabilities and how the e-learning affects students’ outcomes in the e-learning experience. We also make recommendations based on the students’ realistic special-needs experience at Al-Zaytoonah University in Jordan during the COVID-19 pandemic to inspire decision-makers in educational institutions and those interested in this field.&lt;/span&gt;
The Study on Improving Chinese College Students' Intercultural Communication Competence in College English Teaching

Abstract:Intercultural communication competence refers to the ability of people from different cultural backgrounds to come together and communicate.It is also an important skill that college students should possess.However, the college students’ intercultural communication ability is not satisfactory at present.In order to improve college students’ intercultural communication ability at present, this paper puts forward a teaching and training mode to improve students’ intercultural competence under current conditions--the training mode based on English movies, and carries out practical research on this new mode.Finally, this study proves the effectiveness of this new teaching model.
Towards Efficient Cryptographic Data Validation Service in Edge Computing

[J1C2 Presentation Abstract at IEEE SERVICES 2021 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3111208]
An Empirical Framework for Recommendation-based Location Services Using Deep Learning

The large amount of possible online services throws a significant load on the users' service selection decision-making procedure. Α number of intelligent suggestion systems have been created in order to lower the excessive decision-making expense. Taking this into consideration, aν RLSD (Recommendation-based Location Services using Deep Learning) model is proposed in this paper. Alongside robustness, this research considers the geographic interface between the client and the service. The suggested model blends a Multi-Layer-Perceptron (MLP) with a similarity Adaptive Corrector (AC), which is meant to detect high-dimensional and non-linear connections, as well as the location correlations amongst client and services. This not only improves recommendation results but also considerably reduces difficulties due to data sparseness. As a result, the proposed RLSD has strong flexibility and is extensible when it comes to leveraging context data like location.