Reviewerly/exHarmony huggingface dataset:
Voxento 3.0: A Prototype Voice-Controlled Interactive Search Engine for Lifelog

Voxento is an interactive voice-based retrieval system for lifelogs which has been redeveloped and optimised to participate in the fifth Lifelog Search Challenge LSC'22, at ACM ICMR'22. Based on the previous experience in the LSC competition and ranked in the top 4 in the last LSC'21 competition among 17 participants, we present a revised version of Voxento to address the critical points to improve the efficiency of retrieval tasks in lifelog datasets. Basically, Voxento provides a spoken interface to the lifelog data, which facilitates an expert and novice user to interact with a personal lifelog using a range of vocal commands and interactions. Briefly, we made some important improvements to support both the retrieval of content and system interaction. This latest version has been enhanced with the addition of a text-based search feature, new filters based on new metadata provided in lifelog data, rich visual information and features and enhanced speech query. Also, the data preparation tasks comprised a new function to reduce the number of non-relevant images and the latest CLIP model version used to derive features from images. The long term development of Voxento includes a lifelog retrieval that supports speech and conversation interaction with less physical actions required by users such as using a mouse. The system presented here uses a desktop computer in order to participate in the LSC'22 competition with the option to use voice interaction or standard text-based retrieval.
Effects of spatial scale of atmospheric reanalysis data on clear-sky surface radiation modeling in tropical climates: A case study for Singapore

Solar resource assessments most generally require atmospheric information, which is customarily acquired from gridded datasets. The spatial scale mismatch problem, i.e., the difference in spatial representativeness of gridded data and in situ measurements, therefore becomes relevant. This study examines how the gridded data used as inputs to clear-sky radiation models can affect their performance at urban scale. The tropical island of Singapore is selected for the case study. Aerosol optical depth at 550 nm (AOD550), Å ngström exponent (AE), and precipitable water (PW) from both the MERRA-2 reanalysis and ground-based stations (AERONET and SuomiNet) are collected between 2013–2020. Firstly, it is found that, relatively to the AERONET ground truth, the bias in MERRA-2’s AOD550 is more prominent than that in AE or PW. Next, the bias propagation from the gridded inputs (AOD550, AE, and PW) to clear-sky radiation predictions is explored using various models. The estimated clear-sky direct normal irradiance (DNIcs) is more sensitive to AOD550 variation than the clear-sky global horizontal irradiance (GHIcs). Six clear-sky radiation models, five of which accept MERRA-2 gridded inputs, are compared with each other, and with the in situ irradiance measurements recorded at 9 sites. The inter-model difference across Singapore is remarkably consistent because the whole island fits inside a single MERRA-2 grid cell. Under high-AOD550 situations, however, the inter-model deviation becomes large for both GHIcs and DNIcs. The conventional model-versus-measurement comparison shows that each model achieves very different site-to-site performance, largely because the spatially-averaged inputs cannot fully represent the micro-climatic variability. Relatively speaking, no clear-sky radiation model significantly outperforms its peers. The simple MAC2 model and the empirical (locally derived) Yang GHIcs-only model are recommended for Singapore.
Diagnostic Accuracy of Machine Learning Models on Mammography in Breast Cancer Classification: A Meta-Analysis

In this meta-analysis, we aimed to estimate the diagnostic accuracy of machine learning models on digital mammograms and tomosynthesis in breast cancer classification and to assess the factors affecting its diagnostic accuracy. We searched for related studies in Web of Science, Scopus, PubMed, Google Scholar and Embase. The studies were screened in two stages to exclude the unrelated studies and duplicates. Finally, 36 studies containing 68 machine learning models were included in this meta-analysis. The area under the curve (AUC), hierarchical summary receiver operating characteristics (HSROC) curve, pooled sensitivity and pooled specificity were estimated using a bivariate Reitsma model. Overall AUC, pooled sensitivity and pooled specificity were 0.90 (95% CI: 0.85-0.90), 0.83 (95% CI: 0.78-0.87) and 0.84 (95% CI: 0.81-0.87), respectively. Additionally, the three significant covariates identified in this study were country (p = 0.003), source (p = 0.002) and classifier (p = 0.016). The type of data covariate was not statistically significant (p = 0.121). Additionally, Deeks' linear regression test indicated that there exists a publication bias in the included studies (p = 0.002). Thus, the results should be interpreted with caution.
Constructing Better Evaluation Metrics by Incorporating the Anchoring Effect into the User Model

Models of existing evaluation metrics assume that users are rational decision-makers trying to pursue maximised utility. However, studies in behavioural economics show that people are not always rational when making decisions. Previous studies showed that the anchoring effect can influence the relevance judgement of a document. In this paper, we challenge the rational user assumption and introduce the anchoring effect into user models. We first propose a framework for query-level evaluation metrics by incorporating the anchoring effect into the user model. In the framework, the magnitude of the anchoring effect is related to the quality of the previous document. We then apply our framework to several query-level evaluation metrics and compare them with their vanilla version as the baseline in terms of user satisfaction on a publicly available search dataset. As a result, our Anchoring-aware Metrics (AMs) outperformed their baselines in term of correlation with user satisfaction. The result suggests that we can better predict user query satisfaction feedbacks by incorporating the anchoring effect into user models of existing evaluating metrics. As far as we know, we are the first to introduce the anchoring effect into information retrieval evaluation metrics. Our findings provide a perspective from behavioural economics to better understand user behaviour and satisfaction in search interaction.
Symbolic knowledge extraction from opaque ML predictors in PSyKE: Platform design &amp; experiments

A common practice in modern explainable AI is to post-hoc explain black-box machine learning (ML) predictors – such as neural networks – by extracting symbolic knowledge out of them, in the form of either rule lists or decision trees. By acting as a surrogate model, the extracted knowledge aims at revealing the inner working of the black box, thus enabling its inspection, representation, and explanation. Various knowledge-extraction algorithms have been presented in the literature so far. Unfortunately, running implementations of most of them are currently either proofs of concept or unavailable. In any case, a unified, coherent software framework supporting them all – as well as their interchange, comparison, and exploitation in arbitrary ML workflows – is currently missing. Accordingly, in this paper we discuss the design of PSyKE, a platform providing general-purpose support to symbolic knowledge extraction from different sorts of black-box predictors via many extraction algorithms. Notably, PSyKE targets symbolic knowledge in logic form, allowing the extraction of first-order logic clauses. The extracted knowledge is thus both machine- and human-interpretable, and can be used as a starting point for further symbolic processing—e.g. automated reasoning.
Enhancing object detection in aerial images

Unmanned Aerial Vehicles have proven to be helpful in domains like defence and agriculture and will play a vital role in implementing smart cities in the upcoming years. Object detection is an essential feature in any such application. This work addresses the challenges of object detection in aerial images like improving the accuracy of small and dense object detection, handling the class-imbalance problem, and using contextual information to boost the performance. We have used a density map-based approach on the drone dataset VisDrone-2019 accompanied with increased receptive field architecture such that it can detect small objects properly. Further, to address the class imbalance problem, we have picked out the images with classes occurring fewer times and augmented them back into the dataset with rotations. Subsequently, we have used RetinaNet with adjusted anchor parameters instead of other conventional detectors to detect aerial imagery objects accurately and efficiently. The performance of the proposed three step pipeline of implementing object detection in aerial images is a significant improvement over the existing methods. Future work may include improvement in the computations of the proposed method, and minimising the effect of perspective distortions and occlusions.
A Distributed Learning Scheme for Variational Quantum Algorithms

Variational quantum algorithms (VQAs) are prime contenders to gain computational advantages over classical algorithms using near-term quantum machines.As such, many endeavors have been made to accelerate the optimization of modern VQAs in past years.To further improve the capability of VQAs, here we propose a QUantum DIstributed Optimization scheme (dubbed as QUDIO), whose backends support both real quantum devices and various quantum simulators.Unlike traditional VQAs subsuming a single quantum chip or simulator, QUDIO collaborates with multiple quantum machines or simulators to complete learning tasks.In doing so, the required wall-clock time for optimization can be continuously reduced by increasing the accessible computational resources when ignoring the communication and synchronization time.Moreover, through the lens of optimization theory, we unveil the potential factors that could affect the convergence of QUDIO.In addition, we systematically understand the ability of QUDIO to reduce wall-clock time via two standard benchmarks, which are hand-written image classification and the ground energy estimation of the dihydrogen.Our proposal facilitates the development of advanced VQAs to narrow the gap between the state of the art and applications with the quantum advantage.
Improving Personalized Explanation Generation through Visualization

In modern recommender systems, there are usually comments or reviews from users that justify their ratings for different items.Trained on such textual corpus, explainable recommendation models learn to discover user interests and generate personalized explanations.Though able to provide plausible explanations, existing models tend to generate repeated sentences for different items or empty sentences with insufficient details.This begs an interesting question: can we immerse the models in a multimodal environment to gain proper awareness of real-world concepts and alleviate above shortcomings?To this end, we propose a visuallyenhanced approach named METER with the help of visualization generation and text-image matching discrimination: the explainable recommendation model is encouraged to visualize what it refers to while incurring a penalty if the visualization is incongruent with the textual explanation.Experimental results and a manual assessment demonstrate that our approach can improve not only the text quality but also the diversity and explainability of the generated explanations.
Pipelines for Social Bias Testing of Large Language Models

The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks.However, while research has shown how biased and harmful these models are, systematic ways of integrating social bias tests into development pipelines are still lacking.This short paper suggests how to use these verification techniques in development pipelines.We take inspiration from software testing and suggest addressing social bias evaluation as software testing.We hope to open a discussion on the best methodologies to handle social bias testing in language models.
Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals

Warning: This paper contains examples of language that some people may find offensive or upsetting.Current language technology is ubiquitous and directly influences individuals' lives worldwide.Given the recent trend in AI on training and constantly releasing new and powerful large language models (LLMs), there is a need to assess their biases and potential concrete consequences.While some studies have highlighted the shortcomings of these models, there is only little on the negative impact of LLMs on LGBTQIA+ individuals.In this paper, we investigated a state-of-the-art template-based approach for measuring the harmfulness of English LLMs sentence completion when the subjects belong to the LGBTQIA+ community.Our findings show that, on average, the most likely LLM-generated completion is an identity attack 13% of the time.Our results raise serious concerns about the applicability of these models in production environments.
MTRec: Multi-Task Learning over BERT for News Recommendation

Existing news recommendation methods usually learn news representations solely based on news titles.To sufficiently utilize other fields of news information such as category and entities, some methods treat each field as an additional feature and combine different feature vectors with attentive pooling.With the adoption of large pre-trained models like BERT in news recommendation, the above way to incorporate multi-field information may encounter challenges: the shallow feature encoding to compress the category and entity information is not compatible with the deep BERT encoding.In this paper, we propose a multi-task learning framework to incorporate the multi-field information into BERT, which improves its news encoding capability.Besides, we modify the gradients of different tasks based on their gradient conflicts, which further boosts the model performance.Extensive experiments on the MIND news recommendation benchmark show the effectiveness of our approach.
Role of Serverless Computing in Healthcare Systems: Case Studies

Recently, serverless architecture has gained popularity due to its cost-effective policies, independent stateless functions, auto-scaling, and simplified code deployment. It allows the developer to deploy and run their applications without worrying about the underlying architecture. The healthcare services can be made available as a serverless application that consists of distributed cloud services achieving the various requirements in the healthcare industry. The services offered may be available on-premise on a cloud infrastructure to users and health service providers. This paper presents four case studies that have been successfully adapted serverless architecture for health care systems, where the health services are provided as functional units to various stakeholders. We have also discussed various components of healthcare systems that may require serverless services over the cloud infrastructure.
Towards efficient verifiable multi-keyword search over encrypted data based on blockchain

Searchable symmetric encryption (SSE) provides an effective way to search encrypted data stored on untrusted servers. When the server is not trusted, it is indispensable to verify the results returned by it. However, the existing SSE schemes either lack fairness in the verification of search results, or do not support the verification of multiple keywords. To address this, we designed a multi-keyword verifiable searchable symmetric encryption scheme based on blockchain, which provides an efficient multi-keyword search and fair verification of search results. We utilized bitmap to build a search index in order to improve search efficiency, and used blockchain to ensure fair verification of search results. The bitmap and hash function are combined to realize lightweight multi-keyword search result verification, compared with the existing verification schemes using public key cryptography primitives, our scheme reduces the verification time and improves the verification efficiency. In addition, our scheme supports the dynamic update of files and realizes the forward security in update. Finally, formal security analysis proves that our scheme is secure against Chosen-Keyword Attacks (CKA), experimental analysis demonstrations that our scheme is efficient and viable in practice.
Analyzing Modality Robustness in Multimodal Sentiment Analysis

Devamanyu Hazarika, Yingting Li, Bo Cheng, Shuai Zhao, Roger Zimmermann, Soujanya Poria. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
AutoElbow: An Automatic Elbow Detection Method for Estimating the Number of Clusters in a Dataset

The elbow technique is a well-known method for estimating the number of clusters required as a starting parameter in the K-means algorithm and certain other unsupervised machine-learning algorithms. However, due to the graphical output nature of the method, human assessment is necessary to determine the location of the elbow and, consequently, the number of data clusters. This article presents a simple method for estimating the elbow point, thus, enabling the K-means algorithm to be readily automated. First, the elbow-based graph is normalized using the graph’s minimum and maximum values along the ordinate and abscissa coordinates. Then, the distance between each point on the graph to the minimum (i.e., the origin) and maximum reference points, and the “heel” of the graph are calculated. The estimated elbow location is, thus, the point that maximizes the ratio of these distances, which corresponds to an approximate number of clusters in the dataset. We demonstrate that the strategy is effective, stable, and adaptable over different types of datasets characterized by small and large clusters, different cluster shapes, high dimensionality, and unbalanced distributions. We provide the clustering community with a description of the method and present comparative results against other well-known methods in the prior state of the art.
Do You Feel Safe Here? The Role of Psychological Safety in the Relationship between Transformational Leadership and Turnover Intention Amid COVID-19 Pandemic

Despite their significant role in the performance of hotel industry, hotel workers are suffering from high rates of turnover, due to several reasons, particularly amid the COVID-19 pandemic. The pandemic has had numerous negative consequences on hotel workers, including their intention to leave the job or/and career. This study is an attempt to investigate the impact of transformational leadership on turnover intention amid COVID-19 and how psychological safety can intermediate this relationship. The study used a quantitative research approach via a pre-test instrument, self-distributed and collected from hotel workers at different regions in the Kingdom of Saudi Arabia (KSA). Valid responses from 1228 workers, analyzed through a structural equation modeling (SEM) of AMOS version 23, showed that transformational leadership has a significant negative impact on turnover intention as hypothesized. Nevertheless, it has a significant positive impact on psychological safety, whereas psychological safety has a significant negative impact on turnover intention. The most important finding of this study was the perfect mediating effect of psychological safety in the link between transformational leadership and workers’ turnover intention. This finding confirms that psychological safety has the ability to change the above-mentioned link. In other words, the presence of psychological safety ensures negative turnover intention, even if transformational leadership practices do not exist. The findings have implications for scholars and practitioners, especially in tourism and hotel context, in relation to the role of psychological safety and transformational leadership in creating a sustainable working environment to maintain a lower turnover intention.
Security management of BYOD and cloud environment in Saudi Arabia

The increasing trend of Bring Your Own Device (BYOD) to work has led to a significant surge in the risks related to network security. This trend is very beneficial to employers and employees alike in any organisation. The wide infiltration of spyware, malware and similarly suspicious downloads into personal devices has forced the government to reconsider its policies regarding data security. The malicious programs get downloaded onto the personal devices without the user even realising. This could disastrously affect the individuals and the governments. In the case of such an event, the BYODs become risky as they can make unauthorised policy changes and leak sensitive data into the public domain. This type of privacy breach leads to a domino effect with major legal and financial implications, and a decreased productivity for the organisations and governments. This presents a huge challenge as the governments have to consider the user rights and privacy laws and also protect the networks from these attacks. In this study, the researchers have proposed a novel technical framework that could assist the Saudi government. This framework was designed after determining the challenges that were faced by the government, based on the citizen perspectives, to control all risks challenging the use of the BYODs. This framework decreased the number of system restrictions and enforced access control policies for BYODs and cloud environments. The preliminary results of this study were positive and indicated that the framework could decrease the problems related to access control.
Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification

Cervical cancer is the most commonly diagnosed cancer among women globally, with high mortality rate. For early diagnosis, automated and accurate cervical cancer classification approaches can be developed through effective classification of Pap smear cell images. The current study introduces a novel Modified Firefly Optimization Algorithm with Deep Learning-enabled cervical cancer classification (MFFOA-DL3) model for the classification of Pap Smear Images (PSI). The proposed MFFOA-DL3 model examines the PSI for the existence of cervical cancer cells. To accomplish this, the proposed MFFOA-DL3 model primarily applies Bilateral Filtering (BF)-based noise removal approach to get rid of the noise. Then, Kapur's entropy-based image segmentation technique is applied to determine the affected regions. Moreover, EfficientNet technique is also applied to generate the feature vectors. Finally, MFFOA with Stacked Sparse Denoising Autoencoder (SSDA) model is exploited to classify the PSI. In current study, MFFOA is utilized to appropriately modify the parameters related to SSDA model. The proposed MFFOA-DL3 model was experimentally validated using benchmark dataset. The results attained from extensive comparative analysis highlighted the better performance of MFFOA-DL3 model over other recent approaches.