Reviewerly/exHarmony huggingface dataset:
ChromoEnhancer: An Artificial-Intelligence-Based Tool to Enhance Neoplastic Karyograms as an Aid for Effective Analysis

Cytogenetics laboratory tests are among the most important procedures for the diagnosis of genetic diseases, especially in the area of hematological malignancies. Manual chromosomal karyotyping methods are time consuming and labor intensive and, hence, expensive. Therefore, to alleviate the process of analysis, several attempts have been made to enhance karyograms. The current chromosomal image enhancement is based on classical image processing. This approach has its limitations, one of which is that it has a mandatory application to all chromosomes, where customized application to each chromosome is ideal. Moreover, each chromosome needs a different level of enhancement, depending on whether a given area is from the chromosome itself or it is just an artifact from staining. The analysis of poor-quality karyograms, which is a difficulty faced often in preparations from cancer samples, is time consuming and might result in missing the abnormality or difficulty in reporting the exact breakpoint within the chromosome. We developed ChromoEnhancer, a novel artificial-intelligence-based method to enhance neoplastic karyogram images. The method is based on Generative Adversarial Networks (GANs) with a data-centric approach. GANs are known for the conversion of one image domain to another. We used GANs to convert poor-quality karyograms into good-quality images. Our method of karyogram enhancement led to robust routine cytogenetic analysis and, therefore, to accurate detection of cryptic chromosomal abnormalities. To evaluate ChromoEnahancer, we randomly assigned a subset of the enhanced images and their corresponding original (unenhanced) images to two independent cytogeneticists to measure the karyogram quality and the elapsed time to complete the analysis, using four rating criteria, each scaled from 1 to 5. Furthermore, we compared the enhanced images with our method to the original ones, using quantitative measures (PSNR and SSIM metrics).
On the Effectiveness of Transfer Learning for Code Search

The Transformer architecture and transfer learning have marked a quantum leap in natural language processing, improving the state of the art across a range of text-based tasks. This paper examines how these advancements can be applied to and improve code search. To this end, we pre-train a BERT-based model on combinations of natural language and source code data and evaluate it on pairs of StackOverflow question titles and code answers. Our results show that the pre-trained models consistently outperform the models that were not pre-trained. In cases where the model was pre-trained on natural language "and" source code data, it also outperforms an information retrieval baseline based on Lucene. Also, we demonstrated that combined use of an information retrieval-based approach followed by a Transformer, leads to the best results overall, especially when searching into a large search pool. Furthermore, transfer learning is particularly effective when much pre-training data is available and fine-tuning data is limited. We demonstrate that natural language processing models based on the Transformer architecture can be directly applied to source code analysis tasks, such as code search. With the development of Transformer models designed more specifically for dealing with source code data, we believe the results on source code analysis tasks can be further improved.
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers

We present MaX-DeepLab, the first end-to-end model for panoptic segmentation. Our approach simplifies the current pipeline that depends heavily on surrogate sub-tasks and hand-designed components, such as box detection, non-maximum suppression, thing-stuff merging, etc. Although these sub-tasks are tackled by area experts, they fail to comprehensively solve the target task. By contrast, our MaX-DeepLab directly predicts class-labeled masks with a mask transformer, and is trained with a panoptic quality inspired loss via bipartite matching. Our mask transformer employs a dual-path architecture that introduces a global memory path in addition to a CNN path, allowing direct communication with any CNN layers. As a result, MaX-DeepLab shows a significant 7.1% PQ gain in the box-free regime on the challenging COCO dataset, closing the gap between box-based and box-free methods for the first time. A small variant of MaX-DeepLab improves 3.0% PQ over DETR with similar parameters and M-Adds. Furthermore, MaX-DeepLab, without test time augmentation, achieves new state-of-the-art 51.3% PQ on COCO test-dev set. Code is available at https://github.com/google-research/deeplab2.
DialSummEval: Revisiting Summarization Evaluation for Dialogues

Dialogue summarization is receiving increasing attention from researchers due to its extraordinary difficulty and unique application value.We observe that current dialogue summarization models have flaws that may not be well exposed by frequently used metrics such as ROUGE.In our paper, we re-evaluate 18 categories of metrics in terms of four dimensions: coherence, consistency, fluency and relevance, as well as a unified human evaluation of various models in dialogue summarization for the first time.Some noteworthy trends which are different from the conventional summarization tasks are identified.We will release DialSummEval, a multi-faceted dataset of human judgments containing the outputs of 14 models on SAMSum.
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition

Named entity recognition (NER) in a realworld setting remains challenging and is impacted by factors like text genre, corpus quality, and data availability.NER models trained on CoNLL do not transfer well to other domains, even within the same language.This is especially the case for multi-lingual models when applied to low-resource languages, and is mainly due to missing entity information.We propose an approach that with limited effort and data, addresses the NER knowledge gap across languages and domains.Our novel approach uses a token-level gating layer to augment pre-trained multilingual transformers with gazetteers containing named entities (NE) from a target language or domain.This approach provides the flexibility to jointly integrate both textual and gazetteer information dynamically: entity knowledge from gazetteers is used only when a token's textual representation is insufficient for the NER task.Evaluation on several languages and domains demonstrates: (i) a high mismatch of reported NER performance on CoNLL vs. domain specific datasets, (ii) gazetteers significantly improve NER performance across languages and domains, and (iii) gazetteers can be flexibly incorporated to guide knowledge transfer.On cross-lingual transfer we achieve an improvement over the baseline with F1=+17.6%, and with F1=+21.3% for cross-domain transfer.
Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks

Location-based services (LBSs) are widely used in vehicular networks. Privacy leakage from LBS is a key issue to be solved. However, the existing schemes fail to provide differentiated protection for users’ different locations, which may lead to the leakage of location information. In this paper, we propose a personalized location privacy protection scheme based on differential privacy to protect the privacy of location-based services in vehicular networks. Firstly, we propose a normalized decision matrix to describe the efficiency and the privacy effect of navigation recommendations. We then establish a utility model integrated with users’ privacy preferences to compute the effective driving route. Secondly, for different service request locations in the driving route, we define sensitivity distance as an index to quantify their privacy requirements. The privacy budget will be added to the service request location to generate a false location. Moreover, due to the limitation of road range in the driving route, if the privacy budget value allocated is small enough, the false location generated by the Plane Laplace will be deviated. As a result, the attacker can deduce users’ real request locations. Consequently, considering the factors of trajectory leakage, attack strategy and QoS, we establish a multi-objective optimization model to optimize the false location. Based on the real data set, we conduct a series of comparison simulations to evaluate the performance of the proposed scheme. The experimental results demonstrate that our scheme can satisfy users’ personalized services needs and provide an optimal solution to privacy and QoS.
Semantic Diversity in Dialogue with Natural Language Inference

Generating diverse, interesting responses to chitchat conversations is a problem for neural conversational agents.This paper makes two substantial contributions to improving diversity in dialogue generation.First, we propose a novel metric which uses Natural Language Inference (NLI) to measure the semantic diversity of a set of model responses for a conversation.We evaluate this metric using an established framework (Tevet and Berant, 2021) and find strong evidence indicating NLI Diversity is correlated with semantic diversity.Specifically, we show that the contradiction relation is more useful than the neutral relation for measuring this diversity and that incorporating the NLI model's confidence achieves state-of-the-art results.Second, we demonstrate how to iteratively improve the semantic diversity of a sampled set of responses via a new generation procedure called Diversity Threshold Generation, which results in an average 137% increase in NLI Diversity compared to standard generation procedures.
On the Relationship between Shy and Warded Datalog+/-

Datalog^E is the extension of Datalog with existential quantification. While its high expressive power, underpinned by a simple syntax and the support for full recursion, renders it particularly suitable for modern applications on Knowledge Graphs, query answering (QA) over such language is known to be undecidable in general. For this reason, different fragments have emerged, introducing syntactic limitations to Datalog^E that strike a balance between its expressive power and the computational complexity of QA, to achieve decidability. In this short paper, we focus on two promising tractable candidates, namely Shy and Warded Datalog+/-. Reacting to an explicit interest from the community, we shed light on the relationship between these fragments. Moreover, we carry out an experimental analysis of the systems implementing Shy and Warded, respectively DLV^E and Vadalog.
Feudal Latent Space Exploration for Coordinated Multi-Agent Reinforcement Learning

In this article, we investigate how multiple agents learn to coordinate to form efficient exploration in reinforcement learning. Though straightforward, independent exploration of the joint action space of multiple agents will become exponentially more difficult as the number of agents increases. To tackle this problem, we propose feudal latent-space exploration (FLE) for multi-agent reinforcement learning (MARL). FLE introduces a feudal commander to learn a low-dimensional global latent structure that instructs multiple agents to explore coordinately. Under this framework, the multi-agent policy gradient (PG) is adopted to optimize both the agent policy and latent structure end-to-end. We demonstrate the effectiveness of this method in two multi-agent environments that need explicit coordination. Experimental results validate that FLE outperforms baseline MARL approaches that use independent exploration strategy in terms of mean rewards, efficiency, and the expressiveness of coordination policies.
Towards event prediction in temporal graphs

This paper proposes a class of temporal association rules, denoted by TACOs, for event prediction. As opposed to previous graph rules, TACOs monitor updates to graphs, and can be used to capture temporal interests in recommendation and catch frauds in response to behavior changes, among other things. TACOs are defined on temporal graphs in terms of change patterns and (temporal) conditions, and may carry machine learning (ML) predicates for temporal event prediction. We settle the complexity of reasoning about TACOs, including their satisfiability, implication and prediction problems. We develop a system, referred to as TASTE. TASTE discovers TACOs by iteratively training a rule creator based on generative ML models in a creator-critic framework. Moreover, it predicts events by applying the discovered TACOs. Using real-life and synthetic datasets, we experimentally verify that TASTE is on average 31.4 times faster than conventional data mining methods in TACO discovery, and it improves the accuracy of state-of-the-art event prediction models by 23.4%.
Supervised pretraining through contrastive categorical positive samplings to improve COVID-19 mortality prediction

Clinical EHR data is naturally heterogeneous, where it contains abundant sub-phenotype. Such diversity creates challenges for outcome prediction using a machine learning model since it leads to high intra-class variance. To address this issue, we propose a supervised pre-training model with a unique embedded k-nearest-neighbor positive sampling strategy. We demonstrate the enhanced performance value of this framework theoretically and show that it yields highly competitive experimental results in predicting patient mortality in real-world COVID-19 EHR data with a total of over 7,000 patients admitted to a large, urban health system. Our method achieves a better AUROC prediction score of 0.872, which outperforms the alternative pre-training models and traditional machine learning methods. Additionally, our method performs much better when the training data size is small (345 training instances).
Designing intelligence amplification: organizing a design canvas workshop

Based on the related paper about the design of intelligence amplification, in which a design canvas and design principles are introduced, this poster provides researchers, practitioners, and trainers practical guidance to organize a design canvas workshop using the intelligence amplification design canvas. Building on the experience and evaluations from design canvas workshops with practitioners, this poster presents a step-by-step approach with practical guidelines to prepare, deliver, and evaluate a design canvas workshop. The poster complements the paper with a blended workshop design and links to supplementary materials.
How does the blockchain find its way in the UAE The blockchain as a sociotechnical system

Blockchain is emerging as a powerful technological force that promises to change society's status quo. The UAE has taken a proactive approach to blockchain innovation and development. With the framing of blockchain as a sociotechnical system, blockchain initiatives in the UAE are critically examined in terms of prospects, challenges, and implications for future digital societies. Blockchain developmental trajectories are analysed from a sociotechnical lens, i.e., societal, technological, industrial, and economic trends that characterise the interfaces and interaction among people, organisations, technology, and society, eliciting underlying potential influences of cultural, ethical, and legal rhetoric. Despite strong initiatives, a series of challenges, such as trust, privacy, and transparency, has emerged as blockchain begins to develop its full potential. With these issues, sociotechnical concerns have increased regarding how to respond to the possible consequences that such a policy choice would entail, how to govern blockchains, and how to create a sustainable blockchain ecosystem. The socio-political ramifications of UAE blockchains are critically assessed to establish key features and conceptualise issues to unlock opportunities to guarantee a sociotechnical approach to blockchains.
Factors Influencing University Students’ E-Learning Adoption in Bangladesh During COVID-19: An Empirical Study with Machine Learning

The present study investigates the factors that influence Bangladeshi university students' behavior toward adopting the e-learning system during the COVID-19 emergency. A conceptual framework was developed by adopting the variables from several previously published studies to meet the aims of the study. The current study was conducted in the quantitative approach by performing a survey on 393 university students of Bangladesh. All the obtained data were analyzed by using SPSS, AMOS, and machine learning algorithms. The findings of the study indicate that facilitating condition, effort expectancy, performance expectancy, and social influence affect the behavioral intention of the students to adopt e-learning, and the current study provides both practical and theoretical contributions. Theoretically, it provides a research framework and the literature to analyze the factors that trigger the behavioral intention of Bangladeshi university students. Practically, the findings will assist the policymakers of the education industry in Bangladesh.
Opto-electronic bias of a superconducting nanowire single photon detector using a cryogenic photodiode

Superconducting Nanowire Single Photon Detectors (SNSPDs) have become an integral part of quantum optics in recent years because of their high performance in single photon detection. We present a method to replace the electrical input by supplying the required bias current via the photocurrent of a photodiode situated on the cold stage of the cryostat. Light is guided to the bias photodiode through an optical fiber, which enables a lower thermal conduction and galvanic isolation between room temperature and the cold stage. We show that an off-the-shelf InGaAs–InP photodiode exhibits a responsivity of at least 0.55 A/W at 0.8 K. Using this device to bias an SNSPD, we characterize the count rate dependent on the optical power incident on the photodiode. This configuration of the SNSPD and photodiode shows an expected plateau in the single photon count rate with an optical bias power on the photodiode above 6.8 µW. Furthermore, we compare the same detector under both optical and electrical bias, and show there is no significant changes in performance. This has the advantage of avoiding an electrical input cable, which reduces the latent heat load by a factor of 100 and, in principle, allows for low loss RF current supply at the cold stage.
Building a Technology Recommender System Using Web Crawling and Natural Language Processing Technology

Finding, retrieving, and processing information on technology from the Internet can be a tedious task. This article investigates if technological concepts such as web crawling and natural language processing are suitable means for knowledge discovery from unstructured information and the development of a technology recommender system by developing a prototype of such a system. It also analyzes how well the resulting prototype performs in regard to effectivity and efficiency. The research strategy based on design science research consists of four stages: (1) Awareness generation; (2) suggestion of a solution considering the information retrieval process; (3) development of an artefact in the form of a Python computer program; and (4) evaluation of the prototype within the scope of a comparative experiment. The evaluation yields that the prototype is highly efficient in retrieving basic and rather random extractive text summaries from websites that include the desired search terms. However, the effectivity, measured by the quality of results is unsatisfactory due to the aforementioned random arrangement of extracted sentences within the resulting summaries. It is found that natural language processing and web crawling are indeed suitable technologies for such a program whilst the use of additional technology/concepts would add significant value for a potential user. Several areas for incremental improvement of the prototype are identified.
Dual Authentication for Payment Request Verification Over Cloud using Bilinear Dual Authentication Payments Transaction Protocol

There has been a recent explosion in the number of mobile network payment gateways that enable users to access services through a variety of devices.Mobile payment gateway security is complicated by a number of difficult-to-solve issues.As digital technology has progressed over the last decade, mobile payment mechanisms have gained a lot of interest.In the internet industry, these standards might have a significant impact on service quality.However, the most important aspect to consider when using these systems is their accountability, which assures confidence between the parties engaged in the financial transactions.Mobile payments may be easy, quick and secure.On the other hand, they may be rather pricey and are still susceptible to problems caused by technology.Specifically, mobile payments won't be able to go through at all if there are any problems with the host phone.For this reason, in this article a mobile payment mechanism that uses secure bilinear dual authentication.Using Bullet hash Maximum distance separable (MDS) and the mutate Hellman algorithm, our payment protocol incorporates all of the essential security characteristics to establish confidence between the parties.To put it another way, accountability is assured by mutual authentication and nonrepudiation.Conflicts that may emerge in the course of a payment transaction may be resolved using our strategy.Scyther is used to test our suggested protocol's empirical performance.
Automated Expected Amortised Cost Analysis of Probabilistic Data Structures

Abstract In this paper, we present the first fully-automated expected amortised cost analysis of self-adjusting data structures, that is, of randomised splay trees , randomised splay heaps and randomised meldable heaps , which so far have only (semi-)manually been analysed in the literature. Our analysis is stated as a type-and-effect system for a first-order functional programming language with support for sampling over discrete distributions, non-deterministic choice and a ticking operator. The latter allows for the specification of fine-grained cost models. We state two soundness theorems based on two different—but strongly related—typing rules of ticking, which account differently for the cost of non-terminating computations. Finally we provide a prototype implementation able to fully automatically analyse the aforementioned case studies."Image missing"<!-- image only, no MathML or LaTex -->
Earth Observation Satellite Imagery Information Based Decision Support Using Machine Learning

This paper presented a review on the capabilities of machine learning algorithms toward Earth observation data modelling and information extraction. The main purpose was to identify new trends in the application of or research on machine learning and Earth observation—as well as to help researchers positioning new development in these domains, considering the latest peer-reviewed articles. A review of Earth observation concepts was presented, as well as current approaches and available data, followed by different machine learning applications and algorithms. Special attention was given to the contribution, potential and capabilities of Earth observation-machine learning approaches. The findings suggested that the combination of Earth observation and machine learning was successfully applied in several different fields across the world. Additionally, it was observed that all machine learning categories could be used to analyse Earth observation data or to improve acquisition processes and that RF, SVM, K-Means, NN (CNN and GAN) and A2C were among the most-used techniques. In conclusion, the combination of these technologies could prove to be crucial in a wide range of fields (e.g., agriculture, climate and biology) and should be further explored for each specific domain.