Reviewerly/exHarmony huggingface dataset:
Orchestrating Image Retrieval and Storage Over a Cloud System

Since massive numbers of images are now being communicated from, and stored in different cloud systems, faster retrieval has become extremely important. This is more relevant, especially after COVID-19 in bandwidth-constrained environments. However, to the best of our knowledge, a coherent solution to overcome this problem is yet to be investigated in the literature. In this article, by customizing the Progressive JPEG method, we propose a new Scan Script to ensure Faster Image Retrieval. Furthermore, we also propose a new lossy PJPEG architecture to reduce the file size as a solution to overcome our Scan Script's drawback. In order to achieve an orchestration between them, we improve the scanning of Progressive JPEG's picture payloads to ensure Faster Image Retrieval using the change in bit pixels of distinct Luma and Chroma components ( <inline-formula><tex-math notation="LaTeX">$Y$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$C_{b}$</tex-math></inline-formula> , and <inline-formula><tex-math notation="LaTeX">$C_{r}$</tex-math></inline-formula> ). The orchestration improves user experience even in bandwidth-constrained cases. We evaluate our proposed orchestration in a real-world setting across two continents encompassing a private cloud. Compared to existing alternatives, our proposed orchestration can improve user waiting time by up to 54% and decrease image size by up to 27%. Our proposed work is tested in cutting-edge cloud apps, ensuring up to 69% quicker loading time.
AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

Maintaining a healthy cyber society is a great challenge due to the users’ freedom of expression and behavior. This can be solved by monitoring and analyzing the users’ behavior and taking proper actions. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using graph analysis methods. Then, the users’ behavioral patterns are analyzed by applying metadata analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then, in the abnormal behavior detection and filtering component, the interesting profiles are selected for further examinations. Finally, in the contextual analysis component, the contents are analyzed using natural language processing techniques; a binary text classification model (SVM (Support Vector Machine) + TF-IDF (Term Frequency—Inverse Document Frequency) with 88.89% accuracy) is used to detect if a tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN (Feed-Forward Neural Network) with 80% accuracy), because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (the police) with suggestions to control hate speech or terrorist propaganda.
Improving Accuracy and Convergence in Group-Based Federated Learning on Non-IID Data

Federated learning (FL) enables a large number of edge devices to learn a shared model without data sharing collaboratively. However, the imbalanced data distribution among users poses challenges to the convergence performance of FL. Group-based FL is a novel framework to improve FL performance, which appropriately groups users and allows localized aggregations within the group before a global aggregation. Nevertheless, most existing Group-based FL methods are K-means-based approaches that need to explicitly specify the number of groups, which may severely reduce the efficacy and optimality of the proposed solutions. In this paper, we propose a grouping mechanism called Auto-Group, which can automatically group users without specifying the number of groups. Specifically, various grouping strategies with different numbers of groups are generated with our mechanism. In particular, equipped with an optimized Genetic Algorithm, Auto-Group ensures that the data distribution of each group is similar to the global distribution, further reducing the communication delay. We conduct extensive experiments in various settings to evaluate Auto-Group. Experimental results show that, compared with the baselines, our mechanism can significantly improve the model accuracy while accelerating the training speed.
Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning

Urban planning designs land-use configurations and can benefit building livable, sustainable, safe communities. Inspired by image generation, deep urban planning aims to leverage deep learning to generate land-use configurations. However, urban planning is a complex process. Existing studies usually ignore the need of personalized human guidance in planning, and spatial hierarchical structure in planning generation. Moreover, the lack of large-scale land-use configuration samples poses a data sparsity challenge. This paper studies a novel deep human guided urban planning method to jointly solve the above challenges. Specifically, we formulate the problem into a deep conditional variational autoencoder based framework. In this framework, we exploit the deep encoder-decoder design to generate land-use configurations. To capture the spatial hierarchy structure of land uses, we enforce the decoder to generate both the coarse-grained layer of functional zones, and the fine-grained layer of POI distributions. To integrate human guidance, we allow humans to describe what they need as texts and use these texts as a model condition input. To mitigate training data sparsity and improve model robustness, we introduce a variational Gaussian embedding mechanism. It not just allows us to better approximate the embedding space distribution of training data and sample a larger population to overcome sparsity, but also adds more probabilistic randomness into the urban planning generation to improve embedding diversity so as to improve robustness. Finally, we present extensive experiments to validate the enhanced performances of our method.
BERT for Sentiment Analysis: Pre-trained and Fine-Tuned Alternatives

BERT has revolutionized the NLP field by enabling transfer learning with large language models that can capture complex textual patterns, reaching the state-of-the-art for an expressive number of NLP applications. For text classification tasks, BERT has already been extensively explored. However, aspects like how to better cope with the different embeddings provided by the BERT output layer and the usage of language-specific instead of multilingual models are not well studied in the literature, especially for the Brazilian Portuguese language. The purpose of this article is to conduct an extensive experimental study regarding different strategies for aggregating the features produced in the BERT output layer, with a focus on the sentiment analysis task. The experiments include BERT models trained with Brazilian Portuguese corpora and the multilingual version, contemplating multiple aggregation strategies and open-source datasets with predefined training, validation, and test partitions to facilitate the reproducibility of the results. BERT achieved the highest ROC-AUC values for the majority of cases as compared to TF-IDF. Nonetheless, TF-IDF represents a good trade-off between the predictive performance and computational cost.
Interference-Aware Workload Placement for Improving Latency Distribution of Converged HPC/Big Data Cloud Infrastructures

Recently, High Performance, Big Data, and Cloud Computing worlds tend to converge in terms of workload deployment with containerization technology acting as an enabler towards this direction. In such cases of application diversity and multi-tenancy, a universal scheduler able to satisfy the end-user needs for seamless, yet, efficient application deployment is required. While Kubernetes container orchestrator seems to be the answer that enables application-agnostic deployment, it still depends highly on coarse system metrics for its scheduling policies, thus, neglecting the performance degradation due to resource contention in the underlying system. In this paper, we design and implement an interference-aware modular framework, able to balance incoming workload based on low-level metrics monitoring. We evaluate our proposed solution over different workload mixes and co-location scenarios showing that against the state-of-art, but interference unaware Kubernetes scheduler the proposed framework significantly improves the latency distribution of the converged cloud infrastructure, improving median latency up to 27% and reducing standard deviation up to 25%.
Novel Preprocessors for Convolution Neural Networks

Fooling neural networks is a main concern in the process of Artificial Intelligence optimization.Character perturbation make part of a text unnoticeable for some systems, even for human observers.This research focuses the probe on a novel input preprocessing technique, which applies with the Convolutional Neural Networks for character recognition applications.Using Hand-written, Font, and Arabic Alphabet datasets, we show the CNN competitive results in classification.Then, we apply random deformation by mean of reshaping and relocation on the three testing datasets.As a reaction to change, the CNN demonstrates a confused behavior.Therefore, we design a Weightless Neural Network preprocessor that optimizes the shapes of deformed characters.Finally, we compare the impact of corrected input in improving the CNN performance.To measure the success of our preprocessor, we have applied it on well-known CNNs to compare the results between defected and rectified test datasets.Using Keras, we reconstructed LeNet-5, AlexNet and ZFNet for testing.Then, we conduct the test on the three databases to show how the recognition capability of CNNs would decline, then improve after preprocessing.
InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem

Rewards are the critical signals for Reinforcement Learning (RL) algorithms to learn the desired behavior in a sequential multi-step learning task. However, when these rewards are delayed and noisy in nature, the learning process becomes more challenging. The temporal Credit Assignment Problem (CAP) is a well-known and challenging task in AI. While RL, especially Deep RL, often works well with immediate rewards but may fail when rewards are delayed or noisy, or both. In this work, we propose delegating the CAP to a Neural Network-based algorithm named InferNet that explicitly learns to infer the immediate rewards from the delayed and noisy rewards. The effectiveness of InferNet was evaluated on three online RL tasks: a GridWorld, a CartPole, and 40 Atari games; and two offline RL tasks: GridWorld and a real-life Sepsis treatment task. The effectiveness of InferNet rewards is compared to that of immediate and delayed rewards in two settings: with and without noise. For the offline RL tasks, it is also compared to a strong baseline, InferGP [7]. Overall, our results show that InferNet is robust to delayed or noisy reward functions, and it could be used effectively for solving the temporal CAP in a wide range of RL tasks, when immediate rewards are not available or they are noisy.
Compressive Features in Offline Reinforcement Learning for Recommender Systems

In this paper, we develop a recommender system for a game that suggests potential items to players based on their interactive behaviors to maximize revenue for the game provider. Most of today’s recommender systems in e-commerce and retail businesses are built based on supervised learning models and collaborative filtering, while our approach is built on a reinforcement-learning-based technique and is trained on an offline data set that is publicly available on an IEEE Big Data Cup challenge. The limitation of the offline data set and the curse of high dimensionality pose significant obstacles to solving this problem. Our proposed method focuses on improving the total rewards and performance by tackling these main difficulties. More specifically, we utilized sparse PCA to extract important features of user behaviors. Our Q-learning-based system is then trained from the processed offline data set. To exploit all possible information from the provided data set, we cluster user features to different groups and build an independent Q-table for each group. Furthermore, to tackle the challenge of unknown formula for evaluation metrics, we design a metric to self-evaluate our system’s performance based on the potential value the game provider might achieve and a small collection of actual evaluation metrics that we obtain from the live scoring environment. Our experiments show that our proposed metric is consistent with the results published by the challenge organizers. We have implemented the proposed training pipeline, and the results show that our method outperforms current state-of-the-art methods in terms of both total rewards and training speed. By addressing the main challenges and leveraging the state-of-the-art techniques, we have achieved the best public leaderboard result in the challenge. Furthermore, our proposed method achieved an estimated score of approximately 20% better and can be trained faster by 30 times than the best of the current state-of-the-art methods.
Report on the 1st IR for children 2000--2020

We present a brief overview of the discussions and takeaways emerging from the IR for Children 2000--2020: Where Are We Now? Workshop, co-located with the 44 th International ACM SIGIR Conference on Research and Development in Information Retrieval. Date : 15 July, 2021. Website : http://www.fab4.science/IR4C/.
Monocular Depth Distribution Alignment with Low Computation

The performance of monocular depth estimation generally depends on the amount of parameters and computational cost. It leads to a large accuracy contrast between light-weight networks and heavy-weight networks, which limits their application in the real world. In this paper, we model the majority of accuracy contrast between them as the difference of depth distribution, which we call 'Distribution drift'. To this end, a distribution alignment network (DANet) is proposed. We firstly design a pyramid scene transformer (PST) module to capture inter-region interaction in multiple scales. By perceiving the difference of depth features between every two regions, DANet tends to predict a reasonable scene structure, which fits the shape of distribution to ground truth. Then, we propose a local-global optimization (LGO) scheme to realize the supervision of global range of scene depth. Thanks to the alignment of depth distribution shape and scene depth range, DANet sharply alleviates the distribution drift, and achieves a comparable performance with prior heavy-weight methods, but uses only 1% floating-point operations per second (FLOPs) of them. The experiments on two datasets, namely the widely used NYUDv2 dataset and the more challenging iBims-1 dataset, demonstrate the effectiveness of our method. The source code is available at https://github.com/YiLiM1/DANet.
Comparing Lightweight Algorithms to Secure Constrained Objects in Internet of Things

In recent years, the number of objects around us that are able to connect to internet has increased considerably, adding a new dimension to the world of Information and Communication Technology called Internet of Things. This technology allows billions of physical objects to communicate with each other. These things are resource-constrained (i.e., limited in treatment and energy), thus to secure their communications, lightweight cryptography is needed, and good management of energy consumption to ensure their long lifetime is necessary. This paper presents a comparative study of suitable and most frequently used lightweight block ciphers for Internet of Things devices. The comparison focuses on parameters that can reduce energy consumption and optimize battery lifetime. The comparison is done using Cooja simulator to determine the best lightweight cryptography algorithm between PRESENT, HIGHT, LBlock, and LED based on time execution, energy consumption, key size, and hardware implementation. Results show that key size has an impact on execution time and energy consumption; therefore, according to objects resources, the appropriate algorithm is applied. PRESENT and LED algorithms with 80-bit key size perform quickly and consume low energy compared to their 128-bit key size version but they are slower compared to HIGHT and LBlock.
Survey on Formation Verification for Ensembling Collective Adaptive System

The increasing discoveries in the autonomous system had caught researchers attentions. They aimed to find the suitable way to automate the formation of system components in reacting towards the dynamic environments. Among the challenges in designing adaptive systems are to verifying a system’s formation with the consideration of challenges such as uncertainty or scalability. Verified formation indicates the correctness of the formation in handling the changes in the environments. The outcome of the process is the verification of the formation in satisfying the specification of the system. This paper surveys the state-of-the-art formation verification in addressing the formation of collective adaptive systems (CAS) components that applying ensemble concepts. The paper also includes verification techniques used in verifying CAS formation and the tools used for formation verification.
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks

Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alternative that is computationally less demanding, but is unable to capture all of the scales of turbulent transport accurately. Our goal in this work is to build a new data-driven methodology based on super-resolution techniques to reconstruct DNS data from LES predictions. We leverage the underlying physical relationships to regularize the relationships amongst different physical variables. We also introduce a hierarchical generative process and a reverse degradation process to fully explore the correspondence between DNS and LES data. We demonstrate the effectiveness of our method through a single-snapshot experiment and a cross-time experiment. The results confirm that our method can better reconstruct high-resolution DNS data over space and over time in terms of pixel-wise reconstruction error and structural similarity. Visual comparisons show that our method performs much better in capturing fine-level flow dynamics.
Geochemical Association Rules of Elements Mined Using Clustered Events of Spatial Autocorrelation: A Case Study in the Chahanwusu River Area, Qinghai Province, China

The spatial distribution of elements can be regarded as a numerical field of concentration values with a continuous spatial coverage. An active area of research is to discover geologically meaningful relationships among elements from their spatial distribution. To solve this problem, we proposed an association rule mining method based on clustered events of spatial autocorrelation and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China. The elemental data for stream sediments were first clustered into HH (high–high), LL (low–low), HL (high–low), and LH (low–high) groups by using local Moran’s I clustering map (LMIC). Then, the Apriori algorithm was used to mine the association rules among different elements in these clusters. More than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences and occur in the upper reaches of the stream and catchment areas. In addition, we found that the Middle Triassic granodiorite is enriched in sulfophile elements, e.g., Zn, Ag, and Cd, and the Early Permian granite quartz diorite (P1γδο) coexists with Cu and associated elements. Therefore, the proposed algorithm is an effective method for mining coexistence patterns of elements and provides an insight into their enrichment mechanisms.
Friendship Preference: Scalable and Robust Category of Features for Social Bot Detection

Social bots are intelligent programs that control the behavior of fake accounts in an online social network(OSN). They pass themselves off as human accounts and manipulate the health of the ecosystem of OSNs. Thus, it is crucial to distinguish social bots from human accounts. Despite recent advances in social bot detection, state-of-the-art methods face challenges in scalability, generalization, and robustness. Considering these drawbacks, in this article, a new category of features, called friendship preference, is proposed. Friendship preference features are extracted from the profile attributes of the followers. The proposed feature extraction formula is designed to be scalable as the number of followers grows. Two classifiers are developed to evaluate the proposed category of features in terms of efficiency and usefulness. The classifiers are benchmarked on several real datasets. The experimental results indicate that the classifiers outperform Botometer in terms of classification performance. Scalability is assessed by inspecting the detection performance of classifiers when the attributes of a limited number of the accounts in the neighborhood of the users with large egonets are available. Furthermore, generalization is validated by crossover validation on various datasets. Finally, robustness and the ability for early detection of social bots are discussed.
UNO: A Web Application using Django

This project revolves around the usage of a web page and its functionalities. Web has been one of the greatest gifts to mankind and we intend to use its fruitful part too. This project inculcates the usage of Python, HTML, Django and Bootstrap. It is designed to facilitate multiple domains of knowledge from a single place as it is often seen that information is highly specific and found only at their respective domain holders. With this project we sought to bridge the gap between different kinds of beneficiaries of knowledge. This platform will facilitate all sort of knowledge from all sort of domains like Travel, Information Sciences, Tech Advancements, Politics to name a few. The availability of platforms like Django, Bootstrap has not only made the process really timesaving but also hassle free. UNO is to be spoken as You Know? to implicate a wave of curiosity among its users as after all it gives answers to their questions. This web application is supposed to feature a wide domain and global applications. From the world of technology to the vast remains of history and the travelogues all around the world sewed at one place. It will also support multi-lingual features and sections. A separate section for current discount coupons and offers from various websites will also be incorporated. Blogs are supposed to be a very descriptive informative view of a particular experience or a problem and with the advancement of modern era they are one of the most vital places of thorough information from someone experienced.