Reviewerly/exHarmony huggingface dataset:
Finding Missing Person Using Artificial Intelligence

Finding a missing person these days is an extremely difficult effort; even if social media keeps us all informed, it still necessitates a lot of paperwork, takes a lot of time, and offers little guarantees that the search will be successful. This project provides a system that streamlines facial recognition searches for the benefit of the public and the police department. In order for this system to function, the appropriate guardian of the missing individual must upload the image, which will then be stored in our database. The next step is for our system's face recognition algorithm to locate a match for that person in the database. If a match is discovered, the police and that person's guardian of that will be alerted. Face recognition is a biometric technology that creates a face print by mathematically mapping a certain person's or individual's facial traits. Using this method, a person's facial information is mathematically or in the form of graphs kept in the database that is used to identify that specific face. Our system's face recognition model will locate a match for that person in the database. The police and the child's guardian will be alerted if a match is discovered.
Fine-grained sentiment analysis using neural networks to identify guest preferences based on online reviews

Online reviews are a sort of user-generated content that sway not only prospective guests' reservation decisions but also provide timely feedback to hotels on how to enhance their service offerings in response to guest feedback. Customer preferences can be inferred from online hotel evaluations by analysing the reviews for subtle indications of approval or disapproval of specific features. Sentiment component extraction, feature pair identification (FPI), and sentiment orientation analysis are all basic activities in fine-grained sentiment analysis. Yet, current fine-grained sentiment analysis techniques fail to identify FPIs reliably, particularly when dealing with review evaluations. To boost the efficiency of FPI detection, we develop a new convolutional neural network (i.e., CNN) model that can fully exploit unstructured and structured information. In addition, we present a modified fine-grained sentiment analysis methodology that integrates a term clustering algorithm for “aspect terms” to determine a correct “customer sentiment intensity value” for each evaluated aspect, rather than a simple “positive” or “negative” rating. At last, we undertake an empirical analysis of hotel web reviews to demonstrate the rationality and benefits of the suggested methodology. The empirical findings show that our suggested method can effectively identify consumer preferences from online evaluations of hotels and can enhance the effectiveness of FPI identification. Furthermore, we discover that different sorts of clients have distinct tastes in the amenities offered by hotels.
Connecting Database Using Mysol Server

An interface called Java Database Connectivity enables programmers to connect to any database and retrieve data from it. A group of interfaces that define the Java Database Connectivity API are included in the SQL package. Platform-independent interface (JDBC - API) between relational databases and Java programming is also known as Java Database Connectivity. It enables the execution of SQL statements by a Java program and the retrieval of results from a Run a statement, then use the database to get the results. Numerous interfaces with various implementations for various databases are offered by the JDBC API. The implementation, architecture, and JDBC drivers that link to database servers like MySQL, Mango DB, and Oracle are the main topics of this document. Using Java programming, connect to a database and access records.
Secure Federated Training: Detecting Compromised Nodes and Identifying the Type of Attacks

Federated learning (FL) allows a set of clients to collaboratively train a model without sharing private data. As a result, FL has limited control over the local data and corresponding training process. Therefore, it is susceptible to poisoning attacks in which malicious clients use malicious training data or local updates to poison the global model. In this work, we first studied the data level and model level poisoning attacks. We simulated model poisoning attacks by tampering the local model updates during each round of communication and data poisoning attacks by training a few clients on malicious data. And clients under such attacks carry faulty information to the server, poison the global model, and restrict it from convergence. Therefore, detecting clients under attacks as well as identifying the type of attacks are required to recover the clients from their malicious status. To address these issues, we proposed a way under federated framework that enables the detection of malicious clients and attack types while ensuring data privacy. Our clustering-based approach utilizes the neuron’s activations from the local models to identify the type of poisoning attacks. We also proposed to check the weight distribution of local model updates among the participating clients to detect malicious clients. Our experimental results validated the robustness of the proposed framework against the attacks mentioned above by successfully detecting the compromised clients and the attack types. Moreover, the global model trained on MNIST data couldn’t reach the optimal point even after 75 rounds because of malicious clients, whereas the proposed approach by detecting the malicious clients ensured convergence within only 30 rounds and 40 rounds in independent and identically distributed (IID) and non- independent and identically distributed (non-IID) setup respectively.
Implementing Virtual Personal Assistant Through Artificial Intelligence Requirements

For higher productiveness customer service offerings performs a primary role. A device is applied to lessen the employee's necessities in consumer service quarter and stops the excess of client's valuable time. The device is advanced the usage of Natural Language Processing (NLP) and Bidirectional Encoder Representations from Transformers (BERT) set of rules. NLP is used for the speech reputation of the client and BERT is recycled to for the type of the textual content and consequences forecasts. In the prevailing answer Interactive Voice Response (IVR) is used wherein extrade the channels in keeping with the reaction acquired from the client and readdresses it to the particular organization of people. In the suggested answer an automatic respond is produced to the client with none redirection and there's no human intervention involved. Then the device is pre skilled into a barred dataset and every information within the datum are tokenized. The entry acquired for the client is transformed into token after which it unearths the disguised phrase via way of means of the usage of bidirectional search. The BERT set of rules aim is to extract the maximum crucial interactive records among the enquiries and the client reaction. This as beginning the cease strictures are decided because the output with the intention to take delivery of again to the client each in the shape of textual content and voice transcription. Using this method, the customer service offerings may be enhanced, and the consequences are assured in contrast to that of the current answer in which it ultimately relies upon on human interactions.
Towards Improving Cloud Security and Performance by using Proposed Controlled Replication Model

Unlike traditional data centres, cloud computing can be used for data hosting and management, allowing the resources to be used in comparable ways. To host data, cloud companies employ a variety of ways. The most popular data is copied using the fewest resources, while the remainder is not. Placing data fragments in the middle and closest nodes to the user reduces access time. Through replication, data is always available, so its availability is ensured. Data files stored in the cloud should always be available to users. Users can replicate data files based on their reputation degree and replication benefits. The purpose of replication is to ensure consistency, availability, and reliability of data by maintaining different copies of the same data on multiple storage servers. Using a cloud provider creates a sense of trust in the belief that data is available whenever needed. Several factors have been considered when developing the Controlled Replication Model for reducing such instances on the cloud platform. These factors are the level of data reputation, the replication benefit, and the replication cost. As a result, it controls replication costs and lowers resource use. Denial of service and reflection attacks are reduced to a certain level, and the system's stability, availability, load balancing, and fault tolerance are increased.
Trade-off between reconstruction loss and feature alignment for domain generalization

Domain generalization (DG) is a branch of transfer learning that aims to train the learning models on several seen domains and subsequently apply these pre-trained models to other unseen (unknown but related) domains. To deal with challenging settings in DG where both data and label of the unseen domain are not available at training time, the most common approach is to design the classifiers based on the domain-invariant representation features, i.e., the latent representations that are unchanged and transferable between domains. Contrary to popular belief, we show that designing classifiers based on invariant representation features alone is necessary but insufficient in DG. Our analysis indicates the necessity of imposing a constraint on the reconstruction loss induced by representation functions to preserve most of the relevant information about the label in the latent space. More importantly, we point out the trade-off between minimizing the reconstruction loss and achieving domain alignment in DG. Our theoretical results motivate a new DG framework that jointly optimizes the reconstruction loss and the domain discrepancy. Both theoretical and numerical results are provided to justify our approach.
A Preliminary Exploration of the Learning and Engagement Potential of an Intelligent Virtual Environment

This paper presents initial findings from an empirical study utilising an intelligent virtual museum with the aim to explore its learning and engagement capability. The study investigates the participants’ experiences with the environment, their perceptions on its learning and engagement ability, and how they envision future learning scenarios with intelligent virtual environments. Participants found the system interesting, useful, and engaging. Suggestions for improvement highlighted technological, pedagogical, and social aspects which can guide further exploitation of such systems in education.
Evaluating Presence and Technology Acceptance of an Intelligent Reality Virtual Museum Prototype

Intelligent Reality refers to the fusion of integrated technological innovations to develop complex computing systems aiming to blend the real with digital worlds and enable humans and machines to seamlessly work together leveraging each other’s strengths. This paper presents the results of a study evaluating the user experience of visiting an Intelligent Reality virtual museum prototype in terms of presence, technology acceptance, and environment perceptions, as a small-scale Cyber-Physical-Social Eco-System proof of concept. A virtual reality (VR) system was designed, and a real robot was placed in a technology museum to foster cyber-physical synchronisation, for users to visit and navigate in the environment. The study results revealed high degree of presence achieved by users in the VR world, with positive acceptance of technology and environment evaluation. Most importantly, the results highlighted the importance of presence in such systems and how it relates to acceptance and to the overall users’ perception of a VR world.
A Comprehensive Study on Personalized Federated Learning with Non-IID Data

Personalized Federated Learning (PFL) has been extensively studied to overcome the Non-IID challenges of FL. However, given realistic and non-pathologically Non-IID distributions, the elaborate personalized models can even be inferior to a naive single global model. In this paper, we take the first step toward further understanding when PFL is better on a common Non-IID distribution: label skewed distributions. We take Cllustered Federated Learning (CFL) as an example and demonstrate that it is non-trivial to decide whether PFL outperforms a single global model on a given dataset in advance. We propose Hybrid Clustered Federated Learning (HCFL) to tackle the above challenge by alternately aggregating and personalizing the cluster models. HCFL can obtain the advantage of personalized models while preserving the generalization capability. Besides, we propose an efficient clustering algorithm, combining K-Means and Bayesian Information Criterion to ensure the performance superiority of HCFL. Extensive experiments show that HCFL can exhibit superior performance on both slight and pathological label skewed distributions, and outperform state-of-the-art algorithms by up to 10.6% with negligible introduced overhead. Moreover, the performance of HCFL does not rely on the well-tuned aggregation hyper-parameters and can generalize well to new clients, and is thus more suitable for deployment in real FL systems.
Sentence Similarity Recognition in Portuguese from Multiple Embedding Models

Distinct pre-trained embedding models perform differently in sentence similarity recognition tasks. The current assumption is that they encode different features due to differences in algorithm design and characteristics of the datasets employed in the pre-trained process. The perspective of benefiting from different encoded features to generate more suitable representations motivated the assembly of multiple embedding models, so-called meta-embedding. Meta-embedding methods combine different pre-trained embedding models to perform a task. Recently, multiple pre-trained language representations derived from Transformers architecture-based systems have been shown to be effective in many downstream tasks. This paper introduces a supervised meta-embedding neural network to combine contextualized pre-trained models for sentence similarity recognition in Portuguese. Our results show that combining multiple sentence pre-trained embedding models outperforms single models and can be a promising alternative to improve performance sentence similarity. Moreover, we also discuss the results considering our simple extension of a model explainability method to the meta-embedding context, allowing the visual identification of the impact of each token on the sentence similarity score.
A Robust Approach to Fine-tune Pre-trained Transformer-based models for Text Summarization through Latent Space Compression

We proposed a technique to reduce the decoder’s number of parameters in a sequence-to-sequence (seq2seq) architecture for automatic text summarization. This approach uses a pre-trained Autoencoder (AE) trained on top of an encoder’s output to reduce its embedding dimension, which significantly reduces the summarizer model’s decoder size. Two experiments were performed to validate the idea: a custom seq2seq architecture with various pre-trained encoders and incorporating the approach in an encoder-decoder model (BART) for text summarization. Both studies showed promising results in terms of ROUGE score. However, the impressive outcome is the 54% decrease in the inference time and a 57% drop in GPU memory usage while fine-tuning with minimal quality loss (4.5% R1 score). It significantly reduces the hardware requirement to fine-tune large-scale pre-trained models. It is also shown that our approach can be combined with other network size reduction techniques (e.g. Distillation) to further reduce any encoder-decoder model parameters count. The implementation and checkpoints are available on GitHub. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Recent Trends in Neural Architecture Search Systems

Explosive research has been done on Neural Architecture Search (NAS) to automatically create high- performing neural architectures. The majority of the architectures in use today have been created manually, by human specialists, which is a labor-intensive and fault-prone procedure. This has sparked a rise in interest among researchers in automated neural architecture search methods, which has unavoidably led to the development of a wide variety of automated neural architecture search methods. Choosing the right architecture design has been found to be crucial, and many deep learning advancements result from its direct benefits. On the basis of various search strategies employed in NAS, we offer an insight into the literature on the subject and divide the current NAS works into three primary categories with an emphasis on recent trends in each category. The performance comparison of these categories is performed. Challenges and some future research directions in neural architecture search are outlined.
VRFormer: 360-Degree Video Streaming with FoV Combined Prediction and Super resolution

360-degree video has shown great potential to the mainstream since its immersive experience. However, 360-degree video streaming requires ultrahigh bandwidth and low latency, which limit the improvement of user quality of experience (QoE). Currently, methods combining field of view (FoV) prediction and adaptive video streaming provide an effective method for addressing the above issues. However, existing FoV prediction methods based on recurrent neural networks (RNN) cannot capture long-range dependency from input to output. Current deep reinforcement learning (DRL)-based adaptive strategies fail to estimate the future bandwidth with high accuracy and fully explore the capability of VR devices. To ameliorate these limitations, we design a DRL-based 360-degree video streaming method named VRFormer with FoV combined prediction and super resolution (SR). First, we adopt a content-aware transformer-based encoder-decoder network to make the long-term FoV prediction. It combines the user's head movement history, eye-tracking history, and user attention extracted from a convolutional neural network (CNN)-based network. Second, we introduce a DNN-based SR network running on VR devices to reconstruct high-definition video content. Finally, we apply a DRL-based network to adaptively allocate rates for future tiles and dynamically control video content reconstruction. Experiments have verified that the proposed method can effectively improve the quality of experience (QoE) of the user's viewing experience compared to the state-of-the-art methods.
Interpretable Reinforcement Learning with Multilevel Subgoal Discovery

We propose a novel Reinforcement Learning model for discrete environments, which is inherently interpretable and supports the discovery of arbitrary deep subgoal hierarchies. In the model, an agent learns information about environment in the form of probabilistic rules, while policies for (sub)goals are learned as combinations thereof. No reward function is required for learning; an agent only needs to be given a primary goal to achieve. Subgoals of a goal G from the hierarchy are computed as descriptions of states, which if previously achieved increase the total efficiency of the available policies for G. These state descriptions are introduced as new sensor predicates into the rule language of the agent, which allows for sensing important intermediate states and for updating environment rules and policies accordingly.
Intent based Multimodal Speech and Gesture Fusion for Human-Robot Communication in Assembly Situation

Understanding the intent is an essential step for maintaining effective communications. This essential feature is used in communications for assembling, patrolling, and surveillance. A fused and interactive multimodal system for human-robot communication, used in assembly applications, is presented in this paper. Communication is multimodal. Having the options of multiple communication modes such as gestures, text, symbols, graphics, images, and speech increase the chance of effective communication. The intent is the main component that we are aiming to model, specifically in human machine dialogues. For this, we extract the intents from spoken dialogues and fuse the intent with any detected matching gesture that is used in interaction with the robot. The main components of the presented system are: (1) a speech recognizer system using Kaldi, (2) a deep-learning based Dual Intent and Entity Transformer (DIET) based classifier for intent and entity extraction, (3) a hand gesture recognition system, and (4) a dynamic fusion model for speech and gesture based communication. These are evaluated on contextual assembly situation using a simulated interactive robot.
EPGraph: An Efficient Graph Computing Model in Persistent Memory System

With the wide range of requirements to analyse large-scale graphs in many real-world applications (e.g., relationship analysis, fraud detection and product recommendation), graph computing recently receives intensive interests. However, the massive volume and the power-law distribution of graphs are objective obstacles to efficient graph computing. Fortunately, Intel Optane DC Persistent Memory (PMEM) has emerged as a new solution, which is expected to play a crucial role in large-scale graph processing. But compared with main memory, PMEM shows much lower bandwidth and higher access latency. Therefore, it becomes paramount to fully exploit the advantages of PMEM in persistent memory systems. In this paper, we propose EPGraph, a novel efficient graph computing model designed by PMEM. To a considerable extent, it improves the spatial locality and the temporal locality of graph computing at the same time. The main contribution of our work lies in three aspects. Firstly, we design a degree-based data layering strategy to reduce the impact of power-law distribution. The hierarchical strategy makes full use of DRAM and PMEM simultaneously. Secondly, we propose a dynamic migration mechanism during the iterative execution of graph computing. The dynamic mechanism equitably schedules the subgraphs which are used in the next iteration. Thirdly, we evaluate the effectiveness of EPGraph on five public graph data sets. Extensive evaluation results show that EPGraph outperforms state-of-the-art graph computing systems by 22.67%-35.03%.
Utilizing Explainable AI for improving the Performance of Neural Networks

Nowadays, deep neural networks are widely used in a variety of fields that have a direct impact on society. Although those models typically show outstanding performance, they have been used for a long time as black boxes. To address this, Explainable Artificial Intelligence (XAI) has been developing as a field that aims to improve the transparency of the model and increase their trustworthiness. We propose a retraining pipeline that consistently improves the model predictions starting from XAI and utilizing state-of-the-art techniques. To do that, we use the XAI results, namely SHapley Additive exPlanations (SHAP) values, to give specific training weights to the data samples. This leads to an improved training of the model and, consequently, better performance. In order to benchmark our method, we evaluate it on both real-life and public datasets. First, we perform the method on a radar-based people counting scenario. Afterward, we test it on the CIFAR-10, a public Computer Vision dataset. Experiments using the SHAP-based retraining approach achieve a 4% more accuracy w.r.t. the standard equal weight retraining for people counting tasks. Moreover, on the CIFAR-10, our SHAP-based weighting strategy ends up with a 3% accuracy rate than the training procedure with equal weighted samples.