Reviewerly/exHarmony huggingface dataset:
RECBOT: Virtual Museum navigation through a Chatbot assistant and personalized Recommendations

The trend for digitalization of museums has been on the rise in recent years, as museums seek to make their collections and exhibitions more accessible to a wider audience. This has involved the use of technologies such as virtual and augmented reality, online exhibits, and digital archives. These digital initiatives have allowed museums to reach new audiences and provide immersive experiences that enhance visitors' engagement with the exhibits. Following this trend, in the current work, we propose a conversational agent that assists remote visitors in accessing a museum's collection. The proposed architecture includes a chatbot for user interaction that employs Natural Language Processing techniques for understanding the user's input. To increase visitor engagement, a hybrid recommender system is developed that combines content-based and collaborative-filtering components. The available data is modeled in the form of a Knowledge Graph, which allows for useful insights to be extracted from it.
Enhancing Anchor Link Prediction in Information Networks through Integrated Embedding Techniques

There are multiple types of information networks, including: social networks, citation networks, email communications networks, etc. are becoming popular in recent years. They have attracted a lot of researchers in multiple disciplines. Within this research domain, network alignment is considered as one of active topic due to its potential applications in real-world systems. In fact, with the popularity and diversity of information network types, most of users may participate in multiple information networks for many purposes. Thank to tremendous grow in the number of information networks recently, data analysis in information network has become a major challenge in multiple scientific disciplines. Anchor link prediction, also known as network alignment, which is aimed to match the users between different networks who have the same identification, is one of the emerging research directions in this domain. However, recent anchor link prediction baselines still lack the capability of sufficiently preserving global graph-structured features of individual information networks to obtain better prediction results. To overcome this challenge, we propose a novel model to align users between information networks using a seed set of known anchor links. To achieve better representations of individual networks in our proposed model, four embedding techniques are combined and learnt from the same latent space. We then apply the aggregation method to achieve a final network aligned embedding matrix which is later utilized to deal with the anchor link prediction problem. We run comprehensive experiments in real-life network alignment datasets to evaluate the effectiveness and compare them with the up-to-date baseline methods.
A Cross-Modality Person Re-Identification Method Based on Joint Middle Modality and Representation Learning

Modality differences and intra-class differences have been hot research problems in the field of cross-modality person re-identification currently. In this paper, we propose a cross-modality person re-identification method based on joint middle modality and representation learning. To reduce the modality differences, a middle modal generator is used to map different modal images to a unified feature space to generate middle modality images. A two-stream network with parameter sharing is used to extract the combined features of the original image and the middle modality image. In addition, a multi-granularity pooling strategy combining global features and local features is used to improve the representation learning capability of the model and further reduce the modality differences. To reduce the intra-class differences, the model is further optimized by combining distribution consistency loss, label smoothing cross-entropy loss, and hetero-center triplet loss to reduce the intra-class distance and accelerate the model convergence. In this paper, we use the publicly available datasets RegDB and SYSU-MM01 for validation. The results show that the proposed approach in this paper reaches 68.11% mAP in All Search mode for the SYSU-MM01 dataset and 86.54% mAP in VtI mode for the RegDB dataset, with a performance improvement of 3.29% and 3.29%, respectively, which demonstrate the effectiveness of the proposed method.
The Prospect of Quantum Technologies in Space for Defence and Security

Quantum technologies will have enormous applications in space, especially in sensing and communication. Particularly, many current and future space assets are of dual-use character or specifically designed for military and intelligence purposes. This also applies to quantum technologies. In this contribution, we study possible space-born quantum technologies for defence and intelligence that have the greatest potential to exploit these technologies to the maximum and across the full spectrum of quantum technologies. Their utilisation will lead to joint networks of quantum sensing and quantum communications with other emerging technologies such as artificial intelligence and laser communication with deployment around the Earth. On the other side, space-based quantum technology design should reflect threats such as laser anti-satellite weapons in the case of larger conflicts.
A Soldering Motion Analysis System for Monitoring Whole Body of People with Developmental Disabilities

The employment situation for people with developmental disabilities is different for different countries and types of disability. But in many cases, there are significant challenges in finding and keeping employment. For employment, it is essential to consider individual differences. Soldering work in factories is one of the options for persons with developmental disabilities, but it is difficult to ensure safety. In this paper, we propose a soldering motion analysis system for monitoring the whole body of persons with disabilities. We evaluate the proposed system by experiments. The experimental results show that the proposed system is able to monitor the soldering workers or people with developmental disabilities.
Adaptive and Privacy-Aware Persuasive Strategies to Promote Healthy Eating Habits: Position Paper

Food has become a social issue. Developing a healthy relationship with food is a complex process as it implies a behaviour change. At first sight, recent advances in recommender system domain ensure personalised service and assistance of a user in decision-making process. However, traditional recommender systems lack the consideration of health-related factors (e.g., healthy diet guidelines, food allergies, etc.). Moreover, instead of recommending a new item (recipe), an effective system should apply persuasion strategies to accompany a user in a long process, a little step at a time. Furthermore, most of existing recommender models do not take privacy preservation into account. In this position paper, we outline the challenges faced by a persuasive system for promoting eating habits and discuss our vision on the implementation of adaptive and privacy-aware persuasive strategies for healthy food promotion.
FACT: FFN-Attention Co-optimized Transformer Architecture with Eager Correlation Prediction

Transformer model is becoming prevalent in various AI applications with its outstanding performance. However, the high cost of computation and memory footprint make its inference inefficient. We discover that among the three main computation modules in a Transformer model (QKV generation, attention computation, FFN), it is the QKV generation and FFN that contribute to the most power cost. While the attention computation, focused by most previous works, only has decent power share when dealing with extremely long inputs. Therefore, in this paper, we propose FACT, an efficient algorithm-hardware co-design optimizing all three modules of Transformer. We first propose an eager prediction algorithm which predicts the attention matrix before QKV generation. It further detects the unnecessary computation in QKV generation and assigns mixed-precision FFN with the predicted attention, which helps improve the throughput. Further, we propose FACT accelerator to efficiently support eager prediction with three designs. It avoids the large overhead of prediction by using log-based add-only operations for prediction. It eliminates the latency of prediction through an out-of-order scheduler that makes the eager prediction and computation work in full pipeline. It additionally avoids memory access conflict in the mixed-precision FFN with a novel diagonal storage pattern. Experiments on 22 benchmarks show that our FACT improves the throughput of the whole Transformer by 3.59× on the geomean average. It achieves an enviable 47.64× and 278.1× energy saving when computing attention, compared to previous attention-optimization-only SOTA works ELSA and Sanger. Further, FACT achieves an energy efficiency of 4388 GOPS/W performing the whole Transformer layer on average, which is 94.98× higher than Nvidia V100 GPU.
Contiguitas: The Pursuit of Physical Memory Contiguity in Datacenters

The unabating growth of the memory needs of emerging datacenter applications has exacerbated the scalability bottleneck of virtual memory. However, reducing the excessive overhead of address translation will remain onerous until the physical memory contiguity predicament gets resolved. To address this problem, this paper presents Contiguitas, a novel redesign of memory management in the operating system and hardware that provides ample physical memory contiguity. We identify that the primary cause of memory fragmentation in Meta's datacenters is unmovable allocations scattered across the address space that impede large contiguity from being formed. To provide ample physical memory contiguity by design, Contiguitas first separates regular movable allocations from unmovable ones by placing them into two different continuous regions in physical memory and dynamically adjusts the boundary of the two regions based on memory demand. Drastically reducing unmovable allocations is challenging because the majority of unmovable pages cannot be moved with software alone given that access to the page cannot be blocked for a migration to take place. Furthermore, page migration is expensive as it requires a long downtime to (a) perform TLB shootdowns that scale poorly with the number of victim TLBs, and (b) copy the page. To this end, Contiguitas eliminates the primary source of unmovable allocations by introducing hardware extensions in the last-level cache to enable the transparent and efficient migration of unmovable pages even while the pages remain in use.
Optimizing CPU Performance for Recommendation Systems At-Scale

Deep Learning Recommendation Models (DLRMs) are very popular in personalized recommendation systems and are a major contributor to the data-center AI cycles. Due to the high computational and memory bandwidth needs of DLRMs, specifically the embedding stage in DLRM inferences, both CPUs and GPUs are used for hosting such workloads. This is primarily because of the heavy irregular memory accesses in the embedding stage of computation that leads to significant stalls in the CPU pipeline. As the model and parameter sizes keep increasing with newer recommendation models, the computational dominance of the embedding stage also grows, thereby, bringing into question the suitability of CPUs for inference. In this paper, we first quantify the cause of irregular accesses and their impact on caches and observe that off-chip memory access is the main contributor to high latency. Therefore, we exploit two well-known techniques: (1) Software prefetching, to hide the memory access latency suffered by the demand loads and (2) Overlapping computation and memory accesses, to reduce CPU stalls via hyperthreading to minimize the overall execution time. We evaluate our work on a single-core and 24-core configuration with the latest recommendation models and recently released production traces. Our integrated techniques speed up the inference by up to 1.59x, and on average by 1.4x.
TEESec: Pre-Silicon Vulnerability Discovery for Trusted Execution Environments

Trusted execution environments (TEE) are CPU hardware extensions that provide security guarantees for applications running on untrusted operating systems. The security of TEEs is threatened by a variety of microarchitectural vulnerabilities, which have led to a large number of demonstrated attacks. While various solutions for verifying the correctness and security of TEE designs have been proposed, they generally do not extend to jointly verifying the security of the underlying microarchitecture. This paper presents TEESec, the first pre-silicon framework for discovering microarchitectural vulnerabilities in the context of trusted execution environments. TEESec is designed to jointly and systematically test the TEE and underlying microarchitecture against data and metadata leakage across isolation boundaries. We implement TEESec in the Chipyard framework and evaluate it on two open-source RISC-V out-of-order processors running the Keystone TEE. Using TEESec we uncover 10 distinct vulnerabilities in these processors that violate TEE security principles and could lead to leakage of enclave secrets.
Reduction of scan duration and radiation dose in cerebral CT perfusion imaging of acute stroke using a recurrent neural network

Objective. Cerebral CT perfusion (CTP) imaging is most commonly used to diagnose acute ischaemic stroke and support treatment decisions. Shortening CTP scan duration is desirable to reduce the accumulated radiation dose and the risk of patient head movement. In this study, we present a novel application of a stochastic adversarial video prediction approach to reduce CTP imaging acquisition time.Approach. A variational autoencoder and generative adversarial network (VAE-GAN) were implemented in a recurrent framework in three scenarios: to predict the last 8 (24 s), 13 (31.5 s) and 18 (39 s) image frames of the CTP acquisition from the first 25 (36 s), 20 (28.5 s) and 15 (21 s) acquired frames, respectively. The model was trained using 65 stroke cases and tested on 10 unseen cases. Predicted frames were assessed against ground-truth in terms of image quality and haemodynamic maps, bolus shape characteristics and volumetric analysis of lesions.Main results. In all three prediction scenarios, the mean percentage error between the area, full-width-at-half-maximum and maximum enhancement of the predicted and ground-truth bolus curve was less than 4 ± 4%. The best peak signal-to-noise ratio and structural similarity of predicted haemodynamic maps was obtained for cerebral blood volume followed (in order) by cerebral blood flow, mean transit time and time to peak. For the 3 prediction scenarios, average volumetric error of the lesion was overestimated by 7%-15%, 11%-28% and 7%-22% for the infarct, penumbra and hypo-perfused regions, respectively, and the corresponding spatial agreement for these regions was 67%-76%, 76%-86% and 83%-92%.Significance. This study suggests that a recurrent VAE-GAN could potentially be used to predict a portion of CTP frames from truncated acquisitions, preserving the majority of clinical content in the images, and potentially reducing the scan duration and radiation dose simultaneously by 65% and 54.5%, respectively.
Event recognition in marine seismological data using Random Forest machine learning classifier

SUMMARY Automatic detection of seismic events in ocean bottom seismometer (OBS) data is difficult due to elevated levels of noise compared to the recordings from land. Popular deep-learning approaches that work well with earthquakes recorded on land perform poorly in a marine setting. Their adaptation to OBS data requires catalogues containing hundreds of thousands of labelled event examples that currently do not exist, especially for signals different than earthquakes. Therefore, the usual routine involves standard amplitude-based detection methods and manual processing to obtain events of interest. We present here the first attempt to utilize a Random Forest supervised machine learning classifier on marine seismological data to automate catalogue screening and event recognition among different signals [i.e. earthquakes, short duration events (SDE) and marine noise sources]. The detection approach uses the short-term average/long-term average method, enhanced by a kurtosis-based picker for a more precise recognition of the onset of events. The subsequent machine learning method uses a previously published set of signal features (waveform-, frequency- and spectrum-based), applied successfully in recognition of different classes of events in land seismological data. Our workflow uses a small subset of manually selected signals for the initial training procedure and we then iteratively evaluate and refine the model using subsequent OBS stations within one single deployment in the eastern Fram Strait, between Greenland and Svalbard. We find that the used set of features is well suited for the discrimination of different classes of events during the training step. During the manual verification of the automatic detection results, we find that the produced catalogue of earthquakes contains a large number of noise examples, but almost all events of interest are properly captured. By providing increasingly larger sets of noise examples we see an improvement in the quality of the obtained catalogues. Our final model reaches an average accuracy of 87 per cent in recognition between the classes, comparable to classification results for data from land. We find that, from the used set of features, the most important in separating the different classes of events are related to the kurtosis of the envelope of the signal in different frequencies, the frequency with the highest energy and overall signal duration. We illustrate the implementation of the approach by using the temporal and spatial distribution of SDEs as a case study. We used recordings from six OBSs deployed between 2019 and 2020 off the west-Svalbard coast to investigate the potential link of SDEs to fluid dynamics and discuss the robustness of the approach by analysing SDE intensity, periodicity and distance to seepage sites in relation to other published studies on SDEs.
Multibiometric Images Encryption Method Based on Fast Fourier Transform and Hyperchaos

Biometric images are an important means of personal identity verification and identification and are related to personal privacy and property security. To address the problems of poor security and low image reconstruction quality in the encryption and transmission of multibiometric images, a multibiometric images encryption method based on Fast Fourier Transform (FFT) and hyperchaotic system is proposed. First, the FFT is used to transform the multibiometric images from spatial to frequency domain. Then, the initial values of the hyperchaotic Lorenz system are generated using a one-dimensional chaotic logistic system to generate the key stream. Combined with the recoding rules of matrix reconstruction and scrambling without repetition of using multimatrix, the multiple matrices of amplitude and phase in the transform domain are reconstructed to be multiple RGB three-channel color images by using the inverse fast Fourier transform. Then, we combine the two diffusion methods of additive mode and Galois domain diffusion on each color channel to perform confusion and diffusion. Finally, the multiple grayscale images are encrypted to become multiple color images. The experimental results demonstrate that the method can effectively defend against various attacks. In addition, it solves the problem of low reconstruction accuracy that exists in the field of multiple images security.
Detection of SQL Injection Attacks by giving apriori to Q-Learning Agents

Software developers may have created the SQL Injection vulnerability accidentally, or a hacker may have purposefully used it to target vulnerable data. With the recent surge in information, there is an innate quest to safeguard this information from falling into the wrong hand leading to data theft, leak of personal data or loss of property. With relational databases like MySQL being the most popular, it allows users to extract any available information without any significant knowledge of databases. With vast information stored in databases warrants attacker’s attention, potentially risking critical confidential information. The premature detection of SQL Injection Attacks will be very helpful in preventing any malicious attempt by an attacker. In this study, we examine the outcomes of algorithms for reinforcement learning, such as Q-Learning, using a dataset made up of probable SQL Injection queries. We intend to provide a Reinforcement Learning solution to minimise the potential threat posed by SQL Injection and give apriori to the model to learn to detect a SQL attack and prevent any unforeseen mishap more quickly and accurately.
Improving quantum-to-classical data decoding using optimized quantum wavelet transform

One of the challenges facing current noisy-intermediate-scale-quantum devices is achieving efficient quantum circuit measurement or readout. The process of extracting classical data from the quantum domain, termed in this work as quantum-to-classical (Q2C) data decoding, generally incurs significant overhead, since the quantum circuit needs to be sampled repeatedly to obtain useful data readout. In this paper, we propose and evaluate time-efficient and depth-optimized Q2C methods based on the multidimensional, multilevel-decomposable, quantum wavelet transform (QWT) whose packet and pyramidal forms are leveraged and optimized. We also propose a zero-depth technique that uses selective placement of measurement gates to perform the QWT operation. To demonstrate their efficiency, the proposed techniques are quantitatively evaluated in terms of their temporal complexity (circuit depth and execution time), spatial complexity (total gate count), and accuracy (fidelity/similarity) in comparison to existing Q2C techniques. Experimental evaluations of the proposed Q2C methods are performed on a 27-qubit state-of-the-art quantum computing device from IBM Quantum using real high-resolution multispectral images. The proposed QHT-based Q2C method achieved up to $$15\times$$ higher space efficiency than the QFT-based Q2C method, while the proposed zero-depth method achieved up to 14% and 78% improvements in execution time compared to conventional Q2C and QFT-based Q2C, respectively.