Reviewerly/exHarmony huggingface dataset:
Frequency-Based Enhancement Network for Efficient Super-Resolution

Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR).Despite their remarkable performance, the lack of highfrequency information in the recovered images remains a core problem.Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice.A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input.To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output.In particular, this block efficiently decomposes features into low-and high-frequency and assigns more computation to high-frequency ones.Thus, it can help the network generate more discriminative representations by explicitly recovering finer details.Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures.We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model.Moreover, we propose a lightweight SR model -Frequency-based Enhancement Network (FENet) -based on FEB that matches the performance of larger models.Extensive experiments demonstrate that our proposal performs favorably against the state-ofthe-art SR algorithms in terms of visual quality, memory footprint, and inference time.The code is available at https://github.com/pbehjatii/FENet.
Drone Helps Privacy: Sky Caching Assisted $k$-Anonymity in Spatial Querying

As wireless and mobile devices are prevalent and resource-rich, users can frequently query point-of-interests and enjoy diverse location-based services (LBSs) in infrastructure-based wireless networks. However, users often trade their location privacy with services by sharing their location information with an LBS server, without knowing that the server can fully be trusted. In this article, we propose a sky caching-aided spatial querying scheme with the support of a flying drone. The basic idea is that both the user and its drone collaboratively generate a set of dummy locations to hide the current location of the user from the server. We also propose cache admission control techniques to efficiently cache the queried results to minimize the number of duplicated cached copies. The location privacy is analyzed and measured in terms of the location obfuscation and the size of the convex hull area that consists of dummy locations resided within the cloaking areas of user and drone. We conduct extensive simulation experiments using the OMNeT++ for performance evaluation and comparison. The simulation results show that the proposed approach can reduce the number of queries sent to the server, extend the cloaking area, and improve the location privacy of users significantly.
A Simple Strategy for Handling ‘NOT’ Can Improve the Performance of Sentiment Analysis

A major subset of natural language processing at intersection of computer linguistics and text mining is sentiment analysis that makes an effort of encapsulating the broader emotion from the accessible public opinions. The negations have a crucial role in linguistics in context of sentiment analysis as negations have the ability to impact the polarity of the other textual constituents. In a sentence, presence of negation does not impact only the word after the negation but its scope can extend up to a series of words next to the negation depending upon certain criteria. Considering broader level of structural formation negations can exist primarily in two ways, namely syntactic negation and morphological negation. Some common methods followed same strategy for negation handling without taking into consideration the difference among various kinds of negations. This work suggests a technique of handling 'NOT' which is a specific case of syntactic negation. Different linguistic characteristics are taken into account during designing and developing the technique. First sentiment analysis is done using some common classification models on several datasets. Then the datasets are processed with the 'NOT' handling technique and after that the same classification models are employed again. Performance is compared in both the cases with respect to different performance metrics by making use of 10 fold cross validation to show the efficacy of the 'NOT' handling strategy.
A Low-Rank CNN Architecture for Real-Time Semantic Segmentation in Visual SLAM Applications

Real-time semantic segmentation on embedded devices has recently enjoyed significant gain in popularity, due to the increasing interest in smart vehicles and smart robots. In particular, with the emergence of autonomous driving, low latency and computation-intensive operations lead to new challenges for vehicles and robots, such as excessive computing power and energy consumption. The aim of this paper is to address semantic segmentation, one of the most critical tasks for the perception of the environment, and its implementation in a low power core, by preserving the required performance of accuracy and low complexity. To reach this goal a low-rank convolutional neural network (CNN) architecture for real-time semantic segmentation is proposed. The main contributions of this paper are: i) a tensor decomposition technique has been applied to the kernel of a generic convolutional layer, ii) three versions of an optimized architecture, that combines UNet and ResNet models, have been derived to explore the trade-off between model complexity and accuracy, iii) the low-rank CNN architectures have been implemented in a Raspberry Pi 4 and NVIDIA Jetson Nano 2 GB embedded platforms, as severe benchmarks to meet the low-power, low-cost requirements, and in the high-cost GPU NVIDIA Tesla P100 PCIe 16 GB to meet the best performance.
Image Retrieval from Contextual Descriptions

The ability to integrate context, including perceptual and temporal cues, plays a pivotal role in grounding the meaning of a linguistic utterance.In order to measure to what extent current vision-and-language models master this ability, we propose a new multimodal challenge, Image Retrieval from Contextual Descriptions (IMAGECODE).In particular, models are tasked with retrieving the correct image from a set of 10 minimally contrastive candidates based on a contextual description.As such, each description contains only the details that help distinguish between images.Because of this, descriptions tend to be complex in terms of syntax and discourse and require drawing pragmatic inferences.Images are sourced from both static pictures and video frames.We benchmark several state-of-the-art models, including both cross-encoders such as ViLBERT and bi-encoders such as CLIP, on IMAGECODE.Our results reveal that these models dramatically lag behind human performance: the best variant achieves an accuracy of 20.9 on video frames and 59.4 on static pictures, compared with 90.8 in humans.Furthermore, we experiment with new model variants that are better equipped to incorporate visual and temporal context into their representations, which achieve modest gains.Our hope is that IMAGECODE will foster progress in grounded language understanding by encouraging models to focus on fine-grained visual differences.We make code and dataset publicly available.
Explainable Distance-Based Outlier Detection in Data Streams

Explaining outliers is a topic that attracts a lot of interest; however existing proposals focus on the identification of the relevant dimensions. We extend this rationale for unsupervised distance-based outlier detection, and through investigating subspaces, we propose a novel labeling of outliers in a manner that is intuitive for the user and does not require any training at runtime. Moreover, our solution is applicable to online settings and a complete prototype for detecting and explaining outliers in data streams using massive parallelism has been implemented. Our solution is evaluated in terms of both the quality of the labels derived and the performance.
Explainable Artificial Intelligence to Detect Breast Cancer: A Qualitative Case-Based Visual Interpretability Approach

Nowadays, research in the field of artificial intelligence is focusing on the explainability of the developed algorithms, mainly neural networks. This trend is known as XAI and brings certain advantages such as increased confidence in the decision-making process, improved capacity for error analysis, verification of results and possibility of model refinement, among others. In this work we have focused on interpreting the predictions of recently developed deep learning models through different visualization techniques. The use case we introduce is the detection of breast cancer through the classification of mammographies, since the medical field is widely benefited by the contributions of XAI methods. Furthermore, the target neural networks are based on recent and poorly explored architectures: EfficientNet, designed to improve the performance of convolutional networks.
Fuzzy Cognitive Mapping

Abstract This chapter introduces Fuzzy Cognitive Mapping (FCM), a method for developing and analysing ‘semi-quantitative’ (i.e. using and producing indicative rather than predictive numerical values) causal models. We explain in simple language what an FCM map is made up of and the two main ways in which analysis is done. We go into some detail on how to do it yourself and provide reflections on common issues and tricks of the trade. We also discuss its roots and debates in the field since. Finally, we provide some advice and resources for getting started yourself.
An Embarrassingly Simple Method to Mitigate Undesirable Properties of Pretrained Language Model Tokenizers

We introduce FLOTA (Few Longest Token Approximation), a simple yet effective method to improve the tokenization of pretrained language models (PLMs).FLOTA uses the vocabulary of a standard tokenizer but tries to preserve the morphological structure of words during tokenization.We evaluate FLOTA on morphological gold segmentations as well as a text classification task, using BERT, GPT-2, and XLNet as example PLMs.FLOTA leads to performance gains, makes inference more efficient, and enhances the robustness of PLMs with respect to whitespace noise.
One Stage Monocular 3D Object Detection Utilizing Discrete Depth and Orientation Representation

On-road object detection is a critical component in an autonomous driving system. The safety of the vehicle can only be as good as the reliability of the on-road object detection system. Thus, developing a fast and robust object detection algorithm has been the primary goal of many automotive industries and institutes. In recent years, multi-purpose vision-based driver assistance systems have gained popularity with the emergence of a deep neural network. A monocular camera has been developed to locate an object in the image plane and estimate the distance of the said object in the real world or the vehicle plane. In this work, we present a monocular 3D object detection method that utilizes the discrete depth and orientation representation. Our proposed method strives to predict object locations on 3D space utilizing keypoint detection on the object’s center point. To improve the point detection, we employ center regression on the objects segmentation mask, reducing the detection offset significantly. The simplicity of our proposed network architecture and its one-stage approach allows our algorithm to achieve competitive speed compared with prior methods. Our proposed method is able to achieve 26.93% detection score on the Cityscapes 3D object detection dataset, outperforming the preceding monocular method by a margin of 2.8 points.
A (2+1)D Attention Convolutional Neural Network for Video Prediction

Video prediction is a challenging generative modeling task. The objective of algorithms is to generate future scenes from a given image sequence. However, instead of giving a ground-truth prediction, these methods often give blurry predictions. This is a major challenge to predictive models. The causes of the blurry predictions are these models didn’t capture the features of the context and the variation of motion from the input. In this paper, we introduce (2+1)D Convolutional Neural Network (CNN) combined with two attention modules named spatial and temporal attention. The (2+1)D CNN helps the model learn the spatial-temporal features better than 2D CNN. And two attention modules are to boost the representation power of CNNs. The experiment results have shown that our approach achieved state-of-the-art performance in video prediction on standard datasets in terms of both PSNR and SSIM compared to existing methods.
A Provably Secure Mobile User Authentication Scheme for Big Data Collection in IoT-Enabled Maritime Intelligent Transportation System

The emergence of contemporary technologies like cloud computing and the Internet of Things (IoT) has revolutionized the trends in the cyber world to serve humanity. There are plenty of applications in which they are being used, especially in smart cities and their constituents, Maritime Transportation System (MTS) is one of them. The IoT-enabled MTS has the potential to entertain the growing challenges of modern-day ship transportation. Secure real-time data access from numerous smart IoT devices is the most critical and crucial exercise for Big Data acquisition in IoT-enabled MTS. Therefore, we have developed a Physically Unclonable Function (PUF) based authenticated key agreement solution to deal with this challenge. This solution enables the mobile user and IoT node to mutually authenticate each other via Cloud-Gateway before real-time data exchange and transmission in IoT-enabled MTS. The use of PUF in our solution brings invincibility against physical security threats. An inclusive security analysis under the assumption of the specified threat model is carried out to substantiate the security resilience of our solution. The conduct of our solution is realized through security features, communication, and computation cost and It has been observed that our solution achieves efficiency of 37.3&#x0025; and 9.7&#x0025; in communication and computation overhead, respectively. Moreover, the network performance effectiveness of our solution is demonstrated in NS3 implementation.
A Stackelberg Incentive Mechanism for Wireless Federated Learning With Differential Privacy

In recent years, data privacy and security have attracted increasing attention in the age of artificial intelligence. Although federated learning (FL) can avoid data leakage by only sharing the machine learning models, it still suffers from differential attacks which erode the privacy of data owners. In wireless networks, the inherent channel noise can be utilized for differential privacy (DP) protection. However, the problem of incentivizing mobile devices, i.e., data owners, with the demand for DP protection to complete FL tasks has received limited attention so far. In this letter, we establish a system model for DP preserving wireless federated learning and propose an incentive mechanism based on the Stackelberg game. Our theoretical proof and numerical results demonstrate that the proposed game model can achieve the Nash equilibrium and the superior performance in maximizing the server’s utility.
One Class Process Anomaly Detection Using Kernel Density Estimation Methods

We present a one-class anomaly detection method that uses time series sensor data to detect anomalies or faults in semiconductor fabrication processes. Critically, this method is trained using only small amounts of known successful run data, making it possible to implement for many processes and recipes without needing example faults. The proposed method uses kernel density estimation (KDE) to create probability distributions for sensor values during nominal processing. When classifying unseen sensor data, we determine the likelihood that it arose from this (often non-Gaussian) nominal distribution, allowing us to classify new signals as nominal, or faulty. We present model extensions that enable adaptation to changes in the underlying process, i.e., concept drift, as well as transfer learning techniques that enable training of anomaly detectors for new process recipes with less data. The proposed methods are tested on historical data from plasma etch and ion implantation processes, outperforming benchmark methods including traditional statistical process control (SPC), one-class support vector machine (OC-SVM), and variational auto-encoder (VAE) based detectors.
Frequency Feature Pyramid Network With Global-Local Consistency Loss for Crowd-and-Vehicle Counting in Congested Scenes

Context prediction plays a crucial role in implementing autonomous driving applications. As one of important context-prediction tasks, crowd-and-vehicle counting is critical for achieving real-time traffic and crowd analysis, consequently facilitating decision-making processes for autonomous vehicles. However, the completion of crowd-and-vehicle counting also faces challenges, such as large-scale variations, imbalanced data distribution, and insufficient local patterns. To tackle these challenges, we put forth a novel frequency feature pyramid network (FFPNet) in this paper. Our proposed FFPNet extracts the multi-scale information by frequency feature pyramid module, which can tackle the issue of large-scale variations. Meanwhile, the frequency feature pyramid module uses different frequency branches to obtain different scale information. We also adopt the attention mechanism to strength the extraction of different scale information. Moreover, we devise a novel loss function, namely global-local consistency loss, to address the existing problems of imbalanced data distribution and insufficient local patterns. Furthermore, we conduct extensive experiments on six datasets to evaluate our proposed FFPNet. It is worth mentioning that we also construct a novel crowd-and-vehicle dataset (CROVEH), which is the only dataset that contains both crowd-and-vehicle annotations. The experimental results show that FFPNet achieves the best performance on different backbones, e.g., 52.69 mean absolute error (MAE) on P2PNet with FFP module. The codes are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MUST-AI-Lab/FFPNet</uri> .
Large-Scale Crosstalk-Corrected Thermo-Optic Phase Shifter Arrays in Silicon Photonics

We introduce a thermo-optic phase shifter (TOPS) array architecture with independent phase control of each phase shifter for large-scale and high-density photonic integrated circuits with two different control schemes: pulse amplitude modulation (PAM) and pulse width modulation (PWM). We realize a compact spiral TOPS and a 288-element high-density row-column TOPS array with this architecture and drive TOPS with waveforms of both control schemes and of different array sizes. We present a thermal excitation model and a finite difference method-based simulation to simulate large-scale TOPS arrays and compare both schemes experimentally and theoretically. We also analyze the effects of thermal crosstalk in the realized TOPS array and implement a thermal crosstalk correction algorithm with the developed model. The high-density TOPS array architecture and the thermal crosstalk correction algorithm pave the way for high-density TOPS arrays with independent phase control in large-scale photonic integrated circuits interfaced with electronics limited in voltage swing and bandwidth.
Information Extraction from Handwritten Tables in Historical Documents

Recently, significant advances have been made in Document Understanding in structured historical documents. However, not much research has been done in information extraction from handwritten structured historical documents. In this paper, we compare two Machine Learning approaches and another approach that is based on heuristic rules to extract information in historical pre-printed forms with handwritten information. We analyze how each approach performs at each step of the extraction process. The proposed approaches improve the heuristic-rule baseline by up to 0.14 F-measure points throughout the information extraction pipeline.
On IND-qCCA Security in the ROM and Its Applications

Bounded IND-CCA security (IND-qCCA) is a notion similar to the traditional IND-CCA security, except the adversary is restricted to a constant number q of decryption/decapsulation queries. We show in this work that IND-qCCA is easily obtained from any passively secure PKE in the (Q)ROM. That is, simply adding a confirmation hash or computing the key as the hash of the plaintext and ciphertext holds an IND-qCCA KEM. In particular, there is no need for derandomization or re-encryption as in the Fujisaki-Okamoto (FO) transform [15]. This makes the decapsulation process of such IND-qCCA KEM much more efficient than its FO-derived counterpart. In addition, IND-qCCA KEMs could be used in the recently proposed KEMTLS protocol [29] that requires IND-1CCA ephemeral key-exchange mechanisms, or in TLS 1.3. Then, using similar proof techniques, we show that CPA-secure KEMs are sufficient for the TLS 1.3 handshake to be secure, solving an open problem in the ROM. In turn, this implies that the PRF-ODH assumption used to prove the security of TLS 1.3 is not necessary and can be replaced by the CDH assumption in the ROM. We also highlight and briefly discuss several use cases of IND-1CCA KEMs in protocols and ratcheting primitives.
Private Data Inference Attacks against Cloud: Model, Technologies, and Research Directions

Machine learning models are established with a variety of data collected from individual users who are concerned about their privacy. Various cloud service providers (e.g., Amazon, Google, Alibaba) commercialize their models by selling them or providing limited access via APIs. However, existing studies have shown that such cloud-based models are susceptible to training data inference attacks (i.e., membership inference attacks, attribute inference attacks, and model inversion attacks) since the trained models remember information about the training data. In this article, we perform an exhaustive investigation on the current training data inference attacks against cloud-based models. According to the attack scenario, we divide the existing inference attacks into two categories: centralized and collaborative learning scenarios. We first give a comprehensive review of attacks in each category and then give a qualitative comparison of the existing attacks. Further, we quantitatively compare the performance of different attack strategies by experiments. Last but not least, we pinpoint some open issues and potential future directions that need to be investigated in this area.