Reviewerly/exHarmony huggingface dataset:
Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning

This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets to those with attached images and using the characteristics of the images and the relationships between the text and images, a more detailed analysis than that of with text-only tweets can be conducted. Therefore, an analysis method was devised based on a multi-task neural network that uses both the features extracted from the image and text as input and the buzz class (buzz/non-buzz) and the number of “likes (favorites)” and “retweets (RTs)” as output. The predictions made using a single feature of the text and image were compared with the predictions using a combination of multiple features. The differences between buzz and non-buzz features were analyzed based on the cosine similarity between the text and the image. The buzz class was correctly identified with a correctness rate of approximately 80% for all combinations of image and text features, with the combination of BERT and VGG16 providing the highest correctness rate.
EvilModel 2.0: Bringing Neural Network Models into Malware Attacks

Security issues have gradually emerged with the continuous development of artificial intelligence (AI). Earlier work verified the possibility of converting neural network models into stegomalware, embedding malware into a model with limited impact on the model's performance. However, existing methods are not applicable in real-world attack scenarios and do not attract enough attention from the security community due to performance degradation and additional workload. Therefore, we propose an improved stegomalware EvilModel. By analyzing the composition of the neural network model, three new methods for embedding malware into the model are proposed: MSB reservation, fast substitution, and half substitution, which can embed malware that accounts for half of the model's volume without affecting the model's performance. We built 550 EvilModels using ten mainstream neural network models and 19 malware samples. The experiment shows that EvilModel achieved an embedding rate of 48.52\%. A quantitative algorithm is proposed to evaluate the existing embedding methods. We also design a trigger and propose a threat scenario for the targeted attack. The practicality and effectiveness of the proposed methods were demonstrated by experiments and analyses of the embedding capacity, performance impact, and detection evasion.
Describing discontinuous finite 3D scattering objects in Gabor coefficients: fast and accurate methods

In relation to the computation of electromagnetic scattering in layered media by the Gabor-frame-based spatial spectral Maxwell solver, we present two methods to compute the Gabor coefficients of the transverse cross section of three-dimensional scattering objects with high accuracy and efficiency. The first method employs the analytically obtained two-dimensional Fourier transform of the cross section of a scattering object, which we describe by two-dimensional characteristic functions, in combination with the traditional discrete Gabor transform method for computing the Gabor coefficients. The second method concerns the expansion of the so-called dual window function to compute the Gabor coefficients by employing the divergence theorem. Both methods utilize (semi)-analytical approaches to overcome the heavy oversampling requirement of the traditional discrete Gabor transform method in the case of discontinuous functions. Numerical results show significant improvement in terms of accuracy and computation time for these two methods against the traditional discrete Gabor transform method.
DeepDive: The Use of Virtual Worlds to Create an Ethnography of an Ancient Civilization

The DeepDive System is a tool built to assist with the testing of archaeological hypotheses, the visualization of complex data, and the archiving of archaeological and ethnographic data. As such, it allows modern day archaeologists to recreate aspects of ancient cultural behavior through digital time travel. This paper describes how a researcher can configure a version of an ancient Land Bridge landscape and present it to selected participants. Their behavior can then contribute to the development of a hypothetical ethnography. A program that is potentially able to connect the actions of an ancient culture with the ecosystem that it is embedded in. The results of preliminary application of this system with hunters from a similar sub-Arctic landscape is described.
BMI estimation from facial images using residual regression model

Body Mass Index (BMI) has the potential to disclose a variety of health and lifestyle concerns. Predicting BMI from facial images is an interesting but challenging problem in computer vision. Previous works focus mainly on feature extraction step of the whole BMI estimation process. Little attention has been paid to the regression module. In this paper, we propose a new architecture for the regression module which composes of multiple blocks. Each block has several sub-blocks composing of dense layer, batch-normalization, activation, dropout. In addition, we take advantage of the residual principle from ResNet by adding residual connections in the regression blocks. We integrate the proposed regression model just after the state-of-the-art feature extractor ResNet and train the network in an end-to-end manner. Extensive experiments on the VIP Attributes dataset show that thanks to the new residual regression model, the estimation error reduces up to 22% in comparison to the original method.
Design Exploration and Scalability Analysis of a CMOS-Integrated, Polymorphic, Nanophotonic Arithmetic-Logic Unit

Over the past two decades, the clock speed, and hence, the singlecore performance of microprocessors has already stagnated. Following this, the recent faltering of Moore's law due to the CMOS fabrication technology reaching its unavoidable physical limit has presaged daunting challenges for designing power-efficient and ultrafast microprocessors. To overcome these challenges, vigorous efforts have been made to develop new more-than-Moore technologies and architectures for computing. Among these, nanophotonic integrated circuits based computing architectures have shown revolutionary potential. Among recent demonstrations of nanophotonic circuits for computing, a polymorphic, nanophotonic ALU (PoN-ALU) carries a notable importance since it has shown very high flexibility, high speed, and low power consumption for computing. In this paper, we carry out a design space exploration of this PoN-ALU to derive new design guidelines that can help scale the speed and energy efficiency of PoNALU even further.
End-to-End Method With Transformer for 3-D Detection of Oil Tank From Single SAR Image

In recent years, deep learning has been successfully applied in the field of synthetic aperture radar (SAR) image object detection. However, unlike ships and tanks targets, the oil tank targets in SAR image are usually dense and compact with more overlaps and discrete scattering centers, which greatly increases the difficulty of extracting location and structural parameters. Most of the existing methods transfer the methods suitable for natural image to the SAR image field, without considering the unique characteristics of SAR image. Therefore, in this article, we propose an improved model based on the end-to-end transformer network, which is the first model introducing transformer network to 3-D detection of oil tank targets from single SAR image. We input the incidence angle into the transformer model as a priori token. Then, we propose a feature description operator (FDO) based on the scattering centers that are used as an aid to improve the precision of predictions. In addition, we also propose a cylinder IOU as a more suitable evaluation metric for 3-D detection of oil tank. Finally, we evaluate our model on an SAR image dataset that contains SAR images from RADARSAT-2, TerraSAR-X, and GF-3 with different incidence angles. Our experiments demonstrate that our proposed model achieves the AP of 77.6% compared with 60.8% of baseline, which proves the effectiveness of the introduction of observation conditions, cylinder IOU loss (CI Loss), and the FDO based on the scattering centers in our model and is appealing for 3-D detection of oil tank.
A Discourse Coherence Analysis Method Combining Sentence Embedding and Dimension Grid

Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. However, existing coherence models focus on measuring individual aspects of coherence, such as lexical overlap, entity centralization, rhetorical structure, etc., lacking measurement of the semantics of text. In this paper, we propose a discourse coherence analysis method combining sentence embedding and the dimension grid, we obtain sentence-level vector representation by deep learning, and we introduce a coherence model that captures the fine-grained semantic transitions in text. Our work is based on the hypothesis that each dimension in the embedding vector is exactly assigned a stated certainty and specific semantic. We take every dimension as an equal grid and compute its transition probabilities. The document feature vector is also enriched to model the coherence. Finally, the experimental results demonstrate that our method achieves excellent performance on two coherence-related tasks.
Forward Secret Encrypted RAM: Lower Bounds and Applications

In this paper, we study forward secret encrypted RAMs (FS eRAMs) which enable clients to outsource the storage of an n-entry array to a server. In the case of a catastrophic attack where both client and server storage are compromised, FS eRAMs guarantee that the adversary may not recover any array entries that were deleted or overwritten prior to the attack. A simple folklore FS eRAM construction with \(O(\log n)\) overhead has been known for at least two decades. Unfortunately, no progress has been made since then. We show the lack of progress is fundamental by presenting an \(\varOmega (\log n)\) lower bound for FS eRAMs proving that the folklore solution is optimal. To do this, we introduce the symbolic model for proving cryptographic data structures lower bounds that may be of independent interest.
Frames over finite fields: Basic theory and equiangular lines in unitary geometry

We introduce the study of frames and equiangular lines in classical geometries over finite fields. After developing the basic theory, we give several examples and demonstrate finite field analogs of equiangular tight frames (ETFs) produced by modular difference sets, and by translation and modulation operators. Using the latter, we prove that Gerzon's bound is attained in each unitary geometry of dimension d=22l+1 over the field F32. We also investigate interactions between complex ETFs and those in finite unitary geometries, and we show that every complex ETF implies the existence of ETFs with the same size over infinitely many finite fields.
The system of detecting safety helmets based on YOLOv5

The rapid development of computer vision is applied in various aspects and has a wide prospect, especially in safety engineering. In construction sites, the helmet is an important tool to protect workers' life, but actually, because of not wearing a helmet, the accident happened sporadically. To solve this problem, based on deep learning, we collected 2241 images for training and testing, additionally, using YOLOV5 to recognize some common images. The experimental results are shown in the proposed method, and the average accuracy can reach about 95.2%.
Importance of Deep Learning Models to Perform Segmentation on Medical Imaging Modalities

In the area of medical imaging, segmentation method is vital method to outline the area of interest in terms of pixels in 2D images or voxels in 3D images. Researchers are focusing on automated ways for accurate segmentation applied on images captured through different imaging modalities depending on the type of disease or infection in body. Deep neural network-based segmentation models are playing an important part for the accurate and fast segmentation. This paper presents the importance of deep learning models in the field of medical imaging focusing on the segmentation of different body parts from different imaging modalities.
An Improved SSD Object Detection Algorithm For Safe Social Distancing and Face Mask Detection In Public Areas Through Intelligent Video Analytics

Recently, Coronavirus Disease (COVID-19) has spread rapidly across the world and thus social distancing and face mask has become one of the mandatory preventive measures. The Artificial Intelligence community has been focusing on developments for monitoring social distancing and identifying face masks, which have become the hotspot and made the headlines. A powerful approach for fast and more accurate detection and monitoring would greatly enhance the sole purpose, save the lives of many and also alleviate the burden of doing it manually to some extent. You Only Look Once(Yolov3) and improved Single Shot multi-box Detector (SSD) algorithm has been deployed for an improved object detection model to lend a helping hand in the fight against this deadly disease. The proposed framework utilizes Single Shot multi-box Detector (SSD) with MobileNetV2, to enhance the feature extraction ability of the object detection model. After taking into account the significance of developing an accurate model and also the limitations of existing models, herein we have proposed a framework based on deep learning, which would automate and simplify the task of monitoring social distancing and face mask through intelligent video analytics.
Hermes: A reversible language for lightweight encryption

Hermes is a domain-specific language for writing lightweight encryption algorithms: It is reversible, so it is not necessary to write separate encryption and decryption procedures. Hermes uses a type system that avoids several types of side-channel attacks, by ensuring no secret values are left in memory and that operations on secret data spend time independent of the value of this data, thus preventing timing-based attacks. We show a complete formal specification of Hermes, argue absence of timing-based attacks (under reasonable assumptions), and compare implementations of well-known lightweight encryption algorithms in Hermes and C.
Multi-Granularity Control Flow Anomaly Detection with Hardware Counters

Hardware counters are included in processors to count microarchitecture level events affecting performance. When control flow anomalies caused by attacks such as buffer overflow or return oriented programming (ROP) occur, they leave a microarchitectural footprint. Hardware counters reflect such footprints to flag control flow anomalies. This paper is geared towards buffer overflow and ROP control flow anomaly detection in embedded programs. The targeted program entities are main event loops and task/event handlers. Embedded systems also have enhanced need for variable anomaly detection time in order to meet the system response time requirements. We propose a novel repurposing of Patt-Yeh two level branch predictor data structure for abstracting/hashing HW counter signatures to support such variable anomaly detection times. The proposed anomaly detection mechanism is evaluated on some generic benchmark programs and ArduPilot - a popular autopilot software. Experimental evaluation encompasses both Intel X86 and ARM Cortex M processors. DWT within Cortex M provides sufficiently interesting program level event counts to capture these control flow anomalies. We are able to achieve 97-99%+ accuracy with 1-10 micro-second time overhead per anomaly check.
Automatic expression recognition of face image sequence based on key-frame generation and differential emotion feature

Using two key-frames with the neutral expression and maximum expression intensity respectively in face image sequence to alleviate inter-subject variations for Facial Expression Recognition (FER) has become the research focus of computer vision. To determine the two key-frames, an automatic Generation Difference Convolutional Neural Network (GDCNN) framework was presented to reduce the influences of high inter-subject variations caused by individual differences. Firstly, for any given input expression, a trained conditional Generative Adversarial Network (cGAN) is utilized to generate the frame with neutral expression while keeping the identity-related information. Secondly, the fine-tuned triplet distance model is used to detect the frame with the maximum expression intensity. Finally, an optimized two-stream CNN model is designed to reduce the influences of inter-subject variations and trained to extract the differential emotion features for FER. Extensive comparisons were performed on the CK+, MMI, and Beihang University databases and the results demonstrated the superiority of the proposed GDCNN framework over the state-of-the-art methods.
Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm

With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slow convergence speed and easy to fall into local extremum, simulated annealing algorithm is used to optimize the BP neural network and high-order simulated annealing neural network algorithm is constructed. After screening the potential target indicators using the random forest algorithm, based on medical big data, the experiment uses high-order simulated annealing neural network algorithm to establish the obesity monitoring model to realize obesity monitoring and prevention. The results show that the training times of the SA-BP neural network are 1480 times lower than those of the BP neural network, and the mean square error of the SA-BP neural network is 3.43 times lower than that of the BP neural network. The MAE of the SA-BP neural network is 1.81 times lower than that of the BP neural network, and the average output error of the obesity monitoring model is about 2.35 at each temperature. After training, the average accuracy of the obesity monitoring model was 98.7%. The above results show that the obesity monitoring model based on medical big data can effectively complete the monitoring of obesity and has a certain contribution to the diagnosis, treatment, and early warning of obesity.
Text Classification Model Enhanced by Unlabeled Data for LaTeX Formula

Generic language models pretrained on large unspecific domains are currently the foundation of NLP. Labeled data are limited in most model training due to the cost of manual annotation, especially in domains including massive Proper Nouns such as mathematics and biology, where it affects the accuracy and robustness of model prediction. However, directly applying a generic language model on a specific domain does not work well. This paper introduces a BERT-based text classification model enhanced by unlabeled data (UL-BERT) in the LaTeX formula domain. A two-stage Pretraining model based on BERT(TP-BERT) is pretrained by unlabeled data in the LaTeX formula domain. A double-prediction pseudo-labeling (DPP) method is introduced to obtain high confidence pseudo-labels for unlabeled data by self-training. Moreover, a multi-rounds teacher–student model training approach is proposed for UL-BERT model training with few labeled data and more unlabeled data with pseudo-labels. Experiments on the classification of the LaTex formula domain show that the classification accuracies have been significantly improved by UL-BERT where the F1 score has been mostly enhanced by 2.76%, and lower resources are needed in model training. It is concluded that our method may be applicable to other specific domains with enormous unlabeled data and limited labelled data.
Shock filter coupled with a high-order PDE for additive noise removal and image quality enhancement

We are interested in repairing blurry images degraded by additive noise. We present a new filter for image enhancement based on the combination of a shock filter with a fourth order diffusion equation. The algorithm that we propose eliminates not only the blurry but also the noise. The control of the diffusion weight and that of the enhancement of the model is done by a parameter. After experimentation on some test images and on some real images of degraded old documents, the results obtained are satisfactory compared to certain models in the literature.
Visible/near infrared reflectance (VNIR) spectral features of ion-exchangeable Rare earth elements hosted by clay minerals: Potential use for exploration of regolith-hosted REE deposits

Regolith-hosted ion-exchangeable rare earth element (REE) mineralization in South China dominate the global source of heavy REEs. The lack of studies on spectral features of ion-exchangeable REEs limits the application of visible/near infrared reflectance (VNIR, 350–2500 nm) spectroscopy for rapid identification of regolith-hosted REE deposits in prospective areas. This paper presents a systematic study on VNIR spectral features of ion-exchangeable REEs hosted by a series of synthetic REE-adsorbed clay minerals and natural regolith samples. The results show that ion-exchangeable Nd3+, Dy3+, Ho3+, Er3+ and Tm3+ display diagnostic absorption features at the bands of 730–870, 805, 641, 652, and 684 nm. Specifically, the Nd3+ related absorption bands of regolith samples at ~800 nm show a shoulder at ~802 nm that are distinguishable from unweathered primary REE(−bearing) minerals. The maxima of second derivative curves derived from the Nd3+ absorption at ~802 nm, M802_2nd, can be used to quantify variations of ion-exchangeable REEs in regoliths. Nevertheless, the absorption features of REE3+ ions could be subdued due to an overlapping problem caused by the broad absorption bands of Fe-oxides in the range of 700–900 nm. The obtained VNIR spectra of ion-exchangeable REEs provide critical supplement to spectral library for REE-bearing minerals. This study demonstrates that field-based VNIR spectroscopy can be deployed as a powerful tool for regional and deposit-scale exploration of regolith-hosted ion-exchangeable REE resources.
Countering Concurrent Login Attacks in “Just Tap” Push-based Authentication: A Redesign and Usability Evaluations

In this paper, we highlight a fundamental vulnerability associated with the widely adopted “Just Tap” push-based authentication in the face of a concurrency attack, and propose the method REPLICATE, a redesign to counter this vulnerability. In the concurrency attack, the attacker launches the login session at the same time the user initiates a session, and the user may be fooled, with high likelihood, into accepting the push notification which corresponds to the attacker's session, thinking it is their own. The attack stems from the fact that the login notification is not explicitly mapped to the login session running on the browser in the Just Tap approach. REPLICATE attempts to address this fundamental flaw by having the user approve the login attempt by replicating the information presented on the browser session over to the login notification, such as by moving a key in a particular direction, choosing a particular shape, etc. We report on the design and a systematic usability study of REPLICATE. Even without being aware of the vulnerability, in general, participants placed multiple variants of REPLICATE in competition to the Just Tap and fairly above PIN-based authentication.