Reviewerly/exHarmony huggingface dataset:
Towards dense people detection with deep learning and depth images

This paper describes a novel DNN-based system, named PD3net, that detects multiple people from a single depth image, in real time. The proposed neural network processes a depth image and outputs a likelihood map in image coordinates, where each detection corresponds to a Gaussian-shaped local distribution, centered at each person’s head. This likelihood map encodes both the number of detected people as well as their position in the image, from which the 3D position can be computed. The proposed DNN includes spatially separated convolutions to increase performance, and runs in real-time with low budget GPUs. We use synthetic data for initially training the network, followed by fine tuning with a small amount of real data. This allows adapting the network to different scenarios without needing large and manually labeled image datasets. Due to that, the people detection system presented in this paper has numerous potential applications in different fields, such as capacity control, automatic video-surveillance, people or groups behavior analysis, healthcare or monitoring and assistance of elderly people in ambient assisted living environments. In addition, the use of depth information does not allow recognizing the identity of people in the scene, thus enabling their detection while preserving their privacy. The proposed DNN has been experimentally evaluated and compared with other state-of-the-art approaches, including both classical and DNN-based solutions, under a wide range of experimental conditions. The achieved results allows concluding that the proposed architecture and the training strategy are effective, and the network generalize to work with scenes different from those used during training. We also demonstrate that our proposal outperforms existing methods and can accurately detect people in scenes with significant occlusions.
Don't Speak Too Fast: The Impact of Data Bias on Self-Supervised Speech Models

Self-supervised Speech Models (S3Ms) have been proven successful in many speech downstream tasks, like ASR. However, how pretraining data affects S3Ms’ downstream behavior remains an unexplored issue. In this paper, we study how pre-training data affects S3Ms by pre-training models on biased datasets targeting different factors of speech, including gender, content, and prosody, and evaluate these pre-trained S3Ms on selected downstream tasks in SUPERB Benchmark. Our experiments show that S3Ms have tolerance toward gender bias. Moreover, we find that the content of speech has little impact on the performance of S3Ms across downstream tasks, but S3Ms do show a preference toward a slower speech rate.
A Simple Data Augmentation Method to Improve the Performance of Named Entity Recognition Models in Medical Domain

Easy Data Augmentation is originally developed for text classification tasks. It consists of four basic methods: Synonym Replacement, Random Insertion, Random Deletion, and Random Swap. They yield accuracy improvements on several deep neural network models. In this study we apply these methods to a new domain. We augment Named Entity Recognition datasets from medical domain. Although the augmentation task is much more difficult due to the nature of named entities which consist of word or word groups in the sentences, we show that we can improve the named entity recognition performance.
A High-Order Infective Countermeasure Framework

Either based on voltage, LASER or electro-magnetic disturbances, fault attacks represent an ever growing threat to cryptographic implementations, all the more when multiple-fault scenarios are considered. The state-of-the-art of fault countermeasures is essentially divided into two paradigms: Detection-based and infection-based countermeasures. The simple concept of detection-based countermeasures has been proven efficient to counteract fault attacks and easily extendable to handle high-order fault models. On the other hand, the design of infective countermeasures is much more challenging since most proposals have been broken in a single-fault setting. In this paper we present a new infective countermeasure framework fit for any symmetric cryptosystem. This framework takes into account the fatal flaws of previous infective proposals. Our framework can be instantiated in various flavours depending on the context of use and the available software or hardware components. In addition, we describe how our framework can be set-up to handle high-order fault attacks. As far as we know, this is the first time an infective countermeasure proposes such a feature.
Optimizing storage of RDF archives using bidirectional delta chains

Linked Open Datasets on the Web that are published as RDF can evolve over time. There is a need to be able to store such evolving RDF datasets, and query across their versions. Different storage strategies are available for managing such versioned datasets, each being efficient for specific types of versioned queries. In recent work, a hybrid storage strategy has been introduced that combines these different strategies to lead to more efficient query execution for all versioned query types at the cost of increased ingestion time. While this trade-off is beneficial in the context of Web querying, it suffers from exponential ingestion times in terms of the number of versions, which becomes problematic for RDF datasets with many versions. As such, there is a need for an improved storage strategy that scales better in terms of ingestion time for many versions. We have designed, implemented, and evaluated a change to the hybrid storage strategy where we make use of a bidirectional delta chain instead of the default unidirectional delta chain. In this article, we introduce a concrete architecture for this change, together with accompanying ingestion and querying algorithms. Experimental results from our implementation show that the ingestion time is significantly reduced. As an additional benefit, this change also leads to lower total storage size and even improved query execution performance in some cases. This work shows that modifying the structure of delta chains within the hybrid storage strategy can be highly beneficial for RDF archives. In future work, other modifications to this delta chain structure deserve to be investigated, to further improve the scalability of ingestion and querying of datasets with many versions.
IoT Technologies Offer New Potentials for People with Disabilities

The proliferation of Internet of Things (IoT) devices and accompanying technology has opened up new perspectives for everyone, especially for persons with disabilities and elderly people who can greatly benefit from modern technologies in their everyday activities and in general, improve their quality of life. A variety of IoT applications have been designed and developed across several domains based on the type of disability the user has, including applications for people with physical, vision, and hearing impairments, mental and behavioral disorders, and other complex needs. In this paper we briefly outline each of these disabilities and investigate existing IoT applications and systems which provide a basis for identifying relevant IoT devices, as well as, open challenges that need to be addressed that IoT system can be used by people with disabilities.
HandyTrak: Recognizing the Holding Hand on a Commodity Smartphone from Body Silhouette Images

Understanding which hand a user holds a smartphone with can help improve the mobile interaction experience. For instance, the layout of the user interface (UI) can be adapted to the holding hand. In this paper, we present HandyTrak, an AI-powered software system that recognizes the holding hand on a commodity smartphone using body silhouette images captured by the front-facing camera. The silhouette images are processed and sent to a customized user-dependent deep learning model (CNN) to infer how the user holds the smartphone (left, right, or both hands). We evaluated our system on each participant's smartphone at five possible front camera positions in a user study with ten participants under two hand positions (in the middle and skewed) and three common usage cases (standing, sitting, and resting against a desk). The results showed that HandyTrak was able to continuously recognize the holding hand with an average accuracy of 89.03% (SD: 8.98%) at a 2 Hz sampling rate. We also discuss the challenges and opportunities to deploy HandyTrak on different commodity smartphones and potential applications in real-world scenarios.
Aspect-Sentiment-Multiple-Opinion Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion term (opinion) triplets from sentences and can tell a complete story, i.e., the discussed aspect, the sentiment toward the aspect, and the cause of the sentiment. ASTE is a charming task, however, one triplet extracted by ASTE only includes one opinion of the aspect, but an aspect in a sentence may have multiple corresponding opinions and one opinion only provides part of the reason why the aspect has this sentiment, as a consequence, some triplets extracted by ASTE are hard to understand, and provide erroneous information for downstream tasks. In this paper, we introduce a new task, named Aspect Sentiment Multiple Opinions Triplet Extraction (ASMOTE). ASMOTE aims to extract aspect, sentiment and multiple opinions triplets. Specifically, one triplet extracted by ASMOTE contains all opinions about the aspect and can tell the exact reason that the aspect has the sentiment. We propose an Aspect-Guided Framework (AGF) to address this task. AGF first extracts aspects, then predicts their opinions and sentiments. Moreover, with the help of the proposed Sequence Labeling Attention (SLA), AGF improves the performance of the sentiment classification using the extracted opinions. Experimental results on multiple datasets demonstrate the effectiveness of our approach (Data and code can be found at https://github.com/l294265421/ASMOTE).
Enhanced model for fake image detection (EMFID) using convolutional neural networks with histogram and wavelet based feature extractions

In recent scenario of digital world and image forensics image is forged or unforged to be checked. It is a critical process in the areas where the images are taken as crucial proof in decision making in several cases. Hence, for handling the delinquent activities on image, a novel model, which is capable of detecting or classifying the authenticated and altered images, is required. A new model called Enhanced Model for Fake Image Detection (EMFID) is used for classifying the obtained images as AUTHENTIC and FORGED. Moreover, this work also discusses about the images that are altered with the splicing methods that is performed by cropping and pasting certain image part from one to another original image. The proposed model comprises four phases such as Image Pre-processing, Histogram based Feature Extraction, Discrete Wavelet Transform based Feature Extraction, Image Classification with ConvolutionalNeural Networks (CNN). The test images that are obtained from data set are classified under Authentic Class and Forged class. The results are better with classification accuracy and model efficiency.
Towards Class-Oriented Poisoning Attacks Against Neural Networks

Poisoning attacks on machine learning systems compromise the model performance by deliberately injecting malicious samples in the training dataset to influence the training process. Prior works focus on either availability attacks (i.e., lowering the overall model accuracy) or integrity attacks (i.e., enabling specific instance based backdoor). In this paper, we advance the adversarial objectives of the availability attacks to a per-class basis, which we refer to as class-oriented poisoning attacks. We demonstrate that the proposed attack is capable of forcing the corrupted model to predict in two specific ways: (i) classify unseen new images to a targeted "supplanter" class, and (ii) misclassify images from a "victim" class while maintaining the classification accuracy on other non-victim classes. To maximize the adversarial effect as well as reduce the computational complexity of poisoned data generation, we propose a gradient-based framework that crafts poisoning images with carefully manipulated feature information for each scenario. Using newly defined metrics at the class level, we demonstrate the effectiveness of the proposed class-oriented poisoning attacks on various models (e.g., LeNet-5, Vgg-9, and ResNet-50) over a wide range of datasets (e.g., MNIST, CIFAR-10, and ImageNet-ILSVRC2012) in an end-to-end training setting.
Specific Emitter Identification via Squeeze-and-Excitation Neural Network in Frequency Domain

Specific emitter identification (SEI) is a technology that identifies the emitter individual using the characteristic of their transmit signal. SEI has been widely adopted in military and civilian use. As for SEI task, in time domain, general neural network has a poor feature extraction ability that usually gets a low recognition accuracy in a low signal-to-noise ratio (SNR) environment. So, we propose a SEI method based on squeeze-and-excitation neural network (SeNet) in frequency domain. SeNet uses a squeeze-and-excitation module to achieve channel attention mechanism, so the neural network can emphasize the useful feature and weaken the useless feature in different channels. The experiment result shows that, in frequency domain, SeNet has better emitter identification ability and stronger robustness than the general neural network even in a low SNR environment.
Learning Regularizer for Monocular Depth Estimation with Adversarial Guidance

Monocular Depth Estimation (MDE) is a fundamental task in computer vision and multimedia. With the wide applications of deep Convolutional Neural Networks (CNNs), learning-based methods have achieved superior performance on MDE tasks in recent years. Because loss functions are important to train an accurate CNN with good generalization performance, nearly all previous efforts contribute to proposing powerful loss functions with careful hand-crafted regularizers(e.g., gradient loss and normal loss) added to the basic depth L1-Loss. However, the hand-crafted regularizers require rich domain knowledge, while their performance can still not be guaranteed. In this paper, we learn a new regularizer, approximated by a tiny CNN Regularizrer-Net(RN), and train it in an adversarial way. As demonstrated experimentally, our learned regularizer can notably outperform the current state-of-the-art methods by both quantitative evaluation and qualitative visualization on the benchmark NYU-Depth-v2 dataset, and well generalize to the new ScanNet dataset without any further training. Our code will be released soon.
The demand effect analysis of head books and tail books in book recommendation networks

Abstract Many existing studies related to recommendation systems have made great efforts to increase performance‐oriented evaluation metrics such as accuracy, recall, F1 value, MAE, diversity, and so on. However, these metrics for measuring performance do not provide any explanation of how such recommendation systems contribute to a product provider's sales. Recently, to investigate the factors affecting product sales, researchers have started to use the product recommendation network, which is a graphical presentation of the relationship between products that are purchased simultaneously. In this research, we are going to identify factors that influence the demand for head books (top sales books) and tail books (non‐top sales books) in academic and technology books, because the characteristics of specialized books are different from those of general books. To test the model, online sales data of medicine and science books of G Publishing, the leading medical book publishing and distribution company in Korea, are used. We employ social network analysis and multiple regression analysis to investigate demand effect of the book recommendation network. The former is used to measure centralities of the book recommendation network, while the latter is used to measure the demand effect. Furthermore, we inspect whether there are different attributes between head books and tail books that are related to the book demand in recommendation networks. This study is the first attempt to examine the demand effect of the recommendation network of academic and technology books. The following results are obtained general book sales of an online bookstore follow a long‐tail law, while sales of academic and technology books follow Pareto's law distribution. This study investigated attributes that might be linked to demand effect of head books and tail books in the recommendation network of academic and technology books. How to increase book sales using attributes that are differently associated with the demand of head books and tail books in the recommendation network is also presented.
Modeling and prediction of human negotiation behavior in human-computer negotiation

With increasing e-commerce activities, the human–computer negotiation mechanism for online transactions emerges. This study proposes a segmented linear regression strategy (SLR) model, which can predict the opponent's future offer patterns and dynamically adjust the model parameters according to the opponent's behavior. We use the hope-based (behavior-oriented) and game-based (technical-oriented) negotiation theories as the theory ground to explicate the logic of the agent's negotiation strategy. Upon which we propose a theoretical model of bilateral negotiation as the framework for designing the SLR model. We then propose the testable hypotheses to evaluate the utility of the proposed model against the benchmark results. We build a prototype system and empirically test the hypotheses using computer-to-computer (CtC) and human-to-computer (HtC) negotiation experiments. The empirical results show that the proposed system outperforms the benchmark system in both CtC and HtC negotiations regarding deal price and utility. The proposed model provides an automated negotiation solution for online platforms that can improve the efficiency and effectiveness of their negotiation capability.
Effects of IELTS reading education by using new media learning environments effectively

New media are used by language learners and teachers inside and outside the classroom to improve basic language skills. This study investigated the uses of new media for enhancing reading skills, stimulating student motivation, and promoting learner autonomy within the context of the International English Language Testing System (IELTS). This research project enlisted approximately 200 university sophomore students from the course "IELTS Reading and Writing", which was run for non-English major 2016 ISEC (International Scholarly Exchange Curriculum) programme in 2018 at a university in Northeastern China. Students' perceptions of new media applications were investigated, and a pre-test and post-test were used to examine students' IELTS reading progress. The research findings indicate that new media can be adapted for IELTS reading classrooms and autonomous learning, which offers learning resources connected to IELTS reading knowledge in class and outside class. New media learning environments greatly improved students' motivation. Their autonomous learning abilities and advanced academic reading skills were also enhanced. Additionally, the research participants provided advantageous feedback on new media for IELTS reading learning. Finally, the research results could be helpful references for those who intend to conduct the flipped teaching or learning research related to the reform of the advanced English courses teaching such as IELTS education by the effective use of modern educational technology in the future.
Patchworking: Exploring the code changes induced by vulnerability fixing activities

Identifying and repairing vulnerable code is a critical software maintenance task. Change impact analysis plays an important role during software maintenance, as it helps software maintainers to figure out the potential effects of a change before it is applied. However, while the software engineering community has extensively studied techniques and tools for performing impact analysis of change requests, there are no approaches for estimating the impact when the change involves the resolution of a vulnerability bug. We hypothesize that similar vulnerabilities may present similar strategies for patching. More specifically, our work aims at understanding whether the class of the vulnerability to fix may determine the type of impact on the system to repair. To verify our conjecture, in this paper, we examine 524 security patches applied to vulnerabilities belonging to ten different weakness categories and extracted from 98 different open-source projects written in Java. We obtain empirical evidence that vulnerabilities of the same types are often resolved by applying similar code transformations, and, thus, produce almost the same impact on the codebase. On the one hand, our findings open the way to better management of software maintenance activities when dealing with software vulnerabilities. Indeed, vulnerability class information could be exploited to better predict how much code will be affected by the fixing, how the structural properties of the code (i.e., complexity, coupling, cohesion, size) will change, and the effort required for the fix. On the other hand, our results can be leveraged for improving automated strategies supporting developers when they have to deal with security flaws.
ViObject: A Smartwatch-based Object Recognition System via Vibrations

Today, in order to start an interaction with most digital technology, we must perform some sort of action to indicate our intention, such as shaking a computer's mouse to wake it or pressing a coffee maker's start button for your morning cup of coffee. Our system aims to help remove these currently necessary "trigger actions" and aims to support an array of applications to create borderless and fluid interactions between the technological world and our own. Our system as has the potential for application within the world of accessible technology as well. The system we propose is a method of identifying held objects via a smartwatch's accelerometer and gyroscope sensors. A preview demo video is available at https://youtu.be/1YCTzvjcJ18.
Explicit equations for maximal curves as subcovers of the BM curve

Let r≥3 be an odd integer and Fq2r the finite field with q2r elements. A second generalisation of the Giulietti-Korchmáros maximal curve over Fq6 was presented in 2018 by Beelen and Montanucci, the so-called BM curve. This curve is maximal over Fq2r and isomorphic to the Giulietti-Korchmáros curve for r=3. In this paper, benefiting from suitable representations of the automorphism group of the BM curve, we construct explicit equations for families of maximal algebraic curves as Galois subcovers of the BM curve, we also provide the genus and the Galois group associated to the subcover.
Adversarial Imitation Learning with Trajectorial Augmentation and Correction

Deep Imitation Learning requires a large number of expert demonstrations, which are not always easy to obtain, especially for complex tasks. A way to overcome this shortage of labels is through data augmentation. However, this cannot be easily applied to control tasks due to the sequential nature of the problem. In this work, we introduce a novel augmentation method which preserves the success of the augmented trajectories. To achieve this, we introduce a semi-supervised correction network that aims to correct distorted expert actions. To adequately test the abilities of the correction network, we develop an adversarial data augmented imitation architecture to train an imitation agent using synthetic experts. Additionally, we introduce a metric to measure diversity in trajectory datasets. Experiments show that our data augmentation strategy can improve accuracy and convergence time of adversarial imitation while preserving the diversity between the generated and real trajectories.