Reviewerly/exHarmony huggingface dataset:
Web2Vec: Phishing Webpage Detection Method Based on Multidimensional Features Driven by Deep Learning

Phishing is a kind of online attack that attempts to defraud sensitive information of network users. Current phishing webpage detection methods mainly use manual feature collection, and there are problems that feature extraction is complicated and the possible correlation between features cannot be avoided. To solve the problems, a new phishing webpage detection model is proposed, among which the main components are automatic learning representations from multi-aspects features through representation learning and extracting features by hybrid deep learning network. Firstly, the model treats URL, HTML page content, and DOM (Document Object Model) structure of webpages as character sequences respectively, and uses representation learning technology to automatically learn the representation of the webpages; then, sends multiple representations to a hybrid deep learning network composed of a convolutional neural network and a bidirectional long and short-term memory network through different channels to extract local and global features, and use the attention mechanism to strengthen the influence of important features; finally, the output of multiple channels is fused to realize classification prediction. Through four sets of experiments to verify the detection effect of the model, the results show that the overall classification effect of the model is better than the existing classic phishing webpage detection methods, the accuracy reaches 99.05%, and the false positive rate is only 0.25%. It is proved that the strategies of extracting webpage features from all aspects through representation learning and hybrid deep learning network can effectively improve the detection effect of phishing webpages.
Towards Causal Benchmarking of Bias in Face Analysis Algorithms

Measuring algorithmic bias is crucial both to assess algorithmic fairness, and to guide the improvement of algorithms. Current bias measurement methods in computer vision are based on observational datasets, and so conflate algorithmic bias with dataset bias. To address this problem we develop an experimental method for measuring algorithmic bias of face analysis algorithms, which directly manipulates the attributes of interest, e.g., gender and skin tone, in order to reveal causal links between attribute variation and performance change. Our method is based on generating synthetic image grids that differ along specific attributes while leaving other attributes constant. Crucially, we rely on the perception of human observers to control for synthesis inaccuracies when measuring algorithmic bias. We validate our method by comparing it to a traditional observational bias analysis study in gender classification algorithms. The two methods reach different conclusions. While the observational method reports gender and skin color biases, the experimental method reveals biases due to gender, hair length, age, and facial hair. We also show that our synthetic transects allow for more straightforward bias analysis on minority and intersectional groups.
SNAP: An Efficient Sparse Neural Acceleration Processor for Unstructured Sparse Deep Neural Network Inference

Recent developments in deep neural network (DNN) pruning introduces data sparsity to enable deep learning applications to run more efficiently on resourceand energy-constrained hardware platforms. However, these sparse models require specialized hardware structures to exploit the sparsity for storage, latency, and efficiency improvements to the full extent. In this work, we present the sparse neural acceleration processor (SNAP) to exploit unstructured sparsity in DNNs. SNAP uses parallel associative search to discover valid weight (W) and input activation (IA) pairs from compressed, unstructured, sparse W and IA data arrays. The associative search allows SNAP to maintain a 75% average compute utilization. SNAP follows a channel-first dataflow and uses a two-level partial sum (psum) reduction dataflow to eliminate access contention at the output buffer and cut the psum writeback traffic by 22× compared with state-of-the-art DNN accelerator designs. SNAP's psum reduction dataflow can be configured in two modes to support general convolution (CONV) layers, pointwise CONV, and fully connected layers. A prototype SNAP chip is implemented in a 16-nm CMOS technology. The 2.3-mm2 test chip is measured to achieve a peak effectual efficiency of 21.55 TOPS/W (16 b) at 0.55 V and 260 MHz for CONV layers with 10% weight and activation densities. Operating on a pruned ResNet-50 network, the test chip achieves a peak throughput of 90.98 frames/s at 0.80 V and 480 MHz, dissipating 348 mW.
Circumventing Outliers of AutoAugment with Knowledge Distillation

AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization. This paper delves deep into the working mechanism, and reveals that AutoAugment may remove part of discriminative information from the training image and so insisting on the ground-truth label is no longer the best option. To relieve the inaccuracy of supervision, we make use of knowledge distillation that refers to the output of a teacher model to guide network training. Experiments are performed in standard image classification benchmarks, and demonstrate the effectiveness of our approach in suppressing noise of data augmentation and stabilizing training. Upon the cooperation of knowledge distillation and AutoAugment, we claim the new state-of-the-art on ImageNet classification with a top-1 accuracy of $$\mathbf {85.8\%}$$ .
Best-First Beam Search

Decoding for many NLP tasks requires an effective heuristic algorithm for approximating exact search because the problem of searching the full output space is often intractable, or impractical in many settings. The default algorithm for this job is beam search—a pruned version of breadth-first search. Quite surprisingly, beam search often returns better results than exact inference due to beneficial search bias for NLP tasks. In this work, we show that the standard implementation of beam search can be made up to 10x faster in practice. Our method assumes that the scoring function is monotonic in the sequence length, which allows us to safely prune hypotheses that cannot be in the final set of hypotheses early on. We devise effective monotonic approximations to popular nonmonontic scoring functions, including length normalization and mutual information decoding. Lastly, we propose a memory-reduced variant of best-first beam search, which has a similar beneficial search bias in terms of downstream performance, but runs in a fraction of the time.
Bilateral Variational Autoencoder for Collaborative Filtering

Preference data is a form of dyadic data, with measurements associated with pairs of elements arising from two discrete sets of objects. These are users and items, as well as their interactions, e.g., ratings. We are interested in learning representations for both sets of objects, i.e., users and items, to predict unknown pairwise interactions. Motivated by the recent successes of deep latent variable models, we propose Bilateral Variational Autoencoder (BiVAE), which arises from a combination of a generative model of dyadic data with two inference models, user- and item-based, parameterized by neural networks. Interestingly, our model can take the form of a Bayesian variational autoencoder either on the user or item side. As opposed to the vanilla VAE model, BiVAE is "bilateral'', in that users and items are treated similarly, making it more apt for two-way or dyadic data. While theoretically sound, we formally show that, similarly to VAE, our model might suffer from an over-regularized latent space. This issue, known as posterior collapse in the VAE literature, may appear due to assuming an over-simplified prior (isotropic Gaussian) over the latent space. Hence, we further propose a mitigation of this issue by introducing constrained adaptive prior (CAP) for learning user- and item-dependent prior distributions. Empirical results on several real-world datasets show that the proposed model outperforms conventional VAE and other comparative collaborative filtering models in terms of item recommendation. Moreover, the proposed CAP further boosts the performance of BiVAE. An implementation of BiVAE is available on Cornac recommender library.
Drivers of income inequality in Africa: Does institutional quality matter?

Abstract This paper examines the role institutional quality plays amongst the empirical drivers of income inequality in Africa. Using a dynamic two‐step difference GMM with robust standard errors over the period 1990–2017, we find no statistically significant effect of institutions in general, on income inequality. However, we find that institutional quality indicators such as control of corruption and the strict enforcement of the rule of law significantly reduce income inequality. We also find no statistically significant effects of the other institutional quality indicators such as government effectiveness, voice and accountability, regulatory quality and political stability on income inequality in our sample. We suggest that more premium be placed on corruption control and the stringent adherence to the rule of law in ensuring equitable distribution of income in Africa. Furthermore, we re‐echo suggestions that promote institutional development in Africa as institutions in general remain very weak.
VideoSSL: Semi-Supervised Learning for Video Classification

We propose a semi-supervised learning approach for video classification, VideoSSL, using convolutional neural networks (CNN). Like other computer vision tasks, existing supervised video classification methods demand a large amount of labeled data to attain good performance. However, annotation of a large dataset is expensive and time consuming. To minimize the dependence on a large annotated dataset, our proposed semi-supervised method trains from a small number of labeled examples and exploits two regulatory signals from unlabeled data. The first signal is the pseudo-labels of unlabeled examples computed from the confidences of the CNN being trained. The other is the normalized probabilities, as predicted by an image classifier CNN, that captures the information about appearances of the interesting objects in the video. We show that, under the supervision of these guiding signals from unlabeled examples, a video classification CNN can achieve impressive performances utilizing a small fraction of annotated examples on three publicly available datasets: UCF101, HMDB51, and Kinetics.
The Role of the mHealth in the Fight against the Covid-19: Successes and Failures

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Usability Evaluation of Artificial Intelligence-Based Voice Assistants: The Case of Amazon Alexa

Currently, the use of voice-assistants has been on the rise, but a user-centric usability evaluation of these devices is a must for ensuring their success. System Usability Scale (SUS) is one such popular usability instrument in a Graphical User Interface (GUI) scenario. However, there are certain fundamental differences between GUI and voice-based systems, which makes it uncertain regarding the suitability of SUS in a voice scenario. The present work has a twofold objective: to check the suitability of SUS for usability evaluation of voice-assistants and developing a subjective scale in line with SUS that considers the unique aspects of voice-based communication. We call this scale as the Voice Usability Scale (VUS). For fulfilling the objectives, a subjective test is conducted with 62 participants. An Exploratory Factor Analysis suggests that SUS has a number of drawbacks for measuring the voice usability. Moreover, in case of VUS, the most optimal factor structure identifies three main components: usability, affective, and recognizability and visibility. The current findings should provide an initial starting point to form a useful theoretical and practical basis for subjective usability assessment of voice-based systems.
Interaction Relational Network for Mutual Action Recognition

Person-person mutual action recognition (also referred to as interaction recognition) is an important research branch of human activity analysis. Current solutions in the field – mainly dominated by CNNs, GCNs and LSTMs – often consist of complicated architectures and mechanisms to embed the relationships between the two persons on the architecture itself, to ensure the interaction patterns can be properly learned. Our main contribution with this work is by proposing a simpler yet very powerful architecture, named Interaction Relational Network, which utilizes minimal prior knowledge about the structure of the human body. We drive the network to identify by itself how to relate the body parts from the individuals interacting. In order to better represent the interaction, we define two different relationships, leading to specialized architectures and models for each. These multiple relationship models will then be fused into a single and special architecture, in order to leverage both streams of information for further enhancing the relational reasoning capability. Furthermore we define important structured pair-wise operations to extract meaningful extra information from each pair of joints – distance and motion. Ultimately, with the coupling of an LSTM, our IRN is capable of paramount sequential relational reasoning. These important extensions we made to our network can also be valuable to other problems that require sophisticated relational reasoning. Our solution is able to achieve state-of-the-art performance on the traditional interaction recognition datasets SBU and UT, and also on the mutual actions from the large-scale dataset NTU RGB+D. Furthermore, it obtains competitive performance in the NTU RGB+D 120 dataset interactions subset.
A blockchain-based trust management method for Internet of Things

In Internet of things (IoT), one of the main concepts is wireless sensor networks where there are a large of resource-constrained sensor nodes. With the wide application of Internet of things (IoT), these resource-constrained sensor nodes have to suffer from more security threats. One of possible solutions is to establish a trustworthy IoT environment where the interactions are based on the trustworthiness of sensor nodes. More and more scholars focus on studying trust management and trust evaluation in IoT, but there is few research in ensuring that the feedbacks used in the trust evaluating process is trustworthy. Furthermore, a resource-constrained IoT node does not have enough memory to satisfy the usage of trust computation and maintenance. To solve the problem, this paper proposes a blockchain-based trust management mechanism (BBTM), where the trustworthiness of sensor nodes will be evaluated by the mobile edge nodes. BBTM can create smart contracts for trust computation and verify the computation process. We analyze BBTM performance in terms of trust accuracy, convergence, and resiliency against attacks. In addition, we also compare BBTM with other methods. Experimental results demonstrate the effectiveness of the proposed method.
High-Fidelity Face Manipulation With Extreme Poses and Expressions

Face manipulation has shown remarkable advances with the flourish of Generative Adversarial Networks. However, due to the difficulties of controlling structures and textures, it is challenging to model poses and expressions simultaneously, especially for the extreme manipulation at high-resolution. In this article, we propose a novel framework that simplifies face manipulation into two correlated stages: a boundary prediction stage and a disentangled face synthesis stage. The first stage models poses and expressions jointly via boundary images. Specifically, a conditional encoder-decoder network is employed to predict the boundary image of the target face in a semi-supervised way. Pose and expression estimators are introduced to improve the prediction performance. In the second stage, the predicted boundary image and the input face image are encoded into the structure and the texture latent space by two encoder networks, respectively. A proxy network and a feature threshold loss are further imposed to disentangle the latent space. Furthermore, due to the lack of high-resolution face manipulation databases to verify the effectiveness of our method, we collect a new high-quality Multi-View Face (MVF-HQ) database. It contains 120,283 images at 6000 × 4000 resolution from 479 identities with diverse poses, expressions, and illuminations. MVF-HQ is much larger in scale and much higher in resolution than publicly available high-resolution face manipulation databases. We will release MVF-HQ soon to push forward the advance of face manipulation. Qualitative and quantitative experiments on four databases show that our method dramatically improves the synthesis quality.
Neural machine translating from natural language to SPARQL

SPARQL is a highly powerful query language for an ever-growing number of resources and knowledge graphs represented in the Resource Description Framework (RDF) data format. Using it requires a certain familiarity with the entities in the domain to be queried as well as expertise in the language’s syntax and semantics, none of which average human web users can be assumed to possess. To overcome this limitation, automatically translating natural language questions to SPARQL queries has been a vibrant field of research. However, to this date, the vast success of deep learning methods has not yet been fully propagated to this research problem. This paper contributes to filling this gap by evaluating the utilization of eight different Neural Machine Translation (NMT) models for the task of translating from natural language to the structured query language SPARQL. While highlighting the importance of high-quantity and high-quality datasets, the results show a dominance of a Convolutional Neural Network (CNN)-based architecture with a Bilingual Evaluation Understudy (BLEU) score of up to 98 and accuracy of up to 94%.
Electrooculogram signal identification for elderly disabled using Elman network

This study proposes an algorithm for classifying the different eye movements gathered from 20 subjects through Electrooculogram (EOG) by means of neural networks. Reference Power and Plancherel theorem were used to excavate outstanding attributes from raw signals to recognize the eye movement tasks performed by individuals. Elman Recurrent Neural Network (ERNN) was projected for categorization of filtered EOG signals. Classification performance of Reference Power using the ERNN was observed to be better with a range of 90.28 to 91.95% in comparison with Plancherel features. Recognition performance was identified using single trial analysis. The single trial analysis shows that subject S20 and S14 has the maximum recognition and identification rate compared to the other subjects involved in this study for reference power and plancherel theorem, and also this study proves that even though the classification accuracy was the maximum the average recognition rate was minimum for some of the subjects at the same time minimum classification accuracy subject shows the maximum recognition and identification rate for some subjects. Result depicted that average recognition and identification rate was maximum for plancherel features compared to reference features using ERNN and also single trial analysis evident that depends upon the subject performance and involvement the identification and recognition rate was vary for all the subjects.
AI-based mobile context-aware recommender systems from an information management perspective: Progress and directions

In the Artificial Intelligence (AI) field, and particularly within the area of Machine Learning (ML), recommender systems have attracted significant research attention. These systems attempt to alleviate the increasing information overload that users can experience in the current Big Data era, by providing personalized recommendations of items that they may find relevant. Besides, given the importance of mobile computing, these systems have evolved to consider also the dynamic context of the mobile users (location, time, weather conditions, etc.) to offer them more appropriate suggestions and information while on the move. In this paper, we provide an extensive survey of recent advances towards intelligent mobile Context-Aware Recommender Systems (mobile CARS) from an information management perspective, with an emphasis on mobile computing and AI techniques, along with an analysis of existing research gaps and future research directions. We focus on approaches that go beyond just considering the location of the user and exploit also other context information. In this study, we have identified that deep learning approaches are promising artificial intelligence models for mobile CARS. Additionally, in a near future, we expect a higher prominence of push-based recommendation solutions where at least part of the recommendation engine could be executed in the mobile devices, which could share data and tasks in a distributed way.
Box-Point Detector: A Diagnosis Method for Insulator Faults in Power Lines Using Aerial Images and Convolutional Neural Networks

Fault diagnosis of insulators in aerial images is an essential task of power line inspection to maintain the reliability, safety, and sustainability of power transmission. This paper develops a novel method for intelligent diagnosis of electrical insulators based on deep learning, termed Box-Point Detector, which consists of a deep convolutional neural network followed by two parallel branches of convolutional heads. These two branches are utilized to locate the fault region and estimate insulator endpoints, which presents a new representation for insulator faults. Endpoints of the faulty insulator string can provide detailed and correlative information for enhancing the diagnosis capability of component-dependent faults that occur on component bodies. The proposed Box-Point Detector implements all predictions including region and endpoint into one network thus forms an efficient end-to-end structure, and adopts a smaller downsampling ratio to generate high resolution feature-maps in order to preserve more original information for small size faults. Experimental results indicate that Box-Point Detector can accurately diagnose high-voltage insulator faults in real-time under various conditions. Compared with some previous works using Faster R-CNN, SSD, and cascading network, our Box-Point Detector shows more competitively capabilities with high accuracy and robustness.
Learning to detect anomaly events in crowd scenes from synthetic data

Recently, due to its widespread applications in public safety, anomaly detection in crowd scenes has become a hot topic. Some deep-learning-based methods attain significant achievements in this field. Nevertheless, most of them suffer from over-fitting to some extent because of scarce data, which are usually abrupt and low-frequency in the real world. To remedy the above problem, this paper firstly develops a synthetic anomaly event generating system, which could simulate typical specific abnormal events. By utilizing this system, a large synthetic, diverse anomaly event dataset is built, which contains 2,149 video sequences. After getting the dataset, a 3D CNN is designed to detect the abnormal types at the video level. However, we find that there are obvious domain differences (also named as "domain gap/shifts") between synthetic videos and real-world data, which results in performance degradation when applying the model to the real world. Thus, this paper further proposes a cyclic 3D GAN for domain adaption to reduce the domain gap, which translates the synthetic data to the photorealistic video sequences. Then the detection model is trained on the translated data and it can perform well in the real data. Experimental results illustrate that the proposed method outperforms these baselines for the domain adaptation anomaly detection.