Reviewerly/exHarmony huggingface dataset:
L2M-GAN: Learning to Manipulate Latent Space Semantics for Facial Attribute Editing

A deep facial attribute editing model strives to meet two requirements: (1) attribute correctness – the target attribute should correctly appear on the edited face image; (2) irrelevance preservation – any irrelevant information (e.g., identity) should not be changed after editing. Meeting both requirements challenges the state-of-the-art works which resort to either spatial attention or latent space factorization. Specifically, the former assume that each attribute has well-defined local support regions; they are often more effective for editing a local attribute than a global one. The latter factorize the latent space of a fixed pretrained GAN into different attribute-relevant parts, but they cannot be trained end-to-end with the GAN, leading to sub-optimal solutions. To overcome these limitations, we propose a novel latent space factorization model, called L2M-GAN, which is learned end-to-end and effective for editing both local and global attributes. The key novel components are: (1) A latent space vector of the GAN is factorized into an attribute-relevant and irrelevant codes with an orthogonality constraint imposed to ensure disentanglement. (2) An attribute-relevant code transformer is learned to manipulate the attribute value; crucially, the transformed code are subject to the same orthogonality constraint. By forcing both the original attribute-relevant latent code and the edited code to be disentangled from any attribute-irrelevant code, our model strikes the perfect balance between attribute correctness and irrelevance preservation. Extensive experiments on CelebA-HQ show that our L2M-GAN achieves significant improvements over the state-of-the-arts.
Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

We introduce the task of dense captioning in 3D scans from commodity RGB-D sensors. As input, we assume a point cloud of a 3D scene; the expected output is the bounding boxes along with the descriptions for the underlying objects. To address the 3D object detection and description problems, we propose Scan2Cap, an end-to-end trained method, to detect objects in the input scene and describe them in natural language. We use an attention mechanism that generates descriptive tokens while referring to the related components in the local context. To reflect object relations (i.e. relative spatial relations) in the generated captions, we use a message passing graph module to facilitate learning object relation features. Our method can effectively localize and describe 3D objects in scenes from the ScanRefer dataset, outperforming 2D baseline methods by a significant margin (27.61% CiDEr@0.5IoU improvement).
Toward secure distributed data storage with error locating in blockchain enabled edge computing

The technique of Internet of things (IoT) connects the distributed devices via the network that can realize smart applications, such as intelligent transportation, intelligent manufacturing, smart grid and smart home. Edge computing integrates the edge devices together and provides efficient computation and storage services with low latency for users. By combining with blockchain, edge computing can provide more secure data storage and transmission supporting tamper resistance and traceability for IoT. However, the existing data storage schemes are not suitable for processing the gathered data in blockchain enabled edge computing and the efficiency of the data error locating in the previous schemes is very low. In this paper, a secure distributed data storage scheme is proposed, which can be used in blockchain enabled edge computing. In the design of the proposed scheme, the technologies of bilinear pairing and BLS-HLA (BLS-Homomorphic Linear Authenticator) are used, which allows end clients to check the uploaded data storage. In addition, the mechanisms of error locating and data dynamics are supported due to the utilization of CBF (Counting Bloom Filter). Security analysis result indicates that the proposed scheme is correct, data blocks detectable and false positive rate negligible. Performance analysis result shows that the proposed scheme can be executed with low computational cost, which is practical for IOT environment in blockchain enabled edge computing.
The Effect of Strategic Human Resource and Knowledge Management on Sustainable Competitive Advantages at Jordanian Universities: The Mediating Role of Organizational Innovation

The higher education sector faces considerable competition around the world. Accordingly, universities need to make more efforts to increase their competitive advantages. This study aimed to empirically investigate the effect of organizational innovation (OI), knowledge management (KM), and strategic human resource management (SHRM), with a dependent variable of sustainable competitive advantages (SCAs), at Jordanian Universities. For this aim, a specially designed questionnaire has been distributed to study a convenience sample of 400 academic and administrative staff at Jordanian private and public universities, to obtain the required quantitative data. The study’s hypotheses were verified by Baron and Kenny’s mediation regression approach using the software Statistical Package for the Social Sciences (SPSS). The results of the study demonstrate that there is a significant positive relationship between the following pairs of variables: KM and SCA; SHRM and SCA; SHRM and OI; KM and OI; and OI and SCA, whereas OI was found to have a partial and indirect significant mediation impact on the direct relationship between KM and SHRM and universities (organizations) gaining SCAs. Finally, it was concluded that more attention needs to be paid to the OI aspect in organizations and to integrate it with KM and SHRM in a way that promotes SCAs. In addition, we propose that similar studies should be conducted in industries other than education or the education sector in different countries in a way that obtains generalized and representative results.
Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning

To exploit the wealth of data generated and located at distributed entities such as mobile phones, a revolutionary decentralized machine learning setting, known as federated learning, enables multiple clients to collaboratively learn a machine learning model while keeping all their data on-device. However, the scale and decentralization of federated learning present new challenges. Communication between the clients and the server is considered a main bottleneck in the convergence time of federated learning because of a very large number of model's weights that need to be exchanged in each training round. In this paper, we propose and study Adaptive Federated Dropout (AFD), a novel technique to reduce the communication costs associated with federated learning. It optimizes both server-client communications and computation costs by allowing clients to train locally on a selected subset of the global model. We empirically show that this strategy, combined with existing compression methods, collectively provides up to 57× reduction in convergence time. It also outperforms the state-of-the-art solutions for communication efficiency. Furthermore, it improves model generalization by up to 1.7%.
A comprehensive survey on DNS tunnel detection

Domain Name System (DNS) tunnels, established between the controlled host and master server disguised as the authoritative domain name server, can be used as a secret data communication channel for malicious activities. Owing to the ready evasion of the DNS traffic to bypass the network security mechanism, DNS tunnelling can cause severe damage. Thus, an in-depth and comprehensive understanding of the various detection technologies is of considerable importance when facing this type of threat. However, most of the existing reviews focus on a single aspect of the DNS tunnel detection technologies, such as methods based on machine learning, traffic, and payload analysis. In addition, few studies have conducted comprehensive investigation that includes a sequentially integrated range of literature in this researchfield, or have analysed the latest literature on DNS tunnels. This paper reviews these detection technologies from a novel perspective of rule-based and model-based methods with descriptions and analyses of the DNS-based tools and their corresponding features. To the best of our knowledge, this is the first study to comprehensively discuss and analyse DNS tunnel detection in a novel and specific classification fashion from various aspects in detail, covering almost all the detection methods developed from 2006 to 2020. Furthermore, a comparative analysis of detection methods and several suggestions for future research directions are presented.
Boosting people’s ability to detect microtargeted advertising

Abstract Online platforms’ data give advertisers the ability to “microtarget” recipients’ personal vulnerabilities by tailoring different messages for the same thing, such as a product or political candidate. One possible response is to raise awareness for and resilience against such manipulative strategies through psychological inoculation. Two online experiments (total $$N= 828$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>828</mml:mn> </mml:mrow> </mml:math> ) demonstrated that a short, simple intervention prompting participants to reflect on an attribute of their own personality—by completing a short personality questionnaire—boosted their ability to accurately identify ads that were targeted at them by up to 26 percentage points. Accuracy increased even without personalized feedback, but merely providing a description of the targeted personality dimension did not improve accuracy. We argue that such a “boosting approach,” which here aims to improve people’s competence to detect manipulative strategies themselves, should be part of a policy mix aiming to increase platforms’ transparency and user autonomy.
Pre-Trained Convolutional Neural Networks for Breast Cancer Detection Using Ultrasound Images

Volunteer computing based data processing is a new trend in healthcare applications. Researchers are now leveraging volunteer computing power to train deep learning networks consisting of billions of parameters. Breast cancer is the second most common cause of death in women among cancers. The early detection of cancer may diminish the death risk of patients. Since the diagnosis of breast cancer manually takes lengthy time and there is a scarcity of detection systems, development of an automatic diagnosis system is needed for early detection of cancer. Machine learning models are now widely used for cancer detection and prediction research for improving the successive therapy of patients. Considering this need, this study implements pre-trained convolutional neural network based models for detecting breast cancer using ultrasound images. In particular, we tuned the pre-trained models for extracting key features from ultrasound images and included a classifier on the top layer. We measured accuracy of seven popular state-of-the-art pre-trained models using different optimizers and hyper-parameters through fivefold cross validation. Moreover, we consider Grad-CAM and occlusion mapping techniques to examine how well the models extract key features from the ultrasound images to detect cancers. We observe that after fine tuning, DenseNet201 and ResNet50 show 100% accuracy with Adam and RMSprop optimizers. VGG16 shows 100% accuracy using the Stochastic Gradient Descent optimizer. We also develop a custom convolutional neural network model with a smaller number of layers compared to large layers in the pre-trained models. The model also shows 100% accuracy using the Adam optimizer in classifying healthy and breast cancer patients. It is our belief that the model will assist healthcare experts with improved and faster patient screening and pave a way to further breast cancer research.
The Use of Chatbots in Digital Business Transformation: A Systematic Literature Review

The research on chatbots has gained momentum over the past few years. Academics and practitioners investigate how these tools for communication with customers or internal team can be improved in terms of their performance, acceptance, and deployment. Although there is a plethora of recent studies available, not all of them deal with the digital business transformation implications of chatbots. The main aim of the research presented in this paper was to conduct a systematic literature review of high-quality journal research papers in order to summarise the current state of research on chatbots, identify their role in digital business transformation and suggest the areas warranting further attention. 74 papers were included in the research. Topical (focus and applications), methodological (methods used, sample size, sample type, and countries studied) and bibliometric (publication outlet, citations, and Altmetric Attention Score) aspects are evaluated and described. Scholars and practitioners can use the results to identify topics, areas, and applications that are intensely discussed in the literature and require further attention, select a methodology for their research that is well established in the field or is emerging, identify the most influential publications not to be missed in their research or identify publication outlets for publishing their research on chatbots.
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering

We study open-domain question answering with structured, unstructured and semistructured knowledge sources, including text, tables, lists and knowledge bases.Departing from prior work, we propose a unifying approach that homogenizes all sources by reducing them to text and applies the retrieverreader model which has so far been limited to text sources only.Our approach greatly improves the results on knowledge-base QA tasks by 11 points, compared to latest graphbased methods.More importantly, we demonstrate that our unified knowledge (UniK-QA 1 ) model is a simple and yet effective way to combine heterogeneous sources of knowledge, advancing the state-of-the-art results on two popular question answering benchmarks, Nat-uralQuestions and WebQuestions, by 3.5 and 2.6 points, respectively.
Combined RF-Based Drone Detection and Classification

Despite several beneficial applications, unfortunately, drones are also being used for illicit activities such as drug trafficking, firearm smuggling or to impose threats to security-sensitive places like airports and nuclear power plants. The existing drone localization and neutralization technologies work on the assumption that the drone has already been detected and classified. Although we have observed a tremendous advancement in the sensor industry in this decade, there is no robust drone detection and classification method proposed in the literature yet. This paper focuses on radio frequency (RF) based drone detection and classification using the frequency signature of the transmitted signal. We have created a novel drone RF dataset using commercial drones and presented a detailed comparison between a two-stage and combined detection and classification framework. The detection and classification performance of both frameworks are presented for a single-signal and simultaneous multi-signal scenario. With detailed analysis, we show that You Only Look Once (YOLO) framework provides better detection performance compared to the Goodness-of-Fit (GoF) spectrum sensing for a simultaneous multi-signal scenario and good classification performance comparable to Deep Residual Neural Network (DRNN) framework.
A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media

As a constructive mode of information sharing, collaboration and communication, social media platforms offer users with limitless opportunities. The same hypermedia can be transposed into a synthetic and toxic milieu that provides an anonymous, destructive pedestal for online bullying and harassment. Automatic cyberbullying detection on social media using synthetic or real-world datasets is one of a proverbial natural language processing problem. Analyzing a given text requires capturing the existent semantics, syntactic and spatial relationships. Learning representative features automatically using deep learning models efficiently captures the contextual semantics and word order arrangement to build robust and superlative predictive models. This work puts forward a hybrid model, Bi-GRU-Attention-CapsNet (Bi-GAC), that benefits by learning sequential semantic representations and spatial location information using a Bi-GRU with self-attention followed by CapsNet for cyberbullying detection in the textual content of social media. The proposed Bi-GAC model is evaluated for performance using F1-score and ROC-AUC curve as metrics. The results show a superior performance to the existing techniques on the benchmark Formspring.me and MySpace datasets. In comparison to the conventional models, an improvement of nearly 9% and 3% in F-score is observed for MySpace and Formspring.me dataset respectively.
RGBT Tracking by Trident Fusion Network

In recent years, RGBT tracking has become a hot topic in the field of visual tracking, and made great progress. In this paper, we propose a novel Trident Fusion Network (TFNet) to achieve effective fusion of different modalities for robust RGBT tracking. In specific, to deploy the complementarity of features of all convolutional layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in two modalities. Moreover, we design a trident architecture to integrate the fused features and both modality-specific features for robust target representations. There are three main advantages. First, retaining the classification layer of each modality is beneficial to enhance feature learning of single modality, and compared with aggregate branches, single-modality branches pay more attention to the mining of modal specific information. Second, when some modality is noisy or invalid, the modality-specific branches would capture more discriminative features for RGBT tracking. Finally, the integration of aggregation branches and single-modality branches is beneficial to the complementary learning of different modalities. In addition, we also introduce a feature pruning module in each branch to prune the redundant features and avoid network overfitting. Experimental results on four RGBT tracking benchmark datasets suggest that our tracker achieves superior performance against the state-of-the-art RGBT tracking methods.
SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.
GoSPA: An Energy-efficient High-performance Globally Optimized SParse Convolutional Neural Network Accelerator

The co-existence of activation sparsity and model sparsity in convolutional neural network (CNN) models makes sparsity-aware CNN hardware designs very attractive. The existing sparse CNN accelerators utilize intersection operation to search and identify the key positions of the matched entries between two sparse vectors, and hence avoid unnecessary computations. However, these state-of-the-art designs still suffer from three major architecture-level drawbacks, including 1) hardware cost for the intersection operation is high; 2) frequent stalls of computation phase due to strong data dependency between intersection and computation phases; and 3) unnecessary data transfer incurred by the explicit intersection operation.By leveraging the knowledge of the complete sparse 2-D convolution, this paper proposes two key ideas that overcome all of the three drawbacks. First, an implicit on-the-fly intersection is proposed to realize the optimal solution for intersection between one static stream and one dynamic stream, which is the case for sparse neural network inference. Second, by leveraging the global computation structure of 2-D convolution, we propose a specialized computation reordering to ensure that the activation is only transferred if necessary and only once.Based on these two key ideas, we develop GoSPA, an energy-efficient high-performance Globally Optimized SParse CNN Accelerator. GoSPA is implemented with CMOS 28nm technology. Compared with the state-of-the-art sparse CNN architecture, GoSPA achieves average 1.38×, 1.28×, 1.23×, 1.17×, 1.21× and 1.28× speedup on AlexNet, VGG, GoogLeNet, MobileNet, ResNet and ResNeXt workloads, respectively. Also, GoSPA achieves 5.38×, 4.96×, 4.79×, 5.02×, 4.86× and 2.06× energy efficiency improvement on AlexNet, VGG, GoogLeNet, MobileNet, ResNet and ResNeXt, respectively. In more comprehensive comparison including DRAM access, GoSPA also shows significant performance improvement over the existing designs.
Conciseness is better: Recurrent attention LSTM model for document-level sentiment analysis

Long short-term memory (LSTM) or gated recurrent units (GRUs) are usually employed to recurrently learn variable-length sentence representations with long-range dependency in document-level sentiment analysis. However, LSTM and GRUs are biased models in which the words in the tail of sentences are dominant over the words at the beginning. Although some reweighting methods, such as self-attention, are useful for reweighting each word according to its importance, when processing a document with many tokens, they tend to assign equally smaller weights to each word, leading to the keywords being covered by nonsentiment words. In this paper, a recurrent attention LSTM neural network is presented to iteratively locate an attention region covering the key sentiment words. By gradually reducing the range of attention and the number of tokens, the model can leverage the weight of the key sentiment words for final classification. Additionally, a joint loss function is implemented to highlight both keywords and appropriate attention regions. The comparative experiments are conducted on the IMDB, Yelp and Amazon document-level corpora. The results show that the proposed model outperforms several state-of-the-art methods in document-level sentiment classification.