Reviewerly/exHarmony huggingface dataset:
NASB: Neural Architecture Search for Binary Convolutional Neural Networks

Binary Convolutional Neural Networks (CNNs) have significantly reduced the number of arithmetic operations and the size of memory storage needed for CNNs, which makes their deployment on mobile and embedded systems more feasible. However, after binarization, the CNN architecture has to be redesigned and refined significantly due to two reasons: 1. the large accumulation error of binarization in the forward propagation, and 2. the severe gradient mismatch problem of binarization in the backward propagation. Even though substantial effort has been invested in designing architectures for single and multiple binary CNNs, it is still difficult to find an optimized architecture for binary CNNs. In this paper, we propose a strategy, named NASB, which adapts Neural Architecture Search (NAS) to find an optimized architecture for the binarization of CNNs. In the NASB strategy, the operations and their connections define a unique searching space and the training and binarization of the network progress in the three-stage training algorithm. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Due to the flexibility of this automated strategy, the obtained architecture is not only suitable for binarization but also has low overhead, achieving a better trade-off between the accuracy and computational complexity compared to hand-optimized binary CNNs. The implementation of the NASB strategy is evaluated on the ImageNet dataset and demonstrated as a better solution compared to existing quantized CNNs. With insignificant overhead increase, NASB outperforms existing single and multiple binary CNNs by up to 4.0% and 1.0% Top-1 accuracy respectively, bringing them closer to the precision of their full precision counterpart.
Improvement of recommendation algorithm based on Collaborative Deep Learning and its Parallelization on Spark

Collaborative Deep Learning (CDL) utilizes the strong feature learning capability of neural network and the model fitting robustness to solve the problem that the performance of Recommender System drops dramatically when the data is sparse. However, it makes the model training become difficult to maintain when Recommender System faces a large amount of data, and a variety of unpredictable problems will arise. In order to solve the above problems, collaborative deep learning and its parallelization methods were studied in this study, and an improved model CDL-I (CDL with item private node) aiming at item content optimization based on collaborative deep learning was proposed, which improved SDAE on the basis of CDL, added private network nodes; in case of sharing the network parameters of the model, private bias terms were added for each item. As a result, the network may learn the item content parameters in a more targeted manner, thereby enhancing the detection performance of the model on item content in Recommender System. Furthermore, the algorithm was parallelized by splitting the model, and a parallel training CDL-I method was also proposed, which was transplanted to the Spark distributed cluster. The parameters of each part of the model were trained and optimized in parallel to enhance the scale and scalability of data that the model could process. The experiments on multiple real datasets have verified the effectiveness and efficiency of the proposed parallel CDL-I algorithm.
Video Scenes Segmentation Based on Multimodal Genre Prediction

Recent technologies' understanding videos content remain limited due to its complexity and length. However, videos segmentation into small coherent units facilitates indexing and searching task. The subjectivity remains the essential constraint of videos, but the genre (drama, action...) does not present any conflict. In this paper, we present a new approach to video segmentation into scenes based on genre prediction. Initially, the video is divided into shots of equal duration. We used architecture, based on audio-visuals deep features extracted from trained neural networks for genre prediction, and we introduced a transition detection method based on the similarity calculation between shots genre. The originality of this method consists in using the highly level semantic relationship between successive shots for transition detection. We reached good performances on videos of the multi varied genre. We used the RAI dataset and BBC dataset to evaluate our method through a comparison with other state-of-the-art approaches.
Lean UX: A Systematic Literature Review

The software industries often look for ways to remain competitive in terms of cost and time to market. Lean UX is a methodology aiming to achieve this. In this paper, by means of a Systematic Literature Review, authors outline the evolution of Lean UX since its origins, its challenges and benefits, and its definition by means of a systematic literature review. Results showed similarities of the definition of Lean UX, challenges and benefits regarding communication, user testing, team collaboration and the adoption of the concept.
SHOP-VRB: A Visual Reasoning Benchmark for Object Perception

In this paper we present an approach and a benchmark for visual reasoning in robotics applications, in particular small object grasping and manipulation. The approach and benchmark are focused on inferring object properties from visual and text data. It concerns small household objects with their properties, functionality, natural language descriptions as well as question-answer pairs for visual reasoning queries along with their corresponding scene semantic representations. We also present a method for generating synthetic data which allows to extend the benchmark to other objects or scenes and propose an evaluation protocol that is more challenging than in the existing datasets. We propose a reasoning system based on symbolic program execution. A disentangled representation of the visual and textual inputs is obtained and used to execute symbolic programs that represent a 'reasoning process' of the algorithm. We perform a set of experiments on the proposed benchmark and compare to results from the state of the art methods. These results expose the shortcomings of the existing benchmarks that may lead to misleading conclusions on the actual performance of the visual reasoning systems.
<i>AyaTEC</i>

The absence of publicly available reusable test collections for Arabic question answering on the Holy Qur’an has impeded the possibility of fairly comparing the performance of systems in that domain. In this article, we introduce AyaTEC , a reusable test collection for verse-based question answering on the Holy Qur’an, which serves as a common experimental testbed for this task. AyaTEC includes 207 questions (with their corresponding 1,762 answers) covering 11 topic categories of the Holy Qur’an that target the information needs of both curious and skeptical users. To the best of our effort, the answers to the questions (each represented as a sequence of verses) in AyaTEC were exhaustive—that is, all qur’anic verses that directly answered the questions were exhaustively extracted and annotated. To facilitate the use of AyaTEC in evaluating the systems designed for that task, we propose several evaluation measures to support the different types of questions and the nature of verse-based answers while integrating the concept of partial matching of answers in the evaluation.
Inter-rater reliability in Learner Corpus Research

Abstract In Learner Corpus Research (LCR), a common source of errors stems from manual coding and annotation of linguistic features. To estimate the amount of error present in a coded dataset, coefficients of inter-rater reliability are used. However, despite the importance of reliability and internal consistency for validity and, by extension, study quality, interpretability and generalizability, it is surprisingly uncommon for studies in the field of LCR to report on such reliability coefficients. In this Methods Report, we use a recent collaborative research project to illustrate the pertinence of considering inter-rater reliability. In doing so, we hope to initiate methodological discussion on instrument design, piloting and evaluation. We also suggest some ways forward to encourage increased transparency in reporting practices.
Associating Multi-Scale Receptive Fields For Fine-Grained Recognition

Extracting and fusing part features have become the key of fined-grained image recognition. Recently, Non-local (NL) module has shown excellent improvement in image recognition. However, it lacks the mechanism to model the interactions between multi-scale part features, which is vital for fine-grained recognition. In this paper, we propose a novel cross-layer non-local (CNL) module to associate multi-scale receptive fields by two operations. First, CNL computes correlations between features of a query layer and all response layers. Second, all response features are weighted according to the correlations and are added to the query features. Due to the interactions of cross-layer features, our model builds spatial dependencies among multi-level layers and learns more discriminative features. In addition, we can reduce the aggregation cost if we set low-dimensional deep layer as query layer. Experiments are conducted to show our model achieves or surpasses state-of-the-art results on three benchmark datasets of fine-grained classification. Our codes can be found at github.com/FouriYe/CNL-ICIP2020.
OD-SGD

The training of modern deep learning neural network calls for large amounts of computation, which is often provided by GPUs or other specific accelerators. To scale out to achieve faster training speed, two update algorithms are mainly applied in the distributed training process, i.e., the Synchronous SGD algorithm (SSGD) and Asynchronous SGD algorithm (ASGD). SSGD obtains good convergence point while the training speed is slowed down by the synchronous barrier. ASGD has faster training speed but the convergence point is lower when compared to SSGD. To sufficiently utilize the advantages of SSGD and ASGD, we propose a novel technology named One-step Delay SGD (OD-SGD) to combine their strengths in the training process. Therefore, we can achieve similar convergence point and training speed as SSGD and ASGD separately. To the best of our knowledge, we make the first attempt to combine the features of SSGD and ASGD to improve distributed training performance. Each iteration of OD-SGD contains a global update in the parameter server node and local updates in the worker nodes, the local update is introduced to update and compensate the delayed local weights. We evaluate our proposed algorithm on MNIST, CIFAR-10, and ImageNet datasets. Experimental results show that OD-SGD can obtain similar or even slightly better accuracy than SSGD, while its training speed is much faster, which even exceeds the training speed of ASGD.
Minor Privacy Protection Through Real-time Video Processing at the Edge

The collection of a lot of personal information about individuals, including the minor members of a family, by closed-circuit television (CCTV) cameras creates a lot of privacy concerns. Particularly, revealing children's identifications or activities may compromise their well-being. In this paper, we investigate lightweight solutions that are affordable to edge surveillance systems, which is made feasible and accurate to identify minors such that appropriate privacy-preserving measures can be applied accordingly. State of the art deep learning architectures are modified and re-purposed in a cascaded fashion to maximize the accuracy of our model. A pipeline extracts faces from the input frames and classifies each one to be of an adult or a child. Over 20,000 labeled sample points are used for classification. We explore the timing and resources needed for such a model to be used in the Edge-Fog architecture at the edge of the network, where we can achieve near real-time performance on the CPU. Quantitative experimental results show the superiority of our proposed model with an accuracy of 92.1% in classification compared to some other face recognition based child detection approaches.
An Approach For Concept Drift Detection in a Graph Stream Using Discriminative Subgraphs

The emergence of mining complex networks like social media, sensor networks, and the world-wide-web has attracted considerable research interest. In a streaming scenario, the concept to be learned can change over time. However, while there has been some research done for detecting concept drift in traditional data streams, little work has been done on addressing concept drift in data represented as a graph . We propose a novel unsupervised concept-drift detection method on graph streams called Discriminative Subgraph-based Drift Detector (DSDD). The methodology starts by discovering discriminative subgraphs for each graph in the stream. We then compute the entropy of the window based on the distribution of discriminative subgraphs with respect to the graphs and then use the direct density-ratio estimation approach for detecting concept drift in the series of entropy values obtained by moving one step forward in the sliding window. The effectiveness of the proposed method is demonstrated through experiments using artificial and real-world datasets and its performance is evaluated by comparing against related baseline methods. Similarly, the usefulness of the proposed concept drift detection approach is studied by incorporating it in a popular graph stream classification algorithm and studying the impact of drift detection in classification accuracy.
An empirical study on API parameter rules

Developers build programs based on software libraries to reduce coding effort. If a program inappropriately sets an API parameter, the program may exhibit unexpected runtime behaviors. To help developers correctly use library APIs, researchers built tools to mine API parameter rules. However, it is still unknown (1) what types of parameter rules there are, and (2) how these rules distribute inside documents and source files. In this paper, we conducted an empirical study to investigate the above-mentioned questions. To analyze as many parameter rules as possible, we took a hybrid approach that combines automatic localization of constrained parameters with manual inspection. Our automatic approach---PaRu---locates parameters that have constraints either documented in Javadoc (i.e., document rules) or implied by source code (i.e., code rules). Our manual inspection (1) identifies and categorizes rules for the located parameters, and (2) establishes mapping between document and code rules. By applying PaRu to 9 widely used libraries, we located 5,334 parameters with either document or code rules. Interestingly, there are only 187 parameters that have both types of rules, and 79 pairs of these parameter rules are unmatched. Additionally, PaRu extracted 1,688 rule sentences from Javadoc and code. We manually classified these sentences into six categories, two of which are overlooked by prior approaches. We found that 86.2% of parameters have only code rules; 10.3% of parameters have only document rules; and only 3.5% of parameters have both document and code rules. Our research reveals the challenges for automating parameter rule extraction. Based on our findings, we discuss the potentials of prior approaches and present our insights for future tool design.
FANet: Features Adaptation Network for 360$^{\circ }$ Omnidirectional Salient Object Detection

Salient object detection (SOD) in 360° omnidirectional images has become an eye-catching problem because of the popularity of affordable 360° cameras. In this paper, we propose a Features Adaptation Network (FANet) to highlight salient objects in 360° omnidirectional images reliably. To utilize the feature extraction capability of convolutional neural networks and capture global object information, we input the equirectangular 360° images and corresponding cube-map 360° images to the feature extraction network (FENet) simultaneously to obtain multi-level equirectangular and cube-map features. Furthermore, we fuse these two kinds of features at each level of the FENetby a projection features adaptation (PFA) module, for selecting these two kinds of features adaptively. Finally, we combine the preliminary adaptation features at different levels by a multi-level features adaptation (MLFA) module, which weights these different-level features adaptively and produces the final saliency maps. Experiments show our FANet outperforms the state-of-the-art methods on the 360° omnidirectional SOD datasets.
The structure of cybercrime networks: transnational computer fraud in Vietnam

This study examined a sample of transnational computer fraud cases involving bank card fraud and phone scams investigated by the Vietnamese police between 2010 and 2018. This research used social network analysis to evaluate the relationships between computer fraud suspects. The study combined data from criminal profiles and in-depth interviews with police officials to determine the structural characteristics of sample cases. Results showed that transnational computer fraud in Vietnam occurs online and offline, with primary modus operandi including bank card fraud and phone scams. Moreover, the organizational structures of transnational computer fraud in Vietnam differ from those broadly assumed by scholars. Accordingly, this study proposes that the degree of leadership within cybercriminal networks can be used to construct a new typology with four categories: 'swarm networks,' 'distributed networks,' 'single-directed networks,' and 'group-directed networks.' As the first study of this nature conducted in Vietnam, this study contributes to the literature of cybercrime and especially, to the knowledge of transnational computer fraud in Asia and beyond.
SCALE: An Efficient Framework for Secure Dynamic Skyline Query Processing in the Cloud

It is now cost-effective to outsource large dataset and perform query over the cloud. However, in this scenario, there exist serious security and privacy issues that sensitive information contained in the dataset can be leaked. The most effective way to address that is to encrypt the data before outsourcing. Nevertheless, it remains a grand challenge to process queries in ciphertext efficiently. In this work, we shall focus on solving one representative query task, namely dynamic skyline query, in a secure manner over the cloud. However, it is difficult to be performed on encrypted data as its dynamic domination criteria require both subtraction and comparison, which cannot be directly supported by a single encryption scheme efficiently. To this end, we present a novel framework called scale. It works by transforming traditional dynamic skyline domination into pure comparisons. The whole process can be completed in single-round interaction between user and the cloud. We theoretically prove that the outsourced database, query requests, and returned results are all kept secret under our model. Empirical study over a series of datasets demonstrates that our framework improves the efficiency of query processing by nearly three orders of magnitude compared to the state-of-the-art.
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation

Currently, the divergence in distributions of design and operational data, and large computational complexity are limiting factors in the adoption of CNNs in real-world applications. For instance, person re-identification systems typically rely on a distributed set of cameras, where each camera has different capture conditions. This can translate to a considerable shift between source (e.g. lab setting) and target (e.g. operational camera) domains. Given the cost of annotating image data captured for fine-tuning in each target domain, unsupervised domain adaptation (UDA) has become a popular approach to adapt CNNs. Moreover, state-of-the-art deep learning models that provide a high level of accuracy often rely on architectures that are too complex for real-time applications. Although several compression and UDA approaches have recently been proposed to overcome these limitations, they do not allow optimizing a CNN to simultaneously address both. In this paper, we propose an unexplored direction - the joint optimization of CNNs to provide a compressed model that is adapted to perform well for a given target domain. In particular, the proposed approach performs unsupervised knowledge distillation (KD) from a complex teacher model to a compact student model, by leveraging both source and target data. It also improves upon existing UDA techniques by progressively teaching the student about domain-invariant features, instead of directly adapting a compact model on target domain data. Our method is compared against state-of-the-art compression and UDA techniques, using two popular classification datasets for UDA - Office31 and ImageClef-DA. In both datasets, results indicate that our method can achieve the highest level of accuracy while requiring a comparable or lower time complexity.
Weakly Supervised Learning with Side Information for Noisy Labeled Images

In many real-world datasets, like WebVision, the performance of DNN based classifier is often limited by the noisy labeled data. To tackle this problem, some image related side information, such as captions and tags, often reveal underlying relationships across images. In this paper, we present an efficient weakly-supervised learning by using a Side Information Network (SINet), which aims to effectively carry out a large scale classification with severely noisy labels. The proposed SINet consists of a visual prototype module and a noise weighting module. The visual prototype module is designed to generate a compact representation for each category by introducing the side information. The noise weighting module aims to estimate the correctness of each noisy image and produce a confidence score for image ranking during the training procedure. The propsed SINet can largely alleviate the negative impact of noisy image labels, and is beneficial to train a high performance CNN based classifier. Besides, we released a fine-grained product dataset called AliProducts, which contains more than 2.5 million noisy web images crawled from the internet by using queries generated from 50,000 fine-grained semantic classes. Extensive experiments on several popular benchmarks (i.e. Webvision, ImageNet and Clothing-1M) and our proposed AliProducts achieve state-of-the-art performance. The SINet has won the first place in the 5000 category classification task on WebVision Challenge 2019, and outperforms other competitors by a large margin.
Cloud Service Model’s Role in Provider and User Security Investment Incentives

Cloud computing can be viewed as an e‐business in which the cloud provider sells a range of information technology services and operations over the Internet to firms. Security is a major concern for firms that move to the cloud. The security of a cloud is the joint responsibility of the cloud provider and cloud users. However, the extent to which the provider and users can affect cloud security through their efforts depends on the cloud service model. In this study, we develop a game‐theoretical model to study the impact of the cloud service model on provider and user incentives to exert security effort. Our results show that for a given service model, an increase in the user loss from a security breach induces users to exert more security effort. However, if the provider’s service cost is low, the provider profitably free rides on the enhanced user incentives to exert security effort by diminishing his effort. Analogously, the cloud provider is able to profitably free ride on users’ enhanced incentives to exert security effort when the user population is more homogeneous in terms of either cloud valuation or loss from a security breach, depending on the service cost. Our results also have implications for how a cloud provider could possibly target a particular cloud service model to specific user groups based on their characteristics and likelihood of security loss.