Reviewerly/exHarmony huggingface dataset:
Common Subspace Based Low-Rank and Joint Sparse Representation for Multi-view Face Recognition

Multi-view face data are very common in real-world application, since different viewpoints and various types of sensors attempt to better represent face data. However, these data have large pose variation, which dramatically degrades the performance of multi-view face recognition. To address this, we propose a common subspace based low-rank and joint sparse representation (CSLRJSR) method, which provides a framework encompassing divergence mitigation and feature fusion. In CSLRJSR method, common subspace is learnt to bridge the view, then low-rank and joint sparse representation are exploited to learn and then fuse the discriminative features. Experiments on multi-view face dataset demonstrate that CSLRJSR outperforms the state-of-the-art methods both in two-view and multi-view situations.
S3OD: Single Stage Small Object Detector from Scratch for Remote Sensing Images

Small object detection is an important but challenge computer vision task in both natural scene and remote sensing scene. Due to the large difference of density, low contrast, sparse texture and arbitrary orientations, many advanced algorithms for small object detection in natural scene usually experience a sharp performance drop when directly applied to remote sensing images. In addition, most of state-of-the-art object detectors are fine-tuned from the off-the-shelf networks pretrained on large-scale classification dataset like ImageNet, which can incur learning bias and inconvenience of modification for remote sensing object detection tasks. In order to tackle these problems, a robust Single Stage Small Object Detector (S3OD) is trained from scratch, which can efficiently detect small-dense and small-dispersed objects in remote sensing images. The proposed S3OD adopts the small down-sampling factor to keep accurate location information and maintains high spatial resolution by introducing a new dilated residual block in deeper layers for small objects. Especially, the two-branch dilated feature attention module is proposed to enlarge the valid receptive field and make effective attention feature map for small-dense and small-dispersed object detection. S3OD can be trained from scratch stably while keeping the comparable performance by employing BatchNorm on both the backbone and detection head subnetworks. Experiments conducted on our built Remoting Sensing Small Object (RSSO) dataset shows that, our S3OD achieves the state-of-the-art accuracy for small objects detection and even performs better than several one-stage pretrained method.
MALL tools tried and tested

In 2013, Stockwell and Hubbard published an article on emerging principles in Mobile Assisted Language Learning (MALL). In that article, they mentioned three issues that could perhaps impede learning in the domains of the physical, pedagogical, and pyscho-social. The physical issue they imply, refers to the general size of screens, which at the time were deemed small, or not big enough to have an impact on learning. The pedagogical issue involved with MALL, according to Stockwell and Hubbard (2013), is to ensure that tasks introduced with mobile tools are suited to the affordances of the devices. They questioned the need for mobile devices for language learning and whether tasks that were being introduced were suitable and worthwhile and not simply replicating what could normally be done without them, with pen and paper for example. The psycho-social issue they refer to implies that the primary function of mobile devices is generally regarded as one for “personal and social purposes, as opposed to work or study purposes” (Stockwell &amp; Hubbard, 2013, p. 4). In this short paper, we would like to argue this point by introducing seven tools currently available for free on mobile applications that can be adapted to foreign language learning in several ways.
Vision-based mobile application for supporting the user in the vehicle operation

In this paper the automatic vision-based mobile application for supporting the vehicle user is proposed. The system, using the image taken with the typical smartphone camera, recognizes three types of information located on the vehicle body: text data, displayed dashboard icons, and components in the engine compartment. The modern programming tools and recognition algorithms, including deep neural networks, were used in the implementation regarded to the limited efficiency of mobile devices. Preliminary tests using prepared database indicate on a high recognition performance. Recognition results and instructions are presented to the user in an accessible way using techniques of the augmented reality.
Applying a Traditional Software Development Process to Drive Projects in Higher Education

As institutions of higher education attempt to professionalize their curricula, we observe the emergence of learning experiences that simulate industrial software projects and stronger focus on the skill set desired by the industry. A common practice to address that need is to introduce a capstone unit where students complete large assignments in teams and consolidate previously acquired concepts and skills. Increasingly, such projects are supported by a given software development methodology in order to familiarize students with these processes and facilitate implementation. While the growing prevalence of Agile methods in the industry is reflected in a proliferation of studies of their use in academic contexts, the application of traditional approaches does not capture much attention in research. This experience report discusses the introduction of a Waterfall model to support student teams developing Artificial Conversational Entities for an industrial client. The course structure and accompanying methods, adaptations of the process, lessons learned and recommendations for academics wishing to use a traditional method as part of a university class are presented. Our study shows that such an approach yields promising results for complex and state-of-the-art projects and practices associated with it enforce students' software-engineering skills.
Automatically identifying valid API versions for software development tutorials on the Web

Abstract Online tutorials are a valuable source of community‐created information used by numerous developers to learn new APIs and techniques. Once written, tutorials are rarely actively curated and can become dated over time. Tutorials often reference APIs that change rapidly, and deprecated classes, methods, and fields can render tutorials inapplicable to newer releases of the API. Newer tutorials may not be compatible with older APIs that are still in use. In this paper, we first empirically study the tutorial versioning problem, confirming its presence in popular tutorials on the Web. We subsequently propose a technique, based on similar techniques in the literature, for automatically detecting the applicable API version ranges of tutorials, given access to the official API documentation they reference. The proposed technique identifies each API mention in a tutorial and maps the mention to the corresponding API element in the official documentation. The version of the tutorial is determined by combining the version ranges of all of the constituent API mentions. Our technique's precision varies from 61% to 89% and recall varies from 42% to 84% based on different levels of granularity of API mentions and different problem constraints. We observe API methods are the most challenging to accurately disambiguate due to method overloading. As the API mentions in tutorials are often redundant, and each mention of a specific API element commonly occurs several times in a tutorial, the distance of the predicted version range from the true version range is low: 3.61 on average for the tutorials in our sample.
Liveness detection methods implementation to face identification reinforcement in gaming services

Gaming services, including EduTech services, do not use strong user identification and authentication practically. The most serious barrier in terms of information security is biometrics. The problem of replacing the biometric identifier taken from a smartphone or mobile computer camera is considered in the article. Solution of this problem for gaming services is based on the hybrid method called liveness detection. The article describes the requirements and experimental values for enhanced identification. The architecture and parameters of a convolutional neural network that implements the recognition of an additional feature are also shown. The order and results of testing the enhanced identification module are given. The developed identification module has the an additional feature recognition accuracy up to 100% in situation that user's head is turned up to 30°, and up to 70% when the user's head is turned up to 45°. The FAR value of this system was 1%. The FRR value was 0%. The module does not use additional hardware and can be used on a smartphone or mobile computer without upgrading the latter. Attack with the substitution of a biometric feature supplied from the camera is much more difficult to be realized if suggested solution is implemented.
Cryptography of Java Language Using Basa Walikan Methods in Yogyakarta

Basa Walikan can be used as one of the methods in cryptography because the Basa Walikan character exchange process is not easily understood by all people who can speak Javanese. Basa Walikan used in this research is Basa-Walikan in Yogyakarta, which is sourced from Javanese script characters written in a two-line countdown rule. In Roman characters it becomes ha, na, ca, ra, ka, da, ta, sa, wa, la, pa, dha, ja, yes, nya, ma, ga, ba, tha, nga. The characters in Javanese script are written in 4 (four) lines. The process of character reversal in Basa Walikan uses the mechanism of the first line exchanged with the third line and the second line is swapped to the fourth line as well as the opposite. In this research software for message safety with Basa Walikan was made. From the White Box and Black Box test results it can be concluded that the encryption process and the description process are in accordance with the design of the application being made. If the word being tested is two consonants m and p that are close together, then the encryption process and the description process is done separately or by character.
A Smart Contract-Based Risk Warning Blockchain Symbiotic System for Cross-border Products

In the current supervision mode of cross-border products, the government supervises insufficiently due to incomplete and untrustworthy risk data, non-autonomous and human intervened risk evaluation models. A smart contract-based risk warning blockchain symbiotic system is proposed to reform the issues of the current system. The system is a new third-party system that provides risk warning data services for the government. A permissioned blockchain ecosystem has been developed to provide open, equal, and credible services for the government and enterprises. A risk warning model is implemented by smart contracts to provide a non-intervention evaluation for cross-border products. The autonomy of the system is realized through smart contracts such as enterprise access audit, risk data acquisition, risk assessment and feedback. The system effectively improves the science and intelligence of supervision, cut down the customs clearance time and sampling proportion, and has been verified in the Administration of Inspection and Quarantine in Shanghai Airport.
3-D Shape measurement based on the unequal-period combination approach of phase coding and sinusoidal fringe

The phase coding plus sinusoidal fringe method involves embedding the codeword into the phase domain, and is suitable for different contrast variation surfaces. However, as the number of phase coding fringe cycles increase, the fringe order becomes difficult to judge, ultimately leading to phase unwrapping errors. To resolve this issue, an unequal-period combination approach of phase coding and sinusoidal fringe was proposed. The main idea of this method was to project N cycles phase coding fringe, 2N (3N, 4N …) cycles sinusoidal fringe, and the phase coding fringe was ahead of the sinusoidal fringe. This method alleviated the difficulty in judging the fringe order and, consequently, improved measurement accuracy. Experiment results validated the efficiency and accuracy of this novel method.
Quality Estimation with Transformer and RNN Architectures

The goal of China Conference on Machine Translation (CCMT 2019) Shared Task on Quality Estimation (QE) is to investigate automatic methods for estimating the quality of $$ {\text{Chinese}}\!\leftrightarrow\!{\text{English}} $$ machine translation results without reference translations. This paper presents the submissions of our team for the sentence-level Quality Estimation shared task of CCMT19. Considering the good performance of neural models in previous shared tasks of WMT, our submissions also include two neural-based models: one is Bi-Transformer which proposes the model as a feature extractor with a bidirectional transformer and then processes the semantic representations of source and the translation output with a Bi-LSTM predictive model for automatic quality estimation, and the other BiRNN architecture uses only two bi-directional RNNs (bi-RNN) with Gated Recurrent Units (GRUs) as encoders, and learns representation of the source and translation sentence pairs to predict the quality of translation outputs.
A Hybrid Approach on Single Image Dehazing using Adaptive Gamma Correction

Abstract Since the last eruption of Mt. Kelud, a surveillance has been conducted using camera to observe the crater lake and its surroundings. The key point of this observation is based on the captured haze-free images. However, these images suffer from hazy condition because of degassing. Degassing obscures the camera vision so that the observer barely maintain to keep track of lake phenomenon. Furthermore, even many dehazing techniques have been proposed to tackle this such problems, it still leaves some other problems such as oversaturation, color distortion, and halo effects. In this paper, a hybrid method is proposed. This work incorporates two the state-of-the-art visibility restoration and contrast enhancement methods which are color attenuation prior (CAP) and adaptive gamma correction (AGC). This method is separated into three different modules. They are disparity estimation (DispE) module, transmission map enhancement (TME) module, and hazy image restoration module (ImRec). The DispE module adapts the color attenuation model for its powerful depth estimation for handling outdoor image. Subsequently, the TME module is enhanced using AGC. And the last module is calculated from the modified hazy model. The experimental results are quite impressive. It is able to maintain edge and minimize image distortion qualitatively. And the fog density measured with Fog-Aware Density Estimation (FADE) estimated at 1.448.
An Efficient Parallel Implementation of Impossible-Differential Cryptanalysis for Five-Round AES-128

Impossible-differential cryptanalysis finds the correct round-key of a block cipher by eliminating wrong guesses which do not satisfy some impossible path(s). In this paper, we report our parallel implementation of the impossible-differential cryptanalysis of five-round AES-128, originally proposed by Biham and Keller [4]. In this attack, the time complexity is $$2^{31}$$ and the data complexity is $$2^{29.5}$$. But the primary memory requirement is very high, about 4 TB, making the attack somewhat impractical to implement. The first practical implementation of this attack appears in Kakarla et al. [11], where the primary memory requirement is reduced to 128.5 GB, and the running time achieved is 48 h. Here, we propose an improvement of the attack by exploiting data and task parallelism. We use a nine-node cluster (one master node and eight worker nodes) to implement the attack. In our attack, the time complexity and the data complexity remain the same as [11], but the primary memory requirement is reduced to 96.5 GB per node. This parallelism helps us retrieve the full key in only 6.5 mins.
Sensing as a Service Revisited: A Property Rights Enforcement and Pricing Model for IIoT Data Marketplaces

The Industrial Internet of Things (IIoT) has become a valuable data source for products and services based on advanced data analytics. However, evidence suggests that industries are suffering a significant loss of value creation from insufficient IIoT data sharing. We argue that the limited utilization of the Sensing as a Service business model is caused by the economic and technological characteristics of sensor data, and the corresponding absence of applicable digital rights management models. Therefore, we propose a property rights enforcement and pricing model by utilizing digital watermarking in combination with product versioning to address the IIoT data sharing incentive problem.
Compression of Deep Convolutional Neural Networks Using Effective Channel Pruning

Pruning is a promising technology for convolutional neural networks (CNNs) to address the problems of high computational complexity and high memory requirement. However, there is a principal challenge in channel pruning. Although the least important feature-map is removed each time based on one pruning criterion, these pruning may produce considerable fluctuation in classification performance, which easily results in failing to restore its capacity. We propose an effective channel pruning criterion to reduce redundant parameters, while significantly reducing such fluctuations. This criterion adopts the loss-approximating Taylor expansion based on not the pruned parameters but the parameters in the subsequent convolutional layer, which differentiates our method from existing methods, to evaluate the importance of each channel. To improve the learning effectivity and efficiency, the importance of these channels is ranked using a small proportion of training dataset. Furthermore, after each least important channel is pruned, a small fraction of training dataset is used to fine-tune the pruned network to partially recover its accuracy. Periodically, more proportion of training dataset is used for the intensive recovery in accuracy. The proposed criterion significantly addresses the aforementioned problems and shows outstanding performance compared to other criteria, such as Random, APoZ and Taylor pruning criteria. The experimental results demonstrate the excellent compactness performances of our approach, using several public image classification datasets, on some popular deep network architectures. Our code is available at: https://github.com/QingbeiGuo/Based-Taylor-Pruning.git .
High‐quality tweet generation for online behavior security management based on semantics measurement

Abstract Behavior security management refers to monitoring and guiding the user's opinions in online social networks to reduce their harmful influence to social public security. Pushing designed tweets with specific contains to them is one of the promising ways to solve this problem. In this paper, we developed a new method for high‐quality supervision tweet generation, which not only considers the semantics of the tweets but also includes the supervision requirement of different aspects. Firstly, we collect millions of tweets of six typical events. Following, we construct a sentiment lexicon suitable for online behavior analysis, and we also construct a lexicon for tweet preprocessing and emotional score calculation. Secondly, to include the semantics during sentence similarity calculation, we employ the tweets collected to train the word2vec model and employ the sum of the word vectors in specific sentences to form the sentence vector. Finally, we employ the TextRank to generate the supervision tweet. Experimental results based on data collected showed that the proposed methods outperform other related traditional methods, which can be used for effective social security management.
Encoding High-Order Statistics in VLAD for Scalable Image Retrieval

We revisit the implicit design choices in the popular vector of locally aggregated descriptors (VLAD), which aggregates the residuals of local image descriptors. Since original VLAD ignores high-order statistics the resultant vector is not discriminative enough. We address this issue by exploiting high-order statistics for gaining complementary information. Our contributions are two-fold: First, we present a novel high-order VLAD (HO-VLAD) with increased discriminative power. Next, we propose a light-weight retrieval framework to demonstrate HO-VLAD's effectiveness for scalable image retrieval. Systematic experiments on two challenging public databases (INRIA Holidays, UKBench) exhibit a consistent improvement of performance with limited computational costs.
Visual Question Answering with Dynamic Parameter Prediction using Functional Hashing

The dynamic parameter prediction model (DPPnet) established a mapping between the original space and the compression space by HashedNet for Visual Question Answering (VQA). HashedNet is a single-seeded random hash function, so the collision rate is linear to the compression ratio. In order to relieve the interference of the conflict rate to VQA and improve the stability and accuracy of the model, we introduce a multi-seed random hashing compression named functional hashing to be used as the fusion mechanism in this paper. Multiple pairs of independent random hash function are active and the variable parameters among them are trained by a lightweight reconfigurable neural network, making the compression mechanism more flexible. In addition, we use Bi-GRU to extract the historical and future context information from question sentences. At the same time, we propose gated convolution to selectively learn the feature from images questioned dynamically. Experimental results show that our proposed method achieves higher accuracy compared with existing models.
Data Compression Scheme for Fronthaul Based on Vector Quantization

With the rapid development of the mobile communication technology, the number of long-term evolution (LTE) users and the volume of data is continually increasing, which greatly increases the amount of data transferred by fronthaul, resulting in a huge investment in optical fiber resource. In order to control the consumption of optical fiber resource, lower the cost of operator and avoid congestion in the premise of increasing data volume, it is necessary to compress the data for fronthaul. According to the characteristics of the LTE baseband signal, this paper proposes a data compression scheme on the basis of discrete cosine transform (DCT) and vector quantization. Firstly, the time domain signal is transformed in DCT. And then, the k-means clustering algorithm is used for vector quantization on the transformed signal. Finally, the Huffman coding is applied to further increase the compression ratio (CR) under the promise of an acceptable error. The simulation results show that the scheme proposed has a good performance in both compression ratio and error vector Magnitude (EVM) for the LTE baseband signal base on orthogonal frequency division multiplexing (OFDM).