Reviewerly/exHarmony huggingface dataset:
Cover-Source Mismatch in Deep Spatial Steganalysis

In conventional image steganalysis, cover-source mismatch is a serious problem restricting its utility. In our work, we validate that in deep steganalysis, cover-source mismatch still exists. But unlike in conventional scenarios, sharp accuracy reduction just exists in a part of cover-source mismatch scenarios in deep steganalysis. To explain this phenomenon, we use A-distance to measure the texture complexity between databases. Furthermore, to ease the accuracy reduction caused by the mismatch, we adapt JMMD into deep steganalysis and design a new network (J-Net). Extensive experiments prove A-distance and J-Net works well.
Hierarchical Representation Network for Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals

With the volume of Voice over IP (VoIP) traffic rising shapely, more and more VoIP-based steganography methods have emerged in recent years, which poses a great threat to the security of cyberspace. Low bit-rate speech codecs are widely used in the VoIP application due to its powerful compression capability. Previous steganalysis methods mostly focus on capturing the inter-frame correlation or intra-frame correlation features in code-words ignoring the hierarchical structure which exists in speech frame. In this paper, motivated by the complex multi-scale structure, we design a Hierarchical Representation Network (HRN) to tackle the steganalysis of Quantization Index Modulation (QIM) steganography in low-bit-rate speech signal. In the proposed model, Convolution Neural Network (CNN) is used to model the hierarchical structure in the speech frame, and three levels of attention mechanisms are applied at different convolution blocks, enabling it to attend differentially to more and less important contents in speech frame. Experiments demonstrated that the steganalysis performance of the proposed method outperform the state-of-the-art methods especially in detecting both short and low embeded speech samples. Moreover, our model needs less computation and has higher time efficiency to be applied to real online services.
A Motion Vector-Based Steganographic Algorithm for HEVC with MTB Mapping Strategy

Video is considered an ideal hidden communication cover because of its ample signal space and widespread propagation. In this paper, a novel motion vector-based video steganography algorithm is proposed under the HEVC standard. First, the motion trend of each frame is counted and described by creating a Top-list. According to the motion trend, the Motion Trend Based (MTB) mapping strategy is established between motion vectors and binary bitstream. Finally, the cover is embedded using STC to minimize additional distortion. Experiments are carried out on six original YUV sequences. Performance results demonstrate that our algorithm outperforms previous works in general.
Clone Detection on Large Scala Codebases

Code clones are identical or similar code segments. The wide existence of code clones can increase the cost of maintenance and jeopardise the quality of software. The research community has developed many techniques to detect code clones, however, there is little evidence of how these techniques may perform in industrial use cases. In this paper, we aim to uncover the differences when such techniques are applied in industrial use cases. We conducted large scale experimental research on the performance of two state-of-the-art code clone detection techniques, SourcererCC and AutoenCODE, on both open source projects and an industrial project written in the Scala language. Our results reveal that both algorithms perform differently on the industrial project, with the largest drop in precision being 30.7%, and the largest increase in recall being 32.4%. By manually labelling samples of the industrial project by its developers, we discovered that there are substantially less Type-3 clones in the aforementioned project than that in the open source projects.
Weighted Adaptive Huffman Coding

Huffman coding is known to be optimal in case the alphabet is known in advance, the set of codewords is fixed and each codeword consists of an integral number of bits. If one of these conditions is violated, optimality is not guaranteed. In the dynamic variant of Huffman coding the encoder and decoder maintain identical copies of the model; at each position, the model consists of the frequencies of the elements processed so far. After each processed element σ, the model is updated by incrementing the frequency of σ by 1, while the other frequencies remain the same. An enhanced dynamic Huffman coding named forward looking coding [2] starts with the full frequencies, similar to the static variant, and then decreases them progressively. For this method, after each processed element σ, the model is altered by decrementing the frequency of σ by 1, while the other frequencies remain the same. Forward looking Huffman coding has been shown to be always better by at least m-1 bits than static Huffman coding. A hybrid method, exploiting both the classical backward and the new forward approaches is proposed in FKS, and has been shown to be always at least as good as the forward looking Huffman coding. If the model is learned adaptively, as in the traditional backward looking codings, no description of the model is needed, since the model is updated by the encoder and the decoder in synchronization. However, in the other mentioned versions, the details of the chosen model on which the method relies, are needed for the decoding and should be adjoined to the compressed file, for example, as an header. The contribution of this work is as follows: we first define a new generic coding method which we call weighted coding, encompassing all mentioned variants (static, forward and backward) as special cases. Second, a new special case called positional is suggested, and shown to be always at least as good as the forward looking coding. Third, we present empirical results that show practical improvements of the proposed method, even when the encoded file includes the model description. It is important to stress that all the methods can in fact be applied to every adaptive coding technique, in particular to arithmetic coding or PPM.
Neural Network Hyperparameter Tuning based on Improved Genetic Algorithm

In this paper, based on the structural characteristics of neural networks, a series of improvements have been made to traditional genetic algorithms. The algorithm is used to optimize a series of hyper-parameters in the fully connected neural network, and to find the near-global optimal combination of hyper-parameters. In the case of using MNIST data set and 20 rounds of model training, the algorithm is used to optimize the fully connected neural network. Experiments show that when the population evolves to 30 generations, the accuracy of the model can reach 98.81%, which is higher than 98.4% of the official sample model. This shows that the algorithm has played a certain role in the super-parameter optimization of the neural network.
Black-Box Use of One-Way Functions is Useless for Optimal Fair Coin-Tossing

A two-party fair coin-tossing protocol guarantees output delivery to the honest party even when the other party aborts during the protocol execution. Cleve (STOC–1986) demonstrated that a computationally bounded fail-stop adversary could alter the output distribution of the honest party by (roughly) 1/r (in the statistical distance) in an r-message coin-tossing protocol. An optimal fair coin-tossing protocol ensures that no adversary can alter the output distribution beyond 1/r. In a seminal result, Moran, Naor, and Segev (TCC–2009) constructed the first optimal fair coin-tossing protocol using (unfair) oblivious transfer protocols. Whether the existence of oblivious transfer protocols is a necessary hardness of computation assumption for optimal fair coin-tossing remains among the most fundamental open problems in theoretical cryptography. The results of Impagliazzo and Luby (FOCS–1989) and Cleve and Impagliazzo (1993) prove that optimal fair coin-tossing implies the necessity of one-way functions’ existence; a significantly weaker hardness of computation assumption compared to the existence of secure oblivious transfer protocols. However, the sufficiency of the existence of one-way functions is not known. Towards this research endeavor, our work proves a black-box separation of optimal fair coin-tossing from the existence of one-way functions. That is, the black-box use of one-way functions cannot enable optimal fair coin-tossing. Following the standard Impagliazzo and Rudich (STOC–1989) approach of proving black-box separations, our work considers any r-message fair coin-tossing protocol in the random oracle model where the parties have unbounded computational power. We demonstrate a fail-stop attack strategy for one of the parties to alter the honest party’s output distribution by $$1/\sqrt{r}$$ by making polynomially-many additional queries to the random oracle. As a consequence, our result proves that the r-message coin-tossing protocol of Blum (COMPCON–1982) and Cleve (STOC–1986), which uses one-way functions in a black-box manner, is the best possible protocol because an adversary cannot change the honest party’s output distribution by more than $$1/\sqrt{r}$$ . Several previous works, for example, Dachman–Soled, Lindell, Mahmoody, and Malkin (TCC–2011), Haitner, Omri, and Zarosim (TCC–2013), and Dachman–Soled, Mahmoody, and Malkin (TCC–2014), made partial progress on proving this black-box separation assuming some restrictions on the coin-tossing protocol. Our work diverges significantly from these previous approaches to prove this black-box separation in its full generality. The starting point is the recently introduced potential-based inductive proof techniques for demonstrating large gaps in martingales in the information-theoretic plain model. Our technical contribution lies in identifying a global invariant of communication protocols in the random oracle model that enables the extension of this technique to the random oracle model.
Purifying real images with an attention-guided style transfer network for gaze estimation

Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. Image synthesis has been widely accepted as a cost effective way to learn models because it provides training sets that are large, diverse and accurately labeled. However, the realism of the synthetic image is not enough, this affects generalization on naturalistic test image. In an attempt to address this issue, previous methods learn a model to improve the realism of synthetic image. Different from previous methods, we take the first step towards purifying the real image to weaken the influence of light and convert the distribution of an outdoor naturalistic image through a real-time style transfer task to that of indoor synthetic image. In this paper, we first introduce the segmentation masks to construct Red, Green, and Blue-mask (RGB-mask) pairs as inputs, then we design an attention-guided style transfer network to learn style features separately from the attention and background regions, learn content features from full and attention regions. Moreover, we propose a novel region-level task-guided loss to restrain the features learnt from style and content. Experiments were performed using a mixed research (qualitative and quantitative) method to demonstrate the possibility of purifying real images in complex directions. We evaluate the proposed method on three public datasets, including Labeled pupils in the wild (LPW), Common Objects in COntext (COCO) and MPIIGaze. Extensive experimental results show that the proposed method is effective and achieves the state-of-the-art results.
UNCC Biomedical Semantic Question Answering Systems. BioASQ: Task-7B, Phase-B

In this paper, we detail our submission to the 7th year BioASQ competition. We present our approach for Task-7b, Phase B, Exact Answering Task. These Question Answering (QA) tasks include Factoid, Yes/No, List Type Question answering. Our system is based on a contextual word embedding model. We have used a Bidirectional Encoder Representations from Transformers (BERT) based system, fined tuned for biomedical question answering task using BioBERT. In the third test batch set, our system achieved the highest ‘MRR’ score for Factoid Question Answering task. Also, for List type question answering task our system achieved the highest recall score in the fourth test batch set. Along with our detailed approach, we present the results for our submissions, and also highlight identified downsides for our current approach and ways to improve them in our future experiments.
Impacts of Positive and Negative Comments of Social Media Users to Cryptocurrency

Blockchain implementation brought several benefits to many areas. One of the usages of blockchain is in digital currencies. Digital currency (cryptocurrency) is a new era for the global financial system. Cryptocurrencies draw significant attention from researchers because of their advantages. Although there are several risks (e.g., speculation, 51% attack) related to cryptocurrency, billions of dollars are invested in them, because of their transparency, traceability, low transaction cost, and highly profitable potential. In December 2017, the most famous cryptocurrency, Bitcoin, has reached almost $20,000.00 per coin. Such short-term, high gain potential attracts many new small investors. However, speculative movements raise many questions related to the safety and privacy of investors, just to name a few. To understand public opinions about cryptocurrency and speculative movements to protect small investors financial interests, sentiment analysis can be done by using social media activities of individuals who are interested or investing in cryptocurrencies. It is also one of the essential steps in the analysis to understand the profiles of the users. Therefore, in this paper, we determine the attitudes of social network users by analyzing the positivity and negativity of the comments about six cryptocurrencies. Results show that the positivity is higher than negativity, and there exist relations between price changes and attitudes. However, relations vary according to currency types. The results and analysis, which are provided in this paper, help new investors and developers to obtain opinions of social network users who are interested or investing in cryptocurrency.
Deep Learning Algorithms for Vehicle Detection on UAV Platforms: First Investigations on the Effects of Synthetic Training

Vehicle detection on aerial imagery taken with UAVs (unmanned aerial vehicles) plays an important role in many fields of application, such as traffic monitoring, surveillance or defense and rescue missions. Deep learning based object detectors are often used to overcome the resulting detection challenges. The generation of training data under different conditions and with the necessary variance is difficult and costly in real life. Therefore, virtual simulation environments are meanwhile often applied for this purpose. Our current research interests focus on the difference in performance, also called reality gap, of trainable vehicle detectors between both domains and the influence of differently designed training data. A general method for automatic image annotation with the required bounding boxes is described. In the first part of the investigations the training behavior of YOLOv3 on the natural UAVDT data set is analyzed and examined to what extent algorithms trained with natural images can be evaluated in the simulation. Finally, it is shown which performance can be achieved by exclusively synthetic training and how the performance can be improved by synthetic extension of the natural training set.
Pre-trained language model augmented adversarial training network for Chinese clinical event detection

&lt;abstract&gt; &lt;p&gt;Clinical event detection (CED) is a hot topic and essential task in medical artificial intelligence, which has attracted the attention from academia and industry over the recent years. However, most studies focus on English clinical narratives. Owing to the limitation of annotated Chinese medical corpus, there is a lack of relevant research about Chinese clinical narratives. The existing methods ignore the importance of contextual information in semantic understanding. Therefore, it is urgent to research multilingual clinical event detection. In this paper, we present a novel encoder-decoder structure based on pre-trained language model for Chinese CED task, which integrates contextual representations into Chinese character embeddings to assist model in semantic understanding. Compared with existing methods, our proposed strategy can help model harvest a language inferential skill. Besides, we introduce the punitive weight to adjust the proportion of loss on each category for coping with class imbalance problem. To evaluate the effectiveness of our proposed model, we conduct a range of experiments on test set of our manually annotated corpus. We compare overall performance of our proposed model with baseline models on our manually annotated corpus. Experimental results demonstrate that our proposed model achieves the best precision of 83.73%, recall of 86.56% and F1-score of 85.12%. Moreover, we also evaluate the performance of our proposed model with baseline models on minority category samples. We discover that our proposed model obtains a significant increase on minority category samples.&lt;/p&gt; &lt;/abstract&gt;
Cross-level reinforced attention network for person re-identification

Attention mechanism is a simple and effective method to enhance discriminative performance of person re-identification (Re-ID). Most of previous attention-based works have difficulty in eliminating the negative effects of meaningless information. In this paper, a universal module, named Cross-level Reinforced Attention (CLRA), is proposed to alleviate this issue. Firstly, we fuse features of different semantic levels using adaptive weights. The fused features, containing richer spatial and semantic information, can better guide the generation of subsequent attention module. Then, we combine hard and soft attention to improve the ability to extract important information in spatial and channel domains. Through the CLRA, the network can aggregate and propagate more discriminative semantic information. Finally, we integrate the CLRA with Harmonious Attention CNN (HA-CNN) and form a novel Cross-level Reinforced Attention CNN (CLRA-CNN) to optimize person Re-ID. Experiment results on several public benchmarks show that the proposed method achieves state-of-the-art performance.
Design of Blockchain-Based Electronic Election System Using Hyperledger: Case of Indonesia

Indonesia has held simultaneous general elections to elect the President/Vice President and legislative members on April 17, 2019. However, there were at least 4 (four) important problems arised in this election i.e. the problem of logistics distribution, the duration of the ballot counting that is too long, the inconsistent regulation of vote counting, and the error in votes recapitulation. Blockchain technology can be a solution to deal with those problems. In this paper, we propose a design of blockchain-based electronic voting system using Hyperledger for Indonesia case. Our design was analyzed in 3 (three) stages of analysis i.e. blockchain necessity, problem solution, and secure election requirements. Based on the results of the analysis, our proposed design can optimally apply the blockchain to solve the problems of Indonesia general voting system and can meet the secure election requirements to increase the trust of all the involved participants. The proposed design is able to secure votes in blockchain since the voting at the voting booth until the declaration of the elected candidate.
Hybrid taint analysis for Java EE

We present a new approach to protect Java EE web applications against injection attacks, which can handle large commercial systems. We first describe a novel approach to taint analysis for Java EE, which can be characterized by "strings only", "taint ranges", and "no bytecode instrumentation". We then explain how to combine this method with static analysis, based on the JOANA IFC framework. The resulting hybrid analysis will boost scalability and precision, while guaranteeing protection against XSS. The approach has been implemented in the Juturna tool; application examples and measurements are discussed.
Long-Range Dependencies and High-Order Spatial Pooling for Deep Model-Based Full-Reference Image Quality Assessment

Deep Learning based image quality assessment (IQA) has been shown to greatly improve the quality score prediction accuracy of images with single distortion. However, because these models lack generalizability and the accuracy of multidistortion-based image data is relatively low, designing reliable IQA systems is still an open issue. In this paper, we propose to introduce long-range dependencies between local artifacts and high-order spatial pooling into a convolutional neural network (CNN) model to improve the performance and generalizability of the full-reference IQA (FR-IQA). This long-range dependencies model is based on the hypothesis that local apparent artifacts can affect the overall image quality. The proposed network architecture adopts a non-local means algorithm to establish connections between all positions in the deep feature space and uses the Minkowski function to improve the non-linearity of the spatial pooling. Based on this architecture, a robust FR-IQA system has been constructed and evaluated on three well-known single-distortion-based IQA databases (LIVE, CSIQ, and TID2013) and a multidistortion-based IQA database (MDID). Experimental results demonstrate that, compared with the latest FR-IQA systems, the proposed long-range dependencies-boosted CNN-based FR-IQA system can achieve state-of-the-art performance. A comprehensive cross-database evaluation also shows that the proposed system is sufficiently generalized between different databases and multidistortion-based image data is more useful for training robust image quality metrics.
A Preliminary Study on Open-Source Memory Vulnerability Detectors

We present preliminary results of a study on memory vulnerability detectors based on (static and/or dynamic) program analysis. Against a public suite of 520 C/C++ programs as benchmarks which cover 14 different vulnerability categories, we measured the performance of five state-of-the-art detectors in terms of effectiveness and efficiency. Our study revealed that with respect to the particular set of benchmarks we chose: (1) the effectiveness of these studied detectors varied widely: 66.7% to 100% precision, 0% to 100% recall, and 0% to 100% F1 per category, indicating most of the techniques worked extremely well on certain kinds of vulnerabilities yet quite poorly on others, (2) these detectors were generally quite efficient: despite a few outliers, the average (per benchmark) time costs were around one second, (3) except for between the most and least accurate detectors, other pairs of detectors did not have statistically significant and large differences in accuracy in our pair-wise statistical testing. We also share insights into the failures and successes of these detectors obtained from our case studies.