Reviewerly/exHarmony huggingface dataset:
CP-ABE Scheme with Decryption Keys of Constant Size Using ECC with Expressive Threshold Access Structure

With the rapid development of cross-organizational application systems that are geographically distributed, the notion of Virtual Organization (VO) is indispensable. The inevitable existence of global information infrastructure in every field has forced virtual organizations to gain importance as a fitting model for making a large-scale organization of distributed nature. Virtual organization mainly deals with the devolution of responsibilities to other organizations and providing goods and services by having mutual cooperation among organizations. An ancient mechanism is essential to have controlled access to shared resources and proper participation policies. However, this controlled access is challenging in the case of VO because of its distributed nature. Thus, a mechanism which can handle complex access policies is needed. The mechanism is also required to be reasonably scalable and efficient. In this paper, we propose a threshold access structure implemented using Elliptic Curve Cryptography, which is much better in terms of efficiency. Our main contribution is that we propose a threshold access structure that uses ECC and provides constant-size secret keys for the Ciphertext Policy Attribute-based Encryption (CP-ABE) scheme. In essence, we suggest a formal procedure to share secret information using encryption, where secret messages are associated with a policy, and only the users who have this specific attribute set that fulfils the specified policy will be apt to successfully decrypt and gain access to the secret message/information.
An Image Recognition Framework for Oral Cancer Cells

Oral squamous cell carcinoma (OSCC) is a common type of cancer of the oral cavity. Despite their great impact on mortality, sufficient screening techniques for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate recognition of OSCCs would lead to an improved curative result and a reduction in recurrence rates after surgical treatment. The introduction of image recognition technology into the doctor's diagnosis process can significantly improve cancer diagnosis, reduce individual differences, and effectively assist doctors in making the correct diagnosis of the disease. The objective of this study was to assess the precision and robustness of a deep learning-based method to automatically identify the extent of cancer on digitized oral images. We present a new method that employs different variants of convolutional neural network (CNN) for detecting cancer in oral cells. Our approach involves training the classifier on different images from the imageNet dataset and then independently validating on different cancer cells. The image is segmented using multiscale morphology methods to prepare for cell feature analysis and extraction. The method of morphological edge detection is used to more accurately extract the target, cell area, perimeter, and other multidimensional features followed by classification through CNN. For all five variants of CNN, namely, VGG16, VGG19, InceptionV3, InceptionResNetV2, and Xception, the train and value losses are less than 6%. Experimental results show that the method can be an effective tool for OSCC diagnosis.
Deep Learning for Video Based Human Activity Recognition: Review and Recent Developments

The enormous applicability of advanced human-computer interaction technology in daily life has facilitated the interest of the researchers toward the development of more intelligent autonomous systems. These human-computer interaction systems can be successful in real-life by addressing the gap among the existing techniques. This research work focuses on one of the prominent human-computer interaction applications of human activity recognition. Human activity recognition (HAR) is the process to detect human gestures, actions, and different types of interactions. The HAR process requires competent knowledge about the day to day human activities and advanced technology to recognize their activities. The conventional pattern recognition techniques are significant to recognize the human activities using the machine learning techniques but only within the controlled environment for the recognition of limited actions. In recent years, deep learning techniques are developed that can learn the deep attributes of the problem application and determine the outcomes with promising performance. The present paper has presented a systematic review of the deep learning models for video-based human activity recognition. The work describes the recent developments in the field for the analysis of different models. The paper also discusses the process of human activity recognition and the eminent datasets available for experimentation. The summarization of the work is illustrated with the future directions in the field.
Exploring Fusion Strategies in Deep Learning Models for Multi-Modal Classification

When effectively used in deep learning models for classification, multi-modal data can provide rich and complementary information and can represent complex situations. An essential step in multi-modal classification is data fusion which aims to combine features from multiple modalities into a single joint representation. This study investigates how fusion mechanisms influence multi-modal data classification. We conduct experiments on four social media datasets and evaluate multi-modal models with several classification criteria. The results show that the quality of data and class distribution significantly influence the performance of the fusion strategies.
WALT: Watch And Learn 2D amodal representation from Time-lapse imagery

Current methods for object detection, segmentation, and tracking fail in the presence of severe occlusions in busy urban environments. Labeled real data of occlusions is scarce (even in large datasets) and synthetic data leaves a domain gap, making it hard to explicitly model and learn occlusions. In this work, we present the best of both the real and synthetic worlds for automatic occlusion supervision using a large readily available source of data: time-lapse imagery from stationary webcams observing street intersections over weeks, months, or even years. We introduce a new dataset, Watch and Learn Time-lapse (WALT), consisting of 12 (4K and 1080p) cameras capturing urban environments over a year. We exploit this real data in a novel way to automatically mine a large set of unoccluded objects and then composite them in the same views to generate occlusions. This longitudinal self-supervision is strong enough for an amodal network to learn object-occluder-occluded layer representations. We show how to speed up the discovery of unoccluded objects and relate the confidence in this discovery to the rate and accuracy of training occluded objects. After watching and automatically learning for several days, this approach shows significant performance improvement in detecting and segmenting occluded people and vehicles, over human-supervised amodal approaches.
Cryptanalysis of Biometric Based Secure User Authentication Protocol for IoT Applications

In the 21st century various Biometric based user authentication schemes for Internet of Things network applications are evolving. As a part, Vinoth et al. proposed an Biometric authenticated key agreement scheme for industrial IoT. As the IoT environments are rapidly connecting with various public networks for the communication and data transfer, there are high chance for security attacks. The schemes used in these kind of IoT networks needs to maintain the security with lightweight operations. Validation of existing schemes are highly important to protect the environment. We cryptanalysis the Vinoth et al.'s scheme and identified a security design flaw. Their scheme is failed to protect the biometric and not achieving the key freshness. As a result, their scheme is vulnerable to Biometric template attack, user impersonation attack and failed to meet the user anonymity security feature. This work proposes a technique to address the flaws identified. The proposed scheme has been implemented and verified using the AVISPA simulator.
Contrastive Learning of Emoji-Based Representations for Resource-Poor Languages

The introduction of emojis (or emoticons) in social media platforms has given the users an increased potential for expression. We propose a novel method called Classification of Emojis using Siamese Network Architecture (CESNA) to learn emoji-based representations of resource-poor languages by jointly training them with resource-rich languages using a siamese network. CESNA model consists of twin Bi-directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive loss function based on a similarity metric. The model learns the representations of resource-poor and resource-rich language in a common emoji space by using a similarity metric based on the emojis present in sentences from both languages. The model, hence, projects sentences with similar emojis closer to each other and the sentences with different emojis farther from one another. Experiments on large-scale Twitter datasets of resource-rich languages - English and Spanish and resource-poor languages - Hindi and Telugu reveal that CESNA outperforms the state-of-the-art emoji prediction approaches based on distributional semantics, semantic rules, lexicon lists and deep neural network representations without shared parameters.
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information

In this paper, we investigate the use of an unsupervised label clustering technique and demonstrate that it enables substantial improvements in visual relationship prediction accuracy on the Person in Context (PIC) dataset. We propose to group object labels with similar patterns of relationship distribution in the dataset into fewer categories. Label clustering not only mitigates both the large classification space and class imbalance issues, but also potentially increases data samples for each clustered category. We further propose to incorporate depth information as an additional feature into the instance segmentation model. The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver. We have rigorously evaluated the proposed techniques and performed various ablation analysis to validate the benefits of them.
Spears Against Shields

Phishing is the act of using deceptive methods to lure users into taking harmful and dangerous actions against themselves and/or the company they work for. Hackers have been using this method to bypass various security systems, steal personal information, make sensitive data public, and all of this while raking millions of dollars. On the attack side, hackers are using phishing kits and employ all possible phishing techniques to build a successful campaign. On the defense side, we find spam/phishing filters and malicious website detection in addition to anti-phishing simulation training. In this study, we assess the current phishing landscape by testing the tools used on both sides of the war. We generated 1,000 phishing emails containing phishing links semi-automatically using natural language generation technology and tested five popular tools with anti-phishing modules. The number of undetected emails ranged from 77 to 927. We also evaluate several anti-phishing training technologies and reveal their shortcomings. Our results suggest that both anti-phishing filters and current training tools have a long way to go and thus improving these defense mechanisms is still essential.
Ensemble Visual Content Based Search and Retrieval for Natural Scene Images

Background: Content Based Image Retrieval (CBIR) is one of the fields for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, colour, shape, and other information. Objective: The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. Methods: In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the precision and recall for the gained results using single content descriptor is not significant. The proposed system architecture uses the hybrid ensemble feature set for image matching. The hybrid parameters include globally and locally defined feature extraction methodologies. Results: The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes. The overall combination will provide almost 97% accurate results which is better than the existing literature results. Conclusion: In conclusion the experimentation result suggests that the use of the local feature extraction mechanism is better when compared with the global feature extraction methodology.
Hierarchical Stochastic Block Model for Community Detection in Multiplex  Networks

Multiplex networks have become increasingly more prevalent in many fields, and have emerged as a powerful tool for modeling the complexity of real networks. There is a critical need for developing inference models for multiplex networks that can take into account potential dependencies across different layers, particularly when the aim is community detection. We add to a limited literature by proposing a novel and efficient Bayesian model for community detection in multiplex networks. A key feature of our approach is the ability to model varying communities at different network layers. In contrast, many existing models assume the same communities for all layers. Moreover, our model automatically picks up the necessary number of communities at each layer (as validated by real data examples). This is appealing, since deciding the number of communities is a challenging aspect of community detection, and especially so in the multiplex setting, if one allows the communities to change across layers. Borrowing ideas from hierarchical Bayesian modeling, we use a hierarchical Dirichlet prior to model community labels across layers, allowing dependency in their structure. Given the community labels, a stochastic block model (SBM) is assumed for each layer. We develop an efficient slice sampler for sampling the posterior distribution of the community labels as well as the link probabilities between communities. In doing so, we address some unique challenges posed by coupling the complex likelihood of SBM with the hierarchical nature of the prior on the labels. An extensive empirical validation is performed on simulated and real data, demonstrating the superior performance of the model over single-layer alternatives, as well as the ability to uncover interesting structures in real networks.
Bounds on the entanglement of two-qutrit systems from fixed marginals

We discuss the problem of characterizing upper bounds on entanglement in a bipartite quantum system when only the reduced density matrices (marginals) are known. In particular, starting from the known two-qubit case, we propose a family of candidates for maximally entangled mixed states with respect to fixed marginals for two qudits. Interestingly, it turns out such states are always quasidistillable. Moreover, they are extremal in the convex set of two qudit states with fixed marginals. Our observations are supported by numerical analysis.
A Comparative Study of Methods for Hiding Large Size Audio File in Smaller Image Carriers

The last 13 years or more has been characterized by unmatched progress in multimedia content and undercover communication. Sound file is chief among mode of transmitting classified and confidential information across the cyberspace and channels. Steganography is a traditional solution to address the problems of data security on public channels and transmitters. In general, the practice of image steganography favored smaller size secret message transmission in larger size carrier approach. However, there is a major of challenge of concealing classified large size audio data inside a smaller image cover with lesser distortions and quality retention of the cover medium and original message respectively. To achieve these, the audio file is compressed to appropriate size before embedding it into two distinct image files (grayscale and RGB color) at binary level using both Most Significant Bits (MSBs) and Least Significant Bits (LSBs) methods. This paper established that the most suitable of MSBs and LSBs in terms of robustness, payload size, and retention of perception transparency of media understudied.
Efficient Group Proof of Storage With Malicious-Member Distinction and Revocation

Proof of Storage (POS) is a system utilized by a client to verify whether the original data is intact while being possessed by an untrusted server. In a grouping application, multiple members share and verify the same file, and the group manager is responsible for determining if the data has been manipulated based on the responses from group members. However, a malicious member may repudiate a correct proof; therefore, it is important to distinguish the honest members from malicious ones. To the best of our knowledge, none of the existing group-oriented schemes have solved this challenge efficiently and up to the desired satisfaction. In this paper, based on matrix calculation, pseudo-random functions, and commitment functions, we propose a new Group Proof of Storage with Malicious-Member Distinction and Revocation scheme (DR-GPOS). Specifically, in terms of functionality, DR-GPOS can distinguish and revoke the malicious members, as well as, guarantee the integrity and deduplication of the outsourced data. From a security perspective, DR-GPOS can also resist against selective attacks and the collusion attacks from the revoked members (e.g. forging proofs by colluding with the server). The security properties of the proposed schemes have also been formally proven in a standard model. We have further implemented it in a real-world (Baidu) cloud server, to evaluate the performance with large scale data (> 10 G).
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation

Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation. However, 2D convolutions cannot fully leverage the rich spatial information along the third axis, while 3D convolutions suffer from the demanding computation and high GPU memory consumption. In this paper, we propose to automatically search the network architecture tailoring to volumetric medical image segmentation problem. Concretely, we formulate the structure learning as differentiable neural architecture search, and let the network itself choose between 2D, 3D or Pseudo-3D (P3D) convolutions at each layer. We evaluate our method on 3 public datasets, i.e., the NIH Pancreas dataset, the Lung and Pancreas dataset from the Medical Segmentation Decathlon (MSD) Challenge. Our method, named V-NAS, consistently outperforms other state-of-the-arts on the segmentation task of both normal organ (NIH Pancreas) and abnormal organs (MSD Lung tumors and MSD Pancreas tumors), which shows the power of chosen architecture. Moreover, the searched architecture on one dataset can be well generalized to other datasets, which demonstrates the robustness and practical use of our proposed method.
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering

Relation detection is a core step in many natural language process applications including knowledge base question answering. Previous efforts show that single-fact questions could be answered with high accuracy. However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data. But for unseen relations, the performance will drop rapidly. The main reason for this problem is that the representations for unseen relations are missing. In this paper, we propose a simple mapping method, named representation adapter, to learn the representation mapping for both seen and unseen relations based on previously learned relation embedding. We employ the adversarial objective and the reconstruction objective to improve the mapping performance. We re-organize the popular SimpleQuestion dataset to reveal and evaluate the problem of detecting unseen relations. Experiments show that our method can greatly improve the performance of unseen relations while the performance for those seen part is kept comparable to the state-of-the-art. Our code and data are available at https://github.com/wudapeng268/KBQA-Adapter.
Six Shades of AES

Recently there have been various attempts to construct light weight implementations of the AES-128 encryption and combined encryption/ decryption circuits [2, 13]. However no known lightweight circuit exists for AES-192 and AES-256, the variants of AES that use longer keys. Investing in lightweight implementations of these ciphers is important as we enter the post quantum era in which security is, by a rule of the thumb, scaled down to the square-root of the size of the keyspace. In this paper, we propose a single circuit that is able to offer functionalities of both encryption and decryption for AES-128/192/256. Our circuit operates on an 8-bit datapath and occupies around 3672 GE of area in silicon. We outline the challenges that presented themselves while performing the combinatorial optimization of circuit area and the methods we used to solve them.
A Study and Analysis of a Feature Subset Selection Technique Using Penguin Search Optimization Algorithm

In today world of enormous amounts of data, it is very important to extract useful knowledge from it. This can be accomplished by feature subset selection. Feature subset selection is a method of selecting a minimum number of features with the help of which our machine can learn and predict which class a particular data belongs to. We will introduce a new adaptive algorithm called Feature selection Penguin Search optimization algorithm which is a metaheuristic approach. It is adapted from the natural hunting strategy of penguins in which a group of penguins take jumps at random depths and come back and share the status of food availability with other penguins and in this way, the global optimum solution is found. In order to explore the feature subset candidates, the bioinspired approach Penguin Search optimization algorithm generates during the process a trial feature subset and estimates its fitness value by using three different classifiers for each case: Random Forest, Nearest Neighbour and Support Vector Machines. However, we are planning to implement our proposed approach Feature selection Penguin Search optimization algorithm on some well known benchmark datasets collected from the UCI repository and also try to evaluate and compare its classification accuracy with some state of art algorithms.