Reviewerly/exHarmony huggingface dataset:
MACHINE LEARNING MODEL FOR BREAST CANCER DETECTION

Cancer is the second most common cause of death in the world, especially in developing countries. Cancer is a serious and dangerous disease that is usually caused by abnormal growth of the cell. This abnormal growth causes lumps (tumors). Breast cancer is ranked the fifth cause of cancer deaths worldwide and the second most common form of cancer amongst females. Machine learning is one of the most trending technologies in computing. It entails the automated conversion of data into useable information that can be related with. This Paper is concerned with the use of a classification algorithm like Support Vector Machine (SVM) to develop a model for the detection of breast cancer. The developed model is to select/extract features from dataset and then classify it as either benign (non-cancerous) or malignant (cancerous). Dataset was collected from the Wisconsin database (in which features are computed from a digitalized image of a Fine Needle Aspirate (FNA) of a breast mass) then trained and tested using the SVM classifier for prediction of breast cancer. In the algorithm, we plot each data item as a point in n-dimensional (here n=9), with the value of each feature being the value of a particular coordinate. Classification is performed by finding the hyper-plane that differentiates the two classes.  The model accepts values of attributes as inputs and predicts whether a breast mass is benign or malignant, with an accuracy level of 94.3%. This model is recommended for use in the health sector and also for personal use.
Cross Copy Network for Dialogue Generation

In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e.g., LSTM+attention, Pointer Generator Networks and Transformer) to enhance dialogue content generation. While content fluency and accuracy often serve as the major indicators for model training, dialogue logics, carrying critical information for some particular domains, are often ignored. Take customer service and court debate dialogue as examples, compatible logics can be observed across different dialogue instances, and this information can provide vital evidence for utterance generation. In this paper, we propose a novel network architecture - Cross Copy Networks (CCN) to explore the current dialog context and similar dialogue instances’ logical structure simultaneously. Experiments with two tasks, court debate and customer service content generation, proved that the proposed algorithm is superior to existing state-of-art content generation models.
Visuo-Linguistic Question Answering (VLQA) Challenge

Understanding images and text together is an important aspect of cognition and building advanced Artificial Intelligence (AI) systems.As a community, we have achieved good benchmarks over language and vision domains separately, however joint reasoning is still a challenge for state-of-the-art computer vision and natural language processing (NLP) systems.We propose a novel task to derive joint inference about a given image-text modality and compile the Visuo-Linguistic Question Answering (VLQA) challenge corpus in a question answering setting.Each dataset item consists of an image and a reading passage, where questions are designed to combine both visual and textual information i.e., ignoring either modality would make the question unanswerable.We first explore the best existing vision-language architectures to solve VLQA subsets and show that they are unable to reason well.We then develop a modular method with slightly better baseline performance, but it is still far behind human performance.We believe that VLQA will be a good benchmark for reasoning over a visuo-linguistic context.The dataset, code and leaderboard is available at https://shailaja183.github.io/vlqa/.
Quantum Codes From Classical Graphical Models

We introduce a new graphical framework for designing quantum error correction codes based on classical principles. A key feature of this graphical language, over previous approaches, is that it is closely related to that of factor graphs or graphical models in classical information theory and machine learning. It enables us to formulate the description of the recently-introduced `coherent parity check' quantum error correction codes entirely within the language of classical information theory. This makes our construction accessible without requiring background in quantum error correction or even quantum mechanics in general. More importantly, this allows for a collaborative interplay where one can design new quantum error correction codes derived from classical codes.
Exposing cache timing side-channel leaks through out-of-order symbolic execution

As one of the fundamental optimizations in modern processors, the out-of-order execution boosts the pipeline throughput by executing independent instructions in parallel rather than in their program orders. However, due to the side effects introduced by such microarchitectural optimization to the CPU cache, secret-critical applications may suffer from timing side-channel leaks. This paper presents a symbolic execution-based technique, named SymO 3 , for exposing cache timing leaks under the context of out-of-order execution. SymO 3 proposes new components that address the modeling, reduction, and reasoning challenges of accommodating program analysis to the software code out-of-order analysis. We implemented SymO 3 upon KLEE and conducted three evaluations on it. Experimental results show that SymO 3 successfully uncovers a set of cache timing leaks in five real-world programs. Also, SymO 3 finds that, in general, program transformation from compiler optimizations shrink the surface to timing leaks. Furthermore, augmented with a speculative execution modeling, SymO 3 identifies five more leaky programs based on the compound analysis.
Improved k-Anonymize and l-Diverse Approach for Privacy Preserving Big Data Publishing Using MPSEC Dataset

Data exposure and privacy violations may happen when data is exchanged between organizations. Data anonymization gives promising results for limiting such dangers. In order to maintain privacy, different methods of k-anonymization and l-diversity have been widely used. But for larger datasets, the results are not very promising. The main problem with existing anonymization algorithms is high information loss and high running time. To overcome this problem, this paper proposes new models, namely Improved k-Anonymization (IKA) and Improved l-Diversity (ILD). IKA model takes large k-value using a symmetric as well as an asymmetric anonymizing algorithm. Then IKA is further categorized into Improved Symmetric k-Anonymization (ISKA) and Improved Asymmetric k-Anonymization (IAKA). After anonymizing data using IKA, ILD model is used to increase privacy. ILD will make the data more diverse and thereby increasing privacy. This paper presents the implementation of the proposed IKA and ILD model using real-time big candidate election dataset, which is acquired from the Madhya Pradesh State Election Commission, India (MPSEC) along with Apache Storm. This paper also compares the proposed model with existing algorithms, i.e. Fast clustering-based Anonymization for Data Streams (FADS), Fast Anonymization for Data Stream (FAST), Map Reduce Anonymization (MRA) and Scalable k-Anonymization (SKA). The experimental results show that the proposed models IKA and ILD have remarkable improvement of information loss and significantly enhanced the performance in terms of running time over the existing approaches along with maintaining the privacy-utility trade-off.
Exclusive Mining of Blockchain Transactions

After creating a new blockchain transaction, the next step usually is to make miners aware of it by having it propagated through the blockchain’s peer-to-peer network. We study an unintended alternative to peer-to-peer propagation: Exclusive mining. Exclusive mining is a type of collusion between a transaction initiator and a single miner (or mining pool). The initiator sends transactions through a private channel directly to the miner instead of propagating them through the peer-to-peer network. Other blockchain users only become aware of these transactions once they have been included in a block by the miner. We identify three possible motivations for engaging in exclusive mining: (i) reducing transaction cost volatility (“confirmation as a service”), (ii) hiding unconfirmed transactions from the network to prevent frontrunning and (iii) camouflaging wealth transfers as transaction costs to evade taxes or launder money. We further outline why exclusive mining is difficult to prevent and introduce metrics which can be used to identify mining pools engaging in exclusive mining activity.
Senate: A Maliciously-Secure {MPC} Platform for Collaborative Analytics

Many organizations stand to benefit from pooling their data together in order to draw mutually beneficial insights -- e.g., for fraud detection across banks, better medical studies across hospitals, etc. However, such organizations are often prevented from sharing their data with each other by privacy concerns, regulatory hurdles, or business competition. We present Senate, a system that allows multiple parties to collaboratively run analytical SQL queries without revealing their individual data to each other. Unlike prior works on secure multi-party computation (MPC) that assume that all parties are semi-honest, Senate protects the data even in the presence of malicious adversaries. At the heart of Senate lies a new MPC decomposition protocol that decomposes the cryptographic MPC computation into smaller units, some of which can be executed by subsets of parties and in parallel, while preserving its security guarantees. Senate then provides a new query planning algorithm that decomposes and plans the cryptographic computation effectively, achieving a performance of up to 145$\times$ faster than the state-of-the-art.
Blockchain based E-voting System

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Behavior Prediction for Unmanned Driving Based on Dual Fusions of Feature and Decision

Behavioral decision systems may suffer from poor performance due to the failure in capturing the vibrations of environmental information. To better capture such vibrations and then make more accurate predictions, a parallel deep neural network based on dual fusions including feature and decision is proposed, called DFFD-Net. DFFD-NET is composed of two parts, the feature fusion network and the driving data network. The feature fusion model adopts two different operations, deconvolution and linear weighting, to fuse local features and global features, respectively. Deconvolution is applied between the convolutional layers, while linear weighting is operated among the outputs of SPP and LSTM. To further improve the accuracy of the prediction, the decisions generated from both networks are further weighed to get the final decision. Experimentally, DFFD-NET is implemented in the benchmarks BDDV and TORCS, and the results show that the final performance is benefited from both feature fusion and decision fusion. From the comparison, DFFD-NET can get state-of-the-art results on both perplexity and precision by only using the images captured from the front-facing camera as well as a few sensing data.
The Extraction Method of Gearbox Compound Fault Features Based on EEMD and Cloud Model

When a compound fault occurs, the randomness and ambiguity of the gearbox will cause uncertainty in the collected signal and reduce the accuracy of signal feature extraction. To improve accuracy, this research proposes a gearbox compound fault feature extraction method, which uses the inverse cloud model to obtain the signal feature value. First, EEMD is used to decompose the collected vibration signals of gearbox faults in normal and fault states. Then, the mutual information method is used to select the sensitive eigenmode function that can reflect the characteristics of the signal. Subsequently, the inverse cloud generator is used to extract cloud digital features and construct sample feature sets. On this basis, the concept of synthetic cloud is introduced, and the cloud-based distance measurement principle is used to synthesize new clouds, reduce the feature dimension, and extract relevant features. Finally, a simulation experiment on a rotating machinery unit with a certain type of equipment verifies that the proposed method can effectively extract the feature of gearbox multiple faults with less feature dimension. And comparing with the feature set extracted by the single cloud model, the results show that the method can better represent the fault characteristic information of the signal.
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution

One critical issue of zero anaphora resolution (ZAR) is the scarcity of labeled data.This study explores how effectively this problem can be alleviated by data augmentation.We adopt a state-ofthe-art data augmentation method, called the contextual data augmentation (CDA), that generates labeled training instances using a pretrained language model.The CDA has been reported to work well for several other natural language processing tasks, including text classification and machine translation (Kobayashi, 2018;Wu et al., 2019;Gao et al., 2019).This study addresses two underexplored issues on CDA, that is, how to reduce the computational cost of data augmentation and how to ensure the quality of the generated data.We also propose two methods to adapt CDA to ZAR: [MASK]-based augmentation and linguistically-controlled masking.Consequently, the experimental results on Japanese ZAR show that our methods contribute to both the accuracy gain and the computation cost reduction.Our closer analysis reveals that the proposed method can improve the quality of the augmented training data when compared to the conventional CDA.
Incorporating privacy by design in body sensor networks for medical applications: A privacy and data protection framework

Privacy and Data protection are highly complex issues within eHealth/M-Health systems. These systems should meet specific requirements deriving from the organizations and users, as well as from the variety of legal obligations deriving from GDPR that dictate protection rights of data subjects and responsibilities of data controllers. To address that, this paper proposes a Privacy and Data Protection Framework that provides the appropriate steps so as the proper technical, organizational and procedural measures to be undertaken. The framework, beyond previous literature, supports the combination of privacy by design principles with the newly introduced GDPR requirements in order to create a strong elicitation process for deriving the set of the technical security and privacy requirements that should be addressed. It also proposes a process for validating that the elicited requirements are indeed fulfilling the objectives addressed during the Data Protection Impact Assessment (DPIA), carried out according to the GDPR.
Improved Multiview Decomposition for Single-Image High-Resolution 3D Object Reconstruction

As a representative technology of artificial intelligence, 3D reconstruction based on deep learning can be integrated into the edge computing framework to form an intelligent edge and then realize the intelligent processing of the edge. Recently, high-resolution representation of 3D objects using multiview decomposition (MVD) architecture is a fast reconstruction method for generating objects with realistic details from a single RGB image. The results of high-resolution 3D object reconstruction are related to two aspects. On the one hand, a low-resolution reconstruction network represents a good 3D object from a single RGB image. On the other hand, a high-resolution reconstruction network maximizes fine low-resolution 3D objects. To improve these two aspects and further enhance the high-resolution reconstruction capabilities of the 3D object generation network, we study and improve the low-resolution 3D generation network and the depth map superresolution network. Eventually, we get an improved multiview decomposition (IMVD) network. First, we use a 2D image encoder with multifeature fusion (MFF) to enhance the feature extraction capability of the model. Second, a 3D decoder using an effective subpixel convolutional neural network (3D ESPCN) improves the decoding speed in the decoding stage. Moreover, we design a multiresidual dense block (MRDB) to optimize the depth map superresolution network, which allows the model to capture more object details and reduce the model parameters by approximately 25% when the number of network layers is doubled. The experimental results show that the proposed IMVD is better than the original MVD in the 3D object superresolution experiment and the high-resolution 3D reconstruction experiment of a single image.
A Home Appliance Control System with Hand Gesture based on Pose Estimation

Home appliances play a large role in our daily lives. However, with the increase of appliances, each require a dedicated remote controller, which can be cumbersome to users. Existing solutions have the shortcoming of requiring users to acquire devices that can be programmed to act as unified-remote control units. To overcome this challenge, we propose a system that utilizes hand-gestures to control various appliances. Our system provides various advantages, as users do not need to acquire dedicated devices, and controls can be carried out using intuitive hand gestures to perform various actions on the target appliance. We explain our approach and verify it by developing and integrating a system for controlling TV using various hand gestures. We conducted a performance and usability evaluations, which respectively gauged our systems accuracy under different operational and usage conditions, and investigated user impressions of our prototype. Results indicated that our system had generally high gesture-recognition accuracy, and users provided positive feedback about our system's usability within common household usage contexts. Based on the results, we discuss various future research directions to extend our work.
Multitask Learning Mechanism for Remote Sensing Image Motion Deblurring

As a fundamental preprocessing technique, remote sensing image motion deblurring is important for visual understanding tasks. Most conventional approaches formulate the image motion deblurring task as a kernel estimation. Because the kernel estimation is a highly ill-posed problem, many priors have been applied to model the images and kernels. Even though these methods have obtained relatively better performances, they are usually time-consuming and not robust for different conditions. To address this problem, we propose a multitask learning mechanism for remote sensing image motion deblurring in this article, which contains an image restoration subtask and an image texture complexity recognition one. First, we consider the image motion deblurring problem as a domain transformation problem, from the blurred domain to a clear one. Specifically, the blurred domain represents the data space consisted of blurring images, and the definition of clear domain is similar. Second, we design a novel weighted attention map loss to enhance the reconstruction capability of the restoration subbranch for difficult local regions. Third, based on the restoration subbranch, a recognition subbranch is incorporated into the framework to guide the deblurring process, which provides the auxiliary texture complexity information to help the optimization of restoration subbranch. Additionally, in order to optimize the proposed network, we construct three large-scale datasets, and each sample in the dataset contains a clear image, a blurred image, and its texture label obtained by corresponding texture complexity. Finally, the experimental results on three constructed datasets demonstrate the robustness and the effectiveness of the proposed method.
Very deep super-resolution for efficient cone-beam computed tomographic image restoration

As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images.Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images.The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1.VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.
Mining High Utility Itemsets Based on Pattern Growth without Candidate Generation

Mining high utility itemsets (HUIs) has been an active research topic in data mining in recent years. Existing HUI mining algorithms typically take two steps: generating candidates and identifying utility values of these candidate itemsets. The performance of these algorithms depends on the efficiency of both steps, both of which are usually time-consuming. In this study, we propose an efficient pattern-growth based HUI mining algorithm, called tail-node tree-based high-utility itemset (TNT-HUI) mining. This algorithm avoids the time-consuming candidate generation step, as well as the need of scanning the original dataset multiple times for exact utility values, as supported by a novel tree structure, named the tail-node tree (TN-Tree). The performance of TNT-HUI was evaluated in comparison with state-of-the-art benchmark methods on different datasets. Experimental results showed that TNT-HUI outperformed benchmark algorithms in both execution time and memory use by orders of magnitude. The performance gap is larger for denser datasets and lower thresholds.