Reviewerly/exHarmony huggingface dataset:
Deep Bayesian Network for Visual Question Generation

Generating natural questions from an image is a semantic task that requires using vision and language modalities to learn multimodal representations.Images can have multiple visual and language cues such as places, captions, and tags.In this paper, we propose a principled deep Bayesian learning framework that combines these cues to produce natural questions.We observe that with the addition of more cues and by minimizing uncertainty in the among cues, the Bayesian network becomes more confident.We propose a Minimizing Uncertainty of Mixture of Cues (MUMC), that minimizes uncertainty present in a mixture of cues experts for generating probabilistic questions.This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study.We observe that with the addition of more cues and by minimizing uncertainty among the cues, the Bayesian framework becomes more confident.Ablation studies of our model indicate that a subset of cues is inferior at this task and hence the principled fusion of cues is preferred.Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU-n, METEOR, ROUGE, and CIDEr).Here we provide project link for Deep Bayesian VQG https: //delta-lab-iitk.github.io/BVQG/.
Malaria Cell Detection Using Evolutionary Convolutional Deep Networks

With the rapid development of deep learning and computer-vision, accurate identification of medical imaging has become one of the most important factors in medical diagnosis and decision-making. To this end, we propose a data-driven approach named Evolutionary Convolutional Deep Network (ECDN) to detect malaria parasites, which can use evolutionary algorithms to automatically generate deep neural networks, and optimized its network topology structure during the evolution process. Extensive experiments based on the large-scale thin-blood smear images data validate the effectiveness of ECDN for detecting malaria. To be specific, it has the advantage of being able to automatically generate an optimal network structure without the need for any prior knowledge of constructing a neural network, as compared to a traditional artificial convolution network. The experimental results show that the model robustness of ECDN is better. When the training set and test set are divided according to the ratio of 6 and 4, the best result is achieved, and the accuracy rate reaches 99.98%, which provides an important basis for this research.
Fast Nearest Subspace Search via Random Angular Hashing

Subspaces frequently offer powerful representation in many tasks including recognition, retrieval, and optimization. In these tasks, the nearest subspaces (i.e., subspace-to-subspace search) often inevitably arise. Several studies in the literature have attempted to address this hard problem using techniques such as locality-sensitive hashing. Unfortunately, these subspace hashing methods are severely affected by poor scaling, with consequently high computational cost or unsatisfying accuracy, when the subspaces originally distribute with arbitrary dimensions. Accordingly, in this paper, we propose random angular hashing, a new and efficient type of locality-sensitive hashing, for linear subspaces of arbitrary dimension. The method we proposed preserves the angular distances among subspaces by randomly projecting their orthonormal basis and then encoding them with binary codes, meanwhile not only achieving fast computation but also maintaining a powerful collision probability. Moreover, its flexibility to easily get a balance between efficiency and accuracy in terms of performance. The extensive experimental results on tasks of face recognition, video de-duplication, and gesture recognition demonstrate that the proposed approach performs better than the state-of-the-art methods heavily, in terms of both accuracy and efficiency (up to 16× speedup).
Application of Logistic Regression with Fixed Memory Step Gradient Descent Method in Multi-Class Classification Problem

Logistic regression is a supervised binary classification algorithm in machine learning. It is an important part of neural network and convolutional neural network. An important part of the logistic regression algorithm is to find the optimal parameters of the loss function, which is often a non-linear convex optimization problem. Generally, the gradient descent method or the stochastic gradient descent method in linear search are used in this nonlinear problem, but these methods are easy to fall into the trap of local minimum and have weak global convergence. In order to preserve the gradient information better and enhance the global convergence of the algorithm, this paper introduces fixed memory step gradient descent method into the optimization part of logistic regression algorithm, and combines OVR strategy to solve the problem of multi-class classification of data.
Enabling Secure Trustworthiness Assessment and Privacy Protection in Integrating Data for Trading Person-Specific Information

With increasing adoption of cloud services in the e-market, collaboration between stakeholders is easier than ever. Consumer stakeholders demand data from various sources to analyze trends and improve customer services. Data-as-a-service enables data integration to serve the demands of data consumers. However, the data must be of good quality and trustful for accurate analysis and effective decision making. In addition, a data custodian or provider must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a twofold solution: 1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semitrusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup and 2) we incorporate the Vickrey-Clarke-Groves (VCG) auction mechanism for the valuation of data providers' attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements.
Spatial action–effect binding depends on type of action–effect transformation

Abstract Spatial action–effect binding denotes the mutual attraction between the perceived position of an effector (e.g., one’s own hand) and a distal object that is controlled by this effector. Such spatial binding can be construed as an implicit measure of object ownership, thus the belonging of a controlled object to the own body. The current study investigated how different transformations of hand movements (body-internal action component) into movements of a visual object (body-external action component) affect spatial action–effect binding, and thus implicit object ownership. In brief, participants had to bring a cursor on the computer screen into a predefined target position by moving their occluded hand on a tablet and had to estimate their final hand position. In Experiment 1, we found a significantly lower drift of the proprioceptive position of the hand towards the visual object when hand movements were transformed into laterally inverted cursor movements, rather than cursor movements in the same direction. Experiment 2 showed that this reduction reflected an elimination of spatial action–effect binding in the inverted condition. The results are discussed with respect to the prerequisites for an experience of ownership over artificial, noncorporeal objects. Our results show that predictability of an object movement alone is not a sufficient condition for ownership because, depending on the type of transformation, integration of the effector and a distal object can be fully abolished even under conditions of full controllability.
RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation

In this paper, we introduce a self-supervised approach for video object segmentation without human labeled data. Specifically, we present Robust Pixel-level Matching Networks (RPM-Net), a novel deep architecture that matches pixels between adjacent frames, using only color information from unlabeled videos for training. Technically, RPM-Net can be separated in two main modules. The embedding module first projects input images into high dimensional embedding space. Then the matching module with deformable convolution layers matches pixels between reference and target frames based on the embedding features. Unlike previous methods using deformable convolution, our matching module adopts deformable convolution to focus on similar features in spatio-temporally neighboring pixels. Our experiments show that the selective feature sampling improves the robustness to challenging problems in video object segmentation such as camera shake, fast motion, deformation, and occlusion. Also, we carry out comprehensive experiments on three public datasets (i.e., DAVIS-2017, SegTrack-v2, and Youtube-Objects) and achieve state-of-the-art performance on self-supervised video object segmentation. Moreover, we significantly reduce the performance gap between self-supervised and fully-supervised video object segmentation (41.0% vs. 52.5% on DAVIS-2017 validation set).
An Empirical Investigation of the Effects of Individuality on Responses to Data Theft Crimes

E-commerce transactions have been rapidly increasing (over 20% from 2016 to 2017), and thus the number of data thefts has been increasing. Data thefts cause a loss of trust by the customer, but previous research has failed to identify how such events change customer perceptions and also influence future shopping behavior. Our survey of 390 individuals provides insight into how the individuality of the crime victims (targeting of a few named individuals or a large customer database) has strong effects. Specifically, respondents were more likely to blame the breached customers and less likely to blame the business if only a few individuals were targeted. Responses such as blaming the business and possessing a cynical perspective about the thieves and affected customers, along with the individuality of the crime, did have a significant effect on whether customers would choose to revisit the business. However, increased blame on affected customers did not necessarily lead to diminished revisits. We recommend that businesses respond to large database breaches with apologies and blame acceptance, and to small, targeted breaches with an agile mix of apology, blame acceptance, diminishment, and protest.
Scaling HPC Networks with Co-packaged Optics

We propose an HPC network architecture with co-packaged optics enabling 128-port 51.2-Tb/s switches. Simulations for a &gt;34,000-GPU system show up to 11.2x throughput improvement over a Summit-like supercomputer, opening the way to direct-network-attached GPUs.
Federated Deep Learning for Immersive Virtual Reality over Wireless Networks

In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIPs) that can detach the users from their virtual world. To measure the BIPs for wireless VR users, a novel model that jointly considers the VR applications, transmission delay, VR video quality, and users' awareness of the virtual environment is proposed. In the developed model, the base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to increase the data rate of VR users, thus, reducing the number of BIPs for each user. Therefore, the mobility and orientation of VR users must be considered when minimizing BIPs, since the body movements of a VR user may result in blockage of its wireless link. The BIP problem is formulated as an optimization problem which jointly considers the predictions of users' mobility patterns, orientations, and their BS association. To predict the orientation and mobility patterns of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses concept from federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users' mobility patterns and orientations. Using these predictions, the user association policy that minimizes BIPs is derived. Simulation results demonstrate that the developed algorithm reduces the users' BIPs by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.
A Human-in-the-Loop Method for Developing Machine Learning Applications

An intelligent system is usually built based on a machine-learning model trained over offline datasets. However, such a system is difficult to adapt to new patterns or new data in the online environment, i.e. offline model has relatively poor online generalization. Moreover, understanding and solving errors taking place in the online system is also hard because errors arise in offline training pipelines and could propagate. We propose a novel systematic method, including design, architecture, and implementation to mix people's experience and intelligence with a relatively low cost. The method includes three negative-feedback loops and one data loop, supporting iterative and incremental developing procedure. Based on the method, we implemented a general system architecture and conducted two case studies. The results illustrate the effectiveness of the method.
Software Engineering Wastes - A Perspective of Modern Code Review

Identification and eradication of waste are the principal emphases of lean thinking. Waste is defined as any activity that consumes resources but does not deliver any value to the stakeholder and it can also be demarcated as an impediment to process flow. Lean thinking has been applied in the software engineering domain concerning overall software development, however, still, there is a need to take action regarding waste identification and elimination concerning specific software engineering activities. This paper describes the wastes generated during Modern Code Review (MCR). MCR is a socio-technical software engineering activity and acknowledged as a lightweight process for defect identification, code improvement and software quality enhancement. It involves coordination and communication among multiple software engineers having different personalities, preferences, and technical skills, thus it can generate multiple types of wastes. Therefore, the study has two objectives that are to recognize and report various wastes generated during MCR and to map the identified MCR wastes on the existing software engineering wastes. Systematic Literature Review and grounded theory has been utilized to recognize and produce a unique list of the waste generated during MCR. The identified unique list of MCR wastes and their mapping on existing software engineering wastes are validated through software engineering experts. The study findings report 28 unique wastes out of which 25 wastes map to the existing software engineering wastes. However, 3 wastes such as negative emotions, inequality/biasness and insignificant feedback are not reported in the existing software engineering literature. The study will be useful for researchers to identify the wastes in same context or for other software engineering activities and to provide the strategies to minimize the generation of identified wastes.
A multi-period multi-objective optimization framework for software enhancement and component evaluation, selection and integration

Software can be delivered either after it has been entirely developed or it can be delivered in phases. When delivered in phases, software can be enhanced either by upgrading the existing modules in forthcoming phases or by modifying the modules that were developed in previous phases. An incorporated multi-period multiple objective optimization model for the enhancement and establishment of a software system is discussed in this paper. The software system’s total cost is minimized, and the fitness evaluation score of the software components (commercial off-the-shelf and in-house) for modules that are not outsourced, along with vendors for modules that are outsourced, is maximized. The fitness evaluation of module alternatives (software components and vendors) is conducted based on many important qualitative attributes using the Technique for Order Preference by Similarity to Ideal Solution. The model is constrained to critical parameters, such as compatibility, delivery time and integration of alternatives, along with software reliability. To exemplify the applicability of the suggested framework for optimization, the development of e-commerce software is used as a case study.
Joint Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking of Unmanned Cluster Based on Fuzzy Logic Reasoning

The unmanned aerial vehicle (UAV) cluster is gradually attracting more attention, which takes advantage over a traditional single manned platform. Because the size of the UAV platform limits the transmitting power of its own radar, how to reduce the transmitting power while meeting the detection accuracy is necessary. Aim at multiple-target tracking (MTT), a joint radar node selection and power allocation algorithm for radar networks is proposed. The algorithm first uses fuzzy logic reasoning (FLR) to obtain the priority of targets to radars, and designs a radar clustering algorithm based on the priority to form several subradar networks. The radar clustering algorithm simplifies the problem of multiple-radar tracking multiple-target into several problems of multiple-radar tracking a single target, which avoids complex calculations caused by multiple variables in the objective function of joint radar node selection and power allocation model. Considering the uncertainty of the target RCS in practice, the chance-constraint programming (CCP) is used to balance power resource and tracking accuracy. Through the joint radar node selection and power allocation algorithm, the radar networks can use less power resource to achieve a given tracking performance, which is more suitable for working on drone platforms. Finally, the simulation proves the effectiveness of the algorithm.
Spatial Enhanced Rotation Aware Network for Breast Mass Segmentation in Digital Mammogram

Breast cancer is the most common cancer with highest mortality risk among the female worldwide and breast mass is the most effective sign for cancer identification. Thus, accurate segmentation of breast mass is regarded as a key step to reduce the death rate. Traditional segmentation methods require prior knowledge and manually set parameters, while recent studies prefer to construct neural networks based on feature reuse. However, breast mass can display in different orientations and the spatial context is complex, which makes the segmentation remain a challenging task. For these concerns, we propose a Spatial Enhanced Rotation Aware Network (SERAN) for automatic breast mass segmentation. SERAN consists of two critical components: 1) a residual attention encoder with spatial enhancement mechanism for effective feature extraction, and 2) a decoder constructed by multi-stream rotation aware blocks for feature fusion and prediction refinement. To optimize SERAN better and avoid misclassification in background area, a regulation item named Inside-outside Loss (IOL) is used in training procedure. The experimental results tested on a representative subset of Digital Database for Screening Mammography (DDSM) dataset show that SERAN outperforms state-of-the-art methods among most adopted evaluation metrics.
Superposed graphene coherent states

Using an annihilation operator, coherent states related to the electron of graphene layer placed in a magnetic field, can be obtained. In this paper, we define even and odd superposed graphene coherent states and then, we consider their entanglement, squeezing and statistical properties. To study the entanglement, we use concurrence. The results show that odd superposed graphene coherent states are maximally entangled states for all values of coherence parameter. However, the entanglement of graphene coherent states and also even superposed depend on the coherence parameter. In addition, examining the Mandel parameter shows sub-Poissonian statistics for graphene coherent states and their odd superposition; while, even superposed states do not show sub-Poissonian statistics at all. Also, we find that graphene coherent states and even superposition may be squeezed while the odd states do not show squeezing.
An Efficient and Privacy-Preserving Energy Trading Scheme Based on Blockchain

Distributed transaction model has gradually replaced the traditional centralized transaction model and has become the leading direction of development in energy trading. As the underlying support, blockchain technology is attracting more and more attention due to its advantages, i.e., integrity and non-repudiation. However, most blockchain-based trading models face the problem of privacy protection. In this paper, to solve this problem, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is introduced as the core algorithm to reconstruct the transaction model. Specifically, we build a general model for distributed transaction called PP-BCTS (Privacy- Preserving Blockchain Trading Scheme). It can achieve fine-grained access control through transaction arbitration in ciphertext form. This design can maximize the protection of private information and can greatly improve the security and reliability of the transaction model. Additionally, a credibility-based equity proof consensus mechanism is proposed in PP-BCTS, which can greatly improve the operational efficiency. Security analysis and experimental evaluations are conducted to prove the validity and practicability of our proposed scheme.
Robust Text Recognition for Uzbek Language in Natural Scene Images

Text recognition is a challenging and emerging research area in computer vision. The text which is located in images includes semantic data which is practiced in many various applications such as robot navigation, information retrieval of complex images, street signs, beneficial for visually impaired, automatic read the signboard, etc. We propose a text recognition method for Uzbek language using the Tesseract engine. We show that the model can recognize the Uzbek language, including Latin and Cyrillic alphabet text. The experimental results of the proposed method show robust performance than other methods.
Survey on Cloud Computing Security

Cloud computing is considered as one of the renowned computing methods for pooling and providing various computing resources on demand basis. Cloud computing has grabbed its roots in the IT industry and has become a useful choice for small budget business and organizations. As multiple customers are sharing the same cloud, it will have many security challenges such as malicious user attack, user identity management, auditing, resource management, and integrity control. The main aim here is to provide security to the data by protecting it by unauthorized users during the time of information transmission by using different encrypting techniques such as Blowfish algorithm, RSA algorithm, secure hash algorithm 2, and message digest, on the user data in the cloud. In this paper, we present very recent techniques and algorithms proposed by various authors to secure the stored cloud data.