Reviewerly/exHarmony huggingface dataset:
An Efficient Adaptive Algorithm for Electron Microscopic Image Enhancement and Feature Extraction

In this article, a block-based adaptive contrast enhancement algorithm has been proposed, which uses a modified sigmoid function for the enhancement and features extraction of electron microscopic images. The algorithm is based on a modified sigmoid function that adapts according to the input microscopic image statistics. For feature extraction, the contrast of the image is very important and authentic property by which this article enhances the visual quality of the image. In this work, for better contrast enhancement of image, a block based on input value, combined with a modified sigmoid function that is used as contrast enhancer provides better EMF values for a smaller block size. It provides localized contrast enhancement effects adaptively which is not possible using other existing techniques. Simulation and experimental results demonstrate that the proposed technique gives better results compared to other existing techniques when applied to electron microscopic images. After the enhancement of microscopic images of actinomycetes, various important features are shown, like coil or spiral, long filament, spore and rod shape structures. The proposed algorithm works efficiently for different dark and bright microscopic images.
Semantic embeddings of generic objects for zero-shot learning

Zero-shot learning (ZSL) models use semantic representations of visual classes to transfer the knowledge learned from a set of training classes to a set of unknown test classes. In the context of generic object recognition, previous research has mainly focused on developing custom architectures, loss functions, and regularization schemes for ZSL using word embeddings as semantic representation of visual classes. In this paper, we exclusively focus on the affect of different semantic representations on the accuracy of ZSL. We first conduct a large scale evaluation of semantic representations learned from either words, text documents, or knowledge graphs on the standard ImageNet ZSL benchmark. We show that, using appropriate semantic representations of visual classes, a basic linear regression model outperforms the vast majority of previously proposed approaches. We then analyze the classification errors of our model to provide insights into the relevance and limitations of the different semantic representations we investigate. Finally, our investigation helps us understand the reasons behind the success of recently proposed approaches based on graph convolution networks (GCN) which have shown dramatic improvements over previous state-of-the-art models.
Visualising module dependencies in academic recommendations

Starting their academic career can be overwhelming for many young people. Students are often presented with a variety of options within their programmes of study and making appropriate and informed decisions can be a challenge. Compared to many other areas in our everyday life, recommender systems remain underused in the academic setting. In this part of our research we use non-negative matrix factorisation to identify dependencies between modules, visualise sequential recommendations, and bring structure and clarity into the academic module space.
An Integrated Model To Make Cloud Authentication And Multi-Tenancy More Secure

Cloud Computing is an important term of modern technology. The usefulness of Cloud is increasing day by day and simultaneously more and more security problems are arising as well. Two of the major threats of Cloud are improper authentication and multi-tenancy. According to the specialists both pros and cons belong to multi-tenancy. There are security protocols available but it is difficult to claim these protocols are perfect and ensure complete protection. The purpose of this paper is to propose an integrated model to ensure better Cloud security for Authentication and multi-tenancy. Multi-tenancy means sharing of resources and virtualization among clients. Since multi-tenancy allows multiple users to access same resources simultaneously, there is high probability of accessing confidential data without proper privileges. Our model includes Kerberos authentication protocol to enhance authentication security. During our research on Kerberos we have found some flaws in terms of encryption method which have been mentioned in couple of IEEE conference papers. Pondering about this complication we have elected Elliptic Curve Cryptography. On the other hand, to attenuate arose risks due to multi-tenancy we are proposing a Resource Allocation Manager Unit, a Control Database and Resource Allocation Map. This part of the model will perpetuate resource allocation for the users.
Efficient hash function–based duplication detection algorithm for data Deduplication deduction and reduction

Summary Data Deduplication is the foremost technique used for data compression by removing redundant data. It is broadly utilized for cloud storage server, which reduces the un‐utilized memory space and optimized bandwidth. The confidentiality of sensitive information maintains during Deduplication detection. The Efficient Hash Function–based Duplication Detection (EHFDD) algorithm is proposed to encrypt the content before outsourcing in cloud environment. This paper is an attempt to enhance the data security and notify the issues of approved data Deduplication detections. Furthermore, it also presents several novel Deduplication improvisations for helping certified duplicate verification in hybrid cloud. The EHFDD method enables duplicate detection scheme with minimal overhead compared to existing functions. Based on experimental evaluations, proposed EHFDD algorithm reduces the average delay in 28.7 milliseconds, memory utilization in 8%, computation time in 457 milliseconds, communications overhead in 900 milliseconds, and improves the success rate in 3.13%.
Trajectory-Aware Edge Node Clustering in Vehicular Edge Clouds

In vehicular edge clouds, tasks from vehicles are processed nearby edge nodes (ENs) and thus low latency services can be provided. However, under high vehicular mobility, frequent service migration between two ENs and increased handover latency can be observed. In this paper, we introduce a trajectory-aware edge node clustering (TENC) scheme in which multiple ENs form a cluster depending on the trajectory of a target vehicle. To attain the optimal performance, we formulate an optimization problem by means of a constrained Markov decision process (CMDP). Evaluation results demonstrate that the obtained optimal policy can minimize service delay significantly.
Efficient Non-Interactive Zero-Knowledge Proofs in Cross-Domains Without Trusted Setup

With the recent emergence of efficient zero-knowledge (ZK) proofs for general circuits, while efficient zero-knowledge proofs of algebraic statements have existed for decades, a natural challenge arose to combine algebraic and non-algebraic statements. Chase et al. (CRYPTO 2016) proposed an interactive ZK proof system for this cross-domain problem. As a use case they show that their system can be used to prove knowledge of a RSA/DSA signature on a message m with respect to a publicly known Pedersen commitment $$g^m h^r$$ . One drawback of their system is that it requires interaction between the prover and the verifier. This is due to the interactive nature of garbled circuits, which are used in their construction. Subsequently, Agrawal et al. (CRYPTO 2018) proposed an efficient non-interactive ZK (NIZK) proof system for cross-domains based on SNARKs, which however require a trusted setup assumption. In this paper, we propose a NIZK proof system for cross-domains that requires no trusted setup and is efficient both for the prover and the verifier. Our system constitutes a combination of Schnorr based ZK proofs and ZK proofs for general circuits by Giacomelli et al. (USENIX 2016). The proof size and the running time of our system are comparable to the approach by Chase et al. Compared to Bulletproofs (SP 2018), a recent NIZK proofs system on committed inputs, our techniques achieve asymptotically better performance on prover and verifier, thus presenting a different trade-off between the proof size and the running time.
Binarized Knowledge Graph Embeddings

Tensor factorization has become an increasingly popular approach to knowledge graph completion (KGC), which is the task of automatically predicting missing facts in a knowledge graph. However, even with a simple model like CANDECOMP/PARAFAC (CP) tensor decomposition, KGC on existing knowledge graphs is impractical in resource-limited environments, as a large amount of memory is required to store parameters represented as 32-bit or 64-bit floating point numbers. This limitation is expected to become more stringent as existing knowledge graphs, which are already huge, keep steadily growing in scale. To reduce the memory requirement, we present a method for binarizing the parameters of the CP tensor decomposition by introducing a quantization function to the optimization problem. This method replaces floating point–valued parameters with binary ones after training, which drastically reduces the model size at run time. We investigate the trade-off between the quality and size of tensor factorization models for several KGC benchmark datasets. In our experiments, the proposed method successfully reduced the model size by more than an order of magnitude while maintaining the task performance. Moreover, a fast score computation technique can be developed with bitwise operations.
Improved Meet-in-the-Middle Attacks on Generic Feistel Constructions

In this paper, we present improved meet-in-the-middle key-recovery attacks on six-round and seven-round Feistel constructions separately. The attacks are based on Guo et al.'s work which appends one round to the five-round distinguisher to attack the six-round Feistel construction through the meet-in-the-middle method. The proposed method stores only target sequences instead of all the possible sequences, which reduces the memory complexity from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(3/4)n</sup> blocks to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n/2)</sup> blocks. A new key-recovery attack method on the seven-round Feistel construction is proposed by appending one another round after a five-round distinguisher. What is more, is that we propose a new method called the impossible-differential pairs sieve technique which reduces the data complexity from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> chosen plaintexts to 3×2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-2</sup> chosen plaintexts so that the attack complexity is lower than the exhaustive attack. The time complexity is equivalent to about 3×2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-2</sup> encryptions, and the memory complexity is optimized to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(3/4)n</sup> blocks of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n/2)</sup> bits. To the best of our knowledge, it is the first known generic key-recovery attack on the seven-round Feistel construction with a lower attack complexity when compared with the exhaustive attack.
Withshare

Coproductions are reciprocal activities that all parties are actively engaged in and create synergies that cannot be produced by one party alone. Coproduction activities are important for community building, as the social interactions among community members create social values such as new social ties, trust and reciprocal recognition. Mobile technologies bring new opportunity for coproductions by supporting small-scale reciprocal activities that are location and time sensitive. In this article, the authors introduce and study WithShare, a smartphone application that helps people to organize such coproduction activities. A 3-week user study with 38 young adults in a local community of college students shows WithShare facilitates the coordination of opportunistic and lightweight reciprocal activities in their daily life. The results highlight potentials of coproduction activities in strengthening existing social ties, and establishing new weak ties in the local community. The findings suggest important design implications for mobile technologies to support coproduction activities.
User-controlled hybrid recommendation for academic papers

We present Paper Tuner, a user-controlled interface for recommending papers and presentations at a research conference. The availability of multiple sources of information about user interests makes hybrid recommendation approach attractive in a conference context, but traditional static parallel hybridization makes is hard to generate a single ranking that can address different needs. We introduce a novel slider-based user interface that allows users to control the importance of different relevance source and even reverse the impact of specific sources. The log analysis of system usage during in a real conference context revealed an extensive use of sliders. Moreover, nearly half of the users applied the reverse functionality while using the sliders.
A Learning Environment for Geography and History Using Mixed Reality, Tangible Interfaces and Educational Robotics

Integrating ICT technologies in history and geography teaching may promote critical thinking and bridge the gap between unconstructive information accumulation and an explorative and critical learning approach. The aim of this study was to design, deploy and evaluate a low cost and easy-to-use mixed reality learning environment for interdisciplinary and embodied learning of geography, history and computational thinking. The proposed learning environment is comprised of an augmented 3D-tangible model of southern Europe where students interacted using their fingers, and a second treasure hunt augmented interactive floor depicting historical sites, where students performed tasks with Mindstorms EV3 robots. Students swapped between finger-based and robot-based journeys in Europe in two pairs until the end of the game. In order to evaluate our proposal, six groups of four undergraduate participants played with the environment in 6 sessions and for approximately 45 min. Data were collected with a pre and post knowledge test, an attitudes questionnaire and semi-formal group interviews. Students scored significantly higher in the post-tests and their answers in the questionnaires revealed that the multimodal environment enhanced their engagement and motivation, helped them orient themselves better around Europe's geophysical features, while the robotics treasure hunt consolidated their computational thinking skills, inducing a highly entertaining dimension. This approach better conforms to students' interactive experiences and expectations, gamifies learning and exploits embodied learning opportunities. Students engaged in the two augmented spaces in a perceptually immersive experience which became a more authentic and meaningful educational space.
Low size, weight and power quantum key distribution system for small form unmanned aerial vehicles

The security of sensitive information exchange has become a major topic in recent years. Quantum Key Distribution (QKD) provides a highly secure approach to share random encryption keys between two communication terminals. In contrast with traditional public cryptography methods, QKD security relies on the foundations of quantum mechanics and not on computational capabilities. This makes QKD unconditionally secure (if properly implemented) and it is envisaged as a main component in the next–generation cryptographic technology. QKD has already been successfully demonstrated in different contexts such as fibre-to- fibre, and free-space ground-toground as well as ground-to-air communications. However, Size, Weight and Power (SWaP) constraints have prevented previous implementations to be demonstrated on small form airborne platforms such as Unmanned Aircraft Systems (UAS) and High Altitude Pseudo-Satellites (HAPS). Project Q-DOS aims to deliver a QKD module using compact, cutting-edge photonic waveguide technology, which will allow low-SWaP aerospace requirements to be met. This module uses 1550 nm single photons to implement a BB84 protocol, and will enable the demonstration of a secure, high-speed optical communication data link (~0.5 Gbps) between a drone and a ground station. The targeted link range is 1 km. The airborne communications module, including the QKD terminal, tracking modules, traditional communications systems, optics and control electronics, must not exceed a mass of 5 kg and a power consumption of 20 W.
On Measuring the Iconicity of a Face

For a given identity in a face dataset, there are certain iconic images which are more representative of the subject than others. In this paper, we explore the problem of computing the iconicity of a face. The premise of the proposed approach is as follows: For an identity containing a mixture of iconic and non iconic images, if a given face cannot be successfully matched with any other face of the same identity, then the iconicity of the face image is low. Using this information, we train a Siamese Multi-Layer Perceptron network, such that each of its twins predict iconicity scores of the image feature pair, fed in as input. We observe the variation of the obtained scores with respect to covariates such as blur, yaw, pitch, roll and occlusion to demonstrate that they effectively predict the quality of the image and compare it with other existing metrics. Furthermore, we use these scores to weight features for template-based face verification and compare it with media averaging of features.
Classification by Clustering (CbC): An Approach of Classifying Big Data Based on Similarities

Data classification in supervised learning is the process of classifying data for data mining task that helps to analyse data for decision-making. The objective of a classification model is to correctly predict the categorical class labels of known/unknown instances. In machine learning for data mining applications, the classification models are trained based on labelled training datasets. In this paper, we have investigated if we can build a classification model based on the similarities of the instances instead of class labels of instances. Data labelling is always very costly and time-consuming process, and it becomes a very difficult task if the data is big data. The proposed approach clusters the big data and builds the classifier based on the clusters without considering the class labels, which basically improve the performance of the classifier. However, we can relate to the clusters with class labels. We have collected 10 big data from the UC Irvine machine learning repository for experimental analysis and applied three popular decision tree induction algorithms: ID3 (Iterative Dichotomiser 3), C4.5 (extension of ID3 algorithm), and CART (Classification and Regression Tree) for classifier construction.
Sharpness preserved sinogram synthesis using convolutional neural network for sparse-view CT imaging

Sparse view computed tomography (CT) is an effective way to lower the radiation exposure, but results in streaking artifacts in the constructed CT image due to insufficient projection views. Several approaches have been reported for full view sinogram synthesis by interpolating the missing data into the sparse-view sinogram. However, current interpolation methods tend to generate over-smoothed sinogram, which could not preserve the sharpness of the image. Such sharpness is often referred to the region boundaries or tissue texture and of high importance as clinical indicators. To address this issue, this paper aims to propose an efficient sharpness-preserve spare-view CT sinogram synthesis method based on convolutional neural network (CNN). The sharpness preserving is stressed by the zero-order and first-order difference based loss function in the model. This study takes advantage of the residual design to overcome the problem of degradation for our deep network (20 layers), which is capable of extracting high level information and dealing with large sample dimensions (672 x 672). The proposed model design and loss function achieved a better performance in both quantitative and qualitative evaluation comparing to current state-of-the-art works. This study also performs ablation test on the effect of different designs and researches on hyper-parameter settings in the loss function.
Code-to-Code Search Based on Deep Neural Network and Code Mutation

Deep Neural Networks (DNNs) have been often used for the labeling of image files (e.g., object detection). Although they can be applied for the labeling of code fragment (i.e., code-to-code search) in software engineering, a large number of code fragments are required for each label in the learning process of DNNs. In this paper, we present an approach for code-to-code search based on a DNN model and code mutation for generating enough number of code fragments for each label. The preliminary experiment shows high precision and recall of the proposed approach.
Identifying Requirements in Requests for Proposal: A Research Preview

[Context & motivation] Bidding processes are a usual requirement elicitation instrument for large IT or infrastructure projects. An organization or agency issues a Request for Proposal (RFP) and interested companies may submit compliant offers. [Problem] Such RFPs comprise natural language documents of several hundreds of pages with requirements of various kinds mixed with other information. The analysis of that huge amount of information is very time consuming and cumbersome because bidding companies should not disregard any requirement stated in the RFP. [Principal ideas/results] This research preview paper presents a first version of a classification component, OpenReq Classification Service (ORCS), which extracts requirements from RFP documents while discarding irrelevant text. ORCS is based on the use of Naïve Bayes classifiers. We have trained ORCS with 6 RFPs and then tested the component with 4 other RFPs, all of them from the railway safety domain. [Contribution] ORCS paves the way to improved productivity by reducing the manual effort needed to identify requirements from natural language RFPs.
Sequence-to-sequence image caption generator

Recently, image captioning has received much attention from the artificial-intelligent (AI) research community. Most of the current works follow the encoder-decoder machine translation model to automatically generate captions for images. However, most of these works used Convolutional Neural Network (CNN) as an image encoder and Recurrent Neural Network (RNN) as a decoder to generate the caption. In this paper, we propose a sequence-to-sequence model that uses RNN as an image encoder that follows the encoder-decoder machine translation model, such that the input to the model is a sequence of images that represents the objects in the image. These objects are ordered based on their order in the captions. We demonstrate the results of the model on Flickr30K dataset and compare the results with the state-ofthe-art methods that use the same dataset. The proposed model outperformed the state-of-the-art methods on all metrics.