Reviewerly/exHarmony huggingface dataset:
An Advance Cryptographic Solutions in Cloud Computing Security

Cryptographically cloud computing may be an innovative safe cloud computing design. Cloud computing may be a huge size dispersed computing model that ambitious by the economy of the level. It integrates a group of inattentive virtualized animatedly scalable and managed possessions like computing control storage space platform and services. External end users will approach to resources over the net victimization fatal particularly mobile terminals, Cloud's architecture structures are advances in on-demand new trends. That are the belongings are animatedly assigned to a user per his request and hand over when the task is finished. So, this paper projected biometric coding to boost the confidentiality in Cloud computing for biometric knowledge. Also, this paper mentioned virtualization for Cloud computing also as statistics coding. Indeed, this paper overviewed the safety weaknesses of Cloud computing and the way biometric coding will improve the confidentiality in Cloud computing atmosphere. Excluding this confidentiality is increased in Cloud computing by victimization biometric coding for biometric knowledge. The novel approach of biometric coding is to reinforce the biometric knowledge confidentiality in Cloud computing. Implementation of identification mechanism can take the security of information and access management in the cloud to a higher level. This section discusses, however, a projected statistics system with relation to alternative recognition systems to date is a lot of advantageous and result oriented as a result of it does not work on presumptions: it's distinctive and provides quick and contact less authentication. Thus, this paper reviews the new discipline techniques accustomed to defend methodology encrypted info in passing remote cloud storage.
A Novel Approach for Meta-Search Engine Optimization

Search engines are turning out to be the greatest tools for gaining valuable data from the internet. Search engines return the search result to the user query which can be an important result or non-important result. Because, the users naturally look only at the first few pages of search results, and search engine ranking can introduce significant bias to their understanding of the internet and their information gain. When a search query is delivered to several search engines, each individual returns a list of pages based on the ranking. Scientists have confirmed that merging search results in a meta-search engine makes a substantial progress in a search result. Current meta-search engines use several search engines for fetching the results but do not emphasize on the semantic relation of the query for finding the best result. In order tod overcome this limitation, a new approach is proposed. The proposed approach can optimize meta-search results using the combination of linear search and semantic search.
A PCA-like Autoencoder.

An autoencoder is a neural network which data projects to and from a lower dimensional latent space, where this data is easier to understand and model. The autoencoder consists of two sub-networks, the encoder and the decoder, which carry out these transformations. The neural network is trained such that the output is as close to the input as possible, the data having gone through an information bottleneck : the latent space. This tool bears significant ressemblance to Principal Component Analysis (PCA), with two main differences. Firstly, the autoencoder is a non-linear transformation, contrary to PCA, which makes the autoencoder more flexible and powerful. Secondly, the axes found by a PCA are orthogonal, and are ordered in terms of the amount of variability which the data presents along these axes. This makes the interpretability of the PCA much greater than that of the autoencoder, which does not have these attributes. Ideally, then, we would like an autoencoder whose latent space consists of independent components, ordered by decreasing importance to the data. In this paper, we propose an algorithm to create such a network. We create an iterative algorithm which progressively increases the size of the latent space, learning a new dimension at each step. Secondly, we propose a covariance loss term to add to the standard autoencoder loss function, as well as a normalisation layer just before the latent space, which encourages the latent space components to be statistically independent. We demonstrate the results of this autoencoder on simple geometric shapes, and find that the algorithm indeed finds a meaningful representation in the latent space. This means that subsequent interpolation in the latent space has meaning with respect to the geometric properties of the images.
Research on Adaptive Face Recognition Algorithm Under Low Illumination Condition

With the popularization of face recognition technology, the demand for the accuracy of face recognition has greatly increased. In the face image acquisition, the low illumination environment has a significant effect on the quality of human face images. Face images are susceptible to many factors such as image background, brightness, and image noise. This leads to many problems such as low detection rate of face recognition and the decline of recognition accuracy. In this paper, we take the face images under low illumination condition as a sample and present an adaptive algorithm based on the OTSU segmentation algorithm, which realizes the self-adaptation image acquisition under low illumination condition. By using the Adaboost classification detector to verify low-light face images before and after processing, the model we used in this paper successfully improved the accuracy of face detection.
On the Development of Feature-Based Sprint in AGILE

AGILE methodology is widely used throughout the information technology industry for software development. In AGILE methodology, the delivery cycle is broken down into sprints or iterations. Many iteration-based AGILE teams use a time-boxed 1-hour discussion midway through a 2-week iteration (the team selects an iteration duration that provides them frequent enough feedback) (Cohn in AGILE Estimating and Planning, 2005 [1]). A sprint or iteration is expected to deliver a piece of functionality within a fixed period of time. The length of AGILE sprint is measured in number of days. Hence, it can be concluded that the unit of sprint is time, measured in numbers of days. But since AGILE is feature-driven, the unit of sprint should not be limited to time. There is a need to explore other measures that can be used to define or determine the length of the AGILE sprint. The purpose of this paper is to find out any other unit to define or derive the length of sprint. The paper also explores the various ways in which the AGILE developer(s), AGILE tester(s), and AGILE end user(s) can be benefited by changing the unit of sprint length from time to something else. This paper particularly talks about the possibility of using the “feature” as unit of sprint and lists down the advantages and disadvantages of using “feature” over “time” as unit of sprint. The term “sprint” or “iteration” is used as synonyms throughout the discussion.
The analysis of rainwater harvesting carrying capacity on water domestic supply for dwelling areas in Indonesia

This research aims to promote the method to design rainwater harvesting facilities in Indonesian dwelling areas.The estimation of rainwater harvesting (RWH) carrying capacity is undertaken using a simulation involving rainfall, inflow, and outflow data.The research was undertaken in Natar sub-district, Southern Lampung, Indonesia.Daily rainfall data from Branti Airport from 2013 to 2017 are used for the simulation.Research shows that houses in the study area can supply approximately 35% of their domestic water needs by applying RWH.This means that the support capacity of rainwater harvesting in the study area revolves around that value.The research also shows intensity of rainfall will greatly affect the carrying capacity of RWH.In addition to rainfall, the economic ability of a family plays an important role in planning the dimensions of a RWH facility in a house.Finally, maintenance of RWH facilities on a regular basis is important to ensure the operational effectiveness of RWH.
Pengaruh Mailing Groups Sebagai Media Diskusi Dalam Motivasi Belajar Mahasiswa

In the current era of globalization, technology management has touched every part of human life. The combination of these two factors will become a new innovation in the management of education. At Raharja College, the entire academic community has a facility in the form of an official e-mail, Rinfo, which functions as a communication medium. Therefore, it is necessary to optimize the use of RinfoApps which is used as a discussion medium for online learning. Mailing Groups is one of the Google System features that can be used as a formal media for the online learning discussion management process. By creating a forum called the class mailing list on Mailing Groups, students and lecturers can discuss anytime, students can share about learning material, and lecturers can also monitor the activeness of student discussions, this certainly has an important influence on learning motivation college student. Supported by 2 (two) research methods, namely library research and observation methods. The final results of the research that has been achieved are the formation of a discussion media that is easily accessible anywhere and anytime so that the discussion process between students and lecturers becomes more effective and efficient.
Fence GAN: Towards Better Anomaly Detection

Anomaly detection is a classical problem where the aim is to detect anomalous data that do not belong to the normal data distribution. Current state-of-the-art methods for anomaly detection on complex high-dimensional data are based on the generative adversarial network (GAN). However, the traditional GAN loss is not directly aligned with the anomaly detection objective: it encourages the distribution of the generated samples to overlap with the real data and so the resulting discriminator has been found to be ineffective as an anomaly detector. In this paper, we propose simple modifications to the GAN loss such that the generated samples lie at the boundary of the real data distribution. With our modified GAN loss, our anomaly detection method, called Fence GAN (FGAN), directly uses the discriminator score as an anomaly threshold. Our experimental results using the MNIST, CIFAR10 and KDD99 datasets show that Fence GAN yields the best anomaly classification accuracy compared to state-of-the-art methods.
Verifiable Anonymous Identities and Access Control in Permissioned Blockchains.

In this paper we address the issue of identity and access control within shared permissioned blockchains. We propose the ChainAchor system that provides anonymous but verifiable identities for entities on the blockchain. ChainAchor also provides access control to entities seeking to submit transactions to the blockchain to read/verify transactions on the the permissioned blockchain. Consensus nodes enforce access control to the shared permissioned blockchain by a simple look-up to a (read-only) list of anonymous members' public-keys. ChainAnchor also provides unlinkability of transactions belonging to an entity on the blockchain. This allows for an entity to optionally disclose their identity when a transaction is called into question (e.g. regulatory or compliance requirements), but without affecting the anonymity and unlinkability of their remaining transactions.
A Dilated Inception Network for Visual Saliency Prediction

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures. In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction. It captures multi-scale contextual features effectively with very limited extra parameters. Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module, our proposed dilated inception module (DIM) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps. Moreover, the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions. As such, we can formulate saliency prediction as a probability distribution prediction task for global saliency inference instead of a typical pixel-wise regression problem. Experimental results on several challenging saliency benchmark datasets demonstrate that our DINet with proposed loss functions can achieve state-of-the-art performance with shorter inference time.
Using LIDO for Evolving Object Documentation into CIDOC CRM

Over the last years, many projects and institutions have worked on transforming object documentation from several existing cataloguing systems into a CIDOC Conceptual Reference Model (CIDOC CRM) compliant graph representation, as expressed in RDF. There were also various attempts to provide a generally valid path for the transfer of data from Lightweight Information Describing Objects (LIDO), CIDOC’s recommended XML Schema for metadata harvesting, into representations that are suitable for the Semantic Web. They all face the challenge that a detailed mapping, which fully exploits the CRM’s expressiveness and requires semantic assumptions that may not always turn out to be valid. Broad mappings, on the other hand, fail to leverage the potential of Semantic Web technologies. In this paper, we propose a method for using LIDO combined with an associated terminology as a means of evolving existing object documentation into CRM-based RDF representations. By clearly distinguishing between controlled vocabulary and ontology, it is possible to transform object data relatively easily into a minimized, though efficient structure using the CIDOC CRM ontology. This structure will open up the whole world of Semantic Web technologies to be used for further semantic refinement and data quality analysis through exploiting the underlying controlled vocabularies
A Top-N-Balanced Sequential Recommendation Based on Recurrent Network

To solve the low accuracy problem of the recommender system for long term users, in this paper, we propose a top-N-balanced sequential recommendation based on recurrent neural network. We postulated and verified that the interactions between users and items is time-dependent in the long term, but in the short term, it is time-independent. We balance the top-N recommendation and sequential recommendation to generate a better recommender list by improving the loss function and generation method. The experimental results demonstrate the effectiveness of our method. Compared with a state-of-the-art recommender algorithm, our method clearly improves the performance of the recommendation on hit rate. Besides the improvement of the basic performance, our method can also handle the cold start problem and supply new users with the same quality of service as the old users.
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning.

One of the central goals of Recurrent Neural Networks (RNNs) is to learn long-term dependencies in sequential data. Nevertheless, the most popular training method, Truncated Backpropagation through Time (TBPTT), categorically forbids learning dependencies beyond the truncation horizon. In contrast, the online training algorithm Real Time Recurrent Learning (RTRL) provides untruncated gradients, with the disadvantage of impractically large computational costs. Recently published approaches reduce these costs by providing noisy approximations of RTRL. We present a new approximation algorithm of RTRL, Optimal Kronecker-Sum Approximation (OK). We prove that OK is optimal for a class of approximations of RTRL, which includes all approaches published so far. Additionally, we show that OK has empirically negligible noise: Unlike previous algorithms it matches TBPTT in a real world task (character-level Penn TreeBank) and can exploit online parameter updates to outperform TBPTT in a synthetic string memorization task. Code availiable on github.
Lending Orientation to Neural Networks for Cross-view Geo-localization

This paper studies image-based geo-localization (IBL) problem using ground-to-aerial cross-view matching. The goal is to predict the spatial location of a ground-level query image by matching it to a large geotagged aerial image database (e.g., satellite imagery). This is a challenging task due to the drastic differences in their viewpoints and visual appearances. Existing deep learning methods for this problem have been focused on maximizing feature similarity between spatially close-by image pairs, while minimizing other images pairs which are far apart. They do so by deep feature embedding based on visual appearance in those ground-and-aerial images. However, in everyday life, humans commonly use {\em orientation} information as an important cue for the task of spatial localization. Inspired by this insight, this paper proposes a novel method which endows deep neural networks with the `commonsense' of orientation. Given a ground-level spherical panoramic image as query input (and a large georeferenced satellite image database), we design a Siamese network which explicitly encodes the orientation (i.e., spherical directions) of each pixel of the images. Our method significantly boosts the discriminative power of the learned deep features, leading to a much higher recall and precision outperforming all previous methods. Our network is also more compact using only 1/5th number of parameters than a previously best-performing network. To evaluate the generalization of our method, we also created a large-scale cross-view localization benchmark containing 100K geotagged ground-aerial pairs covering a city. Our codes and datasets are available at \url{https://github.com/Liumouliu/OriCNN}.
VoIP Steganography Methods, a Survey

Abstract Achieving secured data transmission is not always an easy job. Secret data sharing requires confidentiality and Undetectability. Steganography is preferred than cryptography to achieve undetectability. Steganography hides the secret data inside the other file such as text, audio, video, so that the existence of the secret data is completely hidden. Recent research focuses much on utilizing Voice over Internet Protocol (VoIP) calls as a carrier for data hiding. VoIP calls are much preferred among internet users for its wide availability, dynamic time limit and low cost. This paper focuses on data hiding methods that uses VoIP as a carrier. The paper also analyzes the performance of the algorithms using the three metrics undetectability, bandwidth and robustness.
A Formal Analysis of Timing Channel Security via Bucketing

This paper investigates the effect of bucketing in security against timing channel attacks. Bucketing is a technique proposed to mitigate timing-channel attacks by restricting a system’s outputs to only occur at designated time intervals, and has the effect of reducing the possible timing-channel observations to a small number of possibilities. However, there is little formal analysis on when and to what degree bucketing is effective against timing-channel attacks. In this paper, we show that bucketing is in general insufficient to ensure security. Then, we present two conditions that can be used to ensure security of systems against adaptive timing channel attacks. The first is a general condition that ensures that the security of a system decreases only by a limited degree by allowing timing-channel observations, whereas the second condition ensures that the system would satisfy the first condition when bucketing is applied and hence becomes secure against timing-channel attacks. A main benefit of the conditions is that they allow separation of concerns whereby the security of the regular channel can be proven independently of concerns of side-channel information leakage, and certain conditions are placed on the side channel to guarantee the security of the whole system. Further, we show that the bucketing technique can be applied compositionally in conjunction with the constant-time-implementation technique to increase their applicability. While we instantiate our contributions to timing channel and bucketing, many of the results are actually quite general and are applicable to any side channels and techniques that reduce the number of possible observations on the channel.
Playing Charades with a Robot: Collecting a Large Dataset of Human Gestures Through HRI

This work documents a playful human-robot interaction, in the form of a game of charades, through which a humanoid robot is able to learn how to produce and recognize gestures by interacting with human participants. We describe an extensive dataset of gesture recordings, which can be used for future research into gestures, specifically for human-robot interaction applications.
Which Way to the Wheat Field? Women of the Radical Right on Facebook

At what rates and in what capacity do women participate in extreme far-right ("radical right") political online communities?Gathering precise demographic details about members of extremist groups in the United States is difficult because of a lack of data.The purpose of this research is to collect and analyze data to help explain radical right participation by gender on social media.We used the public Facebook Graph API to create a large dataset of 700,204 members of 1,870 Facebook groups spanning 10 different far-right ideologies during the time period June 2017 -March 2018, then applied two different gender resolution software packages to infer the gender of all users by name.Results show that users inferred to be women join groups in some ideologies at a greater rate than others, but ideology alone does not determine leadership opportunities for women in these groups.Furthermore, our analysis finds similarities between historical women's organizations such as the 1920s Women's Ku Klux Klan and contemporary online "wheat field" groups designed specifically for women.