Reviewerly/exHarmony huggingface dataset:
Hardware Acceleration of the AES Algorithm using Nios-II Processor

Nowadays, cryptography plays a crucial role in both transmitting and receiving sensitive information, so that any unauthorized person does not have access to it. Advanced Encryption Standard (AES) is the most common symmetric encryption algorithm widely used in many applications. In this document, we present the implementation of the AES algorithm in Very High Speed Integrated Circuit Hardware Description language (VHDL) programming language with 128, 192 and 256 bit key sizes using the Nios II processor in the FPGA Arria 10 GX (10AX115N2F45E1SG). We implemented two techniques without custom instruction and with floating point 2. We compared in the two implementations the three AES key sizes in terms of integration time, number of cycle clocks, and the percentage of used hardware resources (LUTs used in the FPGA). The results have shown that, the larger key size, requires longer clock cycle and longer integration time as well. Implementation with Floating Point 2 shows over 11,07% acceleration in clock cycles and total time on all key sizes. The results of two implementations, are compared with existing similar designs and are found to achieve better performance.
Short-Text Question Classification Based on Dependency Parsing and Attention Mechanism

Question texts analysis is a challenging task of the fine-grained classification due to the few annotation data and unbalanced categories. The existing approaches normally assume that each word contributes the same semantic to the question text, but ignore the different meanings of the words and the dependency relations within the text. In this paper, we propose a deep neural network with multi-layer attention mechanism to capture the extended semantic features by using a dependency parsing tree, which has the capacity to spot the central components of the question. The experimental results demonstrate that our proposed model obtains substantially improvement, comparing with several competitive baselines.
Blockchain: Panacea for Corrupt Practices in Developing Countries

Blockchain technology is one of the most exciting emerging technologies and has the capability to digitally revolutionize many systems for better security, autonomy, transparency, auditability, speed, reduced cost and efficiency. Blockchain can be regarded as a revolutionized distributed ledger technology which uses temper proof database in conjunction with consensus algorithms and cryptography. On the other hand, corruption has been a major problem in developing countries like Nigeria that bedevils their progress and development. In this paper, we investigate corrupt practices in developing countries with Nigeria as a case study. We study some blockchain use cases in some countries like Estonia and Georgia. We propose a blockchain framework to curb corrupt practices related to public funds embezzlement as the biggest corruption act. All transactions made by government agencies are stored in the blockchain shared by anti-corruption agencies, police, judiciaries and other related offices at the same time. We also propose the blockchain architecture and consensus suitable for the framework. Blockchain technology is immune to the influence of central authorities and intermediaries. It is secure against tempering and provides transparency and auditability. Hence using blockchain technology will help to tackle corrupt practices for betterment of developing countries such as Nigeria.
On the Real-World Instantiability of Admissible Hash Functions and Efficient Verifiable Random Functions

Verifiable random functions (VRFs) are essentially digital signatures with additional properties, namely verifiable uniqueness and pseudorandomness, which make VRFs a useful tool, e.g., to prevent enumeration in DNSSEC Authenticated Denial of Existence and the CONIKS key management system, or in the random committee selection of the Algorand blockchain. Most standard-model VRFs rely on admissible hash functions (AHFs) to achieve security against adaptive attacks in the standard model. Known AHF constructions are based on error-correcting codes, which yield asymptotically efficient constructions. However, previous works do not clarify how the code should be instantiated concretely in the real world. The rate and the minimal distance of the selected code have significant impact on the efficiency of the resulting cryptosystem, therefore it is unclear if and how the aforementioned constructions can be used in practice. First, we explain inherent limitations of code-based AHFs. Concretely, we assume that even if we were given codes that achieve the well-known Gilbert-Varshamov or McEliece-Rodemich-Rumsey-Welch bounds, existing AHF-based constructions of verifiable random functions (VRFs) can only be instantiated quite inefficiently. Then we introduce and construct computational AHFs (cAHFs). While classical AHFs are information-theoretic, and therefore work even in presence of computationally unbounded adversaries, cAHFs provide only security against computationally bounded adversaries. However, we show that cAHFs can be instantiated significantly more efficiently. Finally, we use our cAHF to construct the currently most efficient verifiable random function with full adaptive security in the standard model.
Adaptive microphase measuring profilometry for three-dimensional shape reconstruction of a shiny surface

Three-dimensional (3-D) reconstruction of a shiny surface is a great challenge for existing structured light techniques because serious measurement errors will occur in image saturation regions in the captured image. To this end, an adaptive microphase measuring profilometry (AMPMP) is proposed. In this method, an optimal frequency selection strategy is introduced to select the frequencies of phase-shifted fringe patterns. The intensity relation between the projected pattern and the captured image is estimated by fitting a nonlinear function after projecting a series of uniform gray-level patterns onto the shiny surface, and the mapping regions of saturated areas in the captured image are computed with the correspondence between the projector image plane and the camera image plane, which is established through MPMP. Based on the estimated intensity relation and the mapping regions, the optimal projection intensities of saturated areas are adaptively adjusted. Then, the adjusted phase-shifted fringe patterns are projected to obtain the absolute phase distribution of the shiny surface. Therefore, accurate 3-D shape reconstruction is realized. The experimental results demonstrated that the proposed AMPMP can well tackle saturated areas of shiny surfaces and has greater performance than other commonly used adaptive fringe projection techniques.
MoSAR

Today there is a large amount of valuable research on corpora, and the availability of corpora has increased significantly in recent years. Unfortunately, this is not the case for all types of corpora. Research in the field of Arabic language processing suffers from a great lack of annotated educational corpora. In this work, we have tried to constitute a new educational corpus by drawing from Moroccan primary school books. This corpus will help education researchers and computational linguists provide appropriate tools to support school students who are learning formal Arabic. We annotated the corpus with morphosyntactic information that can be used in several natural language processing applications. We also added a text difficulty measure, linked to the Moroccan primary school levels, so that the corpus can be used in the development of readability measurement applications. The result is a Modern Standard Arabic Language corpus dedicated to young learners of Arabic as a first language (L1). The corpus is manually labeled by seven levels, namely the primary levels of the Moroccan educational system from 1st to 6th grade, in addition to a more basic level we called level 0.
Robot Opera: Bridging the Anthropocentric and the Mechanized Eccentric

Abstract The recent emergence of robot opera, in which robots and robotic entities have served polyvalent and at times ontologically ambiguous roles, has challenged the distinction made by Bauhaus artist László Moholy-Nagy between anthropocentric and mechanized eccentric theater, as is outlined in his 1924 essay “Theater, Circus, Variety.” When incorporated into the context of music theater, is the robot dimension intended to replace human activities and modes of expression; to augment, disembody or dislocate them; or rather to absorb them, such that the robot becomes an ersatz human presence in and of itself? If the latter, does the robot adequately emulate human attributes of musical expression, or does it establish its own artificial expressive mode and set of performance techniques? With Moholy-Nagy's criteria for a so-called Theater of Totality and these leading questions in the background, salient robot opera examples of the past several years will be discussed. Repertoire examples include Tod Machover's pioneering Death and the Powers (2010), the Komische Oper Berlin production My Square Lady (2015), Keiichiro Shibuya's Scary Beauty (2018), and works emerging from the University of Sussex Centre for Research in Opera and Music Theatre Robot Opera Mini Symposium, held in 2017.
Detecting Exploit Websites Using Browser-based Predictive Analytics

The popularity of Web-based computing has given increase to browser-based cyberattacks. These cyberattacks use websites that exploit various web browser vulnerabilities. To help regular users avoid exploit websites and engage in safe online activities, we propose a methodology of building a machine learning-powered predictive analytical model that will measure the risk of attacks and privacy breaches associated with visiting different websites and performing online activities using web browsers. The model will learn risk levels from historical data and metadata scraped from web browsers.
Automatic Detection and Analysis of DPP Entities in Legal Contract Documents

Due to introduction of General Data Protection Regulation (GDPR) in EU; all the cloud hosted applications (span across multi geo location) wherein captures the personal data need to first identify data privacy protection (DPP) entities and handle it as per EU norms and regulations. The company's legal contracts or transactions in tie up with other parties (customers, partners, suppliers, etc.) are usually stored in to repository; on termination of the contracts either by agreement or mutual consent then the other parties' information are usually archived for historical reasons. The other parties are usually interested in knowing what all documents or transactions they were participated earlier and expects the data to be pruned on need basis. As these documents are unstructured in nature, this paper proposes a solution in identifying all DPP entities with in legal contract documents, index the corpus level accumulated knowledgebase, apply customized ranking algorithm for the retrieved legal contract documents based on DPP search query, derive DPP entities specific legal contract document dependency relation graph for which the parties are participating by using techniques from Information Retrieval, Information Extraction, Natural Language Processing (NLP) and Ontology.
CNN-based Image Super-Resolution and Deblurring

Image super-resolution and deblurring are two highly ill-posed problems that are usually dealt separately. However, real-world images are often low-resolution and have complex blurring. In this paper, non-uniform motion blur and super-resolution task are combined to reconstruct a clear high-resolution image directly from the blurred low-resolution input. We propose a two-branch network based on convolutional neural network(CNN) to reconstruct images, which mainly include super-resolution module and deblurring module. In addition, we use novel loss functions to generate more realistic images. Experimental results show that the method proposed in this paper can reconstruct sharper high-resolution images than other state-of-the-art algorithms, and the proposed model is lightweight, requiring a low computational cost.
Dynamic composition of services

With the remarkable evolution of information technologies, communication technologies and the familiarization with embedded systems, the current systems are moving towards the use of IT (Information Technology) processes and data sources in a perspective of creating services with ambient and ubiquitous intelligence in addition of the capacity for change and adaptation according to user's requests. As a result, the needs of users are changing and progressing, making atomic services unable to meet their needs. In addition, the context must be completed by the intention of the user to be able to respond correctly to the specified need. Thus, this manuscript propose an overview of service composition approaches based on context and user's intent.
Cloud Services Security Evaluation for Multi-Tenants

Cloud computing is witnessing a fast development and widespread adoption due to its cost-effective and resource efficient services. Nevertheless, security risks are not outsourced and remain under both cloud service providers and consumers responsibility. One of the main challenges is how to evaluate the security level of cloud services. Current methods are mostly focused on solving the problem of cloud services evaluation from a single user perspective. However, cloud computing is a multi-tenant environment that serves a large number of tenants with multiple end-users at the same time. This heterogeneity leads to different, and often, conflicting security requirements. In this paper, we propose a multi-tenant cloud services evaluation framework, whereby service selection is carried out per groups of tenants rather than per a single user. To ensure that all tenants' requirements are satisfied, we first consider the similarity between cloud service providers performances and tenants' requirements. Tenants' weights are determined in an objective way based on their risk attitudes and contributions to the overall group decision using fuzzy TOPSIS technique. The final ranking of the alternatives is further guided by consensus analysis process to guarantee a final solution with the highest level of agreement.
Towards a novel auto-rotating lidar platform for cavity surveying

This paper presents the conceptual design, construction, and preliminary testing of a novel auto-rotating platform for cavity surveying. Cavity surveying involves generating a 3D model of an opening from acquired point cloud data. 3D models are used for volume estimation, stope reconciliation, dilution control, convergence monitoring, and rock mass stability analysis. In contrast to conventional cavity monitoring systems (CMS), which employ a rotating light detection and ranging (LiDAR) sensor attached to a stationary boom, the presented novel platform consists of a single point LiDAR sensor attached to an auto-rotating body as a unique solution to the cavity surveying problem. A proof of concept device consisting of a four-blade rotor and a data acquisition system was constructed and dropped from a five-metre overhang. Although auto-rotation was not reached, the set of indoor drop tests provide preliminary results that illustrate the design concept and describes the impact of design parameters. .
Towards a Probe Design Framework

Since their introduction, probes have been widely used in HCI. Despite this, there have not been much reflections and discussions about the design thinking behind their creation and use. There is also a lack of actionable guidance on designing and using probes. This lack may have contributed to some concerns that the method has been misinterpreted and misunderstood. We reviewed HCI literature surrounding probes and found one of the few papers that offers a nascent framework for probe design and use. We used it to guide the design of a collection of probes and reflected on the framework's usefulness. We extend this framework by offering a more useful way of visualizing and working with probe design properties. We also provide further clarity and advice on how others may think and approach the design and use of probes more effectively, especially those turning to probes for the first time.
Tumor Tissue Segmentation for Histopathological Images

Histopathological image analysis is considered as a gold standard for cancer identification and diagnosis. Tumor segmentation for histopathological images is one of the most important research topics and its performance directly affects the diagnosis judgment of doctors for cancer categories and their periods. With the remarkable development of deep learning methods, extensive methods have been proposed for tumor segmentation. However, there are few researches on analysis of specific pipeline of tumor segmentation. Moreover, few studies have done detailed research on the hard example mining of tumor segmentation. In order to bridge this gap, this study firstly summarize a specific pipeline of tumor segmentation. Then, hard example mining in tumor segmentation is also explored. Finally, experiments are conducted for evaluating segmentation performance of our method, demonstrating the effects of our method and hard example mining.
Investigating Various Cryptographic Techniques Used in Cloud Computing

Cloud computing is the newest buzzword which is rising in its technology as it has reduced cumbersome efforts spent on building infrastructure, software, hardware, or any form of such resources used in a workplace. However, data security which is of primordial importance still remains a huge challenge during cloud implementation. Many traditional encryption algorithms are there to ensure security such as play fair cipher and DES. But all these algorithms only help in encryption of plain text into cipher text during communication. For data analysis, the data is deciphered on the cloud which acts as easy prey for the hackers. Herein we look up to three techniques of cryptography—homomorphic encryption, verifiable computation, and multi-party computation which have the potential to achieve security goals.
Clustering of Female Avatar Face Features Consumers Choice using KMeans and SOM Algorithm

Games are interactive activities that is popular with consumers. However, illustrators are unable to determine of characters face many chosen with consumers. The paper proposes a clustering of face features often chosen by consumers for their female avatars. The face features involved in this research are the face shape, eyebrows, eyes, nose, lips, ears and skin color. The paper uses two methods for clustering, KMeans and SOM. These methods used class partitioning based on shape similarities. Using the K-Means results, 14% chose a triangular face, then 16% chose an diamond face shape, 23% chosen a heart face shape, 6% chosen a round face shape, 23% chosen a oval face shape, 18% chosen a square face shape. Results from SOM shows that 18% chose a triangular face, then 11% chose an diamond face shape, 24% chosen a heart face shape, 22% chosen a round face shape, 12% chosen a oval face shape, 13% chosen a square face shape. It shows that KMeans has better performance than SOM in determining the female avatar face classes that are wanted by customers.
Matrix-variate variational auto-encoder with applications to image process

Variational Auto-Encoder (VAE) is an important probabilistic technology to model 1D vectorial data. However, when applying VAE model to 2D image, vectorization is necessary. Vectorization process may lead to dimension curse and lose valuable spatial information. To avoid these problems, we propose a novel VAE model based on matrix variables named as Matrix-variate Variational Auto-Encoder (MVVAE). In this model, input, hidden and latent variables are all in matrix form, therefore inherent spatial structure of 2D images can be maintained and utilized better. Especially, the latent variable is assumed to follow matrix Gaussian distribution which is more suitable for describing 2D images. To solve the weights and the posterior of latent variable, the variational inference process is given. The experiments are designed for three real-world application: reconstruction, denoising and completion. The experimental results demonstrate that MVVAE shows better performance than VAE and other probabilistic methods for modeling and processing 2D data.
Optimizing Ranking Algorithm in Recommender System via Deep Reinforcement Learning

Recommender system, which attempts to narrow down selections for users based on their preference, plays a crucial role in many E-commerce platforms like Amazon and Taobao. Sponsored search can be regarded as a major revenue contributor of recommender system. The platform sorts the items by a ranking function and charges the advertisers for the users' positive feedback (e.g., click). However, traditional ranking strategies usually apply greedy ranking strategies (e.g.,pCTR*Bid), which considers the recommendation processes to be static and only focuses on the immediate reward. In practice, recommendation processes may be highly correlated with each other and cumulative reward needs to be taken into account. To address these issues, this paper redefines the ranking function and proposes a Deep Reinforcement learning based Ranking Strategy (DRRS) to maximize the cumulative reward of the platform. The experiments conducted on real-world datasets demonstrate the effectiveness of the proposed framework.
Estimating pedestrian intentions from trajectory data

In autonomous driving systems, one of the most crucial aspects is to anticipate the movements of other traffic participants. In this paper, several machine learning methods are used to train classifiers capable of estimating the intention of a pedestrian to cross a zebra crossing. Their results are compared to a Bayesian network-an approach commonly used in autonomous driving. The data used for the estimation contain only position and heading of the pedestrians. The best performing method achieved the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> score of 92.37%.
Global Geochemical Baseline Mapping in India for Environmental Management Using Topsoil

As a part of International Union of Geological Sciences (IUGS)/International Association of Geochemistry (IAGC) Task Group on Global Geochemical Baselines, CSIR-National Geophysical Research Institute (CSIR-NGRI), Hyderabad, India has carried out geochemical baseline mapping of India to provide high quality data for a wide range of environmental applications. A project based on IUGS global baselines project was initiated by NGRI to cover the whole of India which is having 122 Global Terrestrial Network (GTN)/Global Reference Network (GRNs) of 160 x 160 km. Soil samples were studied in 115 out of the 122 GRN cells in India. 544 topsoil samples were collected for these studies. The soil samples were collected as per the guidelines given in the IUGS/IAGC field manual. The samples were analyzed for 10 major and 12 trace elements (SiO2, Al2O3, Fe2O3, MnO, MgO, CaO, Na2O, K2O, TiO2, P2O5, Ba, Co, Cr, Cu, Ni, Pb, Rb, Sr, V, Y, Zn and Zr) by X-ray fluorescence spectrometer at NGRI. The analytical data were computed to prepare geochemical maps showing the distribution of major oxides and trace elements all over India. These maps will help to know the distribution of the toxic and hazardous metals and their relation to human health. These maps also help to see the hot spots for the entire country and in suggesting remedial measures.