Reviewerly/exHarmony huggingface dataset:
Website for remote village empowerment in developing countries

Abstract The village is an area that has a low population density, homogeneous social interactions, works in the field of agriculture and is the lowest government body under the sub-district. The progress of a village varies, depending on its ease in exchanging resources for the lives of its people. Remote villages are villages that are difficult to exchange resources with other regions, so that growth is hampered. The presence of the internet is expected to be able to open access to information to empower disadvantaged villages, among others, by creating village websites. The website is one means of introducing villages to other regions, so that there is an exchange of products and cultures that increase the prosperity of the village. Based on this, the research aims to design a village website. The methodology used in this design uses the mechanism for designing object-oriented Unified Approach websites. This method provides two major stages, namely Object Oriented Analysis and Object Oriented Design, with the help of Unified Modelling Language (UML) modelling. The results of the study are village websites that provide information on agricultural products and processed products offered to other regions. Since this website can be accessed globally, information about the village can be spread throughout the world. Website information can be updated by village officials through training for six hours. System development can be done by providing consultation facilities between village officials and system makers. The conclusion of this study is that the village website produced can be used to provide information about the work of the village community so that it can be marketed to other regions. This website can also be integrated with school and marketplace web.
Comparing Wizard of Oz &amp; Observational Studies for Conversational IR Evaluation

Abstract Systematic and repeatable measurement of information systems via test collections, the Cranfield model, has been the mainstay of Information Retrieval since the 1960s. However, this may not be appropriate for newer, more interactive systems, such as Conversational Search agents. Such systems rely on Machine Learning technologies, which are not yet sufficiently advanced to permit true human-like dialogues, and so research can be enabled by simulation via human agents. In this work we compare dialogues obtained from two studies with the same context, assistance in the kitchen, but with different experimental setups, allowing us to learn about and evaluate conversational IR systems. We discover that users adapt their behaviour when they think they are interacting with a system and that human-like conversations in one of the studies were unpredictable to an extent we did not expect. Our results have implications for the development of new studies in this area and, ultimately, the design of future conversational agents.
Improved collaborative filtering algorithm of restricted boltzmann machine

To overcome the matter of the poor generalization ability resulted by characteristics of homogeneity and the problem that visible layer and hidden layer units only receive 0 and 1 binary data in the unsupervised training of Restricted Boltzmann Machine (RBM) ,the paper introduces the implementation of Collaborative filtering algorithm of RBM with category condition based on real value (R_CCRBM) that can be directly dealing with real value rating data and use category features as additional layer information. For the training of RBM, the category feature information is taken as the training condition for RBM and involved in the activation probability calculation of hidden layer element. Using the Movielens dataset to train and test the model,the experimental results show that this model can improve the efficiency of training speed and feature extraction, and improve the performance of the recommendation system.
Image Generation Using Different Models Of Generative Adversarial Network

Generative adversarial networks (GANs) can be used in modeling highly complex distributions for real world data, especially images. This paper compares between two different models of the Generative Adversarial Networks: the Multi-Agent Diverse Generative Adversarial Networks (MAD-GAN) which consists of multi-generator and one discriminator and the Generative Multi-Adversarial Networks (GMAN) that has multiple discriminators and one generator. The results show that both MAD-GAN and GMAN outperformed the DCGAN. In addition, MAD-GAN performs better than GMAN when avoiding mode collapse or when the dataset contains many different modes.
A Weak and Semi-supervised Segmentation Method for Prostate Cancer in TRUS Images

The purpose of this research is to exploit a weak and semi-supervised deep learning framework to segment prostate cancer in TRUS images, alleviating the time-consuming work of radiologists to draw the boundary of the lesions and training the neural network on the data that do not have complete annotations. A histologic-proven benchmarking dataset of 102 case images was built and 22 images were randomly selected for evaluation. Some portion of the training images were strong supervised, annotated pixel by pixel. Using the strong supervised images, a deep learning neural network was trained. The rest of the training images with only weak supervision, which is just the location of the lesion, were fed to the trained network to produce the intermediate pixelwise labels for the weak supervised images. Then, we retrained the neural network on the all training images with the original labels and the intermediate labels and fed the training images to the retrained network to produce the refined labels. Comparing the distance of the center of mass of the refined labels and the intermediate labels to the weak supervision location, the closer one replaced the previous label, which could be considered as the label updates. After the label updates, test set images were fed to the retrained network for evaluation. The proposed method shows better result with weak and semi-supervised data than the method using only small portion of strong supervised data, although the improvement may not be as much as when the fully strong supervised dataset is used. In terms of mean intersection over union (mIoU), the proposed method reached about 0.6 when the ratio of the strong supervised data was 40%, about 2% decreased performance compared to that of 100% strong supervised case. The proposed method seems to be able to help to alleviate the time-consuming work of radiologists to draw the boundary of the lesions, and to train the neural network on the data that do not have complete annotations.
HFDCM: A low-cost machine learning based class attendance monitoring system

In this paper, a unique camera model was proposed to evaluate class attendance more efficiently. For making the system more robust and reliable, attendance was taken after 15 minutes of class starts and before 15 minutes of class ending. A unique algorithm was also proposed to operate the system properly. This system uses a state-of-the-art methodology to evaluate students attendance. Which is actually based on face detection and face recognition. Accuracy in percentage was the primary concern for choosing the more accurate methodology.
Analysis of Secure Multimedia Communication in Cloud Computing

The exponential up surge of cloud computing and multimedia data communication has alarmed academia-industries to develop more efficient and robust solution, especially secure data transmission system. Unlike classical text data security, multimedia data security requires methods with better signal preserving capacity as well as computational efficacy. Such needs become inevitable for cloud computing environment where there can be gigantically large number of users active simultaneously and demand Quality-of-Service (QoS) provision for respective data communication. On the other hand, the possibility of data breaches can't be ignored in current day scenario. As solution, numerous efforts have been made using cryptography algorithms and steganography concepts; however higher computational overheads and adaptive attack methods have alarmed researchers to develop more efficient solution. Considering it as motivation, in this paper a review has been performed for the different security challenges for multimedia communication over cloud computing environment, different methods including cryptography and steganography are discussed. In addition, hybrid security systems with cryptography and steganography together have been assessed for respective performance towards secure multimedia data communication over cloud infrastructures. This study revealed that both cryptosystem as well as classical steganography can't assure optimal security as standalone solution. Though, hybrid crypto-steganography approaches have performed satisfactory, their efficacy remain suspicious due to increased attack mechanism like steganalysis. To alleviate such issues, augmenting both cryptosystem as well as steganography, especially for attack-resilient embedding optimization and wavelet analysis can be of paramount significance. It can achieve computational efficiency as well as robust attack resilience towards any possible breach during transmission over cloud infrastructures.
Comparative study of feature extraction and classification methods for recognition of characters taken from vehicle registration plates

General Optical Character Recognition system works on the base of several successive steps such as pre-processing, segmentation, feature extraction, classification and post-processing. Feature extraction plays here a major role. In this article, we present an overview and comparison of various methods and approaches for off-line recognition of machine written Latin characters. We assume that individual characters are already segmented in an image. To recognize characters and translate them to text requires that each character must be described by a feature vector, which is then classified into one of the 36 classes corresponding to the uppercase Latin alphabet letters and numbers.
Extending NIST’s CAVP Testing of Cryptographic Hash Function Implementations

This paper describes a vulnerability in Apple's CoreCrypto library, which affects 11 out of the 12 implemented hash functions: every implemented hash function except MD2 (Message Digest 2), as well as several higher-level operations such as the Hash-based Message Authentication Code (HMAC) and the Ed25519 signature scheme. The vulnerability is present in each of Apple's CoreCrypto libraries that are currently validated under FIPS 140-2 (Federal Information Processing Standard). For inputs of about $$2^{32}$$ bytes (4 GiB) or more, the implementations do not produce the correct output, but instead enter into an infinite loop. The vulnerability shows a limitation in the Cryptographic Algorithm Validation Program (CAVP) of the National Institute of Standards and Technology (NIST), which currently does not perform tests on hash functions for inputs larger than 65 535 bits. To overcome this limitation of NIST's CAVP, we introduce a new test type called the Large Data Test (LDT). The LDT detects vulnerabilities similar to that in CoreCrypto in implementations submitted for validation under FIPS 140-2.
Systematic Construction of Nonlinear Product Attacks on Block Ciphers

A major open problem in block cipher cryptanalysis is discovery of new invariant properties of complex type. Recent papers show that this can be achieved for SCREAM, Midori64, MANTIS-4, T-310 or for DES with modified S-boxes. Until now such attacks are hard to find and seem to happen by some sort of incredible coincidence. In this paper we abstract the attack from any particular block cipher. We study these attacks in terms of transformations on multivariate polynomials. We shall demonstrate how numerous variables including key variables may sometimes be eliminated and at the end two very complex Boolean polynomials will become equal. We present a general construction of an attack where multiply all the polynomials lying on one or several cycles. Then under suitable conditions the non-linear functions involved will be eliminated totally. We obtain a periodic invariant property holding for any number of rounds. A major difficulty with invariant attacks is that they typically work only for some keys. In T-310 our attack works for any key and also in spite of the presence of round constants.
Dark Web

The World Wide Web is a part of the Internet that provides data dissemination facility to people. The contents of the Web are crawled and indexed by search engines so that they can be retrieved, ranked, and displayed as a result of users' search queries. These contents that can be easily retrieved using Web browsers and search engines comprise the Surface Web. All information that cannot be crawled by search engines' crawlers falls under Deep Web. Deep Web content never appears in the results displayed by search engines. Though this part of the Web remains hidden, it can be reached using targeted search over normal Web browsers. Unlike Deep Web, there exists a portion of the World Wide Web that cannot be accessed without special software. This is known as the Dark Web. This chapter describes how the Dark Web differs from the Deep Web and elaborates on the commonly used software to enter the Dark Web. It highlights the illegitimate and legitimate sides of the Dark Web and specifies the role played by cryptocurrencies in the expansion of Dark Web's user base.
A Comparative Study on Multilevel Thresholding Using Meta-Heuristic Algorithm

Current research work is designing the biological visual system which can emulate the human visual system. Image segmentation is one of the important initial steps in this area. There are different approaches to perform segmentation. One of the well-known techniques in image segmentation to separate objects from the background is Image thresholding. Segmentation using multiple thresholds is treated as an optimization problem in most of the cases. This can be done by maximizing or minimizing a given objective function. This paper presents a comparison of seven well known meta-heuristic techniques to obtain optimal threshold for multilevel thresholding problem: wind driven optimization, grey wolf optimization, firefly algorithm, whale optimization, crow optimization algorithm, and grasshopper optimization. Experimental results present the quantitative and qualitative measures of the different algorithms on multi-level thresholding problem with advantages and drawbacks.
Application and Analysis of Hyperspectal Imaging

Hyperspectral imaging is an incline approach in far off sensing that excavates its application in many special regions, consisting of agriculture, mapping, goal detection, meals best tracking etc. This approach offers the capability to remotely become aware of the composition of each pixel of the photograph. This work discusses capturing of hyperspectral images using image spectrometer, applications in various fields, its data analysis technique. Data analysis methods for the detection of materials with spectral signatures are broadly classified into three types: whole pixel, sub-pixel and matched filtering methods. This review gives a brief introduction about these methods following instances of the three types of methods.
An Automatic Detection for Avian Blood Cell based on Adaptive Thresholding Algorithm

The chicken industry ranks tenth in the world in term of output in the world meat market. Red blood cell (RBC) count is one of the basic health screening protocol required in an exported meat industry. However, the human's automated blood analyzer cannot be applied to avian RBC, because the shape of avian blood (ellipse) is different from the mammal one (circle). In this paper, we propose 2-stage thresholding technique for automatic avian RBC counting. First, the blood smear slide is binarized into RBC and non-RBC area by applying Otsu thresholding. Then, image morphology and Otsu thresholding are reapplied to detect the blood nucleus. After that, the connected component analysis is applied to count the number of RBC. The experiment demonstrated that the proposed technique was simple and provided the count with error rate (2.23%) much less than the clinically acceptable value (5%).
Local Feature Hashing With Binary Auto-Encoder for Face Recognition

The learning-based hashing has recently made encouraging progress in face recognition. However, most existing hashing methods disregard the discrete constraint during optimization, inducing the accumulated quantization errors. In this work, we develop an effective learning-based hashing model, namely local feature hashing with binary auto-encoder (LFH-BAE), to directly learn local binary descriptors in the Hamming space. It attempts to exploit structure factors to well reconstruct the face image from binary codes. Specifically, we first introduce a binary auto-encoder to learn a hashing function to project each face region into high-quality binary codes. Since the original problem is a tricky combinational function, we then present a softened version to decompose it into separate tractable sub-problems. Next, we propose an effective alternating algorithm based on the augmented Lagrange method (ALM) to solve these sub-problems, which helps to generate strong discriminative and excellent robust binary codes. Moreover, we utilize the discrete cyclic coordinate descent (DCC) method to optimize binary codes to reduce the loss of useful information. Lastly, we cluster and pool the obtained binary codes, and construct a histogram feature as the final face representation for each image. Extensive experimental results on four public datasets including FERET, CAS-PEAL-R1, LFW and PaSC show that our LFH-BAE is superior to most state-of-the-art face recognition algorithms.
ROS-Enabled Hardware Framework for Experimental Robotics

Applying FPGAs and real-time bare-metal code in experimental robots has many benefits but puts high demands on the skills of students and researchers. This contribution presents an interface between FPGA, hard-macro microcontroller cores and the popular middleware ROS implemented on a single chip.
Research on AES Cryptographic Chip Electromagnetic Attack Based on Deep Transfer Learning

Cryptographic chip is an important carrier for implementing cryptographic algorithms. Its side channel leakage seriously threatens information security, which has attracted extensive attention from researchers and business circles around the world. In this paper, the AES-128 cryptographic algorithm is used as the attack object implemented by the commonly FPGA cipher chip. The electromagnetic radiation detection is the main test method while the deep learning method is applied to the side channel attack. For traditional template attack method, its shortcoming is that the plaintext or ciphertext must be known. Combined with the “divide and conquer” approach, we proposed a nibble attack method that targets the key directly. This method can not only reduce the number of templates that need to be built, but also reduce the computational complexity. At the same time, this method can use more training data. For the problem of time-consuming training and large samples collection, transfer learning is used to assign initial weights to the training model. In this way, we fixed the AES-128 plaintext and keys and collected its electromagnetic data, in order to obtain an ideal template to transfer the deep learning situation to the specific case which the keys and plaintext are random to improve deep learning speed and accuracy of key attacks. The experimental results show that the recovery rate of the attack model to the nibble key can reach 77.13% while it has increased 9.47%. compared with the direct training. Besides, the training process is reduced by about 40 rounds, and the speed is increased by nearly 40%.
Mapless Navigation for Autonomous Robots: A Deep Reinforcement Learning Approach

Finding a collision-free path for mobile robots is a challenging task, especially in sceneries where obstacle information is partly observed. Our work presents a decentralized collision avoidance approach based on an innovative application of deep reinforcement learning. The approach takes the spare 10-dimensional range findings and the target position in mobile robot coordinate frame as input and the continuous action commands as output. Traditional method for finding collision-free paths deeply depends on extremely precise laser sensor and the map making work of the roadblocks is inevitable. Our work shows that, using an asynchronous deep reinforcement learning method, a mapless path planer can be trained from start to finish without any manual operations. The trainer is available in other virtual environment directly. We compare a traditional method with the asynchronous method and find that our asynchronous method can decrease training time at beginning.
LG: A clustering framework supported by point proximity relations

Clustering is a research problem based on the data's proximity relationship which is not made full use of by all the existing algorithms. In this paper, we present a novel two-stage LG framework consisting of the proposed Local Energy Gradient Oppression (LEGO) and the Guide Point Assignation (GPA) strategies which are closely related to the data points’ proximity relations. In the LG framework, it is crucial to locate the appropriate centers for the subsequent data label assignment, and therefore we introduce the nuclear model viewing the dataset as a collection of charged particles, which is the basis of LEGO, and the points with local maximum potential energy are ascertained as the cluster centers. Besides, the GPA strategy innovatively adopts the idea that the cluster center actively selects data points as the same cluster, enabling the LG framework still to be effective when dealing with datasets of arbitrary shape distribution. Superiorities of the proposed framework and the two strategies are demonstrated on four synthetic datasets and three real-world faces image datasets in terms of two clustering performance metrics.