Reviewerly/exHarmony huggingface dataset:
Expending Technique Cryptography for Plaintext Messages by Modifying Playfair Cipher Algorithm with Matrix 5 x 19

Cryptography or message hiding techniques can be a solution to avoid cybercrime. One of the techniques is Playfair Cipher that is a cryptographic algorithm. It can hide messages by forming a message key in the form of a 5x5 square-shaped key matrix to accommodate 25 capital letter characters by removing the letter and replacing it with the letter I and making plaintext messages into letter pairs (bigram) to do the encryption process with some rules and provisions in this algorithm. So that a ciphertext is formed which has the same character length as the plaintext. The Playfair Cipher algorithm mechanism is the weakness of Playfair Cipher, which is the limitation in accommodating characters. Eliminate the letter J that causes ambiguity and the length of the plaintext character and the same ciphertext so that it is easy to solve with the frequency of occurrence of the bigram. Modifying Playfair cipher algorithm by extending into 5x19 key matrix with ninety-five characters to hold any types of characters (capital letters, small, numbers, symbols) solving the ambiguity. The expansion techniques will form different ciphertext results with its plaintext to overcome the weakness of Playfair cipher. The aim of this research was to produce a Playfair cipher modification algorithm that could cover the weakness of Playfair cipher and result in differences in character length on plaintext and ciphertext implementation results from Playfair cipher modification.
Advances in the application of Ontologies in the area of Digital Forensic Electronic Mail

This article presents a descriptive review of the research published in the last five years to identify areas of unavailability in the study of Digital Forensics problems. Particularly, it is important to define the state of the art related to the application of ontologies, especially in the forensics of emails. The following objectives of the review are proposed: identify and study the most up-to-date research contributions on Ontologies and Digital Forensics; establish the gaps in current research related with the application of Ontologies to Digital Forensics; and correlate these works from attributes of proximity (or distance) with the application of ontologies to the forensic analysis of emails. In addition, a systematic method is defined to select the research works that are considered of interest for this review. It is expected that it will lead to the identification of gaps in the investigation of characteristic problems in digital forensic analysis, and the definition of an updated theoretical framework linked to the forensic analysis of emails with the application of ontologies.
A Study: Multiple-Label Image Classification Using Deep Convolutional Neural Network Architectures

Multiple-label image classification is a kind of supervised learning approach, in which each image can belong to the set of multiple classes. The area of multiple-label image classification increases in recent years, in the field of machine learning. Multiple-label image classification has created significant scope from research people and it has been employed in an image labeling and object prediction tasks. Many conventional machine learning algorithms used for multiple-label image prediction by using relationship between objects in the image, co-occurrence objects, and rank between multiple instances in an image. Deep ConvNet architectures have proved increasing classification performance in image object classification in recent time. This study presents the task of multiple-label classification, the various literatures in area of multiple-label image classification using deep ConvNet architecture, also provides evaluation measures and performs a comparative analysis of the architecture models on PASCAL VOC dataset and various future challenges in the multiple-label image area.
Probe position sensor to track image location in 2D ultrasound

Two-dimensional ultrasounds (2D US) have low reproducibility of given images in later sessions and often depend on the operator’s experience. Due to difficulties in reproducing images in 2D US separated in time, a device to track the position of the ultrasound probe was created. This device aims to measure the probe position angle in two axes for 2D US to provide accurate repositioning of the 2D US probe for repeatability of the desired imaging plane. The device uses an inclinometer with an Arduino microprocessor to determine the angle of the ultrasound probe to subsequently establish the specific position coordinates of the probe. To test the effectiveness of this device, a 2D US probe was placed on a frontal burr hole of a neonatal swine to image the brain. When fixing the point of contact, using the probe with the angle sensing device helped the operator reproduce images that were both visually and positionally similar. This attachment allows the probe to return to the same image with at least 95% similarity in between different sessions, based on evaluations using an image analysis Matlab program. The sensor can return images of the same location with landmarks within the tolerance of 1.400°. This device could potentially improve 2D US image reproducibility such that accuracy of images between sessions is increased, thereby increasing the reproducibility of 2D US in diagnostic and interventional procedures.
Deformation Gesture-based Input Method for Non-visual Primitive Geometric Shape Drawing

Reading and creating graphical information is a difficult task for users with visual impairment and blindness. It becomes even more challenging on touchscreen devices due to the lack of tactile buttons. However, advancement in flexible displays and electronics offers the potential use of physical deformation as an additional input modality. These deformation-based gestures provide innate tactile and kinesthetic feedback, which are essential for non-visual interaction. In this paper, I describe my Ph.D. research on non-visual drawing using the deformation gesture-based input method. This work is currently in the initial phase. This research aims to understand the preference and performance of deformation-based gestures on a smartphone-sized flexible handheld device and evaluate the effect of deformation and touch input modalities in the non-visual primitive geometric shape drawing. The expected outcome of this research can be useful for user interface designers and developers and researchers in developing accessible applications for future flexible devices.
Data Mining Hardware Acceleration for Object Detection

Over the past decade, object detection has been an active research area in the field of computer vision due to its many applications such as intelligent robots, smart homes, monitoring and surveillance. The need for real-time detection in most of these applications and the frequent failure of software-based implementations in achieving real-time capability, has motivated researchers to utilize hardware acceleration. Due to the potential for parallelism, low power and reconfigurability, Field Programmable Gate Array (FPGA) devices are well-suited for object detection system implementation. In this survey, we investigate FPGA-based implementation of object detection systems using data mining algorithms.
Optical methods of banknotes sorting for Ukrainian Hryvnia: results and problems

General soiling is the main reason for withdrawing banknotes from circulation. Optical characteristics are fundamental for determining deterioration during automated banknote sorting. A comparative study of the results of the Ukrainian hryvnia (UAH) banknote sorting by optical characteristics was conducted for banknotes withdrawn in 2009, 2012 and 2017. If in 2009 and 2012 due to general soiling, which is the main reason for the withdrawal of circulation for banknotes, 89,19% of the Ukrainian hryvnia were withdrawn from circulation, then in 2017 this figure has decreased already to 50%, depending on the denomination. Thus, the determined before and implemented the direction of priority research and development to increase the durability of the UAH - increasing the resistance to general soiling - has significantly improved the durability of UAH notes, since for other currencies the withdrawing due to the general soiling is 64 - 81%. It was found that the use of processing complexes allows to reliably assess the quality of the banknotes, although there are certain problems: there is a mistake not taking into account the simultaneous presence of various types of damage on one note and the return to circulation of deteriorated banknotes and unjustified removal of circulation of suitable banknotes due to an erroneous assessment of their status to be deteriorated.
Exploring the Memorization-Generalization Continuum in Deep Learning

Human learners appreciate that observations usually form hierarchies of regularities and sub-regularities. For example, English verbs have irregular cases that must be memorized (e.g., go -> went) and regular cases that generalize well (e.g., kiss -> kissed, miss -> missed). Likewise, deep neural networks have the capacity to memorize rare or irregular forms but nonetheless generalize across instances that share common patterns or structures. We analyze how individual instances are treated by a model via a consistency score. The score is the expected accuracy of a particular architecture for a held-out instance on a training set of a given size sampled from the data distribution. We obtain empirical estimates of this score for individual instances in multiple data sets, and we show that the score identifies out-of-distribution and mislabeled examples at one end of the continuum and regular examples at the other end. We explore two categories of proxies to the consistency score: pairwise distance based proxy and the training statistics based proxies. We conclude with two applications using C-scores to help understand the dynamics of representation learning and filter out outliers, and discussions of other potential applications such as curriculum learning, and active data collection.
The Implementation of Data Mining to Show UKT (Students’ Tuition) Using Fuzzy C-Means Algorithm : (Case Study: Universitas Pendidikan Ganesha)

This research aimed to show the result of clustering students’ tuition (UKT) at Undiksha using algorithm FCM. The characteristics of each cluster, measurement of level implementing algorithm FCM accuracy in determining UKT. Students’ tuition data used in this research include students’ tuition from SBMPTN year 2017. The students’ data came from 30 students with 7 parameters, namely, parents’ occupation, parents’ income, number of dependents, assets, water payment, electronic voltage, and varieties of vehicles. The data of students’ tuition grouped into four groups, namely, UKT 1, UKT 2, UKT 3, and UKT 4. The data from grouping students’ tuition using FCM method in determining students’ tuition supported with Matlab Software 2017 a showed UKT 1 into 89 students, UKT 2 into 91 students, UKT 3 into 79 students, and UKT 4 into 46 students. The data characteristics of each student’s tuition were gathered from each parameter based on the result of the center vector (v) in the last iteration. Besides, the result showed an FCM method has high accuracy in 0.78. The result of factor analysis showed 3 factors determined students’ tuition from 7 parameters, namely, income factor, expulsion factor, and load factor. On the other hand, future research can be developed by grouping the 3 factors as computation variable in algorithm FCM and to use other methods, so that the results of clustering are more optimal.
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Black Box Simulators

As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions. A significant hurdle to allowing for such explanatory dialogue could be the vocabulary mismatch between the user and the AI system. This paper introduces methods for providing contrastive explanations in terms of user-specified concepts for sequential decision-making settings where the system's model of the task may be best represented as a blackbox simulator. We do this by building partial symbolic models of the task that can be leveraged to answer the user queries. We empirically test these methods on a popular Atari game (Montezuma's Revenge) and modified versions of Sokoban (a well known planning benchmark) and report the results of user studies to evaluate whether people find explanations generated in this form useful.
Fuzzy Lattice-Based Orthogonal Image Transformation Technique for Natural Image Analysis

Fuzzy lattice theory have been widely used in image processing as it allows map functions residing in an original space to functions in a transformed space, resulting in powerful knowledge extraction and image pattern recognition. Despite recognition efficiency and best means of knowledge extraction, the computational complexity and the noise rate involved have been an open problem to be addressed. In this paper, to reduce the computational complexity by optimizing the number of granules between pixels and improving the PSNR through linear fuzzy transform, a method called Euclidean Fuzzy Lattice Orthogonal Image Transform (EFL-OIT) has been presented.
Deep Residual Learning for Burst Denoising

The goal of image denoising is to recover a clean image from noisy input(s). For single image denoising, we look for similarities (or priors) within and across an image dataset to help recover clean images. As noise level increases, using multiple frames become feasible, which is defined as burst denoising. In this paper, we propose a deep residual model for burst denoising. Unlike previous methods, our model does not need an explicit aligning procedure, which is light-weighted and fast. Since denoising performance is closely related to noise level, frame displacement and number of frames (burst length), intensive experiments and ablation study are performed. Results show that the proposed method performs significantly better than previous state-of-the-art methods V-BM4D and KPN in all valid test cases.
Pedestrian Presence Detection System Based on Image Processing

The study presents a methodology for detection of pedestrian presence at road crossings. It is based on image processing and a recognition background pattern. For each camera image is applied a filter in order to exclude pedestrians outside the waiting enclosure from affecting the system's decisions. Next, the object is converted to black and white format and is divided into sections. Each section is compared with the corresponding section from the recognition pattern pixel-by-pixel. The relative error between the two images is then compared to a critical value, and a decision is made. The analysis of the images by sections reduces the influence of potential vibrations, thus significantly increasing the probability for correct recognitions.
SARC: Split-and-Recombine Networks for Knowledge-Based Recommendation

Utilizing knowledge graphs (KGs) to improve the performance of recommender systems has attracted increasing attention recently. Existing path-based methods rely heavily on manually designed meta-paths, while embedding-based methods focus on incorporating the knowledge graph embeddings (KGE) into recommender systems, but rarely model user-entity interactions, which can be used to enhance the performance of recommendation. To overcome the shortcomings of previous works, we propose SARC, an embedding-based model that utilizes a novel Split-And-ReCombine strategy for knowledge-based recommendation. Firstly, SARC splits the user-item-entity interactions into three 2-way interactions, i.e., the user-item, user-entity and item-entity interactions. Each of the 2-way interactions can be cast as a graph, and we use Graph Neural Networks (GNN) and KGE to model them. Secondly, SARC recombines the representation of users and items learned from the first step to generates recommendation. In order to distinguish the informative part and meaningless part of the representations, we utilize a gated fusion mechanism. The advantage of our SARC model is that through splitting, we can easily handle and make full use of the 2-way interactions, especially the user-entity interactions, and through recombining, we can extract the most useful information for recommendation. Extensive experiments on three real-world datasets demonstrate that SARC outperforms several state-of-the-art baselines.
Decentralized Incentive-Compatible and Sybil-Proof Transaction Advertisement

In a blockchain network, transaction advertisement is the announcement of the new transactions to the participants (miners) who are responsible to validate them. Existing blockchain protocols lack an incentive-compatible advertisement process where a rational participant would gain from advertising a transaction. The deficiency can be solved by a Sybil-proof rewarding function which divides the transaction fee among the round leader and the nodes who advertise it. Up to now, there have been three rewarding function proposals, all of which require special constraints on the blockchain network model, e.g., tree-structured connections. In this work, we formulate the rewarding function and obtain the necessary conditions for Sybil-proofness and incentive-compatibility properties. To the best of our knowledge, we present the first rewarding function which is suitable for any blockchain network model. We introduce path length dependent rewarding for the nodes involved in the advertisement process, which helps us to overcome the impossibility results given in the previous works. Our rewarding function divides the transaction fee among the nodes who advertise it, the current round leader and the next round leader. In addition to these achievements, unlike previous proposals, our rewarding function provides resistance against the forking attacks where an adversary rejects a valid block and creates a fork to gain the transaction fees in the original block.
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

We prove that two-layer (Leaky)ReLU networks initialized by e.g. the widely used method proposed by He et al. (2015) and trained using gradient descent on a least-squares loss are not universally consistent. Specifically, we describe a large class of one-dimensional data-generating distributions for which, with high probability, gradient descent only finds a bad local minimum of the optimization landscape. It turns out that in these cases, the found network essentially performs linear regression even if the target function is non-linear. We further provide numerical evidence that this happens in practical situations, for some multi-dimensional distributions and that stochastic gradient descent exhibits similar behavior.
Word Meaning, Data Semantics, Crowdsourcing, and the BKM/A-Lex Approach

The lexical definition of concepts is an integral part of Fact Based Modelling. More in general, structured description of term meaning, in many forms and guises, has since the early days played a role in information systems (data dictionaries, data modelling), data management (business glossaries for data governance), knowledge engineering (applied logic, rule definition and management), and the Semantic Web (RDF). We observe that at the core of many different approaches to lexical meaning lies the combination of semantic networks and textual definitions, and propose to re-appreciate these relatively simple basics as the theoretical but also, and perhaps more so, the practical core of dealing with Data Semantics. We also explore some fundamental concepts from cybernetics, providing some theoretical basis for advocating crowdsourcing as a way of taking up a continuous lexical definition in and across domain communities. We discuss and compare various combined aspects in lexical definition approaches from various relevant fields in view of the A-Lex tool, which supports a crowdsourcing approach to the lexical definition in a data management context: Business Knowledge Mapping. We explain why this approach indeed applies most of the core concepts of “word meaning as a vehicle for dealing with data semantics in and across communities”.
Incorporating Domain Knowledge in Learning Word Embedding

Word embedding is a Natural Language Processing (NLP) technique that automatically maps words from a vocabulary to vectors of real numbers in an embedding space. It has been widely used in recent years to boost the performance of a variety of NLP tasks such as named entity recognition, syntactic parsing and sentiment analysis. Classic word embedding methods such as Word2Vec and GloVe work well when they are given a large text corpus. When the input texts are sparse as in many specialized domains (e.g., cybersecurity), these methods often fail to produce high-quality vectors. In this paper, we describe a novel method, called Annotation Word Embedding (AWE), to train domain-specific word embeddings from sparse texts. Our method is generic and can leverage diverse types of domain knowledge such as domain vocabulary, semantic relations and attribute specifications. Specifically, our method encodes diverse types of domain knowledge as text annotations and incorporates the annotations in word embedding. We have evaluated AWE in two cybersecurity applications: identifying malware aliases and identifying relevant Common Vulnerabilities and Exposures (CVEs). Our evaluation results have demonstrated the effectiveness of our method over state-of-the-art baselines.
A satellite image target detection model based on an improved single-stage target detection network

Aiming at the problem that it is difficult to detect small targets in satellite images, this paper proposes an improved method based on deep convolutional neural network YOLO V3. Firstly, the network structure of the original YOLO V3 was modified, and the target detection layer of three scales was reset. Then, during the detection process, since the test image is too large, the image is cut through the sliding window and then detected. During the experiment, the original YOLO V3 network and the improved network were used to train and test on the dataset. The experimental results show that the improved network improves the detection accuracy by 1.79% and the recall rate by 4.55%, the AP increased by 4.34%.