Reviewerly/exHarmony huggingface dataset:
High-Capacity Constructive Steganography Using Optimal Texture Block Synthesis

In this paper, we design a new high-capacity constructive steganographic scheme by optimal texture image block synthesis. First, original texture pattern is divided into multiple seed patches, which are hashed into a blank stego pattern by the key. Furthermore, we employ generated seed patches to reconstruct a candidate pattern, which is scanned by sliding window to generate a series of candidate texture blocks. Subsequently, we sort these candidate texture blocks and select the appropriate ones that their serial number equals to the secret messages to achieve information hiding. After theses texture blocks containing secret messages are hashed into the stego pattern by the key, the remaining positions of stego pattern are sequentially filled with the optimal candidate texture patches by calculating the least square error. Finally, the stego pattern is completely synthesized as the constructive stego image. Compared with the existing schemes, our proposed scheme has a significant high embedding capacity, while keeping a higher visual quality of stego images.
No-reference stereoscopic images quality assessment method based on monocular superpixel visual features and binocular visual features

No-reference quality assessment of images has received considerable attention. However, the accuracy of such assessment remains questionable because of its weak biological basis. In this paper, we propose a novel quality assessment model based on the superpixel index and biological binocular mechanisms. The technical contributions of our model are the introduction of local monocular superpixel features and three global binocular visual features. We utilize monocular superpixel segmentation to extract two types of entropies as the local visual features for accurate quality-aware feature extraction. In addition, natural scene statistics features are extracted from the binocular visual information to complement the local monocular features and quantify the naturalness of the stereoscopic images. Finally, a regression model is learned to evaluate the quality of the stereoscopic images. Experimental results from three popular databases demonstrate that the proposed model has a more reliable performance than earlier models in terms of prediction accuracy and generalizability.
Qualitative software engineering research: Reflections and guidelines

Abstract Researchers are increasingly recognizing the importance of human aspects in software development. Because qualitative methods are used to explore human behavior in‐depth, we believe that studies using such methods will become more common. Existing qualitative software engineering guidelines do not cover the full breadth of qualitative methods and the knowledge on how to use them like in social sciences. The purpose of this study was to extend the software engineering community's current body of knowledge regarding available qualitative methods and their quality assurance frameworks and to provide recommendations and guidelines for their use. With the support of an epistemological argument and a survey of the literature, we suggest that future research would benefit from (1) utilizing a broader set of research methods, (2) more strongly emphasizing reflexivity, and (3) employing qualitative guidelines and quality criteria. We present an overview of three qualitative methods commonly used in social sciences but rarely seen in software engineering research, namely interpretative phenomenological analysis, narrative analysis, and discourse analysis. Furthermore, we discuss the meaning of reflexivity in relation to the software engineering context and suggest means of fostering it. Our paper will help software engineering researchers better select and then guide the application of a broader set of qualitative research methods.
A Detection Tool for Code Bad Smells in Java Source Code

Code bad smells are the indication of an unstructured codeGupta, Aakanshi snippet that can be a problem in software design. Code bad smell deteriorates the source code quality that resulting in unnecessary maintenance cost.Suri, Bharti This paper presents a tool to automatically detect bad smells in Java software. This tool can detect eight bad smells, namely God class/large class, data class and empty catch block, comments, nested try statement, exception thrown in finally block, unprotected main program by understanding their definitions, empty catch block, dummy handler, nested try statement,Wadhwa, Bimlesh exception thrown in finally block, dummy handler, and unprotected main program considered as exception handling bad smells, and these bad smells received less attention from researchers. No single tool is available to detect all considered eight code bad smells in one go.
Performance traits of a newly proposed modularity function for spatial networks: Better assessment of clustering for unsupervised learning

Abstract The “best” partition of a given network helps in revealing its naturally identifiable structures. The most modular structure is often considered as the best partition. Modularity function, is an objective measure of the quality of partitioning in a given network with that of a random graph (“Null model”), where edge between any two nodes is equally probable, are inappropriate to use for spatially embedded networks. Earlier we have proposed a new modularity function, which does not compare the network with a null model. We have analyzed a 2D and 3D granular networks which can be considered as a spatially embedded network. In all considered systems new method identifies the better partition. New function properly detects the better modular partition in 2D as well as in 3D granular assemblies as compared to the most commonly used modularity function, known as Newman modularity function, and thus is more suitable for unsupervised machine learning.
Hierarchical Three-module Method of Text Classification in Web Big Data

Text analysis is a method for extracting knowledge from text. Memory and time limitations in processing big data is crucial due to data sources distributed in web, search engines and socials network sites. In addition, due to automatizing search process, summarizing and finding the interests of users, immediate classification of various texts in a streaming manner has gained attention in industrial and scientific fields. Hierarchical classification of text is among common issues which is simply possible in traditional methods using bag of words; however, while talking about big data and when there are a lot of labels of classes, employing traditional methods will not meet the needs of societies. With the improvement of data in internet and social networks, more powerful methods are needed which can classify the data closely and immediately. Through abstraction in textual data, deep learning can deal with these challenges. In this paper a deep learning method will be introduced which is based on hierarchical classification (HAN) named HAN-MODI and which can classify texts from social networks and web sites with an accuracy of 98.81% at the real time bilingually in English and Farsi. This paper also shows that this complex network with three modules word, sentence and document can work better at word level and there is no need to know syntactic or semantics structure of language. The novelty of the proposed method is adding a third level to the hierarchical structure for general detection and for more exact detection of the class. In addition, classification using this method will be multi-level classification and finally with a change in HAN, this method can be used with Farsi texts. Model improvement is done by adding a new layer above the architecture HAN. We called it as segmentation of sentences into expressions Bag of Sentences and added a dynamicity window in any stage that applied attention mechanism simultaneously.
Distributed Deep Reinforcement Learning Method Using Profit Sharing for Learning Acceleration

Abstract Profit Sharing (PS), a reinforcement learning method that strongly reinforces successful experiences, has been shown to contribute to the improvement of learning speed when combined with a deep Q‐network (DQN). We expect a further improvement in learning speed by integrating PS‐based learning and Ape‐X DQN that has state‐of‐the‐art learning speed instead of the DQN. However, PS‐based learning does not use replay memory. In contrast, the Ape‐X DQN requires the use of replay memory because the exploration of the environment for collecting experiences and network training are performed asynchronously. In this study, we propose Learning‐accelerated Ape‐X, which integrates the Ape‐X DQN and PS‐based learning with some improvements including the use of replay memory. We show through numerical experiments that the proposed method improves the scores in Atari 2600 video games in a shorter time than the Ape‐X DQN. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Fast and Simple Compact Hashing via Bucketing.

Compact hash tables store a set S of n key-value pairs, where the keys are from the universe U = {0,…,u-1}, and the values are v-bit integers, in close to B(u, n) + nv bits of space, where {b(u, n)} = log₂ binom(u,n) is the information-theoretic lower bound for representing the set of keys in S, and support operations insert, delete and lookup on S. Compact hash tables have received significant attention in recent years, and approaches dating back to Cleary [IEEE T. Comput, 1984], as well as more recent ones have been implemented and used in a number of applications. However, the wins on space usage of these approaches are outweighed by their slowness relative to conventional hash tables. In this paper, we demonstrate that compact hash tables based upon a simple idea of bucketing practically outperform existing compact hash table implementations in terms of memory usage and construction time, and existing fast hash table implementations in terms of memory usage (and sometimes also in terms of construction time). A related notion is that of a compact Hash ID map, which stores a set S of n keys from U, and implicitly associates each key in S with a unique value (its ID), chosen by the data structure itself, which is an integer of magnitude O(n), and supports inserts and lookups on S, while using close to B(u,n) bits. One of our approaches is suitable for use as a compact Hash ID map.
Financing Development Without Tears: An Empirical Investigation on Sub-saharan Africa

We examined both traditional and ‘innovative’ sources of financing development in SSA. The panel results for 36 SSA countries showed mixed results. While revenues, savings and remittances had a positive relationship with growth and per capita income, inflation, governance and external debt indicated negative relationship with growth. It is apparent that SSA economies must improve on governance and strive to mobilize domestic resources such as savings, tax revenues and private–public partnership arrangements to finance development.
WITNESS: Wide InTegration of Sensor Networks to Enable Smart Surveillance

In the recent years the need of advanced security systems has grown in an unpredictable manner. We saw how modern cities are very susceptible to terrorist attacks and the population asks to feel safer. In this perspective WITNESS project was designed, to help security forces to preview terrorist attacks and to facilitate disaster recovery scenarios. In this paper we break down some aspects of modern surveillance systems within the context of the Wide InTegration of sensor Networks to Enable Smart Surveillance (WITNESS) NATO project. A brief description of the architecture of the system and the methodology are presented, as well as several use case scenarios which WITNESS will cater for.
Inherent Mapping Analysis of Agile Development Methodology Through Design Thinking

Software engineering is a systematic set of activities which leads to the development of software solutions of the user’s requirement. In today’s information era, there is a need to fulfill the user’s requirements quickly in a swift and systematic manner, which is done through agile development methodology. Design thinking facilitates the software development team not only to identify the explicit requirements but also the implicit requirements very keenly. This paper introduces the new framework—Intelli-A, which inherently maps the design thinking approach in agile methodology.
Curriculum for Business Information Technology Studies at BSc and MSc Levels – Observations from a Long-term Educational Endeavour

The long-term development experience of undergraduate (BSc) and graduate (MSc) curriculum for the Business Information Technology study programs at Tallinn University of Technology (TalTech) are presented and analysed in this paper. The three-year undergraduate program aims to prepare junior specialists for organisations and businesses who have the theoretical knowledge and practical skills for planning, development and maintenance of enterprise information systems. In the two-year graduate program, the target is to educate leading and senior specialists in the fields of (a) business analysis and enterprise architecture, (b) information systems analysis and architecture, and (c) data analysis and business intelligence. We analyse the development of the Business Information Technology curriculum at TalTech over the last ten years, evaluate our experiences and present the resulting curriculum together with their underlying unified curriculum model. The main contribution of this paper is to exemplify and analyse the use of modern teaching theories in curriculum development practice.
Violin training with a magnetostrictive actuator

Professional players claim that stringed musical instruments exhibit better acoustic behaviour if they are frequently played. Although in literature there is no clear relationship between these phenomena, this work aims to develop a device capable of “making a violin sound” autonomously, so that it can always be kept in optimal conditions. The paper shows the design aspects of the magnetostrictive actuator, its operating principle, the prototype development and the experimental characterization. The prototype is then mounted on a new violin, simulating its continuous use for a week. During the test the acoustic measurements showed a large change in the acoustic response of the instrument, thus demonstrating the effectiveness of the magnetostrictive actuator in the training of a stringed instrument.
Legal Amount Recognition in Bank Cheques Using Capsule Networks

Legal amount detection is a decade old conundrum hindering the efficiency of automatic cheque detection systems. Ever since the advent of legal amount detection as a use-case in the computer vision ecosystem, it has been hampered by the deficiency of effective machine learning models to detect the language-specific legal amount on bank cheques. Currently, convolutional neural networks are the most widely used deep learning algorithms for image classification. Yet the majority of deep learning architectures fail to capture information like shape, orientation, pose of the images due to the use of max pooling. This paper proposes a novel way to extract, process and segment legal amounts into words from Indian bank cheques written in English and recognize them. The paper uses capsule networks to recognize legal amounts from the bank cheques, which enables the shape, pose and orientation detection of legal amounts by using dynamic routing and routing by agreement techniques for communication between capsules and thus improves the recognition accuracy.
FPGA as a Service Solutions Development Strategy

the most effective integrated development environments (IDEs) from leading manufacturers of programmable logic devices and FPGA platform in particular have been analyzed in this article. The characteristics of development environments based on practical experience, including undocumented ones, are formulated. The SDAccel platform of the Xilinx SDx family, as well as its role in creating the FPGA computing platform as a service was considered. An example of using SDAccel to develop parallel processing based on FPGA is given. The results of comparing the indicators of the simulation speed of a system described using programming and hardware description languages are presented. The advantages of modeling complex systems, especially for testing solutions involving the processing of tens of gigabytes of data and the constraints of creating truncated test sets are discussed.
Recent Advances in Text-to-Pattern Distance Algorithms

Computing text-to-pattern distances is a fundamental problem in pattern matching. Given a text of length n and a pattern of length m, we are asked to output the distance between the pattern and every n-substring of the text. A basic variant of this problem is computation of Hamming distances, that is counting the number of mismatches (different characters aligned), for each alignment. Other popular variants include $$\ell _1$$ distance (Manhattan distance), $$\ell _2$$ distance (Euclidean distance) and general $$\ell _p$$ distance. While each of those problems trivially generalizes classical pattern-matching, the efficient algorithms for them require a broader set of tools, usually involving both algebraic and combinatorial insights. We briefly survey the history of the problems, and then focus on the progress made in the past few years in many specific settings: fine-grained complexity and lower-bounds, $$(1+\varepsilon )$$ multiplicative approximations, k-bounded relaxations, streaming algorithms, purely combinatorial algorithms, and other recently proposed variants.
Sarcasm Detection in Tweets as Contrast Sentiment in Words Using Machine Learning and Deep Learning Approaches

Sarcasm is a remark that clearly means the opposite of what is said, made to criticize something in a humorous way. The main goal of the proposed work is to identify sarcasm in plain text. Sarcasm detection in a text is a difficult task due to lack of context of the tweet, lack of user's character and personality and lack of expression and body language. We present a novel method in feature engineering based on the contrast in phrases of the sarcastic sentence. It can recognize the positive phrases followed by negative situation phrase (example- I can't wait for the algebra exam tomorrow!) and the inverse of it in sarcastic tweets. It has been done by getting the sentiment of various parts of the tweet by dividing the tweet. Features to know the context of words used in a sentence were extracted by many different methods. We used a neural network model using the ReLU activation function for the detection of sarcasm in tweets, which improves the f1-score as compared to machine learning approaches. It is found that the neural network model outperforms the machine learning model in terms of accuracy and f1-score.
A Robust Video Steganographic Method against Social Networking Transcoding Based on Steganographic Side Channel

The social networks transcode uploaded videos in a lossy way, which makes most video steganographic methods become unusable. In this paper, a robust video steganographic method is proposed to resist video transcoding on social networking sites. The luminance component of the raw video is selected as the cover and Quantization Index Modulation (QIM) algorithm based on block statistical features is applied to embed secret messages. To make a good tradeoff between the robustness and visual quality, an iteration in the local transcoder is designed to determine the minimum quantization step for each video. Then, a strategy of selecting robust video frames is proposed to further improve the robustness and security. To avoid sharing information beforehand between the sender and the receiver, a steganographic side channel is built for correct message extraction. Experimental results have shown that our proposed method can provide strong robustness against social networks transcoding, the average bit error rate is less than 1%. Meanwhile, our proposed method achieves a satisfactory level of security performance. It's a robust and secure method for covert communication on social networking sites such as YouTube and Vimeo.
Domain of Competency of Classifiers on Overlapping Complexity of Datasets Using Multi-label Classification with Meta-Learning

A classifier’s performance can be greatly influencedGupta, Shivani by the characteristics of the underlying dataset. We aim at investigating the connection between the overlapping complexity of dataset and the performance of a classifier in order to understand the domain of competence of these machine learning classifiers. In this paper, we report the results and implications of a study investigating the connection between four overlapping measures and the performance of three classifiers, namely KNN, C4.5 and SVM.Gupta, Atul In this study, we first evaluated the performance of the three classifiers over 1060 binary classification datasets. Next, we constructed a multi-label classification dataset by computing the four overlapping measures as features and multi-labeled with the competent classifiers over these 1060 binary classification datasets. The generated multi-label classification dataset is then used to estimate the domain of the competence of the three classifiers with respect to the overlapping complexity. This allowed us to express the domain of competence of these classifiers as a set of rules obtained through multi-label rule learning. We found classifiers’ performance invariably degraded with the datasets having high values of complexity measures (N1 and N3). This suggested for the existence of a strong negative correlation between the classifiers’ performance and class overlapping present in the data.
Spatio-temporal tracking of vehicles using Deep Learning, applied on aerial videos

Deep learning methods, in their latest progress, enable key steps in object detection and recognition to be performed with convolutional neural networks (CNN). In this article, we apply Yolo (You Only Look Once) algorithm on areal imagery for spatiotemporal tracking of vehicles, for GIS applications. Data augmentation technics are used to increase the number of training and test data. After augmenting the aerial images of vehicles and their corresponding labels, Yolo is trained in iterations until reaching an acceptable precision. Then the trained algorithm is applied on frames of an aerial video of a parking lot, whose outputs (bounding boxes) are used in the LinktheDot algorithm in order to track vehicles in space and time.
A New Class of Minimal Asymptotic Bases

A set A of nonnegative integers is an asymptotic basis of order h if every sufficiently large integer can be represented as the sum of h not necessarily distinct elements of A. An asymptotic basis A is minimal if removing any element of A destroys every representation of infinitely many integers. In this paper, a new class of minimal asymptotic bases is constructed.