Reviewerly/exHarmony huggingface dataset:
Regularization of boosted decision stumps using tabu search

This study proposes a novel method to improve the well-known AdaBoost algorithm by combining it with a procedure inspired by tabu search. After each iteration of AdaBoost, the attribute used by the weak learner is placed on the tabu list, which prevents it from being utilized by the subsequent weak learners. The length of the tabu list becomes a new meta-parameter of the learning process and can be tuned based on the cross-validation procedure. This study shows that the proposed approach can improve the original AdaBoost procedure, preventing it from over-fitting to training data. This study also demonstrates that the novel method can act as a regularization procedure. Finally, the paper presents results for the proposed algorithm for 20 classification problems from the UCI repository and for face verification and gender recognition problems.
Survey on Software Clone Detection Research

In order to improve the efficiency of software development, developers often copy-paste code. It is found that the clone code may affect the quality of the software system, especially the maintenance and comprehension of the software, so it is necessary to find and locate it. Many clone detection techniques and tools have been proposed in the search for clone code. How to make better use of these detection techniques and tools will be very important. This paper describes the clone code and general process of clone code detection; introduces different clone code detection methods and related technologies; then conducts a summary analysis, the challenges and development direction faced by clone detection technology.
What Storage Access Privacy is Achievable with Small Overhead?

Oblivious RAM (ORAM) and private information retrieval (PIR) are classic cryptographic primitives used to hide the access pattern to data whose storage has been outsourced to an untrusted server. Unfortunately, both primitives require considerable overhead compared to plaintext access. For large-scale storage infrastructure with highly frequent access requests, the degradation in response time and the exorbitant increase in resource costs incurred by either ORAM or PIR prevent their usage. In an ideal scenario, a privacy-preserving storage protocols with small overhead would be implemented for these heavily trafficked storage systems to avoid negatively impacting either performance and/or costs. In this work, we study the problem of the best \em storage access privacy that is achievable with only \em small overhead over plaintext access. To answer this question, we consider \em differential privacy access which is a generalization of the \em oblivious access security notion that are considered by ORAM and PIR. Quite surprisingly, we present strong evidence that constant overhead storage schemes may only be achieved with privacy budgets of ε = Ømega(łog n)$. We present asymptotically optimal constructions for differentially private variants of both ORAM and PIR with privacy budgets ε = Θ(łog n)$ with only $O(1)$ overhead. In addition, we consider a more complex storage primitive called key-value storage in which data is indexed by keys from a large universe (as opposed to consecutive integers in ORAM and PIR). We present a differentially private key-value storage scheme with ε = Θ(łog n)$ and $O(łogłog n)$ overhead. This construction uses a new oblivious, two-choice hashing scheme that may be of independent interest.
A Survey on the Multiple Classifier for New Benchmark Dataset of Vietnamese News Classification

In this paper, we have summarized the well-known multi-class classifiers in the literature and applied them to evaluate on a new benchmark dataset of Vietnamese News (VNNews-01). This database is created from more than thirty Vietnamese online newspaper websites and grouped into twenty-five categories. The proposition and evaluation of this work might promote the related research for Vietnamese text mining.
A Framework for Analyzing Real-Time Tweets to Detect Terrorist Activities

Terrorist organizations use different social media as a tool for spreading their views and influence general people to join their terrorist activities. Twitter is the most common and easy way to reach mass people within a small amount of time. In this paper, we have focused on the development of a system that can automatically detect terrorism-supporting tweets by real-time analyzation. In this system, we have developed a frontend for realtime viewing of the tweets that are detected using this system. We have also compared the performance of two different machine learning classifiers, Support Vector Machine (SVM) and Multinomial Logistic Regression and foundthe first one works better. As our system is highly dependent on data, for more accuracy we added a re-train module. By using this module wrongly classified tweets can be added to the training dataset and train the whole system again for better performance. This system will help to ban the terrorist accounts from twitter so that they can't promote their views or spread fear among general people.
M-vectors: Sub-band Based Energy Modulation Features for Multi-stream Automatic Speech Recognition

In this paper, we propose a novel method to capture energy modulations from different frequency bands in speech into frame-level feature vectors, called Modulation-vectors or M-vectors, for use in Automatic Speech Recognition (ASR) systems. We show that in different multi-stream setups, with parallel streams for M-vectors and the popular Mel-frequency Cepstral Coefficient (MFCC) features, we can realize a boost in word recognition performance of end-to-end systems by ≈ 5%, and that of a monophone and triphone HMM-GMM ASR system by ≈ 18% and ≈ 16% respectively over using the traditional MFCC features.
CFRaaS: Architectural design of a Cloud Forensic Readiness as-a-Service Model using NMB solution as a forensic agent

The proliferation of cloud resources among organizations has had numerous benefits with regard to how business processes are conducted. However, despite the benefits, the cloud has not been very resilient due to how it is distributed and its open nature. Due to this, there have been numerous reports on how the security of organizational information has been compromised. In any organization, Digital Forensic Readiness (DFR) is employed as a pre-incident phase whose aim is to maximize the use of Potential Digital Evidence (PDE) while minimizing the cost of performing a Digital Forensic Investigation (DFI). Therefore, it is on this premise that this paper makes a contribution to the architectural design of a Cloud Forensic Readiness as-a-Service (CFRaaS) that uses a Non-Malicious Botnet (NMB) solution as a forensic agent. The authors argue that the architectural design of a CFRaaS is an important aspect, which brings out the requirements that are needed in order for the cloud to be forensically ready for digital investigations when a modified NMB acting as an Agent-Based Solution (ABS) is used. To support this claim, the authors have identified important dependencies and indicators that will provide a synergistic relationship while coming up with CFRaaS design decisions. The main objective of this paper is to present the requirements, design and implementation for achieving DFR in the cloud using a CFRaaS. This study complies with the ISO/IEC 27043: 2015 international standard which presents guidelines for Information Technology, Security Techniques and Incident Investigation Principles and Processes. The result of the study has indicated that it is possible to achieve DFR in the cloud environment using a botnet with modified functionalities.
A New Crossover Technique to Improve Genetic Algorithm and Its Application to TSP

Optimization problem like Travelling Salesman Problem (TSP) can be solved by applying Genetic Algorithm (GA) to obtain perfect approximation in time. In addition, TSP is considered as a NP-hard problem as well as an optimal minimization problem. Selection, crossover and mutation are the three main operators of GA. The algorithm is usually employed to find the optimal minimum total distance to visit all the nodes in a TSP. Therefore, the research presents a new crossover operator for TSP, allowing the further minimization of the total distance. The proposed crossover operator consists of two crossover point selection and new offspring creation by performing cost comparison. The computational results as well as the comparison with available well-developed crossover operators are also presented. It has been found that the new crossover operator produces better results than that of other cross-over operators.
Agile Development in Practice: Lessons from the Trenches

In the twenty years since the agile approach to systems development was introduced, a wide range of organizations have adopted its principles. Despite numerous reports that highlight agile success stories, there are relatively few recent publications that provide practical guidance on the potential pitfalls and areas of risk related to agile. In response, this practice-oriented paper draws on 56 practitioner interviews to identify a series of “lessons learned” pertaining to agile adoption, tailoring, and use.
Automated analysis of two-layered feature models with feature attributes

The proliferation of features and platforms in variability intensive systems, coupled with substantial technological progress, imposes several challenges for software developers and equipment manufacturers—in some cases referred as technical sustainability. For instance, in the mobile application domain, developers often need to know the requirements and limitations of their applications to be supported on a specific platform. Conversely, an equipment manufacturer is interested in knowing what additional features become accessible on the application layer when the or platform is being upgraded. To date, analyzing such interdependencies between specific feature and platform combinations is a tough problem, but important to solve. There are well-established approaches in the literature to analyze variability–intensive systems using feature models. However, there is a lack of approaches to analyze application and platform features in multiple layers. In this paper we present a framework towards the analysis of multi-layered feature models. First, modeling the two layers including their respective interdependencies. Second, a definition of operations that can be imposed on such models. We also provide a reference implementation for analysis of multiple layers. Finally, we present two empirical evaluations demonstrating the feasibility of the approach in practice.
Efficient implementation of digit‐serial Montgomery modular multiplier architecture

The importance of security in communications has made cryptography one of the most important research for designers. Montgomery modular multiplication is one of the best methods used in cryptosystems. These multipliers can be implemented in different ways including digit-serial architecture. The digit-serial multiplier is an efficient structure for low power and high-speed applications. The architecture of digit-serial multiplier can benefit from the advantage of both serial and parallel structures. This study presents a new digit-serial Montgomery modular multiplier architecture, which takes up less area by reducing internal blocks of the structure. The latency and design complexity of the proposed digit-serial Montgomery multiplier is less than many other similar designs. The critical path delay of the proposed architecture is reduced compared to similar works. The proposed circuit has the flexibility to be used in a large number of bits and can be used for any size of the digit. The simulation results of the proposed multiplier are presented.
Feature Extraction Method Based on Social Network Analysis

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‘Why Don’t You Block Them?’ Police Officers’ Constructions of the Ideal Victim When Responding to Reports of Interpersonal Cybercrime

This chapter explores police officers’ responses to reports of interpersonal cybercrime by considering their construction of the ‘ideal victim’. It contributes to knowledge on police officers’ perceptions of cybercrime and their support for victims. The discussion draws on Nils Christie’s (1986) concept of the ‘ideal victim’ to explore which individuals police officers most readily give the legitimate status of victim to. Three themes are discussed including: police officers’ constructions of the ‘ideal victim’; their attitudes towards victims in relation to prevention of cybercrime (i.e. ‘block them’) and; negotiations over responsibility for dealing with the emerging issue of cybercrime. The chapter argues that police forces must advance beyond an approach which entails victim-blaming and instead recognise the centrality of social media and online spaces in individuals’ lives.
Extended power-based aggregation of distance functions and application in image segmentation

In this paper, we psropose a novel method for construction of a distance function and demonstrate its application in image segmentation. In algorithms for image segmentation, distance functions represent a criterion which divides pixels into groups of segments. We introduce two extended aggregation functions, extended powers productand extended weighted arithmetic mean of powers. Their relevant properties are examined, as well as certain resulting properties of distance functions, which are constructed by an application of mentioned aggregation functions. In addition, one pixel descriptor, which is motivated by Local Binary Patternfamily of descriptors (LBPs), is introduced and discussed. In the experimental section, we present an application of the introduced extended aggregation functions and descriptor, by a construction of a new distance function, used in Fuzzy c-Means Clustering Algorithm (FCM) for image segmentation.
Managing to release early, often and on time in the OpenStack software ecosystem

The dictum of “Release early, release often.” by Eric Raymond as the Linux modus operandi highlights the importance of release management in open source software development. However, there are very few empirical studies addressing release management in this context. It is already known that most open source software communities adopt either a feature-based or time-based release strategy. Both have their own advantages and disadvantages that are also context-specific. Recent research reports that many prominent open source software projects have overcome a number of recurrent problems by moving from feature-based to time-based release strategies. In this longitudinal case study, we address the release management practices of OpenStack, a large scale open source project developing cloud computing technologies. We discuss how the release management practices of OpenStack have evolved in terms of chosen strategy and timeframes with close attention to processes and tools. We discuss the number of practical and managerial issues related to release management within the context of large and complex software ecosystems. Our findings also reveal that multiple release management cycles can co-exist in large and complex software ecosystems such as OpenStack.
A Fast Circle Detection Method Based on a Tri-Class Thresholding for High Detail FPC Images

In advanced integrated circuit packing, a very important substrate is high-density flexible printed circuit (FPC) board, which is small and foldable. The detection of the circular holes on FPCs' surfaces through machine vision is very useful to identify root causes in FPC manufacturing process. However, it is a challenge as FPC images always contain many high details. This paper proposes a circle detection method with a modified tri-class thresholding-based contour extraction to detect and measure the circular holes on FPCs' surfaces. It first extracts object contours by using a modified tri-class thresholding and then uses two circle validation criterions to eliminate noncircles: one is based on geometrical features and the other is based on the Helmholtz principle. Moreover, an iterative circle parameter refinement strategy is proposed. Experiments are carried out on 120 FPC images and the results indicate that the modified tri-class thresholding-based contour extraction method is more efficient than Canny detector, and the proposed circle detection method shows a better performance in terms of false detection rate, computation cost, and robustness than three other popular algorithms for detecting the circles in FPC images.
Small target detection for search and rescue operations using distributed deep learning and synthetic data generation

It is important to find the target as soon as possible for search and rescue operations. Surveillance camera systems and unmanned aerial vehicles (UAVs) are used to support search and rescue. Automatic object detection is important because a person cannot monitor multiple surveillance screens simultaneously for 24 hours. Also, the object is often too small to be recognized by the human eye on the surveillance screen. This study used an UAVs around the Port of Houston and fixed surveillance cameras to build an automatic target detection system that supports the US Coast Guard (USCG) to help find targets (e.g., person overboard). We combined image segmentation, enhancement, and convolution neural networks to reduce detection time to detect small targets. We compared the performance between the autodetection system and the human eye. Our system detected the target within 8 seconds, but the human eye detected the target within 25 seconds. Our systems also used synthetic data generation and data augmentation techniques to improve target detection accuracy. This solution may help the search and rescue operations of the first responders in a timely manner.
Understanding the Shared Experience of Runners and Spectators in Long-Distance Running Events

Increasingly popular, long-distance running events (LDRE) attract not just runners but an exponentially increasing number of spectators. Due to the long duration and broad geographic spread of such events, interactions between them are limited to brief moments when runners (R) pass by their supporting spectators (S). Current technology is limited in its potential for supporting interactions and mainly measures and displays basic running information to spectators who passively consume it. In this paper, we conducted qualitative studies for an in-depth understanding of the R&S' shared experience during LDRE and how technology can enrich this experience. We propose a two-layer DyPECS framework, highlighting the rich dynamics of the R&S multi-faceted running journey and of their micro-encounters. DyPECS is enriched by the findings from our in depth qualitative studies. We finally present design implications for the multi-facet co-experience of R&S during LDRE.
Parameterizable FPGA Implementation of SHA-256 using Blockchain Concept

This paper concentrates on the Design of Parameterizable Implementation of SHA-256 algorithm in FPGA imparting Blockchain Concepts. SHA-256 is the key principle utilized in Blockchain architecture to impart security and privacy into a system. This one way hash function generates unique output for a given input ensuring data authenticity and non- repudiation. Blockchain technology is gaining popularity in the Internet world due to its property of decentralization. Through this implementation, main goal is to introduce this new technology into VLSI domain for securing hardware digital system designs and SOC's(System On Chip). The proposed methodology enables any bit length input message to get converted to fixed length message digest known as Hash. The design for the proposed architecture was simulated in Modelsim and synthesized in Xilinx Vivado Design Suite using Artix 7 FPGA.
Pseudoinverse Learners: New Trend and Applications to Big Data

Pseudoinverse learner (PIL), a kind of multilayer neural networks (MLP) trained with pseudoinverse learning algorithm, is a novel learning framework. It has drawn increasing attention in the areas of large-scale computing, high-speed signal processing, artificial intelligence and so on. In this paper, we briefly review the pseudoinverse learning algorithm and discuss the characteristics as well as its variants. Some new viewpoints to PIL algorithm are presented, and currently developments of PIL algorithm for autoencoder is presented under the framework of deep learning. Some new trends on PIL-based learning are also discussed. Moreover, we present several interesting PIL applications to demonstrate the practical advances on the big data analysis topic.
PicMe

PicMe is a mobile application that provides interactive on-screen guidance that helps the user take pictures of a composition that another person requires. Once the requester captures a picture of the desired composition and delivers it to the user (photographer), a 2.5D guidance system, called the virtual frame, guides the user in real-time by showing a three-dimensional composition of the target image (i.e., size and shape). In addition, according to the matching accuracy rate, we provide a small-sized target image in an inset window as feedback and edge visualization for further alignment of the detail elements. We implemented PicMe to work fully in mobile environments. We then conducted a preliminary user study to evaluate the effectiveness of PicMe compared to traditional 2D guidance methods. The results show that PicMe helps users reach their target images more accurately and quickly by giving participants more confidence in their tasks.
Domain Classifier: Compromised Machines Versus Malicious Registrations

In “phishing attacks”, phishing websites disguised as trustworthy websites attempt to steal sensitive information. Remediation and mitigation options differ depending on whether the phishing website is hosted on a legitimate but compromised domain, in which case the domain owner is also a victim, or whether the domain itself is maliciously registered. We accordingly attempt to tackle here the important question of classifying known phishing sites as either compromised or maliciously registered. Following the recent adoption of GDPR standards now putting off-limits any personal data, few relevant literature criteria still satisfy those standards. We propose here a machine-learning based domain classifier, introducing nine novel features which exploit the internet presence and history of a domain, using only publicly available information. Evaluation of our domain classifier was performed with a corpus of phishing websites hosted on over 1,000 compromised domains and 10,000 malicious domains. In the randomized evaluation, our domain classifier achieved over 92% accuracy with under 8% false positive rate, with compromised cases as the positive class. We have also collected over 180,000 phishing website instances over the past 3 years. Using our classifier we show that 73% of the websites hosting attacks are compromised while the remaining 27% belong to the attackers.