Reviewerly/exHarmony huggingface dataset:
Outlining Software Project Management Education by Surveying Educators

This research full paper presents an investigation on how project management education is taught in undergraduate degree programs of higher education in computing. Projects are a key way to create value and benefits in organizations. In today's business environment, organizational leaders need to be able to manage with tighter budgets, shorter timelines, scarcity of resources, and rapidly changing technology. Software project management (SPM) encompasses the knowledge, techniques and tools necessary to manage the development of software products, being one of the most relevant units in the area of software engineering that should be addressed in computing programs. Teaching students effectively any subject is a challenging endeavor by itself. A big issue that may discourage graduate and undergraduate students is the disassociation between theory and practice. Thus, investigating SPM Education is relevant, especially in terms of improving SPM courses. In this paper we investigated the panorama of SPM Education in undergraduate degree programs of higher education in Computing. We surveyed educators who teach or taught software project management to gather information from the academia point of view. In general, the results indicated that SPM is being taught in different contexts in computing undergraduate degree programs. However, despite the efforts expended, there is still a lot to do for graduates to meet industry expectations. There is a need to change paradigms, moving from the traditional approach to teaching approaches that bring students closer to the challenges they will find in the software industry.
The creation and evaluation of a grammar pattern list for the most frequent academic verbs

The current research project was designed according to the conceptualization that vocabulary knowledge is multidimensional (Jaen, 2007; Qian & Schedl, 2004) and the learning of grammatical and collocational patterns should be considered as important as, if not more important than, the acquisition of semantic knowledge of single words. The purposes of the project were to (1) propose a method of extracting grammar patterns for the most frequent content words (verbs, nouns, adjectives and adverbs) in a given corpus; and (2) derive a tentative grammar pattern list for the 115 most frequent academic verbs. The extraction of grammar patterns was realized through a rule-based programming approach, where the researchers coded all possible patterns of the most frequent academic verbs and used the script to extract grammar patterns from a given corpus. The extracted grammar patterns were compiled with corpus-based frequency information and developed into a grammar pattern list for the top-frequency academic verbs. The grammar pattern list is expected to complement the widely used EAP (English for academic purposes) word list, the Academic Vocabulary List (Gardner & Davies, 2014), by specifying the frequency of each verb patterns (e.g. provide n with n, select n from n, base n on n).
Leveraging Big Data to Identify Corruption as an SDG Goal 16 Humanitarian Technology

Corruption is a serious impediment to global goals of ensuring sustainable development and is now a threat specifically recognized in the UN Sustainable Development Goals under Target 16.5. Though corruption remains challenging to identify, measure, and combat, technology advances provide new opportunities to advance humanitarian goals, including the detection of corruption reported by the public. In this study, we address this challenge by developing a method using an unsupervised machine learning model to detect reports of corruption-related activity on the micro-blogging platform Twitter. In total, we collected over 6 million tweets containing keywords related to corruption between January and February 2019. We use the Biterm Topic Model to then isolate tweets from users who report corruption and found that most topics focus on police bribery and corruption in health-care. Though preliminary, these results shave the potential of identifying the scope and prevalence of corruption in society and also advance shared goals of combating corruption and advancing sustainable development in the 21st century. Index TermsCorruption, Machine Learning, Natural Language Processing, Topic modeling.
Special Issue on Cryptographic Currency and Blockchain Technology

People’s behavior depends on extremely complex, multidimensional processes. This poses challenges when trying to model their behavior. In the transportation modeling community, great effort is spent to model the activity schedules of people. Remarkably however, the frequency of occurrence of day-long activity schedules obeys a ubiquitous power law distribution, commonly referred to as Zipf’s law. Previous research established the universal nature of this distribution and proposed potential application areas. However, these application areas require additional information about the distribution’s properties. To stress-test this universal power law, this paper discusses the role of aggregation within the phenomenon of Zipf’s law in activity schedules. Aggregation is analyzed in three dimensions: activity type encoding, aggregation over time and the aggregation of individual data. Five data sets are used: the household travel survey from the USA (2009) and from GBR (2009–2014), two six-week travel surveys (DEU MobiDrive 1999 and CHE Thurgau 2003) and a donated 450-day data set from one individual. To analyze the effect of aggregation in the first dimension, five different activity encoding aggregation levels were created, each aggregating the activity types somewhat differently. In the second dimension, the distribution of schedules is compared over multiple years and over the days of the week. Finally, in the third dimension, the analysis moves from study area-wide aggregated data to subsets of the data, and finally to individual (longitudinal) data.
Pedestrian Detection Based on HOG Features and SVM Realizes Vehicle-Human-Environment Interaction

Autonomous driving has to deal with human-vehicle interaction, in which one of the key tasks is to detect pedestrians. In this paper, HOG, a classical algorithm in the pedestrian detection field is used for extracting features and SVM for pedestrian classifier training. The pedestrian feature classifier is obtained through training and testing using INRIA pedestrian dataset and data acquired by autonomous vehicles. Meanwhile, we design a pedestrian detection visualization system for better application on autonomous vehicles to detecting pedestrian. This system detects the image data input by users and calls the pedestrian feature classifier that has been trained to effectively mark pedestrians.
Analyzing Web Application Vulnerabilities

Internet based application services become the essential part of peoples' daily activities. Web applications are a complex organized mess of codes; the software industry often they are built-in extreme pressure to meet a deadline. Therefore, it often retains the vulnerability holes from the development phase to the operational phase. Coding flaws, improper input sanitization, server misconfiguration, etc. causes the application vulnerable to an attacker, which may lead to service interruption and theft of valuable information. This paper conducts an empirical analysis of e-commerce based web applications to evaluate the current web application security scenario in Bangladesh. The most prominent web application security scanner named as Acunetix and Nikto were used to evaluate this study. A breakdown has conjured based on analytical results to find out the types of vulnerability. It is concluded that Cross-Site Request Forgery shows most frequent vulnerability where maximum apps were found having this vulnerability so aptly ranked as number one. However, cross site scripting positioned top for high-level risk vulnerability in Bangladeshi e-commerce platform.
High-Efficiency Wavelet Compressive Fusion for Improving MEMS Array Performance

With the rapid development of microelectromechanical systems (MEMS) technology, low-cost MEMS inertial devices have been widely used for inertial navigation. However, their application range is greatly limited in some fields with high precision requirements because of their low precision and high noise. In this paper, to improve the performance of MEMS inertial devices, we propose a highly efficient optimal estimation algorithm for MEMS arrays based on wavelet compressive fusion (WCF). First, the algorithm uses the compression property of the multiscale wavelet transform to compress the original signal, fusing the compressive data based on the support. Second, threshold processing is performed on the fused wavelet coefficients. The simulation result demonstrates that the proposed algorithm performs well on the output of the inertial sensor array. Then, a ten-gyro array system is designed for collecting practical data, and the frequency of the embedded processor in our verification environment is 800 MHz. The experimental results show that, under the normal working conditions of the MEMS array system, the 100 ms input array data require an approximately 75 ms processing delay when employing the WCF algorithm to support real-time processing. Additionally, the zero-bias instability, angle random walk, and rate slope of the gyroscope are improved by 8.0, 8.0, and 9.5 dB, respectively, as compared with the original device. The experimental results demonstrate that the WCF algorithm has outstanding real-time performance and can effectively improve the accuracy of low-cost MEMS inertial devices.
Ultra Low-Power Encryption/Decryption Core for Lightweight IoT Applications

The fact that security is not the main functionality of an IoT device means that even lesser portion of its computing power is available for security. Security measures implemented in traditional computers such as cryptography, present a challenge from this context when applied in IoT devices. Different algorithms have been presented in the literature that meet IoT requirements and yet maintain the needed security level. Most of the studies have focused on popular algorithms, especially AES finalists. However, more IoT oriented algorithms have been presented to provide better performance in terms of power and capacity, those are known as lightweight cryptographic algorithms. PRESENT as one of the lightweight cryptographic algorithms is the main focus of this study. The algorithm has been implemented using MATLAB, ASIC and FPGA flows. Lowest achievable power consumption is 120.9 μW at throughput of 20.64 Mbps for the ASIC. For the FPGA, best achievable results was for 16 s-box design utilizing a power of 225.95 mW and 352 LUTs.
Natural language processing and Machine learning based phishing website detection system

Because of the colossal growth of internet, most of the users have changed their preference from traditional shopping, banking etc. to online mode. This paved the way for a lot of cybercrimes including phishing into existence. The attackers try to extract sensitive/personal details such as user ID, passwords and debit card/credit card information by disguising themselves as reliable websites. Identifying whether the Uniform Resource Locator (URL) of a website is legitimate or phishing is a difficult task because it exploits the user's vulnerabilities. Although many products are available for detecting phishing websites, they are just making use of heuristic approach and black lists and hence they can't prevent phishing in a more effective way. A system that detects phishing websites in real time has been proposed in this paper. It uses five different classification algorithms with two different feature sets using natural language processing and word vectors to identify which performs better. After analyzing the accuracy of different machine learning classification algorithms like naive bayes, logistic regression, support vector machine, decision tree and random forest using different features, it has been found that random Forest algorithm with features based on natural language processing has performed better with an accuracy of 97.99.
Non-parallel Voice Conversion with Controllable Speaker Individuality using Variational Autoencoder

We propose a flexible non-parallel voice conversion (VC) system that is capable of both performing speaker adaptation and controlling speaker individuality. The proposed VC framework aims to tackle the inability to arbitrarily modify voice characteristics in the converted waveform of conventional VC model. To achieve this goal, we use the speaker embedding realized by a Variational Autoencoder (VAE) instead of one-hot encoded vectors to represent and modify the target voice's characteristics. Neither parallel training data, linguistic label nor time alignment procedure is required to train our system. After training on a multi-speaker speech database, the proposed VC system can adapt an arbitrary source speaker to any target speaker using only one sample from a target speaker. The speaker individuality of converted speech can be controlled by modifying the speaker embedding vectors; resulting in a fictitious speaker individuality. The experimental results showed that our proposed system is similar to conventional non-parallel VAE-based VC and better than the parallel Gaussian Mixture Model (GMM) in both perceived speech naturalness and speaker similarity; even when our system only uses one sample from target speaker. Moreover, our proposed system can convert a source voice to a fictitious target voice with well perceived speech naturalness of 3.1 MOS.
Automatic Image Recommendation for Economic Topics using Visual and Semantic Information

This paper proposes an image recommendation system for automatic report generation on economic topics. For a given specific headline query on daily economic events, the proposed system collects candidate images through a public search engine and choose the most appropriate one for summary report on the event. The proposed system is composed of two deep learning-based modules of different modalities: image filtering module and text matching module. In the image filtering module, an image classifier network is adopted to filter out non-photo images such as graph and tables. In the text matching module, a sentence embedding network is adopted to get text query vector and image caption vector and calculate their matching scores. By analyzing image and text information together, the proposed system can recommend suitable images both visually and semantically. Through computational experiments using a number of recent economic topics, we confirm that recommended images of the proposed system are more appropriate than that of conventional search engine.
A Study of Digital Banking: Security Issues and Challenges

In the extensive sense, mobile banking (M-Banking) is defined as the financial transaction execution through smart devices or electronic devices. The clients are utilizing mobile devices with the combination of mobile communication systems for purpose of electronic transactions (E-transactions). The major reason for the expansion of electronic banking (E-Banking) is due to continuous growth of Internet speed, facilities, personal Digital Assistants (PDAs), Internet-empowered phones, and designing of banking application. This made another new subset of E-Banking and M-Banking. We mainly concentrated on E-Banking and M-Banking aspects of banking area. In this paper, we examined the general structure of Indian banks and identified relevant digital banking types, which are classified into E-Banking and M-Banking. We carried a survey on modern M-Banking and determined five different categories of M-banking services and listed out the functionalities, advantages, disadvantages, and security issues of it. We also examine the security attacks on mobile devices and wireless networks. Finally, a related work is carried on the existing system based on the security concern issues in M-Banking.
MMiDaS-AE

Systematic review (SR) is an essential process to identify, evaluate, and summarize the findings of all relevant individual studies concerning health-related questions. However, conducting a SR is labor-intensive, as identifying relevant studies is a daunting process that entails multiple researchers screening thousands of articles for relevance. In this paper, we propose MMiDaS-AE, a Multi-modal Missing Data aware Stacked Autoencoder, for semi-automating screening for SRs. We use a multi-modal view that exploits three representations, of: 1) documents, 2) topics, and 3) citation networks. Documents that contain similar words will be nearby in the document embedding space. Models can also exploit the relationship between documents and the associated SR MeSH terms to capture article relevancy. Finally, related works will likely share the same citations, and thus closely related articles would, intuitively, be trained to be close to each other in the embedding space. However, using all three learned representations as features directly result in an unwieldy number of parameters. Thus, motivated by recent work on multi-modal auto-encoders, we adopt a multi-modal stacked autoencoder that can learn a shared representation encoding all three representations in a compressed space. However, in practice one or more of these modalities may be missing for an article (e.g., if we cannot recover citation information). Therefore, we propose to learn to impute the shared representation even when specific inputs are missing. We find this new model significantly improves performance on a dataset consisting of 15 SRs compared to existing approaches.
BLAMM: BLAS-based algorithm for finding position weight matrix occurrences in DNA sequences on CPUs and GPUs

Abstract Background The identification of all matches of a large set of position weight matrices (PWMs) in long DNA sequences requires significant computational resources for which a number of efficient yet complex algorithms have been proposed. Results We propose BLAMM, a simple and efficient tool inspired by high performance computing techniques. The workload is expressed in terms of matrix-matrix products that are evaluated with high efficiency using optimized BLAS library implementations. The algorithm is easy to parallelize and implement on CPUs and GPUs and has a runtime that is independent of the selected p -value. In terms of single-core performance, it is competitive with state-of-the-art software for PWM matching while being much more efficient when using multithreading. Additionally, BLAMM requires negligible memory. For example, both strands of the entire human genome can be scanned for 1404 PWMs in the JASPAR database in 13 min with a p -value of 10 −4 using a 36-core machine. On a dual GPU system, the same task can be performed in under 5 min. Conclusions BLAMM is an efficient tool for identifying PWM matches in large DNA sequences. Its C++ source code is available under the GNU General Public License Version 3 at https://github.com/biointec/blamm .
Eye Blink Controlled Low Cost Smart Wheel Chair Aiding Disabled People

In this paper, a smart wheelchair is proposed to help out physically disabled people by using their eye blink activities. A prototype is implemented by processing eye ball images of the subject to detect intentional eye blinks. An algorithm is proposed to produce wheelchair movement command from consecutive intentional eye blinks. To identify intentional eye blinks from eye ball images, advanced image processing techniques are applied using raspberry-pi. The commands are wirelessly transferred to the motor driver of the wheel chair. The HC-05 Buletooth receiver is connected to the wheelchair which receives transmitted data from raspberry-pi and motor driver implements the command according to predefined decision table. It can also sense obstacle using APDS 9960 proximity sensor to avoid collision with surroundings. The proposed smart wheelchair has shown False Command Rate (FCR) of 2.1 % to 13.2%. The experiment is performed on four subjects and each of them has attended three sessions. Overall, the eye blink controlled aiding wheelchair is going to create a humanitarian impact on physically disabled people.
Sluggish Mining: Profiting from the Verifier’s Dilemma

Miners in Ethereum need to make a choice when they receive a block: they can fully validate the block by executing every transaction in order to validate the new state, but this consumes precious time that could be used on mining the next block. Alternatively, miners could skip some of the verification stages and proceed with the mining, taking the risk of building on top of a potentially invalid block. This is referred to as the verifier’s dilemma. Although the gas limit imposed on Ethereum blocks mitigates this attack by forcing an upper bound on the time spent during verification, the slowdown that can be achieved within a block can still be enough to have an impact on profitability. In this paper we present a mining strategy based around sluggish contracts; these computationally intensive contracts are purposely designed to have a slow execution time in the Ethereum Virtual Machine to provide an advantage over other miners by slowing their contract verification time. We validate our proposed mining strategy by designing and evaluating a set of candidate sluggish smart contracts. Furthermore, we provide a detailed analysis that shows under which conditions our strategy becomes profitable alongside a series of suggestions to detect this type of strategy in the future.
Error Detection using Augmented Reality in the Subtractive Manufacturing Process

A small error in subtractive manufacturing can adversely impact an entire production if not caught right away. By utilizing an Augmented Reality (AR) simulation, workers can view in real-time when the machine malfunctions, stop the process and correct it. AR will also improve the visualization of the object. This paper sets the initial stage for our future work in which the manufacturing defects can be detected in real-time. In this paper, error detection in dimensions was tested using OpenCV-Python and compared using a single web camera and intel real-sense D435i stereo camera, Augmenting AR block over the original block using Unity software in real-time, and operation of the switch to control the machine using AR was successfully tested.