Reviewerly/exHarmony huggingface dataset:
Toward a reliability measurement framework automated using deep learning

We propose a framework to detect software bugs based on code pattern detection. Our framework will mine and generate bug patterns, detect those patterns in code, and calculate a vulnerability measure of software. While our framework performs well, we realize that it requires heavy manual tasks that render the framework infeasible to use in practice. However, we believe that recent advancements in machine learning will allow us to apply deep learning techniques to source code, which will help automate our framework for better practicality in the real world.
Correction to: Fear of heights in virtual reality saturates 20 to 40 m above ground

The original version of this article unfortunately contained a mistake. The surnames of all authors have been interchanged. The corrected author names are given below.
Integrating Antonyms in Fuzzy Inferential Systems via Anti-membership

Numerous authors have proposed extending fuzzy inferential systems to include the antonyms in fuzzy rules. To date, however, those efforts require significant changes to the nature of a linguistic variable, directly implying substantial additional computation. We propose a new mechanism for incorporating antonyms into fuzzy rules, based on allowing negative-valued memberships along with two new union and intersection operations developed by Dick et al. We prove that these operations form a total ordering over [−1,1], and then show how they integrate antonyms into fuzzy rules seamlessly and require little additional computation.
Detecting Traces of Bullying in Twitter Posts Using Machine Learning

There are over 100,000 bullying-related tweets sent through Twitter every week. These social media posts show the prevalence of bullying both online and in the physical world. Researchers find that victims of bullying are more likely to have symptoms of depression. Oftentimes, these tweets are unreported, leaving the victim to face mental consequences when kept in a bullying situation. Natural language processing (NLP) and machine learning, however, can efficiently detect bullying-related tweets and bring help to those in need. The purpose of this research was to explore various methods of identifying bullying related tweets posted by various groups, including perpetrators and victims, through NLP and machine learning. We produced word embeddings (word vectors) and used the term frequency-inverse document frequency (TF-IDF) algorithm. Then, we trained various machine learning models, including multinomial Naive Bayes classifier, convolutional neural networks (CNN) and recurrent neural networks (RNN), to improve the predictions for the labels of tweets and observe patterns among them. For binary classification (bullying traces vs. no bullying traces), our CNN model produced the highest area under receiver operating curve (AUROC) of 0.924. AUROC was a more comprehensive metric which accounted for our skewed dataset. For multiclass classification, the tweets were separated into six classes: NA (the tweet was not bullying-related and was not annotated), self-disclosure, report, accusation, denial, and cyberbullying. Our RNN model produced AUROC value for each class, ranging from .854 to .936, for multiclass classification. We aim to implement a script to Twitter to help identify users who are being impacted by bullying and may require mental health assistance. This research has important real world applications because as social media usage increases so do the number of malicious posts online.
Inventory Level Optimization of Raw Materials for Ready-Made Garment Industry XYZ Pty Ltd using Mamdani Method of Fuzzy Interference System

Abstract Every company who performs production activities needs inventory of raw materials. The most important thing which is mandatory for all companies when performing their production activities is management of inventory, as inventory is an asset for a company. Availability of raw materials in the industry is expected to facilitate production/service activities to meet consumer needs. Ready-made garment industry, XYZ Pty Ltd is a family business established in 2004 located in Jakarta which engages in textile manufacture and has shown a relatively high demand. While XYZ Pty Ltd attempted to meet the demand, the inventory level was not able to balance with it, resulting in after-hour work of employees. In order to provide appropriate solutions, we proposed some strategies using Mamdani method and TFN (Triangular Fuzzy Number) range which selected appropriate rules to optimize inventory level of the company. Average of range score Fuzzy Interference System obtained for both production level and demand level were 36,500, while average of range score Fuzzy Interference System obtained for Inventory Cost and Inventory Level were 2,250,000,000 and 13,900, respectively. Rules resulting in a high inventory level are very good for use either together with other levels or not. Rules resulting in an intermediate inventory level are also good for use, in particular together with low and intermediate demand levels. Rules resulting in a high demand level are still good for use. On the other hand, rules resulting in a low inventory level are not good for use.
Comparative Analysis of Blockchain Technologies Under a Coordination Perspective

Many research works apply blockchain technologies to several different application domains ranging from supply chain and logistics to healthcare and real-estate. There, nevertheless, the blockchain performs the same two core tasks: identity management and asset tracking. In this paper we analyse how the blockchain can be exploited beyond these traditional tasks, towards coordination of distributed processes. To this end, we design and develop proof-of-concept implementations of the Linda model on top of different blockchains, and compare their strengths and shortcomings.
FamilySong

FamilySong (FS) helps remote intergenerational family members create opportunities for connection by facilitating a synchronized shared-music environment between participants' homes. These shared experiences can be referenced in subsequent mediated conversation which reinforces familial bonds. This demo of FS showcases scenarios for music selection and playback. Demo visitors will play user roles (parents, children, remote grandparents) to understand the ecological context in which the system operates.
A Cloud-based Educational and Career Guidance Model using Fuzzy Logic Concepts

Choosing a career is an important objective for students and graduates, since the choice is done at a relatively early age, and based on several criteria. The right decision for choosing a career will affects positively on the academic level of students and professional life of the graduates, and vice versa. As a result the importance of career guidance increases. Career guidance services include three main components: career information, career counseling and career education. The main services models for cloud computing includes: software, platform and infrastructure (as a service), to satisfy the needs of different kinds of organizations. By using cloud computing concepts, we will open some important area that is likely to have positive implications for career guidance. From cloud computing point of view, we introduce the proposed application as Career-as-a-Service (CAAS) model. The identification of system inputs, outputs, and rules are controlled by fuzzy logic operations. The main objectives of the proposed system model includes: (1) help students to choose appropriate colleges that suitable to their academic ability; (2) help and support graduates to choose the appropriate job (or career) to their ability, scientific skills, and practical experiences; (3) provide graduates with the required training courses to accept them in a specific career; (4) to contribute for implementing an applied software product that can be used in relevant organization and companies in KSA and Egypt. This research study presents a prototype development of a career guidance system that provides a career guidance services to students and graduates automatically using fuzzy logic and cloud computing concepts.
Human-Agent Interaction for Human Space Exploration

Human space exploration creates unique challenges and opportunities for many scientific disciplines. From the human-agent interaction perspective, these require significant advances in the way that agents model, adapt and personalize their behavior to individual astronauts and groups of astronauts. In this paper, we highlight the key challenges and opportunities that human space exploration provides to the agent and UMAP communities and present two avenues for future research. We further propose a viable way to explore these challenges and opportunities through the world-wide analogue space programs which solicit research proposals from all scientific disciplines.
A robust integration algorithm for out-of-plane displacement field measurements applied to multiple images of shearography

Speckle shear interferometry, or shearography, has been more and more frequently used in the aerospace and oil and gas industry for in-field nondestructive inspections of flaws in composite materials. Nowadays new applications have emerged demanding the ability to operate in harsher environments, requiring more robust systems to meet this type of application. A recent modified shearography device allows multiple and simultaneous measurements with different shearing directions on a single grabbed image. This work proposes a robust integration algorithm by error minimization to obtain full-field displacement measurement. Simulated images are used to validate the effectiveness of the integration algorithm. Further, experiments are performed on a clamped circular plate with uniform loading. The proposed algorithm leads to a more accurate estimate of defect size measurement in composite materials.
Poster: ClearTH Test Automation Framework: A Running Example of a DLT-Based Post-Trade System

The paper presents an overview of a test automation framework aimed at end-to-end functional and non-functional testing of DLT-based hybrid financial software for post-trade. The proposed solution comprises the components designed for testing user-facing parts of the SUT as well as business logic specific for different DLT-based architectures. This combined approach is seen as a viable solution of the problem of the SUT complexity as well the variety of possible DLT architectural decisions.
A Link Between the Multiplicative and Additive Functional Asplund’s Metrics

Functional Asplund's metrics were recently introduced to perform pattern matching robust to lighting changes thanks to double-sided probing in the Logarithmic Image Processing (LIP) framework. Two metrics were defined, namely the LIP-multiplicative Asplund's metric which is robust to variations of object thickness (or opacity) and the LIP-additive Asplund's metric which is robust to variations of camera exposure-time (or light intensity). Maps of distances-i.e. maps of these metric values-were also computed between a reference template and an image. Recently, it was proven that the map of LIP-multiplicative As-plund's distances corresponds to mathematical morphology operations. In this paper, the link between both metrics and between their corresponding distance maps will be demonstrated. It will be shown that the map of LIP-additive Asplund's distances of an image can be computed from the map of the LIP-multiplicative Asplund's distance of a transform of this image and vice-versa. Both maps will be related by the LIP isomorphism which will allow to pass from the image space of the LIP-additive distance map to the positive real function space of the LIP-multiplicative distance map. Experiments will illustrate this relation and the robustness of the LIP-additive Asplund's metric to lighting changes.
Differential Phase Encoding Scheme for Measurement-Device-Independent Quantum Key Distribution

This paper proposes a measurement-device-independent quantum key distribution (MDI-QKD) scheme based on differential phase encoding. The differential phase shift MDI-QKD (DPS-MDI-QKD) couples the advantages of DPS-QKD with that of MDI-QKD. The proposed scheme offers resistance against photon number splitting attack and phase fluctuations as well as immunity against detector side-channel vulnerabilities. The design proposed in this paper uses weak coherent pulses in a superposition of three orthogonal states, corresponding to one of three distinct paths in a delay-line interferometer. The classical bit information is encoded in the phase difference between pulses traversing successive paths. This 3-pulse superposition offers enhanced security compared to using a train of pulses by decreasing the learning rate of an eavesdropper and unmasking her presence with an increased error rate upon application of intercept and resend attack and beamsplitter attack. The proposed scheme employs phase locking of the sources of the two trusted parties so as to maintain the coherence between their optical signal, and uses a beamsplitter (BS) at the untrusted node (Charlie) to extract the key information from the phase encoded signals.
Context by Proxy

Contextual anomalies arise only under special internal or external stimuli in a system, often making it infeasible to detect them by a rule-based approach. Labelling the underlying problem sources is hard because complex, time-dependent relationships between the inputs arise. We propose a novel unsupervised approach that combines tools from deep learning and signal processing, working in a purely data-driven way. Many systems show a desirable target behaviour which can be used as a proxy quantity removing the need to manually label data. The methodology was evaluated on real-life test car traces in the form of multivariate state message sequences. We successfully identified contextual anomalies during the cars' timeout process along with possible explanations. Novel input encodings allow us to summarise the entire system context including the timing such that more information is available during the decision process.
Location Based Augmented Reality Navigation Application

We will be integrating Augmented Reality to the traditional maps which will give a new perspective to the maps. We will be using Location based and Markerless Aug- mented Reality which will track the user's current location and calculate the destination by calculating distance between them and will facilitate information about the places which appears within the journey. The problem occuring in traditional mapping is not proper direction representation. A traditional map cannot provide a proper view for instance if there is a flyover and a road below it, traditional map cannot differentiate between them and will show both as a single blue line (path) which will confuse the user which way to go. This problem can be solved as we would be providing a 3 Dimensional view . Along with a generous assistant which will help the user in interactive manner. The assistant will direct the user till the actual destination which will solve our above problem whether to take flyover or the road??It will also give information about the places nearby in the route with their details on for example if we come across a restaurant, it will augment name of the cafe its ratings etc which help user to make decision if he requires anything during the journey!!In this way one of the latest technology can be used for the betterment of the user and to solve the problem of users getting confused while finding their destination.
Experimental Detection of the Parallel Organization of Mental Calculations of a Person on the Basis of Two Algebras Having Different Associativity

In this work, we research a human’s ability to use the parallel calculations for improving and accelerating of an information processing. We measured the amount of information that a person processes per unit of time. In the described experiment, the ability of a person to parallel pattern recognition in a large array of graphic information was determined. A person receives a series of tasks, the solution of which requires the processing of a certain amount of information. Computer recorded the time and correctness of the decision and calculated the dependence of the average solution time on the amount of information in the task for correctly solved problems. In accordance with the earlier proposed method (Koganov and Rakcheeva in Advances in Intelligent Systems and Computing. Springer, Warsaw, Poland, pp. 68–78, 2017; Koganov and Rakcheeva in The Computer Research and Modeling. The Computer Research Institute (UGU), pp. 621–638, 2017) [1, 2], the problems contain calculations of expression in two algebras, one of which is associative and the other is nonassociative. To facilitate the work of the subjects, we used in the experiment the figurative graphic images for representation of an algebraic elements. The computations in associative algebra allow the parallel counting. In the algebra where the associativity is absenting, the computations may be only sequential. Therefore, the analysis of the solution time in a series of problems allows to discover the computing strategy: uniformly sequential or accelerated sequential or parallel computing. The experiment was showed that all subjects used a uniform sequential strategy in nonassociative algebra, and parallel computations for associative problems.
Histogram-Equalized Hypercube Adaptive Linear Regression for Image Quality Assessment

Image Quality Assessment (IQA) becomes intensely salient in several applications, namely, acquisition of images, watermarking, image compression, image transmission, enhancement of images and so on, due to the extensive use of digital images. In the past decades, considerable advancements have been developed in IQA using Region of Interest (ROI). However, ROI localization is a labour-intensive process that takes multiple passes of sliding-window in search of proper ROI. The efficiency of examination, reduction in the time taken for ROI localization by multiple passes and the quality of the image can be improved by the proposed method, Histogram-Equalized Hypercube Adaptive Linear Regression (HE-HALR) scheme. HE-HALR scheme first performs the pre-processing step for input images. In this step, the features used to describe the quality of images are analysed using Histogram-Equalization-based Contrast Masking (HE-CM) model. The HE-CM model performs ROI localization with the parallelization programming that identifies the contrast masking and luminance value in a parallel manner. With the resultant feature vectors, dimensional reduction is performed using machine learning technique, namely, hypercubical neighbourhood. Finally, IQA is performed with the dimensionality-reduced features using Adaptive Linear Regression.
Contiguity Theory and Pied-Piping

In this article, I apply the conditions of Contiguity Theory ( Richards 2016 ) to the problem of pied-piping. I derive the conditions on pied-piping discovered by Cable ( 2007 , 2010a , b ) and account for the connection discovered by Uribe-Etxebarria (2002) between the conditions on wh-in-situ in a given language and the conditions on pied-piping in that language. In the end, as in Cable’s approach, pied-piping dissolves as a phenomenon; the same conditions that determine when overt movement must take place also determine how much material may be pied-piped.
Controllable Privacy Preserving Blockchain

Central banks are reluctant to accept cryptocurrency, because current implementations of decentralized privacy preserving transactions make it impossible to apply know your customer (KYC) and anti-money laundering (AML) procedures. In this paper, we augment a distributed privacy preserving cyptocurrency known as Monero with KYC and AML procedures. The proposed solution relies on secretly sharing of the clients' private view keys and private transaction keys among a large number of permissioned signers (PSs). The resulting cryptocurrency maintains the notion of distributed trust while allowing a group of PSs to cooperate, collectively applying KYC and AML procedures.
Slicing Based Code Recommendation for Type Based Instance Retrieval

It is common for developers to retrieve an instance of a certain type from another instance of other types. However, it is quite often that developers do not exactly know how to retrieve the instance although they know exactly what they need (the instance to re retrieved, also known as the target instance) and where it could be retrieved (i.e., the source instance). Such kind of instance retrieval is popular and thus their implementations, in different forms, are often publicly available on the Internet. Consequently, a number of approaches have been proposed to retrieve such implementations (code snippets) and release developers from reinventing such snippets. However, the performance of such approaches deserves further improvement. To this end, in this paper, we propose a slicing based approach to recommending code snippets that could retrieve the target instance from the source instance. The approach works as follows. First, from a large code base, it retrieves methods that contain the source instance and the target instance. Second, for each of these methods, it locates the target instances, and extracts related code snippets that generate the target instances by backward code slicing. Third, from the extracted code snippets, it removes those that do not contain the source instance. Fourth, it merges code snippets whose corresponding target instances are at parallel execution paths. Fifth, it removes duplicate code snippets. Finally, it ranks the resulting code snippets, and presents the top ones. We implement the approach as an Eclipse plugin called TIRSnippet. We also evaluate it with real type based instance retrieval queries. Evaluation results suggest that compared to the state-of-the-art approaches, the proposed approach improves the precision and recall by 8.8%, and 25%, respectively.
FFnet: Residual Block-Based Convolutional Neural Network for Crowd Counting

Due to the nonuniform scale variations and severe occlusion, most current state-of-the-art approaches use multicolumn CNN architectures with different receptive fields to tackle these obstacles. We design a single-column network to verify the necessity of multicolumn network, and we find that under similar number of parameters and size of receptive field, single network is able to perform as well as multicolumn network. Following that, we propose a single-column network called FFnet based on residual block. FFnet is a fully convolutional network and easy to train. We perform extensive experiments on Shanghaitech dataset and the UCF_CC_50 dataset, and the results show that our method achieves a better performance than Switch-CNN with nearly half number of parameters, and a closing performance to the state-of-the-art model CP-CNN with almost one-tenth parameters.
A Gray-Box Approach for Curriculum Learning

Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors. Numerical methods for curriculum learning in the literature provides only initial heuristic solutions, with little to no guarantee on their quality. We define a new gray-box function that, including a suitable scheduling problem, can be effectively used to reformulate the curriculum learning problem. We propose different efficient numerical methods to address this gray-box reformulation. Preliminary numerical results on a benchmark task in the curriculum learning literature show the viability of the proposed approach.