Reviewerly/exHarmony huggingface dataset:
An Empirical Evaluation of Machine Learning Algorithms for Identifying Software Requirements on Stack Overflow: Initial Results

Context: The recent developments made during the last decade or two in requirements engineering (RE) methods have seen a rise in using different machine-learning (ML) algorithms to solve some complex RE problems. One such problem is identifying and classifying software requirements on Stack Overflow (SO). The suitability of ML-based techniques to this tackle problem has shown convincing results, much better than those generated by some traditional natural language processing (NLP) techniques. Nevertheless, a comprehensive and systematic comprehension of these ML based techniques is still deficient. Objective: To identify and classify the type of ML algorithms used for identifying software requirements on SO. Method: This article reports systematic literature review (SLR) gathering evidence published up to August, 2019. Results: This study identified 1073 published papers related to RE and SO. Only 12 primary papers were selected. The data extraction process revealed that; 1) Latent Dirichlet Allocation (LDA) topic modeling is the most widely used ML algorithm in the selected studies, and 2) Precision and recall are the most commonly used evaluation method to measure the performance of these ML algorithms. Conclusion: The SLR finds that while ML algorithms have great potential in the identification of RE on SO, they face some open issues that will ultimately affect their performance and practical application. The SLR calls for the collaboration between RE and ML researchers, to tackle the open issues facing the development of real-world ML systems.
False Glitch Cells as a Countermeasure Against Power Analysis Attacks in Cryptographic Circuits

Cryptographic systems can potentially suffer from various attacks which are a consequence of vulnearability of side channel information to analysis attacks. The nature of digital circuits is prone to electromagnetic and power analysis (EMA and PA) attacks. Interconnects in cryptographic circuits emanate information through electromagnetic signals which can be exploited using EMA. Complementary Metal Oxide Semiconductor (CMOS) circuits are vulnearable to leak of computational information through leakage and dynamic power consumption. Dynamic power consumption of cryptographic engines is a rich source to sink in the data under process. The hardware implementation of cryptographic algorithms undermine the security of encrypted system against non brute-force-attacks. The attackers collect and analyse the leakage information of cryptographic circuits to detect the secrete key. The parameters sensitivity of the device and circuits make the cryptographic engines vulnearable to fault injection and inherent inconsistency of the circuits. Dynamic and static power comprise two major components of power consumption of digital circuits. Both power components vary with data under process. This exhibits power consumption has data dependency in digital circuits. To make noisy power profile, various countermeasures have been proposed against Differential Power Analysis (DPA) attacks. In this research, false glitch cells is proposed to work as a countermeasure to generate random transitions and power spikes to scramble power profile. The mathematical foundation of security implications of false glitch cells against Correlation Power Analysis (CPA) attacks is investigated. Moreover, the security efficiency of utilizing false glitch cells is verified with different metrics.
A Semantic Segmentation Approach to Recognize Assault Rifles in ISIS Propaganda Images

Between 2014 and 2018, the Islamic State in Iraq and Syria (ISIS) perfected the use of social media for its propaganda. To understand and counter these efforts by ISIS, it is critical to analyze their propaganda materials. During the past few years, a systematic effort has been made to catalog and annotate these materials which appear in the form of images, video and text. However due to the sheer volume of the material, it is an extremely onerous task to maintain. In this work, we present a deep learning solution to automatically identify and tag images for assault rifles. We present our experiments of a semantic segmentation approach to localization of assault rifles in a self-collected and maintained data set. Our goal is to consume minimal amount of data and cater to an analysis platform. The state of the art for object localization is the Convolutional Neural Network (CNN). A limitation of CNN is that it only handles images of fixed dimensions. One way to deal with this limitation is to re-size the input images, however this is not an ideal solution. A more flexible approach is to use a Fully Convolutional Network (FCN), which provides a robust solution for varied sizes of input images. We show that FCNs can achieve high performance in detecting and localizing objects in a real world setting, with non-curated data. We also show that by using a step wise training pipeline it is possible to learn a representation of the object using a bounding box annotation.
An Enhanced Algorithm for Matching Arabic Names Entered by Mobile Phones

The field of matching Arabic names is a serious challenge when dealing with medical data, especially in decision making through social networking programs while using the mobile phone. Although many algorithms have been proposed in matching Arabic names, most studies focus on errors introduced by computers through the keyboard. To our knowledge, there is no study on matching the Arabic names entered by mobile phones. So in this work, we propose a new algorithm based on the convergence and spacing between the Arabic characters in the keyboard of the mobile phone. The experimental results show that the proposed algorithm give more accurate results than the levenshtein algorithm.
Performance Evaluation of Inverted Files, B-Tree and B+ Tree Indexing Algorithm on Malay Text

Indexing is a part of Information Retrieval that helps to overcome the results yield on certain information. This paper show the result of a study on indexing techniques that implementing three types of indexing technique. The indexing techniques applied are Inverted Files Indexing, B-Tree Indexing and B+ Tree Indexing. This research uses 500 Malay text documents as the test collection. The performance of the indexing technique was measured and compared in its retrieval time taken. There are five experiments done in this paper that helps to determine which of the techniques are said have a good performance. The experiments done are on the time taken to index the terms or keywords to its data structure which are either linked list as in Inverted Files or B-Tree as implemented in B-Tree and B+ Tree indexing technique.
A Compositional Semantics for a Wide-Coverage Natural-Language Query Interface to a Semantic Web Triplestore

Many Natural Language (NL) Query Interfaces to data stores convert queries to a formal query language and then execute the formal query in order to obtain the result. This is problematic when handling chained prepositional phrases. An alternative approach is to treat the NL query language as a formal language and to execute the NL query directly with respect to the data store. This approach can accommodate a wide range of NL queries and is relatively easy to implement if it is based on an extension of Richard Montague's denotational semantics (MS) for natural language. The higher-order functional capability of the programming language Haskell facilitates both the implementation of MS and the close integration of syntactic analysis with semantic processing. A publicly accessible web-based NL interface to a remote Semantic Web data store has been constructed to demonstrate the viability of this approach. The approach can be directly adapted for use with relational databases.
A Class-Incremental Classification Method Based on Support Vector Machine

Incremental classification has become a hot research topic due to ever increasing availability of data, finding its application in fields like image and sound analysis. As more data is collected, we may get instances which do not belong to any of the classes previously seen by the classification model. To incorporate the new class, typical multi-class classification methods require retention of complete previous data, making the training process memory intensive. This paper puts forth a novel approach to class-incremental classification using support vector machines (SVM) and a subset of training data known as candidate support vectors (CSV). By using these CSVs, the proposed method facilitates addition of a new class to multiclass SVM classifier trained in one-vs-all fashion. Experiments on different datasets achieve accuracy comparable to complete batch training, while retaining only a small subset of the training data.
Automatic block dimensioning on GPU-accelerated programs through particle swarm optimization

Nowadays, the use of GPU to improve performance of computationally expensive systems are widely explored. On GPU-accelerated programs, performance is related to the partition of the problem into blocks of threads in such a way that the parallel tasks to be executed better fit the GPU architecture. Although there exists some general guidelines to help defining block dimensions, finding the optimum partition is still a complex and problem dependent task. In this work, it has been investigated the use of particle swarm optimization (PSO) to optimize blocks dimensions aiming to minimize programs execution time. The approach was evaluated on a GPU-accelerated wind field calculation program, in which block dimensioning was based on literature guidelines and empirical adjusts. Before PSO optimization, the program was about 25 times faster than the sequential program. After applying PSO, speedup increased to about 60 times. Unexpected optimized configurations were observed, ratifying that finding optimum dimensioning is a complex task. So the use of a robust optimization tool, such as PSO, demonstrated to be very profitable, allowing automatic optimization of blocks dimensions without necessity of a priori knowledge about problem, programs peculiarities and GPU architecture. Improve speedup of GPU-accelerated programs by automatic defining optimized block dimensions using PSO. A GPU-accelerated wind field calculation problem has been focused. A PSO was interfaced to the program in order to find the block dimensions that leads to a minimum execution time. Results were compared to literature results. The speedup obtained with the proposed approach is more than 2 times the original speedup. PSO, demonstrated to be very profitable, allowing automatic optimization of blocks dimensions without necessity of a priori knowledge about problem/programs peculiarities and/or GPU architecture.
Explainability in knowledge discovery from data streams

Interpretability of machine learning algorithms become an emerging research area over last decade. Most powerful algorithms such as deep neural networks, gradient boosting trees, deep reinforcement learning and other do not offer human-understandable justification of their decisions. However, such explanations are desired in high-risk domains (such as health, autonomous vehicles), and recently required by law (GDPR regulations in EU). Several methods were developed to assure that feature, with LIME and SHAP among most robust approaches. In this paper we focus on the issue of interpretable decision making in time series data streams. In our previous work we developed causal rule-mining algorithm that provided contrastive explanations via rule-based notation. We work on extending this work to exploit strengths of process mining and Bayesian networks to better assure counterfactual explanations.
A Cryptographic Algorithm Based on ASCII and Number System Conversions along with a Cyclic Mathematical Function

Data encryption and decryption in an efficient manner are the challenging aspects of modern information theory. An efficient cryptology algorithm is introduced in this paper to offer comparatively higher security of data. In this algorithm, the plaintext to be encrypted is converted into unprintable characters. For encryption, a competent technique is applied based on ASCII and number system conversions which make this algorithm different from others. In the steps of the algorithm, each character of the plaintext is first converted into its equivalent ASCII (decimal) which is further converted to its equivalent octal and hexadecimal numbers. Then, using some matrix manipulations on the decimal, octal and hexadecimal representation of each character the character is transformed to 5 unprintable characters to form the intermediate cipher text. After that, every unprintable character in the intermediate cipher text is further converted into a different unprintable character using a cyclic mathematical function. Performing three steps of processing, the final encrypted text is produced that gives higher level of security. In this way, as there can have total 32 unprintable characters, it will take much time (for a plaintext of only 4 characters it takes 1.53 years to break down) if the intruders try to decrypt the original message with every probable combination. Though the length of the encrypted message is larger than original message in this proposed algorithm, it offers higher security for the real-time communications.
Design and Implementation of KPI Evaluation System

The usual KPI evaluation method adopts manual scoring, which requires a lot of manpower and paper for scoring and statistics. Its scoring process is very complex, complicated, and easy to lose data. It not only consumes a lot of manpower and material resources, but takes a lot of time as well. Sometimes inaccurate calculation causes unnecessary mistakes. Therefore, it is necessary for schools to develop a complete KPI Evaluation System, which is developed with PHP and MySQL database. The system is divided into two modules: foreground and background. The foreground module is divided into Login module, Self-evaluation and Mutual Evaluation module and KPI Evaluation module. In the foreground Login module, the whole school staff needs to log in before the evaluation. After the login, they can carry out self-evaluation and mutual evaluation, and the staff who have administrative positions also need to carry out KPI evaluation. The background module includes the Informant module and the Administrator module. After the administrator logs in to the background, he can manage all modules in the background and save the summary results to an Excel file.
Prohibition of Mobile Phones in Restricted Areas Using Ibeacon Broadcaster and Device Manager

In the recent times, smartphones or cell phones have become the most widely used for communication. This application mainly aimed to provide a simplified management of prohibitions enforced in public places by developing an application to control the different mobile features. There are many places which includes hospital, religious places, research centres, museums, corporate offices, petrol pumps, universities etc., where they clearly mentioned that, “usage of mobile phones is prohibited”, “keep your mobile phones silent”, “usage of camera is restricted”, etc. many times people forget to switch off their mobile phone or forget to switch their mobile phone to silent or vibrate mode, which is not feasible. Capturing of photos and videos even in the places where the mobile phones usage is restricted. In this paper, we develop an application that provides complete control on automated profile switching and camera disabling of mobile phones.
Distributing and Sharing Resources for Automatic Speech Recognition Applications

Implementation of automatic speech recognition (ASR) system to the real scenarios has been discovered many difficulties in two main topics: processing time and resource demands. These obstructions are such big issues in deploying ASR system. This paper proposed three approaches to deal with those problems, which are applying multithread processing to separate sub-processes, exploiting multiplexing and demultiplexing technique to network socket, and improving the distribution of speech recognition engine in audio streaming. In the experiment, we evaluated our approaches with two types of speech input (audio files and audio streams). The results showed that our approaches are using fewer resources (sharing working memory) and also reduce the processing time since the real-time factor (RTF) is reduced by 15 % approximately comparing with the baseline system.
Weakly Supervised Complets Ranking for Deep Image Quality Modeling

Despite the competitive prediction performance, recent deep image quality models suffer from the following limitations. First, it is deficiently effective to interpret and quantify the region-level quality, which contributes to global features during deep architecture training. Second, human visual perception is sensitive to compositional features (i.e., the sophisticated spatial configurations among regions), but explicitly incorporating them into a deep model is challenging. Third, the state-of-the-art deep quality models typically use rectangular image patches as inputs, but there is no evidence that these rectangles can reflect arbitrarily shaped objects, such as beaches and jungles. By defining the complet, which is a set of image segments collaboratively characterizing the spatial/geometric distribution of multiple visual elements, we propose a novel quality-modeling framework that involves two key modules: a complet ranking algorithm and a spatially-aware dual aggregation network (SDA-Net). Specifically, to describe the region-level quality features, we build complets to characterize the high-order spatial interactions among the arbitrarily shaped segments in each image. To obtain complets that are highly descriptive to image compositions, a weakly supervised complet ranking algorithm is designed by quantifying the quality of each complet. The algorithm seamlessly encodes three factors: the image-level quality discrimination, weakly supervised constraint, and complet geometry of each image. Based on the top-ranking complets, a novel multi-column convolutional neural network (CNN) called SDA-Net is designed, which supports input segments with arbitrary shapes. The key is a dual-aggregation mechanism that fuses the intracomplet deep features and the intercomplet deep features under a unified framework. Thorough experimental validations on a series of benchmark data sets demonstrated the superiority of our method.
Integrating Spectral-CF and FP-Growth for Recommendation

In the era of information overload, both information consumers and information producers have encountered great challenges: for information consumers, it is very difficult to find information of interest from a large amount of information. The recommendation system is an important tool to resolve this contradiction. Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start and data sparseness problems. This paper took commodity recommendation as to the research object and proposed a recommendation algorithm that combines Spectral-CF and FP-Growth. Firstly, Firstly, the association rule algorithm FP-Growth is mine the association rules of the target user and the target item directly, and recommend the collection of items with higher similarity for the user. Secondly, using a spectral collaborative filtering algorithm Perform convolution operations in the spectral domain. Finally, providing the final result by combining the Spectral-CF and FP-Growth recommendation. The experimental results on the MovieLens dataset show that the proposed method can better solve the problem of data sparseness and cold-start problems, improvement the accuracy of recommendation.
Resilient Trajectory Planning in Adversarial Environments

Autonomous systems, including ground and aerial robots, must plan trajectories in order to satisfy performance requirements in uncertain environments. Current trajectory planning approaches do not incorporate resilience to malicious adversaries. We develop a framework for trajectory planning under false data injection attacks, in which a subset of sensors is vulnerable to compromise by an adversary. We propose a two-step control policy, in which the set of feasible control inputs is constrained based on the observations of the non-vulnerable sensors, and then an optimal control is chosen at each time step. We develop a differential dynamic programming algorithm for selecting a nominal trajectory that achieves a desired trade-off between performance and attack resilience, and prove that the chosen trajectory is locally optimal. Our approach is illustrated through numerical study.
<i>CANavi</i>: Synthesizing Cartoon-Like Animation for Street Navigation Based on Google Maps

Today, Google Maps has become one of the most important tools for exploring maps. In addition, Google Street View further provides images for improving users’ cognition of new smart cities. Many route recommendation services are available online, but most provide recommended routes on a 2D planar map. This can create a gap between a user’s perception and reality. It is not easy for users to understand an unfamiliar place through linked line-segments on a 2D map only. Many users find themselves lost while they attempt to virtually walkthrough an area in Google Street View via a mouse. Moreover, these street images are static and do not depict current weather conditions. Therefore, in this paper, we propose a novel approach for synthesizing cartoon-like animation for street navigation (CANavi). To achieve our goal, we utilized the abundant resources from Google Maps and Google Street View. The results show that our street-navigation animations create interesting and vivid sceneries that can successfully improve a user’s cognition and experience when exploring new smart cities.
The Role of Debitage Size in Assessing the Spatial Organization of Lithic Production. The Case of Lake Onega Axe and Adze Workshops (Russia)

The paper discusses the potential of metrical values of lithic debris as a proxy for distinguishing between lithic production sites where different stages of a generally identical reduction sequence took place. One of the most striking features of lithic workshops is the huge number of waste flakes, and this factor makes the use of aggregate analytical approaches unavoidable and allows obtaining essential (for the needs of archaeological interpretation) information at relatively reasonable expenditure of time. In the approach advocated in this paper, the previously suggested methods were supplemented with a new technique of artifact processing using image recognition systems. The approach was tested on the assemblages of the Eneolithic workshops for the production of stone axes and adzes from the western shores of Lake Onega (Russia).
The Design and Implementation of Trade Finance Application based on Hyperledger Fabric Permissioned Blockchain Platform

Blockchain has grown beyond cryptocurrency. It has found a sweet spot in applications that required increased trust and transparency among multi-party transactions. This paper describes our experience in the design, implementation and architecture of blockchain-based trade finance application. The implementation is based on permissioned blockchain Hyperledger Fabric. Recently, the number of projects embarked on blockchain application have grown significantly over the year. However, the current level of understanding of blockchain application is insufficient and the architectural aspects of the system has remained largely unexplored. This paper attempts to solve this problem. It applies the concept of software connectors as a medium to explore fundamental building blocks of software interaction and how they are composed into a more complex interaction.