Reviewerly/exHarmony huggingface dataset:
The Design of a Survey on Bridging the Gap between Software Industry Expectations and Academia

Software practitioners have a variety of different roles, whose academic backgrounds are not only based on “Computing Disciplines” but also non-computing ones such as Electrical and Electronics Engineering. Many engineering graduates in the software industry often face difficulties after beginning their professional careers due to misalignment of the skills learnt in the university education with what is needed in the job. It is also reported that software practitioners in the embedded industry, who were graduated from any non-computing disciplines are lacking knowledge in key software engineering (SE) topics and knowledge areas (KAs), which they learn or improve themselves during the job (e.g., after university education). Companies spend crucial resources to train these personnel, who are not “ready” for the industry. Hence, academia must know what skills are needed to adapt the educational programs via an effective curriculum. Although SE is shaped and used by both practitioners and academicians, these two separate worlds have different objectives and concerns. In order to increase Industry-Academia Collaboration (IAC) in SE, it is necessary to understand different perceptions of practitioners about academia. After inspiring different curriculum guidelines and related studies and then identifying the expectations and opinions of practitioners about the academic world (as expert opinions via semi-structured interviews), a survey, which explores the gap between the industry expectations and academic activities, was designed. In this article, the design phase activities of this survey, whose results will shed light on software development education and more IACs, are presented.
How to produce complementary explanations using an Ensemble Model

In order to increase the adoption of machine learning models in areas like medicine and finance, it is necessary to have correct and diverse explanations for the decisions that the models provide, to satisfy the curiosity of decision-makers and the needs of the regulators. In this paper, we introduced a method, based in a previously presented framework, to explain the decisions of an Ensemble Model. Moreover, we instantiate the proposed approach to an ensemble composed of a Scorecard, a Random Forest, and a Deep Neural Network, to produce accurate decisions along with correct and diverse explanations. Our methods are tested on two biomedical datasets and one financial dataset. The proposed ensemble leads to an improvement in the quality of the decisions, and in the correctness of the explanations, when compared to its constituents alone. Qualitatively, it produces diverse explanations that make sense and convince the experts.
Design Procedure of Knowledge Base for Practical Attack Graph Generation

Cyber security assessment is an essential activity for understanding the security risks in an enterprise environment. While many tools have been developed in order to evaluate the security risks for individual hosts, it is still a challenge to identify multi-hop cyber security risks in a large-scale environment. An attack graph, which provides a comprehensive view of attacks, assists in identifying high-risk attack paths and efficiently deploying countermeasures. Several frameworks which generate an attack graph from system information and knowledge base have also been developed in the past. Although these tools are widely adopted, their expression capabilities are insufficient. The expansion of knowledge base is needed to handle comprehensive attack scenario. In this research, we developed an attack graph generation system by extending the MulVAL framework which is widely adopted due to its high extensibility. We designed and implemented knowledge base (also known as "interaction rules" in the MulVAL framework) for practical attack graph generation. A structured design procedure is necessary to construct a knowledge base that enables comprehensive analysis, which is highly important for actual risk assessment. We describe the design procedure, design considerations and implementation of our rule set. Additionally, we demonstrate the improvement to the generated attack graph by the implemented rules in a case study.
Collaborative Augmented Reality in Engineering Education

A continuous trend in education is the use of new technologies like Augmented Reality (AR). These technologies are assumed to make teaching and learning processes more hands-on and more tangible, particularly in terms of abstract learning contents. Due to this practical nature, AR is assumed to foster the motivation of concerning oneself with a specific learning content and supports, thus, the comprehension of it and its purpose for e.g. the future work life. In academic learning contexts such as engineering education, however, AR can also be used for collaborative purposes despite its currently most common purpose of demonstration and instruction. The present paper investigates the effects of using AR in collaborative team processes with special respect to motivation and emotional activation. A mixed methods approach is chosen in order to examine qualitative and quantitative data to gather both a subjective and an objective perspective on the subject of research. As the investigation shows, the motivation to use this technology is high which derives, amongst other factors, from the better comprehension of the learning content when compared to the results of the control group.
Cognitive and Computational Complexity: Considerations from Mathematical Problem Solving

Abstract Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in cognitive processes that make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general.
Are Bitcon returns predictable?: Evidence from technical indicators

We examine the predictive ability of technical indicators for excess returns to Bitcoin prices. Our out-of-sample evidence suggests the existence of significant return predictability. Combining all technical information results in out-of-sample R2 as high as 0.523%. The dynamic strategy based on the return forecasts from combining technical information achieves the CER gains greater than 130%.
On classification of abstracts obtained from medical journals

Classification of medical documents was mostly carried out on English data sets and these studies were performed on hospital records rather than academic texts. The main reasons behind this situation are the lack of publicly available data sets and the tasks being costly and time-consuming. As the first contribution of this study, two data sets including Turkish and English counterparts of the same abstracts published in Turkish medical journals were constructed. Turkish is one of the widely used agglutinative languages worldwide and English is a good example of non-agglutinative languages. While English abstracts were obtained automatically from MEDLINE database with a computer program, Turkish counterparts of these documents were collected manually from the Internet. As the second contribution of this study, an extensive comparison on classification of abstracts obtained from Turkish medical journals was made by using these two equivalent data sets. Features were extracted from text documents with three different approaches: unigram, bigram and hybrid. Hybrid approach includes a combination of unigram and bigram features. In the experiments, three different feature selection methods and seven different classifiers were utilised. According to the results on both data sets, classification performance of the English abstracts outperformed the Turkish counterparts. Maximum accuracies were obtained from the combination of unigram features, distinguishing feature selector (DFS) and multinomial naïve Bayes (MNB) classifier for both data sets. Unigram features were generally more efficient than bigram and hybrid features. However, analysis of top-10 features indicated that nearly half of the features were translations of each other for Turkish and English data sets.
Current State of Mixed Reality Technology for Digital Retail: A Literature Review

Abstract Immersive 3D environments have been a major research area in different scientific domains such as Human-Computer Interaction, display devices, etc. Mixed Reality (MR) technologies are one of the most interesting sections of immersive environments that have a huge potential of deployment in a diverse range of industries, Retail being one of them. Retail started from traditional physical in-store setups and has been evolving ever since. Integration of technologies gave birth to Digital and Omnichannel Retail and for efficient development and deployment of MR in Omnichannel Retail, appropriate research must be made to provide proper frameworks and guidelines to developers and managers to optimize User Experiences. This paper investigates the current state of the art of MR in Retail by reviewing the present literature in the domain. It proposes important research gaps based on the analysis and understanding to provide further researches a clear picture of the different aspects associated with optimal application of MR technology in Retail sector.
Construction of a Polarization Multiphoton Controlled One‐Photon Unitary Gate Assisted by the Spatial and Temporal Degrees of Freedom

Abstract In order to fulfill quantum information processing tasks such as quantum computation and scalable quantum networks, quantum logic gates are indispensable parts. A multiqubit logic gate can efficiently reduce the cost of physical resources and complicated operations that are necessary in the combination of fewer‐qubit (e.g., two‐qubit) logic gates to fulfill the same tasks. However, the construction of multiphoton quantum logic gates is a large challenge. Assisted by spatial and temporal degrees of freedom, a polarization multiphoton controlled one‐photon unitary gate is proposed, which can be realized with three processes including the control‐path process based on weak cross‐Kerr nonlinearities, the unitary transformation process, and the output process. In the control‐path process, the control‐path modules are applied, where the entanglement between polarization, spatial and temporal degrees of freedom is generated by beam splitters, time delayers, and nonlinear interaction in cross‐Kerr media. The scheme presented here, assisted by spatial and temporal degrees of freedom reduces the experimental complexity of the schemes assisted by only spatial degree of freedom. Moreover, it will also be a useful reference in optimally constructing multiqubit quantum logic gates with other multiple degrees of freedom in many physical systems.
Fully End-to-End Composite Recurrent Convolution Network for Deformable Facial Tracking In The Wild

Human facial tracking is an important task in computer vision, which has recently lost pace compared to other facial analysis tasks. The majority of current available tracker possess two major limitations: their little use of temporal information and the widespread use of handcrafted features, without taking full advantage of the large annotated datasets that have recently become available. In this paper we present a fully end-to-end facial tracking model based on current state of the art deep model architectures that can be effectively trained from the available annotated facial landmark datasets. We build our model from the recently introduced general object tracker Re <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , which allows modeling the short and long temporal dependency between frames by means of its internal Long Short Term Memory (LSTM) layers. Facial tracking experiments on the challenging 300-VW dataset show that our model can produce state of the art accuracy and far lower failure rates than competing approaches. We specifically compare the performance of our approach modified to work in tracking-by-detection mode and showed that, as such, it can produce results that are comparable to state of the art trackers. However, upon activation of our tracking mechanism, the results improve significantly, confirming the advantage of taking into account temporal dependencies.
Running Language Interpreters Inside SGX

Recent advances in trusted execution environments, specifically with Intel's introduction of SGX on consumer processors, have provided unprecedented opportunities to create secure applications with a small TCB. While a large number of SGX solutions have been proposed, nearly all of them focus on protecting native code applications, leaving scripting languages unprotected. To fill this gap, this paper presents SCRIPTSHIELD, a framework capable of running legacy script code while simultaneously providing confidentiality and integrity for scripting code and data. In contrast to the existing schemes that either require tedious and time-consuming re-development or result in a large TCB by importing an entire library OS or container, SCRIPTSHIELD keeps the TCB small and provides backwards compatibility (i.e., no changes needed to the scripting code itself). The core idea is to customize the script interpreter to run inside an SGX enclave and pass scripts to it. We have implemented SCRIPTSHIELD and tested with three popular scripting languages: Lua, JavaScript, and Squirrel. Our experimental results show that SCRIPTSHIELD does not cause noticeable overhead. The source code of SCRIPTSHIELD has been made publicly available as an open source project.
An Efficient Supply Chain Architecture Based on Blockchain for High-value Commodities

The blockchain is the innovative technology in recent year. It brings a revolution to both industrial and academic area. In fact, it is a distributed storage system based on cryptography and consensus algorithms. Currently, many applications have been implemented in finance, agriculture, payment, and medicine area. We proposed a platform called High-value Commodities as a Service (HCS) to combine blockchain and high-value assets together. Traditionally, real estates, jewelry and artworks are main luxuries word-widely. We provide a sound and reliable environment for those investors to trade. Due to characteristics of the blockchain, users on our platform can track the source for each item. And all private data would be pushed on chains which can avoid some privacy-related problems.
A study of the use of mobile learning technology in Taiwan for language learning

Although there have been numerous mobile learning studies, relatively few concerns the moderating impact of perceived fit on mobile learning outcomes. A total of 335 adult learners with a mobile language learning experience in Taiwan took part in this study. Partial least squares structural equation modelling (PLS-SEM) is used to determine the key factors that lead to better mobile learning performance. The results of this study show that perceived usefulness, playfulness and self-management of learning are positively linked to the intention to continue with mobile learning. The results also show that perceived flexibility and personal innovativeness have a positive impact on perceived usefulness and playfulness. It is also shown that perceived fit plays a moderating role in determining mobile learning continuance intention.
A Stenography Application for Hiding Student Information into an Image

Information security is a major problem today. Different approaches and methods are introduced every day for data protection. One of them is steganography. The word steganography combines the Greek words steganos , meaning “covered, concealed, or protected,” and graphein meaning “writing”. The purpose of steganography is to construct the stego object by placing important information invisible into the ordinary cover object (image, sound, video, text, etc.) and to transmit it to the recipient. In this study, it is aimed to strengthen the LSB technique which is one of the steganography methods by suggesting the use of mask which will provide the least change on the image while hiding the data into a digital image. In the proposed method, the data is also compressed by the LZW algorithm, thus allowing more data to be hidden.
Development of Android Application for Gender, Age and Face Recognition Using OpenCV

The idea behind the face recognition system is the fact that every individual has a unique face. Like the fingerprint, an individual's face has many unique structures and features. Facial authentication and facial recognition are challenging tasks. For facial recognition systems to be reliable, they must work with great precision and accuracy. Images captured taking into account different facial expressions or lighting conditions allow greater precision and accuracy of the system compared to a case where only one image of each individual is stored in the database. The face recognition method handles the captured image and compares it to the images stored in the database. If a matching template is found, an individual is identified. Otherwise, the person is reported as unidentified. This paper describes and explains in detail the entire process of developing Android mobile application for recognizing person's gender, age and face. Face detection and recognition methods that have been used are described and explained as well as development tools used in the development of Android mobile application. The software solution describes the details of using the OpenCV library and shows the actual results of the mobile application through the images.
Tell Me Where I Am: Object-Level Scene Context Prediction

Contextual information has been shown to be effective in helping solve various image understanding tasks. Previous works have focused on the extraction of contextual information from an image and use it to infer the properties of some object(s) in the image. In this paper, we consider an inverse problem of how to hallucinate missing contextual information from the properties of a few standalone objects. We refer to it as scene context prediction. This problem is difficult as it requires an extensive knowledge of complex and diverse relationships among different objects in natural scenes. We propose a convolutional neural network, which takes as input the properties (i.e., category, shape, and position) of a few standalone objects to predict an object-level scene layout that compactly encodes the semantics and structure of the scene context where the given objects are. Our quantitative experiments and user studies show that our model can generate more plausible scene context than the baseline approach. We demonstrate that our model allows for the synthesis of realistic scene images from just partial scene layouts and internally learns useful features for scene recognition.
Algebraic Differential Fault Analysis on SIMON Block Cipher

Algebraic differential fault attack (ADFA) is an attack in which an attacker combines a differential fault attack and an algebraic technique to break a targeted cipher. In this paper, we present three attacks using three different algebraic techniques combined with a differential fault attack in the bit-flip fault model to break the SIMON block cipher. First, we introduce a new analytic method which is based on a differential trail between the correct and faulty ciphertexts. This method is able to recover the entire master key of any member of the SIMON family by injecting faults into a single round of the cipher. In our second attack, we present a simplified Grobner basis algorithm to solve the faulty system. We show that this method could totally break SIMON ciphers with only 3 to 5 faults injected. Our third attack combines a fault attack with a modern SAT solver. By guessing some key bits and with only a single fault injected at the round T - 6, where T is the number of rounds of a SIMON cipher, this combined attack could manage to recover a master key of the cipher. For the last two attacks, we perform experiments to demonstrate the effectiveness of our attacks. These experiments are implemented on personal computers and run in very reasonable timing.
Improving Resource Usages of Containers Through Auto-Tuning Container Resource Parameters

Recently, the container-based virtualization has gained increasing attention and been widely used in cloud computing. In container products such as Docker, there are a number of parameters that can control container resource usages, to avoid the resource contention occurred when running too many containers concurrently. However, it is difficult to set parameter values accurately only based on experience while tuning the parameters manually is too time-consuming to be impractical. Therefore, it becomes a challenge to set appropriate resource parameter values automatically and quickly to optimize the resource usages of container. In this paper, we present an adaptive tuning framework, conTuner, to optimize the resource configuration of container online for a new application. conTuner contains two components: an optimized configuration pool that offers candidate resource configurations, as well as a configuration optimizer that gets the appropriate optimized configuration from the pool. We have deployed conTuner in a Docker cluster. The experimental results demonstrated that, for a new application, compared to the pre-set upper limit of container resource usages, the container performance is equal or better when using conTuner, and the set resource usage constraint is more accurate. Besides, conTuner can also forecast whether resource contention among multiple containers occurs before running them concurrently. The evaluation results indicate that the prediction accuracy is 87%.