Reviewerly/exHarmony huggingface dataset:
A Transactive Memory System Perspective in Software Engineering Education

Contribution: Results and conclusions on the use of transactive memory system (TMS) in software engineering (SE) education in student project teams that consist of undergraduate and postgraduate students are documented. This can be valuable for the adoption of this learning approach and team formation by other universities and institutes. Background: Preparing students for the SE industry requires bringing them in close collaboration to real-world tasks. This is usually performed via students carrying out software projects, working in teams. In such environments, the knowledge sharing and the interactions within the team are worth investigating, as they can affect the outcome of the student collaboration and can provide insight on how the collaboration is valued by the team members. Research Questions: 1) What is the effect of TMS on students in SE education? 2) Do parameters, such as gender, affect TMS development? 3) Does the inclusion of a project manager with more expertise affect TMS? Methodology: Students were divided into teams working on SE projects. Teams consist of both undergraduate and postgraduate students. Data from students on TMS for consecutive academic years were gathered. Findings: TMS is present in student teams and some of its scale items are affected by parameters, such as the team composition and the project type. TMS also affects performance.
Minimising using the Method of Quine-McCluskey with Generalised Nets

This article describes one of the methods of minimizing a logical function. The method is based on Quine-McCluskey algorithm for a completely defined logical function. The generalized nets apparatus is used for modelling and description. The generalized nets are one of the most popular techniques for describing the processes in a formal and abstract manner.
Immature Digital Expertise of the Educational Institution’s Managerial Staff as HR Risk to Education Development

The article presents the findings of the research on the impact immature digital expertise of the educational institution’s managerial staff on the results of education development. The term “HR risks in the education development” has been clarified as situations of a potential hazard that have a direct or indirect negative impact on the development (digitalization) of education, which occurrence is associated with real uncertainty caused by the inefficient HR management system and HR performance. The term “digital expertise of the managerial staff of an educational institution” is defined as an integrative personal quality expressed in the ability and willingness of the managerial staff of an educational institution to design and/or apply digital systems and technologies to achieve development (digitalization) of education and educational institution. The main criteria for the digital expertise of the educational institution’s managerial staff are their abilities and willingness to provide: (1) the continuous development of the digital competency of the teaching and managerial staff of the educational institution; (2) designing a graduate’s expertise model that meets the requirements of the digital economy; (3) introduction and application of digital systems and technologies in the fulfillment of educational programs; (4) adoption and employment of digital systems and technologies in the management of the educational process and educational institution. The regular relationships between immature digital expertise of the educational institution’s managerial staff and the results of the education development (digitalization) have been substantiated. The findings can be useful in designing a system for management of education development and a system for the development of an educational institution’s managerial staff. In the course of the research, we have applied statistical methods, questioning, interviewing, observation, analysis of management activity results, and factor analysis.
Video Indexing Using Face Appearance and Shot Transition Detection

The possibility to automatically index human faces in videos could lead to a wide range of applications such as automatic video content analysis, data mining, on-demand streaming, etc. Most relevant works in the literature gather full indexing of videos in real scenarios by exploiting additional media features (e.g. audio and text) that are fused with facial appearance information to make the whole frameworks accurate and robust. Anyway, there exist some application contexts where multimedia data are either not available or reliable and for which available solutions are not well suited. This paper tries to explore this challenging research path by introducing a new fully computer vision based video indexing pipeline. The system has been validated and tested in two different typical scenarios where no-multimedia data could be exploited: broadcasted political video documentaries and healthcare therapies sessions about non-verbal skills.
From Text Classification to Keyphrase Extraction for Short Text

Existing keyphrase extraction approaches often suffer from issues such as the sparsity and brevity of short text (e.g., headlines, queries, and tweets). In this paper, we propose a novel keyphrase extraction method for short text by utilizing recurrent neural networks. The main idea behind our approach is to classify short text into a relevant class or category and extract keyphrases from important words in the class or category. Unlike previous supervised approaches that need the information of annotated keyphrases, our approach requires only a text classification dataset (i.e., DBpedia), which is easier to use and requires less human effort. In our approach, we first feed short text into the attention-based neural network for text classification. We then compute attention weights of each word in input short text. Subsequently, we detect keyphrase candidates by chunking phrases and summing the attention weights of compositional words in the chunked phrase. The experimental results clearly show the efficacy of our approach on real-world datasets, such as headlines, queries, and tweets. The proposed method outperforms the Microsoft Cognitive Services and IBM Watson Natural Language Understanding service for keyphrase extraction in terms of F1-score and acceptable percentage on the NYT and Question datasets. Further, we confirm that the proposed method is comparable to supervised methods for keyphrase extraction from short text in the Tweet dataset.
Online Bagging for Anytime Transfer Learning

Transfer learning techniques have been widely used in the reality that it is difficult to obtain sufficient labeled data in the target domain, but a large amount of auxiliary data can be obtained in the relevant source domain. But most of the existing methods are based on offline data. In practical applications, it is often necessary to face online learning problems in which the data samples are achieved sequentially. In this paper, We are committed to applying the ensemble approach to solving the problem of online transfer learning so that it can be used in anytime setting. More specifically, we propose a novel online transfer learning framework, which applies the idea of online bagging methods to anytime transfer learning problems, and constructs strong classifiers through online iterations of the usefulness of multiple weak classifiers. Further, our algorithm also provides two extension schemes to reduce the impact of negative transfer. Experiments on three real data sets show that the effectiveness of our proposed algorithms.
STEAM for STEM - Include “Art” in STEM (Science, Technology, Engineering and Mathematics)

After a fairly recent study discussed in IEEE Spectrum, with the catchy title, “want-girls-attracted-to-tech-put-a-for-art-in-stem”, on the strategies of attracting more female students to opt for studies in STEM (Science, Technology, Engineering and Mathematics), the acronym STEM has been modified to STEAM (Science, Technology, Engineering, Art and Mathematics), by adding “art” as a topic in the curricula. In a recent study, the authors of this paper noted that there has been and there is still an on-going discussion on the low percentage of females in STEM (Science, Technology, Engineering and Mathematics) related studies. Some initiatives of engineers in the USA and Canada and current discussions in the IEEE community support the idea of adding an A (for Arts) in STEM leading to STEAM. With the element of gamification in many subjects and associated elements of Art in many disciplines, this strategy may help to improve the number of female students, as confirmed by a study performed in 2016. Topics with the hue of art are found in many STEM subjects and the staff responsible for courses can tune the content to accommodate these in already existing programs by putting more emphasis on “Art”. This paper addresses possibilities of adding elements of “Art” in existing courses with Biggs' Teaching and Learning, Intended Learning Outcomes and Assessment with gender balance in focus. The example of the Shad, Canada's successful STEAM and Entrepreneurship Live-in program for high-school (i.e. pre-university) students are cited. With respect to Biggs' model and using problem based learning (PBL), we point out, how the curricula in our universities in electrical engineering, IT and system engineering have many similarities with the pedagogical goals of the Shad program. This paper presents possibilities of incorporating elements of “art” related topics in disciplines under focus in the EAEEIE-community. Experiences from some courses in our universities are also provided with some pertinent conclusions for STEAM studies with enhanced gender balance on national, regional and international levels.
An Empirical Study of Deep Neural Networks Models for Sentiment Classification on Movie Reviews

Sentiment classification is one of the new absorbing parts appeared in natural language processing with the emergence of community sites on the web. Taking advantage of the amount of information now available, research and industry have been seeking ways to automatically analyze the sentiments expressed in texts. The challenge for this task is the human language ambiguity, and also the lack of labeled data. In order to solve this issue, Deep learning models appeared to be effective due to their automatic learning capability. In this paper, we provide a comparative study on IMDB movie review dataset, we compare word embeddings methods and further deep learning models on sentiment analysis and give broad empirical outcomes for those keen on taking advantage of deep learning for sentiment analysis in real-world settings.
A Hierarchical Model for StarCraft II Mini-Game

StarCraft II is one of the most challenging real-time strategy games, due to huge action space, large observation space, imperfect information, etc. Therefore, it is hard to learn the full game of StarCraft II. To reduce the learning complexity, DeepMind and Blizzard released several mini-games, in which the BuildMarines mini-game is most challenging, due to long time horizons, partially-observed state, high-dimensional, continuous action space and observation space. In this paper, we propose a hierarchical modeling method to solve those challenges in BuildMarines mini-game. Our approach consists of two levels, combining learning-based (high-level) and rule-based (low-level) method. The learning-based method leverages DQN reinforcement learning algorithm, while the rule-based method leverages script to realize. Experimental results show that the proposed approach is effective for an agent to learn the long planning horizon game, BuildMarines.
Twin-field quantum key distribution based on twisted photon

Quantum key distribution (QKD) is a promising application to establish unconditional secure communications by quantum mechanics. However, its widespread application still faces a great challenge, that is, the fundamental linear key-rate constraint called Pirandola-Laurenza-Ottaviani-Banchi (PLOB) bound. Recently, twin-field QKD (TF-QKD) was proposed (Lucamarini et al., 2018 [4]), it overcomes the constraint mentioned above. However, the original TF-QKD is based on the phase-encoding strategy, which requires active alignment. In this paper, we improve the original TF-QKD with the photon orbital angular momentum (OAM), and propose a novel reference frame independent protocol to overcome the reference frame dependence. No more alignment procedure is needed, and the intrinsic misalignment errors are eliminated by utilizing the rotation-invariance of OAM photons. Besides, the security performance is also improved.
Predicting Dynamic Product Price by Online Analysis: Modified K-Means Cluster

In data mining, various techniques are applied on large sets of data. These large sets of data are known as big data. For extraction and retrieval of desired information or pattern from big amount of data and quantity of knowledge, we tend to use huge data processing. To maintain working of “big data,” various logics are used. Today, because of many online applications, product price is highly dynamic and current price can be compared by the customers by using different applications. In our work, we have used modified K-means clustering method and divided online data. Dataset is divided into 10 clusters for analysis. In the last section, the conclusion based on I/O time and computational time is given.
Uncertain Ontology-Aware Knowledge Graph Embeddings

Much attention has recently been given to knowledge graphs embedding by exploiting latent and semantic relations among entities and incorporating the structured knowledge they contain into machine learning. Most of the existing graph embedding models can only encode a simple model of the data, while few models are designed for ontology rich knowledge graphs. Furthermore, many automated knowledge construction tools produce modern knowledge graphs with rich semantics and uncertainty. However, there is no graph embedding model which includes uncertain ontological information into graph embedding models. In this paper, we propose a novel embedding model UOKGE (Uncertain Ontology-aware Knowledge Graph Embeddings), which learns embeddings of entities, classes, and properties on uncertain ontology-aware knowledge graphs according to confidence scores. The proposed method preserves both structures and uncertainty of knowledge in the embedding space. Specifically, UOKGE encodes each entity in a knowledge graph as a point of n-dimensional vector, each class as a n-sphere and each property as 2n-sphere in the same semantic space. This representation allows for the natural expression of uncertain ontological triples. The preliminary experimental results show that UOKGE can robustly learn representations of uncertain ontology-aware knowledge graphs when evaluated on a benchmark dataset.
Revisiting Cloud Security Threats: IP Spoofing

In the computing architecture, cloud is emerging as a major giant in the networking area/s for providing services over the Internet. Despite security issues, a continuous deployment of business applications over cloud has been seen. The main security issues addressed in cloud are confidentiality, integrity, availability (CIA) and non-repudiation (N-R). Distributed denial-of-service (DDoS) attack is the main threat encountered by cloud services which attempt to make an unavailability of information of business applications by flooding traffic passages from numerous sources. IP spoofing is the main part of distributed denial-of-service in which the IP address is forged to prohibit effortless traceback. The focal point of the paper is re-examining the IP spoofing attack and its root causes. The aim of the paper is to present a broad indication of “IP spoofing attack”, emerging as an important security concern in cloud computing. The research aims to summarize the previous literature available on IP spoofing attack in context with distributed denial-of-service (DDoS) attack. The paper also presents conclusive findings having various directions for future research in IP spoofing based on the related published work and industry/organization reports.
CTC-Attention based Non-Parametric Inference Modeling for Clinical State Progression

Predictive modeling of patient state to state medical conditions in ICU is a critical yet challenging task in health informatics and machine learning. Prior critical stages from the same ICU admission may contribute differently to the next stages. That said, stages are interdependent, and disease progression is a multi-step temporal observation. In this paper, we formally name this problem as “Clinical State Progression Prediction (CSPP).” Conventional temporal modeling may fit well to predictions of fixed size observations and number of stages, but have troubles and less flexibility when addressing CSPP. To that end, we cast this problem as multi-label learning on time series data in which each stage is marked by a label. The implementation of entire framework includes two phases. In learning, an RNN based Encoder-Decoder deep model is developed for basic temporal modeling. In addition, Attention mechanism and Connectionist Temporal Classification (CTC) are integrated to explicitly model the temporal dependency as well as monotonic relation between input time series and output label space. In inference, based on the observed multi-stage labels, a non-parametric retrieval is carried out first to build up the reference patient records. Then, based on CTC-Attention learning model, consistent progressions are computed and ranked to contribute to the prediction of the clinical state progression in the next few hours. Extensive experiments on MIMIC III and Parkinson datasets demonstrate that the proposed predictive modeling for CSPP outperforms state-of-the-art works on Sepsis, Kidney-Sepsis-Mortality, Heart-Sepsis-Mortality, and Parkinson Progression.
On collecting semantics for program analysis

Reasoning on a complex system in the abstract interpretation theory starts with a formal description of the system behavior specified by a collecting semantics. We take the common point of view that a collecting semantics is a very precise semantics from which other abstractions may be derived. We elaborate on both the concepts of precision and derivability, and introduce a notion of adequacy which tell us when a collecting semantics is a good choice for a given family of abstractions. We instantiate this approach to the case of first-order functional programs by considering three common collecting semantics and some abstract properties of functions. We study their relative precision and give a constructive characterization of the classes of abstractions which are adequate for the collecting semantics.
Towards Secure and Efficient Equality Conjunction Search Over Outsourced Databases

Searchable symmetric encryption enables a cloud server to answer queries directly over encrypted data. Two key requirements are a strong security guarantee and a sub-linear search performance. The bucketization approach in the literature addresses these requirements at the expense of downloading false positives and requiring the local search at the client side. In this article, we propose a novel approach to meet these requirements while minimizing the clients work and communication cost. First, a relaxed notion of ciphertext indistinguishability on partitioned data is formalized, called class indistinguishability, which provides a level of ciphertext indistinguishability similar to that of bucketization but allows the server to perform search of relevant data and filter false positives. We present a construction for achieving these goals through a two-phase search algorithm. The first phase finds a candidate set through a sub-linear search. The second phase finds the exact query result using a linear search applied to the candidate set. The experiment results on large real-world data-sets show that our approach outperforms the state-of-the-art. This article focuses on the class of equality conjunction search, but it applies to the general class of Boolean queries of equalities because the latter can be reduced to several equality conjunction queries.
Memes As Marketing

How many fast-food restaurants have released a chart-topping mixtape? The answer is exactly one—Wendy’s. Their mixtape, We Beefin?, tears down competitors like McDonald’s and Burger King while also managing to reach the number one spot on Spotify’s Global Viral 50 and garnering almost 800 million impressions. But, Wendy’s isn’t just a one-hit wonder. Their Twitter account is also a hit, managing to achieve a 113% jump in Twitter mentions. Through consistently clever tweets, Wendy’s brands themselves as "the sassy friend you want to go to lunch with". These marketing wins have also demonstrated a major business impact. After Wendy’s began this unique social media approach in 2017, they experienced a 49.7% growth in profit that year. Using the power of Internet memes, Wendy’s was able to vitalize its brand even as an already successful company.
College Curriculum Evaluation Frame Based on Blockchain

Blockchain is an emerging decentralized architecture and distributed computing paradigm underlying a large number of cryptocurrencies and has recently attracted many high-tech institutions. It has a wide range of applications in various fields. With the continuous development of higher education, the curriculum evaluation system plays a vital role in the construction of college curriculum. Owing to the defects of non-transparent and centralization in college curriculum, a new frame based on blockchain is proposed in this paper. The proposed framework separates the curriculum evaluation from the school educational system and uses the characteristics of the blockchain to achieve the validity and openness of the evaluation results. Performance analysis and practical instances show the proposed frame can improve the authenticity and effectiveness of the evaluation.
Asymmetric Node Similarity Embedding for Directed Graphs

Node embedding is the process of mapping a set of vertices from a graph onto a vector space. Modern deep learning embedding methods use random walks on the graph to sample relationships between vertices. These methods rely on symmetric affinities between nodes and do not translate well to directed graphs. We propose a method to learn vector embeddings of nodes in a graph as well as the parameters of an asymmetric similarity function that can be used to retain the direction of relationships in the embedding space. The effectiveness of our approach is illustrated visually by the 2D embedding of a lattice graph as well quantitatively in multiple link prediction experiments on real world datasets.
Novel Image State Ensemble Decomposition Method for M87 Imaging

This paper proposes a new method of image decomposition with a filtering capability. The image state ensemble decomposition (ISED) method has generative capabilities that work by removing a discrete ensemble of quanta from an image to provide a range of filters and images for a single red, green, and blue (RGB) input image. This method provides an image enhancement because ISED is a spatial domain filter that transforms or eliminates image regions that may have detrimental effects, such as noise, glare, and image artifacts, and it also improves the aesthetics of the image. ISED was used to generate 126 images from two tagged image file (TIF) images of M87 taken by the Spitzer Space Telescope. Analysis of the images used various full and no-reference quality metrics as well as histograms and color clouds. In most instances, the no-reference quality metrics of the generated images were shown to be superior to those of the two original images. Select ISED images yielded previously unknown galactic structures, reduced glare, and enhanced contrast, with good overall performance.