Reviewerly/exHarmony huggingface dataset:
Job2Vec

Job Title Benchmarking (JTB) aims at matching job titles with similar expertise levels across various companies. JTB could provide precise guidance and considerable convenience for both talent recruitment and job seekers for position and salary calibration/prediction. Traditional JTB approaches mainly rely on manual market surveys, which is expensive and labor intensive. Recently, the rapid development of Online Professional graph has accumulated a large number of talent career records, which provides a promising trend for data-driven solutions. However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for a same position (\eg,Programmer, Software Development Engineer, SDE, Implementation Engineer ), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness for modeling job transition patterns. To overcome these challenges, we aggregate all the records to construct a large-scale Job Title Benchmarking Graph (Job-Graph), where nodes denote job titles affiliated with specific companies and links denote the correlations between jobs. We reformulate the JTB as the task of link prediction over the Job-Graph that matched job titles should have links. Along this line, we propose a collective multi-view representation learning method (Job2Vec) by examining the Job-Graph jointly in (1) graph topology view (the structure of relationships among job titles), (2) semantic view (semantic meaning of job descriptions), (3) job transition balance view (the numbers of bidirectional transitions between two similar-level jobs are close), and (4) job transition duration view (the shorter the average duration of transitions is, the more similar the job titles are). We fuse the multi-view representations in the encode-decode paradigm to obtain an unified optimal representations for the task of link prediction. Finally, we conduct extensive experiments to validate the effectiveness of our proposed method.
LevioSA

We study the problem of secure two-party computation of arithmetic circuits in the presence of active ("malicious") parties. This problem is motivated by privacy-preserving numerical computations, such as ones arising in the context of machine learning training and classification, as well as in threshold cryptographic schemes. In this work, we design, optimize, and implement anactively secure protocol for secure two-party arithmetic computation. A distinctive feature of our protocol is that it can make a fully modular black-box use of any passively secure implementation of oblivious linear function evaluation (OLE). OLE is a commonly used primitive for secure arithmetic computation, analogously to the role of oblivious transfer in secure computation for Boolean circuits. For typical (large but not-too-narrow) circuits, our protocol requires roughly 4 invocations of passively secure OLE per multiplication gate. This significantly improves over the recent TinyOLE protocol (Döttling et al., ACM CCS 2017), which requires 22 invocations of actively secure OLE in general, or 44 invocations of a specific code-based passively secure OLE. Our protocol follows the high level approach of the IPS compiler (Ishai et al., CRYPTO 2008, TCC 2009), optimizing it in several ways. In particular, we adapt optimization ideas that were used in the context of the practical zero-knowledge argument system Ligero (Ames et al., ACM CCS 2017) to the more general setting of secure computation, and explore the possibility of boosting efficiency by employing a "leaky" passively secure OLE protocol. The latter is motivated by recent (passively secure) lattice-based OLE implementations in which allowing such leakage enables better efficiency. We showcase the performance of our protocol by applying its implementation to several useful instances of secure arithmetic computation. On "wide" circuits, such as ones computing a fixed function on many different inputs, our protocol is 5x faster and transmits 4x less data than the state-of-the-art Overdrive (Keller et al., Eurocrypt 2018). Our benchmarks include a general passive-to-active OLE compiler, authenticated generation of "Beaver triples", and a system for securely outsourcing neural network classification. The latter is the first actively secure implementation of its kind, strengthening the passive security provided by recent related works (Mohassel and Zhang, IEEE S&P 2017; Juvekar et al., USENIX 2018).
Hybrid and lightweight detection of third party tracking: Design, implementation, and evaluation

A common practice for websites is to rely on services provided by third party sites to track users and provide personalized experiences. Unfortunately, this practice has strong implications for both users and performance. From one hand, the privacy of individuals is at a risk given the use of valuable information used for the reconstruction of personal profiles. From the other hand, many existing countermeasures to protect privacy, having been implemented into Web browsers, exhibit performance issues, mainly due to the use of huge (and difficult to maintain up to date) lists of resources that have to be filtered out, given their privacy intrusiveness. To overcome these limitations, we propose the use of a hybrid mechanism exploiting blacklisting and machine learning for the automatic identification of privacy intrusive services requested while browsing Web pages. The idea is to use the blacklisting technique (widely used by the majority of privacy tools), in combination with a machine learning model which distinguishes between malicious and functional resources, and hence updates the blacklist, accordingly. We found out that machine learning models are able to classify JavaScript programs and HTTP requests with accuracy up to 91% and 97%, respectively. We provided a prototype implementation of this hybrid mechanism, named GuardOne, and we performed an exhaustive evaluation study to assess its effectiveness and performance. Results showed that GuardOne is able to filter out malicious resources from users’ requests without performance degradation when compared with traditional systems that leverage on the use of static lists for filtering. Moreover, results about effectiveness show that our mechanism, even with some small improvements, is able to efficiently filter out malicious requests and reduce in a substantial way personal information leakage.
A Secure and Robust Frequency and Time Diversity Aided OFDM–DCSK Modulation System Not Requiring Channel State Information

In this paper, we propose a novel two-dimensional frequency and time diversity aided orthogonal frequency division multiplexing based differential chaos shift keying (OFDM-DCSK) system. Our aim is to provide secure and robust transmissions for practical wireless systems, where perfect channel state information (CSI) may not be available at the receiver, by exploiting the natural high security of chaotic sequences and frequency diversity gains brought by the frequency hopping (FH). In our design, the information bits are firstly modulated by chaotic chips, then non-repetitive FH operations are performed on both reference chips and chaotic modulated symbols. After the inverse fast Fourier transform (IFFT), the non-repetitive reference chips and chaotic modulated symbols are respectively transmitted over different subcarriers. Subsequently, the receiver recovers the information using the received reference chaotic chips which naturally embed the channel frequency response (CFR) of all subcarriers. We then analyze the energy and spectral efficiencies, derive the bit error rate (BER) and information leakage expressions, and provide a detailed complexity analysis of the proposed scheme. Simulation results verify the effectiveness of our derivations and demonstrate that the proposed system achieves better BER and security performances compared with the benchmark system, especially when the CSI is imperfect or unknown.
Becoming information centric: the emergence of new cognitive infrastructures in education policy

New cognitive infrastructures are emerging as digital platforms and artificial intelligence enable new forms of automated thinking that shape human decision-making. This paper (a) offers a new theoretical perspective on automated thinking in education policy and (b) illustrates how automated thinking is emerging in one specific policy context. We report on a case study of a policy analysis unit (‘The Centre’) in an Australian state education department that has been implementing a BI strategy since 2013. The Centre is now focused on using BI to support complex decision making and improve learning outcomes, and their strategy describes this focus as becoming ‘information centric’. The theoretical framework for our analysis draws on infrastructure studies and philosophy of technology, particularly Luciana Parisi’s recent work on automated thinking. We analyse technical documentation and semi-structured interview data to describe the enactment of a BI strategy in The Centre, with a focus on how new approaches to data analytics are shaping decision-making. Our analysis shows that The Centre is developing a cognitive infrastructure that is already creating new conditions for education policy making, and we conclude with a call for research designs that enable pragmatic exploration of what these infrastructures can do.
A Hybrid Ensemble Algorithm Combining AdaBoost and Genetic Algorithm for Cancer Classification with Gene Expression Data

The diversity of base classifiers and integration of multiple classifiers are two key issues in the field of ensemble learning. This paper puts forward a hybrid ensemble algorithm combining AdaBoost and genetic algorithm(GA) for cancer classification with gene expression data. The decision group is designed to increase the diversity of base classifier pool, and the GA is used to assign weight to each base classifier, thus to improve the classification performance by avoiding local extrema. The decision groups composed by using base classifiers, including K-nearest neighbor (KNN), Naïve Bayes (NB), and Decision Tree (C4.5). Experimental results show that the proposed algorithm is superior to those existing ensemble learning methods, such as Bagging, Random Forest (RF), Rotation Forest (RoF), AdaBoost, AdaBoost-BPNN, AdaBoost-SVM, and AdaBoost-RF, especially it has better performance on small samples and unbalanced gene expression data processing.
A secure data deletion scheme for IoT devices through key derivation encryption and data analysis

With the widespread adoption of mobile devices in various IoT services, an increasing amount of personal sensitive data are stored in IoT devices using flash memory as storage medium. Personal sensitive data are subject to privacy leakage due to unauthorized access, accidentally loss or resale of IoT devices. To tackle this challenge, in this paper, we present a novel key derivation encryption (KDE) algorithm, which is then used to construct a secure data deletion (SDDK) scheme for IoT devices. Initially, we design a nodal key tree based on flash memory's hierarchical structure, and present a KDE algorithm to generate data key for encrypting user's sensitive data and simplify key management. Meanwhile, based on KDE, we propose an SDDK scheme by combining partial block erasure with key deletion to remove both the ciphertext and the key components after data expiration, thereby implementing secure data deletion on IoT devices. Furthermore, we formally describe the process of SDDK using a mathematical analysis model, and give an optimal solution to reduce the page transfer overhead by employing implicit enumeration analysis algorithm. Finally, security analysis shows that the KDE algorithm is provably secure and the SDDK scheme implements data privacy protection and secure deletion of invalid data. Performance analysis and experimental results indicate that the SDDK scheme is effective and efficient.
Towards Accurate and Interpretable Sequential Prediction

With the influence of information explosion, there are more and more choices exposed to public view. Next item recommendation is being a significant and challenging task. Recently, attention mechanism, Convolutional Neural Networks (CNN) and other kinds of deep components are used to model user behaviors. However, the proposed models often fail to extract the feature of user behaviors in different time periods and the CNN-based models before are hard to make the used CNN interpretable. In this paper, we propose a CNN & Attention-based Sequential Feature Extractor (CASFE) module to capture the possible features of user behaviors at different time intervals. Specifically, we import CNN to extract multi-level features of user behaviors with different time periods. After each CNN layer, we use attention module to emphasize the different effect of behaviors on the prediction result. Besides, the features we try to extract here have the similar concept and meaning with the hand-crafted features in Feature Engineering, which proves the validity of CASFE. Accordingly, CASFE becomes a general sequential feature extractor that can be used in various sequential prediction tasks. With Multi-Layer Perceptron (MLP), CASFE would be a state-of-the-art next item recommendation model. The model obtains good performance on Last.fm_1K dataset and MovieLens_1M dataset. Besides, as a compatible extractor module, it can also promote CTR prediction models as well as other sequential prediction tasks.
Banking on Blockchain: An Evaluation of Innovation Decision Making

Blockchain technology has received significant attention following the introduction of Bitcoin in 2008. In particular, the financial services industry has sought to capitalize on the underlying technology, known as distributed ledger technology, to generate new products, services, and innovative business models. In this article, through an in-depth investigation of the technology innovation evaluation process, we utilize a grounded theory approach to study 12 financial service organizations. This article is unique in that it explores the technology evaluation process concurrently as decision makers reacted to the potential uses, as opposed to a retrospective view after a technology innovation had been adopted. Evidence suggests that, organizations applied a specific process to determine the value of blockchain that consisted of understand, organize, and test, which collectively helped create the proof-of-value model. Surprisingly, we find that financial service organizations tend to view blockchain innovation as a lower priority due to the lack of a clear path to value. Additionally, financial service organizations consistently leverage industry consortiums to link to external knowledge and help with the decision-making process. Our findings have direct implications to both innovation researchers as well as practitioners seeking to evaluate blockchain technology.
SVD: A Large-Scale Short Video Dataset for Near-Duplicate Video Retrieval

With the explosive growth of video data in real applications, near-duplicate video retrieval (NDVR) has become indispensable and challenging, especially for short videos. However, all existing NDVR datasets are introduced for long videos. Furthermore, most of them are small-scale and lack of diversity due to the high cost of collecting and labeling near-duplicate videos. In this paper, we introduce a large-scale short video dataset, called SVD, for the NDVR task. SVD contains over 500,000 short videos and over 30,000 labeled videos of near-duplicates. We use multiple video mining techniques to construct positive/negative pairs. Furthermore, we design temporal and spatial transformations to mimic user-attack behavior in real applications for constructing more difficult variants of SVD. Experiments show that existing state-of-the-art NDVR methods, including real-value based and hashing based methods, fail to achieve satisfactory performance on this challenging dataset. The release of SVD dataset will foster research and system engineering in the NDVR area. The SVD dataset is available at https://svdbase.github.io.
A Deep Neural Network Framework for English Hindi Question Answering

In this article, we propose a unified deep neural network framework for multilingual question answering (QA). The proposed network deals with the multilingual questions and answers snippets. The input to the network is a pair of factoid question and snippet in the multilingual environment (English and Hindi), and output is the relevant answer from the snippet. We begin by generating the snippet using a graph-based language-independent algorithm, which exploits the lexico-semantic similarity between the sentences. The soft alignment of the question words from the English and Hindi languages has been used to learn the shared representation of the question. The learned shared representation of question and attention-based snippet representation are passed as an input to the answer extraction layer of the network, which extracts the answer span from the snippet. Evaluation on a standard multilingual QA dataset shows the state-of-the-art performance with 39.44 Exact Match (EM) and 44.97 F1 values. Similarly, we achieve the performance of 50.11 Exact Match (EM) and 53.77 F1 values on Translated SQuAD dataset.
A Survey on Blockchain Technology and its Proposed Solutions

Blockchain technology had become evident since 2008 when Santoshi Nakamoto aimed to serve blockchain as a bond ledger of the cryptocurrency bitcoin. It should never be compared to any existing technologies like the internet. Blockchain is pleased with the fact that it provides high satisfaction and a trust bond to its users. There are significant implementations of blockchain technology across various sectors of a country which includes the agricultural venture, education venture, and supply chain management systems. Blockchain technology can be useful in countries like India, where the agricultural sector perturb about one-sixth of the total GDP of the country and also about half of the employees working in that country. So we will discuss several implementations of blockchain, which are spoonful in transforming a nation.
Distributed entropy filtering subject to DoS attacks in non‐Gauss environments

Summary This paper is concerned with distributed entropy filtering for a class of time‐varying systems subject to non‐Gaussian noises and denial‐of‐service attacks. A distributed Kalman‐type filter is constructed by fusing the information from neighboring sensors in the one‐step prediction estimation. The maximum correlation entropy criterion with weighted Gaussian kernels instead of the traditional least squares and minimum variance indices is then employed to evaluate the filtering performance under the more general case of non‐Gaussian noises. For the considered scenario, a new algorithm of distributed maximum correntropy criterion Kalman filters is developed by utilizing traditional fixed‐point iterative rules, and the desired filter gains are dependent on both the one‐step prediction cross‐variance and the weighted Gaussian kernel function. In light of a similar analysis process and an introduced auxiliary cost, its suboptimal version is proposed to avoid the calculation of cross‐variance and thereby satisfying the requirement of real time. Furthermore, a degenerated result is also derived under the traditional correntropy criterion (ie, with unit weighted matrices). Finally, the simulation results show the merit of proposed distributed maximum correntropy criterion Kalman filters in the presence of denial‐of‐service attacks and non‐Gaussian noises.
On the Accuracy of SonarQube Technical Debt Remediation Time

[Context] The popularity of tools for software quality analysis has increased over the years, with special attention to tools that calculate technical debt based on violations of a set of rules. SonarQube is one of the most used tools and provides an estimation of the time needed to remediate technical debt. However, practitioners are still skeptical about the accuracy of its remediation time estimation. [Objective] In this paper, we analyze the accuracy of SonarQube remediation time on a set of 15 open source Java projects. [Method] We designed and conducted a case study where we asked 65 novice developers to remove rule violations and reduce the technical debt of 15 projects. [Results] The results point out that SonarQube remediation time, compared to the actual time for reducing technical debt, is generally overestimated, and that the most accurate estimation relates to code smells, while the least accurate concerns bugs. [Conclusions] Practitioners and researchers could benefit from the results of this work to understand up to which extent technical debt is overestimated and have a more accurate estimation of the remediation time.?
Performance and Economic Evaluation of Solar Rooftop Systems in Different Regions of Thailand

Solar rooftop systems in the residential sector have been rapidly increased in the term of installed capacity. There are various factors, such as climate, temperature, and solar radiation, that have effects on solar power generation efficiency. This paper presents a performance assessment of a solar system installed on the rooftop of residence in different regions of Thailand by using PSIM simulation. Solar rooftop installation comparison in different regions is carried out to evaluate the suitable location. In addition, three types of solar panels are used in research: monocrystalline, polycrystalline, and thin-film. The electrical parameters of real power and energy generated from the systems are investigated and analyzed. Furthermore, the economic evaluation of different solar rooftop system sizes using the monocrystalline module is investigated by using economic indicators of discounted payback period (DPP), net present value (NPV), internal rate of return (IRR), and profitability index (PI). Results show that the central region of Thailand is a suitable place for installing solar rooftop in terms of solar radiation, and the temperature has more solar power generation capacity than the other regions. The monocrystalline and polycrystalline solar panels can generate maximum power close to each other. All solar rooftop sizes with the Feed-in Tariff (FiT) scheme give the same DPP of 6.1 years, IRR of 15%, and PI of 2.57 which are better than the cases without the FiT scheme. However, a large-scale installation of solar rooftop systems can receive more electrical energy produced from the solar rooftop systems. As a result, the larger solar rooftop system sizes can achieve better economic satisfaction.
On activities and affordances for mobile learning

Across the articles in this special issue, there is a clear and important focus on how people learn through mobility, which allows them to move across contexts as they learn. This commentary considers ways mobile technologies can support learning with a focus on understanding the affordances to of the mobile technologies develop new learning practices that could not be accomplished without this technology. With this in mind, we return to the definition of mobile learning that suggests mobile learning is learning across multiple contexts, through social interaction, using personal electronic devices that can immediately capture information about, or provide information to the user. To explore how to implement this definition in truly powerful ways, we suggest explicitly unpacking this into its four component parts, so that we can explore and discuss the unique affordances of mobile learning: (1) multiple contexts, (2) social interactions, (3) content interactions, and (4), capturing information and providing information to users in real-time. We further suggest a 5th element, which is the synergies among these different dimensions. We conclude with the challenges in doing research in mobile learning environments and the need to understand both how and what people learn in such environments.