Reviewerly/exHarmony huggingface dataset:
Blockchain as the “trust-building machine” for supply chain management

Abstract This paper aims to investigate the impact of blockchain application on trust levels in supply chains. Through the systematic review of the relevant literature, three dimensions of trust, i.e., the trustor–trustee perspective, forms of trust, and time orientation, are investigated. Our findings show that, first, there are three pairs of trustors and trustees involved in blockchain implementation: (a) the user and the blockchain, (b) two supply chain partners, and (c) the consumer/public and a supply chain unit. Second, the two forms of trust, namely cognition-based and institution-based trust, are likely to be enhanced by blockchain execution, while affect-based trust may not be directly impacted by the technology. Third, the presence of blockchain technology would facilitate swift trust-building between unknown supply chain partners under specific circumstances. Moreover, we also find contradicting assertions among scholars on the implications of blockchain for trust in supply chains. While some studies pointed out that blockchain will enable a trustless trusted scheme, others expected the reinforcement of interorganizational trust. To test these assertions, we develop the blockchain-entrusted supply chain models to present the three-step process of how trust is developed through the blockchain and diffused to supply chain partners and external stakeholders.
Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

Mobile payment systems are becoming more popular due to the increase in the number of smartphones, which, in turn, attracts the interest of fraudsters. Extant research has therefore developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available and their detection performance is negatively affected by the extreme class imbalance in financial fraud data. The purpose of this study is to propose an XGBoost-based fraud detection framework while considering the financial consequences of fraud detection systems. The framework was empirically validated on a large dataset of more than 6 million mobile transactions. To demonstrate the effectiveness of the proposed framework, we conducted a comparative evaluation of existing machine learning methods designed for modeling imbalanced data and outlier detection. The results suggest that in terms of standard classification measures, the proposed semi-supervised ensemble model integrating multiple unsupervised outlier detection algorithms and an XGBoost classifier achieves the best results, while the highest cost savings can be achieved by combining random under-sampling and XGBoost methods. This study has therefore financial implications for organizations to make appropriate decisions regarding the implementation of effective fraud detection systems.
The choice of scaling technique matters for classification performance

Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary within the same range. This transformation is known to improve the performance of classification models, but there are several scaling techniques to choose from, and this choice is not generally done carefully. In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios. Results show that the choice of scaling technique matters for classification performance, and the performance difference between the best and the worst scaling technique is relevant and statistically significant in most cases. They also indicate that choosing an inadequate technique can be more detrimental to classification performance than not scaling the data at all. We also show how the performance variation of an ensemble model, considering different scaling techniques, tends to be dictated by that of its base model. Finally, we discuss the relationship between a model’s sensitivity to the choice of scaling technique and its performance and provide insights into its applicability on different model deployment scenarios. Full results and source code for the experiments in this paper are available in a GitHub repository.1
Connect the dots: Accessibility, readability and site ranking – An investigation with reference to top ranked websites of Government of India

With the emergence of World Wide Web (WWW) as the primary reference channel of information, the need for making it barrier-free for all categories of users has evolved into a critical factor. Making the web resources barrier-free for users requires action across various dimensions such as accessibility and readability. This paper presents an analysis of accessibility, readability, and site-ranking of top ranked (N = 20) government websites of India. The accessibility analysis has been carried out using aChecker and WAVE tools. The readability of the contents of the website is measured with six different indices such as Flesch-Kincaid reading ease, Flesch-Kincaid grade level, Gunning fog, SMOG, Coleman-Liau index and Automated Readability Index. The ranking of sites by National Informatics Centre (NIC) has been utilized to select the top ranked websites and their corresponding rankings are compared with global site ranking services such as Alexa. The correlation among these three factors of accessibility, readability, and site-ranking has been carried out with Spearman's rank correlation method and the inferences derived from the results are presented.
AP-PRE: Autonomous Path Proxy Re-Encryption and Its Applications

In this paper, we introduce a new cryptographic primitive, called autonomous path proxy re-encryption (AP-PRE), which is motivated by several application scenarios where the delegator would like to control the whole delegation path in a multi-hop delegation process. Compared with the traditional proxy re-encryption, AP-PRE provides much better fine-grained access control to delegation path. Briefly speaking, in an AP-PRE scheme, the delegator designates a path of his preferred delegatees. The path consists of several delegatees with the privilege from high to low. If the delegatee in the path cannot complete the decryption, the decryption right is automatically delegated to the next one in the path. In this way, the delegator can ensure that the delegation has always been done among those delegatees the delegator trusts. Moreover, an AP-PRE scheme has to obey the following path rules. The delegation, for ciphertexts of a delegator i, can only be carried out on the autonomous path Pa <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> designated by the delegator i, in the sense that (1) re-encrypted ciphertexts along the autonomous path Pa cannot branch off Pa with meaningful decryption, and (2) original ciphertexts generated under pk <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> for j ≠ i (i.e., for a path Pa <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> different from Pa <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ) cannot be inserted into (i.e., cannot be transformed along) the autonomous path Pa <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> with meaningful decryption. We give the formal definition, as well as the formal security model, for this cryptographic primitive. Under this concept, we construct an IND-CPA secure AP-PRE scheme under the decisional bilinear DiffieHellman (DBDH) assumption in the random oracle model. Our scheme is with the useful properties of proxy re-encryption, i.e., unidirectionality and multi-hop.
Social emotion classification based on noise-aware training

Social emotion classification draws many natural language processing researchers’ attention in recent years, since analyzing user-generated emotional documents on the Web is quite useful in recommending products, gathering public opinions, and predicting election results. However, the documents that evoke prominent social emotions are usually mixed with noisy instances, and it is also challenging to capture the textual meaning of short messages. In this work, we focus on reducing the impact of noisy instances and learning a better representation of sentences. For the former, we introduce an “emotional concentration” indicator, which is derived from emotional ratings to weight documents. For the latter, we propose a new architecture named PCNN, which utilizes two cascading convolutional layers to model the word-phrase relation and the phrase–sentence relation. This model regards continuous tokens as phrases based on an assumption that neighboring words are very likely to have internal relations, and semantic feature vectors are generated based on the phrase representation. We also present a Bayesian-based model named WMCM to learn document-level semantic features. Both PCNN and WMCM classify social emotions by capturing semantic regularities in language. Experiments on two real-world datasets indicate that the quality of learned semantic vectors and the performance of social emotion classification can be improved by our models.
Combining speech-based and linguistic classifiers to recognize emotion in user spoken utterances

In this paper we propose to combine speech-based and linguistic classification in order to obtain better emotion recognition results for user spoken utterances. Usually these approaches are considered in isolation and even developed by different communities working on emotion recognition and sentiment analysis. We propose modeling the users emotional state by means of the fusion of the outputs generated with both approaches, taking into account information that is usually neglected in the individual approaches such as the interaction context and errors, and the peculiarities of transcribed spoken utterances. The fusion approach allows to employ different recognizers and can be integrated as an additional module in the architecture of a spoken conversational agent, using the information generated as an additional input for the dialog manager to decide the next system response. We have evaluated our proposal using three emotionally-colored databases and obtained very positive results.
One-Class Convex Hull-Based Algorithm for Classification in Distributed Environments

In this paper, a new one-class classification algorithm capable of working in distributed environments is presented. In it, convex hull is used to build the boundary of the target class defining the one-class problem in each of the distributed nodes. Therefore, we will consider several classifiers, each one determined using a given local data partition, and the goal is to obtain a global classification decision. In order to obtain this final decision, two different algebraic combination rules were proposed: 1) sum and 2) majority voting. Experimental results show that this method opens the possibility of tackling practical one-class classification problems in distributed big data scenarios in an efficient and accurate way.
Efficient Secure Outsourcing of Large-Scale Convex Separable Programming for Big Data

Big data has become a key basis of innovation and intelligence, potentially making our lives more convenient and bringing new opportunities to the modern society. Towards this goal, a critical underlying task is to solve a series of large-scale fundamental problems. Conducting such large-scale data analytics in a timely manner requires a large amount of computing resources, which may not be available for individuals and small companies in practice. By outsourcing their computations to the cloud, clients can solve such problems in a cost-effective way. However, confidential data stored at the cloud is vulnerable to cyber attacks, and thus needs to be protected. Previous works employ cryptographic techniques like homomorphic encryption, which significantly increase the computational complexity of solving a large-scale problem at the cloud and is impractical for big data applications. For the first time in the literature, we present an efficient secure outsourcing scheme for convex separable programming problems (CSPs). In particular, we first develop efficient matrix and vector transformation schemes only based on arithmetic operations that are computationally indistinguishable both in value and in structure under a chosen-plaintext attack (CPA). Then, we design a secure outsourcing scheme in which the client and the cloud collaboratively solve the transformed problems. The client can efficiently verify the correctness of returned results to prevent any malicious behavior of the cloud. Theoretical correctness and privacy analysis together show that the proposed scheme obtains optimal results and that the cloud cannot learn private information from the client's concealed data. We conduct extensive simulations on Amazon Elastic Cloud Computing (EC2) platform and find that our proposed scheme provides significant time savings to the clients.
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions

This paper investigates a fundamental problem of scene understanding: how to parse a scene image into a structured configuration (i.e., a semantic object hierarchy with object interaction relations). We propose a deep architecture consisting of two networks: i) a convolutional neural network (CNN) extracting the image representation for pixel-wise object labeling and ii) a recursive neural network (RsNN) discovering the hierarchical object structure and the inter-object relations. Rather than relying on elaborative annotations (e.g., manually labeled semantic maps and relations), we train our deep model in a weakly-supervised learning manner by leveraging the descriptive sentences of the training images. Specifically, we decompose each sentence into a semantic tree consisting of nouns and verb phrases, and apply these tree structures to discover the configurations of the training images. Once these scene configurations are determined, then the parameters of both the CNN and RsNN are updated accordingly by back propagation. The entire model training is accomplished through an Expectation-Maximization method. Extensive experiments show that our model is capable of producing meaningful scene configurations and achieving more favorable scene labeling results on two benchmarks (i.e., PASCAL VOC 2012 and SYSU-Scenes) compared with other state-of-the-art weakly-supervised deep learning methods. In particular, SYSU-Scenes contains more than 5,000 scene images with their semantic sentence descriptions, which is created by us for advancing research on scene parsing.
Prediction of drive-by download attacks on Twitter

The popularity of Twitter for information discovery, coupled with the automatic shortening of URLs to save space, given the 140 character limit, provides cybercriminals with an opportunity to obfuscate the URL of a malicious Web page within a tweet. Once the URL is obfuscated, the cybercriminal can lure a user to click on it with enticing text and images before carrying out a cyber attack using a malicious Web server. This is known as a drive-by download. In a drive-by download a user's computer system is infected while interacting with the malicious endpoint, often without them being made aware the attack has taken place. An attacker can gain control of the system by exploiting unpatched system vulnerabilities and this form of attack currently represents one of the most common methods employed. In this paper we build a machine learning model using machine activity data and tweet metadata to move beyond post-execution classification of such URLs as malicious, to predict a URL will be malicious with 0.99 F-measure (using 10-fold cross-validation) and 0.833 (using an unseen test set) at 1 s into the interaction with the URL. Thus, providing a basis from which to kill the connection to the server before an attack has completed and proactively blocking and preventing an attack, rather than reacting and repairing at a later date.
Intrinsic Motion Stability Assessment for Video Stabilization

This paper presents a novel algorithm for assessing the motion stability of a video after stabilization. The assessment works in a non-reference manner that directly measures the intrinsic smoothness of the video motion path. Specifically, the motion path is cast as a curve embedded in the Lie group of homographies, and its smoothness is mathematically characterized by the intrinsic geodesic curvature. A bundle of paths are adopted to handle spatially variant motions through the frames. Then, we compute the weighted curvature for a holistic assessment on the motion stability. Other factors related to video stabilization, e.g., distortion and cropping, are also investigated as supplement. We collect 160 shaky video clips and their stabilized results for verification, and the experimental evidence shows the effectiveness of our algorithm in good correlation with human subjective judgements.
Nested quantum annealing correction at finite temperature: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi></mml:math> -spin models

Quantum annealing in a real device is necessarily susceptible to errors due to diabatic transitions and thermal noise. Nested quantum annealing correction is a method to suppress errors by using an all-to-all penalty coupling among a set of physical qubits representing a logical qubit. We show analytically that nested quantum annealing correction can suppress errors effectively in ferromagnetic and antiferromagnetic Ising models with infinite-range interactions. Our analysis reveals that the nesting structure can significantly weaken or even remove first-order phase transitions, in which the energy gap closes exponentially. The nesting structure also suppresses thermal fluctuations by reducing the effective temperature.
A Taxonomy of Software Integrity Protection Techniques

Tampering with software by man-at-the-end (MATE) attackers is an attack that can lead to security circumvention, privacy violation, reputation damage, and revenue loss. In this model, adversaries are end users who have full control over software as well as its execution environment. This full control enables them to tamper with programs to their benefit and to the detriment of software vendors or other end users. Software integrity protection research seeks for means to mitigate those attacks. Since the seminal work of Aucsmith, a great deal of research effort has been devoted to fight MATE attacks, and many protection schemes were designed by both academia and industry. Advances in trusted hardware, such as TPM and Intel SGX, have also enabled researchers to utilize such technologies for additional protection. Despite the introduction of various protection schemes, there is no comprehensive comparison study that points out advantages and disadvantages of different schemes. Constraints of different schemes and their applicability in various industrial settings have not been studied. More importantly, except for some partial classifications, to the best of our knowledge, there is no taxonomy of integrity protection techniques. These limitations have left practitioners in doubt about effectiveness and applicability of such schemes to their infrastructure. In this work, we propose a taxonomy that captures protection processes by encompassing system, defense and attack perspectives. Later, we carry out a survey and map reviewed papers on our taxonomy. Finally, we correlate different dimensions of the taxonomy and discuss observations along with research gaps in the field.
Privacy Aware Data Deduplication for Side Channel in Cloud Storage

Cloud storage services enable individuals and organizations to outsource data storage to remote servers. Cloud storage providers generally adopt data deduplication, a technique for eliminating redundant data by keeping only a single copy of a file, thus saving a considerable amount of storage and bandwidth. However, an attacker can abuse deduplication protocols to steal information. For example, an attacker can perform the duplicate check to verify whether a file (e.g., a pay slip, with a specific name and salary amount) is already stored (by someone else), hence breaching the user privacy. In this paper, we propose ZEUS (zero-knowledge deduplication response) framework. We develop ZEUS and ZEUS+, two privacy-aware deduplication protocols: ZEUS provides weaker privacy guarantees while being more efficient in the communication cost, while ZEUSþ guarantees stronger privacy properties, at an increased communication cost. To the best of our knowledge, ZEUS is the first solution which addresses two-side privacy by neither using any extra hardware nor depending on heuristically chosen parameters used by the existing solutions, thus reducing both cost and complexity of the cloud storage. In summary, through the evaluation on real datasets and comparison to existing solutions, our proposed framework demonstrates its capability of eliminating data deduplication-based side channel and at the same time keeping the deduplication benefits.
Using big data database to construct new GFuzzy text mining and decision algorithm for targeting and classifying customers

After an enterprise builds a data warehouse, it can record information related to customer interactions using structured and unstructured data. The intention is to convert these data into useful information for decision-making to ensure business continuity. Hence, this study proposes a new Chinese text classification model for the project management office (PMO) using fuzzy semantics and text mining techniques. First, content analysis is performed on the unstructured data to convert important textual information and compile it into a keyword index. Next, a classification and decision algorithm for grey situations and fuzzy (GFuzzy) is used to categorize textual data by three characteristics: maximum impact, moderate impact, and minimum impact. The purpose is to analyze consumer behaviors for the accurate classification of customers. Lastly, a more effective marketing strategy is formulated to target the various customer combinations, growth models, and the best mode of service. A company database of interactions with customers is used to construct a text mining model and to analyze the decision process of its PMO. The purpose is to test the feasibility and validity of the proposed model so that enterprises are provided with better marketing strategies and PMO processes aimed at their customers.
Remittances, ICT and doing business in Sub-Saharan Africa

Purpose The purpose of this paper is to examine how linkages between information and communication technology (ICT) and remittances affect the doing of business. Design/methodology/approach The focus is on a panel of 49 Sub-Saharan African (SSA) countries for the period 2000–2012. The empirical evidence is based on the generalized method of moments. Findings While the authors establish some appealing results in terms of net negative effects on constraints to the doing of business (i.e. time to start a business and time to pay taxes), some positive net effects are also apparent (i.e. number of start-up procedures, time to build a warehouse and time to register a property). The authors also establish ICT penetration thresholds at which the unconditional effect of remittances can be changed from positive to negative, notably: for the number of start-up procedures, an internet level of 9.00 penetration per 100 people is required, while for the time to build a warehouse, a mobile phone penetration level of 32.33 penetration per 100 people is essential. Practical and theoretical implications are discussed. Originality/value To the best of the authors’ knowledge, this is the first study to assess linkages between ICT, remittances and doing business in SSA.