Reviewerly/exHarmony huggingface dataset:
Blockchain-based Learning Credential Verification System with Recipient Privacy Control

Various forms of learning credentials play an important role in society. For example, a degree certificate is usually required to apply for many jobs. Currently, paper-based learning credentials such as diploma or degree certificates are still widely used. However, proof of such credentials' authenticity has become increasingly more difficult in recently years due to availability of low-cost desktop publishing tools. Recently, a blockchain-based digital certificate issuing and verification system has been proposed to address these problems. However, existing blockchain-based certification verification solutions have a few shortcomings in common. One of them is the lack of mechanism to control the amount of information and anonymity exposed during the verification process. This paper proposed a blockchain-based learning credential verification system, where a credential recipient can control the amount of credential-related information to be exposed during the verification process. The proposed solution makes use of a Merkle tree to encode causal relationship among pieces of information the credential is composed of. The abstract data model built on top of the binary Merkle tree has been proposed so that a recipient can choose to disclose part of the credential information, including the recipient profile. This means it is possible for credential verification to be performed anonymously. This can be achieved without increasing blockchain transaction cost and complexity. Example of use case scenarios that require such anonymous verification are described.
A Topic and Concept Integrated Model for Thread Recommendation in Online Health Communities

Online health communities (OHCs) provide a popular channel for users to seek information, suggestions and support during their medical treatment and recovery processes. To help users find relevant information easily, we present CLIR, an effective system for recommending relevant discussion threads to users in OHCs. We identify that thread content and user interests can be categorized in two dimensions: topics and concepts. CLIR leverages Latent Dirichlet Allocation model to summarize the topic dimension and uses Convolutional Neural Network to encode the concept dimension. It then builds a thread neural network to capture thread characteristics and builds a user neural network to capture user interests by integrating these two dimensions and their interactions. Finally, it matches the target thread's characteristics with candidate users' interests to make recommendations. Experimental evaluation with multiple OHC datasets demonstrates the performance advantage of CLIR over the state-of-the-art recommender systems on various evaluation metrics.
Towards unsupervised text multi-style transfer with parameter-sharing scheme

Text style transfer is an important task in the field of natural language generation. Because of the lack of parallel data, it is a challenge to address this problem in an unsupervised manner. Existing methods mainly focus on the two-style transfer task, i.e. from one source style to one target style. In this paper, we first propose the task of unsupervised text multi-style transfer to address the problem of efficient text transfer from a source style to multiple target styles. To tackle this new task, we present a novel model based on Non-Autoregressive Transformer (NAT). The model consists of two parts: a parameter-shared style-independent module and a style-dependent module. In practice, we only need to reinitialize the parameter of style-dependent modules and retrain the whole model which can converge fast. Experimental results show that our model not only performs well in two-style transfer task, but also promises good results in the multi-style scenario.
Into the DDoS maelstrom: a longitudinal study of a scrubbing service

Distributed denial-of-service (DDoS) attacks are nowadays easy and cheap to carry out, and have become bigger and more frequent over the last years. Cloud-based scrubbers have emerged as a service which victims can hire on demand to fend off attacks. There are many industry players, but not much insights into their operations. This work unravels for the first time the inner workings of a DDoS scrubber - NaWas- a non-profit scrubber in the Netherlands. We analyze 1800+ DDoS attacks spanning over a period of 22 months, and show that while most attacks are not very large, they are still large enough to disrupt services and likely to disturb links. We estimate the collateral damage incurred by DDoS attacks, and demonstrate that the number of victims of is at least quadratically larger (IP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) than the targeted addresses. Last, by correlating attacks metadata with authoritative DNS traffic, we show that DDoS attacks leave fingerprints on DNS traffic, which, in turn can be used to detect DDoS attacks at early stages, even if attackers attempt to deceive DNS based detection.
Lightweight Hardware Architectures for the Piccolo Block Cipher in FPGA

The Piccolo block cipher is a lightweight block encryption for hardware use. Hardware devices are equipped with limited computation resources and small memory. In this paper, we propose an implementation to carry out through several trade-offs between area and speed. We implemented the Piccolo block cipher algorithm with 128-bit key in two different architectures on FPGA: the iterative and the 4-bit serial architectures. The proposed implementation was performed on Xilinx Spartan-3. The iterative implementation achieves 76% of resource utilization. This implementation takes 31 clock cycles to perform the encryption or decryption. So, it results in a throughput of 151.1 Mbps. The serial implementation was optimized in terms of area to reduce the cost. It achieves 54% of resource utilization and takes 496 clock cycles resulting in a throughput of 6.39 Mbps.
Extensible and modular abstract syntax for feature modeling based on language constructs

Since the definition of feature models in 1990, a large number of language constructs have emerged. Each language construct usually comes with its own abstract and concrete syntax, its semantics, and even its complete language dialect and tool support. Nowadays, there is a consensus in the Software Product Line community about a need for defining a common variability modeling language. But the fact of the matter is that it is very complex to achieve a good compromise between how expressive the language should be and the effort of developing practical tools for a language with all possible language constructs. In this paper, we propose an extensible model-driven engineering approach for defining the abstract syntax of feature modeling language constructs that could be tailored to different needs and domains. We formalize our approach as a set of modular and reusable metamodels that allows practitioners to decide which subset of language constructs to use through: (1) generating a new variability language; and (2) managing feature models with different level of expressiveness. We provide an instantiation and implementation of our approach.
Dissecting the Communication Latency in Distributed Deep Sparse Learning

Distributed deep learning (DDL) uses a cluster of servers to train models in parallel. This has been applied to a multiplicity of problems, e.g. online advertisement, friend recommendations. However, the distribution of training means that the communication network becomes a key component in system performance. In this paper, we measure the Alibaba's DDL system, with a focus on understanding the bottlenecks introduced by the network. Our key finding is that the communications overhead has a surprisingly large impact on performance. To explore this, we analyse latency logs of 1.38M Remote Procedure Calls between servers during model training for two real applications of high-dimensional sparse data. We reveal the major contributors of the latency, including concurrent write/read operations of different connections and network connection management. We further observe a skewed distribution of update frequency for individual parameters, motivating us to propose using in-network computation capacity to offload server tasks.
Ratings meet reviews in the monitoring of online products and services

User-generated content including both review texts and user ratings provides important information regarding the customer-perceived quality of online products and services. This article proposes a modeling and monitoring method for online user-generated content. A unified generative model is constructed to combine words and ratings in customer reviews based on their latent sentiment and topic assignments, and a two-chart scheme is proposed for detecting shifts of customer responses in dimensions of sentiments and topics, respectively. The proposed method shows superior performance in shift detection, especially for the sentiment shifts in customer responses, based on the results of simulation and a case study.
Face Emotion Recognition From Static Image Based on Convolution Neural Networks

Human-Machine Interaction systems have not yet reached all the emotional and social capacities. In this paper, we propose a face emotion recognition system from static image based on the Xception convolution neural network architecture and the K-fold-cross-validation strategy. The proposed system was improved using the fine-tuning method. The Xception model pre-trained on ImageNet database for objects recognition was fine-tuned to recognize seven emotional states. The proposed system is evaluated on the database recorded during the Empathic project and the AffectNet database. Our experimental results achieve an accuracy of 62%, 69% on Empathic and AffectNet databases respectively using the fine-tuning strategy. Combined the AffectNet and Empathic databases to train our proposed model, show significant improvement in the emotion recognition that achieves an accuracy of 91.2% on Empathic database.
Refugee information consumption on Twitter

The purpose of the study is to describe how society expresses itself in regard to people who, due to war, politics or religion, migrate from their country of origin to foreign countries. Refugees are considered minority groups. Thus, examining their behavior reveals significant data on sociocultural factors (Tsai, 2011). Under a qualitative approach and the netnography method, we used Twitter as an instrument for data collection. The model proposed by Perez-Cepeda and Arias-Bolzmann (2020), which describes cultural factors, is used to analyze the Twitter conversation flow that evidences the sociocultural factors of minority subcultures. Studies have shown that the data structure, classification, categorization, and purpose are based on the information production and consumption of the users who participate in Twitter conversations with @Refugees—the official account of the ACNUR/UNHCR Américas. The findings showed recurrent subjects and sets of ideas related to sociocultural factors, human rights, economic news, critical reflection, and religious topics. Tweets about refugees describe the sociocultural issues around this minority group. This paper focuses on refugees, the consumption of refugee information, text writing, sociocultural factors, and the environment on Twitter. This research contributes to the social sciences, especially to sociocultural interaction studies. The originality of the work is the identification of sociocultural factors related to the social sciences that emerge from minority groups' tweets through images, texts, and objects.
Double JPEG Compression Detection for Distinguishable Blocks in Images Compressed with Same Quantization Matrix

Detection of compression history is a crucial step in verifying the authenticity of a JPEG image. Previous approaches for double compression detection with the same quantization matrix are designed for full-sized images or large patches. In this paper, we propose a novel deep learning based approach that utilizes spatial and frequency domain information from the error blocks obtained from multiple compression stages and uses a multi-column CNN architecture to classify distinguishable blocks of size 8×8. Three successive error blocks are obtained from the given JPEG block and its repeated compression by taking the difference between inverse discrete cosine transform (DCT) of de-quantized DCT coefficients and the reconstructed blocks. On average, the performance gain of the proposed approach over the baseline method in terms of TPR, TNR, and balanced accuracy is 4.04%,1.6%, and 2.8%, respectively. We also show the applicability of the method for unseen quality factors.
Quantify Co-Residency Risks in the Cloud Through Deep Learning

Cloud computing, while becoming more and more popular as a dominant computing platform, introduces new security challenges. When virtual machines are deployed in a cloud environment, virtual machine placement strategies can significantly affect the overall security risks of the entire cloud. In recent years, the attacks are specifically designed to co-locate with target virtual machines in the cloud. The virtual machine placement without considering the security risks may put the users, or even the entire cloud, in danger. In this article, we present a fine-grained model to quantify the risk level caused by co-residency. Using a large scale dataset collected from Microsoft Azure Platform, we profile the behavior patterns of normal service subscribers (tenants) using our proposed feature metrics. Tenants are clustered into multiple categories. After the baseline is established based on the normal behavior pattern, the derivation can be evaluated for each category and the high-risk group can be labeled accordingly. With the labeled datasets, a classification component and a quantification component are constructed to dynamically quantify the co-residency risks for a specific virtual machine. Our experimental results demonstrate the robustness of our model to the new data and the accuracy is verified by examination of F-score Matrix.
High Voltage Cable X-Ray Digital Image Depth Processing and Technology of Buffer Layer Defect Intelligent Recognition

High voltage cable is an important basic equipment for city power supply. Existing research shows that: the method of digital X-ray imaging can realize the non-destructive detection of power cable, it belongs to the new detection technology in the field of cable defect detection, at present, there is no deep X-ray digital image processing and defect recognition method, and it can not directly detect the cable ontology and defect recognition from the original digital image. Therefore, this article on the depth of power cable X-ray digital image processing and intelligent recognition technology carried out research, buffer layer defects using gray processing technology, the original image grayscale range compression to the human eye can recognize, and defect identification, and the traditional convolution neural network and the convolution neural network (CNN) FCN image data for training, implementation of the intelligent identification of power cable buffer layer defects. The results show that compared with traditional convolutional neural network (CNN), the full convolutional neural network (FCN) proposed in this paper has a clearer and more intuitive recognition effect.
Software Requirements Classification and Prioritisation Using Machine Learning

Software Development Lifecycle (SDLC) is a systematic process used to achieve high quality software that meets customer requirements. During SDLC requirements, engineering plays an important role. Prioritisation helps to focus on the most important requirements in terms of importance, cost, penalty, time and risk. Stakeholders (users, developers) of the software product identify requirements. The two major activities of requirement engineering process are requirements classification and requirements prioritisation. Sometimes requirement mentioned by stakeholder can be of both types, i.e. functional and non-functional. So it is challenging to classify requirements separately in two different categories. There are many fundamental prioritisation techniques available to prioritise software requirements. In this paper, we have compared existing requirements prioritisation techniques based on ease of use, speed, scalability and accuracy. Our literature study suggests that the appropriate requirements prioritisation technique has to be selected that can help software developer to minimise the risk, penalty. In automating various tasks of software engineering, machine learning (ML) has shown useful positive impact. This paper discusses the various algorithms used to classify and prioritise the software requirements. The results in terms of performance, scalability and accuracy from different studies are contradictory in nature due to variations in research methodologies and the type of dataset used. Based on the literature survey conducted, we propose a new architecture that will use both types of datasets, i.e. Software Requirement Specifications (SRS) and user text reviews to create a generalised model. Our proposed architecture will attempt to extract features which can be used to train the model using ML algorithms. The ML algorithms for classifying and prioritising software requirements will be developed and assessed based on performance, scalability and accuracy.
The feature generator of hard negative samples for fine-grained image recognition

The key to solving the fine-grained image recognition is exploring more discriminative features for capturing tiny hints. In particular, the triplet objective function fits well with the fine-grained image recognition task because they capture the semantic similarity between images. However, triplet loss needs many pairs of tuples with hard negative samples, and it takes too much cost. To alleviate this problem, we propose a new framework that generates features of the hard negative samples. The proposed framework consists of three stages: learning part-wise features, enriching refined hard negative samples, and fine-grained image recognition. Our proposed method has achieved state-of-the-art performance in CUB-200-2011, Stanford Cars, FGVC-Aircraft, and DeepFashion datasets. Also, our extensive experiments demonstrate that each stage has a good effect on the final goal.
Temporal Image Forensic Analysis for Picture Dating with Deep Learning

Estimating the acquisition date of digital photographs is crucial in image forensics. The task of dating images by processing their contents should be reasonably accurate as this can be used in court to resolve high profile cases. The goal of temporal forensics analysis is to find out the links in time between two or more pieces of evidence. In this paper, the problem of picture dating is addressed from a machine learning perspective, precisely, by adopting a deep learning approach for the first time in temporal image forensics. In this work, the acquisition time of digital images is estimated in such a way that the analyst can identify the timeline of unknown digital photographs given a set of pictures from the same source whose time ordering is known. By applying Convolutional Neural Networks (CNN), namely the AlexNet and GoogLeNet architectures in both feature extraction and transfer learning modes, results have shown that the networks can successfully learn the temporal changes in the content of the digital pictures that are acquired from the same source. Interestingly, although images are divided into non-overlapping blocks in order to increase the number of training samples and feed CNNs, the obtained estimation accuracy has been from 80% to 88%. This suggests that the temporal changes in image contents, modelled by CNNs, are not dependent on block location. This has been demonstrated on a new database called `Northumbria Temporal Image Forensics' (NTIF) database which has been made publicly available for researchers in image forensics. NTIF is the first public database that captures a large number of images at different timeslots on a regular basis using 10 different digital cameras. This will serve the research community as a solid ground for research on picture dating and other image forensics applications.
Financial Instruments Generation via Tokenization into Commodity

The diversification is a classic approach to reduce investor risks. Blockchain and smart contracts allow tokenizing heterogeneous assets into a commodity and open a new tool for it. The authors describe how to apply the approach to the various finance cases from peer-to-peer lending to real estate and leases, and theoretically estimate minimum token package size to ensure the given reliability parameter.
TABLE: A Task-Adaptive BERT-based ListwisE Ranking Model for Document Retrieval

Document retrieval (DR) is a crucial task in NLP. Recently, the pre-trained BERT-like language models have achieved remarkable success, obtaining a state-of-the-art result in DR. In this paper, we come up with a new BERT-based ranking model for DR task, named TABLE. In the pre-training stage of TABLE, we present a domain-adaptive strategy. More essentially, in the fine-tuning stage, we develop a two-phase task-adaptive process, i.e., type-adaptive pointwise fine-tuning and listwise fine-tuning. In the type-adaptive pointwise fine-tuning phase, the model can learn different matching patterns regarding different query types. In the listwise fine-tuning phase, the model matches documents with regard to a given query in a listwise fashion. This task-adaptive process makes the model more robust. In addition, a simple but effective exact matching feature is introduced in fine-tuning, which can effectively compute matching of out-of-vocabulary (OOV) words between a query and a document. As far as we know, we are the first who propose a listwise ranking model with BERT. This work can explore rich matching features between queries and documents. Therefore it substantially improves model performance in DR. Notably, our TABLE model shows excellent performance on the MS MARCO leaderboard.
Diffusion and adoption of Linked Data among libraries

Abstract Through content analyses of literature about the Linked Data (LD) adoption of individual libraries, this study found that the diffusion of LD among libraries is a decentralized process with high‐degree of reinvention and a continuous process that includes multiple stages and might last for many years. The diffusion of LD among libraries involves the diffusion of numerous related innovations, follows three paths (inter‐library, intra‐library, and inter‐librarian), and is facilitated by four types of institutions (professional associations, vendors, external funders, and leading libraries) and three factors (avoiding re‐inventing the wheel, standardization, and commercialization).