Reviewerly/exHarmony huggingface dataset:
A dynamic taint analyzer for distributed systems

As in other software domains, information flow security is a fundamental aspect of code security in distributed systems. However, most existing solutions to information flow security are limited to centralized software. For distributed systems, such solutions face multiple challenges, including technique applicability, tool portability, and analysis scalability. To overcome these challenges, we present DistTaint, a dynamic information flow (taint) analyzer for distributed systems. By partial-ordering method-execution events, DistTaint infers implicit dependencies in distributed programs, so as to resolve the applicability challenge. It resolves the portability challenge by working fully at application level, without customizing the runtime platform. To achieve scalability, it reduces analysis costs using a multi-phase analysis, where the pre-analysis phase generates method-level results to narrow down the scope of the following statement-level analysis. We evaluated DistTaint against eight real-world distributed systems. Empirical results showed DistTaint’s applicability to, portability with, and scalability for industry-scale distributed systems, along with its capability of discovering known and unknown vulnerabilities. A demo video for DistTaint can be downloaded from https://www.dropbox.com/l/scl/AAAkrm4p63Ffx0rZqblY3zlLFuaohbRxs0 or viewed here https://youtu.be/fy4yMIaKzPE online. The tool package is here: https://www.dropbox.com/sh/kfr9ixucyny1jp2/AAC00aI-I8Od4ywZCqwZ1uaa?dl=0
Aspect-Based Sentiment Analysis Using Aspect Map

Aspect-based sentiment analysis (ABSA) is the task of classifying the sentiment of a specific aspect in a text. Because a single text usually has multiple aspects which are expressed independently, ABSA is a crucial task for in-depth opinion mining. A key point of solving ABSA is to align sentiment expressions with their proper target aspect in a text. Thus, many recent neural models have applied attention mechanisms to learning the alignment. However, it is problematic to depend solely on attention mechanisms to achieve this, because most sentiment expressions such as “nice” and “bad” are too general to be aligned with a proper aspect even through an attention mechanism. To solve this problem, this paper proposes a novel convolutional neural network (CNN)-based aspect-level sentiment classification model, which consists of two CNNs. Because sentiment expressions relevant to an aspect usually appear near the aspect expressions of the aspect, the proposed model first finds the aspect expressions for a given aspect and then focuses on the sentiment expressions around the aspect expressions to determine the final sentiment of an aspect. Thus, the first CNN extracts the positional information of aspect expressions for a target aspect and expresses the information as an aspect map. Even if there exist no data with annotations on direct relation between aspects and their expressions, the aspect map can be obtained effectively by learning it in a weakly supervised manner. Then, the second CNN classifies the sentiment of the target aspect in a text using the aspect map. The proposed model is evaluated on SemEval 2016 Task 5 dataset and is compared with several baseline models. According to the experimental results, the proposed model does not only outperform the baseline models but also shows state-of-the-art performance for the dataset.
Learning from ambiguous and misspecified models

We model inter-temporal ambiguity as the scenario in which a Bayesian learner holds more than one prior distribution over a set of models and provide sufficient conditions for ambiguity to fade away because of learning. Our conditions apply to most learning environments: iid and non-iid model-classes, well-specified and misspecified model-classes/prior support pairs. We show that ambiguity fades away if the empirical evidence supports a set of models with identical predictions, a condition much weaker than learning the truth.
Efficient FPGA Implementations of LowMC and Picnic

Post-quantum cryptography has received increased attention in recent years, in particular, due to the standardization effort by NIST. One of the second-round candidates in the NIST post-quantum standardization project is Picnic, a post-quantum secure signature scheme based on efficient zero-knowledge proofs of knowledge. In this work, we present the first FPGA implementation of Picnic. We show how to efficiently calculate LowMC, the block cipher used as a one-way function in Picnic, in hardware despite the large number of constants needed during computation. We then combine our LowMC implementation and efficient instantiations of Keccak to build the full Picnic algorithm. Additionally, we conform to recently proposed hardware interfaces for post-quantum schemes to enable easier comparisons with other designs. We provide evaluations of our Picnic implementation for both, the standalone design and a version wrapped with a PCIe interface, and compare them to the state-of-the-art software implementations of Picnic and similar hardware designs. Concretely, signing messages on our FPGA takes 0.25 ms for the L1 security level and 1.24 ms for the L5 security level, beating existing optimized software implementations by a factor of 4.
Three-button gateway smart home interface (TrueSmartface) for elderly: Design, development and deployment

With the growing age, the older adults experience gradual health, physical as well cognitive, declining. Oftentimes, the degree of developed health concerns is severe enough for the inevitable need of most likely a permanent caregiver or resource-rich but overly expensive assisted or nursing facility. The increasingly technical richness of the science advancements helps elevate at least most aspects of human life and the existence of smart home is one such outcome that has attained popularity, maturity and acceptability over the short period of time, especially in the communities of seniors, where it promises them an independent daily living. The smart home is an intelligent, technical, and automated cyber-physical infrastructure, which consists of a number of appliances and devices embedded or attached in physical environment. Some of them will be contextually-triggered, however, there is also a potential need for an efficient interface (graphical or non-graphical) to operate some. The design of such graphical interfaces is not so rudimentary, rather ought to be a comprehensive human-centered design that imposes very minimal physical and cognitive stress on a senior inhabitant. In this research work, we design and develop one such graphical interface (apt for desktop, laptop and mobile devices), which is based on some newly devised hypotheses, validated through literature and informal surveys. At every stage of the design, a three-button constraint is precisely persevered and the interface is successfully deployed into the Smart Home Lab at Iowa State University.
Cascade and Chain Effects in Big Data Cybercrime: Lessons from the TalkTalk hack

Big data and cybercrime are creating 'upstream', big data related cyber-dependent crimes such as data breaches. They are essential components in a cybercrime chain which forms a cybercrime ecosystem that cascades 'downstream' to give rise to further crimes, such as fraud, extortion, etc., where the data is subsequently monetized. These downstream crimes have a massive impact upon victims and data subjects. The upstream and downstream crimes are often committed by entirely different offending actors against different victim groups, which complicates and frustrates the reporting, recording, investigative and prosecution processes. Taken together the crime stream's cascade effect creates unprecedented societal challenges that need addressing in the face of the advances of AI and the IoT. This phenomenon is explored here by unpacking the TalkTalk case study to conceptualize how big data and cloud computing are creating cascading effects of disorganized, distributed and escalating data crime. As part of the larger CRITiCal project, the paper also hypothesizes key factors triggering the cascade effect and suggests a methodology to further investigate and understand it.
Blockchain technology and smart contracts in the agro-industrial complex of Russia

Abstract Progressive technologies associated primarily with the use of the latest information systems are developing very quickly and are being introduced in the modern world. Recently, such phenomena as cryptocurrency and the smart contract, functioning thanks to the blockchain technology, have become very popular. The application of this technology is used in many areas: information technology, law, state, and municipal administration, etc. This article discusses the mechanisms of functioning and features of the latest Internet technologies in the context of the agro-industrial complex of Russia. Their practical significance for optimizing the relationship between the seller and the buyer has been investigated. The advantages of the blockchain for all market participants when using technology in the supply of agri-food products are justified. The research shows the possible difficulties and shortcomings of the existing content system, gives recommendations on the use of the blockchain and its evaluation for domestic producers of agricultural products. The authors propose a mechanism for using the blockchain technology in agriculture, which will change the existing practice of trade and settlement transactions between counterparties of the agricultural market.
Multi-Party WebRTC Services Using Delay and Bandwidth Aware SDN-Assisted IP Multicasting of Scalable Video Over 5G Networks

At present, multi-party WebRTC videoconferencing between peers with heterogenous network resources and terminals is enabled over the best-effort Internet using a central selective forwarding unit (SFU), where each peer sends a scalable encoded video stream to the SFU. This connection model avoids the upload bandwidth bottleneck associated with mesh connections; however, it increases peer delay and overall network load (resource consumption) in addition to requiring investment in servers since all video traffic must go through SFU servers. To this effect, we propose a new multi-party WebRTC service model over future 5G networks, where a video service provider (VSP) collaborates with a network service providers (NSP) to offer an NSP-managed service to stream scalable video layers using software-defined networking (SDN)-assisted Internet protocol (IP) multicasting between peers using NSP infrastructure. In the proposed service model, each peer sends a scalable coded video upstream, which is selectively duplicated and forwarded as layer streams at SDN switches in the network, instead of at a central SFU, in a multi-party WebRTC session managed by multicast trees maintained by the SDN controller. Experimental results show that the proposed SDN-assisted IP multicast service architecture is more efficient than the SFU model in terms of end-to-end service delay and overall network resource consumption, while avoiding peer upload bandwidth bottleneck and distributing traffic more evenly across the network. The proposed architecture enables efficient provisioning of premium managed WebRTC services over bandwidth-reserved SDN slices to provide videoconferencing experience with guaranteed video quality over 5G networks.
A Hierarchical Attention Based Seq2Seq Model for Chinese Lyrics Generation

In this paper, we comprehensively study on context-aware generation of Chinese song lyrics. Conventional text generative models generate a sequence or sentence word by word, failing to consider the contextual relationship between sentences. Taking account into the characteristics of lyrics, a hierarchical attention based Seq2Seq (Sequence-to-Sequence) model is proposed for Chinese lyrics generation. With encoding of word-level and sentence-level contextual information, this model promotes the topic relevance and consistency of generation. A large Chinese lyrics corpus is also leveraged for model training. Eventually, results of automatic and human evaluations demonstrate that our model is able to compose complete Chinese lyrics with one united topic constraint.
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In Mohammadi et al. (Evolutionary computation, optimization and learning algorithms for data science. arXiv preprint, arXiv: 1908.08006, 2019), we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyse data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale datasets, curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies that successfully tackled some application domains, such as image processing, sentiment analysis, network traffics/anomalies analysis, credit score analysis and other benchmark functions/datasets analysis.
Multitask TSK Fuzzy System Modeling by Jointly Reducing Rules and Consequent Parameters

Existing multitask Takagi-Sugeno-Kang (TSK) fuzzy modeling methods always produce high complex fuzzy models with numerous redundant rules and consequent parameters. To this end, we propose a novel multitask TSK fuzzy modeling method called mtSparseTSK, which learns a compact set of fuzzy rules and shared consequent parameters across tasks in a unified procedure. Specifically, we consider the fuzzy rule reduction and consequent parameter selection across tasks by devising novel group sparsity regularizations in the learning criterion of the model. We also integrate the intertask relations in the proposed TSK model for multitask learning. We fully utilize the block structure in the TSK fuzzy models in formulating a joint block sparse optimization problem and develop a procedure for alternating direction method of multipliers (ADMMs) to find the optimal solution of the problem. Experiments on the synthetic and real-world datasets demonstrate the distinctive performance of the proposed methods over the existing ones on multitask fuzzy system modeling.
Extended smoothing joint data association for multi‐target tracking in cluttered environments

In heavily cluttered environments, it is difficult to estimate the uncertain motion of an unknown number of targets with low detection probabilities. In particular, for tracking multiple targets, standard multi-target data association algorithms such as joint integrated probabilistic data association (JIPDA), face complexity and severely limited applicability due to a combinatorially increasing number of possible measurement-to-track associations. Smoothers refine the target estimates based on future scan information. However, in this complex surveillance scenario, existing smoothing algorithms often fail to track the true target trajectories. To overcome such difficulties, this study proposes a new smoothing joint measurement-to-track association algorithm called fixed-interval smoothing JIPDA for tracking extended target trajectories (FIsJIPDA). The algorithm employs two independent JIPDA filters: forward JIPDA (fJIPDA) and backward JIPDA (bJIPDA). fJIPDA tracks the target state forward in time and is computed after the smoothing is achieved. bJIPDA estimates the target state in the backward time sequence. The numerical simulation is performed in a heavily populated cluttered environment with low target-detection probabilities. The results show better target trajectory accuracy and false-track discrimination performance of FIsJIPDA compared with that of existing algorithms for tracking multiple extended targets.
Stable adaptive probabilistic Takagi–Sugeno–Kang fuzzy controller for dynamic systems with uncertainties

In this study, an adaptive probabilistic Takagi-Sugeno-Kang fuzzy PID (APTSKF-PID) scheme is developed to control nonlinear systems. The proposed controller merges the features of the TSK fuzzy logic system, which possess a superior performance in system size and learning accuracy than the Mamdani-type fuzzy systems and the probabilistic processing method in nonlinear control, which handles the system uncertainties. To achieve controlled system stability, Lyapunov function is used for tuning the controller parameters. Tuning the probability parameters provides an extra degree of flexibility in controller design and improves the control performance. Furthermore, to ensure the effectiveness of the developed scheme for engineering applications, the proposed control technology is introduced to control nonlinear dynamical plants and its performance is compared with existing schemes. Simulation tasks indicate that the efficiency of APTSKF-PID scheme has high superiority over the other controller for external disturbances, random noise and a large scope of system uncertainties.
ELM and KELM based software defect prediction using feature selection techniques

Context: Software defect prediction (SDP) models help in delivering a dependable and a genuine product to the clients. However, the performance of these models is affected by the presence of irrelevant features in the datasets. This problem is addressed by feature selection techniques.Objectives: (1) To determine the performance of feature selection based classification models in the context of software defect prediction, and (2) To determine if the removal of insignificant features makes a significant difference in the performance of the SDP models.Method: SDP models are built using two classifiers – Extreme learning machine (ELM) and Kernel based extreme learning machine (KELM) based on five wrapper and seven filter based feature selection techniques. Experiments are performed using seven datasets from the PROMISE repository. Testing accuracy is used for performance comparison of the feature selection based ELM and KELM defect classification models.Results: (1) ELM based classifiers achieved a higher testing accuracy with wrapper based feature selection methods while KELM classifiers performed better with filter based methods. (2) It is also found that even after eliminating over 85 percent of the attributes from the original software project data, the classification performance of the models is comparable before and after removing the insignificant features in most of the cases and it improved in very few experiments.Conclusion: With respect to the feature selection based defect classification, the performance of ELM and KELM based models is better with wrapper and filter based methods, respectively. Overall, a dimensionally reduced space does not significantly affect the prediction performance of the SDP models. In a way, it is indicated that the feature subsets obtained after removing the insignificant software metrics provide more significance to the output class.
Automatic Classification of Open-Ended Questions: Check-All-That-Apply Questions

Text data from open-ended questions in surveys are challenging to analyze and are often ignored. Open-ended questions are important though because they do not constrain respondents’ answers. Where open-ended questions are necessary, often human coders manually code answers. When data sets are large, it is impractical or too costly to manually code all answer texts. Instead, text answers can be converted into numerical variables, and a statistical/machine learning algorithm can be trained on a subset of manually coded data. This statistical model is then used to predict the codes of the remainder. We consider open-ended questions where the answers are coded into multiple labels (all-that-apply questions). For example, in the open-ended question in our Happy example respondents are explicitly told they may list multiple things that make them happy. Algorithms for multilabel data take into account the correlation among the answer codes and may therefore give better prediction results. For example, when giving examples of civil disobedience, respondents talking about “minor nonviolent offenses” were also likely to talk about “crimes.” We compare the performance of two different multilabel algorithms (random k-labelsets [RAKEL], classifier chains [CC]) to the default method of binary relevance (BR) which applies single-label algorithms to each code separately. Performance is evaluated on data from three open-ended questions (Happy, Civil Disobedience, and Immigrant). We found weak bivariate label correlations in the Happy data (90th percentile: 7.6%), and stronger bivariate label correlations in the Civil Disobedience (90th percentile: 17.2%) and Immigrant (90th percentile: 19.2%) data. For the data with stronger correlations, we found both multilabel methods performed substantially better than BR using 0/1 loss (“at least one label is incorrect”) and had little effect when using Hamming loss (average error). For data with weak label correlations, we found no difference in performance between multilabel methods and BR. We conclude that automatic classification of open-ended questions that allow multiple answers may benefit from using multilabel algorithms for 0/1 loss. The degree of correlations among the labels may be a useful prognostic tool.
SelfIs: Self-Sovereign Biometric IDs

We live in a connected world that requires us to identify ourselves every time we want to access our emails, work stations, bank accounts, health care records, etc. Every system we interact with requires us to remember a username/password combination, have access to some private/public key pair, a hardware token, or some third party authentication software. Our digital identity is owned by the services we are trying to access, no longer under our control. Self-Sovereign Identity promises to give back control of his or her identity to the user. It is in this context that we explore the use of biometrics in order to empower users to be their own passwords, their own keys, their own means to authenticate themselves. We propose Self-Sovereign Biometric IDs (SelfIs), a novel approach that marries the concepts of decentralization, cancelable biometrics, bloom filters, and machine learning to develop a privacy-first solution capable of allowing users to control how their biometrics are used without risking their raw biometric templates.