Reviewerly/exHarmony huggingface dataset:
Subscription-Based Data-Sharing Model Using Blockchain and Data as a Service

In modern times, many individuals, businesses and the Internet of Things (IoT) integrated industries collect huge amounts of meaningful data daily, which may be beneficial for other individuals and businesses as well. By utilizing this data, future trends to make the right decisions on the bases of facts and figures are analyzed efficiently. In addition to that, many new ways are paved for researchers to utilize this data in their upcoming research. However, due to some major issues like security, privacy and access control of data, data owners avoid sharing data among themselves. Another main problem is the selfish behavior of data owners. Businesses also act selfishly and invest huge amounts of money to collect and maintain the data for their benefits. Therefore, data owners are hesitant to share their data with others without the availability of a fair profit and secure data-sharing platform. Moreover, consumers are not much motivated to buy data from Data Providers (DPs) due to its bad quality and inconsistency. The data provided by data owners is mostly incomplete, outdated, heterogeneous and costly. In this paper, a subscription-based data-sharing model is proposed by leveraging the blockchain technology and Data as a Service (DaaS) concept. In this model, users subscribe to a DP for a specific period to get access to the data and pay according to the subscription plan. The DP keeps receiving revenue recurrently for a long-time, which has a huge profit margin in comparison with selling data at once. Furthermore, two major pricing models, Flat Rate Pricing (FRP) and Usage-Based Pricing (UBP), are discussed to set standards for data owners to monetize their data, and a new hybrid pricing model is also proposed. Blockchain technology is utilized in the proposed model to make it secure, transparent and immutable. To investigate the performance of the proposed model, a private blockchain network is deployed using a web interface provided by MultiChain blockchain. The simulation results demonstrate that the proposed model is feasible and efficient. The theoretical discussion proves that the proposed model is beneficial for both data owners and data consumers and has a good scope in the future for data management and trading processes.
Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks

Summary In the age of the Internet of Things (IoT), large numbers of sensors and edge devices are deployed in various application scenarios; Therefore, collaborative learning is widely used in IoT to implement crowd intelligence by inviting multiple participants to complete a training task. As a collaborative learning framework, federated learning is designed to preserve user data privacy, where participants jointly train a global model without uploading their private training data to a third party server. Nevertheless, federated learning is under the threat of poisoning attacks, where adversaries can upload malicious model updates to contaminate the global model. To detect and mitigate poisoning attacks in federated learning, we propose a poisoning defense mechanism, which uses generative adversarial networks to generate auditing data in the training procedure and removes adversaries by auditing their model accuracy. Experiments conducted on two well‐known datasets, MNIST and Fashion‐MNIST, suggest that federated learning is vulnerable to the poisoning attack, and the proposed defense method can detect and mitigate the poisoning attack.
Evaluating and comparing ontology alignment systems: An MCDM approach

Ontology alignment is vital in Semantic Web technologies with numerous applications in diverse disciplines. Due to diversity and abundance of ontology alignment systems, a proper evaluation can portray the evolution of ontology alignment and depicts the efficiency of a system for a particular domain. Evaluation can help system designers recognize the strength and shortcomings of their systems, and aid application developers to select a proper alignment system. This article presents a new evaluation and comparison methodology based on multiple performance metrics that accommodates experts' preferences via a multi-criteria decision-making (MCDM) method, i.e., Bayesian best–worst method (BWM). First, the importance of a performance metric for a specific task/application is determined according to experts' preferences. The alignment systems are then evaluated based on proposed expert-based collective performance (ECP) that takes into account multiple metrics as well as their calibrated importance. For comparison, the alignment systems are ranked based on a probabilistic scheme, where it includes the extent to which one alignment system is preferred over another. The proposed methodology is applied to six tracks from ontology alignment evaluation initiative (OAEI), where the importance of performance metrics are calibrated by designing a survey and eliciting the preferences of ontology alignment experts. Accordingly, the participating alignment systems in the OAEI 2018 are evaluated and ranked. While the proposed methodology is applied to six OAEI tracks to demonstrate its applicability, it can also be applied to any benchmark or application of ontology alignment.
Performance Improvement of Chaotic Baseband Wireless Communication Using Echo State Network

The inter-symbol interference (ISI) caused by multipath propagation in wireless channel is one of the main reasons for high bit error rate (BER) in wireless communication system. Chaos was proved to be an ideal communication baseband signal because of its special properties, including the corresponding simple matched filter and multipath resistance ability. Although the ISI caused by the multipath is promised to be completely eliminated by using the optimal symbol decoding threshold, the future symbols are needed to be known for calculating the optimal threshold, which is hard to be practically implemented. To deal with such problem, an echo state network (ESN), because of its short-term memory ability, is proposed to predict the future chaotic baseband signal based on the signal received after the matched filter. Thus, the first future symbol is estimated, which is used to improve the accuracy of the decoding threshold in the chaotic baseband wireless communication system (CBWCS) in order to further relieve the effect of ISI. Compared to the sub-optimal decoding threshold considering only past symbols, the improved threshold proposed here considers not only the ISI from past symbols, but also the ISI from one future symbol. The simulation in both the static and time-varying wireless channels are performed, the results show that the BER performance of CBWCS is improved more significantly in multipath channels as compared to single path channel. The experiments based on wireless open-access research platform verify the ISI resistance performance under the practical scenarios. Both simulation and experimental results show the effectiveness and the superiority of the proposed method.
Machine Learning based Sarcasm Detection on Twitter Data

Sarcasm is a subtle type of irony, which can be widely used in social networks. It is usually used to transmit hidden information to criticize and ridicule a person and to recognize. The sarcastic reorganization system is very helpful for the improvement of automatic sentiment analysis collected from different social networks and microblogging sites. Sentiment analysis refers to internet users of a particular community, expressed attitudes and opinions of identification and aggregation. In this paper, to detect sarcasm, a pattern-based approach is proposed using Twitter data. Four sets of features that include a lot of specific sarcasm is proposed and classify tweets as sarcastic and non-sarcastic. The proposed feature sets are studied and evaluate its additional cost classifications.
Point-cloud avatars to improve spatial communication in immersive collaborative virtual environments

Collaborative virtual environments allow remote users to work together in a shared 3D space. To take advantage of the possibilities offered by such systems, their design must allow the users to interact and communicate efficiently. One open question in this field concerns the avatar fidelity of remote partners. This can impact communication between the remote users, more particularly when performing collaborative spatial tasks. In this paper, we present an experimental study comparing the effects of two partner's avatars on collaboration during spatial tasks. The first avatar was based on a 2.5D streamed point-cloud and the second avatar was based on a 3D preconstructed avatar replicating the remote user movements. These avatars differ in their fidelity levels described through two components: visual and kinematic fidelity. The collaborative performance was evaluated through the efficacy of completing two spatial communication tasks, a pointing task and spatial guidance task. The results indicate that the streamed point-cloud avatar permitted a significant improvement of the collaborative performance for both tasks. The subjective evaluation suggests that these differences in performance can mainly be attributed to the higher kinematic fidelity of this representation as compared with the 3D preconstructed avatar representation. We conclude that, when designing spatial collaborative virtual environments, it is important to reach a high kinematic fidelity of the partner's representation while a moderate visual fidelity of this representation can suffice.
Intelligent system for English translation using automated knowledge base

In the process of globalization, machine translation has undergone a long period of evolution and development. Although the development level of machine translation has been greatly improved, the quality of machine translation is still not very high, and it is difficult to meet the needs of users. Artificial intelligence is the science that studies the laws of human intelligent activity. The application of artificial intelligence technology in the English depression and depression, combined with the Internet and intelligent knowledge base, can develop English translation systems to solve the problem of English translation to a certain extent. Based on the above background, the research content of this article is a neural network-based artificial intelligence technology English translation system based on the intelligent knowledge base. This article is mainly based on the existing English-Chinese machine translation to find a more favorable method for English long sentence translation. By improving part-of-speech tagging and rules, the rules can match more sentence patterns to improve the quality of existing machine translations. This paper proposes an improved hybrid recommendation algorithm, and through experimental simulation, the results show that the accuracy of the algorithm is not very high. The highest is 35.64%. The possible reason may be that the k value is selected during k-means text clustering, or the N value recommended by TopN is not selected properly, but the hybrid recommendation is still better than ordinary collaborative filtering.
Complexity-Scalable Neural-Network-Based MIMO Detection With Learnable Weight Scaling

This paper introduces a framework for systematic complexity scaling of deep neural network (DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. This allows for weight scaling across and within the different DNN layers in order to achieve accuracy-vs.-complexity scalability during inference. In order to further improve the performance of our proposal, we introduce a sparsity-inducing regularization constraint in conjunction with trainable weight-scaling functions. In this way, the network learns to balance detection accuracy versus complexity while also increasing robustness to changes in the activation patterns, leading to further improvement in the detection accuracy and BER performance at the same inference complexity. Numerical results show that our approach is 10-fold and 100-fold less complex than classical approaches based on semi-definite relaxation and ML detection, respectively.
ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks

Deep Quantization (below eight bits) can significantly reduce the DNN computation and storage by decreasing the bitwidth of network encodings. However, without arduous manual effort, this deep quantization can lead to significant accuracy loss, leaving it in a position of questionable utility. We propose a systematic approach to tackle this problem, by automating the process of discovering the bitwidths through an end-to-end deep reinforcement learning framework (ReLeQ). This framework utilizes the sample efficiency of proximal policy optimization to explore the exponentially large space of possible assignment of the bitwidths to the layers. We show how ReLeQ can balance speed and quality, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks with minimal accuracy loss (≤ 0.3% loss) while minimizing the computation and storage costs. With these DNNs, ReLeQ enables conventional hardware and custom DNN accelerator to achieve 2.2× speedup over 8-bit execution.
Classifying Papanicolaou cervical smears through a cell merger approach by deep learning technique

Early detection of cancer is important to improve survival and reduce associated morbility. Nowadays, there is no automatic classification process with enough accuracy to be recommended to its use in population cervical cancer screening. In most automatic medical image classifications, these images are clean in background and without overlap between elements, which means that these images do not reflect reality and the model cannot be applied to directly obtained images from medical samples. The objectives of this study are to design and implement a Cell Merger Approach to improve the efficiency and realism of the PAP-smears classification model, by allowing overlapping and folding of different cells, to design and implement a Convolutional Neural Network for PAP-smears image classification, and to optimize and integrate the cell fusion approach with the neural network building a feasible, reliable and highly accurate system for cervical smears classification. The carried out experiments have validated both the CNN and the proposed Cell Merger Approach with very interesting results. The most outstanding results show that the Convolutional Neural Network models together with the Cell Merger Approach have a classification accuracy of 88.8% with a standard deviation of 1%, obtaining a sensitivity and specificity of 0.92 and 0.83 respectively. This classification level depicts a robust and accurate model that is comparable to an expert pathologist competencies.
Impact of Wearable Measurement Properties and Data Quality on ADLs Classification Accuracy

In the field of automatic recognition and classification of Activities of Daily Living (ADLs), a paramount role to determine the classification accuracy is played by sensor technologies, as the algorithms’ performance is highly affected by the nature and quality of the collected measurement data. This work aims to investigate the influence of the wearable device characteristics and measurement uncertainty on the classification accuracy. For this study, two wearables devices are considered: a top-quality smartwatch (Empatica E4) and a low-cost Arduino-based wristband prototype. These devices have been used to measure the acceleration signal at the dominant wrist of subjects performing some relevant activities in real-life conditions. The experimental evaluation of some ADLs classification algorithms shows that their accuracy fluctuates depending on the choice of the sensor, which in turn affects the amount and type of relevant features to process. As such, the combination of features’ domain, i.e. time or frequency, number and type, which leads to the best classification accuracy has to be tuned on a specific sensor basis, despite the same type of signal, i.e. acceleration, is measured and processed under identical circumstances and processed. Accuracy values of 50-99% and 66-95% in the ADLs classification, are obtained for Empatica E4 and Arduino-based prototype, respectively; the best performance among classifiers is obtained with J48 and Random Forest, confirming that, with an appropriate configuration, satisfactory accuracy may be attained, even by resorting to the use of simple sensors.
Unexplainability and Incomprehensibility of AI

Explainability and comprehensibility of AI are important requirements for intelligent systems deployed in real-world domains. Users want and frequently need to understand how decisions impacting them are made. Similarly, it is important to understand how an intelligent system functions for safety and security reasons. In this paper, we describe two complementary impossibility results (Unexplainability and Incomprehensibility), essentially showing that advanced AIs would not be able to accurately explain some of their decisions and for the decisions they could explain people would not understand some of those explanations.
A Survey of Temporal Antialiasing Techniques

Abstract Temporal Antialiasing (TAA), formally defined as temporally‐amortized supersampling, is the most widely used antialiasing technique in today's real‐time renderers and game engines. This survey provides a systematic overview of this technique. We first review the history of TAA, its development path and related work. We then identify the two main sub‐components of TAA, sample accumulation and history validation, and discuss algorithmic and implementation options. As temporal upsampling is becoming increasingly relevant to today's game engines, we propose an extension of our TAA formulation to cover a variety of temporal upsampling techniques. Despite the popularity of TAA, there are still significant unresolved technical challenges that affect image quality in many scenarios. We provide an in‐depth analysis of these challenges, and review existing techniques for improvements. Finally, we summarize popular algorithms and topics that are closely related to TAA. We believe the rapid advances in those areas may either benefit from or feedback into TAA research and development.
Speculative Data-Oblivious Execution: Mobilizing Safe Prediction For Safe and Efficient Speculative Execution

Speculative execution attacks are an enormous security threat. In these attacks, malicious speculative execution reads and exfiltrates potentially arbitrary program data through microarchitectural covert channels. Correspondingly, prior work has shown how to comprehensively block such attacks by delaying the execution of covert channel-creating instructions until their operands are a function of non-speculative data. This paper's premise is that it is safe to execute these potentially dangerous instructions early, improving performance, as long as their execution does not require operand-dependent hardware resource usage, i.e., is data oblivious. While secure, this idea can easily reduce, not improve, performance. Intuitively, data obliviousness implies doing the worst case work all the time. Our key idea to get net speedup is that it is safe to predict what will be, and to subsequently perform, the work needed to satisfy the common case, as long as the prediction itself does not leak privacy. We call the complete scheme-predicting the form of data-oblivious execution-Speculative Data-Oblivious Execution (SDO). We build SDO on top of a recent comprehensive and state-of-the-art protection called STT. Extending security arguments from STT, we show how the predictions do not reveal private information, enabling safe and efficient speculative execution. We evaluate the combined scheme, STT + SDO, on a set of SPEC17 workloads and find that it improves the performance of stand-alone STT by an average 36.3% to 55.1%, depending on the microarchitecture and attack model-and without changing STT's security guarantees.
On the diffuseness of technical debt items and accuracy of remediation time when using SonarQube

Context. Among the static analysis tools available, SonarQube is one of the most used. SonarQube detects Technical Debt (TD) items—i.e., violations of coding rules—and then estimates TD as the time needed to remedy TD items. However, practitioners are still skeptical about the accuracy of remediation time estimated by the tool. Objective. In this paper, we analyze both diffuseness of TD items and accuracy of remediation time, estimated by SonarQube, to fix TD items on a set of 21 open-source Java projects. Method. We designed and conducted a case study where we asked 81 junior developers to fix TD items and reduce the TD of 21 projects. Results. We observed that TD items are diffused in the analyzed projects and most items are code smells. Moreover, the results point out that the remediation time estimated by SonarQube is inaccurate and, as compared to the actual time spent to fix TD items, is in most cases overestimated. Conclusions. The results of our study are promising for practitioners and researchers. The former can make more aware decisions during project execution and resource management, the latter can use this study as a starting point for improving TD estimation models.
Here we go again

Once a programmer knows one language, they can leverage concepts and knowledge already learned, and easily pick up another programming language. But is that always the case? To understand if programmers have difficulty learning additional programming languages, we conductedan empirical study of Stack Overflow questions across 18 different programming languages. We hypothesized that previous knowledge could potentially interfere with learning a new programming language. From our inspection of 450 Stack Overflow questions, we found 276 instances of interference that occurred due to faulty assumptions originating from knowledge about a different language. To understand why these difficulties occurred, we conducted semi-structured interviews with 16 professional programmers. The interviews revealed that programmers make failed attempts to relate a new programming language with what they already know. Our findings inform design implications for technical authors, toolsmiths, and language designers, such as designing documentation and automated tools that reduce interference, anticipating uncommon language transitions during language design, and welcoming programmers not just into a language, but its entire ecosystem.
Rare Feature Selection in High Dimensions

It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which many columns are highly sparse. The challenge posed by such "rare features" has received little attention despite its prevalence in diverse areas, ranging from natural language processing (e.g., rare words) to biology (e.g., rare species). We show, both theoretically and empirically, that not explicitly accounting for the rareness of features can greatly reduce the effectiveness of an analysis. We next propose a framework for aggregating rare features into denser features in a flexible manner that creates better predictors of the response. Our strategy leverages side information in the form of a tree that encodes feature similarity. We apply our method to data from TripAdvisor, in which we predict the numerical rating of a hotel based on the text of the associated review. Our method achieves high accuracy by making effective use of rare words; by contrast, the lasso is unable to identify highly predictive words if they are too rare. A companion R package, called rare, implements our new estimator, using the alternating direction method of multipliers. Supplementary materials for this article are available online.