Reviewerly/exHarmony huggingface dataset:
In-Time Explainability in Multi-Agent Systems: Challenges, Opportunities, and Roadmap

In the race for automation, distributed systems are required to perform increasingly complex reasoning to deal with dynamic tasks, often not controlled by humans. On the one hand, systems dealing with strict-timing constraints in safety-critical applications mainly focused on predictability, leaving little room for complex planning and decision-making processes. Indeed, real-time techniques are very efficient in predetermined, constrained, and controlled scenarios. Nevertheless, they lack the necessary flexibility to operate in evolving settings, where the software needs to adapt to the changes of the environment. On the other hand, Intelligent Systems (IS) increasingly adopted Machine Learning (ML) techniques (e.g., subsymbolic predictors such as Neural Networks). The seminal application of those IS started in zero-risk domains producing revolutionary results. However, the ever-increasing exploitation of ML-based approaches generated opaque systems, which are nowadays no longer socially acceptable—calling for eXplainable AI (XAI). Such a problem is exacerbated when IS tend to approach safety-critical scenarios. This paper highlights the need for on-time explainability. In particular, it proposes to embrace the Real-Time Beliefs Desires Intentions (RT-BDI) framework as an enabler of eXplainable Multi-Agent Systems (XMAS) in time-critical XAI.
Incitement to discriminatory hatred, illocution and perlocution

Abstract Even though there seem to be no objectively defined criteria about what constitutes hate speech, a lot of legislation and policy making currently aims at combating it. This paper sets out to define hate speech under its standard legal understanding of ‘incitement to discriminatory hatred’, by adopting a speech-act theoretic perspective. My main proposal is that the Austinian distinction between illocution and perlocution can be pivotal in this process, since hate speech may be an illocutionary act that is typically tied to the recognition of a speaker’s intention to incite discriminatory hatred, but one which can only be defined if one takes into account its speaker’s intended perlocutionary effects; that is, the intention of the speaker to trigger a particular kind of response from some audience. Against this backdrop, I turn to show how a reworked Searlean notion of felicity conditions can be usefully applied in the delineation of hate speech under this legal conception.
Micro‐blog sentiment classification using Doc2vec + SVM model with data purification

As a Chinese version of twitter, micro-blog has been popular for many years. On this platform, a lot of comments are generated explosively every day. These comments contain the public's opinions on various topics, which have wide applications in both academic and industrial fields. In recent years, deep learning and some classification algorithms have been applied to sentiment analysis, and good results have been achieved. However, micro-blog sentiment classification is a challenging task, because micro-blog messages are short and noisy, and contain massive user-invented acronyms and informal words. Unfortunately, most researchers pay more attention to analyse the data after deep learning, but only simply remove the noisy data before using algorithm, so the result of sentiment analysis has reached a bottleneck. Here, the authors first purify the data using varied methods before deep learning, then, the Support Vector Machine (SVM) classification algorithm is applied to sentiment classification of micro-blog using many types of features. Through comparing with the method of simply pre-processing data, the results show that their approach can improve the performance of micro-blog sentiment classification effectively and efficiently.
Achieving higher precision in quantum parameter estimation with feedback controls

We study the effect of feedback controls in quantum parameter estimation. In particular, we study the estimation of a magnetic field with a spin in the presence of the spontaneous emission where feedback control, triggered by the detection of the emitted photons, is employed to improve the precision limit. We also study the effect of additional Hamiltonian controls and show that they can provide further improvement on the precision limit.
Capturing creative requirements via requirements reuse: A machine learning-based approach

The software industry has become increasingly competitive as we see multiple software serving the same domain and striving for customers. To that end, modern software needs to provide creative features to improve sustainability. To advance software creativity, research has proposed several techniques, including multi-day workshops involving experienced requirements analysts, and semi-automated tools to support creative thinking in a limited scope. Such approaches are either useful only for software with already rich issue tracking systems, or require substantial engagement from analysts with creative minds. In a recent work, we have demonstrated a novel framework that is beneficial for both novel and existing software and allows end-to-end automation promoting creativity. The framework reuses requirements from similar software freely available online, utilizes advanced natural language processing and machine learning techniques, and leverages the concept of requirement boilerplate to generate candidate creative requirements. An application of our framework on software domains: Antivirus, Web Browser, and File Sharing followed by a human subject evaluation have shown promising results. In this invited extension, we present further analysis for our research questions and report an additional evaluation by human subjects. The results exhibit the framework's ability in generating creative features even for a relatively matured application domain, such as Web Browser, and provoking creative thinking among developers irrespective of their experience levels.
Analysis of a Pipelined Architecture for Sparse DNNs on Embedded Systems

Deep neural networks (DNNs) are increasing their presence in a wide range of applications, and their computationally intensive and memory-demanding nature poses challenges, especially for embedded systems. Pruning techniques turn DNN models into sparse by setting most weights to zero, offering optimization opportunities if specific support is included. We propose a novel pipelined architecture for DNNs that avoids all useless operations during the inference process. It has been implemented in a field-programmable gate array (FPGA), and the performance, energy efficiency, and area have been characterized. Exploiting sparsity yields remarkable speedups but also produces area overheads. We have evaluated this tradeoff in order to identify in which scenarios it is better to use that area to exploit sparsity, or to include more computational resources in a conventional DNN architecture. We have also explored different arithmetic bitwidths. Our sparse architecture is clearly superior on 32-bit arithmetic or highly sparse networks. However, on 8-bit arithmetic or networks with low sparsity it is more profitable to deploy a dense architecture with more arithmetic resources than including support for sparsity. We consider that FPGAs are the natural target for DNN sparse accelerators since they can be loaded at run-time with the best-fitting accelerator.
A Multi-System Chaotic Path Planner for Fast and Unpredictable Online Coverage of Terrains

Coverage path planning (CPP) algorithms customize an autonomous robot's trajectory for various applications. In surveillance and exploration of unknown environments, random CPP can be very effective in searching and finding objects of interest. Robots with random-like search algorithms must be unpredictable in their motion and simultaneously scan an uncertain environment, avoiding intruders and obstacles in their path. Inducing chaos into the robot's controller system makes its navigation unpredictable, accounts for better scanning coverage, and avoids any hurdles (obstacles and intruders) without the need for a map of the environment. The unpredictability, however, will come at the cost of increased coverage time. Due to the associated challenges, previous studies have ignored the coverage time and focused instead on the coverage rate only. This letter establishes a novel method that addresses the coverage time challenge of chaotic path planners. The method here combines the properties of two chaotic systems and manipulates them to achieve a fast coverage of the environment. The outcome has been a technique that can fully cover an area in at least 81% less time compared to state-of-the-art methods.
Contrastive Fairness in Machine Learning

Was it fair that Harry was hired but not Barry? Was it fair that Pam was fired instead of Sam? How can one ensure fairness when an intelligent algorithm takes these decisions instead of a human? How can one ensure that the decisions were taken based on merit and not on protected attributes like race or sex? These are the questions that must be answered now that many decisions in real life can be made through machine learning. However, research in fairness of algorithms has focused on the counterfactual questions “what if?” or “why?”, whereas in real life most subjective questions of consequence are contrastive: “why this but not that?”. We introduce concepts and mathematical tools using causal inference to address contrastive fairness in algorithmic decision-making with illustrative examples.
Genetic state-grouping algorithm for deep reinforcement learning

Although Reinforcement learning has already been considered one of the most important and well-known techniques of machine learning, its applicability remains limited in the real-world problems due to its long initial learning time and unstable learning. Especially, the problem of an overwhelming number of the branching factors under real-time constraint still stays unconquered, demanding a new method for the next generation of reinforcement learning. In this paper, we propose Genetic State-Grouping Algorithm based on deep reinforcement learning. The core idea is to divide the entire set of states into a few state groups. Each group consists of states that are mutually similar, thus representing their common features. The state groups are then processed with the Genetic Optimizer, which finds outstanding actions. These steps help the Deep Q Network avoid excessive exploration, thereby contributing to the significant reduction of initial learning time. The experiment on the real-time fighting video game (FightingICE) shows the effectiveness of our proposed approach.
Public-Key Infrastructure for Smart Meters using Blockchains

Public-Key Infrastructures are elementary building blocks to implement digital signatures (i.e., digital notarization) and, hence, ensure information integrity, authenticity, and nonrepudiation. This feature is a promising alternative to improve the reliability of smart meters. However, conventional PKIs can be too expensive in scenarios that consist of a significant number of meters. In this work, we propose a blockchain-based PKI, describing how to tailor this solution to deal with specific aspects related to smart meter protection. We also implement our proposal as a smart contract with the Hyperledger Fabric platform. The results show our solution's feasibility and provide a reliable model to manage digital certificates for a wide diversity of smart devices.
On the Limits of Parallelizing Convolutional Neural Networks on GPUs

GPUs are currently the platform of choice for training neural networks. However, training a deep neural network (DNN) is a time-consuming process even on GPUs because of the massive number of parameters that have to be learned. As a result, accelerating DNN training has been an area of significant research in the last couple of years.
Incremental and Approximate Computations for Accelerating Deep CNN Inference

Deep learning now offers state-of-the-art accuracy for many prediction tasks. A form of deep learning called deep convolutional neural networks (CNNs) are especially popular on image, video, and time series data. Due to its high computational cost, CNN inference is often a bottleneck in analytics tasks on such data. Thus, a lot of work in the computer architecture, systems, and compilers communities study how to make CNN inference faster. In this work, we show that by elevating the abstraction level and re-imagining CNN inference as queries , we can bring to bear database-style query optimization techniques to improve CNN inference efficiency. We focus on tasks that perform CNN inference repeatedly on inputs that are only slightly different . We identify two popular CNN tasks with this behavior: occlusion-based explanations (OBE) and object recognition in videos (ORV). OBE is a popular method for “explaining” CNN predictions. It outputs a heatmap over the input to show which regions (e.g., image pixels) mattered most for a given prediction. It leads to many re-inference requests on locally modified inputs. ORV uses CNNs to identify and track objects across video frames. It also leads to many re-inference requests. We cast such tasks in a unified manner as a novel instance of the incremental view maintenance problem and create a comprehensive algebraic framework for incremental CNN inference that reduces computational costs. We produce materialized views of features produced inside a CNN and connect them with a novel multi-query optimization scheme for CNN re-inference. Finally, we also devise novel OBE-specific and ORV-specific approximate inference optimizations exploiting their semantics. We prototype our ideas in Python to create a tool called Krypton that supports both CPUs and GPUs. Experiments with real data and CNNs show that Krypton reduces runtimes by up to 5× (respectively, 35×) to produce exact (respectively, high-quality approximate) results without raising resource requirements.
Security analysis of a dynamic threshold secret sharing scheme using linear subspace method

A dealer-free and non-interactive dynamic threshold secret sharing scheme has been proposed by Harn and Hsu in Information Processing Letters in 2015. In this scheme, a (t,n) secret sharing scheme in secret sharing phase can turn into a (m,n) scheme in secret reconstruction phase, where m is the number of participating shareholders. It has been claimed that the secrecy of shares and the secrecy of the secret are unconditionally preserved if m∈(t,1+t(t+1)/2]. This paper provides a security analysis of this scheme by introducing two attacks. In the first attack, we show that this scheme does not have the dynamic threshold property. In more details, any t+1 released values are sufficient to reconstruct the secret, while the updated threshold has any larger value. In the second attack, we show that any t+1 released values are sufficient to forge the released value of a non-participating shareholder. The technique enjoyed for these attacks is the linear subspace cryptanalysis. It basically measures the information leaked by the known parameters of the scheme by computing the dimension of the linear subspace spanned by these parameters. This method has shown to be capable of cryptanalysis of some secret sharing based schemes, whose security relies on keeping the coefficients of the underlying polynomial(s) secret.
Modelling clinical experience data as an evidence for patient-oriented decision support

Abstract Background Evidence-based Clinical Decision Support Systems (CDSSs) usually obtain clinical evidences from randomized controlled trials based on coarse-grained groups. Individuals who are beyond the scope of the original trials cannot be accurately and objectively supported. Also, patients’ opinions and preferences towards the health care delivered to them have rarely been considered. In this regards, we propose to use clinical experience data as an evidence to support patient-oriented decision-making. Methods The experience data of similar patients from social networks as subjective evidence and the argumentation rules derived from clinical guidelines as objective evidence are combined to support decision making together. They are integrated into a comprehensive decision support architecture. The patient reviews are crawled from social networks and sentimentally analyzed to become structured data which are mapped to the Clinical Sentiment Ontology (CSO). This is used to build a Patient Experience Knowledge Base (PEKB) that can complement the original clinical guidelines. An Experience Inference Engine (EIE) is developed to match similar experience cases from both patient preference features and patient conditions and ultimately, comprehensive clinical recommendations are generated. Results A prototype system is designed and implemented to show the feasibility of the decision support architecture. The system allows patients and domain experts to easily explore various choices and trade-offs via modifying attribute values to select the most appropriate decisions. Conclusions The integrated decision support architecture built is generic to solving a wide range of clinical problems. This will lead to better-informed clinical decisions and ultimately improved patient care.
Multiple-image hybrid encryption based on compressive sensing and diffractive imaging

In this paper, the Hybrid-II method for encrypting multiple images by combining simple modified diffractive imaging-based encoding (SMDIBE) and compressive sensing (CS) algorithm is developed. Separately encrypted images are combined in a single plane with the space multiplexing technique and authorization is provided for different users. The matrix size of the ciphertext is also reduced by a method that reproduces the plane containing images as amplitude and phase information. With the Hybrid-II method, a different solution has been introduced to the limited space and noiseless images transmission issues where the multiple-image encryption methods are experiencing problems. Reliability is enhanced by combining SMDIBE and CS techniques with a strong non-linear structure. After the space multiplexing, the pixel scrambling and the CS technique have provided additional key space in the method. A maximum of 121 images can be encrypted without noise for individual users with a single carrier. Experimental studies have also shown that the Hybrid-II method is resistant to chosen-plaintext and known plain-text attacks.
The histogram Poisson, labeled multi-Bernoulli multi-target tracking filter

A Random Finite Set (RFS) based multi-target filter is proposed, which utilizes a labeled Multi-Bernoulli distribution to model the multi-target state, together with a Poisson RFS distribution to model target birth. Referred to as the Poisson Labeled Multi-Bernoulli (PLMB) filter, results show that, in simulated environments, it outperforms the Labeled Multi-Bernoulli (LMB), δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) and Labeled Multi-Bernoulli Mixtures (LMBM) filters under general target birth scenarios. An algorithm based on a histogram of Gibbs samples is also proposed which efficiently generates a posterior labeled Multi-Bernoulli distribution in a simple manner using a histogram of the state-measurement associations obtained by a Gibbs sampler. The histogram approach is readily applicable to all Multi-Bernoulli based filters and is demonstrated in the form of the Histogram-PLMB (HPLMB) filter.
Personalizing Information Exploration with an Open User Model

Over the past two decades, several information exploration approaches were suggested to support a special category of search tasks known as exploratory search. These approaches creatively combined search, browsing, and information analysis steps shifting user efforts from recall (formulating a query) to recognition (i.e., selecting a link) and helping them to gradually learn more about the explored domain. More recently, a few projects demonstrated that personalising the process of information exploration with models of user interests can add value to information exploration systems. However, the current model-based information exploration interfaces are very sophisticated and focus on highly experienced users. The project presented in this paper attempted to assess the value of open user modeling in supporting personalized information exploration by novice users. We present an information exploration system with an open and controllable user model, which supports undergraduate students in finding research advisors. A controlled study of this system with target users demonstrated its advantage over a traditional search interface and revealed interesting aspects of user behavior in a model-based interface.
Hierarchical Pricing Mechanism With Financial Stability for Decentralized Crowdsourcing: A Smart Contract Approach

Software crowdsourcing is an emerging approach to software engineering with great potential for the subdivision and assignment of large-scale tasks. However, because of the centralization of the traditional crowdsourcing platform, information disclosure and nontransparent accounting may be difficult to avoid. To address this issue, we first introduce a novel blockchain-enabled crowdsourcing platform that integrates the functions of task assignment and resource lending via two dedicated smart contracts. Second, to ensure financial stability in the blockchain-enabled market and to match the difficulty of the received tasks with the ability of the workers, we design a dynamic, hierarchical pricing mechanism based on economic modeling methods and heterogeneous agent theory. With this mechanism, the market is divided dynamically into multiple levels according to the remuneration of the customers' offer and the market value of the workers' resources. Additional constraints are proposed to avoid possible malicious trading behavior from workers in the resource lending process. We prove theoretically the rationality of our model and demonstrate the dynamics of the model. We show that the market price and demand can be convergent and test the cost of executing the two smart contracts. Finally, extensive experimental results demonstrate the correctness and feasibility of the platform and confirm that the hierarchical pricing mechanism can maintain the stability of the market.
Performance of a method for weighting a range in the number of contributors in probabilistic genotyping

Uncertainty in the assignment of the number of contributors (NoC) can be encountered, particularly in higher-order mixtures, where alleles may be shared between contributors, may have dropped out, or may be masked by the stutter artefacts or allelic peaks of a more dominant contributor. Most probabilistic genotyping software requires the assignment of NoC prior to interpretation. NoC has been described as a nuisance parameter. Taylor et al. [1] describe a method to weigh the probability of the profile under different values of N and incorporate this into a likelihood ratio (LR). Within this paper we explore the performance of this variable number of contributors (varNoC) method programmed within the probabilistic genotyping software STRmix™. The desired combination of performance and runtime was obtained using the default STRmix™ version 2.7 MCMC settings in conjunction with a 2.5 % hyper-rectangle range, at least 10,000 naïve MC iterations and 8 MCMC chains. The varNoC LR demonstrated the typical sensitivity and specificity behaviour seen in previous studies, with a high level of reproducibility given repeat analyses. Profiles previously demonstrating ambiguity in the NoC assigned using conventional estimation methods, were able to be reliably interpreted and a varNoC LR assigned.