Reviewerly/exHarmony huggingface dataset:
Multi-armed Bandits for Boolean Connectives in Hybrid System Falsification

Hybrid system falsification is an actively studied topic, as a scalable quality assurance methodology for real-world cyber-physical systems. In falsification, one employs stochastic hill-climbing optimization to quickly find a counterexample input to a black-box system model. Quantitative robust semantics is the technical key that enables use of such optimization. In this paper, we tackle the so-called scale problem regarding Boolean connectives that is widely recognized in the community: quantities of different scales (such as speed [km/h] vs. rpm, or worse, rph) can mask each other's contribution to robustness. Our solution consists of integration of the multi-armed bandit algorithms in hill climbing-guided falsification frameworks, with a technical novelty of a new reward notion that we call hill-climbing gain. Our experiments show our approach's robustness under the change of scales, and that it outperforms a state-of-the-art falsification tool.
Building Jiminy Cricket

An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and is interacting with end-users. We address the challenge of how the moral values and views of all stakeholders can be integrated and reflected in the moral behavior of the autonomous system. We propose an artificial moral agent architecture that uses techniques from normative systems and formal argumentation to reach moral agreements among stakeholders. We show how our architecture can be used not only for ethical practical reasoning and collaborative decision-making, but also for the explanation of such moral behavior.
Automatic knowledge extraction of any Chatbot from conversation

Acquiring knowledge for conversation modeling is an important task in the process of building a Conversational Agent (Chatbot). However, it is a quite difficult process that requires too much time and efforts. To overcome these difficulties, in this paper, we demonstrate a novel methodology for the automatic conversational knowledge extraction from an existing Chatbot. Extracted knowledge will be used as training dataset for building a Neural Network Conversational Agent. The experiments in the paper show that our proposed approach can be used for the automatic knowledge extraction from any type of Chatbot on the Internet. The experiment that is presented in this paper has two phases. In the first phase, we present a methodology for the conversational knowledge extraction. In the second phase of the experiment, we introduce a methodology for building a new Neural Conversational Agent using a deep learning Neural Network framework. The key novelty of our proposed approach is the automated machine-machine conversational knowledge sharing and reuse. This is an important step towards building the new conversational agents skipping the difficult and time-consuming procedure of knowledge acquisition.
Blockchain speculation or value creation? Evidence from corporate investments

Abstract Many corporate executives believe blockchain technology is broadly scalable and will achieve mainstream adoption, yet there is little evidence of significant shareholder value creation associated with corporate adoption of blockchain technology. We collect a broad sample of firms that invest in blockchain technology and examine the stock price reaction to the “first” public revelation of this news. Initial reactions average close to +13% and are followed by reversals over the next 3 months. However, we report a striking difference based on the credibility of the investment. Blockchain investments that are at an advanced stage or are confirmed in subsequent financial statements are associated with higher initial reactions and little or no reversal. The results suggest that credible corporate strategies involving blockchain technology are viewed favorably by investors.
Parkchain: A Blockchain Powered Parking Solution for Smart Cities

With the advances happening in the area of urbanization, with increased demand for dwellings and ever growing number of vehicles on road, finding a parking spot has become one of the major pain points for the citizens. This is mainly due to the limited parking spaces in the urban areas. To overcome this limitation, one solution is to create more parking spaces. In this work we propose a Blockchain based solution, where parking pools can be created by developing a transparent platform where individuals can rent out their unused land for a stipulated amount of time with little or no legal hassles. A non-fungible token system representing unique parking lots will be created for transparency of the entire system. This also has the added advantage of generating revenue from their otherwise unused property. Smart contracts over Blockchain enforces the contractual agreement between the participants ensuring financial transparency in the proposed system.
Creating consensus group using online learning based reputation in blockchain networks

One of the biggest challenges to blockchain technology is the scalability problem. The choice of consensus algorithm is critical to the practical solution of the scalability problem. To increase scalability, Byzantine Fault Tolerance (BFT) based methods have been most widely applied. This study proposes a new model instead of Proof of Work (PoW) for forming the consensus group that allows the use of BFT based methods in the public blockchain network. The proposed model uses the adaptive hedge method, which is a decision-theoretic online learning algorithm (Qi et al., 2016). The reputation value is calculated for the nodes that want to participate in the consensus committee, and nodes with high reputation values are selected for the consensus committee to reduce the chances of the nodes in the consensus committee being harmful. Since the study focuses on the formation of the consensus group, a simulated blockchain network is used to test the proposed model more effectively. Test results indicate that the proposed model, which is a new approach in the literature making use of machine learning for the construction of consensus committee, successfully selects the node with the higher reputation for the consensus group.
Joint Attention Mechanism for Person Re-Identification

Although person re-identification (ReID) has drawn increasing research attention due to its potential to address the problem of analysis and processing of massive monitoring data, it is very challenging to learn discriminative information when the people in the images are occluded, in large pose variations or from different perspectives. To address this problem, we propose a novel joint attention person ReID (JA-ReID) architecture. The idea is to learn two complementary feature representations by combining a soft pixel-level attention mechanism and a hard region-level attention mechanism. The soft pixel-level attention mechanism learns a discriminative embedding for the fine-grained information by exploring the salient parts in the feature maps. The hard region-level attention mechanism conducts uniform partitions on the convolutional feature maps for learning local features. We have achieved competitive results in three popular benchmarks, including Market1501, DukeMTMC-reID, and CUHK03. The experimental results verify the adaptability of the joint attention mechanism to non-rigid deformation of the human body, which can effectively improve the accuracy of ReID.
Observational Measures for Effective Profiling of Healthcare Staffs' Security Practices

The healthcare sector is characterized with variant situations and services such as emergency services, collaborations in patient care and patient referrals. These activities require erratic accesses and electronic exchange of personal health information (PHI) between health professionals and healthcare organizations. Also, healthcare information is deemed to be among the most confidential of all types of personal data. Analyzing and modeling the security threats emanating from healthcare staffs' security practices therefore need an efficient approach. There is a need for tailored measures to be adopted in assessing healthcare personnel security practices in relation to Confidentiality, Integrity and Availability (CIA) threats. Standards and technical security implementations, required by regulatory bodies, have resulted in tracking healthcare staffs' security practices in various data sources which can be explored for security countermeasures. A literature survey was adopted to obtain the most appropriate observational measures that can be used to empirically study healthcare staffs' security practice analysis, modeling and incentivization (HSPAMI). The survey was conducted in journal and conference articles, healthcare security breaches reports and AI tools for detecting anomalous healthcare staff security practices. The survey results did not find a comprehensive and tailed observational measures suitable for the HSPAMI project. A comprehensive and tailored observational measures were therefore developed from healthcare standards, legal, regulatory aspects, and the code of conduct. Observational measures relating to healthcare security practices such as self-authorization, inter-organizational accesses to PHI and ICT readiness were found to be unique and have not been factored in existing observational measures for efficient profiling of healthcare staffs.
Practical Aggregate Signature from General Elliptic Curves, and Applications to Blockchain

Aggregate signature (AS) allows non-interactively condensing multiple individual signatures into a compact one. Besides faster verification, it is useful to reduce storage and bandwidth, and is especially attractive for blockchain and cryptocurrency. In this work, we first demonstrate the subtlety of achieving AS from general groups, by a concrete attack that actually works against the natural implementations of AS based on almost all the variants of DSA and Schnorr's. Then, we show that aggregate signature can be de- rived from the -signature scheme proposed by Yao, et al. To the best of our knowledge, this is the first aggregate signature scheme from general elliptic curves without bilinear maps (in particular, the secp256k1 curve used by Bitcoin). The security of aggregate -signature is proved based on a new assumption proposed and justified in this work, referred to as non-malleable discrete-logarithm (NMDL), which might be of independent interest. When applying the resultant aggregate -signature to Bitcoin, the storage volume of signatures reduces about 49.8%, and the signature verification time can even reduce about 72%. Finally, we specify in detail the application of the proposed AS scheme to Bitcoin, with the goal of maximizing performance and compatibility. We adopt a Merkle-Patricia tree based implementation, and the resulting system is also more friendly to segregated witness and provides better protection against transaction malleability attacks.
Lifestyles and routine activities: Do they enable different types of cyber abuse?

The emergence of new technology-facilitated types of crime following the advent of the Internet necessitated the re-examination of the utility of traditional theories such as lifestyle-routine activity theory to explain crimes that occur in the new and unique environment of cyberspace. The objective of this study was to investigate whether victims' lifestyles and routine activities can help explain the risk of victimization from direct, indirect and mixed types of cyber abuse. To achieve this objective, the data from a large nationwide (US) crowd-sourced sample (N = 1463) of adult members of an online labour portal Mechanical Turk was collected using an online survey platform Qualtrics and then analyzed using Bayesian Profile Regression to identify clusters of lifestyles and routine activities of victims associated with victimization from different types of cyber abuse. Our analyses were able to distinguish between victims and non-victims as well as between victims of different sub-classifications of cyber abuse. Specifically, we have identified five population subgroups based on their lifestyles and routine activities in terms of the associated risk of personal victimization from different types of cyber abuse. This paper discusses the differences in lifestyles and routine activities between the identified groups. Our findings generally support the empirical utility of lifestyle-routine activity theory to explain cyber abuse victimization; as with other traditional types of crime (e.g. robbery or assault), victims' lifestyles and routine activities play a significant role in their risk of various types of cyber abuse victimization.
Blind Deblurring of Barcodes via Kullback-Leibler Divergence

Barcode encoding schemes impose symbolic constraints which fix certain segments of the image. We present, implement, and assess a method for blind deblurring and denoising based entirely on Kullback-Leibler divergence. The method is designed to incorporate and exploit the full strength of barcode symbologies. Via both standard barcode reading software and smartphone apps, we demonstrate the remarkable ability of our method to blindly recover simulated images of highly blurred and noisy barcodes. As proof of concept, we present one application on a real-life out of focus camera image.
Facial Expression Editing with Continuous Emotion Labels

Recently deep generative models have achieved impressive results in the field of automated facial expression editing. However, the approaches presented so far presume a discrete representation of human emotions and are therefore limited in the modelling of non-discrete emotional expressions. To overcome this limitation, we explore how continuous emotion representations can be used to control automated expression editing. We propose a deep generative model that can be used to manipulate facial expressions in facial images according to continuous two-dimensional emotion labels. One dimension represents an emotion's valence, the other represents its degree of arousal. We demonstrate the functionality of our model with a quantitative analysis using classifier networks as well as with a qualitative analysis.
Hierarchical Clustering-Based Graphs for Large Scale Approximate Nearest Neighbor Search

This paper presents a novel approach to perform fast approximate nearest neighbors search in high dimensional data, using a nearest neighbor graph created over large collections. This graph is created based on the fusion of multiple hierarchical clustering results, where a minimum-spanning-tree structure is used to connect all elements in a cluster. We propose a novel search technique to guide the navigation on the graph without computing exhaustively the distances to all neighbors in each step of the search, just to those in the direction of the query. The objective is to determine the nearest point to the query with a few number of distance calculations. We experimented in three datasets of 1 million SIFT, GIST, and GloVe features. Results show better speedups than another graph-based technique, and competitive speedups at high recall values when compared to classic and recent state-of-the-art techniques.
The Impact of Error Dependencies on Ring/Mod-LWE/LWR Based Schemes

Current estimation techniques for the probability of decryption failures in Ring/Mod-LWE/LWR based schemes assume independence of the failures in individual bits of the transmitted message to calculate the full failure rate of the scheme. In this paper we disprove this assumption both theoretically and practically for schemes based on Ring/Mod-Learning with Errors/Rounding. We provide a method to estimate the decryption failure probability, taking into account the bit failure dependency. We show that the independence assumption is suitable for schemes without error correction, but that it might lead to underestimating the failure probability of algorithms using error correcting codes. In the worst case, for LAC-128, the failure rate is $$2^{48}$$ times bigger than estimated under the assumption of independence. This higher-than-expected failure rate could lead to more efficient cryptanalysis of the scheme through decryption failure attacks.
Reservoir Computing Using Laser Networks

Reservoir computing is a neuromorphic computing scheme inspired by the human brain.It has found great success as a versatile hardware-compatible application of machine learning concepts.In this paper, we highlight the fundamental working principles and important characteristics of reservoir computing with a particular focus on photonic systems and networks.These systems can further be enhanced by the inclusion of delayed variables to produce complex spatiotemporally mixed "time-multiplexed" networks.We use a simple nonlinear oscillator model, that is not only applicable to lasers, but can also describe a variety of other oscillating systems.
Introducing Low-Cost Sensors into the Classroom Settings: Improving the Assessment in Agile Practices with Multimodal Learning Analytics

Currently, the improvement of core skills appears as one of the most significant educational challenges of this century. However, assessing the development of such skills is still a challenge in real classroom environments. In this context, Multimodal Learning Analysis techniques appear as an attractive alternative to complement the development and evaluation of core skills. This article presents an exploratory study that analyzes the collaboration and communication of students in a Software Engineering course, who perform a learning activity simulating Scrum with Lego® bricks. Data from the Scrum process was captured, and multidirectional microphones were used in the retrospective ceremonies. Social network analysis techniques were applied, and a correlational analysis was carried out with all the registered information. The results obtained allowed the detection of important relationships and characteristics of the collaborative and Non-Collaborative groups, with productivity, effort, and predominant personality styles in the groups. From all the above, we can conclude that the Multimodal Learning Analysis techniques offer considerable feasibilities to support the process of skills development in students.
Unsupervised semantic deep hashing

In recent years, deep hashing methods have been proved to be effective since it employs convolutional neural network to learn features and hashing codes simultaneously. However, these methods are mostly supervised. In real-world applications, it is a time-consuming and overloaded task for annotating a large number of images. In this paper, we propose a novel unsupervised deep hashing method for large-scale image retrieval. Our method, namely unsupervised semantic deep hashing (USDH), uses semantic information preserved in the CNN feature layer to guide the training of network. We enforce four criteria on hashing codes learning based on VGG-19 model: 1) preserving relevant information of feature space in hashing space; 2) minimizing quantization loss between binary-like codes and hashing codes; 3) improving the usage of each bit in hashing codes by using maximum information entropy, and 4) invariant to image rotation. Extensive experiments on CIFAR-10, NUSWIDE have demonstrated that USDH outperforms several state-of-the-art unsupervised hashing methods for image retrieval. We also conduct experiments on Oxford 17 datasets for fine-grained classification to verify its efficiency for other computer vision tasks.
A Three-Player GAN: Generating Hard Samples to Improve Classification Networks

We propose a Three-Player Generative Adversarial Network to improve classification networks. In addition to the game played between the discriminator and generator, a competition is introduced between the generator and the classifier. The generator's objective is to synthesize samples that are both realistic and hard to label for the classifier. Even though we make no assumptions on the type of augmentations to learn, we find that the model is able to synthesize realistically looking examples that are hard for the classification model. Furthermore, the classifier becomes more robust when trained on these difficult samples. The method is evaluated on a public dataset for traffic sign recognition.
On Implicit Filter Level Sparsity in Convolutional Neural Networks

We investigate filter level sparsity that emerges in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradient descent techniques and L2 regularization or weight decay. We conduct an extensive experimental study casting our initial findings into hypotheses and conclusions about the mechanisms underlying the emergent filter level sparsity. This study allows new insight into the performance gap obeserved between adapative and non-adaptive gradient descent methods in practice. Further, analysis of the effect of training strategies and hyperparameters on the sparsity leads to practical suggestions in designing CNN training strategies enabling us to explore the tradeoffs between feature selectivity, network capacity, and generalization performance. Lastly, we show that the implicit sparsity can be harnessed for neural network speedup at par or better than explicit sparsification / pruning approaches, with no modifications to the typical training pipeline required.
Refined selfish herd optimizer for global optimization problems

The selfish herds optimizer (SHO) is a recently developed swarm optimization algorithm for solving global optimization problems. SHO mimics the widely observed selfish herd behaviors of avoiding predation risks. In SHO, a set of unique evolutionary operators inspired by the prey-predator relationship are used in dealing with optimization problems. Although SHO can provide a good performance when finding an optimal solution, this algorithm has some oversights that cause problems in the exploitation ability of SHO. Additionally, SHO still has some shortcomings that influence the performance of exploration and avoiding stagnation in local optima. In this paper, some refinements and modifications are proposed for these oversights and shortcomings. In order to validate whether the refinements and modifications are appropriate for improving the performance of SHO, two suites of benchmark functions are employed to compare the proposed method with the original SHO and other well-known and recently developed algorithms, such as Standard Particle Swarm Optimization, Artificial Bee Colony algorithm, Differential Evolution, and Crow Search Algorithm, etc. Finally, the efficiency of the proposed method is justified using the nonparametric Wilcoxon rank-sum test. The experimental results show that the proposed modifications are appropriate for improving the performance of the original SHO. In addition, the proposed method can also give competitive results in finding global optima when compared with other algorithms.