Reviewerly/exHarmony huggingface dataset:
Secure IoT Access at Scale Using Blockchains and Smart Contracts

Blockchains and smart contracts are an emerging, promising technology, that has received considerable attention. We use the blockchain technology, and in particular Ethereum, to implement a large-scale event-based Internet of Things (IoT) control system. We argue that the distributed nature of the “ledger,” as well as, Ethereum's capability of parallel execution of replicated “smart contracts”, provide the sought after automation, generality, flexibility, resilience, and high availability. We design a realistic blockchain-based loT architecture, using existing technologies while by taking into consideration the characteristics and limitations of IoT devices and applications. Furthermore, we leverage blockchain's immutability and Ethereum's support for custom tokens to build a robust and efficient token-based access control mechanism. Our evaluation shows that our solution is viable and offers significant security and usability advantages.
Cryptanalysis of OCB2: Attacks on Authenticity and Confidentiality

We present practical attacks on OCB2. This mode of operation of a blockcipher was designed with the aim to provide particularly efficient and provably-secure authenticated encryption services, and since its proposal about 15 years ago it belongs to the top performers in this realm. OCB2 was included in an ISO standard in 2009. An internal building block of OCB2 is the tweakable blockcipher obtained by operating a regular blockcipher in $$ \text {XEX} ^*$$ mode. The latter provides security only when evaluated in accordance with certain technical restrictions that, as we note, are not always respected by OCB2. This leads to devastating attacks against OCB2's security promises: We develop a range of very practical attacks that, amongst others, demonstrate universal forgeries and full plaintext recovery. We complete our report with proposals for (provably) repairing OCB2. To our understanding, as a direct consequence of our findings, OCB2 is currently in a process of removal from ISO standards. Our attacks do not apply to OCB1 and OCB3, and our privacy attacks on OCB2 require an active adversary.
Textual Variability Modeling Languages

During the three decades since the invention of the first variability modeling approach [28], there have been multiple attempts to introduce advanced variability modeling capabilities. More recently, we have seen increased attention on textual variability modeling languages. In this paper, we summarize the main capabilities of state of the art textual variability modeling languages, based on [23], including updates regarding more recent work. Based on this integrated characterization, we provide a discussion of additional concerns, opportunities and challenges that are relevant for designing future (textual) variability modeling languages. The paper also summarizes relevant contributions by the authors as input to further discussions on future (textual) variability modeling languages.
HRNN4F: HYBRID DEEP RANDOM NEURAL NETWORK FOR MULTI-CHANNEL FALL ACTIVITY DETECTION

Falls are a major health concern in older adults. Falls lead to mortality, immobility and high costs to social and health care services. Early detection and classification of falls is imperative for timely and appropriate medical aid response. Traditional machine learning models have been explored for fall classification. While newly developed deep learning techniques have the ability to potentially extract high-level features from raw sensor data providing high accuracy and robustness to variations in sensor position, orientation and diversity of work environments that may skew traditional classification models. However, frequently used deep learning models like Convolutional Neural Networks (CNN) are computationally intensive. To the best of our knowledge, we present the first instance of a Hybrid Multichannel Random Neural Network (HMCRNN) architecture for fall detection and classification. The proposed architecture provides the highest accuracy of 92.23% with dropout regularization, compared to other deep learning implementations. The performance of the proposed technique is approximately comparable to a CNN yet requires only half the computation cost of the CNN-based implementation. Furthermore, the proposed HMCRNN architecture provides 34.12% improvement in accuracy on average than a Multilayer Perceptron.
Combining Bayesian optimization and Lipschitz optimization

Bayesian optimization and Lipschitz optimization have developed alternative techniques for optimizing black-box functions. They each exploit a different form of prior about the function. In this work, we explore strategies to combine these techniques for better global optimization. In particular, we propose ways to use the Lipschitz continuity assumption within traditional BO algorithms, which we call Lipschitz Bayesian optimization (LBO). This approach does not increase the asymptotic runtime and in some cases drastically improves the performance (while in the worst case the performance is similar). Indeed, in a particular setting, we prove that using the Lipschitz information yields the same or a better bound on the regret compared to using Bayesian optimization on its own. Moreover, we propose a simple heuristics to estimate the Lipschitz constant, and prove that a growing estimate of the Lipschitz constant is in some sense “harmless”. Our experiments on 15 datasets with 4 acquisition functions show that in the worst case LBO performs similar to the underlying BO method while in some cases it performs substantially better. Thompson sampling in particular typically saw drastic improvements (as the Lipschitz information corrected for its well-known “over-exploration” pheonemon) and its LBO variant often outperformed other acquisition functions.
NV-DNN: Towards Fault-Tolerant DNN Systems with N-Version Programming

Employing deep learning algorithms in real-world applications becomes a trend. However, a bottleneck that impedes their further adoption in safety-critical systems is the reliability issue. It is challenging to develop reliable neural network models as the theory of deep learning has not yet been well-established and neural network models are very sensitive to data perturbations. Inspired by the classic paradigm of N-version programming for fault tolerance, this paper investigates the feasibility of developing fault-tolerant deep learning systems through model redundancy. We hypothesize that if we train several simplex models independently, these models are unlikely to produce erroneous results for the same test cases. In this way, we can design a fault-tolerant system whose output is determined by all these models cooperatively. We propose several independence factors that can be introduced for generating multiple versions of neural network models, including training, network, and data. Experimental results on MNIST and CIFAR-10 both verify that our approach can improve the fault-tolerant ability of a deep learning system. Particularly, independent data for training plays the most significant role in generating multiple models sharing the least mutual faults.
Research on Cooperative Trajectory Planning and Tracking Problem for Multiple Carrier Aircraft on the Deck

Based on the concepts of the reciprocal velocity obstacle (RVO) and Dubins methods, an improved Dubins-RVO method that can be applied to the scenario with noncircular obstacles is developed. Based on the Dubins-RVO method and one-sided Symplectic Pseudospectral method, a multiaircraft collaborative trajectory planning method was proposed. The kinematic constraint and terminal boundary conditions can be strictly satisfied in the proposed compound trajectory planning method. In addition, a symplectic pseudospectral receding horizon control (RHC) controller is designed to solve the trajectory tracking problem where the constraints on the state and control variables are considered. The proposed compound trajectory planning method is first applied to a two-aircraft problem. The obtained results demonstrate that compared to the RVO and Dubins-RVO methods, the proposed method can effectively avoid the “dead-lock” phenomenon and generate feasible trajectory efficiently satisfying all constraints. In addition, simulation experiment for four aircrafts is conducted. Smooth trajectories are obtained. Despite the presence of disturbances in the environment, the trajectories can be tracked with high precision using the symplectic pseudospectral RHC controller.
Toward Objective Evaluation of Working Memory in Visualizations: A Case Study Using Pupillometry and a Dual-Task Paradigm

Cognitive science has established widely used and validated procedures for evaluating working memory in numerous applied domains, but surprisingly few studies have employed these methodologies to assess claims about the impacts of visualizations on working memory. The lack of information visualization research that uses validated procedures for measuring working memory may be due, in part, to the absence of cross-domain methodological guidance tailored explicitly to the unique needs of visualization research. This paper presents a set of clear, practical, and empirically validated methods for evaluating working memory during visualization tasks and provides readers with guidance in selecting an appropriate working memory evaluation paradigm. As a case study, we illustrate multiple methods for evaluating working memory in a visual-spatial aggregation task with geospatial data. The results show that the use of dual-task experimental designs (simultaneous performance of several tasks compared to single-task performance) and pupil dilation can reveal working memory demands associated with task difficulty and dual-tasking. In a dual-task experimental design, measures of task completion times and pupillometry revealed the working memory demands associated with both task difficulty and dual-tasking. Pupillometry demonstrated that participants' pupils were significantly larger when they were completing a more difficult task and when multitasking. We propose that researchers interested in the relative differences in working memory between visualizations should consider a converging methods approach, where physiological measures and behavioral measures of working memory are employed to generate a rich evaluation of visualization effort.
Learning part-aware attention networks for kinship verification

In this paper, we present a method for facial kinship verification, which uses an attention network to focus on extracting information of local facial parts. Unlike most existing approaches which use low-level features for verification, we introduce an attention mechanism in the deep network to extract high-level features for face representation. We also propose a self-supervised approach to directing the attention network. Specifically, we randomly include a mask to five facial feature parts of each face to help the network focus on extracting more discriminative information at these locations. Experimental results on the KinFaceW-I and KinFaceW-II datasets are presented to show the effectiveness of our proposed approach.
Randomness cost of masking quantum information and the information conservation law

Masking quantum information, which is impossible without randomness as a resource, is a task that encodes quantum information into bipartite quantum state while forbidding local parties from accessing to that information. In this work, we disprove the geometric conjecture about unitarily maskable states [K. Modi et al., Phys. Rev. Lett. 120, 230501 (2018)], and make an algebraic analysis of quantum masking. First, we show a general result on quantum channel mixing that a subchannel's mixing probability should be suppressed if its classical capacity is larger than the mixed channel's capacity. This constraint combined with the well-known information conservation law, a law that does not exist in classical information theory, gives a lower bound of randomness cost of masking quantum information as a monotone decreasing function of evenness of information distribution. This result provides a consistency test for various scenarios of fast scrambling conjecture on the black hole evaporation process. The results given here are robust to incompleteness of quantum masking.
HAPE: A programmable big knowledge graph platform

Heaven Ape (HAPE) is an integrated big knowledge graph platform supporting the construction, management, and operation of large to massive scale knowledge graphs. Its current version described in this paper is a prototype, which consists of three parts: a big knowledge graph knowledge base, a knowledge graph browser on the client side, and a knowledge graph operating system on the server side. The platform is programmed in two high level scripting languages: JavaScript for programming the client side functions and Python for the server side functions. For making the programming more suitable for big knowledge processing and more friendly to knowledge programmers, we have developed two versions of knowledge scripting languages, namely K-script-c and K-script-s, for performing very high level knowledge programming of client resp. server side functions. HAPE borrows ideas from some well-known knowledge graph processing techniques and also invents some new ones as our creation. As an experiment, we transformed a major part of the DBpedia knowledge base and reconstructed it as a big knowledge graph. It works well in some application tests and provides acceptable efficiency.
Enhancing the Quality of Image Tagging Using a Visio-Textual Knowledge Base

Auto-tagging of images is important for image understanding and for tag-based applications viz. image retrieval, visual question-answering, image captioning, etc. Although existing tagging methods incorporate both visual and textual information to assign/refine tags, they lag in tag-image relevance, completeness, and preciseness, thereby resulting in the unsatisfactory performance of tag-based applications. In order to bridge this gap, we propose a novel framework for tag assignment using knowledge embedding (TAKE) from a proposed external knowledge base, considering properties such as Rarity, Newness, Generality, and Naturalness (RNGN properties). These properties help in providing a rich semantic representation to images. Existing knowledge bases provide multiple types of relations extracted through only one modality, either text or visual, which is not effective in image related applications. We construct a simple yet effective Visio-Textual Knowledge Base (VTKB) with only four relations using reliable resources such as Wikipedia, thesauruses, dictionaries, etc. Our large scale experiments demonstrate that the proposed combination of TAKE and VTKB assigns a large number of high quality tags in comparison to the ConceptNet and ImageNet knowledge bases when used in conjunction with TAKE. Also, the effectiveness of knowledge embedding through VTKB is evaluated for image tagging and tag-based image retrieval (TBIR).
Joint Banknote Recognition and Counterfeit Detection Using Explainable Artificial Intelligence

We investigated machine learning-based joint banknote recognition and counterfeit detection method. Unlike existing methods, since the proposed method simultaneously recognize banknote type and detect counterfeit detection, it is significantly faster than existing serial banknote recognition and counterfeit detection methods. Furthermore, we propose an explainable artificial intelligence method for visualizing regions that contributed to the recognition and detection. Using the visualization, it is possible to understand the behavior of the trained machine learning system. In experiments using the United State Dollar and the European Union Euro banknotes, the proposed method shows significant improvement in computation time from conventional serial method.
Virtual Environments for Substance Abuse Assessment and Treatment

The previous scenario is based on a first-person recollection of attending a party that was developed into a virtual reality scenario by Dr. Bordnick at the Virtual Reality Clinical Research Lab. This scenario was used to construct a virtual reality platform to assess and eventually decrease alcohol craving, offering a novel approach to substance abuse treatment.
Disentangling the relationship between Bitcoin and market attention measures

In the last few years Bitcoin price dynamics has been the subject of intense research. One of the main stream of investigation is the identification of relevant factors affecting its returns and volatility; empirical evidence suggests a positive association between returns and sentiment proxies about the Bitcoin network, such as Wikipedia inquiries, internet search intensity on the topic, trading volume in main exchanges or sentiment measures obtained via natural language processing algorithms applied on specialized forums comments or social media posts on the theme. In this paper we investigate the association of trading volume and internet search intensity with Bitcoin returns and volatility, complementing the outcomes in Figá-Talamanca and Patacca (Decis Econ Fin ISSN: 1129-6569, https://doi.org/10.1007/s10203-019-00258-7, 2019) and Urquhart (Econ Lett 166:40–44, ISSN: 0165-1765, https://doi.org/10.1016/j.econlet.2018.02.017, 2018): we find no direct relationship between the two market attention measures and returns while both the trading volume and the internet search intensity affect positively Bitcoin volatility. Conversely, an increase in Bitcoin returns does increase both trading volume and internet search intensity, evidencing an inverse relationship between returns and attention measures. As a byproduct, we also detect a positive association between trading volume and the internet search intensity and no reverse relationship. Since market attention, especially internet search volume, do increase around relevant events and corresponding news or announcements for the Bitcoin market, we also analyze whether and to which extent the above relationships change, after specific events are taken into account. Indeed, by applying two different approaches, we show that the relationships may change significantly.
Uncertain Data Modeling Based on Evolving Ellipsoidal Fuzzy Information Granules

Dealing with uncertain data requires effective methods to properly describe their real meaning in terms of a tradeoff between interpretability and generality on the process of knowledge formation based on data abstraction. This article proposes an online granulation process based on evolving ellipsoidal fuzzy information granules (EEFIG) and the principle of justifiable granularity (PJG) for data streams parameterization. The granulation process consists in the information granule development taking into consideration the data stream with a simplified optimal granularity allocation. In the sequel, an evolving Takagi-Sugeno fuzzy model based on the ellipsoidal granules is proposed for data reconstruction and one-step ahead prediction from past data numerical evidence. Experimental studies concerning clustering, data granulation, and time-series forecasting are performed to illustrate the effectiveness of the proposed method.
Pedagogical Aspects of Cyber Security Exercises

Cyber security exercises (CSE) are complex learning experiences aimed at developing expert knowledge and competence through simulation. In this paper we examine pedagogical issues relating to CES, from exercise design to training results and evaluation. In addition, we present a Deliberate Practice -oriented view on expert and competence development for CSEs. We use data gathered from multiple CSE cases, where we have collected field notes, observations, questionnaire results, and other documentary data while organizing these training events. Based on our observations and analysis, integrating pedagogical knowledge and focus with each phase in the CSE lifecycle, i.e. planning, implementation, and feedback phases, the training effectiveness can be improved. We also note that CSE evaluation requires systematic measurements of change ranging from customer experience to organizational change. We also outline avenues for further work relating to various aspects of expert knowledge development and training evaluation in the context of CSEs.
A square lattice oriented reversible information hiding scheme with reversibility and adaptivity for dual images

This paper proposes an adaptive dual-image-based reversible information hiding scheme. First, for a pixel pair in the original image, an associated square lattice is selected to determine the maximum distortion that will be induced by hiding information. Then, an embedding rule, which combines the selected square lattice with integer rounding, is constructed. In addition, a novel histogram shifting mechanism to solve the problems of overflow and underflow is designed to decrease the distortions that are caused by the shifting of a large number of pixels. In our approach, both the original image and the secret messages can be recovered losslessly. Our experimental results show that the proposed scheme achieved performance that was superior to the state-of-the-art schemes, resulting in embedding rates up to 1.40 bpp and average PSNRs of approximately 47.60 dB. The proposed scheme also can be used in real-time systems due to the simplicity of its computations.
Smart Traffic Light Scheduling in Smart City Using Image and Video Processing

The growing population and increased vehicles lead to the main challenges in urban life. Therefore, the role of traffic management will save time and fuel consumption and reduce environmental pollution. In recent years, Internet of Things (IoT) and smart cities drive a new field of intelligent traffic management. In this paper, a new method for traffic light control is presented by using the combination of IoT and image and video processing techniques. In the proposed models, traffic light scheduling is determined based on the density and the number of passing vehicles. Moreover, it is implemented by Raspberry-Pi board and OpenCV tool. The analytical and experimental results indicate the efficiency provided by the proposed models in intelligent traffic management.
An Energy-Efficient Sparse Deep-Neural-Network Learning Accelerator With Fine-Grained Mixed Precision of FP8–FP16

Recently, several hardware have been reported for deep-neural-network (DNN) acceleration, however, they focused on only inference rather than DNN learning that is crucial ingredient for user adaptation at the edge-device as well as transfer learning with domain-specific data. However, DNN learning requires much heavier floating-point (FP) computation and memory access than DNN inference, thus, dedicated DNN learning hardware is essential. In this letter, we present an energy-efficient DNN learning accelerator core supporting CNN and FC learning as well as inference with following three key features: 1) fine-grained mixed precision (FGMP); 2) compressed sparse DNN learning/inference; and 3) input load balancer. As a result, energy efficiency is improved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.76\times $ </tex-math></inline-formula> compared to sparse FP16 operation without any degradation of learning accuracy. The energy efficiency is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.9\times $ </tex-math></inline-formula> higher than NVIDIA V100 GPU and its normalized peak performance is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.47\times $ </tex-math></inline-formula> higher than previous DNN learning processor.