Reviewerly/exHarmony huggingface dataset:
Low-Cost and Programmable CRC Implementation Based on FPGA

Cyclic redundancy check (CRC) is a well-known error detection code that is widely used in Ethernet, PCIe, and other transmission protocols. The existing FPGA-based implementation solutions encounter the problem of excessive resource utilization in high-performance scenarios. The padding zeros problem and the introduction of programmability further exacerbate this problem. In this brief, the stride-by-5 algorithm is proposed to achieve the optimal utilization of FPGA resources. The pipelining go back algorithm is proposed to solve the padding zeros problem. The method of reprogramming by HWICAP is proposed to realize programmability with small and constant resource utilization. The experimental results show that the resource utilization of the proposed non-segmented architecture is 80.7%-87.5% and 25.1%-46.2% lower than that of two state-of-the-art FPGA-based CRC implementations, and the proposed segmented architecture has lower resource utilization, by 81.7%-85.9% and 2.9%-20.8%, than two state-of-the-art architectures. Furthermore, throughput and programmability are guaranteed. The source code has been made available on -1.5mm]Please cite reference [1] in the text of the paper. It was removed from the abstract as having references in an abstract is contrary to IEEE journal style. GitHub.
New Algorithms for Solving Zero-Sum Stochastic Games

Zero-sum stochastic games, henceforth stochastic games, are a classical model in game theory in which two opponents interact and the environment changes in response to the players’ behavior. The central solution concepts for these games are the discounted values and the value, which represent what playing the game is worth to the players for different levels of impatience. In the present manuscript, we provide algorithms for computing exact expressions for the discounted values and for the value, which are polynomial in the number of pure stationary strategies of the players. This result considerably improves all the existing algorithms.
Learning-Based Real-Time Process-Aware Anomaly Monitoring for Assured Autonomy

Various learning-based approaches have been proposed recently for robot control in complex unknown environments. However, ensuring safety in these systems is essential. Learning-based approaches are usually black-box systems which are difficult to interpret/verify. We propose a real-time continuous safety checking system which monitors the validity of the mapping from the sensor input to the actuator command (i.e., controller-focused anomaly detection) and from the actuator command to the sensor input (i.e., system-focused anomaly detection). Our controller-focused anomaly monitor is an image conditioned energy based generative adversarial network in which the discriminator acts as an energy function attributing low energies to proper actuator commands and high energy to anomalous actuator commands. Our system-focused anomaly monitor is based on an action condition video prediction framework, which finds the action conditioned future frame most similar to the actual future frame and compares it with the true action to detect anomaly. We experimentally demonstrate the effectiveness of our framework for an autonomous navigation system in indoor (on datasets from our ground vehicle) and outdoor environments (on Udacity dataset). Furthermore, we show through experimental evaluations that our proposed framework is resilient to various data corruptions common in a vision based system.
SALI360

In accordance with the recent enhancement of display technology, users demand a higher quality of streaming service, which escalates the bandwidth requirement. Considering the recent advent of high FPS (frame per second) 4K and 8K resolution 360° videos, such bandwidth concern further intensifies in 360° Virtual Reality (VR) content streaming even at a larger scale. However, the currently available bandwidth in most of the developed countries can hardly support the bandwidth required to stream such a scale of content. To address the mismatch between the demand on higher quality of streaming service and the saturated network improvement, we propose SALI360 that practically solves the mismatch by utilizing the characteristics of the human vision system (HVS). By pre-rendering a set of regions - where viewers are expected to fixate - on 360° VR content in higher quality than the other regions, SALI360 improves viewers' quality of perception (QoP) while reducing content size with geometry-based 360° content encoding. In our user experiment, we compare the performance of SALI360 to the existing 360° content-encoding techniques based on 20 viewers' head movement and eye gaze traces. To evaluate viewers' QoP, we propose FoL (field of look) that captures viewers' quality perception area in the visual focal field (8°) rather than a wide (around 90°) field of view (FoV). Results of our experimental 360° VR video streaming show that SALI360 achieves 53.3% of PSNR improvement in FoL, while gaining 9.3% of PSNR improvement in FoV. In addition, our subjective study on 93 participants verifies that SALI360 improves viewers' QoP in the 360° VR streaming service.
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning

Deep neural networks (DNNs) are increasingly powering high-stakes applications such as autonomous cars and healthcare; however, DNNs are often treated as "black boxes" in such applications. Recent research has also revealed that DNNs are highly vulnerable to adversarial attacks, raising serious concerns over deploying DNNs in the real world. To overcome these deficiencies, we are developing Massif, an interactive tool for deciphering adversarial attacks. Massif identifies and interactively visualizes neurons and their connections inside a DNN that are strongly activated or suppressed by an adversarial attack. Massif provides both a high-level, interpretable overview of the effect of an attack on a DNN, and a low-level, detailed description of the affected neurons. Massif's tightly coupled views help people better understand which input features are most vulnerable and important for correct predictions.
Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.
Student Engagement in Sensitive Design Contexts

There is a growing body of HCI work that seeks to understand and enhance the lived experience of people with dementia. The majority of this work involves researchers working alongside people with dementia and their carers, focused on the design project outcomes. In order to enrich the social context of this work, we explore broadening participation to include student volunteers. To encourage mutually engaging experiences in this design context, careful consideration of how to support both students and people with dementia is needed. In this paper, we present two case- studies of co-design projects between students and people with dementia. Our findings detail the use of design methods to reconfigure the role of the residents in care contexts and the students learning process. We discuss the project learning outcomes as well as practical and ethical considerations to support the use of design methods to support mutual engagement in sensitive contexts.
Hardening SAML by Integrating SSO and Multi-Factor Authentication (MFA) in the Cloud

Even though the cloud paradigm and its associated services has been adopted in various enterprise applications, there has been major issues with regard to authenticating users' critical data. Single Sign on (SSO) is a user authentication technique through which a server authenticates and allows a user to use a single aspect of login credentials, for example, to access multiple services in the cloud. Even though SSO reduces the number of logins that are needed over heterogeneous environments, the risk that might be associated with the security of SSO might be detrimental if, for example, a Man-in-the Middle (MITM) attacker manages to gain control of the SSO credentials. It is also possible to get the identity of the users who have logged into Active Directory or intranet and this identity can easily be used to log into other web-based applications, and this requires the use of the Security Assertion Mark-up Language (SAML). SAML is basically a standard that allows users to be logged into applications as per their sessions. The problem that this paper addresses is the lack of a proactive technique of hardening cloud-based SAML while combining SSO with a Multi-Factor Authentication (MFA) at the time of writing this paper. The authors have, therefore, proposed an effective approach that unifies SSO with MFA in this context. Based on the base score index conducted over Common Vulnerability Scoring System (CVSS), the architecture proves to be reliable, feasible and with better performance.
Training Technology Probes Across Fitness Practices: Yoga, Circus and Weightlifting

Wearable technology for sports and fitness have increased in popularity in the last decade, but most technological solutions in research are designed for a single specific fitness practice and target group. Towards validating a design approach and resulting wearable designs across several fitness practices, we used three wearable Training Technology Probes (TTPs) originally designed for, and tested in, the context of Yoga and Circus training. They were used in a design activity with the goal of exploring and opening up the design space of technology for weightlifting. Our exploration proved fruitful and substantiated the versatility, adaptability and usefulness of the TTPs on account of their design features. Here we present initial insights from deploying the TTPs in that domain. The TTPs served as probing tools, helping to surface goals and challenges of weightlifting. They were appropriated to fit and assist in new TTP uses for weightlifting exercises, leading to interesting design iterations that will inform future work.
LATTE: Visual Construction of Smart Contracts

Smart contracts enable developers to run instructions on blockchains (eg. Ethereum) and have broad range of real-world applications. Solidity is the most popular high-level smart contract programming language on Ethereum. Coding in such language, however, demands a user to be proficient in contract programming and debugging to construct smart contracts correctly. In practice, such expectation makes it harder for non-programmers to take advantage of smart contracts. In this demonstration, we present a novel visual smart contract construction system on Ethereum called latte to make smart contract development accessible to non-programmers. Specifically, it allows a user to construct a contract without writing Solidity code by manipulating visual objects in a direct manipulation-based interface. Furthermore, latte interactively guides users and makes them aware of the cost (in units of Gas) of visual actions undertaken by them during contract construction.
Augmented Displays: Seamlessly Extending Interactive Surfaces With Head-Mounted Augmented Reality

We present Augmented Displays, a new class of display systems combining high-resolution interactive surfaces with head-coupled Augmented Reality. This extends the screen estate beyond the display and enables placing AR content directly at the display's borders or within the real environment. Furthermore, it enables people to interact with AR objects with natural pen and touch input in high precision on the surface. This combination allows a variety of interesting applications. To illustrate them, we present two use cases: An immersive 3D modeling tool and an architectural design tool. Our goal is to demonstrate the potential of Augmented Displays as a foundation for future work in the design space of this exciting new class of systems.
Feasibility of Internet of Things and Agnostic Blockchain Technology Solutions: A Case in the Fisheries Supply Chain

Along with Internet of Things -IoT-, distributed ledger/blockchain technology can provide substantial benefits to the management of supply chains. However the adoption of blockchain-based solutions may face substantial challenges such as scalability, which happens when several parties have to access and record information in a single blockchain. The use of two or more blockchains may help to mitigate such problem but it may require the use of an alternative architecture that enables interoperability. This work investigates the feasibility of adopting an agnostic blockchain architecture based on the particularities of the supply chain commonly found in the fisheries sector. A case in the fisheries sector in Atlantic Canada, characterized for the capture of different species destined for markets worldwide, is used to explore the adoption of an agnostic blockchain architecture. Emerging concepts such as agnotic blockchain applied to the supply chains of perishable goods may open the door to the development of innovative solutions.
Geochemical and petrographic identification of stone quarries used for the construction of the Anahita Temple of Kangavar (West Iran)

Abstract The archaeological site of the Anahita Temple in Kangavar is one of the best-known stone monuments in western Iran and dates back to the Persian Empire period of Iranian history (about 550 BC to 650 AD). The Temple was constructed on a semi-artificial hill out of large stone blocks, many of which are cubic in shape, and short thick columns. In the Kangavar area (basin) there are various sites in which there is evidence of stone extraction. The aim of this paper is to characterize the stones used in the Anahita Temple of Kangavar and their correlations with the quarrying activities observed in the Kangavar regions. In fact, we tried to find out which of these quarries may have been the source of the stone blocks used in the construction of the Anahita Temple. To this end the stones used in the Temple have been characterized by conducting a multi-analytical study involving chemical, mineralogical and textural analyses including XRF, XRD and thin section petrography. The results show that although all the quarries could have provided the stone used in the construction of the Temple, the largest, most probable source was the Chel Maran quarry. It is due to the more similarity of stones used in the Anahita Temple and the samples from this quarry from geochemical point of view as well as the archaeological evidences present in this quarry. The results are also useful for conservators to select appropriate materials for reconstruction of the decayed parts of the Anahita Temple.
Computational Efficient Authenticated Digital Content Distribution Frameworks for DRM Systems: Review and Outlook

Advancement in digital technologies presents a user-friendly environment for the digital content distribution. However, it makes digital content prone to piracy issues. Digital rights management (DRM) systems aim to ensure the authorized content usage. As the digital content broadcasts through the public network, a secure and authorized content access mechanism is required. As digital media users are increasing, data centers draw vast amounts of power to manage consumer demands for instant access. Thus, design of efficient protocols is required for digital industry. Several authenticated content key distribution frameworks have been introduced for DRM systems to support efficient computing and mutual authentication. The main focus of recent devolvement is security and energy consumption by increasing communication and computing efficiency. Most of the existing DRM models efficiently address the consumer concerns and make the system user-friendly and computationally efficient. Thus, it is essential to analyze the security, efficiency, and related issues in the existing framework before adopting them for a DRM system. This article presents a study on the existing authenticated content key distribution frameworks of DRM systems. It proves the vulnerabilities of the existing structures against known attacks and challenges in their implementation. It also discusses the desirable attributes of DRM systems and provides possible future direction.
Multi-Source Data Stream Online Frequent Episode Mining

Online frequent episode mining is more complicated than the traditional static frequent episode mining due to the continuous, unbounded and time-varying data stream.Especially in the multiple data streams, online frequent episode mining is more difficult than the single-source stream, due to the concurrency, global clock loss, and uncertainty of delay caused by the distributed environment.To cope with these problems, we propose a new algorithm.Firstly, the data stream with ''happen-before'' relationship among multiple sources is combined on the global data lattice.Next, the traversal on global data lattice generates effective parallel and serial candidate data streams, which guarantee the accuracy of subsequent mining and reduce the number of global sequences during searching process.Then, we use the frequent episode tree to detect the expanding online serial episodes and parallel episodes.Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments.
Anomaly detection with inexact labels

We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measure the performance with inexact anomaly labels, we define the inexact AUC, which is our extension of the area under the ROC curve (AUC) for inexact labels. The proposed method trains an anomaly score function so that the smooth approximation of the inexact AUC increases while anomaly scores for non-anomalous instances become low. We model the anomaly score function by a neural network-based unsupervised anomaly detection method, e.g., autoencoders. The proposed method performs well even when only a small number of inexact labels are available by incorporating an unsupervised anomaly detection mechanism with inexact AUC maximization. Using various datasets, we experimentally demonstrate that our proposed method improves the anomaly detection performance with inexact anomaly labels, and outperforms existing unsupervised and supervised anomaly detection and multiple instance learning methods.
A Secure IoT Data Integrity Auditing Scheme Based on Consortium Blockchain

With the development of IoT, a large amount of valuable data is stored in remote cloud services by enterprises or individuals, which raises concerns about the security of data. In order to ensure the integrity of the data in the cloud, scholars have proposed various auditing scheme, among which Third Party Auditor (TPA) is a distinctive feature of a class of scheme. But the security of a centralized TPA is difficult to ensure, and blockchain technology makes up for this shortcoming. In order to solve this, we propose a secure auditing scheme based on the consortium blockchain. Our scheme owns following feature: anonymity, fairness, accountability, security. The experiment results show that our scheme is efficient.
Detecting Visual Objects by Edge Crawling

Abstract Content-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.
OntoPLC: Semantic Model of PLC Programs for Code Exchange and Software Reuse

Regarding programmable logic controller (PLC) development, improving programming efficiency and encouraging code exchange and software reuse are necessary to increase the productivity and safety of smart manufacturing and industrial automation. However, differences between implementations are significant even though all manufacturers claim to conform to the IEC 61131-3 and IEC 61131-10 standards, resulting in incompatibilities inside heterogeneous systems, preventing projects from interoperating between vendors. In this article, we present an approach that utilizes an ontology-based semantic model named OntoPLC to enable automatic porting of PLC projects between development environments and also prevent significant information loss during the translation process. High-level semantics of PLC projects are added into OntoPLC including software resources, which can be required for software reuse leveraging semantic query. To demonstrate the usefulness of the ontology model, the proposed methodology is applied to a turbine-driven boiler feed pump control module.
"I simply watched where she was looking at"

Mixed reality (MR) cooperation scenarios are more and more interesting for business and research as powerful wearable devices like head mounted displays (HMD) become commercially available. A lot of work focuses on remote MR cooperation settings like remote maintenance support and co-located scenarios in which participants cooperate over a longer period time. Despite this, MR also has great potential for real-time co-located cooperation support with the need of short-term decisions and interactions. However, little is known on how this support can be provided. To bridge this gap, we conducted an experiment using a MR visual search task performed by dyads. Based on related work, visual search was chosen to represent typical challenges of short-term cooperative MR tasks. The aim of the experiment was to explore how the participants coordinate their searches and how this influences their performance in a task. We found that participants mainly used embodied and verbal cues to coordinate their searches (rather than virtual cues provided by the HMD) and that less communication worked significantly better, which is in (partial) contrast to existing findings. We discuss potential reasons for and impacts of these findings.