Reviewerly/exHarmony huggingface dataset:
Using mental models to quantify linear and non-linear relationships in complex fishery systems

Reducing uncertain outcomes of fishery management actions requires an understanding of how fishers will be affected by policy actions. This requires integrating the local expertise of commercial fishers and seafood processors into management planning to predict how regulatory changes could impact social-ecological systems. Participatory modeling between managers, scientists, and industry can be useful in integrating different perspectives and gaining a more comprehensive understanding of how different scenarios can cause direct and indirect impacts throughout fishery systems. We used group Fuzzy Cognitive Mapping exercises to describe the structure and dynamics of two economically important commercial fishery systems in Alaska, USA: the Bering Sea Pollock Catcher/Processor and Amendment 80 Groundfish fishery. Several components were identified by the fleets that had a relatively high number of links to other components in each system, including chum salmon catch for the pollock fleet and the deck sorting of halibut for the groundfish fleet. After identifying other critical relationships between regulatory, environmental, and operational changes, we developed a survey technique and regression analytical approach to define the shape of these relationships. Our results show both linear and non-linear relationships between bycatch regulations and fishing operations; thus, providing evidence counter to the traditional assumption that the relationships between policy and fishing operations are always linear and constant across variable levels of change. Our novel approach permits for more specific explorations of curvilinear patterns, social tipping points, and asymptotic limits to change, thereby allowing for improved assessments of the effects of regulatory and environmental change on fishing communities.
Performance Analysis and Auto-tuning for SPARK in-memory analytics

Recently the Apache Spark in-memory computing framework has gained a lot of attention, due to its increased performance on large-scale data processing. Although Spark is highly configurable, its manually tuning is time consuming, due to the high-dimensional configuration space. Prior research has emerged frameworks able to analyze and model the performance of Spark applications, however they either rely on empirical selection of important parameters or/and follow a pure application-specific modeling approach. In this paper, we propose an end-to-end performance auto-tuning framework for Spark in-memory analytics. By adopting statistical hypothesis testing techniques, we manage to extract the higher order effects among differing parameters and their significance in performance optimization. In addition, we propose a new systematic meta-model driven approach utilizing cluster-, rather than application-wise performance modeling for traversing the configuration search space. We evaluate our approach using real scale analytic benchmarks from HiBench suite and show that the proposed framework achieves an average performance gain of × 3.07 for known and × 2.01 for unknown applications, compared to the default configuration.
PoliCT: Flexible Policy in Certificate Transparency Enabling Lightweight Self-monitor

Certificate Transparency (CT) is proposed to detect maliciously or mistakenly issued certificates by recording all certificates in publicly-visible logs. CT assumes that any individual can undertake the role of a CT monitor which fetches all the certificates in the logs and discovers suspicious ones from them. However, studies in recent years shows that ordinary individuals have to pay an unbearable price to operate a monitor by themselves, which makes the originally distributed trust be concentrated on several third-party monitors. Unfortunately, some researches indicate that problems of timeliness, security, and reliability exist in third-party monitors. In this paper, we propose the PoliCT, a flexible and customizable certificate transparency management solution where domain owners can designate how their certificates should be submitted and validated. It enables domain owners (a) to release their CT policies to monitor a few logs purposefully, thereby greatly reducing monitoring costs; (b) to demand more SCTs to increase the transparency of their certificates. After that, we discuss the design of a reliable lightweight self-monitor in detail. Expectably, the actual data collection and the theoretical analysis of the prototype system show that PoliCT enables a common individual to maintain its CT policies with negligible overhead, and significantly improves the performance of monitoring service.
Nonlinear photonic dynamical systems for unconventional computing

Driven by the remarkable breakthroughs during the past decade, photonics neural networks have experienced a revival. Here, we provide a general overview of progress over the past decade, and sketch a roadmap of important future developments. We focus on photonic implementations of the reservoir computing machine learning paradigm, which offers a conceptually simple approach that is amenable to hardware implementations. In particular, we provide an overview of photonic reservoir computing implemented via either spatio temporal or delay dynamical systems. Going beyond reservoir computing, we discuss recent advances and future challenges of photonic implementations of deep neural networks, of the quest for learning methods that are hardware-friendly as well as realizing autonomous photonic neural networks, i.e. with minimal digital electronic auxiliary hardware.
Comparison of tow two cryptocurrencies: Bitcoin and Litecoin

Abstract The increasing daily use of virtual currency (cryptocurrency) is being adopted worldwide for many legal and illegal transactions. Cryptocurrency technology operates on a network that allows people to make payments around the world without any middleman. Since the technology was first developed, it became popular, and the price of cryptocurrencies started to rise and became unstable. In terms of the returns gained from investing in cryptocurrencies, they have been huge in recent times, but there has always been a question about their existence and reliability. A cryptocurrency is a digital process that relies on the use of an encryption system for security primarily. Despite the challenges and problems facing cryptocurrencies, the success of Bitcoin has led several companies to search for alternative digital currencies. This paper tries to compare two cryptocurrencies - Bitcoin (BTC) and Litecoin (LTC) with respect to their stability and understanding its trends in the recent period.
Influence of the Software Development Project Context on the Requirements Elicitation Techniques Selection

Elicitation (requirements gathering) is a core part of software requirements engineering activities that helps to gather and extract information regarding the current or desired state of enterprise/particular process or product from stakeholders or other sources. Elicited information is used as an input for requirement analysis, design definition, development, and quality assurance activities in IT projects. Different elicitation techniques may be used separately or in conjunction with other techniques to accomplish the elicitation. Best-suited technique choice influences the project management plan, namely its part with the business analysis activities plan and business analysis approach. The goal of the paper is to analyze the influence of project context on requirement elicitation techniques selection, define factors influencing technique selection based on the Machine Learning model, and predict usage of a particular elicitation technique depending on the project attributes and business analyst’s background.
Quantum Fair Machine Learning

In this paper, we inaugurate the field of quantum fair machine learning. We undertake a comparative analysis of differences and similarities between classical and quantum fair machine learning algorithms, specifying how the unique features of quantum computation alter measures, metrics and remediation strategies when quantum algorithms are subject to fairness constraints. We present the first results in quantum fair machine learning by demonstrating the use of Grover's search algorithm to satisfy statistical parity constraints imposed on quantum algorithms. We provide lower-bounds on iterations needed to achieve such statistical parity within ε-tolerance. We extend canonical Lipschitz-conditioned individual fairness criteria to the quantum setting using quantum metrics. We examine the consequences for typical measures of fairness in machine learning context when quantum information processing and quantum data are involved. Finally, we propose open questions and research programmes for this new field of interest to researchers in computer science, ethics and quantum computation.
Efficient Multi-Encoding Algorithms for HTTP Adaptive Bitrate Streaming

Since video accounts for the majority of today's internet traffic, the popularity of HTTP Adaptive Streaming (HAS) is increasing steadily. In HAS, each video is encoded at multiple bitrates and spatial resolutions (i.e., representations) to adapt to a heterogeneity of network conditions, device characteristics, and end-user preferences. Most of the streaming services utilize cloud-based encoding techniques which enable a fully parallel encoding process to speed up the encoding and consequently to reduce the overall time complexity. State-of-the-art approaches further improve the encoding process by utilizing encoder analysis information from already encoded representation(s) to improve the encoding time complexity of the remaining representations. In this paper, we investigate various multi-encoding algorithms (i.e., multi-rate and multi-resolution) and propose novel multi-encoding algorithms for large-scale HTTP Adaptive Streaming deployments. Experimental results demonstrate that the proposed multi-encoding algorithm optimized for the highest compression efficiency reduces the overall encoding time by 39% with a 1.5% bitrate increase compared to stand-alone encodings. Its optimized version for the highest time savings reduces the overall encoding time by 50% with a 2.6% bitrate increase compared to standalone encodings.
Haze Removal Based on Refined Transmission Map for Aerial Image Matching

A novel strategy is proposed to address block artifacts in a conventional dark channel prior (DCP). The DCP was used to estimate the transmission map based on patch-based processing, which also results in image blurring. To enhance a degraded image, the proposed single-image dehazing technique restores a blurred image with a refined DCP based on a hidden Markov random field. Therefore, the proposed algorithm estimates a refined transmission map that can reduce the block artifacts and improve the image clarity without explicit guided filters. Experiments were performed on the remote-sensing images. The results confirm that the proposed algorithm is superior to the conventional approaches to image haze removal. Moreover, the proposed algorithm is suitable for image matching based on local feature extraction.
Exploiting Secrets by Leveraging Dynamic Cache Partitioning of Last Level Cache

Dynamic cache partitioning for shared Last Level Caches (LLC) is deployed in most modern multicore systems to achieve process isolation and fairness among the applications and avoid security threats. Since LLC has visibility of all cache blocks requested by several applications running on a multicore system, a malicious application can potentially threaten the system that can leverage the dynamic partitioning schemes applied to the LLCs by creating a timing-based covert channel attack. We call it as Cache Partitioned Covert Channel (CPCC) attack. The malicious applications may contain a trojan and a spy and use the underlying shared memory to create the attack. Through this attack, secret pieces of information like encryption keys or any secret information can be transmitted between the intended parties. We have observed that CPCC can target single or multiple cache sets to achieve a higher transmission rate with a maximum error rate of 5% only. The paper also addresses a few defense strategies that can avoid such cache partitioning based covert channel attacks.
A Context-aware Black-box Adversarial Attack for Deep Driving Maneuver Classification Models

In a connected autonomous vehicle (CAV) scenario, each vehicle utilizes an onboard deep neural network (DNN) model to understand its received time-series driving signals (e.g., speed, brake status) from its nearby vehicles, and then takes necessary actions to increase traffic safety and roadway efficiency. In the scenario, it is plausible that an attacker may launch an adversarial attack, in which the attacker adds unnoticeable perturbation to the actual driving signals to fool the DNN model inside a victim vehicle to output a misclassified class to cause traffic congestion and/or accidents. Such an attack must be generated in near real-time and the adversarial maneuver must be consistent with the current traffic context. However, previously proposed adversarial attacks fail to meet these requirements. To handle these challenges, in this paper, we propose a Context- aware Black-box Adversarial Attack (CBAA) for time-series DNN models in CAV scenarios. By analyzing real driving datasets, we observe that specific driving signals at certain time points have a higher impact on the DNN output. These influential spatio-temporal factors differ in different traffic contexts (a combination of different traffic factors (e.g., congestion, slope, and curvature)). Thus, CBAA first generates the perturbation only on the influential spatio-temporal signals for each context offline. In generating an attack online, CBAA uses the offline perturbation for the current context to start searching the minimum perturbation using the zeroth-order gradient descent method that will lead to the misclassification. Limiting the spatio-temporal searching scope with the constraint of context greatly expedites finding the final perturbation. Our extensive experimental studies using two different real driving datasets show that CBAA requires 43% fewer queries (to the DNN model to verify the attack success) and 53% less time than existing adversarial attacks.
Built-in Encrypted Health Cloud Environment for Sharing COVID-19 Data

During the COVID-19 pandemic, the increasing amount of personal and sensitive data collected in the healthcare inistiutions, make it necessary to use the cloud not only to store the data, but also to process it in the cloud. Obviously, most people would like to get a secure information related to their health. Therefore, with the recent growth of cloud computing privacy and data protection requirements have been evolving toto protect individuals from surveillance and data leakage. However, due to security concerns about frequent data breaches and recently upgraded legal data protection requirements (such as the EU's General Data Protection Regulation), it is recommended not to outsourcing unprotected sensitive data to public clouds. Cloud computing offers on-demand services including platforms, infrastructure and software that have been developed. To ensure confidentiality, the encryption algorithm is the most commonly used technique. However, Using a single algorithm is not effective for the high-level security of data in cloud computing. To fix this issue, we have introduced a new security mechanism using a built-in encrypted cloud storage system, which makes the proposed system strong against vulnerabilities. Various symmetric and asymmetric encryption techniques have been used to protect data from external threats. These techniques can help restrict the access to the stored data in order to be available for authorized users only. Combining Advanced Encryption Standard (AES) and Cryptographic Curve Cryptography (ECC), the sensitive files are stored in client side and uploaded to the server. The results show that the impact of this built-in approach is more important than the other secure algorithms. In addition, it also showed that in the built-in algorithm, adding additional layers of security did not cause an increase in the download and upload time from and to the cloud.
Augmented Reality Passenger Information on Mobile Public Displays – an Iterative Evaluation Approach

The use of augmented reality is becoming more and more commonplace. Whether via smartphone apps, on television or while driving a car, almost everyone has already encountered this technology more or less knowingly. In this paper, we investigate to what extent Augmented Reality (AR) can be used profitably in public transport, especially on semi-transparent display screens. These semi-transparent and interactive public displays are built-in as windows in public transport vehicles. In our research project SmartMMI these mobile public displays are called “SmartWindows”. We implemented an iterative evaluation concept containing of four phases to assess different types of AR content to be displayed on such a SmartWindow as well as the special interest of individual types of users. Based on the results of two conducted online studies, specific user scenarios were created and evaluated in a lab-based user study. Finally, we designed an online survey to determine which AR concepts are most accepted by users. In this video-based survey, we evaluated the running speed of AR object inserts, the colors, the fade in’s and the types of information displayed on a semi-transparent display. In this paper, we introduce all four stages of our iterative evaluation process, and focus further on study methodology, set-up and the results of the video-based online survey.
An Identity-Based Framework for Generalizable Hate Speech Detection

This paper explores the viability of leveraging an identity-based framework for generalizable hate speech detection. Across a corpus of seven benchmark datasets, we find that hate speech consistently features higher levels of abusive and identity terms, robust to social media platforms of origin and multiple languages. Using only lexical counts of abusives, identities, and other psycholinguistic features, heuristic and machine learning models achieve high precision and weighted F1 scores in hate speech prediction, with performance on a three-language dataset comparable to recent state-of-the-art multilingual models. Cross-dataset predictions further reveal that our proposed identity-based models map hate and non-hate categories with each other in a conceptually coherent fashion across diverse classification schemes. Our findings suggest that conceptualizing hate speech through an identity lens offers a generalizable, interpretable, and socio-theoretically robust framework for computational modelling of online conflict and toxicity.
A Rational Protocol Treatment of 51% Attacks

Game-theoretic analyses of cryptocurrencies and—more generally—blockchain-based decentralized ledgers offer insight on their economic robustness and behavior when even their underpinning cryptographic assumptions fail. In this work we utilize the recently proposed blockchain adaptation of the rational protocol design (RPD) framework [EUROCRYPT '18] to analyze 51% double-spending attacks against Nakamoto-style proof-of-work based cryptocurrencies. We first observe a property of the originally proposed utility class that yields an unnatural conclusion against such attacks, and show how to devise a utility that avoids this pitfall and makes predictions that match the observable behavior—i.e., that renders attacking a dominant strategy in settings where an attack was indeed observed in reality. We then propose a generic remedy to the underlying protocol parameters that provably deter adversaries controlling a majority of the system's resources from attacks on blockchain consistency, including the 51% double-spending attack. This can be used as guidance to patch systems that have suffered such attacks, e.g., Ethereum Classic and Bitcoin Cash, and serves as a demonstration of the power of game-theoretic analyses.
Towards a New Chemistry Learning Platform with Virtual Reality and Haptics

With COVID-19, colleges had to change the way they handle education. Students had to adapt to remote learning instead of the face-to-face learning they are used to. The transition to remote learning has proved to be difficult, but most courses have translated over without any problems. Unfortunately, the same cannot be said for the lab courses, which traditionally have students conduct hands-on experiments. In order to achieve the same level of effectiveness that the labs had prior to COVID-19; a new system is required. The long-term goal of this work is to develop a complete immersive Virtual Reality (VR) chemistry lab. In the VR lab space, the student can handle chemicals and equipment to simulate an actual chemistry lab. Using a VR headset and haptic gloves, the user will be able to freely move and interact with the virtual lab, fellow students, and their teacher. Due to COVID-19 restrictions, instead of the students testing the virtual experience, we presented a 6-min video demonstration of our immersive VR chemistry lab to 109 General Chemistry students. Students filled out pre- and post-questionnaires. 80% of the students either strongly agreed or agreed with the statement, “I think learning general chemistry with VR and haptics will be more engaging than a standard lecture”. For the statement, “I think undergoing the chemistry lab training in a VR environment rather than watching videos will help me learn the basics of chemistry lab better”, 71.8% of the students strongly agreed or agreed with the statement.
A Design of Key Distribution Mechanism in Decentralized Digital Rights Management Based on Blockchain and Zero-Knowledge Proof

Digital Rights Management (DRM) is a basic requirement in the Internet era. In order to solve the disadvantages caused by centralization in traditional DRM, many decentralized DRM (DDRM) structure are proposed, and they have achieved decentralization in copyright registration, copyright trading, etc. However, the distribution of content keys is their bottleneck. Different from public copyright and transaction information, content key which is used for digital content decryption cannot be directly disclosed to decentralized nodes. The existing DDRM relies on specific nodes or an ideal trust environment to issue encrypted content keys, which makes the system unable to be completely decentralized. In this paper, we proposed a decentralized key distribution system, and it can be compatible with the existing DDRM structure. We use zero-knowledge proof technology to prove to others the correctness of the encrypted content key without revealing it. We use blockchain technology to achieve decentralized key distribution, without relying on any trusted center. Our system allows consumers and agents to apply for and distribute content keys without trusting each other. Malicious users cannot commit fraud against others, and damaged nodes will not affect the stability and reliability of the system.