Reviewerly/exHarmony huggingface dataset:
Initial Coin Offerings (ICOs): Risks, Regulation, and Accountability

The emergence of the cryptocurrency as an investment vehicle has brought the phenomenon of Initial Coin Offerings (ICOs) into the spotlight, since they provide rapid access to capital for new ventures, but suffer from drawbacks relating to regulation and accountability. In that regard, this chapter provides a review of the recent literature on ICOs before proceeding with a discussion of the regulatory and other risks that ICOs pose for market participants, thereby encouraging a broader discussion about where such a novel capital-raising mechanism may lie in the investment universe, and how the weaknesses of ICOs may be addressed so as to better leverage its strengths towards value creation and innovation.
Initialization of active contours for segmentation of breast cancer via fusion of ultrasound, Doppler, and elasticity images

Active contours (snakes) are an efficient method for segmentation of ultrasound (US) images of breast cancer. However, the method produces inaccurate results if the seeds are initialized improperly (far from the true boundaries and close to the false boundaries). Therefore, we propose a novel initialization method based on the fusion of a conventional US image with elasticity and Doppler images. The proposed fusion method (FM) has been tested against four state-of-the-art initialization methods on 90 ultrasound images from a database collected by the Thammasat University Hospital of Thailand. The ground truth was hand-drawn by three leading radiologists of the hospital. The reference methods are: center of divergence (CoD), force field segmentation (FFS), Poisson Inverse Gradient Vector Flow (PIG), and quasi-automated initialization (QAI). A variety of numerical tests proves the advantages of the FM. For the raw US images, the percentage of correctly initialized contours is: FM-94.2%, CoD-0%, FFS-0%, PIG-26.7%, QAI-42.2%. The percentage of correctly segmented tumors is FM-84.4%, CoD-0%, FFS-0%, PIG-16.67%, QAI-22.44%. For reduced field of view US images, the percentage of correctly initialized contours is: FM-94.2%, CoD-0%, FFS-0%, PIG-65.6%, QAI-67.8%. The correctly segmented tumors are FM-88.9%, CoD-0%, FFS-0%, PIG-48.9%, QAI-44.5%. The accuracy, in terms of the average Hausdorff distance, is respectively 2.29 pixels, 33.81, 34.71, 7.7, and 8.4, whereas in terms of the Jaccard index, it is 0.9, 0.18, 0.19, 0.63, and 0.48.
Student perceptions of marker personality and intelligence: The effect of emoticons in online assignment feedback

Online marking is playing a growing role in universities, affording a new range of communicative opportunities through paralinguistic textual cues (such as emoticons). However, little is known of how the impression of a marker's personality might be formed using this extra-linguistic information provided in assignment feedback. In this study (N = 210 university students), the inclusion of emoticons in feedback on a faux assignment was manipulated between groups. For both groups, the assignment and the marker's feedback were identical, but for one group, three emoticons were embedded in feedback, as well as in the overall comments (in total: 4 × ☺; 2 × :/). Participants then responded to questions about the marker's personality and intelligence. When emoticons were used, the marker was perceived as being significantly more extraverted, agreeable, and open to experience, suggesting that only minimal cues are necessary to manipulate perceptions of an educator's personality online.
Aggregation of Rich Depth-Aware Features in a Modified Stacked Generalization Model for Single Image Depth Estimation

Estimating scene depth from a single monocular image is a crucial component in computer vision tasks, enabling many further applications such as robot vision, 3-D modeling, and above all, 2-D to 3-D image/video conversion. Since there are an infinite number of possible world scenes, that can produce a unique image, single image depth estimation is a highly challenging task. This paper tackles such an ambiguous problem by using the merits of both global and local information (structures) of a scene. To this end, we formulate single image depth estimation as a regression problem via (on) rich depth related features which describe effective monocular cues. Exploiting the relationship between these image features and depth values is adopted via a learning model which is inspired by modified stacked generalization scheme. The experiments demonstrate competitive results compared with existing data-driven approaches in both quantitative and qualitative analysis with a remarkably simpler approach than previous works.
Classification of masses in digital mammograms using Biogeography-based optimization technique

Breast cancer is a leading cause of death in women in both developed and developing countries. Design and development of computer-based systems can assist radiologists in the effective treatment of breast cancer. For the design of an efficient classification system, efficient feature selection techniques must be used to reduce complexity of feature space in digital mammogram classification. The proposed methodology aims to explore use of Biogeography-based optimization to select a subset of features. Adaptive neuro-fuzzy inference system and artificial neural network are employed to evaluate fitness of the selected features. The features selected are used to train and test adaptive neuro-fuzzy inference system and artificial neural network classifiers. The experiment employed over 651 mammograms. The classification results shows that Biogeography-based optimization with adaptive neuro-fuzzy inference system is superior to Biogeography-based optimization with artificial neural network. Adaptive neuro-fuzzy inference system classifier achieve an accuracy of 98.92% with sensitivity of 99.10%, specificity of 98.72% and area under curve AZ = 0.999 ± 0.000. Outcomes achieved with the proposed Biogeography-based optimization with adaptive neuro-fuzzy inference system are far better as compared to some recent work.
EPICS: A Framework for Enforcing Security Policies in Composite Web Services

With advances in cloud computing and the emergence of service marketplaces, the popularity of composite services marks a paradigm shift from single-domain monolithic systems to cross-domain distributed services, which raises important privacy and security concerns.Access control becomes a challenge in such systems because authentication, authorization and data disclosure may take place across endpoints that are not known to clients.The clients lack options for specifying policies to control the sharing of their data and have to rely on service providers which provide limited selection of security and privacy preferences.This lack of awareness and loss of control over data sharing increases threats to a client's data and diminishes trust in these systems.
Review on VLSI design using optimization and self-adaptive particle swarm optimization

Today, VLSI technology has taken a fundamental role in developing most of the high-tech electronic circuits. Even though VLSI design is renowned for its smaller size, lower cost, lower power, high reliability and high functionality, the design process takes long time and produces high risk. So, to obtain the knowledge regarding the different contributions on VLSI design, about 52 papers on the design of VLSI using optimization are reviewed here. Accordingly, VLSI design optimization is analyzed through different bio-inspired algorithms and the performance measures of different VLSI experimentations are compared. Further, various improvements on Self-Adaptive Particle Swarm Optimization (SA-PSO) and VLSI design optimization, without the adoption of bio-inspired algorithms, are examined. Additionally, the floor planning problem of VLSI is considered and reviewed. Eventually, this paper provides the diverse research gaps and challenges in VLSI design, which may be helpful for the authors and the philosophers to contribute for further research.
Biometric surveillance using visual question answering

Surveillance of individuals using visual data requires human-level capabilities for understanding the characteristics that differentiate one person from another. However, because the influx of both video and imagery is increasing at a greater rate than humans can cope with, biometric-based surveillance systems are required to assist with the triage of information based on human-generated queries. Unfortunately, current systems are not robust enough to tackle new tasks, as they involve specialized models that do not leverage existing, pre-trained components. To mitigate these issues, we propose a novel system for biometric-based surveillance that utilizes models that are relevance-aware to triage images and videos based on interaction with single or multiple users. As the system is initially focused on detection of people via their appearance and clothing, we have named the system Context and Collaborative (C2) Visual Question Answering (VQA) for Biometric Object-Attribute Relevance and Surveillance (C2VQA-BOARS). To validate the usefulness of C2VQA-BOARS in real-world scenarios, we provide an implementation of two novel components (Relevance and Triage) and apply them in tasks against two datasets created for biometric surveillance. Our results outperform baseline approaches, proving that a system with a minimal amount of fine-tuned components can robustly handle new datasets and problems as needed.
Enhancing Binocular Depth Estimation Based on Proactive Perception and Action Cyclic Learning for an Autonomous Developmental Robot

In humans, perception and action (PA) possess cyclically causal relations. In this paper, we propose a new PA-based cyclic learning framework to autonomously enhance the depth-estimation accuracy of a humanoid robot and perform given behavioral tasks. The proposed method integrates the concepts of sensory invariance-driven action and object-size invariance to autonomously enhance the depth-estimation accuracy. If the depth estimation is reliable, the reinforcement learning framework is used to generate goal-directed actions of a humanoid robot based on a perceived environment. Iterative PA cycles of a robot autonomously refine its depth-estimation. The proposed method is evaluated using a humanoid robot (NAO) with stereo cameras, and the experimental results demonstrate that the proposed framework is effective for autonomously enhancing both the depth-estimation accuracy and the action-generation performance.
Worst-Case Hardness for LPN and Cryptographic Hashing via Code Smoothing

We present a worst case decoding problem whose hardness reduces to that of solving the Learning Parity with Noise (LPN) problem, in some parameter regime. Prior to this work, no worst case hardness result was known for LPN (as opposed to syntactically similar problems such as Learning with Errors). The caveat is that this worst case problem is only mildly hard and in particular admits a quasi-polynomial time algorithm, whereas the LPN variant used in the reduction requires extremely high noise rate of $$1/2-1/\mathrm{poly}(n)$$ . Thus we can only show that "very hard" LPN is harder than some "very mildly hard" worst case problem. We note that LPN with noise $$1/2-1/\mathrm{poly}(n)$$ already implies symmetric cryptography. Specifically, we consider the (n, m, w)-nearest codeword problem ((n, m, w)-NCP) which takes as input a generating matrix for a binary linear code in m dimensions and rank n, and a target vector which is very close to the code (Hamming distance at most w), and asks to find the codeword nearest to the target vector. We show that for balanced (unbiased) codes and for relative error $$w/m \approx {\log ^2 n}/{n}$$ , (n, m, w)-NCP can be solved given oracle access to an LPN distinguisher with noise ratio $$1/2-1/\mathrm{poly}(n)$$ . Our proof relies on a smoothing lemma for codes which we show to have further implications: We show that (n, m, w)-NCP with the aforementioned parameters lies in the complexity class $$\mathrm {{Search}\hbox {-}\mathcal {BPP}}^\mathcal {SZK}$$ (i.e. reducible to a problem that has a statistical zero knowledge protocol) implying that it is unlikely to be $$\mathcal {NP}$$ -hard. We then show that the hardness of LPN with very low noise rate $$\log ^2(n)/n$$ implies the existence of collision resistant hash functions (our aforementioned result implies that in this parameter regime LPN is also in $$\mathcal {BPP}^\mathcal {SZK}$$ ).
Super-Fine Attributes with Crowd Prototyping

Recognising human attributes from surveillance footage is widely studied for attribute-based re-identification. However, most works assume coarse, expertly-defined categories, ineffective in describing challenging images. Such brittle representations are limited in descriminitive power and hamper the efficacy of learnt estimators. We aim to discover more relevant and precise subject descriptions, improving image retrieval and closing the semantic gap. Inspired by fine-grained and relative attributes, we introduce super-fine attributes, which now describe multiple, integral concepts of a single trait as multi-dimensional perceptual coordinates. Crowd prototyping facilitates efficient crowdsourcing of super-fine labels by pre-discovering salient perceptual concepts for prototype matching. We re-annotate gender, age and ethnicity traits from PETA, a highly diverse (19K instances, 8.7K identities) amalgamation of 10 re-id datasets including VIPER, CUHK and TownCentre. Employing joint attribute regression with the ResNet-152 CNN, we demonstrate substantially improved ranked retrieval performance with super-fine attributes in comparison to conventional binary labels, reporting up to a 11.2 and 14.8 percent mAP improvement for gender and age, further surpassed by ethnicity. We also find our 3 super-fine traits to outperform 35 binary attributes by 6.5 percent mAP for subject retrieval in a challenging zero-shot identification scenario.
Using GitHub in large software engineering classes. An exploratory case study

Background and Context GitHub has been recently used in Software Engineering (SE) classes to facilitate collaboration in student team projects as well as help teachers to evaluate the contributions of their students more objectively.Objective We explore the benefits and drawbacks of using GitHub as a means for team collaboration and performance evaluation in large SE classes.Method Our research method takes the form of a case study conducted in a senior level SE class with 91 students. Our study also includes entry and exit surveys, an exit interview, and a qualitative analysis of students' commit behavior.Findings Different teams adapt GitHub to their workflow differently. Furthermore, despite the steep learning curve, using GitHub should not affect the quality of students' submissions. However, using GitHub metrics as a proxy for evaluating team performance can be risky.Implications We provide several recommendations for integrating Web-based configuration management tools in SE classes.
Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design

The intelligence of Smart Cities (SC) is represented by its ability in collecting, managing, integrating, analyzing, and mining multi-source data for valuable insights. In order to harness multi-source data for an informed place design, this paper presents “Public Sentiments and Activities in Places” multi-source data analysis flow (PSAP) in an Informed Design Platform (IDP). In terms of key contributions, PSAP implements 1) an Interconnected Data Model (IDM) to manage multi-source data independently and integrally, 2) an efficient and effective data mining mechanism based on multi-dimension and multi-measure queries (MMQs), and 3) concurrent data processing cascades with Sentiments in Places Analysis Mechanism (SPAM) and Activities in Places Analysis Mechanism (APAM), to fuse social network data with other data on public sentiment and activity comprehensively. As proved by a holistic evaluation, both SPAM and APAM outperform compared methods. Specifically, SPAM improves its classification accuracy gradually and significantly from 72.37 to about 85 percent within nine crowd-calibration cycles, and APAM with an ensemble classifier achieves the highest precision of 92.13 percent, which is approximately 13 percent higher than the second best method. Finally, by applying MMQs on “Sentiment&Activity Linked Data”, various place design insights of our testbed are mined to improve its livability.
On the Strength of Privacy Metrics for Vehicular Communication

Vehicular communication plays a key role in near-future automotive transport, promising features such as increased traffic safety and wireless software updates. However, vehicular communication can expose drivers' locations and thus poses privacy risks. Many schemes have been proposed to protect privacy in vehicular communication, and their effectiveness is usually evaluated with privacy metrics. However, to the best of our knowledge, (1) different privacy metrics have never been compared to each other, and (2) it is unknown how strong the metrics are. In this paper, we evaluate and compare the strength of 41 privacy metrics in terms of four novel criteria: Privacy metrics should be monotonic, i.e., indicate decreasing privacy for increasing adversary strength; their values should be spread evenly over a large value range to support within-scenario comparability; and they should share a large portion of their value range between traffic conditions to support between-scenario comparability. We evaluate all four criteria on real and synthetic traffic with state-of-the-art adversary models and create a ranking of privacy metrics. Our results indicate that no single metric dominates across all criteria and traffic conditions. We therefore recommend to use metrics suites, i.e., combinations of privacy metrics, when evaluating new privacy-enhancing technologies.
Avoiding Critical Members in a Team by Redundant Assignment

It is an important topic to organize a team efficiently and keep it in a good state. In most cases, administrators try to avoid critical members. With this requirement, administrators prefer that their team members are able to be good at many things and expert in one (GMEO). However, that too many people are “good at many things” is definitely a waste. Role-based collaboration and its environments-classes, agents, roles, groups, and objects (E-CARGO) model are a good means to provide modeling and solutions to such a challenging problem. This paper formalizes the problem of GMEO into GMEO-1 (the fundamental form of GMEO) with the support of E-CARGO, clarifies two different forms of GMEO-1 and provides two highly practical solutions. The proposed solutions are verified by comparing with initial solutions using a linear programming solver, i.e., the IBM ILOG CPLEX Optimization Platform. The contributions of this paper are a thorough investigation of GMEO-1 that has no exact solutions even for a small group (e.g., ten people) and is normally managed by highly qualified administrators. The proposed solutions provide digital results with algorithms that form a solid foundation for decision making in dealing with similar issues.
Enhancing Performance and Energy Efficiency for Hybrid Workloads in Virtualized Cloud Environment

Virtualization has attained mainstream status in enterprise IT industry. Despite its widespread adoption, it is known that virtualization also introduces non-trivial overhead when tasks are executed on a virtual machine (VM). In particular, a combined effect from device virtualization overhead and CPU scheduling latency can cause performance degradation when computation intensive tasks and I/O intensive tasks are co-located on a VM. Such an interference also causes extra energy consumption. In this paper, we present Hylics, a novel solution that enables efficient data traverse paths for both I/O and computation intensive workloads. This is achieved with the provision of in-memory file system and network service at the hypervisor level. Several important design issues are pinpointed and addressed during our prototype implementation, including efficient intermediate data sharing, network service offloading, and QoS-aware memory usage management. Based on our real-world deployment on KVM, we show that Hylics can significantly improve computation and I/O performance for hybrid workloads. Moreover, this design also alleviates the existing virtualization overhead and naturally optimizes the overall energy efficiency.
An Extended Energy-Aware Cost Recovery Approach for Virtual Machine Migration

Energy supply problems, fuel costs, and global warming all suggest the continued need to explore possible savings through efficient scheduling and consolidation techniques. However, consolidation involves migrations that cost additional energy, so it can be more energy efficient not to consolidate. In several experiments, we found that efficient resource allocation methods can be more energy efficient than migration-based techniques, where it is possible to avoid inefficient migrations, i.e., those that can never recover the energy required for migration. In this paper, we elaborate our previous results and demonstrate through extensive experiments, using the Google workload traces for 12 583 hosts and 20 071 200 tasks, how different virtual machine (VM) allocation heuristics, combined with different migration techniques, will impact on energy efficiency. We suggest, using reasonable assumptions for datacenter setup, that a combination of energy-aware Fill-Up VM allocation and migration, and migration only for relatively long-running VMs, provides optimal energy efficiency. Furthermore, switching <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> the idle hosts provides more energy efficiency, however, at a nonnegligible cost in terms of scheduling delay.
The Externalities of Exploration and How Data Diversity Helps Exploitation

chapter Share on The Externalities of Exploration and How Data Diversity Helps Exploitation Author: Manish Raghavan Massachusetts Institute of Technology, Sloan School of Management and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology, Sloan School of Management and Department of Electrical Engineering and Computer ScienceView Profile Authors Info & Claims The Societal Impacts of Algorithmic Decision-MakingSeptember 2023https://doi.org/10.1145/3603195.3603208Published:08 September 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Petri net-based methods for analyzing structural security in e-commerce business processes

The rapid development of e-commerce worldwide, means more e-commerce business processes adopting the structure of multiple participants; these include shopper clients, merchant and third-party payment platforms (TPPs), banks, and so on. It is a distributed and complex system, where communications among these participants rely on the web services and Application Programming Interfaces (APIs) such as Cashier-as-a-Service or CaaS. This introduces new security challenges due to complex interactions among multiple participants, and any design flaws in procedure structures may result in serious security issues. We study the structural security issues based on Petri nets, and a framework for analyzing structural security in e-commerce business process is proposed. Petri net-based modeling and analysis methods are also provided. Given the specifications of e-commerce business processes, the proposed methods can help designers analyze structural security issues of an e-commerce business process.