Reviewerly/exHarmony huggingface dataset:
BoPF

Even though batch, interactive, and streaming applications all care about performance, their notions of performance are different. For instance, while the average completion time can suffciently capture the performance of a throughout-sensitive batch-job queue (TQ) [5], interactive sessions and streaming applications form latencysensitive queues (LQ): each LQ is a sequence of small jobs following an ON-OFF pattern. For these jobs [7], individual completion times or latencies are far more important than the average completion time or the throughput of the LQ. Indeed, existing "fair" schedulers are inherently unfair to LQ jobs: when LQ jobs are present (ON state), they must share the resources equally with TQ jobs, but when they are absent (OFF state), batch jobs get all the resources. In the long run, TQs receive more resources than their fair shares because today's schedulers such as Dominant Resource Fairness [4] make instantaneous decisions Clearly, it is impossible to achieve the best response time for LQ jobs under instantaneous fairness. In other words, there is a hard tradeoff between providing instantaneous fairness for TQs and minimizing the response time of LQs. However, instantaneous fairness is not necessary for TQs because average-completion time over a relatively long time horizon is their most important metric. This sheds light on the following question: how well can we simultaneously accommodate multiple classes of workloads with performance guarantees, in particular, isolation protection for TQs in terms of long-term fairness and low response times for LQs? This work serves as our first step in answering the question by designing BoPF: the first multi-resource scheduler that achieves both isolation protection for TQs and response time guarantees for LQs in a strategyproof way. The key idea is "bounded" priority for LQs: as long as the burst is not too large to hurt the long-term fair share of TQs and other LQs, they are given higher priority so jobs can be completed as quickly as possible.
An Adaptive Trust Model for Achieving Emergent Cooperation in Ad Hoc Networks

Cooperation is a fundamental part of both the Next Generation Networks (NGNs) and the expected applications of Industry 4.0. In such systems, there is no centralized control, and the system components require self-organize themselves to capturing, processing and analyzing real-world information with the purpose of delivering useful data to the final user. In this article, we aim to explore the cooperation mechanisms that could be used in the next generation of communication systems to produce collective behaviors that allow the member of the system join efforts to achieve individual and collective goals in environments without a centralized controller. We used socially inspired computing to introduced an adaptive trust model based on a theoretical analysis of cooperation through game theory and genetic algorithms. The results show the cooperation process can adapt itself even in environments with dynamic populations, selfish agents and communication failures.
Multimodal Data Fusion-Moving From Domain-Specific Algorithms to Transdomain Understanding for Accelerated Solution Development

The exploding availability of new data streams now enables insights to be garnered through the integration (fusion) of multiple data sources (modalities); however, currently, it remains difficult to predict <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> which multimodal data fusion (MMDF) methods and architectures will be best suited for a novel application, leading to trial-and-error approaches that are inefficient in both time and cost. Although MMDF strategies are being applied <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> in many different fields (e.g., healthcare, autonomous navigation, robotics, manufacturing, transportation, etc.), results are reported in different journals using different terminology for different target audiences. These applications have diverse needs (assessment, prediction, detection, etc.), resources (number and quality of available data streams), and constraints (social, economic, and physics); however, they share many properties when these lists are evaluated at the metalevel. The authors propose that a shared MMDF framework is urgently needed to allow aggregation of insights across applications, with the end goal of developing an MMDF recommendation tool that overcomes the need for trial-and-error approaches to expedite implementation of new solutions across a wide range of disciplines. To provide context for this Special Issue and provide an overview of key related issues, this editorial presents an abbreviated summary of the current state of MMDF usage from multiple perspectives, based on input from a March 2018 workshop, and finally presents four key research priorities for the field of MMDF.
On the detection of applications in co-resident virtual machines via a memory deduplication side-channel

Nowadays, hosting services of multiple customers on the same hardware via virtualiation techniques is very common. Memory deduplication allows to save physical memory by merging identical memory pages of multiple Virtual Machines (VMs) running on the same host. However, this mechanism can leak information on memory pages to other. In this paper, we propose a timing-based side-channel to identify software versions running in co-resident VMs. The attack tests whether pages that are unique to a specific software version are present in co-resident VMs. We evaluate the attack in a setting without background load and in a more realistic setting with significant background load on the host memory. Our results indicate that, with few repetitions of our attack, we can precisely identify software versions within reasonable time frames and nearly independent of the background load. Finally, we discuss potential coun-termeasures against the presented side-channel attack.
Modified Erdös–Ginzburg–Ziv constants for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e22" altimg="si12.gif"><mml:mi mathvariant="double-struck">Z</mml:mi><mml:mo>∕</mml:mo><mml:mi>n</mml:mi><mml:mi mathvariant="double-struck">Z</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e33" altimg="si13.gif"><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mi mathvariant="…

For an abelian group G and an integer t > 0 , the modified Erdös–Ginzburg–Ziv constant s t ′ ( G ) is the smallest integer ℓ such that any zero-sum sequence of length at least ℓ with elements in G contains a zero-sum subsequence (not necessarily consecutive) of length t . We compute s t ′ ( G ) for G = Z ∕ n Z and for G = ( Z ∕ n Z ) 2 when t = n .
An Explainable Password Strength Meter Addon via Textual Pattern Recognition

Textual passwords are still dominating the authentication of remote file sharing and website logins, although researchers recently showed several vulnerabilities about this authentication mechanism. When a user creates or changes a password, a website usually leverages a password strength meter (PSM for short) to show the strength of the password. When the password is evaluated as a weak one, the user may replace the password with a stronger or securer one. However, the user is usually confused when the password, especially a frequently used password, is shown as a weak one. We argue that an explainable password strength meter addon, which could show the reasons of weak , may help users to more effectively create a secure password. Unfortunately, we find few sites in Alexa global top 100 showing these details. Motivated to help users with an explainable PSM, this paper proposes an addon to PSMs providing feedbacks in the form of pattern passwords explaining why a password is weak. This PSM addon can detect twelve types of patterns, which cover a very large proportion among 70 million of leaked real passwords from high-profile websites. According to our evaluation and user study, our PSM addon, which leverages textual pattern passwords, can effectively detect these popular patterns and effectively help users create securer passwords.
Performance analysis of mobile applications developed with different programming tools

This study examines the efficiency of certain software tasks in applications developed using three frameworks for the Android system: Android SDK, Qt and AppInventor. The results obtained using the Android SDK provided the benchmark for comparison with other frameworks. Three test applications were implemented. Each of them had the same functionality. Performance in the following aspects was tested: sorting a list of items using recursion by means of the Quicksort algorithm, access time to a location from a GPS sensor, duration time for reading the entire list of phone contacts, saving large and small files, reading large and small files, image conversion to greyscale, playback time of a music file, including the preparation time. The results of the Android SDK are good. Unexpectedly, it is not the fastest tool, but the time for performing most operations can be considered satisfactory. The Qt framework is overall about 34% faster than the Android SDK. The worst in terms of overall performance is the AppInventor: it is, on average, over 626 times slower than Android SDK.
Social Influence-Based Similarity Measures for User-User Collaborative Filtering Applied to Music Recommendation

Social characteristics present in current music streaming services allow to use methods for endowing these systems with more reliable recommendation functionalities. There are many proposals in the literature that take advantage of that information and use it in the context of recommender systems. However, in the specific application domain of music the studies are much more limited, and the methods developed for other domains cannot be often applied since they require social interaction data that are not available in the streaming systems. In this paper, we present a method to determine social influence of users uniquely from friendship relations. The degree of influence obtained is used to define new similarity metrics for collaborative filtering (CF) where more weight is given to more influential users.
The open incentive kit (OINK)

Incentives are a key facet of human studies research, yet the state-of-the-art often designs and implements incentive systems in an ad-hoc, on-demand manner. We introduce the first vocabulary for formally describing incentive systems and develop a software infrastructure that enables UI-based graphical generation of complex, auditable, reliable, and reproducible incentive systems. We call this infrastructure the Open INcentive Kit (OINK). A review of recent literature from several communities finds that of the one hundred and twenty-one publications that incorporate incentives, only thirty-one describe their incentive system in detail, and all of these could be implemented using OINK. We evaluate OINK in practice by using it for an active energy monitoring deployment in Ghana and find that OINK successfully facilitates thousands of individual incentive payments. Finally, we describe our efforts to generalize OINK for different research communities, specifically focusing on architectural decisions around extensibility to support unanticipated use cases. OINK is free and open-source software.
Analysis of Public Sentiments About Mega Online Sale Using Tweets on Big Billions Day Sale

The opinion of others significantly influences our decision-making process about any product or service. The positive or negative opinions of prospective clients or customers may promote or demote the profit margin of any business activities. Therefore, analyzing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections, and predicting socioeconomic phenomena such as stock exchange, sale of products, etc. With the emergence of Web 2.0 services, a wide range of online platforms including micro-blogging, social networking, and many other review platforms are available. The automated process for public sentiment analysis from a large amount of social data present on the web helps to improve customer satisfaction. This chapter discusses the process of sentiment analysis of prospective buyers of mega online sales using their posted tweets about the big billions day sale.
Attacking Noisy Secret CRT-RSA Exponents in Binary Method

In this paper, we perform a security evaluation on the RSA encryption scheme with the Chinese remainder theorem (CRT-RSA), against side-channel attacks. We discuss the methods for recovering the CRT-RSA secret keys by observing physical information. In the CRT-RSA scheme, we calculate the exponentiations by repeated squaring and multiplication operations during decryption. The square-and-multiply sequences of the exponentiation can be obtained by side-channel attacks. However, errors occur in the square and multiply sequences because of physical-information observation errors, due to which the secret keys cannot be recovered by using Bernstein et al.’s method, even if window size $$w=1$$ in sliding window exponentiation. In this paper, we propose an algorithm for correcting the errors in the square-and-multiply sequences, and for obtaining the correct secret keys, when the square-and-multiply sequences are generated at $$w=1$$ , namely, the binary method. Moreover, we theoretically prove that the expected time complexity of our algorithm is in polynomial time, when the error rate is less than 5.8%.
Ontology-Oriented Software Effort Estimation System for E-commerce Applications Based on Extreme Programming and Scrum Methodologies

Abstract Presently, software industry is severely suffering from inaccurate effort estimation and inadequate unstructured or semi-structured project history management. In fact, both are difficult to accomplish and hence badly impact the software projects. We proposed improvements in the effort estimation and the project history management of e-commerce projects focusing on Extreme Programing (XP) and Scrum methodologies using ontology models in our software effort estimation system. Proposed system infers suitable estimate in the form of time, resources and lessons learnt as per the project leader’s requirements by using description logic and HermiT reasoner. To validate our approach, we have performed a case study comprising 20 Business-to-Consumer (B2C) web projects and performed comparative analysis on the collected efforts in both XP and Scrum contexts by applying (Mean Magnitude of Relative Error) MMRE and PRED(25) prediction accuracy measures. Likewise, software functional size of understudy e-commerce projects was measured using COSMIC functional size measurement methodology. Regression analysis of relations among actual COSMIC function points, estimated effort, and actual effort spent for the projects show better significance-F and R2 values for our approach. The comparative results show that overall proposed approach provides accurate estimates and significantly improves over planning poker and delphi methods by 10% and 30%, respectively.
Similar Terms Grouping Yields Faster Terminological Saturation

This paper reports on the refinement of the algorithm for measuring terminological difference between text datasets (THD). This baseline THD algorithm, developed in the OntoElect project, used exact string matches for term comparison. In this work, it has been refined by the use of appropriately selected string similarity measures (SSM) for grouping the terms, which look similar as text strings and presumably have similar meanings. To determine rational term similarity thresholds for several chosen SSMs, the measures have been implemented as software functions and evaluated on the developed test set of term pairs in English. Further, the refined algorithm implementation has been evaluated against the baseline THD algorithm. For this evaluation, the bags of terms have been used that had been extracted from the three different document collections of scientific papers, belonging to different subject domains. The experiment revealed that the use of the refined THD algorithm, compared to the baseline, resulted in quicker terminological saturation on more compact sets of source documents, though at an expense of a noticeably higher computation time.
Inference on Two-Part Latent Variable Analysis Model With Multivariate Longitudinal Data

Semicontinuous variable analysis is a widely appreciated statistical method in such disciplines as social science, medicines, and economics. In detecting underlying structure and representing possible interrelationships, statistical analysis using a two-part model is appropriated. In this paper, we present a general extension of two-part model to the situation where the unobserved factors are included in the two parts to interpret external variability in semicontinuous variable. Auxiliary information on these factors is manifested by continuous responses via measurement model, while the interrelationships among factors are exploited through structural equation model. Moreover, under longitudinal setting, dynamic characteristics of responses between any two occasions are represented by transition model. Procedures for model fitting, parameter estimation, model selection and prediction are developed within the Bayesian paradigm. Markov Chains Monte Carlo method is used to implement posterior analysis. Empirical results including a simulation and a real example are used to illustrate the proposed methodology.
Predictive Crawling for Commercial Web Content

Web crawlers spend significant resources to maintain freshness of their crawled data. This paper describes the optimization of resources to ensure that product prices shown in ads in a context of a shopping sponsored search service are synchronized with current merchant prices. We are able to use the predictability of price changes to build a machine learned system leading to considerable resource savings for both the merchants and the crawler. We describe our solution to technical challenges due to partial observability of price history, feedback loops arising from applying machine learned models, and offers in cold start state. Empirical evaluation over large-scale product crawl data demonstrates the effectiveness of our model and confirms its robustness towards unseen data. We argue that our approach can be applicable in more general data pull settings.
Complementing the DTW based speaker verification systems with knowledge of specific regions of interest

In recent times, Dynamic Time Warping (DTW) based template matching systems have again come to the forefront in the field of text-dependent speaker verification. Its integration with the latest technology, like i-vector/Probabilistic Linear Discriminant Analysis (PLDA) and Deep Neural Network (DNN) , has resulted in significant improvement in the performance of the systems. DTW algorithm time-aligns two templates and gives a similarity score based on the optimal warping path. It however weighs all the local distances equally, along the optimal path. In this paper, we propose complementing the DTW based text-dependent speaker verification systems with local scores derived from the vicinity of speaker-identity-rich regions. The vowel regions are used to determine portions along the warping path that are more important in terms of speaker discriminating information content. Two systems, namely the DTW/ Mel-frequency Cepstral Coefficients (MFCC) system and the online i-vector/PLDA/DTW system have been extended to incorporate the knowledge of specific regions of interest. The results have been evaluated on Part 1 of RSR2015 database. Relative improvements of upto 11.85% and 49.41% are observed for the extended systems based on MFCC and i-vector respectively.
Machine learning improves image restoration

A neural network increases image quality when it knows what to look for.
Censors Get Smart: Evidence from Psiphon in Iran

Abstract Information controls are actions intended to deny, disrupt, monitor, or secure information for political ends. They can be implemented using a wide variety of technical and nontechnical means. Political contests over the control of information are heightened around important events, such as major anniversaries, armed conflicts, protests, and elections. In this paper, we offer a comparative case study of online censorship of the circumvention tool Psiphon during the Iranian elections in 2016 and 2013, drawing on unique access to analytics data from Psiphon. We find that the Iranian regime developed its censorship approach in two ways, deploying blocking that was more targeted and strategically timed in the more recent case. Evidence suggests that the regime relaxed censorship of Psiphon during the official campaign period for the 2016 election. The apparent objective of this new approach was to control access to information while minimizing the political consequences of doing so.
FrAppLe

Human and AI collaboration has evolved drastically over the past decade. Most of the tasks are now partly handled by machines and partly by humans, resulting in human and AI partnership. Humans share their tasks with the AI agents as they are good at performing predictive and repetitive tasks. The tasks assigned to the agents can be dynamic in nature and can change over time. This may lead to acquiring new skills or re-training to improvise on the same task. The developer needs to programme these agents every time for a new domain. In this work, we propose a framework that helps in developing apprentice agents that learn from Human demonstration. Apprenticeship learning is the process of learning by observing an expert performing actions to achieve a specific goal. The agent observes the expert's actions, the sensory inputs and then trains itself using the observed data. We also propose a process to learn and self-diagnose the agent's action for apprenticeship learning. The framework alleviates the need to explicitly program the apprentice agents. We demonstrate our framework on a case study.
A motion strategy for exploration driven by an automaton activating feedback-based controllers

This paper addresses the problem of exploring an unknown, planar, polygonal and simply connected environment. To explore the environment, the robot follows the environment boundary. In the first part of this paper, we propose a motion policy based on simple sensor feedback and a complete exploration strategy is represented as a Moore machine. The proposed motion policy is based on the paradigm of avoiding the state estimation; there is a direct mapping from observation to control. We present the theoretical conditions guaranteeing that the robot discovers the largest possible region of the environment. In the second part of the paper, we propose an automaton that filters spurious observations to activate feedback-based controllers. We propose a practical control scheme whose objective is to maintain a desired distance between the robot and the boundary of the environment. The approach is able to deal with imprecise robot’s observations and controls, and to take into account variations in the robot’s velocities. The control scheme switches controllers according to observations obtained from the robots sensor. Our control scheme aims to maintain the continuity of angular and linear velocities of the robot in spite of the switching between controllers. All the proposed techniques have been implemented and both simulations and experiments in a real robot are presented.