Reviewerly/exHarmony huggingface dataset:
Fast modular subset sum using linear sketching

Given n positive integers, the Modular Subset Sum problem asks if a subset adds up to a given target t modulo a given integer m. This is a natural generalization of the Subset Sum problem (where m = +∞) with ties to additive combinatorics and cryptography.Recently, in [Bri17, KX17], efficient algorithms have been developed for the non-modular case, running in near-linear pseudo-polynomial time. For the modular case, however, the best known algorithm by Koiliaris and Xu [KX17] runs in time O(m5/4).In this paper, we present an algorithm running in O(m) randomized time, which matches a recent conditional lower bound of [ABHS17] based on the Strong Exponential Time Hypothesis. Interestingly, in contrast to most previous results on Subset Sum, our algorithm does not use the Fast Fourier Transform. Instead, it is able to simulate the textbook Dynamic Programming algorithm much faster, using ideas from linear sketching. This is one of the first applications of sketching-based techniques to obtain fast algorithms for exact combinatorial problems in an offline setting.
A characterisation of S-box fitness landscapes in cryptography

Substitution Boxes (S-boxes) are nonlinear objects often used in the design of cryptographic algorithms. The design of high quality S-boxes is an interesting problem that attracts a lot of attention. Many attempts have been made in recent years to use heuristics to design S-boxes, but the results were often far from the previously known best obtained ones. Unfortunately, most of the effort went into exploring different algorithms and fitness functions while little attention has been given to the understanding why this problem is so difficult for heuristics. In this paper, we conduct a fitness landscape analysis to better understand why this problem can be difficult. Among other, we find that almost each initial starting point has its own local optimum, even though the networks are highly interconnected.
Polygon-Net: A General Framework for Jointly Boosting Multiple Unsupervised Neural Machine Translation Models

Neural machine translation (NMT) has achieved great success. However, collecting large-scale parallel data for training is costly and laborious. Recently, unsupervised neural machine translation has attracted more and more attention, due to its demand for monolingual corpus only, which is common and easy to obtain, and its great potentials for the low-resource or even zero-resource machine translation. In this work, we propose a general framework called Polygon-Net, which leverages multi auxiliary languages for jointly boosting unsupervised neural machine translation models. Specifically, we design a novel loss function for multi-language unsupervised neural machine translation. In addition, different from the literature that just updating one or two models individually, Polygon-Net enables multiple unsupervised models in the framework to update in turn and enhance each other for the first time. In this way, multiple unsupervised translation models are associated with each other for training to achieve better performance. Experiments on the benchmark datasets including UN Corpus and WMT show that our approach significantly improves over the two-language based methods, and achieves better performance with more languages introduced to the framework.
Semantic Profiles for Easing SensorML Description: Review and Proposal

The adoption of Sensor Web Enablement (SWE) practices by sensor maintainers is hampered by the inherent complexity of the Sensor Model Language (SensorML), its high expressiveness, and the scarce availability of editing tools. To overcome these issues, the Earth Observation (EO) community often recurs to SensorML profiles narrowing the range of admitted metadata structures and value ranges. Unfortunately, profiles frequently fall short of providing usable editing tools and comprehensive validation criteria, particularly for the difficulty of checking value ranges in the multi-tenanted domain of the Web of Data. In this paper, we provide an updated review of current practices, techniques, and tools for editing SensorML in the perspective of profile support and introduce our solution for effective profile definition. Beside allowing for formalization of a broad range of constraints that concur in defining a metadata profile, our proposal closes the gap between profile definition and actual editing of the corresponding metadata by allowing for ex-ante validation of the metadata that is produced. On this basis, we suggest the notion of Semantic Web SensorML profiles, characterized by a new family of constraints involving Semantic Web sources. We also discuss implementation of SensorML profiles with our tool and pinpoint the benefits with respect to the existing ex-post validation facilities provided by schema definition languages.
Themis: a tool for validating ontologies through requirements

The validation of ontologies, whose aim is to check whether an ontology matches the conceptualization it is meant to specify, is a key activity for guaranteeing the quality of ontologies.This work is focused on the validation through requirements, with the aim of assuring, both the domain experts and ontology developers, that the ontologies they are building or using are complete regarding their needs.Inspired by software engineering testing processes, this work proposes a web-based tool called Themis, independent of any ontology development environment, for validating ontologies by means of the application of test expressions which, following lexicosyntactic patterns, represent the desired behaviour that will present an ontology if a requirement is satisfied.
Role of cytotechnologists in rapid onsite adequacy assessment of cytology materials for diagnostic workup and specimen allocation for ancillary testing using a standardized protocol

Data on the performance of cytotechnologists in assessing specimen adequacy of needle core biopsies (NCB) is scant and their role in specimen triaging for ancillary studies have not been well established. We retrospectively analyzed rapid onsite evaluation (ROSE) performed exclusively by cytotechnologists on 248 NCB and fine-needle aspiration (FNA) specimens. Overall adequacy and accuracy rates were determined by comparing to final diagnosis. We also reviewed the process of specimen allocation for ancillary testing to determine whether specimens were appropriately triaged at the time of ROSE. Of the 248 cases, 222 (89.5%) were touch imprint and 26 (10.5%) were FNA smears. The overall adequacy rate was 73.4% (182 of 248). Concordance for “adequate” interpretation by ROSE with unequivocal malignant or benign diagnoses on final interpretation was 95.6%. The sensitivity, specificity, and accuracy of ROSE for a final “positive for malignancy” were 89.2% (95% CI 83.04% to 93.69%), 43.24% (95% CI 31.77% to 55.28%), and 73.87% (95% CI 67.57% to 55.28%), respectively. Cases with “positive for malignancy” on final diagnosis were “adequate” by ROSE in 89.1% (132 of 148) and “inadequate” in 10.8% (16 of 148), P < 0.0001. Ancillary tests were performed in 168 of 248 (67.7%); the majority were immunohistochemical stains for determining tumor subtype. Predictive biomarkers were performed successfully in 100% of metastatic breast cancers. Cytotechnologists performed at a high level of competency in providing ROSE and allocating specimens for ancillary testing, which were performed successfully in the majority of cases. Implementation of a standardized protocol for tissue management/prioritization is of paramount importance to maximize tissue preservation and minimize wastage.
Agricultural Researchers' Attitudes Toward Open Access and Data Sharing

This study involved a thorough examination of attitudes and opinions of agricultural researchers toward open access publishing and data sharing. Utilizing the results of the Ithaka S+R Agriculture Research Support Services project, we reanalyzed our institutional interview transcripts and synthesized information from the project's publicly available reports. For comparison, we also searched and coded scientific and library literature. Our findings reveal common attitudes related to open access publishing and data sharing and point to potential roles for libraries to help address common impediments, such as lack of trust, time, and money. Overall, this study provides disciplinary context that can inform how librarians approach agricultural researchers about open access publishing and data sharing.
A Novel Model for Optimizing Selection of Cloud Instance Types

With the development of cloud computing, the cloud market is becoming more and more complicated. In a cloud data center, there are many cloud instance types with different computing capacity and price, which brings users some confusions when they select cloud instance types. In order to solve this selection problem, a cloud brokering architecture is proposed. In this architecture, the selection problem is modeled as a multi-objective optimization problem, and through analysis, we get the relationship between complete Pareto set and solution space. Based on this, a two-stage Cloud Instance Type Selection Model (CITSM) is proposed to help users select the cloud instance types. The first stage is Complete Pareto Set Generation Algorithm (CPSGA) which can generate a complete Pareto set of the cloud instance type selection schemes. Then, the Optimal cloud instance type selection Scheme Screening Algorithm (OSSA) is used to select one scheme from the complete Pareto set. We perform some experiments to prove the proposed CITSM is efficient and effective. The proposed method can also solve the single objective optimization problem by modifying OSSA, which illustrates the scalability.
Data Quality Control in Blockchain Applications

This paper discusses the problem of data quality in blockchain applications at three levels of abstraction, i.e., conceptual, logical and physical. Conceptually, it makes explicit the need for information of typical data quality metrics for their online assessment. Logically, it analyzes how the adoption of blockchain technology affects the availability of the data needed for quality assessment. Physically, it identifies a set of implementation options that take into account the information needs of metrics and the restrictions by the technology; special attention at this level is paid to Ethereum and Solidity. Two case studies put the identified patterns and abstractions into context and showcase their importance in real-world distributed applications and processes.
Normalization Helps Training of Quantized LSTM

The long-short-term memory (LSTM), though powerful, is memory and computa\x02tion expensive. To alleviate this problem, one approach is to compress its weights by quantization. However, existing quantization methods usually have inferior performance when used on LSTMs. In this paper, we first show theoretically that training a quantized LSTM is difficult because quantization makes the exploding gradient problem more severe, particularly when the LSTM weight matrices are large. We then show that the popularly used weight/layer/batch normalization schemes can help stabilize the gradient magnitude in training quantized LSTMs. Empirical results show that the normalized quantized LSTMs achieve significantly better results than their unnormalized counterparts. Their performance is also comparable with the full-precision LSTM, while being much smaller in size.
Fast and Accurate Stochastic Gradient Estimation

Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems. SGD estimates the gradient by uniform sampling with sample size one. There have been several other works that suggest faster epoch-wise convergence by using weighted non-uniform sampling for better gradient estimates. Unfortunately, the per-iteration cost of maintaining this adaptive distribution for gradient estimation is more than calculating the full gradient itself, which we call the chicken-and-the-egg loop. As a result, the false impression of faster convergence in iterations, in reality, leads to slower convergence in time. In this paper, we break this barrier by providing the first demonstration of a scheme, Locality sensitive hashing (LSH) sampled Stochastic Gradient Descent (LGD), which leads to superior gradient estimation while keeping the sampling cost per iteration similar to that of the uniform sampling. Such an algorithm is possible due to the sampling view of LSH, which came to light recently. As a consequence of superior and fast estimation, we reduce the running time of all existing gradient descent algorithms, that relies on gradient estimates including Adam, Ada-grad, etc. We demonstrate the effectiveness of our proposal with experiments on linear models as well as the non-linear BERT, which is a recent popular deep learning based language representation model.
Comparing factors that influence learning management systems use on computers and on mobile

Purpose Learning management systems (LMSs) have been embraced for their potential to create a ubiquitous learning that is free from time and space constraints. Mobile devices afford enhanced mobility that enables flexible learning with LMSs. Thus, understanding students’ use of mobile devices to interact with LMSs and the influencing factors is essential. This paper aims to examine the factors that influenced students’ behavioural intention in using Web-based LMSs via mobile phones and compared the factors with those that affect students’ general acceptance of Web-based LMSs. Design/methodology/approach This study surveyed 356 university students and interviewed 17 students on the various factors that might affect their LMS adoption. Structural equation modelling was used to analyse the survey data. Findings This study identified that perceived usefulness, perceived ease of use, social influence and facilitating conditions were significant determinants of students’ usage intention in both contexts. However, social factors exerted greater influence on students’ behavioural intentions of mobile access than the attitudinal factors. The results also pinpointed some sociocultural and tempo-spatial factors that might have minimized the influence of perceived usefulness in the mobile context. Originality/value The study calls for special attention to the potential influences of sociocultural norms and tempo-spatial circumstances of mobile use in shaping the nature of learners’ voluntary mobile use of LMSs.
Effect of economically friendly acustimulation approach against cybersickness in video-watching tasks using consumer virtual reality devices

Consumer virtual reality (VR) devices are becoming more prevalent in the market, but cybersickness induced by VR devices limits their potential application and promotion. Acustimulation has been found effective in reducing cybersickness symptoms. However, in previous forms, the more effective way of acustimulation is either intrusive or electrical which is hard to be applied to daily VR use. In this study, we aimed to find a both simple and more effective acustimulation approach, acupressure plus acupaste (AcP+) to reducing the adverse effects caused by cybersickness from VR applications. In this study, we set three conditions: acupressure plus acupaste (AcP+) (main condition of interest), acupressure with fake acupaste (AcP), and a no acustimulation condition (NoAcP). In AcP and AcP + conditions, we applied acupressure or acupressure with true acupaste on P6 point before conducting video-watching tasks using VR headsets, while in NoAcP condition, participants received no special treatment before video-watching tasks. We used questionnaires to measure symptoms of cybersickness and compared the results between these 3 conditions, especially between acupressure plus acupaste (AcP+) and acupressure (AcP) to examine the effect of AcP+, and compared AcP and AcP+ with NoAcP to confirm the effect of acustimulation. Participants reported significant fewer symptoms of cybersickness nausea feelings in both acustimulation methods, compared with NoAcP; and AcP+ was more effective than AcP against cybersickness on visual oculomotor aspect, and facilitated cybersickness recovery. It would be promising to develop acupressure equipment and apply stimulation before VR application to reduce cybersickness.
Drinking Water Monitoring in Mosul City Using IoT

Mosul is one of the cities of Iraq, which was attacked by fierce terrorist organizations called ISIS since 2014-2017 destroyed all the infrastructure of the city. After liberation the city from ISIS by the Iraqi forces had to return to normal life and repair all the destroyed topic of the city. The most important of these topics is for return drinking water and elimination of water pollution by testing and monitoring the water in real time with modern technology to use the best drinking water in addition to reach Mosul city as a smart city. In this paper, designed and implemented the proposed system by using internet of thing with low cost and instant results for the purpose of testing drinking water in real time for many important parameters of the water, such as Temperature, pH, TDS, Turbidity, and Conductivity,. Each parameter can be measured by the sensor. All these sensors connected to the Arduino UNO model can be used as a core controller and send the data to the internet through the Wi-Fi module (ESP 8266). Finally, the sensor data can be viewed on the internet using ThingSpeak API for the purpose of collecting information, analyzing and sending it for display on LCD or Mobile or Laptop. The results were compared with laboratory results in the “Right water project/Mosul”. The proposed system was very powerful, short time to execute, very cheap, and highly efficient. The results for the proposed system is approximate to the Laboratory results by 98.6 %.
Evaluation of multiple prediction models: A novel view on model selection and performance assessment

Model selection and performance assessment for prediction models are important tasks in machine learning, e.g. for the development of medical diagnosis or prognosis rules based on complex data. A common approach is to select the best model via cross-validation and to evaluate this final model on an independent dataset. In this work, we propose to instead evaluate several models simultaneously. These may result from varied hyperparameters or completely different learning algorithms. Our main goal is to increase the probability to correctly identify a model that performs sufficiently well. In this case, adjusting for multiplicity is necessary in the evaluation stage to avoid an inflation of the family wise error rate. We apply the so-called maxT-approach which is based on the joint distribution of test statistics and suitable to (approximately) control the family-wise error rate for a wide variety of performance measures. We conclude that evaluating only a single final model is suboptimal. Instead, several promising models should be evaluated simultaneously, e.g. all models within one standard error of the best validation model. This strategy has proven to increase the probability to correctly identify a good model as well as the final model performance in extensive simulation studies.
Cold Boot Attacks on Bliss

In this paper, we examine the feasibility of cold boot attacks against the BLISS signature scheme. We believe this to be the first time that this has been attempted. Our work is the continuation of the trend to develop cold boot attacks for different schemes as revealed by the literature. But it is also the continuation of the evaluation of post-quantum cryptographic schemes against this class of attack. Particularly, we review the BLISS implementation provided by the strongSwan project. This implementation particularly stores its private key in memory in an interesting way therefore requiring novel approaches to key recovery. We present various approaches to key recovery. We first analyse the key recovery problem in this particular case via key enumeration algorithms, and so propose different techniques for key recovery. We then turn our attention to exploit further the algebraic relation among the components of the private key, and we thus establish a connection between the key recovery problem in this particular case and an instance of Learning with Errors Problem (LWE). We then explore various key recovery techniques to tackle this instance of LWE. In particular, we show a key recovery strategy combining lattice techniques and key enumeration. Finally, we report results from experimenting with one of the key recovery algorithms for a range of parameters, showing it is able to tolerate a noise level of $$ \alpha = 0.001$$ and $$\beta = 0.09$$ for a parameter set when performing a $$2^{40}$$ enumeration.
Second-order Co-occurrence Sensitivity of Skip-Gram with Negative Sampling

We simulate first-and second-order context overlap and show that Skip-Gram with Negative Sampling is similar to Singular Value Decomposition in capturing second-order cooccurrence information, while Pointwise Mutual Information is agnostic to it.We support the results with an empirical study finding that the models react differently when provided with additional second-order information.Our findings reveal a basic property of Skip-Gram with Negative Sampling and point towards an explanation of its success on a variety of tasks.
Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures

Despite the success of reinforcement learning methods in various simulated robotic applications, end-to-end training suffers from extensive training times due to high sample complexity and does not scale well to realistic systems. In this work, we speed up reinforcement learning by incorporating domain knowledge into policy learning. We revisit an architecture based on the mean of multiple computations (MMC) principle known from computational biology and adapt it to solve a “reacher task”. We approximate the policy using a simple MMC network, experimentally compare this idea to end-to-end deep learning architectures, and show that our approach reduces the number of interactions required to approximate a suitable policy by a factor of ten.
Variational Bayesian methods for cognitive science.

Bayesian inference has become a powerful and popular technique for understanding psychological phenomena. However, compared with frequentist statistics, current methods employing Bayesian statistics typically require time-intensive computations, often hindering our ability to evaluate alternatives in a thorough manner. In this article, we advocate for an alternative strategy for performing Bayesian inference, called variational Bayes (VB). VB methods posit a parametric family of distributions that could conceivably contain the target posterior distribution, and then attempt to identify the best parameters for matching the target. In this sense, acquiring the posterior becomes an optimization problem, rather than a complex integration problem. VB methods have enjoyed considerable success in fields such as neuroscience and machine learning, yet have received surprisingly little attention in fields such as psychology. Here, we identify and discuss both the advantages and disadvantages of using VB methods. In our consideration of possible strategies to make VB methods appropriate for psychological models, we develop the differential evolution variational inference algorithm, and compare its performance with a widely used VB algorithm. As test problems, we evaluate the algorithms on their ability to recover the posterior distribution of the linear ballistic accumulator model and a hierarchical signal detection model. Although we cannot endorse VB methods in their current form as a complete replacement for conventional methods, we argue that their accuracy and speed warrant inclusion within the cognitive scientist's toolkit. (PsycInfo Database Record (c) 2020 APA, all rights reserved).