Reviewerly/exHarmony huggingface dataset:
Is Rank Aggregation Effective in Recommender Systems? An Experimental Analysis

Recommender Systems are tools designed to help users find relevant information from the myriad of content available online. They work by actively suggesting items that are relevant to users according to their historical preferences or observed actions. Among recommender systems, top- N recommenders work by suggesting a ranking of N items that can be of interest to a user. Although a significant number of top- N recommenders have been proposed in the literature, they often disagree in their returned rankings, offering an opportunity for improving the final recommendation ranking by aggregating the outputs of different algorithms. Rank aggregation was successfully used in a significant number of areas, but only a few rank aggregation methods have been proposed in the recommender systems literature. Furthermore, there is a lack of studies regarding rankings’ characteristics and their possible impacts on the improvements achieved through rank aggregation. This work presents an extensive two-phase experimental analysis of rank aggregation in recommender systems. In the first phase, we investigate the characteristics of rankings recommended by 15 different top- N recommender algorithms regarding agreement and diversity. In the second phase, we look at the results of 19 rank aggregation methods and identify different scenarios where they perform best or worst according to the input rankings’ characteristics. Our results show that supervised rank aggregation methods provide improvements in the results of the recommended rankings in six out of seven datasets. These methods provide robustness even in the presence of a big set of weak recommendation rankings. However, in cases where there was a set of non-diverse high-quality input rankings, supervised and unsupervised algorithms produced similar results. In these cases, we can avoid the cost of the former in favor of the latter.
An Efficient Sentiment Analysis Approach for Product Review using Turney Algorithm

Sentiment analysis can be done by means of Classification and its most important tasks are text categorization, tone recognition, image classification etc. Mostly the extant methods of supervised classification are based on traditional statistics, which can provide ideal results. The main aim is to increase the accuracy and to report the manufacturer about the negatives of the product. The major problem is categorization of sentiment polarity, which is the problem of sentiment analysis. There are two levels of categorization and they are Review-level Categorization and Sentence-level Categorization. Categorization of review-level becomes arduous when we attempt to classify the reviews respect with their specific rating related to star-scaled. Second, Review-level Categorization has a drawback in Implicit-level sentiment analysis. Mostly SVM, Naïve Bayesian and Decision Tree are mainly used to improve the efficiency of classification. Amazon Dataset is used as Dataset in proposed system to improve the accuracy of Turney algorithm. Semantic Orientation (SO) with Point wise Mutual Information yields good results than other classification methods. The review level gets subjected as positive value, on acquaintance of positive average SO. On the other hand, the review level acquires a negative level in accordance with attainment of negative average SO.
Towards Incentive for Electrical Vehicles Demand Response With Location Privacy Guaranteeing in Microgrids

The rapid and wide adoption of microgrids (MGs) and the increasing popularity of electric vehicles (EVs) have created a unique opportunity for the integration of these technologies. In this article, we address the issue of demand response of EVs during MG outages by leveraging Vehicle-to-Grid (V2G) technology. Particularly, we investigate an auction trading market that allows EVs with surplus energy to act as sellers, and EVs that want to be charged to act as buyers. A novel distributed double auction scheme is proposed to allow each buyer EV to submit multiple bids to seller EVs in different parking lots. Nonetheless, the locations of buyer EVs could be inferred by an adversary through analyzing the valuations, posing serious privacy and security risks. In this regard, a valuation-based attack scheme is investigated to validate the potential privacy risk. To defend against such an attack, we present a location privacy-preserving double auction scheme, in which the MicroGrid Central Controller (MGCC) acts as the auctioneer, solving the social welfare maximization problem of matching buyers to sellers, and the cloud is used to conduct calculations for the auctioneer, protecting the privacy of participants via homomorphic encryption. Theoretical analysis is conducted to validate our auction scheme in satisfying the designed economic and privacy properties (e.g., strategy-proofness and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity). The experimental results show that our auction scheme can not only mitigate the demand response problem in MGs, but also provides good performance with respect to social welfare, satisfaction ratio, computational and communication overhead, and privacy leakage.
On the solvability of bipolar max-product fuzzy relation equations with the standard negation

Bipolar fuzzy relation equations arise when unknown variables together with their logical negations appear simultaneously in fuzzy relation equations. This paper gives a characterization of the solvability of bipolar max-product fuzzy (relation) equations with the standard negation. In addition, some properties associated with the existence of the greatest/least solution or maximal/minimal solutions are shown, when these (relation) equations are solvable. Different examples are included in order to clarify the developed theory.
Recurrent Semantic Preserving Generation for Action Prediction

In this paper, we propose a recurrent semantic preserving generation (RSPG) method for action prediction. Unlike most existing methods which don't make full use of information from partially observed sequences, we develop a generation architecture to complement the sequence of skeletons for predicting the action, which can exploit more potential information of the movement tendency. Our method learns to capture the tendency of observed sequences and complement the subsequent action with adversarial learning under some constrains, which preserves the consistency between the generation sequence and the observed sequence. By generating the subsequent action, our method can predict the action with the most probability. Moreover, the redundant generation introduces the noise and disturbs the prediction. The insufficient generation cannot exploit the potential information for improving the effect of predicting the action. Our RSPG controls the generation step in a recurrent manner for maximizing the discriminative information of actions, which can adapt to the variable length of different actions. We evaluate our method on four popular action datasets: NTU, UCF101, BIT, and UT-Interaction, and experimental results show that our method achieves very competitive performance with the state-of-the-art.
Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm

In contrast with conventional structural optimization benchmark problems real size structures mostly contain a large number of members and their optimal design provide a serious challenging area for optimization methods. In this regard, current study deals with assessing the search performance of the recently developed fuzzy reinforced metaheuristic technique so called Interactive Fuzzy Search Algorithm on the optimization of large-scale structures. This method is a self-adaptive and parameter-free algorithm which applies a dual-module fuzzy decision mechanism to adjust its search behavior during the optimization process. This mechanism employs two nine rule fuzzy modules which permanently monitor the agents updating process and based on the governing conditions of the problem emphasize their exploration or exploitation search behavior. Attained results show that proposed method can adopt itself with the extensive search space of the studied problems. Form both accuracy and stability aspects Interactive Fuzzy Search Algorithm provides promising results on solving large-scale structural optimization problems.
Cybersecurity and platform competition in the cloud

Cloud computing services are examples of platforms in the classic sense of the economics of two-sided markets. In particular, indirect externalities arise in the cloud from connecting users with developers of complementary products or services. The premise investigated here is that a cloud provider's security is subject to the same rigors of two-sided market competition that shape a cloud provider's pricing and strategy. In so doing, this study demonstrates how cybersecurity shapes cloud competition and cloud competition shapes cybersecurity. Specifically, cybersecurity is shown to be a user-side phenomenon with characteristics that can lead to the coexistence of cloud services providers rather than monopoly. In addition, a user's switching costs are characterized in terms of the ratio of the potential consequence of a class break under a user's current provider against that of the competition. Finally, a component of cybersecurity is identified for which there is no technical solution.
Deriving a usage-independent software quality metric

The extent of post-release use of software affects the number of faults, thus biasing quality metrics and adversely affecting associated decisions. The proprietary nature of usage data limited deeper exploration of this subject in the past. To determine how software faults and software use are related and how, based on that, an accurate quality measure can be designed. Via Google Analytics we measure new users, usage intensity, usage frequency, exceptions, and release date and duration for complex proprietary mobile applications for Android and iOS. We utilize Bayesian Network and Random Forest models to explain the interrelationships and to derive the usage independent release quality measure. To increase external validity, we also investigate the interrelationship among various code complexity measures, usage (downloads), and number of issues for 520 NPM packages. We derived a usage-independent quality measure from these analyses, and applied it on 4430 popular NPM packages to construct timelines for comparing the perceived quality (number of issues) and our derived measure of quality during the lifetime of these packages. We found the number of new users to be the primary factor determining the number of exceptions, and found no direct link between the intensity and frequency of software usage and software faults. Crashes increased with the power of 1.02-1.04 of new user for the Android app and power of 1.6 for the iOS app. Release quality expressed as crashes per user was independent of other usage-related predictors, thus serving as a usage independent measure of software quality. Usage also affected quality in NPM, where downloads were strongly associated with numbers of issues, even after taking the other code complexity measures into consideration. Unlike in mobile case where exceptions per user decrease over time, for 45.8% of the NPM packages the number of issues per download increase. We expect our result and our proposed quality measure will help gauge release quality of a software more accurately and inspire further research in this area.
FarSight: Long-Term Disease Prediction Using Unstructured Clinical Nursing Notes

Accurate risk stratification using patient data is a vital task in channeling prioritized care. Most state-of-the-art models are predominantly reliant on digitized data in the form of structured Electronic Health Records (EHRs). Those models overlook the valuable patient-specific information embedded in unstructured clinical notes, which is the prevalent medium employed by caregivers to record patients’ disease timeline. The availability of such patient-specific data presents an unprecedented opportunity to build intelligent systems that provide exclusive insights into patients’ disease physiology. Moreover, very few works have attempted to benchmark the performance of deep neural architectures against the state-of-the-art models on publicly available datasets. This article presents significant observations from our benchmarking experiments on the applicability of deep learning models for the clinical task of ICD-9 code group prediction. We present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FarSight</i> , a long-term aggregation mechanism intended to recognize the onset of the disease with the earliest detected symptoms. Vector space and topic modeling approaches are utilized to capture the semantic information in the patient representations. Experiments on MIMIC-III database underscored the superior performance of the proposed models built on unstructured data when compared to structured EHR based state-of-the-art model, achieving an improvement of 19.34 percent in AUPRC and 5.41 percent in AUROC.
Learning from Hometown and Current City

With more and more frequent population movement between different cities, like users' travel or business trip, recommending personalized cross-city Point-of-Interests (POIs) for these users has become an important scenario of POI recommendation tasks. However, traditional models degrade significantly due to sparsity problem because travelers only have limited visiting behaviors. Through a detailed analysis of real-world check-data, we observe 1) the phenomenon of travelers' interest drift and transfer co-exist between hometown and current city; 2) differences between popular POIs among locals and travelers. Motivated by this, we propose a POI Recommendation framework with User Interest Drift and Transfer (PR-UIDT), which jointly considers above two factors when designing user and POI latent vector. In this framework, user vector is divided into a city-independent part and another city-dependent part, and POI is represented as two independent vectors for locals and travelers, respectively. To evaluate the proposed framework, we implement it with a square error based matrix factorization model and a ranking error based matrix factorization model, respectively, and conduct extensive experiments on three real-world datasets. The experiment results demonstrate the superiority of PR-UIDT framework, with a relative improvement of 0.4% ~ 20.5% over several state-of-the-art baselines, as well as the practicality of applying this framework to real-world applications and multi-city scenarios. Further qualitative analysis confirms both the plausibility and validity of combining user interest transfer and drift into cross-city POI recommendation.
Small-Footprint Keyword Spotting with Graph Convolutional Network

Despite the recent successes of deep neural networks, it remains challenging to achieve high precision keyword spotting task (KWS) on resource-constrained devices. In this study, we propose a novel context-aware and compact architecture for keyword spotting task. Based on residual connection and bottleneck structure, we design a compact and efficient network for KWS task. To leverage the long range dependencies and global context of the convolutional feature maps, the graph convolutional network is introduced to encode the nonlocal relations. By evaluated on the Google Speech Command Dataset, the proposed method achieves state-of-the-art performance and outperforms the prior works by a large margin with lower computational cost.
Learning skeleton information for human action analysis using Kinect

Human action recognition plays an important role in modern intelligent systems, such as human–computer interaction (HCI), sport analysis, and somatosensory game. Compared with conventional 2-D based human action analysis, using Kinect sensor can obtain depth information of human action, which is significant for human action recognition. In this paper, we propose a joint angle sequence model for recognizing human actions, where depth images are acquired by using Kinect sensor. We design an improved DTW method to improve the matching accuracy. Comprehensive experiments show the effectiveness and robustness of our proposed method.
A Usable and Robust Continuous Authentication Framework Using Wearables

One-time login process in conventional authentication systems does not guarantee that the identified user is the actual user throughout the session. However, it is necessary to re-verify the user identity periodically throughout a login session, which is lacking in existing one-time login systems. Continuous authentication, which re-verifies the user identity without breaking the continuity of the session, can address this issue. However, existing methods for Continuous Authentication are either not reliable or not usable. In this paper, we introduce a usable and reliable Wearable-Assisted Continuous Authentication (WACA), which relies on the sensor-based keystroke dynamics and the authentication data is acquired through the built-in sensors of a wearable (e.g., smartwatch) while the user is typing. The acquired data is periodically and transparently compared with the registered profile of the initially logged-in user with one-way classifiers. With this, WACA continuously ensures that the current user is the user who logged-in initially. We implemented the WACA framework and evaluated its performance extensively on real devices with real users. The empirical evaluation of WACA reveals that WACA is feasible, and its error rate is as low as 1 percent with 30 seconds of processing time and 2-3 percent for 20 seconds. The computational overhead is minimal. Furthermore, WACA is capable of identifying insider threats with very high accuracy (99.2 percent) and also robust against powerful adversaries such as imitation and statistical attackers. We believe that this work has practical and far-reaching implications for the future of the usable authentication field.
An Effective and Scalable Data Modeling for Enterprise Big Data Platform

The enormous growth of the internet, enterprise applications, social media, and IoT devices in the current time caused a huge spike in enterprise data growth. Big data platform provided scalable storage to manage enterprise data growth and served easier data access to decision-makers, stakeholders and business users. It is a well-known challenge to classify, organize and store all this data and process it to provide business insights. Due to nature, variety, velocity, volume and value of data make it difficult to effectively process big data. Enterprises face challenges to apply complex business rules, to generate insights and to support data-driven decisions in a timely fashion. As big data lake integrates streams of data from a bunch of business units, stakeholders usually analyze enterprise-wide data from various data models. Data models are a vital component of Big data platform. Users may do complex processing, run queries and perform big table joins to generate required metrics depending on the available data models. It is usually a time consuming and resource-intensive process to find the value from data. It is a no-brainer that big data platform in the enterprise needs high-quality data modeling methods to reach an optimal mix of cost, performance, and quality. This paper addresses these challenges by proposing an effective and scalable way to organize and store data in Big Data Lake. It presents some of the basic principles and methodology to build scalable data models in a distributed environment. It also describes how it overcomes common challenges and presents findings.
Decryption of historical manuscripts: the DECRYPT project

Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology.
Model assisted variable clustering: Minimax-optimal recovery and algorithms

The problem of variable clustering is that of estimating groups of similar components of a p-dimensional vector X = (X 1 , . . ., X p ) from n independent copies of X.There exists a large number of algorithms that return data-dependent groups of variables, but their interpretation is limited to the algorithm that produced them.An alternative is model-based clustering, in which one begins by defining population level clusters relative to a model that embeds notions of similarity.Algorithms tailored to such models yield estimated clusters with a clear statistical interpretation.We take this view here and introduce the class of G-block covariance models as a background model for variable clustering.In such models, two variables in a cluster are deemed similar if they have similar associations will all other variables.This can arise, for instance, when groups of variables are noise corrupted versions of the same latent factor.We quantify the difficulty of clustering data generated from a G-block covariance model in terms of cluster proximity, measured with respect to two related, but different, cluster separation metrics.We derive minimax cluster separation thresholds, which are the metric values below which no algorithm can recover the model-defined clusters exactly, and show that they are different for the two metrics.We therefore develop two algorithms, COD and PECOK, tailored to G-block covariance models, and study their minimax-optimality with respect to each metric.Of independent interest is the fact that the analysis of the PECOK algorithm, which is based on a corrected convex relaxation of the popular K-means algorithm, provides the first statistical analysis of such algorithms for variable clustering.Additionally, we compare our methods with another popular clustering method, spectral clustering.Extensive simulation studies, as well as our data analyses, confirm the applicability of our approach.
Tomography of a Feedback Measurement with Photon Detection

Quantum measurement is essential to both the foundations and practical applications of quantum information science. Among many possible models of quantum measurement, feedback measurements that dynamically update their physical structure are highly interesting due to their flexibility, which enables a wide range of measurements that might otherwise be hard to implement. Here we investigate by detector tomography a measurement consisting of a displacement operation combined with photon detection followed by a real time feedback operation. We design the measurement in order to discriminate the superposition of vacuum and single photon states---the single-rail qubit---and find that it can discriminate the superposition states with a certainty of 96%. Such a feedback-controlled photon counter will facilitate the realization of quantum information protocols with single-rail qubits as well as the nonlocality test of certain entangled states.