Reviewerly/exHarmony huggingface dataset:
Multi-User Distillation of Common Randomness and Entanglement from Quantum States

The tasks of converting noisy multipartite quantum correlations into noiseless classical and quantum ones using local operations and classical communications (LOCC) are studied. For the former, known as common randomness (CR) distillation, two novel lower bounds on the "distillable common randomness", an operational measure of the total genuine (classical) correlations in a quantum state, are obtained. Our proof relies on a generalization of communication for omniscience (CO) [Csiszár and Narayan, IEEE Trans. Inf. Theory 50: 3047-3061, 2004]. For the latter, we derive two lower bounds on the rate at which Greenberger-Horne-Zeilinger (GHZ) states can be asymptotically distilled from any given pure state under LOCC. Our approach consists in "making coherent" the proposed CR distillation protocols and recycling of resources [Devetak, Harrow and Winter, IEEE Trans. Inf. Theory 54:4587-4618, 2008]. The first lower bound is identical to a recent result by Vrana and Christandl [IEEE Trans. Inf. Theory 65:5945-5958, 2019], which is based on a combinatorial approach to achieve the same rate. Our second lower bound generalises and improves upon this result, and unifies a number of other known lower bounds on GHZ distillation. Full details in the long version [1].
Optimal Portfolio Sold-Out via Blockchain Tokenization

Financial institutions own balanced portfolios with many assets and hence a small risk. But they are not able to split them into smaller parts with comparable risk for resale due to regulators' restrictions caused by a lack of auditability. Blockchain and smart contracts allow overcoming this problem via tokenizing assets into a commodity. The paper seeks to answer the question: how to assemble as many as possible standardized packages from a given portfolio. The optimal algorithms for two special cases-discrete and continuous homogeneous-are provided.
Development of Product Recommendation Engine By Collaborative Filtering and Association Rule Mining Using Machine Learning Algorithms

Recommendation engines are a subclass of information filtering system that seeks to predict the ‘rating’ or ‘preference’ that user would give to an item. It finds information designs in the informational index by learning customers decisions and produces the results that co-identifies with their requirements. Real time examples like Amazon, have been using a recommendation engine for suggesting the goods or products that customers might also like. As the database used in this paper consists of large amount of data, it becomes a difficult and cumbersome process to provide viable choice of products for all the customers. The need of state of art recommendation engine is a necessity in real world e-commerce platforms to solve the issue and fulfil the customers’ needs. There are numerous ways such as collaborative filtering, content-based filtering, hybrid filtering, etc. to build a recommendation system. This paper developed a product recommendation engine that uses collaborative filtering approach, which finds similarity between items bought by the customers with other customers, purchase pattern, and association rule mining framework. The recommendations were generated in order to facilitate ‘cross-sell’ across various items. The collaborative filtering (CF) approach produced the top 10 recommendations for each user. The association rule mining produced the rules based on minimum support (at 0.001) and minimum confidence (at 0.8) values. These values produced around 40,000 viable rules. It can be inferred that selection of metrics and the computation speed is important for quality recommendations.
Grounding Visual Concepts for Zero-Shot Event Detection and Event Captioning

The flourishing of social media platforms requires techniques for understanding the content of media on a large scale. However, state-of-the art video event understanding approaches remain very limited in terms of their ability to deal with data sparsity, semantically unrepresentative event names, and lack of coherence between visual and textual concepts. Accordingly, in this paper, we propose a method of grounding visual concepts for large-scale Multimedia Event Detection (MED) and Multimedia Event Captioning (MEC) in zero-shot setting. More specifically, our framework composes the following: (1) deriving the novel semantic representations of events from their textual descriptions, rather than event names; (2) aggregating the ranks of grounded concepts for MED tasks. A statistical mean-shift outlier rejection model is proposed to remove the outlying concepts which are incorrectly grounded; and (3) defining MEC tasks and augmenting the MEC training set by the videos detected in MED in a zero-shot setting. To the best of our knowledge, this work is the first time to define and solve the MEC task, which is a further step towards understanding video events. We conduct extensive experiments and achieve state-of-the-art performance on the TRECVID MEDTest dataset, as well as our newly proposed TRECVID-MEC dataset.
Visual–spatial attention as a comfort measure in human–robot collaborative tasks

In this paper, we propose a new formulation to consider visual–spatial attention in order to improve the comfort of a human standing or operating near a collaborative robot. This formulation is based on the principle of having the robot’s end-effector in the human visual–spatial attention as much as possible. The integration of this new constraint into the Inverse Kinematics (IK) problem is thoroughly studied and efficient solutions are proposed. Moreover, to allow the robot to react rapidly in the case of unforeseen events, adding the manipulability index to the IK problem is also studied and its impact is analyzed. The proposed method is then extensively tested in simulation and verified on the real Baxter research robot, these experiments pointed out the method efficiency to improve the task visibility while avoiding self-occlusion and singular configurations. Moreover, real experiments revealed the method robustness to uncertainties such as imprecision of detecting human gaze direction.
Conditional Disclosure of Secrets: A Noise and Signal Alignment Approach

In the conditional disclosure of secrets (CDS) problem, Alice and Bob (each holds an input and a common secret) wish to disclose, as efficiently as possible, the secret to Carol if and only if their inputs satisfy some function. The capacity of CDS is the maximum number of bits of the secret that can be securely disclosed per bit of total communication. We characterize the necessary and sufficient condition for the extreme case where the capacity of CDS is the highest and is equal to 1/2.
Meaning and attention in scenes

Perception of a complex visual scene requires that important regions be prioritized and attentionally selected. What is the basis for this selection? Although much research has focused on the spatial distribution of image salience as an important factor guiding attention, relatively little work has focused on the spatial distribution of semantic features (meaning) across a scene. To address this imbalance, we have recently developed a new method for measuring, representing, and evaluating the spatial distribution of meaning in scenes and its influence on attention. In this method, the spatial distribution of meaning is represented as a meaning map. Meaning maps are generated from crowd-sourced responses given by naïve subjects who rate a large number of scene patches drawn from each scene. Meaning maps are coded in the same format as traditional saliency maps, and therefore both types of maps can be directly compared and evaluated against the spatial distribution of attention derived from viewers' eye fixations. In this review I provide an overview of my lab's research comparing the influences of meaning and image salience on attentional guidance in real-world scenes. Overall, we have found that both the spatial distribution of meaning and physical salience across a scene predict the spatial distribution of attention, but when the correlation between meaning and image salience is statistically controlled, only meaning uniquely accounts for variance in attention. I discuss the theoretical implications of these findings and point to new questions for the future.
A Fast Inference and Type-Reduction Process for Constrained Interval Type-2 Fuzzy Systems

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 to interval type-2 (IT2) membership functions. Although they can be used to produce type-2 fuzzy systems with enhanced explainability, so far, the latter comes at the expense of high computational cost. Specifically, the exhaustive type-reduction method for CIT2 Mamdani systems has been shown to be too slow to be used in practical applications and even the current approximation procedure is much slower than modern type-reduction algorithms used for IT2 fuzzy sets. In this article, a novel type-reduction procedure for CIT2 sets is presented, based on the concept of switch indices. The algorithm is applied on a real-world classification problem and compared to other type-reduction approaches used in IT2 and CIT2 systems. In the case studies presented, the new algorithm is significantly faster than the exhaustive and sampling CIT2 approaches while keeping the high level of interpretability of the type-reduction operation that characterizes CIT2 fuzzy sets.
Sentiment Classification of Movie Reviews by Supervised Machine Learning Approaches Using Ensemble Learning &amp; Voted Algorithm

In this paper Authors explained their experimental works. In this work we choose movies review from corpora community. corpora contain numbers of dataset from where we selected movies review. In this dataset we have two categories negative & positive each category contains 1000 files. In our file we have movies review by different reviewer. we took. txt file extension. Here we are finding sentiment polarity against those movies. we extend some more in comparison to our base papers. we apply numbers of classification algorithm like NaiveBayes, SkLearn, Support Vector Machine. We applied multiple classifier like MultinomialNB, GaussianNB, BernoulliNB and many more. we select different features (in count or numbers) to understand Accuracy Level of different Algorithms. Here Authors used Ensemble Learning where we combined number of classification algorithms and try voted algorithm to find best accuracy.
Re-identification Attack to Privacy-Preserving Data Analysis with Noisy Sample-Mean

In mining sensitive databases, access to sensitive class attributes of individual records is often prohibited by enforcing field-level security, while only aggregate class-specific statistics are allowed to be released. We consider a common privacy-preserving data analytics scenario where only a noisy sample mean of the class of interest can be queried. Such practice is widely found in medical research and business analytics settings. This paper studies the hazard of re-identification of entire class caused by revealing a noisy sample mean of the class. With a novel formulation of the re-identification attack as a generalized positive-unlabeled learning problem, we prove that the risk function of the re-identification problem is closely related to that of learning with complete data. We demonstrate that with a one-sided noisy sample mean, an effective re-identification attack can be devised with existing PU learning algorithms. We then propose a novel algorithm, growPU, that exploits the unique property of sample mean and consistently outperforms existing PU learning algorithms on the re-identification task. GrowPU achieves re-identification accuracy of 93.6% on the MNIST dataset and 88.1% on an online behavioral dataset with noiseless sample mean. With noise that guarantees 0.01-differential privacy, growPU achieves 91.9% on the MNIST dataset and 84.6% on the online behavioral dataset.
Towards a digital learning ecology to address the grand challenge in adult literacy

The adult learning ecology has expanded to include digital access, requiring enhancement of theories in the specific context of low-literate learners who may not have previously benefited from educational technology. The Digital Learning Ecology (DLE) framework identifies the three dimensions of context, design, and motivation, considering theories of cognitive learning, multimedia design, and language learning ecology that help support low-literate adults. A mobile learning solution based on DLE, designed for the Barbara Bush Foundation XPRIZE Adult Literacy competition, is described. The app design utilizes context proximal to adult learners' lives to motivate and maintain active engagement, improve multiple literacies, foster engagement, and encourage persistence. DLE dimensions are mapped to specific features of the app, along with the underlying cognitive principles that support learning. Both internal evaluations by authors and external evaluations by XPRIZE demonstrate significant improvement in learning outcomes for low-literate adults. Further, many low-literate adults in this study demonstrated low digital literacy, indicating a design need for a more accessible user interface with enhanced signifiers compared to those provided by universal design principles. Carefully designed mobile solutions based on the DLE framework can enhance and expand the learning ecology for low-literate adults, supporting both formal and non-formal learning environments.
Detection of Cervix Cancer from Pap-smear Images

Pap-smear test is used to detect cervical cancer, which ranks fourth in the ranking of cancer diseases in women worldwide. In this study, it is aimed to design a computer based decision system that can detect cervical cancer at an early stage. Normal and abnormal cells are found in the cervix images obtained as a result of the pap-smear test and the abnormal cells are marked on the image. The features extracted from the images were examined with pathologists and a dataset was created. For each of the 917 images in the Herlev dataset, these features were extracted and stored in a dataset. Support Vector Machines (SVM), Naive Bayes, Random Forest (RF), Multilayer Perceptron (MLP), Logistic Regression (LR), K- Nearest Neighbor (KNN) methods were applied to the created dataset, and accuracy values between 83% and 92% were obtained.
Data breaches in hospitality: is the industry different?

Purpose The purpose of this study is to expand on the existing literature by specifically examining data security incidents within the hospitality industry, assessing origins and causes, comparing breaches within the industry with those of other industries and identifying areas of concern. Design/methodology/approach A sample of data breach incidents is drawn from the Verizon VERIS Community Database (VCDB). Statistical comparisons between hospitality and non-hospitality industry firms are conducted following the Verizon A4 threat framework. Findings The results reveal that breaches between hospitality and non-hospitality firms differ significantly in terms of actors, actions, assets and attributes. Specifically, proportions of breaches in the hospitality industry are larger in terms of external actors, hacking and malware, user devices compromised and integrity violations. Additionally, compared to other industries, point-of-sales (POS) system breaches occur at a higher rate in the hospitality industry. The study finds that company size, hacking and malware predict the likelihood of a POS breach. Research limitations/implications The study uses secondary data and does not include the entire universe of data breaches. Originality/value In the quest to reduce data breach incidents, it is imperative to identify and assess the nature of data breach incidents between industries. Doing so permits the development of targeted industry-specific solutions rather than generic ones. This study systematically identifies differences between hospitality and non-hospitality data security incidents and then suggests areas where hospitality companies should focus future attention to mitigate breach incidents.
SGFA

In this paper we propose a user authentication method on smartphone devices based on the popular pattern lock mechanism, called the Simple Game For Authentication (SGFA). The SGFA mechanism resembles a simple game involving connecting points on the touch screen that is relatively easy to perform. To provide increased security, the mechanism utilizes both the user's knowledge and behavioral biometrics based on touch screen interaction an active layer of defence against unauthorized access. Based on an initial experiment, we determine the minimum number of strokes forming a password to reach a satisfying level of success rate. We discuss possible problems and attacks that can potentially break the process and evaluate the impact of over-the-shoulder attacks on the security of the password-matching layer. We further evaluate the biometric layer in terms of user authentication error rates. In an experiment involving 33 participants, the biometric layer achieved the false acceptance rate (FAR) and false rejection rate (FRR) of approx. 1.4% and 2%, respectively. Combined with the password-matching layer, the SGFA mechanism provides a more secure approach than pattern locks.
Finding counterfeited banknotes: the roles of vision and touch

Abstract Central banks incorporate various security features in their banknotes to enable themselves, the general public, retailers and professional cash handlers to detect counterfeits. In two field experiments, we tested central bank counterfeit experts and non-experts (the general public) in their ability to detect counterfeited euro banknotes. We varied exposure duration and perceptual modality (sight, touch or both). The counterfeit banknotes were actual counterfeits taken out of circulation. Experiment 1, in which participants only viewed the banknotes, showed that experts did reasonably well in detecting counterfeits even when exposure duration was limited to 500 ms. Non-experts did not reach the criterion for decent performance, marked by d’ = 1.25, although they did perform above chance. In Experiment 2, participants could both see and touch the banknotes, which resulted in better performance especially with longer exposure durations. The main finding of the current study is that visual information mostly impacts the decision-making process during the first glance, whereas tactile information increasingly aids performance as it continues to be accrued over time. Implications for the design of security features of new banknotes are discussed.
MSL Telecom Automated Anomaly Detection

The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.
Software Defect Density Prediction based on Multiple Linear Regression

The software quality is an indicator of the ability of a software system to function in accordance with the customer requirements. In maintaining an acceptable software quality level, defect density plays a major role. With the increasing complexity of the software, the chances of defects are also increasing. In this paper, a machine learning-based model for the prediction of the defect density of the future version of an open-source software (OSS) using the multiple linear regression technique is presented. The data of the OSS versions has been collected from the version control system, Git. Software metrics such as source lines of code, number of developers, number of commits, and code churn affect the defect density of the software, therefore, these metrics are selected as the predictor variables. The normality test of each of the variables is performed to show that the variable data belongs to a normal distribution. The results show that how much variability of defect density is predicted by these software metrics.
Decentralised Freelancing using Ethereum Blockchain

Ever since peer to peer transaction paradigm was implemented, there has been a certain belief that it can be used effectively in the commercial sector. The freelance economy has witnessed massive growth over the years, while India is the largest freelancing market with over 10 million freelancers. Freelancer is the person who works under different employers to earn money, with no assurance of long-time commitment to a particular employer. There are multiple centralised freelancing websites present but the problem with the centralised system is that it can be manipulated. In these platforms, freelancers and employers are dependent on the third-party for payment contracts. Hence, we are proposing a decentralised system based on public Blockchain Ethereum to resolve the issues generated by the third party. The proposed system will have peer to peer transaction using cryptocurrency and peer to peer reviews on the distributed ledger of Ethereum.