Reviewerly/exHarmony huggingface dataset:
GeoSecure-R: Secure Computation of Geographical Distance using Region-anonymized GPS Data

Today Location-Based Services (LBS) are used by millions of users all over the world. These services widely use multimedia sensory data such as GPS location. LBS provides enormous convenience to the users, but at the expense of continuously tracking their location. This raises severe privacy concerns. The distance computation using users' GPS coordinates is a crucial component in many LBS, such as the fitness tracker app and understanding driving habits. In this paper, we propose a method, called GeoSecure-R, to calculate the distance without revealing the users' exact location. The proposed method ensures users' privacy with region anonymity while maintaining utility, i.e., it provides LBS that requires only the traveled distance rather than the actual location of the user. Experimental results on Microsoft's GeoLife dataset show that the proposed method calculates the distance metric very close to what we get using the actual location of the user.
A primer on coupled state-switching models for multiple interacting time series

State-switching models such as hidden Markov models or Markov-switching regression models are routinely applied to analyse sequences of observations that are driven by underlying non-observable states. Coupled state-switching models extend these approaches to address the case of multiple observation sequences whose underlying state variables interact. In this article, we provide an overview of the modelling techniques related to coupling in state-switching models, thereby forming a rich and flexible statistical framework particularly useful for modelling correlated time series. Simulation experiments demonstrate the relevance of being able to account for an asynchronous evolution as well as interactions between the underlying latent processes. The models are further illustrated using two case studies related to (a) interactions between a dolphin mother and her calf as inferred from movement data and (b) electronic health record data collected on 696 patients within an intensive care unit.
Convolution Neural Network Learning Features for Handwritten Digit Recognition

The classical six-layer neural network is considered. This network is used to recognize handwritten digit patterns from the MNIST database. The influence of the size of mini-batches on the learning speed and pattern recognition accuracy is analyzed. The optimal sizes of mini-batch are obtained. The relationship between the accuracy of training and the accuracy of test samples is considered. The change in the values of the radius vector of the scales during training is shown. Conclusions are drawn about the influence of the initial value of the balance on the recognition accuracy. A more accurate formula is obtained for the limits, in which the initial values of the weights of the neural network are generated.
Application of Time Series Analysis and Forecasting Methods for Enterprise Decision-Management

The management of a complex manufacturing enterprise requires great attention to all interrelated business processes. Such processes are characterized by a high level of complexity, and additionally, by massive volumes of aggregated information. The current approach of production management is based on using a standard industrial methodology adopted for various enterprises. The industrial methodology contains algorithms and coefficients, accumulated from the statistic of the whole industry. The principal disadvantage of this approach is a strong contrariety between real production indicators and indicators described in the methodology. Enterprise management tasks must be solved using new automation and intellectualization approaches to analysis and forecasting of production indicators. Information systems of the enterprise contain all the necessary information to evaluate the state of production. The production processes can easily be represented by a discrete time series that could be extracted from information systems. It is necessary to use time series that modeling with type 2 fuzzy sets to account for the fuzziness of the real world. Using the fuzzy approach allows creating models that can improve the quality of the decision-making. The fuzzy approach and ontology engineering methods are used in this research. The hybridization of these approaches allows analyze the data about production processes and makes linguistic summarization of the production state in the process of decision-making.
Focused Questions and Answer Generation by Key Content Selection

Automatic Question Answer Generation(AQAG) as a part of Natural Language Processing is an ongoing research trend. AQAG is extremely helpful for Computer-Assisted Assessments where it reduces the expense of manual construction of questions and satisfies the need for a constant supply of new questions. Exam styled questions generated from Automatic Question Generation are mostly "WH"("What", "Who", and "Where") or reading comprehension type. In order for the questions to be most natural or human-like, they need to be diverse or semantically different, based on their levels of assessment, while their answers might remain the same. Hence generating diverse sequences as a part of question generation has become an important NLP task, especially in the education and publishing industry. In this paper, we propose a method of automatically generating answers and diversified sequences corresponding to those answers by introducing a new module called the "Focus Generator". This module guides the decoder in an existing "encoder-decoder" model to generate questions based on selected focus contents. We use a keyword generation algorithm to generate answer tags and a pool of candidate focus from which three best focus are chosen according to the level of information contained in them. We then use this focus content to generate questions that are semantically different from each other. Our work uses a simple architecture by using a single "Focus Generator" module and experimental results show that our module demonstrates 1.2% improvements in BLEU4 score and 20% less training time over the current state-of-the-art model. Our model is also user friendly and provides ease of deriving inference.
The Impact of Implicit Information Exchange in Human-agent Negotiations

Intelligent virtual agents have been developed to study, assess and teach a variety of human interpersonal skills. Here we examine the impact of an agent's perspective-taking sophistication on human negotiators. Good perspective-takers can discover creative solutions that benefit both parties, but many have difficulty with this skill. In particular, novices focus on explicit goal-statements (e.g., "I want apples more than bananas") but discount goal-relevant information implicit in the opponent's offers. Many human-agent negotiation agents similarly ignore implicit information. We examined the influence of implicit information on human negotiators by independently enhancing agents in two ways: do agents communicate implicit information and do they attend to implicit information communicated by users. We find that communicating implicit information seems to confuse user's perspective-taking ability, yet paradoxically, helps lead them to better outcomes. In contrast, an agent that attends to user's implicit communications shows better perspective-taking but fails to translate this into better outcomes. These results emphasize the challenges associated with implicit information. We discuss how these results impact the design of negotiation agents for applications, analysis and pedagogy.
Sensor2Vec: an Embedding Learning for Heterogeneous Sensors for Activity Classification

Based on the idea of word2vec embedding method in NLP, this paper presents a novel idea called sensor2vec which captures the contextual information of the heterogeneous sensory information in the ambient assisted living setting. The contextual information is essential in order to classify and understand the human activity using multi-modal sensory data. In the activity classification, the sensor2vec embedding method is able to do the pre-processing which produce the embedding layer which represents the semantic value in the high-dimensional space. The preliminary experiment based on LSTM shows that the sensor2vec performs better classification result than the one-hot inputs.
20 Years of Software Security

This article examines how the software security industry has evolved and where it must still improve.
Using Machine Learning to Identify Code Fragments for Manual Review

Code reviews are one of the first quality assurance tasks in continuous software integration and delivery. The goal of our work is to reduce the need for manual reviews by automatically identify which code fragments should be further reviewed manually. We conducted an action research study with two companies where we extracted code reviews and build machine learning classifiers (AdaBoost and Convolutional Neural Network– CNN). Our results show that the accuracy of recognizing code fragments that require manual review, measured with Matthews Correlation Coefficient, was 0.70 in the combination of our own feature extraction and CNN. We conclude that this way of combining automation with manual code reviews can improve the speed of reviews while providing organizations with the possibility to support knowledge transfer among the designers.
Design of Distributed Power Trading Mechanism Based on P2P Contract

With the increase in the number of renewable energy power generation, the scale of prosumers on the distribution network side is increasing. At the same time, P2P with decentralized features can provide mechanism support for distributed power transactions. Based on this, this paper designs a distributed electrical energy trading mechanism based on P2P contracts. First of all, this paper designs the method of bidding for prosumers based on P2P combining prospect theory and economic theory. Secondly, the trading members are bilaterally matched according to the principle of priority matching. Finally, the transaction revenue and expenditure of each node are calculated. Combining with calculation examples, this paper analyzes the influence of prosumer rationality on P2P decision-making and final transaction revenue.
From features to roles

The detection of interactions is a challenging task present in almost all stages of software development. In feature-oriented system design, this task is mainly investigated for interactions of features within a single system, detected by their emergent behaviors. We propose a formalism to describe interactions in hierarchies of feature-oriented systems (hierarchical interactions) and the actual situations where features interact (active interplays). Based on the observation that such interactions are also crucial in role-based systems, we introduce a compositional modeling framework based on concepts and notions of roles, comprising role-based automata (RBAs). To describe RBAs, we present a modeling language that is close to the input language of the probabilistic model checker Prism. To exemplify the use of RBAs, we implemented a tool that translates RBA models into Prism and thus enables the formal analysis of functional and non-functional properties including system dynamics, contextual changes, and interactions. We carry out two case studies as a proof of concept of such analyses: First, a peer-to-peer protocol case study illustrates how undesired hierarchical interactions can be discovered automatically. Second, a case study on a self-adaptive production cell demonstrates how undesired interactions influence quality-of-service measures such as reliability and throughput.
Propensities and Second Order Uncertainty: A Modified Taxi Cab Problem

The study of people's ability to engage in causal probabilistic reasoning has typically used fixed-point estimates for key figures. For example, in the classic taxi-cab problem, where a witness provides evidence on which of two cab companies (the more common 'green'/less common 'blue') were responsible for a hit and run incident, solvers are told the witness's ability to judge cab color is 80%. In reality, there is likely to be some uncertainty around this estimate (perhaps we tested the witness and they were correct 4/5 times), known as second-order uncertainty, producing a distribution rather than a fixed probability. While generally more closely matching real world reasoning, a further important ramification of this is that our best estimate of the witness' accuracy can and should change when the witness makes the claim that the cab was blue. We present a Bayesian Network model of this problem, and show that, while the witness's report does increase our probability of the cab being blue, it simultaneously decreases our estimate of their future accuracy (because blue cabs are less common). We presented this version of the problem to 131 participants, requiring them to update their estimates of both the probability the cab involved was blue, as well as the witness's accuracy, after they claim it was blue. We also required participants to explain their reasoning process and provided follow up questions to probe various aspects of their reasoning. While some participants responded normatively, the majority self-reported 'assuming' one of the probabilities was a certainty. Around a quarter assumed the cab was green, and thus the witness was wrong, decreasing their estimate of their accuracy. Another quarter assumed the witness was correct and actually increased their estimate of their accuracy, showing a circular logic similar to that seen in the confirmation bias/belief polarization literature. Around half of participants refused to make any change, with convergent evidence suggesting that these participants do not see the relevance of the witness's report to their accuracy before we know for certain whether they are correct or incorrect.
Secret Sharing MPC on FPGAs in the Datacenter

Multi-Party Computation (MPC) is a technique enabling data from several sources to be used in a secure computation revealing only the result while protecting the original data, facilitating shared utilization of data sets gathered by different entities. The presence of Field Programmable Gate Array (FPGA) hardware in datacenters can provide accelerated computing as well as low latency, high bandwidth communication that bolsters the performance of MPC and lowers the barrier to using MPC for many applications. In this work, we propose a Secret Sharing FPGA design based on the protocol described by Araki et al. We compare our hardware design to the original authors' software implementations of Secret Sharing and to work accelerating MPC protocols based on Garbled Circuits with FPGAs. Our conclusion is that Secret Sharing in the datacenter is competitive and when implemented on FPGA hardware was able to use at least 10× fewer computer resources than the original work using CPUs.
Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of breast tissue are discussed. The algorithms are based on recent advances in multi-dimensional signal processing and aim to advance current state-of-the-art computer-aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi-parametric computer-aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi-supervised deep learning and self-supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE-MRI. Since some of the approaches discussed are also based on time-lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.
Smart Crawler for Harvesting Deep web with Multi-Classification

In recent era data available on the internet is playing a vital role. According to research, most precious and valuable data is present in the deep web so interest in techniques to efficiently site invisible web is increasing. The challenges to extract the deep web are requirement of huge volume of resources, dynamic nature of the deep web, coverage of a wider area of deep web and higher efficiency of collected results from deep web with accuracy. Along with all the above challenges, user demand of privacy and identity is to be maintained. In this paper we proposed a smart crawler that efficiently searches the deep web and avoids visiting irrelevant pages. A smart crawler starts crawling from the center page of seed URL and goes on crawling till the last link available. The crawler has an ability to separate active and inactive links based on requests to sever of hyperlink. The crawler also contains text-based site classifier using neural network and natural language processing as Term Frequency Inverse Document Frequency and Bag of Words with supervised machine learning techniques as logistic regression, support vector machine and naive bayes. Also HTML tags are extracted from hyperlinks along with data which plays a huge role in data analysis and all this is separately saved in a centralized database. Our experimental results with efficient link reaping rate and classification show higher accuracy compared to different crawlers.
Disentangled features with direct sum decomposition for zero shot learning

The core problem of zero-shot learning (ZSL) is extracting concise feature from cumbersome and intricate semantic space. While semantic vectors were mainly used to constrain the distance between different classes in embedding space in existing ZSL methods, we first emphasize the importance of the relationship between different dimensions in semantic space. In this paper, we propose a novel inductive approach, Multiple Semantic Subspaces Network (MSSN), to simplify the complex and intractable semantic features. Our method generates multiple disentangled subfeatures via direct sum decomposition, which not only retain completely semantic information, but also obtain simple independent hierarchical features. Specially, instead of original space, using projection subspace to map embedding space can reduce the difficulty of model optimization and enhance the generalization ability of the model. Full experiments are achieved on almost all datasets and comparision with many algorithms which exist in the present latest literature. Compared with nineteen competitors, the results show that our model outperforms the state-of-the-art on conventional ZSL setting and has a very competitive performance on generalized ZSL setting.
Class-Aware Image Search for Interpretable Cancer Identification

In recent times, the performance of computer-aided diagnosis systems in classification of malignancies has significantly improved.Search and retrieval methods are specifically important as they assist physicians in making the right diagnosis in medical imaging owing to their ability of obtaining similar cases for a query image.Supervised classification algorithms are generally more accurate than unsupervised search-based classifications; however, the latter may more easily provide insights into the decision-making process by providing a group of similar cases and their corresponding metadata (i.e., diagnostic reports) and not simply a class probability.In this study, we propose a class-aware search operating on deep image embeddings to increase the accuracy of content-based search.We validate our methodology using two different publicly available datasets, one containing endometrial cancer images and the other containing colorectal cancer images.The proposed class-aware scenarios can enhance the accuracy of the searchbased classifier, thereby making them more feasible in practice.With search results providing access to the metadata of retrieved cases (i.e., pathology reports of evidently diagnosed cases), such a combination has clear benefits for assisting experts with explainable results.
Flexible weighted dirichlet process mixture modelling and evaluation to address the problem of forecasting return distribution

Forecasting volatility has been widely addressed in the fields of finance, environmetrics, and other areas involving massive time series. The important part of addressing this problem is how to specify the error term's distribution. With a weaker distribution assumption, we achieve greater model flexibility. In this paper, we present a flexible semiparametric Bayesian framework to address the problem of forecasting volatility in time series data by introducing the weighted Dirichlet process mixture (WDPM). We illustrate the advantages of WDPM using simulation data and stock return data.
She talks to me as if she were alive

It has long been observed that people treat technical entities like computers as social actors. Numerous studies have been conducted on media equation theory, which describes users' social reactions toward devices to be unconscious. These users consider this inappropriate when thought about consciously. However, both long-term trends as well as the reactions of children have been neglected - although children might indeed differ in their reactions, as they are especially prone to the tendency to humanize and anthropomorphize. This study examined the social behavior of 20 children aged six to twelve toward a voice assistant during a five-week field study. In the course of three sessions, interviews were conducted to assess the children's tendency to socially react to the devices and to what extent they perceive such reactions as appropriate. Additionally, the tendency to anthropomorphize was assessed together with qualitative data to gain insight into cues that would trigger anthropomorphization. Results indicate that age and duration have an effect on the tendency to anthropomorphize. However, age and duration have only a partial effect on social reactions and the evaluation of their appropriateness. Furthermore, natural speech appeared to be a rather strong cue in triggering tendencies to anthropomorphize in the children.