Reviewerly/exHarmony huggingface dataset:
SPARR: Spintronics-based private aggregatable randomized response for crowdsourced data collection and analysis

The rapid development of information and communication technologies has revolutionized people’s lives. A large amount of user behavioral data is generated by client software and is valuable for organizations (e.g., Apple, Google, and Samsung) to adjust their commercial strategies or to promote the quality of service. However, data collection raises privacy concerns. Either malicious or accidental leaking of sensitive information may cause serious consequences. Randomized response is a candidate solution, and it can provide “plausible deniability” for each individual. The aggregators can only collect sanitized data, but they can still conduct analyses and make predictions. Existing randomized response solutions can either apply pseudo-randomized functions with a predicable period of random numbers or have severely restricted functionality rather than providing practical privacy protection and efficient usability. We propose the spintronics-based private aggregatable randomized response (SPARR) for crowdsourced data collection while satisfying differential privacy. SPARR utilizes a spintronics-based true random number generator and multilayer randomized response to guarantee true data randomness and high data utility. Through multilayer randomized response and the corresponding subtle decoding algorithm, SPARR can obtain exceptionally high accuracy and efficiency in the frequency estimation of client strings even for small collections of applications, compared to state-of-the-art methods.
An UAV Path Planning Method in Complex Mountainous Area Based on a New Improved Ant Colony Algorithm

The route planning of unmanned aerial vehicles (UAVs) is being studied to carry out emergency transport missions in mountainous areas. Based on performance analysis and drone comparison, the route planning constraints are discussed, and route safety is proposed. The ant colony algorithm first establishes Tyson polygons based on mountain positions and obtains feasible solutions for drones that avoid mountain obstacles. Second, in order to avoid dense mountain areas, the safety of the orbit is limited and feasible solutions are reduced. In addition, the shortest path is searched using the ant colony algorithm, and finally, eliminate unnecessary obstacles in the path to shorten the distance, and gain a comprehensive understanding of the drone performance criteria to smooth corners to optimize the actual stroke. safe way. The example analysis shows that the improved ant colony algorithm converges faster than the traditional algorithm and produces a shorter path.
Application of Deep Learning to Pre-processing of Cousumer's Eye Tracking Data in Supermarket

The purpose of this study is to automate pre-processing of eye tracking data. In an investigation with eye tracking in a field such as supermarket, pre-processing of data has enormous cost. This is because it is very difficult to map consumer's gaze points to certain snapshot due to her/his movement. This study uses deep learning for automating pre-processing of eye tracking data. General object recognition using deep learning can classify an image into various classes. The proposed method attempts to classify fixated object of consumer into three classes, product, promotion and etc., based on the result of general object recognition. This paper discusses the applicability of proposed method through cross validation using eye tracking data preprocessed manually.
Organizing Tagged Knowledge: Similarity Measures and Semantic Fluency in Structure Mining

Abstract Recovering a system’s underlying structure from its historical records (also called structure mining) is essential to making valid inferences about that system’s behavior. For example, making reliable predictions about system failures based on maintenance work order data requires determining how concepts described within the work order are related. Obtaining such structural information is challenging, requiring system understanding, synthesis, and representation design. This is often either too difficult or too time consuming to produce. Consequently, a common approach to quickly elicit tacit structural knowledge from experts is to gather uncontrolled keywords as record labels—i.e., “tags.” One can then map those tags to concepts within the structure and quantitatively infer relationships between them. Existing models of tag similarity tend to either depend on correlation strength (e.g., overall co-occurrence frequencies) or on conditional strength (e.g., tag sequence probabilities). A key difficulty in applying either model is understanding under what conditions one is better than the other for overall structure recovery. In this paper, we investigate the core assumptions and implications of these two classes of similarity measures on structure recovery tasks. Then, using lessons from this characterization, we borrow from recent psychology literature on semantic fluency tasks to construct a tag similarity measure that emulates how humans recall tags from memory. We show through empirical testing that this method combines strengths of both common modeling paradigms. We also demonstrate its potential as a preprocessor for structure mining tasks via a case study in semi-supervised learning on real excavator maintenance work orders.
Face Recognition Using RLDA Method Based on Mutated Cuckoo Search Algorithm to Extract Optimal Features

Regularized-LDA (R-LDA) is one of the most successful holistic approaches that is introduced to overcome the “small sample size” (SSS) problem of the LDA method, which is often encountered in Face Recognition (FR) tasks. R-LDA is based on reducing the high variance of principal components of the within-class scatter matrix to optimize the regularized Fisher's criterion. In this article, the authors assume that some of these components do not have significant information and they can be discarded. To this end, the authors propose CS-RLDA that uses a Cuckoo search (CS) algorithm to select the optimal eigenvectors from a within-class matrix. However, the CS algorithm has a slow convergence speed. To deal with this problem, and to create more diversity and better trade-off between exploitation and exploration around the best solutions, the authors have modified the basic cuckoo algorithm by using a mutation operator. The experimental results performed on the ORL and UMIST databases indicate that the proposed method enhances the performance of FR.
Analyzing Social Network Data Using Deep Neural Networks: A Case Study Using Twitter Posts

The limitation on the total number of characters compels Twitter users to compose their messages more succinctly, suggesting a stronger association between text and image. In this paper, we employ computer vision and word embedding techniques to analyze the relationship between image content and text messages and explore the rich information entangled. Specifically, we collected all tweets which include keywords related to Taiwan during 2017. After data cleaning, we apply machine learning techniques to classify tweets into travel and non-travel types. This is achieved by employing deep neural networks to process and integrate text and image information. Within each class, we use hierarchical clustering to further partition the data into different clusters and investigate their characteristics. Through this research, we expect to identify the relationship between text and images in a tweet and gain more understanding of the properties of tweets on social networking platforms. The proposed framework and corresponding analytical results should also prove useful for qualitative research.
Developing Distributed PV in Beijing: Deployment Potential and Economics

The building sector consumed a total of 580 million tons-coal equivalent (Mtce) terminal energy in China in 2018 including 1888 terawatt-hours (TWh) electricity, accounting for 20.2% of total terminal energy consumption in this country. As the capital of China, Beijing is striving to improve the air quality while ensuring power and heat supply due to heavy reliance on electricity intake from other energy-rich provinces. The distributed photovoltaic, as a flexible application of renewable energy systems in urban and rural regions, can contribute to the power supply for rapid urbanization and mitigate the negative environmental impact of fossil energy use. In the context of grid parity, this article provides a systematic analysis of solar resource potential, power generation economics and policy support for the rooftop photovoltaic (PV) system in Beijing. The deployment potential of rooftop PV is estimated to be 11.47 GW and the large-scale commercial rooftop PV is approaching grid parity. Furthermore, this article discusses the feasibility of large-scale distributed PV deployment in Beijing by considering distributed electricity trade envisioned the ongoing power market reform in China.
Clan-Based Cultural Algorithm for Feature Selection

Feature selection is the process of selecting a subset of relevant features from datasets, which can then be used for building machine learning models. In this study we propose a novel multi-population Clan-based Cultural algorithm (CCA) for feature selection. CCA models the complex social interactions within a clan-based society. Each population or clan contains solutions that are closely related and are managed by the population's own local belief space. There is also a global belief space that is shared by clans creating a tiered belief space structure. The global belief space contains the best solution across all the clans and provides the algorithm with a local and global view of the search landscape. At given generations, individual solutions migrate to another clan, different from its source clan, and combines with the clans population to form a single mating pool. Crossover and mutation operators are then applied and the fittest solutions are selected. This ensures that the search is directed toward the global optimum. The proposed algorithm was tested on benchmark datasets and its performance was compared with other evolutionary based feature selection methods. Results show that CCA achieved the highest accuracy, AUC and F1 values of 99.3%, 99.2% and 99.1% respectively across most of the datasets. The proposed CCA model selected fewer features to achieve its highest performance values than alternative approaches, suggesting CCA is more effective as a feature selection model.
Trust Mechanism of Cloud Manufacturing Service Platform Based on Blockchain

Cloud manufacturing is a new kind of manufacturing paradigm, in which geographically distributed various manufacturing resources belonging to different enterprises are encapsulated as manufacturing services (service providers) for providing users (service consumers) with services and users are able to find, select, and trade with MSs via Networks on a cloud manufacturing platform. To solve the trust issues between service providers and service consumers, a trust mechanism based on blockchain(BC) for services' information was proposed. Based on the decentralized, distributed storage and non-tamperable of BC, using public BC and consortium BC to construct a trusted mechanism for cloud manufacturing service platforms. Public BC is high credible, consortium BC is efficient and has larger capacity. The trusted mechanism can ensure that the source and storage of the service information is trusted, to solve the problem that the cloud manufacturing platform service data is untrustworthy and easily falsified. It can provide trusted choices to service consumers while operating efficiently and at low cost.
Speech Recognition Model Compression

Deep Neural Network-based speech recognition systems are widely used in most speech processing applications. To achieve better model robustness and accuracy, these networks are constructed with millions of parameters, making them storage and compute-intensive. In this paper, we propose Bin & Quant (B&Q), a compression technique using which we were able to reduce the Deep Speech 2 speech recognition model size by 7 times for a negligible loss in accuracy. We have shown that our algorithm is generally beneficial based on its effectiveness across two other speech recognition models and the VGG16 model. In this paper, we have empirically shown that Recurrent Neural Networks (RNNs) are more sensitive to model parameter perturbation than Convolutional Neural Networks (CNNs), followed by fully connected (FC) networks. Using our B&Q technique, we have shown that we can establish parameter sharing across layers instead of just within a particular layer.
Intelligent System for Generation and Evaluation of e-Learning Tests Using Integer Programming

The major challenge in e-learning is the assessment as a tool to measure students’ knowledge. In this regard an intelligent system for generation and evaluation of e-learning tests using integer programming is proposed. The described system aims to determine a number of questions with different degree of difficulty from a predefined set of questions that will compose the test. It allows also generating tests with different level of complexity. To realize the selection of the questions for different levels of tests two optimization models are proposed. Both of these models are of linear integer programming. The first of them determines the minimum number of questions by selecting among more difficult questions, while the second one aims to maximize the number of questions by selecting among less difficult questions. The proposed intelligent system for generating and evaluating e-learning tests with different levels of complexity is implemented as web-based application. The numerical testing of the developed prototype of the intelligent system for generation of tests for e-learning purposes is demonstrated in a web programming course.
Retroactive Packet Sampling for Traffic Receipts

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network transparency'' system. In this system, networks emit receipts for a small sample of the packets they observe, and a monitor collects these receipts to estimate each network's loss and delay performance. Sampling is a good building block for this system, because it enables a solution that is flexible and combines low resource cost with quantifiable accuracy. The challenge is cheating resistance: when a network's performance is assessed based on the conditions experienced by a small traffic sample, the network has a strong incentive to treat the sampled packets better than the rest. We contribute a sampling algorithm that is provably robust to such prioritization attacks, enables network performance estimation with quantifiable accuracy, and requires minimal resources. We confirm our analysis using real traffic traces.
Credit Risk Analysis Model in Microfinance Institutions in Peru Through the use of Bayesian Networks

In this paper, we propose a risk analysis model to obtain the probability of default of microfinance clients in Peru. Our model uses trends of predictive analysis through variants of neural network algorithms; and data processing methodologies such as the Knowledge Discovery in Databases (KDD). The analysis method is used through Bayesian networks which allows the customer data evaluation and is related to our model data. This model is composed of 5 phases: 1. The input elements for the analysis; 2. The process of evaluation and analysis; 3. The regulatory standards; 4. The technological architecture; 5. The output elements. This model allows knowing the probability of compliance of a client with 84% prediction accuracy. The model validation was carried out in a microfinance institution in Lima, Peru, using cross-validation, evaluating the sensitivity and specificity of the results.
Spell checking algorithm for agglutinative languages “Central Kurdish as an example”

Kurdish language is an agglutinative language. Currently, in Kurdistan all legal paperwork is written in Kurdish using word processors. Spelling is an important aspect of language writing. Poor spelling can be interfering with communication between the writer and the reader. Unfortunately, current word processors lack the existence of built-in spell checkers for every language. Individual nations create their own customized dictionaries and add them to the word processors for error correction. Presently, the Kurdish language lacks a reliable spell checker. This imperfection needs to be resolved. Overcoming this problem by this study will flourish the Kurdish writing and helps word spelling standardization. This paper proposes an algorithm to design and build a reliable and comprehensive Kurdish spell checker that can be used by the public in Kurdistan. The algorithm identifies the misspelled words in the language and suggests one or more alternative words as the correct spelling. The spell checker was implemented in C# and as a. Net standard library, so it can be integrated into popular word processing applications. The performance and accuracy of the algorithm has been approved by experimental results.
Self-Play for Training General Fighting Game AI

In this paper, we train a general fighting game AI from self-play games to outperform an unseen opponent AI. It has been reported that an AI using Deep Q Network (DQN) can outperform the training partner. However, according to our experience, the DQN AI is not always superior to a new opponent, unseen before. By learning from self-play, we overcome this drawback while maintaining the DQN AI’s strong points. Our experimental results show that it is more effective to use a variety of AIs with different behaviors as training partners.
Managing collaborative research data for integrated, interdisciplinary environmental research

Abstract The consistent management of research data is crucial for the success of long-term and large-scale collaborative research. Research data management is the basis for efficiency, continuity, and quality of the research, as well as for maximum impact and outreach, including the long-term publication of data and their accessibility. Both funding agencies and publishers increasingly require this long term and open access to research data. Joint environmental studies typically take place in a fragmented research landscape of diverse disciplines; researchers involved typically show a variety of attitudes towards and previous experiences with common data policies, and the extensive variety of data types in interdisciplinary research poses particular challenges for collaborative data management. In this paper, we present organizational measures, data and metadata management concepts, and technical solutions to form a flexible research data management framework that allows for efficiently sharing the full range of data and metadata among all researchers of the project, and smooth publishing of selected data and data streams to publicly accessible sites. The concept is built upon data type-specific and hierarchical metadata using a common taxonomy agreed upon by all researchers of the project. The framework’s concept has been developed along the needs and demands of the scientists involved, and aims to minimize their effort in data management, which we illustrate from the researchers’ perspective describing their typical workflow from the generation and preparation of data and metadata to the long-term preservation of data including their metadata.
Control-based algorithms for high dimensional online learning

In the era of big data, the high-dimensional online learning problems require huge computing power. This paper proposes a novel approach for high-dimensional online learning. Two new algorithms are developed for online high-dimensional regression and classification problems, respectively. The problems are formulated as feedback control problems for some low dimensional systems. The novel learning algorithms are then developed via the control problems. Via an efficient polar decomposition, we derive the explicit solutions of the control problems, substantially reducing the corresponding computational complexity, especially for high dimensional large-scale data streams. Comparing with conventional methods, the new algorithm can achieve more robust and accurate performance with faster convergence. This paper demonstrates that optimal control can be an effective approach for developing high dimensional learning algorithms. We have also for the first time proposed a control-based robust algorithm for classification problems. Numerical results support our theory and illustrate the efficiency of our algorithm.
VIKOR Algorithm Based on Cuckoo Search for Multi-document Text Summarization

Due to the huge amount of documents on the internet and the redundancy contained in each document makes it difficult for the user to get useful information. Automatic text summarization is a solution to such problems of information overload. Text summarization is the process of generating a single document summary from a set of documents or from a single document. This paper proposes a method based on the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) algorithm. In the first stage based on extracting six features for each sentence in the document collection, the next cuckoo search algorithm was applied to compute weights for these extracted features. Finally, the VIKOR algorithm used to rank the sentences. The sentences with a high score and less redundancy selected to be included in the final summary. The proposed model is evaluated by using the dataset supplied by the Text Analysis Conference (TAC-2011) for English documents. The performance of the proposed model is evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric. The obtained results support the effectiveness of the proposed model.
Victim or Attacker? A Multi-dataset Domain Classification of Phishing Attacks

In “phishing attacks”, phishing websites disguised as trustworthy websites attempt to steal sensitive information from end users. Remediation options differ depending on whether the phishing website is hosted on a legitimate but compromised domain, in which case the domain owner is also a victim, or whether the domain itself is maliciously registered by an attacker. We propose here a novel machine-learning domain classifier, introducing features based on the internet presence and history of a domain, using only publicly available information. Using a phish feed and malicious domain feed from the Anti-Phishing Working Group (APWG), evaluation of our domain classifier achieves 94% accuracy on future malicious domains, while maintaining 88% and 92% accuracy on malicious and compromised datasets respectively from two other sources. To increase our training set we introduce a semi-supervised technique to label part of APWG's phish feed. For the rest of the feed we use our classifier and show that 62% of the websites hosting attacks are compromised while the remaining 38% belong to the attackers. The result of this research is a tool which gives some important insights on phisher's current modus operandi. It also provides a quick mechanism, entirely based on freely available data, to assess crucial information about the server and better respond to an attack by having it taken down as quickly as possible.