Reviewerly/exHarmony huggingface dataset:
Enhanced anonymous models for microdata release based on sensitive levels partition

Depending on the inherent characteristic of sensitive attributes, even those existing enhanced anonymous models still permit the private information to be disclosed or have other limitations so far, such as skewness attacks or sensitive attacks. Based on this, three novel enhanced anonymous models, i.e., (p,αisg)-sensitive k-anonymity model, (p+,αisg)-sensitive k-anonymity model and (pi+,αisg)-sensitive k-anonymity model, are proposed to improve the privacy preservation by fully considering the sensitivity of different sensitive values on the sensitive attribute so as to realize better personalized privacy preservation. Different from the traditional methods, which basically quantify the sensitive level of the specific sensitive value focusing on user-defined classification approaches, the sensitivity of different sensitive values based on self-information is fully considered to obtain sensitive levels partition (SLP) so as to achieve better privacy by designing SLP from qualitative status towards quantitative status in our models. The conception of identical sensitive group (ISG), which is generated from the idea of hierarchical clustering method by using SLP algorithm, is introduced to design the anonymous models for better defending against sensitive attacks. In this case, the sensitive values with the most similar sensitivity are most likely clustered in the same ISG. Moreover, the frequency of each ISG is confined to no more than the specific personalized threshold αisg in any equivalence class without ending up with “one size for all” restrictions. Here, higher sensitive of ISG should be assigned a lower frequency constraint for resisting sensitive attacks so as to achieve better privacy. Then, two clustering algorithms are devised by using the idea of bottom-up greedy methods. Experiment results based on two real-world datasets show that our three anonymous models can effectively protect data privacy and enhance data security and practicality with certain information loss.
HumTouch: Finger gesture recognition on hydrogel-painted paper using hum-driven signals

We propose a passive touch sensor that can be implemented on semi-conductive materials, such as paper. This sensor detects electric currents leaked from a human body when touched with bare fingers. Such electric currents are mostly produced by AC power lines in the environment. We tested the potential of the sensor for gesture recognition by using a paper sheet painted with a hydrogel ink. A gesture classification algorithm combining the k-means method and Mahalanobis' generalized distance could successfully classify 90% of the gestures drawn on the paper with a fingertip. Gestures including multi-directional lines and circles were nearly perfectly classified whereas digits, such as 2 and 8 exhibited difficulties with classification. The hydrogel ink can turn other non-conductive materials comprising woods and plastic into touch sensors, and such hum-based touch sensors are expected to find practical applications in gesture recognition.
Mining Non-redundant Periodic Frequent Patterns

Discovering periodic frequent patterns has been useful in various decision making. Traditional algorithms, however, often report a large number of such patterns, most of which are often redundant since their periodic occurrences can be inferred from other periodic frequent patterns. Employing such redundant periodic frequent patterns in decision making would often be detrimental if not trivial. To address this challenge and report only non-redundant periodic frequent patterns, this paper employs the concept of deduction rules in mining the set of non-redundant periodic frequent patterns. A Non-redundant Periodic Frequent Pattern Miner (NPFPM) is subsequently proposed to achieve this purpose. Experimental analysis on benchmark datasets show that NPFPM is efficient and can effectively prune the set of redundant periodic frequent patterns.
Exploiting Caching and Prediction to Promote User Experience for a Real-Time Wireless VR Service

In this paper, we propose a novel wireless virtual reality scheme by caching resource on head-mounted displays (HMD) and exploiting head movement prediction to improve user experience. We render images for the future request based on the user's head movement prediction at the server-end to reduce the motion-to- photon latency and cache those rendered images on the HMD to form an image pool. We then develop a corresponding algorithm to select and warp the exact image from the image pool for display. Furthermore, a performance metric - warping distance is defined and used to evaluate the image quality of the proposed scheme. Finally, real dataset-driven results show that the proposed scheme is able to provide higher image quality as well as experience consistency compared with existed schemes.
Fairness issues in AI systems that augment sensory abilities

Systems that augment sensory abilities are increasingly employing AI and machine learning (ML) approaches, with applications ranging from object recognition and scene description tools for blind users to sound awareness tools for Deaf users. However, unlike many other AI-enabled technologies, these systems provide information that is already available to non-disabled people. In this paper, we discuss unique AI fairness challenges that arise in this context, including accessibility issues with data and models, ethical implications in deciding what sensory information to convey to the user, and privacy concerns both for the primary user and for others.
Photo-realistic emoticon generation using multi-modal input

Emojis have changed the way humans communicate today. They are the most convenient non-linguistic social cues available to us in this era of social media. But there is no methodology wherein users can creatively interact with their systems to generate personalised emojis or edit existing ones. While there have been some experiments that enable networks to create images, there is no comprehensive solution that gives users the control to create personalised emoticons. In this work, we propose an end-to-end architecture to create a realistic emoji from a roughly drawn sketch. Our generated emojis show a PSNR value of 20.30dB and a SSIM of 0.914. Additionally, we look at a multi-modal architecture which generates an emoji when given an incomplete sketch along with a handwritten word describing the associated emotion.
Cross-projects software defect prediction using spotted hyena optimizer algorithm

Cross-projects software defect prediction improves the quality of new software projects or projects with a shortage of historical data. Therefore, various data mining techniques are recommended in this field. The classification accuracy issue is considered one of the most significant problems due to the shortage and heterogeneous in historical data. To address this challenge, this research utilizes a spotted hyena optimizer algorithm as a classifier to predict defects through cross-projects. Confidence and Support are utilized as a multi-objective fitness function to look for the best classification rules. These classification rules are used to predict defects for new projects or other projects with insufficient data. The datasets of NASA such as JM1, KC1, and KC2 are used. By applying spotted hyena optimizer algorithm as a classifier on one dataset and predicting defects in the other two datasets, accuracy is reported 84.6, 92.0, 82.4, 90.7, 86.6 and 81.8 for JM1, KC1, and KC2 respectively. These accuracy values are better than the most significant data mining techniques in the field such as Support Vector Machine, Naïve Bayes, Boosting, C4.5, and Bagging. Also, the proposed research discusses other performance measures such as precision, recall, and f-measure. The conclusion proves that there are many features of McCabe and Halstead that have a strong impact to generate highly accurate predictors for defects such as McCabe's line count of code, McCabe's cyclomatic complexity, McCabe's essential complexity, McCabe's design complexity iv, Halstead's effort, Halstead's time estimator, Halstead's line count, Halstead's count of line of comments and total operators.
Assessment of Image Enhancement Procedures for Matching Sketches to Photos

Image supported examination procedures are widely considered in the variety of fields. Typically, the image examination involves in the extracting the region of interest or features for the further investigation. In human detection system and also in criminal science, identification of the correct person is essential for the authentication requirements. In these applications, usually, the sketch drawn by an expert or a computer is matched against the digital photographs available in the criminal or the public database. During this examination, essential facial features are extracted from the sketch and digital photography is compared to identify the percentage of fit. The proposed work in this paper aims to examine the existing image enhancement and identification procedures existing in the literature. After creating the essential sketch for a person, a comparative analysis against the digital photo (Gray / RGB scale) of the person is performed, and the image similarity measures, such as PSNR, MSE, and SSIM are computed. In this paper, the benchmark datasets, like CUHK and PRIP-HDC are considered for the examination, and the empirical trial is achieved with the help of Matlab software.
Scalable Blockchain Implementation for Edge-based Internet of Things Platform

State-of-the-art centralized internet of things (IoT) architectures suffer from scalability issues in terms of both devices and transactions. Centralized architecture infers a central server with finite resources to process the communication demands of large IoT networks. The security of such IoT infrastructures is contingent on the protection of the central server. To address the challenges of security and scalability of transactions, we design and implement an edge-based blockchain network, where a set of edge nodes are incorporated to serve the requests of field devices. The edge nodes of our network offer security and processing functionalities to data transactions made by field devices. Besides the proposed architecture, a proof of concept of our edge-based blockchain platform is implemented to study its feasibility and performance. The platform achieves the fundamental security properties and addresses the transaction scalability issue. Blockchain technology and edge computing have the potential to enable a robust, and flexible architecture for IoT networks while maintaining low latency and high availability. The effectiveness of the proposed architecture is evaluated in a simulated network.
Anomaly Detection in Crowd Scenes using Streak Flow Analysis

A new approach is proposed to analysis and localize abnormal events in crowded scenes, such us detecting suspicious behavior, sudden assemble and dispersion of pedestrian in the crowd scenes. The proposed methodology uses streak flow technique along with LDA (Latent Dirichlet Allocation)to detect anomalies in crowd scenes. This method divides the frames into blocks for accurate representation of spatial and temporal changes in scene. Optical flow algorithm is used to estimate the motion and direction of the pedestrian in crowd scene. Crowd flow motion is extracted using streak flow with the combination of streak line and potential functions. Experimental result shows that the detection of abnormalities in crowd scenes using streak flow approach gives better accuracy of 93.37% than the state of Art of techniques.
Quantifying data sensitivity

Until recently an assumption within the predictive modelling community has been that collecting more student data is always better. But in reaction to recent high profile data privacy scandals, many educators, scholars, students and administrators have been questioning the ethics of such a strategy. Suggestions are growing that the minimum amount of data should be collected to aid the function for which a prediction is being made. Yet, machine learning algorithms are primarily judged on metrics derived from prediction accuracy or whether they meet probabilistic criteria for significance. They are not routinely judged on whether they utilize the minimum number of the least sensitive features, preserving what we name here as data collection parsimony. We believe the ability to assess data collection parsimony would be a valuable addition to the suite of evaluations for any prediction strategy and to that end, the following paper provides an introduction to data collection parsimony, describes a novel method for quantifying the concept using empirical Bayes estimates and then tests the metric on real world data. Both theoretical and empirical benefits and limitations of this method are discussed. We conclude that for the purpose of model building this metric is superior to others in several ways, but there are some hurdles to effective implementation.
Improving the Quality of Education on the Galapagos Islands through a Community Intranet

The widespread use of mobile devices has increased the impact of Information and Communication technologies influencing various sectors including Education. Research into community networks and wireless communications show great promise in providing offline educational servers to communities without access to the Internet. This paper presents the implementation of a Community Intranet initiative to share sustainable education resources among schools on the island of San Cristobal in the Galapagos Islands. The implementation of the system comprises a combination of a fiber optic network infrastructure and WiFi routers to enable access to mobile devices and computers at each school. The paper also describes the delivery of workshops to the teachers and students of the schools focusing on an introduction to communications, introduction to web design, and the operation of the intranet system.
Sentiment Analysis of Conservation Studies Captures Successes of Species Reintroductions

Learning from the rapidly growing body of scientific articles is constrained by human bandwidth. Existing methods in machine learning have been developed to extract knowledge from human language and may automate this process. Here, we apply sentiment analysis, a type of natural language processing, to facilitate a literature review in reintroduction biology. We analyzed 1,030,558 words from 4,313 scientific abstracts published over four decades using four previously trained lexicon-based models and one recursive neural tensor network model. We find frequently used terms share both a general and a domain-specific value, with either positive (success, protect, growth) or negative (threaten, loss, risk) sentiment. Sentiment trends suggest that reintroduction studies have become less variable and increasingly successful over time and seem to capture known successes and challenges for conservation biology. This approach offers promise for rapidly extracting explicit and latent information from a large corpus of scientific texts.
Encrypted JPEG image retrieval using histograms of transformed coefficients

This work proposes an encrypted JPEG image retrieval mechanism based on the histograms of transformed coefficients. With this scheme, JPEG image is encrypted during its compression process by using other orthogonal transforms for blocks' transformation, rather than 8×8 DCT. Then the encrypted images are transferred to and stored in the cloud server. When receiving an encrypted query image from the authorized user, the server calculates the histograms of transformed coefficients located at different frequency positions. By computing the distance between the histograms of encrypted query image and database cipherimages, encrypted images with plaintext content similar to the query image are retured to the authorized user for decryption. Experiments are conducted to show that our scheme can provide effective cipherimage retrieval service, while ensure format compliance and compression friendly.
Personalized Entity Search by Sparse and Scrutable User Profiles

Prior work on personalizing web search results has focused on considering query-and-click logs to capture users' individual interests. For product search, extensive user histories about purchases and ratings have been exploited. However, for general entity search, such as for books on specific topics or travel destinations with certain features, personalization is largely underexplored. In this paper, we address personalization of book search, as an exemplary case of entity search, by exploiting sparse user profiles obtained through online questionnaires. We devise and compare a variety of re-ranking methods based on language models or neural learning. Our experiments show that even very sparse information about individuals can enhance the effectiveness of the search results.
Online streaming feature selection with incremental feature grouping

Abstract Today, the dimensionality of data is increasing in a massive way. Thus, traditional feature selection techniques are not directly applicable. Consequently, recent research has led to the development of a more efficient approach to the selection of features from a feature stream, known as streaming feature selection. Another active research area, related to feature selection, is feature grouping. Feature grouping selects relevant features by evaluating the hidden information of selected features. However, although feature grouping is a promising technique, it is not directly applicable to feature streams. In this paper, we propose a novel and efficient algorithm that uses online feature grouping, embedded within a new incremental technique, to select features from a feature stream. This technique groups similar features together; it assigns new incoming features to an existing group or creates a new group. To the best of our knowledge, this is the first approach that proposes the use of incremental feature grouping to perform feature selection from features. We have implemented this algorithm and evaluated it, using benchmark datasets, against state‐of‐the‐art streaming feature selection algorithms that use feature grouping or incremental selection techniques. The results show superior performance by the proposed technique through combining the online selection and grouping, in terms of prediction accuracy and running time. This article is categorized under: Algorithmic Development &gt; Spatial and Temporal Data Mining Technologies &gt; Data Preprocessing Technologies &gt; Classification Technologies &gt; Machine Learning
Order-preserving Encryption as a Tool for Privacy-Preserving Machine Learning

An order-preserving encryption is an encryption scheme based on strictly increasing functions. It allows mapping a set of plaintext into a set of ciphertexts with the order relation specified on a set of plaintexts. Thus, data comparisons and search for the minimum or maximum elements become possible without decrypting data. This type of encryption is mainly used to protect cloud databases in the case when it is necessary to make queries to them. However, it is almost not used in privacy-preserving data mining and machine learning. Nevertheless, it is possible to use this type of encryption in privacy-preserving machine learning, but only for certain algorithms. In this paper, we consider some existing order-preserving encryption schemes and suggest some cases of machine learning, where they can be applied to obtain correctly working privacy-preserving machine learning algorithms. An explanation is also given how and why it is possible to apply order-preserving encryption to these algorithms.
Talking With Your Hands: Scaling Hand Gestures and Recognition With CNNs

The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. As the technology advances and communication between humans and machines becomes more complex, HCI systems should also be scaled accordingly in order to accommodate the introduced complexities. In this paper, we propose a methodology to scale hand gestures by forming them with predefined gesture-phonemes, and a convolutional neural network (CNN) based framework to recognize hand gestures by learning only their constituents of gesture-phonemes. The total number of possible hand gestures can be increased exponentially by increasing the number of used gesture-phonemes. For this objective, we introduce a new benchmark dataset named Scaled Hand Gestures Dataset (SHGD) with only gesture-phonemes in its training set and 3-tuples gestures in the test set. In our experimental analysis, we achieve to recognize hand gestures containing one and three gesture-phonemes with an accuracy of 98.47% (in 15 classes) and 94.69% (in 810 classes), respectively. Our dataset, code and pretrained models are publicly available.
Developing a machine learning-based grade level classifier for Filipino children’s literature

Reading is an essential part of children’s learning. Identifying the proper readability level of reading materials will ensure effective comprehension. We present our efforts to develop a baseline model for automatically identifying the readability of children’s and young adult’s books written in Filipino using machine learning algorithms. For this study, we processed 258 picture books published by Adarna House Inc. In contrast to old readability formulas relying on static attributes like number of words, sentences, syllables, etc., other textual features were explored. Count vectors, Term FrequencyInverse Document Frequency (TF-IDF), n-grams, and character-level n-grams were extracted to train models using three major machine learning algorithms–Multinomial Naive-Bayes, Random Forest, and K-Nearest Neighbors. A combination of K-Nearest Neighbors and Random Forest via voting-based classification mechanism resulted with the best performing model with a high average training accuracy and validation accuracy of 0.822 and 0.74 respectively. Analysis of the top 10 most useful features for each algorithm show that they share common similarity in identifying readability levels–the use of Filipino stop words. Performance of other classifiers and features were also explored.
Prioritized Multi-Robot Velocity Planning for Trajectory Coordination of Arbitrarily Complex Vehicle Structures

Prioritized planning is a widely used technique to coordinate motions of multiple robots along fixed paths. Velocity profiles are planned sequentially avoiding previously computed trajectories represented as dynamic obstacles. To derive these, the vehicle structures are usually approximated with simple shapes, which fails for complex robots like automated truck-trailer combinations. A novel method to derive the spatiotemporal conflict zones directly from arbitrarily complex geometries swept along pre-planned paths is proposed and it is shown that the resulting obstacles in the distance-time-space allow velocity planning with precise space utilization and short execution times.
Policy Text Analysis Based on Text Mining and Fuzzy Cognitive Map

With the introduction of computer methods, the amount of material and processing accuracy of policy text analysis have been greatly improved. In this paper, Text mining(TM) and latent semantic analysis(LSA) were used to collect policy documents and extract policy elements from them. Fuzzy association rule mining(FARM) technique and partial association test (PA) were used to discover the causal relationships and impact degrees between elements, and a fuzzy cognitive map (FCM) was developed to deduct the evolution of elements through a soft computing method. This non-interventionist approach avoids the validity defects caused by the subjective bias of researchers and provides policy makers with more objective policy suggestions from a neutral perspective. To illustrate the accuracy of this method, this study experimented by taking the state-owned capital layout adjustment related policies as an example, and proved that this method can effectively analyze policy text.