Reviewerly/exHarmony huggingface dataset:
A digital hardware implementation of spiking neural networks with binary FORCE training

The brain, a network of spiking neurons, can learn complex dynamics by adapting its spontaneous chaotic activity. One of the dominant approaches used to train such a network, the FORCE method, has recently been applied to spiking neural networks. This method employs a pool of randomly connected spiking neurons, called a reservoir, to create chaos and uses the recursive least square (RLS) method to change its dynamic to what is required to follow a teacher signal. Here, we propose a digital hardware architecture for spiking FORCE with some modifications to the original method. First, to reduce the memory usage in hardware implementation, we show that careful binarization of the reservoir weights could preserve its initial chaotic activity. Second, we generate the connection matrix on-the-fly instead of storing the whole matrix. Third, a single processor systolic array implementation of the RLS using the inverse QR decomposition is exploited to update the readout layer weights. This implementation is not only more hardware-friendly but also more numerically stable in reduced precision than the standard RLS implementation. Fourth, we implement the design in both single and custom-precision floating-point number systems. Finally, we implement a network of 510 Izhikevic neurons on a Xilinx Artix-7 FPGA with 32, 24, and 18 bits floating-point numbers. To confirm the correctness of our architecture, we successfully train our hardware using three different teacher signals.
To Warn or Not to Warn: Online Signaling in Audit Games

Routine operational use of sensitive data is often governed by law and regulation. For instance, in the medical domain, there are various statues at the state and federal level that dictate who is permitted to work with patients' records and under what conditions. To screen for potential privacy breaches, logging systems are usually deployed to trigger alerts whenever a suspicious access is detected. However, such mechanisms are often inefficient because 1) the vast majority of triggered alerts are false positives, 2) small budgets make it unlikely that a real attack will be detected, and 3) attackers can behave strategically, such that traditional auditing mechanisms cannot easily catch them. To improve efficiency, information systems may invoke signaling, so that whenever a suspicious access request occurs, the system can, in real time, warn the user that the access may be audited. Then, at the close of a finite period, a selected subset of suspicious accesses are audited. This gives rise to an online problem in which one needs to determine 1) whether a warning should be triggered and 2) the likelihood that the data request event will be audited. In this paper, we formalize this auditing problem as a Signaling Audit Game (SAG), in which we model the interactions between an auditor and an attacker in the context of signaling and the usability cost is represented as a factor of the auditor's payoff. We study the properties of its Stackelberg equilibria and develop a scalable approach to compute its solution. We show that a strategic presentation of warnings adds value in that SAGs realize significantly higher utility for the auditor than systems without signaling. We perform a series of experiments with 10 million real access events, containing over 26K alerts, from a large academic medical center to illustrate the value of the proposed auditing model and the consistency of its advantages over existing baseline methods.
A clinical decision support system for micro panoramic melanoma detection and grading using soft computing technique

Computer aided algorithms plays pivotal role in disease diagnosis and treatment planning for therapeutic applications. This research work proposes a clinical decision support system for melanoma detection using SVM machine learning algorithm. Prior to classification, preprocessing, lesion segmentation, feature extraction and feature optimization are done. The performance of the SVM classifier was compared with the other classical classifiers and superior results are produced. A novel total dermascopic score was proposed in this work that generates efficient results, when compared with the classical total dermascopic score. The SVM when coupled with RFE for feature selection and ranking generates efficient results, when compared with the other machine learning classifiers. The performance evaluation was done by metrics like sensitivity, specificity and accuracy. The SVM has highest classification accuracy of 96.4%, when compared with the other algorithms.
Evaluation and Summarization of Student Feedback Using Sentiment Analysis

Educational dataSharma, Neeraj miningJain, Vaibhav facilitates educational institutions to discover useful patterns and apply them to improve the overall quality of education. Analysing student feedback may help institutions to enhance student's learning capabilities in the classroom. We propose a student feedback analysis system that helps in identifying sentiments from student reviews, and it further helps in generating the summary of feedback. It is implemented using sentiment analysis and text summarization techniques. Based on our evaluation, the lexicon-based approach did better than traditional machine learning-based techniques. Finally, we were able to generate a precise summary of student feedback.
Interacting with Literary Style through Computational Tools

Style is an important aspect of writing, shaping how audiences interpret and engage with literary works. However, for most people style is difficult to articulate precisely. While users frequently interact with computational word processing tools with well-defined metrics, such as spelling and grammar checkers, style is a significantly more nuanced concept. In this paper, we present a computational technique to help surface style in written text. We collect a dataset of crowdsourced human judgments of style, derive a model of style by training a neural net on this data, and present novel applications for visualizing and browsing style across broad bodies of literature, as well as an interactive text editor with real-time style feedback. We study these interactive style applications with users and discuss implications for enabling this novel approach to style.
Querying Shared Data with Security Heterogeneity

There has been increasing need for secure data sharing. In practice a group of data owners often adopt a heterogeneous security scheme under which each pair of parties decide their own protocol to share data with diverse levels of trust. The scheme also keeps track of how the data is used. This paper studies distributed SQL query answering in the heterogeneous security setting. We define query plans by incorporating toll functions determined by data sharing agreements and reflected in the use of various security facilities. We formalize query answering as a bi-criteria optimization problem, to minimize both data sharing toll and parallel query evaluation cost. We show that this problem is PSPACE-hard for SQL and Σ_3^p-hard for SPC, and it is in NEXPTIME. Despite the hardness, we develop a set of approximate algorithms to generate distributed query plans that minimize data sharing toll and reduce parallel evaluation cost. Using real-life and synthetic data, we empirically verify the effectiveness, scalability and efficiency of our algorithms.
Miniature Water Surface Garbage Cleaning Robot

In light of the problem for garbage cleaning in small water area, an intelligent miniature water surface garbage cleaning robot with unmanned driving and convenient operation is designed. Based on STC12C5A60S2 as the main controller in the design, power module, transmission module and cleaning module are controlled together to realize the function of cleaning and transporting garbage, intelligent remote control of miniature water surface garbage cleaning robot is realized by the WiFi module. Then the prototype is developed and tested, which will verify the rationality of the design. Compared with the traditional manual driving water surface cleaning devices, the designed robot realizes the intelligent control of unmanned driving, and achieves the purpose of saving human resources and reducing labor intensity, and the system operates security and stability, which has certain practical value.
Co-Design Futures for AI and Space: A Workbook Sprint

Artificial Intelligence (AI) is continuously moving into our surroundings. In its various forms, it has the potential to disrupt most aspects of human life. Yet, the discourse around AI has long been by experts and for experts. In this paper, we argue for a participatory approach towards designing human-AI interactions. We outline how we used design methodology to organise an interdisciplinary workshop with a diverse group of students – a workbook sprint with 45 participants from four different programs and 13 countries – to develop speculative design futures in five focus areas. We then provide insights into our findings and share our lessons learned regarding our workshop topic – AI and Space – our process, and our research. We learned that involving non-experts in complex technical discourses – such as AI – through the structural rigour of design methodology is a viable approach. We then conclude by laying out how others might use our findings and initiate their own workbook sprint to explore complex technologies in a human-centred way.
Two-Level Complex-Valued Hopfield Neural Networks

In multistate neural associative memories, some neurons have small noise and the others have large noise. If we know which neurons have small noise, the noise tolerance could be improved. In this brief, we provide a novel method to reinforce neurons with small noise and apply our new method to images with the Gaussian noise. A complex-valued multistate neuron is decomposed to two neurons, referred to as high and low neurons. For the Gaussian noise, the high neurons are expected to have small noise. The noise tolerance is improved by reinforcement of high neurons. The computer simulations support the efficiency of reinforced neurons.
Development of the formal model for the presentation of poorly structured and unstructured information

Abstract The article describes the tasks of data mining, data modeling, presents a classification of data models, and also develops a formal model for representing various types of weakly structured and unstructured information resources. The developed formal model for representing various types of weakly structured and unstructured information resources within the framework of the software environment represents the theoretical basis for creating tools for solving the applied problem of automatic analysis of documentation on paper and digital media for the subsequent cataloging of poorly structured information.
Increasing Video Accessibility for Visually Impaired Users with Human-in-the-Loop Machine Learning

Video accessibility is crucial for blind and visually impaired individuals for education, employment, and entertainment purposes. However, professional video descriptions are costly and time-consuming. Volunteer-created video descriptions could be a promising alternative, however, they can vary in quality and can be intimidating for novice describers. We developed a Human-in-the-Loop Machine Learning (HILML) approach to video description by automating video text generation and scene segmentation while allowing humans to edit the output. Our HILML system was significantly faster and easier to use for first-time video describers compared to a human-only control condition with no machine learning assistance. The quality of the video descriptions and understanding of the topic created by the HILML system compared to the human-only condition were rated as being significantly higher by blind and visually impaired users.
Chinese Data Extraction and Named Entity Recognition

Extracting effective information from a large amount of Chinese text data is an important task for data analysis in the era of big data. The key to extracting information is whether it can quickly identify named entities in Chinese text. This paper analyzes the structure of text data about news text and specialized medical text, propose IDCNN-BiLSTM-CRF (Iterated Dilated Convolutions-Bidirectional Long Short Memory Network-Condition Random Field) model. In this paper, medical text data is processed by analyzing the structure of the public news dataset. This paper analyzes the structure of public news datasets to process special medical text data, and uses public news datasets and special medical text datasets to compare and analyze the model proposed in this paper and the BiLSTM-CRF model to verify the recognition result.
PROUD: PaRallel OUtlier Detection for Streams

We introduce PROUD, standing for PaRallel OUtlier Detection for streams, which is an extensible engine for continuous multi-parameter parallel distance-based outlier (or anomaly) detection tailored to big data streams. PROUD is built on top of Flink. It defines a simple API for data ingestion. It supports a variety of parallel techniques, including novel ones, for continuous outlier detection that can be easily configured. In addition, it graphically reports metrics of interest and stores main results into a permanent store to enable future analysis. It can be easily extended to support additional techniques. Finally, it is publicly provided in open-source.
Aerodynamic analysis and car body optimalization of saving energy “WARAK” using software Ansys Fluent R15.0

Abstract Designing vehicle prototype saving energy is a thing should be seriously calculated, which is purpose to produce design are aerodynamic and streamline, also it drag force coefficient and less lift force to minimize the big engine work because the fraction cause of air so it could be saving the fuels. In the laboratory simulations, it test using scale as same as actual is 1:1. The yield of simulation gives coefficient value of drag in Warak is 0,09 as we want to be gained less than 0,1. In this simulation also present coefficient testing average value of lift about 0,17. From this, we can conclude that is very aerodynamic.
Unsupervised Deep Collaborative Filtering Recommender System for E-Learning Platforms

With the rapid development of the online learning resources, trying to respect the differences between learners in terms of cognitive ability and knowledge structure. Traditional collaborative filtering recommendation algorithms cannot identify useful learning resources which will be interesting and simple to understand. Furthermore, the redundant recommended content and the high-dimensional and nonlinear data on online learning users cannot be effectively handled, leading to inefficient resource recommendations. To enhance learning resource recommendations efficiency, this paper presents a two steps efficient resource recommendation model based on, Kohonen card unsupervised deep learning to identify the instrumental approximation of learning styles, and deep auto-encoder, whose interest is not the prediction of resource in as such, but the transformation learned by the self-encoder, which serves as an alternative representation of the input and estimate the success rate of the proposed resource to the learner. This model need deeply mines learner features course content attribute features assessment attribute features and incorporates learner platform interactions features to build Learner features vector as input for the first step and Learner-Content ratings vector to choose the more efficient learning resource to recommend.
Detection of Skin Cancer Lesions from Digital Images with Image Processing Techniques

Melanoma, a kind of skin cancer, could be a category of cancer that originates from the pigment which includes the cells named melanocytes. Melanomas normally appear within not only the skin, but also may rarely occur within the eyes, intestines, or mouth. In women melanomas most ordinarily found on the legs, on the other hand in men, they are most typical on the rear side. Generally, these are originated from a mole with regarding changes as well as they increase in size, asymmetrical edges, and modification in skin breakdown, itchiness, or color. Melanoma is one of the life-threatening kinds of carcinoma. There is an increase in incidence rates of skin cancer, specifically between non-Hispanic white females and males, however, the chances of survival are high detected in an early stages. In this project, we are taking the help of the image processing techniques for detecting melanoma in the image. Firstly, we will apply the preprocessing technique in order to make the image noise free. Median filters will be used for filtration of the image. After that, we will transform the image into an HSI color image. Active shape segmentation and texture segmentation techniques will be used for segmenting the image. For feature extraction, GLCM Feature Extraction algorithm is used. Finally, we will apply the probabilistic neural network (PNN) classifier to classify the image either as normal or melanoma.
Iterative Prototyping of Urban CoBuilder: Tracking Methods and User Interface of an Outdoor Mobile Augmented Reality Tool for Co-Designing

This research presents results from a study developing a smartphone app, Urban CoBuilder, in which citizens can collaboratively create designs for urban environments using augmented reality technology and game mechanics. Eight prototypes were developed to refine selected design criteria, including tracking strategies, design elements, user experience and the interface with game mechanics. The prototypes were developed through an iterative design process with assessments and incremental improvements. The tracking was especially challenging and using multiple bitonal markers combined with the smartphone’s gyroscope sensor to average the user position was identified as the most suitable strategy. Still, portability and stability linked to tracking need to be improved. Design elements, here building blocks with urban functions textures, were realistic enough to be recognizable and easy to understand for the users. Future studies will focus on usability tests with larger user groups.
Intersection distribution, non-hitting index and Kakeya sets in affine planes

In this paper, we propose the concepts of intersection distribution and non-hitting index, which can be viewed from two related perspectives. The first one concerns a point set S of size q+1 in the classical projective plane PG(2,q), where the intersection distribution of S indicates the intersection pattern between S and the lines in PG(2,q). The second one relates to a polynomial f over a finite field Fq, where the intersection distribution of f records an overall distribution property of a collection of polynomials {f(x)+cx|c∈Fq}. These two perspectives are closely related, in the sense that each polynomial produces a (q+1)-set in a canonical way and conversely, each (q+1)-set with certain property has a polynomial representation. Indeed, the intersection distribution provides a new angle to distinguish polynomials over finite fields, based on the geometric property of the corresponding (q+1)-sets. Among the intersection distribution, we identify a particularly interesting quantity named non-hitting index. For a point set S, its non-hitting index counts the number of lines in PG(2,q) which do not hit S. For a polynomial f over a finite field Fq, its non-hitting index gives the summation of the sizes of q value sets {f(x)+cx|x∈Fq}, where c∈Fq. We derive lower and upper bounds on the non-hitting index and show that the non-hitting index contains much information about the corresponding set and the polynomial. More precisely, using a geometric approach, we show that the non-hitting index is sufficient to characterize the corresponding point set and the polynomial, when it is very close to the lower and upper bounds. Moreover, we employ an algebraic approach to derive the non-hitting index and the intersection distribution of several families of point sets and polynomials. As an application, we consider the determination of the sizes of Kakeya sets in affine planes. The polynomial viewpoint of intersection distributions enable us to compute the size of a few families of Kakeya sets with nice algebraic properties. Finally, we describe the connection between these new concepts and various known results developed in different contexts and propose some future research problems.
A Blockchain-Encryption-Based Approach to Protect Fog Federations from Rogue Nodes

People have used cloud computing approach to store their data remotely. As auspicious as this approach is, it brings forth many challenges: from data security to time latency issues with data computation as well as delivery to end users. Fog computing has emerged as an extension for cloud computing to bring data processing and storage close to end-users; however, it minimizes the time latency issue but still suffers from data security challenges. For instance, when a fog node providing services to end users is compromised, the users' data security can be violated. Thus, this paper proposes a secure and fine-grained data access control scheme by integrating the Ciphertext Policy Attribute-Based Encryption (CP-ABE) algorithm and blockchain concept to prevent fog nodes from violating end users' data security in a situation where a compromised fog node is being ousted. We also classify the fog nodes into fog federations, based on their attributes such as services and locations, to minimize the time latency and communication overhead between fog nodes and cloud server. Further, the exploitation and integration of the blockchain concept and the CP-ABE algorithm enables fog nodes in the same fog federation to perform the authorization process in a distributed manner. In addition, to solve time latency and communication overhead problems, we equip every fog node with an off-chain database to store most frequently accessed data files for specific time, and with an on-chain access control policies table (On-chain Files Tracking Table) which must be protected from being tampered by malicious (rogue) fog nodes. Therefore, blockchain plays a vital role here as it is tamper-proof by nature. We demonstrate our scheme's efficiency and feasibility by designing algorithms and conducting a security analysis. The provided analysis shows that the proposed scheme is efficient and feasible in ousting malicious (rogue) fog nodes.
Privado

Online Social Networks (OSNs) offer free storage and social networking services through which users can communicate personal information with one another. The personal information of the users collected by the OSN provider comes with privacy problems when being monetized for advertising purposes. To protect user privacy, existing studies propose utilizing data encryption that immediately prevents OSNs from monetizing users data and hence leaves secure OSNs with no convincing commercial model. To address this problem, we propose Privado as a privacy-preserving group-based advertising mechanism to be integrated into secure OSNs to re-empower monetizing ability. Privado is run by N servers, each provided by an independent provider. User privacy is protected against an active malicious adversary controlling N − 1 providers, all the advertisers, and a large fraction of the users. We base our design on the group-based advertising notion to protect user privacy, which is not possible in the personalized variant. Our design also delivers advertising transparency; the procedure of identifying target customers is operated solely by the OSN servers without getting users and advertisers involved. We carry out experiments to examine the advertising running time under various number of servers and group sizes. We also argue about the optimum number of servers with respect to user privacy and advertising running time.