Reviewerly/exHarmony huggingface dataset:
Selfies Save Lives (Digital Strategies for Flood Response in Indonesia)

Abstract Flooding is very serious problem for cities in Asia; according to Dartmouth Flood Observatory, of the 20 megacities in East and Southeast Asia, 14 are sitting on river deltas and 18 have experienced major flood events in the past decade. In Indonesia, there are millions people exposed to flooding conditions during the monsoon season. There are also millions of residents who are connected to the internet, and one another, through their mobile devices. During flood situations, Indonesians are especially active on the social media; they share crucial information with each other through social media and instant messaging platforms. By sharing local information about their neighbourhood during the flood, others use this real-time information as to make decisions, reduce risk, and improve safety. Though the local government has received some support from NGOs, private companies, and other agencies in terms of collecting information from the field during disaster events, this information is usually not well-integrated with their operational system, which can cause problems during emergency situation. By beginning from the chaotic reality experienced by residents and first responders during flood events, PetaBencana.id team created a design brief for their platform that made sure it would easily collect disaster reports from social media and integrate with real-time updates from governmental agencies. In the same time, the platform was designed to support peer-to-peer sharing and to democratize decision support by allowing residents with internet access to freely use the platform. PetaBencana.id also runs on free and open source software, which is a key to maintain public transparency and make the project sustainable.
Pruning High-Similarity Clusters to Optimize Data Diversity when Building Ensemble Classifiers

Diversity is a key component for building a successful ensemble classifier. One approach to diversifying the base classifiers in an ensemble classifier is to diversify the data they are trained on. While sampling approaches such as bagging have been used for this task in the past, we argue that since they maintain the global distribution, they do not create diversity. Instead, we make a principled argument for the use of [Formula: see text]-means clustering to create diversity. Expanding on previous work, we observe that when creating multiple clusterings with multiple [Formula: see text] values, there is a risk of different clusterings discovering the same clusters, which would in turn train the same base classifiers. This would bias the ensemble voting process. We propose a new approach that uses the Jaccard Index to detect and remove similar clusters before training the base classifiers, not only saving computation time, but also reducing classification error by removing repeated votes. We empirically demonstrate the effectiveness of the proposed approach compared to the state of the art on 19 UCI benchmark datasets.
Network Presentation of Texts and Clustering of Messages

For the purposes of searching for various communities on the Internet, automatic typology of text messages defined via application of methods of cluster analysis may be used. In this paper, we address one of the significant issues in text classification via cluster analysis, namely determination of the number of clusters. For clustering based on semantics, text documents are typically represented in the form of vectors within n-dimensional linear space. What we suggest as a method for determining the number of clusters is the agglomerative clustering of vectors in the linear space. In our work, statistical analysis is combined with neural network algorithms to obtain a more accurate semantic portrait of a text. Then, using the techniques of distributive semantics, mapping of the derived network structures into a vector form is constructed. A statistical criterion for the completion of the clustering process is derived, defined as a Markovian moment. By obtaining automatic partitioning into clusters, one can compare texts that are closest to the centroids with actual content samples or evaluate such texts with the help of experts. If the display of texts in a vector form is adequate, all informational messages from a fixed cluster have the same meaning and the same emotional coloring. In addition, we discuss a possibility to use vector representation of texts for sentiment detection in short texts like search engines input or tweets.
Accelerate Black-Box Attack with White-Box Prior Knowledge

We propose an efficient adversarial attack method in the black-box setting. Our Multi-model Efficient Query Attack (MEQA) method takes advantage of the prior knowledge on different models’ relationship to guide the construction of black-box adversarial instances. The MEQA method employs several gradients from different white-box attack models and further the “best” one is selected to replace the gradient of black-box model in each step. The gradient composed by different model gradients will lead a significant loss to the black-box model on these adversarial pictures and then cause misclassification. Our key motivation is to estimate the black-box model with several existing white-box models, which can significantly increase the efficiency from the perspectives of both query sampling and calculating. Compared with gradient estimation based black-box adversarial attack methods, our MEQA method reduces the number of queries from 10000 to 40, which greatly accelerates the black-box adversarial attack. Compared with the zero query black-box adversarial attack method, which also called transfer attack method, MEQA boosts the attack success rate by 30%. We evaluate our method on several black-box models and achieve remarkable performance which proves that MEQA can serve as a baseline method for fast and effective black-box adversarial attacks.
Secure and privacy‐preserving keyword search retrieval over hashed encrypted cloud data

Summary Cloud computing (CC) is the universal area in which the data owners will contract out their pertinent data to the untrusted public cloud that permits the data users to retrieve the data with complete integrity. To give data privacy along with integrity, majority of the research works were concentrated on single data owner for secure searching of encrypted data via the cloud. Also, searchable encryption supports data user to retrieve the particular encrypted document from encrypted cloud data via keyword search (KS). However, these researches are not efficient for keyword search retrieval. To trounce such drawbacks, this paper proposes efficient secure and privacy‐preserving keyword search retrieval (SPKSR) system, in which the user retrieves the hashed encrypted documents over hashed encrypted cloud data. The proposed system includes three entities explicitly, (a) data owner (DO), (b) cloud server (CS), and (c) data users (DU). The owner outsources hashed encrypted documents set, along with generated searchable index tree to the CS. The CS hoards the hashed encrypted document collection and index tree structure. DU performs the “search” over the hashed encrypted data. Experimental results of the proposed system are analyzed and contrasted with the other existent system to show the dominance of the proposed system.
An Improved objective detection of road scenes based on SSD model

Aiming at the problem that how to quickly and accurately detect the target object in front of the running vehicle is difficult to achieve real-time target detection on mobile devices, this paper adopts the framework of the lightweight network model Mobile Net-SSD, and designs a combined network LSTM-SSD for road video multi-target detection. The temporal dimension features between the video frames are extracted by the combined network, so the results of the detection model are corrected. This method effectively improves the confidence of detection and reduces the instability problem in single image detection. Compared with the two detection network models of VGG-SSD\MobileNet-SSD, the average accuracy of the detection results is increased by 2%~8%, which indicates that the LSTM-SSD combined detection network model can achieve good detection results when dealing with the interference situations such as fuzziness\occlusion\etc. This model can provide basis and reference for real-time target detection of unmanned driving.
Modified Ant Algorithm VLSI Planning on the Basis of the Composite Model of the Solution Space

In this paper, the crystal plan is formed by the recursive use of a "guillotine cut". To set the plan means to set the structure of the binary tree of the cuts, i.e. sequence of binary cuts; for internal tree vertices, to indicate the type of the cut H or V; to number the leaves of the tree and indicate the orientation of the modules. The structure of the binary tree of the cuts can be set using the Polish expression on the base of the alphabet A = {M, TR}, where the set of letters M = {mi|i = 1, 2, ..., nМ} corresponds to the leaves of the section tree (regions), and the set R = {H, V} corresponds to the cuts. We propose a way and methods for solving the problems of planning VLSI based on a modified ant colony. The task of synthesizing the section tree of the plan with the choice of types of sections, identification and orientation of the modules in the work is reduced to the task of forming a modified Polish expression with the identification of elements on the composite model of the solution space, including many alternative vertices. To keep the collective evolutionary memory during the life of the ant population and to form the solution of the problem, we use the complete graph G = (X, U) with alternative vertex states. Each vertex may be in one of two alternative states, i.e., α or β, corresponding to the orientation of the module or the type of the cut. The task of synthesizing the Polish expression is formulated as the task of finding the least-cost route on the solution search graph G = (X, U). A distinctive feature is that when building a route, simultaneously with the choice of the vertex xi∈ X, the state of this vertex is selected. The time complexity of the algorithm is O(n2). Experiments have shown that for large dimensions, the time indicators of the developed algorithm exceed those of the compared algorithms with the best values of the objective function.
Cross-Domain Interpolation for Unpaired Image-to-Image Translation

Unpaired Image-to-image translation is a brand new challenging problem that consists of latent vectors extracting and matching from a source domain A and a target domain B. Both latent spaces are matched and interpolated by a directed correspondence function F for $$A \rightarrow B$$ and G for $$B \rightarrow A$$. The current efforts point to Generative Adversarial Networks (GANs) based models due they synthesize new quite realistic samples across different domains by learning critical features from their latent spaces. Nonetheless, domain exploration is not explicit supervision; thereby most GANs based models do not achieve to learn the key features. In consequence, the correspondence function overfits and fails in reverse or loses translation quality. In this paper, we propose a guided learning model through manifold bi-directional translation loops between the source and the target domains considering the Wasserstein distance between their probability distributions. The bi-directional translation is CycleGAN-based but considering the latent space Z as an intermediate domain which guides the learning process and reduces the inducted error from loops. We show experimental results in several public datasets including Cityscapes, Horse2zebra, and Monet2photo at the EECS-Berkeley webpage ( http://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/ ). Our results are competitive to the state-of-the-art regarding visual quality, stability, and other baseline metrics.
Fuzzy cognitive maps and induced fuzzy cognitive maps approach to traffic flow

Abstract This paper presents a method to analyze the traffic flow pattern at a crowded junction in Chennai, one of the metropolitan cities in India, using waiting time in the signal in different time intervals with the help of Fuzzy Cognitive Map and Induced Fuzzy Cognitive Map.
A Catalogue of Scenario Patterns for Validating and Verifying Model Behavior

For validating and verifying behavioral model properties, typically dynamic scenarios that essentially consist of operation call sequences can be used. A developer then has to cover all necessary scenarios for checking the crucial model properties. In this contribution, we propose a catalogue of different scenario patterns in order to develop guidelines and advice for constructing scenarios, and manifest the usefulness of the catalogue through exemplary models.
TRUST: TRust Unguarded Service Terminals

Nowadays, plenty of digital services are provided to citizens by means of terminals located in public unguarded places. In order to access the desired service, users, authenticate themselves by providing their credentials through such terminals. This approach opens up to the problem of fraudulent devices that could be installed in place of regular terminals to capture users’ confidential information. Indeed, despite the development of increasingly secure systems aiming at guaranteeing an acceptable security level, users are frequently unable to distinguish between terminals on which security measures are enforced (trusted terminals) and malicious terminals that pretend to be trusted. We deal with this problem by presenting a human-compatible authentication protocol, leveraging Graphical Passwords, helps user to authenticate a terminal before using it. We also present a prototype implementation of this protocol, called TRUST (TRust Unguarded Service Terminals). The usability of our solution has been analyzed by means of a preliminary experimentation.
Zero-knowledge proofs for set membership: efficient, succinct, modular

Abstract We consider the problem of proving in zero knowledge that an element of a public set satisfies a given property without disclosing the element, i.e., for some u , “ $$u \in S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>u</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>S</mml:mi> </mml:mrow> </mml:math> and P ( u ) holds”. This problem arises in many applications (anonymous cryptocurrencies, credentials or whitelists) where, for privacy or anonymity reasons, it is crucial to hide certain data while ensuring properties of such data. We design new modular and efficient constructions for this problem through new commit-and-prove zero-knowledge systems for set membership , i.e. schemes proving $$u \in S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>u</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>S</mml:mi> </mml:mrow> </mml:math> for a value u that is in a public commitment $$c_u$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>c</mml:mi> <mml:mi>u</mml:mi> </mml:msub> </mml:math> . We also extend our results to support non-membership proofs , i.e. proving $$u \notin S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>u</mml:mi> <mml:mo>∉</mml:mo> <mml:mi>S</mml:mi> </mml:mrow> </mml:math> . Being commit-and-prove, our solutions can act as plug-and-play modules in statements of the form “ $$u \in S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>u</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>S</mml:mi> </mml:mrow> </mml:math> and P ( u ) holds” by combining our set (non-)membership systems with any other commit-and-prove scheme for P ( u ). Also, they work with Pedersen commitments over prime order groups which makes them compatible with popular systems such as Bulletproofs or Groth16. We implemented our schemes as a software library, and tested experimentally their performance. Compared to previous work that achieves similar properties—the clever techniques combining zkSNARKs and Merkle Trees in Zcash—our solutions offer more flexibility, shorter public parameters and $$3.7 \times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3.7</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> – $$30\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>30</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> faster proving time for a set of size $$2^{64}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>64</mml:mn> </mml:msup> </mml:math> .
State-of-the-Art in Action: Unconstrained Text Detection

In this paper, we stage five real-world scenarios for six state-of-the-art text detection methods in order to evaluate how competent they are with new data without any training process. Moreover, this paper analyzes the architecture design of those methods to reveal the influence of pipeline choices on the detection quality. The setup of experimental studies are straight-forward: we collect and manually annotate test data, we reimplement the pretrained models of the state-of-the-art methods, then we evaluate and analyze how well each method achieve in each of our collected datasets. We found that most of the state-of-the-art methods are competent at detecting textual information in unseen data, however, some are more readily used for real-world applications. Surprisingly, we also found that the choice of a post-processing algorithm correlates strongly with the performance of the corresponding method. We expect this paper would serve as a reference for researchers as well as application developers in the field. All collected data with ground truth annotation and their detected results is publicly available at our Github repository: https://github.com/chupibk/HBlab-rlq19.
Design of Automatic Braking System Using Interval Type-2 Fuzzy Logic System

Abstract The car braking system is a very important part of car safety. The effectiveness of Conventional braking system is strongly influenced by the ability and the driver’s experience. It is very vulnerable to accidents if the driver fails to operate the braking system. Because of that, an automation of the braking system is needed that can decrease the accident’s impact. The Automatic braking systems require the reasoning to manage information before making a decision that brakes or slows down. There is uncertainty in determining membership function of distance and velocity. It is difficult to determine an exact membership function. In this paper, the system use Interval type-2 fuzzy logic control that can work well in an unstructured environment and have the ability to overcome the uncertainty of information. This system will be compared with the braking system using T1FLS. The result is that the pressure on the brake pads is gradually increased. At a distance of 34 m, the pressure is at maximum value. The changes produced by the automatic braking system that uses IT2FLS increase the average pressure of 4,167% while in T1FLS system, a change in growth greater pressure by an average of 5%. In IT2FLS braking system, braking is gradually increasing smoother than T1FLS.
Anomalous Behaviour Detection using Transfer Learning Algorithm of Series and DAG Network

Transfer learning is highly recommended in image recognition studies due to its ability in leveraging the finest architecture of pre-trained convolution neural networks (CNNs) for instance AlexNet, GoogLeNet and several others in learning new dataset at a faster learning process with smaller input images and could yield better classification rate as well. Hence, this study discussed deep learning with transfer learning approach in recognizing and classifying normal behavior and anomalous behavior referring to housebreaking crime behavior at the gate of the residential unit. Firstly, the dataset of normal behavior and housebreaking crime behavior are acquired. Next, these images are extracted and classified using remodeled AlexNet and GoogLeNet that have been fine-tuned using transfer learning technique. Results attained showed that the classification accuracy for both AlexNet and GoogLeNet are within 97%.
On Perfectly Secure 2PC in the OT-Hybrid Model

A well known result by Kilian [22] (ACM 1988) asserts that general secure two computation (2PC) with statistical security, can be based on OT. Specifically, in the client-server model, where only one party – the client – receives an output, Kilian’s result shows that given the ability to call an ideal oracle that computes OT, two parties can securely compute an arbitrary function of their inputs with unconditional security. Ishai et al. [19] (EUROCRYPT 2011) further showed that this can be done efficiently for every two-party functionality in $$\mathrm {NC}^1$$ in a single round. However, their results only achieve statistical security, namely, it is allowed to have some error in security. This leaves open the natural question as to which client-server functionalities can be computed with perfect security in the OT-hybrid model, and what is the round complexity of such computation. So far, only a handful of functionalities were known to have such protocols. In addition to the obvious theoretical appeal of the question towards better understanding secure computation, perfect, as opposed to statistical reductions, may be useful for designing secure multiparty protocols with high concrete efficiency, achieved by eliminating the dependence on a security parameter. In this work, we identify a large class of client-server functionalities $$f:\mathcal {X}\times \mathcal {Y}\mapsto \{0,1\}$$, where the server’s domain $$\mathcal {X}$$ is larger than the client’s domain $$\mathcal {Y}$$, that have a perfect reduction to OT. Furthermore, our reduction is 1-round using an oracle to secure evaluation of many parallel invocations of $$\left( {\begin{array}{c}2\\ 1\end{array}}\right) \text {-bit-OT}$$, as done by Ishai et al. [19] (EUROCRYPT 2011). Interestingly, the set of functions that we are able to compute was previously identified by Asharov [2] (TCC 2014) in the context of fairness in two-party computation, naming these functions full-dimensional. Our result also extends to randomized non-Boolean functions $$f: \mathcal {X}\times \mathcal {Y}\mapsto \left\{ 0,\ldots ,k-1\right\} $$ satisfying $$|\mathcal {X}|>(k-1)\cdot |\mathcal {Y}|$$.
Human Body Three-Dimensional Motion Skeleton Reconstruction of Moving Image Sequence

In the field of computer vision, the research of human motion analysis has a wide range of application prospects. Due to the complexity of human motion, the existing research methods have imposed many restrictions on the human body of the research object. A new method is proposed in this paper. The information of various types of human motion mainly discusses the reconstruction part of the human body's three-dimensional motion skeleton. The basic idea is to establish the image of each image based on the camera calibration and the application of three-dimensional human body model knowledge and motion continuity. On the basis of obtaining the binarized motion image, automatic annotation of joint feature points is realized. Firstly, the Canny operator is used to extract the target contour, and the Douglas-Peucker vector compression algorithm is used to obtain a suitable human contour shape. According to the joint proportional structure, the position of each joint point is determined, and the joint feature points are automatically labeled. The three-dimensional human motion tracking technology is studied. The characteristic optical flow algorithm is used to track the marked joint points, and the Kalman filter is used to correct the joint points of the tracking error. The automatic labeling method of the mannequin joint points used reduces the manual intervention in the system to some extent.