Reviewerly/exHarmony huggingface dataset:
Call Me Back, I Have a Type Invariant

Callbacks in Smart Contracts on blockchain-based distributed ledgers are a potential source of security vulnerabilities: callbacks may lead to reentrancy, which has been previously exploited to steal large sums of money. Unfortunately, analysis tools for Smart Contracts either fail to support callbacks or simply detect and disallow patterns of callbacks that may lead to reentrancy. As a result, many authors of Smart Contracts avoid callbacks altogether, and some Smart Contract programming languages, including Solidity, recommend using primitives that avoid callbacks. Nevertheless, reentrancy remains a threat, due to the utility of and frequent reliance on callbacks in Smart Contracts. In this paper, we propose the use of type invariants, a feature of some languages supporting formal verification, to enable proof of correctness for Smart Contracts, including Smart Contracts that permit or rely on callbacks. Our result improves upon existing research because it neither forbids reentrancy nor relies on informal, meta-arguments to prove correctness of reentrant Smart Contracts. We demonstrate our approach using the SPARK programming language, which supports type invariants and moreover can be compiled to relevant blockchains.
Meta-heuristic inspired by the behavior of the humpback whale tuned by a fuzzy inference system

The Whale Optimization Algorithm (WOA) is a recent meta-heuristic that can be explored in global optimization problems. This paper proposes a new parameter adjustment mechanism that influences the probability of the food recognition process in the whale algorithm. The adjustment is performed using a fuzzy inference system that uses the current iteration number as input information. Our simulation results are compared with other meta-heuristics such as the conventional version of WOA, Particle Swarm Optimization (PSO) and Differential Evolution (DE). All algorithms are used to optimize ten test functions (Sphere, Schwefel 2.22, Quartic, Rosenbrock, Ackley, Rastrigin, Penalty 1, Schwefel 2.21, Six hump camel back and Shekel 1) in order to obtain their respective optimal values for be used as criteria for analysis and comparison. The results of the simulations show that the proposed fuzzy inference system improves the convergence of WOA and also is competitive in relation to the other algorithms, i.e., classical WOA, PSO and DE.
Reconsidering Leakage Prevention in MapReduce

Trusted Execution Environment introduces a promising avenue for protecting MapReduce jobs on untrusted cloud environment. However existing works pointed out that simply protecting MapReduce workers with trusted execution environment and protecting cross-worker communications with encryption still leak information via cross-worker traffic volumes. Although several countermeasures were proposed to defeat such a side-channel attack, in this paper, we showed that previous countermeasures not only fail in completely eliminating such a side-channel, but also have limitations from other aspects. To address all the discovered limitations, we further discussed possible strategies.
A Novel CAD System for Breast DCE-MRI Based on Textural Analysis Using Several Machine Learning Methods

In this paper, we propose a Computer-Aided Detection (CAD) system based on the novel Multi-Classifier Fusion-based Classification Model (MCFM) of the breast lesions using textural features in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed system consists of four principal phases: Region Of Interest (ROI) segmentation, feature extraction, fusion and selection and finally Classification. We have extracted a complementary set of features from the DCE-MRI database on two statistical descriptors such as the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) methods. Then, we have concatenated the extracted features using Serial method to exploit the complementary between these sets. Next, we have applied a features selection phase through Principal Component Analysis (PCA) and Feature Subset Selection (FSS) techniques to remove redundant information and improve the overall performance of breast DCE-MRI in a computer-aided system. For the classification, we have used a set of classifiers (K-Nearest Neighbors (K-NN), Support Vector Machines (SVM) and Multilayer Perceptron-Artificial Neural Network (MLP-ANN)). Then, a fusion Model between them using several metrics is applied. Experimental validation is performed over an MRI dataset, which is comprised of 286 patients with 143 Masses and 143 No Masses (Normal) breast tissues. These experiments report encouraging performances for mass detection, achieved using ten-fold cross-validation model, in terms of the averaged measurements of different metrics such as Sensitivity, Specificity, Area Under Curve (AUC) and Accuracy, which are 0.8972, 0.9572, 0.9630 and 0.9501, respectively. The proposed DCE-MRI analysis model can offer an efficient system for radiologists to identify the breast tissues.
HURRA! Human-Readable Router Anomaly Detection

Automated troubleshooting tools must be based on solid and principled algorithms to be useful. However, these tools need to be easily accessible for non-experts, thus requiring to also be usable. This demo combines both requirements by combining an anomaly detection engine inspired by Auto-ML principles, that combines multiple methods to find robust solutions, with automated ranking of results to provide an intuitive interface that is remindful of a search engine. The net result is that HURRA! simplifies as much as possible human operators interaction while providing them with the most useful results first. In the demo, we contrast manual labeling of individual features gathered from human operators from real troubleshooting tickets with results returned by the engine - showing an empirically good match at a fraction of the human labor.
Modified inverse neural controller using adaptive gain factor for DC motor

Abstract Disturbances and input changes effects lead to condition of error in the action integrity of control systems especially when the system is nonlinear. Adaptive controllers are being used to solve this problem. However they may add cost and complexity to the system design, and may not give the required action. This paper presents a DC motor speed controller using Inverse Neural controller and adaptive gain factor. The adaptive gain factor designed to be a function of the error signal (will be used to cancel the error in the controller signal), also a positive feedback from the output (to overcome the error of the plant) will be added to the adaptive gain factor signal to create a new input to the plant. Simulation results proved that the proposed method excellently controls the DC motor speed and evicts the steady-state response error thus; makes the output correctly tracking the desired input with minimum rising time, reduced peak time and settling time, and minimum peak overshoot, with average enhancement of 50% for each versus the results of PID controller for the same plant and same inputs. MATLAB R2015b, SIMULINK simulation has been used to simulate the system and obtain the results.
A Solution for State Conflicts of Smart Contract in Interaction with Non-blockchain

Smart contract helps to broaden the application of blockchain to various fields. In many cases, the non-blockchain systems (NBS) are involved. However, when a smart contract directly interacts with NBS, there is a non-deterministic issue (or a state conflict issue). The reason is that different nodes may obtain different statuses of NBS when the state changes in NBS. If we can solve this issue, a smart contract can be used to interact with current systems without an additional adaptor or bridge required by the existing solutions. In this paper, we regard the states of NBS as a sequence of states instead of a single state and propose to solve the non-deterministic issue by state synchronization. Blockchain nodes synchronize the state of the non-blockchain server via the P2P protocol. The state synchronization has requirements for both blockchain and NBS. For blockchain, it should provide the mechanism to synchronize states obtained from NBS. For NBS, it should sign the query result with a timestamp; the signature prevents the cheat of the observer (the blockchain), and the timestamp ensures the order of a state in the state sequence. At last, we show our verification results with respect to our proposed model and method.
Demo Abstract: FingerLite: Finger Gesture Recognition Using Ambient Light

Free hand interaction with devices is a promising trend with the advent of Internet of Things (IoT). The unmodulated ambient light, which can be an exciting modality for interaction, is still deficient in research and practice when most of the efforts in the field of visible light sensing are put into solutions based on modulated light. In this paper, we propose a low-cost ambient light-based system which performs finger gesture recognition in real-time. The system relies on a recurrent neural network (RNN) architecture without complicated pre-processing algorithms for the gesture classification task. The results of experimental evaluation proves that the solution that we put forward achieves a rather high recognition accuracy with our proposed sensor layout across a certain group of users.
Filtering-Based Text Sentiment Analysis for Twitter Dataset

Text sentiment analysis for the blogs appearing in popular microblogging Web sites has become a very important area of research. Among the popular microblogging Web sites, twitter is most popular and has a limit of 140/280 characters per tweet. Due to the geopolitical and socioeconomic diversity of its users and summarized content, in this work twitter has been chosen for collecting corpus required for text sentiment analysis. Till date, two main approaches are into consideration for text sentiment analysis—lexicon-based and machine learning-based. No research work till date has focused on filtering-based text sentiment analysis that has been used here. The training data has been filtered based on six human emotions, and all their English synonyms and this filtered training dataset were used for training the classifier model. This model gives significantly better accuracy than traditional machine learning-based models on the test data.
Handwritten Signature Verification System Using Sound as a Feature

In this paper, the sound arising from the friction of paper and pen is considered as a biometric data for handwritten signature verification. Sounds were recorded with two different mobile phone models with different operating systems. To expand the scale of the study, the data collection process was carried out by taking samples from participants of different age ranges using different types of pen, paper, and mobile phones. Time sequential peak values were obtained from the onset strength envelopes of sound signals in the feature extraction phase. Similarity distances of the signatures were calculated by Dynamic Time Warping (DTW) algorithm. Equal Error Rate (EER) was used to measure the classification success. When calculated with signer-specific thresholds, EER values vary between %8.14 and %16.61. When calculated using a single threshold for all participant signers, EER values vary between % 15.29 and %28.45 according to different pen, paper, and mobile phone combinations.
Word Webbing as an Effective Technique to Teach Descriptive Writing

One language which is popular nowadays is English.English is an international language that is very popular for global communication.English itself is an international language, which is used as a medium of interaction and communication among people from different parts of the world.The purpose of this research is to propose an alternative technique to help students learn descriptive text better.This research aims at investigating a writing teaching technique using word webbing since the teaching and learning process, particularly in descriptive text, is challenging.The effectiveness of using word webbing in enhancing students' ability to compose descriptive text is measured by using the quantitative method.Using an experimental and control group, the researchers used the quasi experimental method to collect the data and counted it using the t-test formula.According to the statistical calculation, it is found that the word webbing technique is significantly effective in supporting the students' ability to learn descriptive text.
Small sample face recognition based on ensemble deep learning

The problem of small sample in face recognition is still a hot topic nowadays. How to improve the accuracy of face recognition in the case of small sample is very important, for this, this paper proposes a small sample face recognition method based on ensemble deep learning. In order to reduce the problem that the recognition rate decreases greatly due to the too small sample size, first, pixel transformation is carried out on the training database to obtain more training data of different scales; then, the training sets of different scales are classified, and deep learning convolutional neural network (CNN) is used for training respectively; finally, the CNN output results are trained as the meta-features of back propagation (BP) neural network by Stacking strategy of integrated learning, so as to obtain the model data of integrated deep learning and identify it. The ORL face database is used to verify the training sample set of 5 and 6 pieces per person. The experimental results show that the deep neural network using ensemble learning improves 7.5% and 5% respectively compared with the training method using CNN network alone, indicating the excellence of this algorithm.
GPON Traffic Analysis with TensorFlow

The paper presents the latest research results of gigabit passive optical network analysis using machine learning algorithms. TensorFlow has been used to learn the frames of GPON management traffic of the G.984.3 protocol and detect outliers to the standard. The frames are captured by FPGA programmable network card and processed with a software parser and further processed with the TensorFlow using JSON format. The proposed technique can bring a way how to test if vendors of network devices follow the standard and if the content of the frames can be treated as secure and trustworthy.
A Novel Location Privacy Protection Scheme with Generative Adversarial Network

With the booming development of technology and location tracking, location-based services (LBSs) are widely used in mobile phone applications. According to LBS, we can know the location information of relatives at any time, search the surrounding hospitals, restaurants and so on. Moreover, the police can use the LBS to catch escaped prisoners. Generally speaking, LBS brings a lot of convenience to our life and keep our lives safe. However, there are also hidden threats to LBS. Many criminals use LBS to steal location information to track or analyze the preferences of their victims to promote their products. This kind of harassment will affect people’s normal life. It may threaten the location privacy of users. What more serious is it may damage the life and property of users who use an unsafe location system on the internet. To protect the location privacy of users more efficiently, we come up with a new model that includes the traditional model of location privacy and generative-adversarial net (GANs). As we all know generative-adversarial net (GANs) can generate fake data that is closed to real data. Therefore, we use the generative-adversarial net (GANs) to generate a cloaking region (CR) (as same as real location and attacker cannot perceive its false) so that it protects users’ location privacy more efficiently. In our simulation, we generate the cloaking region (CR) in generative-adversarial net (GANs) by the eigenvalues of the initial location. Hence, the cloaking region (CR) we generated is harder to detect than others. But we cannot get details of the eigenvalues and it is bounded to the range of cloaking region (CR) now. We hope we can deal with these problems someday in the future.
Imperfect and Cooperative Guandan Game System

Guandan is a type of imperfect information Card games with four players which are divided into two teams. In recent years, great progress has been made in the field of imperfect information games, AI Pluribus and AI Suphx defeat multiple expert players in Texas Hold'em or Mahjong successfully. However, in Guandan game, the cooperation of two players in the same team must be considered. The cooperation relationship in Texas Hold'em or Mahjong is not exist. Therefore, the successful methods mentioned above are not suitable for solving Guandan game. In this paper, we design and implement Guandan game system based on the framework of UCT (Upper Confidence Bound Apply to Tree) algorithm. Firstly, we sample the multiple possible game states through Monte Carlo Sampling algorithm. Secondly, UCT algorithm is used to searching game tree based on every sampled game state. Finally, the best action is selected based on searching results. According to the cooperation relationships in Guandan game, the payoffs of the two players in the same team is considered in the simulation phase of UCT algorithm. It can reduce the card ability loss in the team. The experimental results show that the improved UCT algorithm is better than random strategy in terms of intelligence, and it has some meaning for solving the game problems involving cooperation relationship.
Duplicated Audio Segment Detection with Local Binary Pattern

Due to the advantages of convenience, and difficulty of detection, copy-move forgery is one of the most common audio forgery forms. It is significant to decide whether there is a duplicated segment in the audio. In order to detect copy-move forgery, a new method is proposed in this paper. In the proposed algorithm, first the audio is segmented into syllables using a pitch tracking method. Second, each syllable analyzed with a 1-D local binary pattern operator. This operator calculates the histogram of each syllable. Mean square error is used to compute the similarities of histograms. The syllables which have similar histograms are detected as forged audio segments. Compared to another 1-D local binary pattern method, the proposed method has better detection results for copy-move forgery. Our experiments show that the proposed algorithm is feasible and effective, and robust against many common postprocessing operations such as noise addition, filtering, and compression.
Evaluating execution strategies of GraphQL queries

The microservices pattern is an effective way for architecting modern cloud-based applications. Communication between microservices depends on extensible and scalable interfaces. However, existing solutions are limited when used in such tasks. In many cases, client applications do not have full control over delivered data. It often leads to under-fetching or over-fetching scenarios. In our work, we evaluate the effectiveness of different execution strategies of GraphQL queries when dealing with the N+1 problem. Our results show that the selection of execution strategies has a significant influence on performance. The results also support some of the general IT industry observations regarding the GraphQL query processing.
Pseudo 3-D ISAR Imaging by Barker’s Phase Code Modulated Waveforms

A three-dimensional geometrical model of inverse synthetic aperture radar (ISAR), and a model of Barker's phase code modulation (BPCM) signal are suggested. A spatial correlation with a known reference function is applied to extract a target's pseudo-3-D image from 2-D ISAR data. To prove the correctness of the geometry, signal model, and imaging algorithm a numerical experiment is carried out.
An Educational Mobile Application Development with AR-Based as a Promotion Media Alian Butterfly Park

Information technology has developed rapidly and has changed various things in human life, from how to communicate, do business, to the way humans learn and work. The development of this technology has helped humans in various fields. One of the technologies that are currently being developed is Augmented Reality (AR). This study discusses the development of AR-based mobile application to promote Alian Butterfly Park. Application development uses the Feature- Driven Development (FDD) method, where the FDD method and application are evaluated using the System Usability Scale (SUS). The SUS score analysis result shows that this application is acceptable and good for the category application acceptance and adjective assessment.
Tool for Optimizing Webpages on a Mobile Phone

This paper discusses the tool that serve to optimize web pages. The internet has become part of our life. People use different devices to browse web pages, and not everyone is adapted to the device. In this article, we design and implement a tool that allows people to optimize any page in their device. The tool contains an algorithm for identifying the main content of a web page and then extracting text and images from it. The goal of the algorithm is to remove redundant information from the webpage and create a new view that contains only useful information. The document created in this way can be further used in web content viewing applications with minimized data transfer or as direct printing of the main web page content without unnecessary data.
Earthquake Early Warning System

Earthquake early warning is considered as one of the real-time earthquake damage mitigation measures which detects seismic waves, analyzes and transmits information of the impending ground shaking at the potential user site. Earthquake warning is issued in order to reduce the damage caused by earthquake such as trains speed can be reduced, and people can move themselves to safer place. The basic requirement of an EEW system is the development of real-time algorithm for fast calculation of earthquake parameters. Earthquake warning is possible by detecting primary waves that travel more quickly than the secondary and Rayleigh waves. The reliable issuance of the warning depends on the accuracy of the calculated parameters. The parameters are estimated on the basis of initial portion of P-wave. Body waves are of two types, namely compressional waves and shear waves. Compressional waves or the P-waves are fast in velocity as compared to shear waves and are recorded first in the seismograph. On the other hand, shear wave or secondary waves are slower than P-wave, and most of the damage is caused due to S-wave. An EEW system warns about an area of forthcoming strong shaking, normally with a few tens of seconds of warning time before the arrival of destructive S-wave. Even a few sec warning will be useful for some emergency measures such as shut off gas pipelines to minimize fire hazards, to slow down rapid transit vehicles and safeguarding of computer facilities to avoid loss of data bases. This can be done by calculating EEW parameters such as τc and peak displacement from the initial 3 s of the P-waveform. With the help of these parameters, magnitude can be estimated, and warning can be provided.
Route planning method for UAV in unknown environment based on improved SAS algorithm

Unmanned aerial vehicle (UAV) real-time target tracking in an unknown environment can be considered as a route planning problem. Aiming at this problem, this paper proposes a three-dimensional (3D) route planning method based on an improved sparse A* search(SAS) algorithm. This method improves the evaluation function. The actual cost in the evaluation function of the SAS algorithm is removed, and the threat information in the mission environment is constructed as part of the evaluation function to guide the UAV to move away from the threat area. The planned trajectory in this method takes into account the updating of real-time environmental information and can achieve real-time trajectory planning. Furthermore, the filtering effect of singer model, current "statistical" model, and interactive multi-model (IMM) algorithm are compared and the current "statistical" model is finally chosen as the maneuvering target tracking model.