Reviewerly/exHarmony huggingface dataset:
Behavior Compatibility Analysis for Service Mashup with Model Checking

In existing mashup environment, end-users take charge of the behavioral compatibility of mashup services. Actually, because of the limited ability of end-users in both programming technique and domain knowledge, they cannot always find the mismatch of services’ behavior. We present an automatic approach for analyzing the behavioral compatibility of service mashup with verification techniques of Communication Sequence Processes (CSP). The feature of this approach is that if a service process includes some incompatibilities, the counterexample would be returned to end-users to track the source of errors. In this approach, a Service Behavior Model (SBM) is presented to facilitate the modeling of services with CSP, and an automatic modeling approach is proposed for service processes through a modular way. Furthermore, two types of behavioral compatibility are introduced based on the development practice, and an automatic specification approach is presented for each type of compatibility properties. Finally, a case study is presented to analyze the behavioral compatibility based on our approach with PAT checker.
Detecting Anomalies in Production Quality Data Using a Method Based on the Chi-Square Test Statistic

This paper describes the capability of the Chi-Square test statistic at detecting outliers in production-quality data. The goal is automated detection and evaluation of statistical anomalies for a large number of time series in the production-quality context. The investigated time series are the temporal course of sensor failure rates in relation to particular aspects (e.g. type of failure, information about products, the production process, or measuring sites). By means of an industrial use case, we show why in this setting our chosen approach is superior to standard methods for statistical outlier detection.
Split Feature Model For Adversarial Condition

The use of machine learning applications has become very common in recent years. It has an important place in many technological fields such as Cyber Security, Language Processing, Biometry and Task Automation. It is possible to see machine learning applications in many places from personal assistants to autonomous vehicles. However, like any system, machine learning has a number of security risks. In addition to general security vulnerability, there are also vulnerabilities specific to machine learning systems. Attacks targeting these vulnerabilities can be encountered in training or production and cause serious problems on the system. In this study, the split feature model (SFM, Split Feature Model) approach has been developed against Evasion Attacks. In Evasion Attacks, it is tried to evade the machine learning system or the performance of the system is reduced with the adversarial inputs. In SFM, features are splitted and distributed to different models. Thus, the features are prevented from directly affecting the output of the entire model. As a result, it is aimed to prevent manipulation in features from directly affecting the output of the machine learning system. The effect of security attacks on the spam filter applied with the traditional learning model and SFM have been examined. Then, performance of SFM has been compared with the traditional machine learning model under security attacks. Experimental results demonstrate the effectiveness of SFM in terms of maintaining accuracy against adversarial inputs.
A Simulator for Analyzing the Balance of Mafia Game

Mafia, a game with high logic, is very popular around the world. Players from all over the world constantly alter the rules of the game to increase the complexity and the entertainment of the game. With increasing the character and number of participants, the balance between the citizens and Mafias is more difficult to maintain. Although little study has been done on Mafia Game, there are some studies which indicate to us the criteria and methods to maintain balance. Among them, there are two main theories: 1) Some studies present the best ratio of the number of Citizens and Mafias, from the perspective of the winning-chance, and use a mathematical model to prove the correctness of the ratio for simple version. 2) Some studies focus on selecting the best ratio from the perspective of the entertainment by analyzing the overall death toll based on the Game Refinement Theory. Although the two above theories have different perspectives and their conclusions are not absolute, both of them are necessary for players. However, most of the studies only do their evaluation according to the theories of themselves, and few studies combine the two theories together to analysis the balance of the Mafia Game between the different characters. In this paper, we proposed simulator which can simulate the various of actions more realistically in Mafia Game. Different from other simulators, it adds a weight of credibility to each character, which can change continuously as the game progresses, and make it more clearly to distinguish the identity of each player. Additionally, the simulation results are analyzed according to the above two theories, which helps us to understand the relationship between the them, and to find a best combination solution dynamically as the change of the total number of players.
Supervised Topic Modeling Using Word Embedding with Machine Learning Techniques

Large amounts of text are collected on the internet every day. As more text documents become available, it becomes essential to categorize them for efficient archiving, retrieval and search. In this paper, we investigate both statistical and machine learning techniques like (HMM & Deep learning network) combined with two well-known word embedding models (word2vec & Glove) for supervised document classification. The investigated combinations are compared with state-of-the-art approaches applied on the same data. The main contribution of this paper is to demonstrate the importance of both the meaning and the order of the word on topic modeling. This has often been overlooked in previous work as neither were taken into consideration where in some others only one was taken. This paper shows that one of our proposed models; which employed a hybrid between LSTM and CNN neural networks, obtained better accuracy on the same dataset than all state-of-the-art models in the literature.
A service-oriented adaptive anonymity algorithm

Recently, a large amount of research studies aiming at the privacy-preserving data publishing have been conducted. We find that most K-anonymity algorithms fail to consider the characteristics of attribute values distribution in data and the contribution value differences in quasi-identifier attributes when service-oriented. In this paper, the importance of distribution characteristics of attribute values and the differences in contribution value of quasi-identifier attributes to anonymous results are illustrated. In order to maximize the utility of released data, a service-oriented adaptive anonymity algorithm is proposed. We establish a model of reaction dispersion degree to quantify the characteristics of attribute value distribution and introduce the concept of utility weight related to the contribution value of quasi-identifier attributes. The priority coefficient and the characterization coefficient of partition quality are defined to optimize selection strategies of dimension and splitting value in anonymity group partition process adaptively, which can reduce unnecessary information loss so as to further improve the utility of anonymized data. The rationality and validity of the algorithm are verified by theoretical analysis and multiple experiments.
Adaptive IMM-CFusion for a Remote IMM Track and Local Measurements

The problem addressed in this paper is the tracking a maneuvering target in a distributed sensor network using a real-world-motivated fusion configuration. The local node SL receives a track from the remote node SR generated by its Interacting Multiple Model (IMM) estimator, and the “inside information” such as motion models, mode probabilities and mode-conditioned estimates of the remote track is unknown. The local node SL has its own measurements which need to be fused with the remote IMM track. This problem can be solved using an existing technique with the following steps: 1) generate a SL track based on its own measurements; 2) perform Track-to-Track Fusion (T2TF) on SR and SL tracks. This paper will develop an alternative approach to fuse the remote IMM track and the local measurements directly. We called it the IMM Cumulated information Fusion (IMM-CFusion). The IMM-CFusion estimates the cumulated information of the local S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> measurements with multiple models, and then fuses this cumulated information with the remote IMM track state. The IMM-CFusion shows better performance than the T2TF approach in a test case.
Review on Removing color & dehazing using Underwater Image enhancement.

Typical underwater images exhibit poor visibility because of light scattering and absorption in turbid water. To resolve the ill-posed problem, a novel underwater image enhancement method based on Removing Light Source Color and Dehazing (RLSCD) is proposed. Scene depth, which his assumed strongly correlated with attenuations, is often ignored in most of the previous methods. A new scene depth estimation, which takes the attenuations of the different light conditions into account, is ﬁrst presented. Additionally, the background light is estimated based on gray open operation and scene depth to avoid that the pixels in the white objects and artiﬁcial lighting regions may be wrongly estimated as the background light. With the estimated background light and transmission map, the dehazed image is achievable. Moreover, the theoretical analysis of the Lambertian Model indicates that the disturbance of the light source color in dehazed image can be estimated, this phenomenon enables us to correct color distortion and light overcompensation by removing the disturbance. Experimental results demonstrate the RLSCD method outperforms state-of-the art methods in terms of relatively genuine color, increased contrast, and brightness.
Joint Model-Based Attention for Spoken Language Understanding Task

Intent determination (ID) and slot filling (SF) are two critical steps in the spoken language understanding (SLU) task. Conventionally, most previous work has been done for each subtask respectively. To exploit the dependencies between intent label and slot sequence, as well as deal with both tasks simultaneously, this paper proposes a joint model (ABLCJ), which is trained by a united loss function. In order to utilize both past and future input features efficiently, a joint model based Bi-LSTM with contextual information is employed to learn the representation of each step, which are shared by two tasks and the model. This paper also uses sentence-level tag information learned from a CRF layer to predict the tag of each slot. Meanwhile, a submodule-based attention is employed to capture global features of a sentence for intent classification. The experimental results demonstrate that ABLCJ achieves competitive performance in the Shared Task 4 of NLPCC 2018.
Design of Robot Routing Controller Based on OpenWrt

Based on the method that most robots use app to control data collection and the router and development board combined to control the robot, there are problems of long development cycle, large power consumption and high cost. We propose the design of a routing controller based on OpenWrt. The controller uses LuCI as the configuration page for control data acquisition, the designed control software processes and sends control commands, and the router directly controls the robot. The results show that the power consumption and cost of the robot are significantly reduced, and the development cycle is shortened by 5 days. In addition, the routing controller is suitable for multi-robot control in the state of the Internet of Things.
Increasing the Efficiency of Text Input in the 8pen Method

The purpose of this work is to improve the 8pen method to increase text input speed. Authors proposed calculation of Layout Factor based on gesture energy cost calculated as number of Curve Drawn Per Character (CDPC) and the cost of transition between two letters neighboring in a text. Then the letters were arranged in four different layouts: alphabetically, using the frequency of unigrams, bigrams and trigrams in English. The best result was estimated for letter order based on bigrams. However, after user evaluation the best results were achieved for trigrams (almost 20 WPM). That underlines the importance of easy transition between letters while entering the text.
Development of Smart-Phone Interfaces for Tongue Controlled Assistive Devices

Enabling individuals with upper limb disabilities to interact independently with smartphones is challenging. Our team has developed discreet Bluetooth-enabled intraoral wearable devices that utilize the tongue in order to allow for independent interaction with smartphones. This paper presents on custom applications that are paired with the developed button-based intraoral device and the trackpad-based intraoral device. Initially, user study surveys were conducted in order to identify the most common needs of the target population. The prominent needs identified were the ability to independently type and place phone calls. These identified features were implemented in the developed applications. The applications also featured six tongue-training environments. Each tongue-training environment is designed to enhance an aspect of the tongue’s strength and precision for optimized interaction with the intraoral wearable devices. To assess the implementation of the developed applications, an expert user participated in the iterative design process. Also, in order to assess the target population’s feedback in regard to the developed applications, a follow-up survey was distributed. Our aim is to be able to utilize the intraoral wearable devices and applications to bridge the technological divide which affects individuals with paralyzing disabilities to the upper limbs.
MAC-in-the-Box: Verifying a Minimalistic Hardware Design for MAC Computation

We study the verification of security properties at the state machine level of a minimalistic device, called the MAC-in-the-Box (MITB). This device computes a message authentication code based on the SHA-3 hash function and a key that is stored on device, but never output directly. It is designed for secure password storage, but may also be used for secure key-exchange and second-factor authentication. We formally verify, in the HOL4 theorem prover, that no outside observer can distinguish this device from an ideal functionality that provides only access to a hashing oracle. Furthermore, we propose protocols for the MITB’s use in password storage, key-exchange and second-factor authentication, and formally show that it improves resistance against host-compromise in these three application scenarios.
Joint Lattice of Reconstructability Analysis and Bayesian Network General Graphs

This paper integrates the structures considered in Reconstructability Analysis (RA) and those considered in Bayesian Networks (BN) into a joint lattice of probabilistic graphical models. This integration and associated lattice visualizations are done in this paper for four variables, but the approach can easily be expanded to more variables. The work builds on the RA work of Klir [22], Krippendorff [26], and Zwick [44], and the BN work of Pearl [33–36], Verma and Pearl [39], Heckerman et al. [20], Chickering [9], Andersson et al. [1], and others. The RA four variable lattice and the BN four variable lattice partially overlap: there are ten unique RA general graphs, ten unique BN general graphs, and ten general graphs common to both RA and BN. For example, the specific graph having probability distribution p(A)p(C)p(B | AC) is unique to BN, the RA specific graph AB:AC:BC, which contains a loop, is unique to RA, and the specific graph ACD:BCD with probability distribution p(A | CD)p(B | CD)p(D | C)p(C) is common to both RA and BN. The joint RA-BN lattice of general graphs presented in this paper expands the set of general graphs with unique independence structures beyond what was previously available by either RA alone or BN alone, thus allowing for representations of complex systems which are (i) more accurate relative to data and/or (ii) simpler and thus more comprehensible and more generalizable than would be possible by modeling only with RA or only with BN.
Obstacle Avoidance-based Control System Design of UAV with Suspended Payload

In this paper, the flight safety of quadrotor UAV with suspended payload in presence of obstacle is addressed. The trajectory planning and stability control are discussed to minimize the payload swing. Firstly, a comprehensive model of quadrotor UAV with payload system is established. Then, for given waypoints, Cubic curve is used to generate a feasible reference trajectory in urban environment. In suburban, Dubins trajectory method is introduced to generate smooth and continuous obstacle avoidance trajectory. Predictive control is used to find the shortest route with minimizing payload swing by avoiding unknown obstacles in real-time. The hierarchical control structure of inner-loop attitude and outer-loop position using PID is adopted in the design of trajectory tracking. The simulation results verify the feasibility of obstacle avoidance strategy for UAV flying with a suspended payload.
Soupa and Integration of Ontologies Verl for Conceptualizing Contexts in Video Surveillance and Ubiquitous Computing

Research describes the design and Ontology for Ubiquitous Surveillance Systems (OSU). OSU arises from the combination of Video Event Ontology (VEO), used generally to conceptualize events surveillance [2] with Standard Ontology for Ubiquitous and Pervasive Applications (SOUPA), used generally to conceptualize events ubiquitous computing [3]. The goal of combining VEO and SOUPA is complementarity, because there classes SOUPA that can be used to describe events surveillance (Person, for example), while there are classes VEO that can be used to describe events ubiquitous computing (Object, for example). OSU allowed understand and conceptualize, better, the events recorded in CASAS [4] projects and start [5].
Research Progress in Blind Forensics of Digital Image Smooth Filtering

Digital image blind forensics technology is a hot research direction in the field of information security. It can realize the authenticity and integrity verification of digital images without embedding the authentication information in the image in advance. In recent years, it has been widely researched and quickly developed. In this paper, a post-processing method-smoothing filter commonly used in digital image processing is summarized. The traditional forensic methods and representative work proposed in the early stage are summarized and discussed. Combined with deep learning technology, the main deep learning based smooth filtering is introduced in detail. Combined with deep learning technology, the main detection method based on smooth filtering is introduced in detail. Finally, we discussed the problems that still needed to be solved by blind learning technology based on deep learning and future research trends.
IPDH: An Improved Pairwise-Based Deep Hashing Method for Large-Scale Image Retrieval

Hashing technique has been extensively utilized in approximate nearest neighbor (ANN) search for large-scale image retrieval by virtue of its storage simplicity and computational efficiency. Recently, many researches show that hashing methods based. on deep neural networks (DNNs) can improve retrieval accuracy by simultaneously learning both deep feature representation and hashing functions in an end-to-end framework. Most deep supervised hashing methods aim to preserve the distance or similarity between data points using the similarity relationships constructed based on semantic labels of images, while ignoring the classification ability of the generated hash codes. However, the semantic labels themselves carry more information than the corresponding similarity labels. We propose an Improved Pairwise-based Deep Hashing (IPDH) method to generate hash codes with powerful classification ability by exploring the global distribution of semantic labels. Specifically, the proposed IPDH method aims to minimize the information loss generated during the process of classification prediction to ensure that the output predicted labels of the network model has a similar distribution with those from the original semantic labels. Comprehensive experiments show that the proposed IPDH method can obtain better improvement than other state-of-the-art algorithms.
Round Robin Based Efficient Resource Allocation and Utilization in an Organization

In an organization, resource allocation to a request is a complex task. Traditionally, resource allocation is done through manually with high time consumption. Similarly, collision is occurring for allocating a single resource to multiple requests. Thus, leads to complex problems and slow-down the working process. The existing resource allocation technique, allocate resources continuously to a specific request and omit another request. This kind of allocation technique also leads to lots of critical issues. That is the non-allocated process never gets a resource. To overcome these issues, the Round Robin based Resource allocation and Utilization technique is proposed in this work. The Round Robin technique allocates resources to the request in an efficient with equal priority. Similarly, the proposed technique reduces collision and takes less time for mapping a resource with a request. The experimental results shows improved accuracy than the traditional resource allocation technique.
Learning from Past Observations: Meta-Learning for Efficient Clustering Analyses

Many clustering algorithms require the number of clusters as input parameter prior to execution. Since the “best” number of clusters is most often unknown in advance, analysts typically execute clustering algorithms multiple times with varying parameters and subsequently choose the most promising result. Several methods for an automated estimation of suitable parameters have been proposed. Similar to the procedure of an analyst, these estimation methods draw on repetitive executions of a clustering algorithm with varying parameters. However, when working with voluminous datasets, each single execution tends to be very time-consuming. Especially in today’s Big Data era, such a repetitive execution of a clustering algorithm is not feasible for an efficient exploration. We propose a novel and efficient approach to accelerate estimations for the number of clusters in datasets. Our approach relies on the idea of meta-learning and terminates each execution of the clustering algorithm as soon as an expected qualitative demand is met. We show that this new approach is generally applicable, i.e., it can be used with existing estimation methods. Our comprehensive evaluation reveals that our approach is able to speed up the estimation of the number of clusters by an order of magnitude, while still achieving accurate estimates.