Reviewerly/exHarmony huggingface dataset:
One-More Assumptions Do Not Help Fiat-Shamir-type Signature Schemes in NPROM

On the Fiat-Shamir-type signature schemes, there are several impossibility results concerning their provable security. Most of these impossibility results employ the non-programmable random oracle model (NPROM), and to the best of our knowledge, all impossibilities deal with the security reductions from the non-interactive cryptographic assumptions except for the result on the security of Schnorr signature scheme from the One-More DL (OM-DL) assumption in ProvSec2017. In this paper, we extend the impossibility result above concerning Schnorr signature scheme and the OM-DL assumption to a wider class of the Fiat-Shamir-type signature schemes, and aim to find out the conditions so that such impossibility results hold. We show that a specific class of the Fiat-Shamir-type signature schemes, including Schnorr signature scheme, cannot be proven to be euf-cma secure in NPROM from the generalized One-More cryptographic assumptions. This is just a generalization of the impossibility concerning Schnorr signature scheme and the OM-DL assumption. Our result also suggests that for some Fiat-Shamir-type signature schemes, which is not covered by our impossibility (e.g. the RSA-based schemes), there may exist a successful security proof in NPROM from the interactive cryptographic assumption.
Vehicle Face Detection Based on Cascaded Convolutional Neural Networks

Vehicle face detection is an important and challenging task in face detection. On one hand, the face image is affected by the complex environment during driving so that the facial details are easily lost. On the other hand, there is no large public vehicle face dataset. In order to solve these problems, firstly, inspired by the convolutional neural network, an improved method based on cascaded convolutional neural networks is proposed in this paper. The cascaded convolutional networks consist of three lightened convolutional neural networks. Each convolutional neural network uses multi-task learning. Feature fusion technology focusing on learning details of vehicle face image is also adopted in cascaded convolutional networks. Secondly, due to the lack of vehicle face image samples, we build a small vehicle face dataset by ourselves, named Driver FACE dataset. Experiments on Driver FACE dataset show that our method improves the performance of vehicle face detection compared with baseline.
Weakening Feature Independence of Naïve Bayes Using Feature Weighting and Selection on Imbalanced Customer Review Data

E-commerce sites have provided review section for users to take advantage of and express their opinion about products or services. Decision makers, on other hand, can also take advantage of the abundant reviews to analyze which aspects of products or services to be improved, which is known as sentiment analysis. naïve Bayes (NB) is popular method for sentiment analysis because it is considerably faster than other methods but has comparable performance. One weakness of NB however, is that NB assumes each feature is independent with other features. This assumption is not fulfilled in sentiment analysis because terms are correlated with others. Two approaches, i.e. feature weighting (FW) and feature selection (FS) are used to weaken this assumption. Two approaches use genetic algorithm (GA) to find optimal weights and subset based on correlation and odds ratio to take imbalanced review data into account. Experiments on Women Ecommerce Clothing Review dataset show that FW approach has comparable results to non-weighted NB and FS yield worse results than NB. It can be concluded that proposed FW and FS scheme cannot improve standard NB.
Performance Evaluation of Multi-instance Multi-label Classification using Kernel based K-Nearest Neighbour Algorithm

A new learning methodology Multi-instance Multi-label learning (MIML) developed to address the problems of identifying multiple instances belonging to many class labels. This framework is used to understand how multiple instances or complex objects with different semantic meanings are related to many labels. In existing approaches algorithms like Support Vector Machines, AdaBoost, K-Nearest Neighbor, are adapted for the MIML framework. In this paper, a varied approach of adapting Polynomial Kernel and Linear Kernel before applying K-Nearest Neighbour algorithm for MIML framework is proposed. The Kernel provides a better linear separation of the data in the projected space and removes noise. Hausdorff metric is used in K-Nearest Neighbour algorithm to compute the distance between the training sample and the test sample. On performance evaluation it can be seen that the integration of the Polynomial Kernel and Linear Kernel method with the K-Nearest Neighbour algorithm for MIML framework (MIML-KKNN) has better performance in terms of ranking loss, and coverage errors when compared to KNN without Kernel function (MIML-KNN).
Aiming for advanced intelligibility and proficiency using mobile ASR

Abstract This experimental study aimed to determine the impact of mobile-based Automatic Speech Recognition (ASR) in Gmail on intelligibility and proficiency, as well as whether any individual factors influenced learning outcomes. It focused on 26 Intermediate learners of French as a foreign language enrolled in two university courses geared towards the development of advanced oral skills but with different approaches to integrated instruction. It innovatively combined human-based and machine-based ratings within an ecological paradigm, following Levis’s (2005) intelligibility principle and Thomson and Derwing’s (2015) call for research that is readily useful for language instructors. Results show that ASR users significantly outperformed non-ASR users on intelligibility, particularly when exposed to instruction on spelling-to-sound patterns, and demonstrated the biggest growth in proficiency. Gender was also found to impact results. Pedagogical implications and venues for future research are offered.
Feedback Motion Plan Verification for Vehicles with Bounded Curvature Constraints

The kinematic approximation of Dubin's Vehicle has been largely exploited in the formulation of various motion planning methods. In the majority of these methods, planning and control phases are decoupled, and the burden of rejecting disturbances is left to the controller. An alternative to this approach is the use of a feedback motion plan, where for each state there is a specific pre-computed action that will be executed. This planning approach provides the ability to verify all trajectories off-line. The verification can be performed using backward reachability, which provides the set of configurations from which a region is reachable. In this paper, we formulate a verification process that relies on the computation of the backward reachable set using geometric principles. In addition to the theoretical foundation of the method, we provide a numerical implementation of the method and we illustrate a practical example.
Learning to Drive via Apprenticeship Learning and Deep Reinforcement Learning

With the implementation of reinforcement learning (RL) algorithms, current state-of-art autonomous vehicle technology have the potential to get closer to full automation. However, most of the applications have been limited to game domains or discrete action space which are far from the real world driving. Moreover, it is very tough to tune the parameters of reward mechanism since the driving styles vary a lot among the different users. For instance, an aggressive driver may prefer driving with high acceleration whereas some conservative drivers prefer a safer driving style. Therefore, we propose an apprenticeship learning in combination with deep reinforcement learning approach that allows the agent to learn the driving and stopping behaviors with continuous actions. We use gradient inverse reinforcement learning (GIRL) algorithm to recover the unknown reward function and employ REINFORCE as well as Deep Deterministic Policy Gradient algorithm (DDPG) to learn the optimal policy. The performance of our method is evaluated in simulation-based scenario and the results demonstrate that the agent performs human like driving and even better in some aspects after training.
VNet: A Versatile Network for Efficient Real-Time Semantic Segmentation

Many recent excellent methods for efficient real-time semantic segmentation are of low precision and heavily rely on multiple GPUs for training. In this paper, we rethink the critical factors affecting the accuracy of efficient segmentation models. The previous works usually reduce the input resolution prior to training the parameters of models by cropping or resizing the images. On the contrary, our empirical study shows that the reduced images lose the important content information and details, which are vital to the high precision. However, the previous methods are unable to train the original high-resolution images due to the memory-limited GPUs. To tackle this problem, we propose a novel versatile network (VNet), which employs reversible mechanism and asymmetric convolution to achieve highly efficient and extremely low memory consumption in backward propagation. In particular, we keep all the detailed spatial information of the input images without cropping or resizing to pursue decent prediction accuracy. It is worth noting that VNet can train multiple 1024×2048 high-resolution images on only one standard GPU card. Under the same conditions, our model achieves a new state-of-the-art result on Cityscapes datasets. Specifically, it can process the 1024×2048 high-resolution inputs at a rate of 37.4 and 15.5 frames per second (fps) on a standard GPU and an edge device, respectively, with only 0.16 million parameters.
Fuzzy Based Threat Assessment Model (FTAM)

Threat assessment is the process of identifying and characterizing a cybersecurity threat by evaluating and assessing its properties. This is a tool used by many organizations around the globe in order to evaluate their systems and perform a risk analysis against common threats. SAE J3061 guidelines provide a set of principles for automotive cybersecurity which includes threat identification, assessment, and analysis. This standard defines threat assessment as “an analysis technique that is applied in the concept phase to help identify potential threats to a feature and to assess the risk associated with the identified threats”. This paper presents an innovative, Fuzzy Based Threat Assessment Model (FTAM). FTAM leverages the threat characterization from established threat assessment models, while focusing on improving its assessment capabilities by using Fuzzy Logic. Through this methodology, FTAM is able to improve the efficiency and accuracy of the threat assessment process to determine the “degree” of the threat. This is different from using subjective assessment processes based on table look-ups or scoring like some of the current threat assessment models do. This paper provides an analysis on the design and performance of FTAM as well as benchmarking with other existing models.
A Hive and SQL Case Study in Cloud Data Analytics

The digital universe is expanding at a very fast pace generating massive datasets. In order to keep up with the processing and storage needs for this big data, and to discover knowledge, we need scalable infrastructure and technologies that can access data from multiple disks simultaneously. Cloud computing provides paradigms for data analytics over such huge datasets. While SQL continues to be popular among database and data mining professionals, in recent years Hive has established itself as a rapidly advancing technology for big data which makes it highly suitable for use over the cloud. In this paper, we present investigatory research on Hive and SQL with a detailed case study between them, considering cloud data management and mining. Our work here constitutes a thorough scrutiny, focusing on processing Hive queries on cloud infrastructure considering three different approaches, and also delving into SQL processing on the cloud with similar approaches. Real datasets are used for conducting various operations using Hive and SQL. This paper conducts performance comparisons of the two technologies and explains the environment in which one is preferred over the other for processing and analyzing data. It provides recommendations for cloud data analytics, based on the case study.
Learning discriminative hashing codes for cross-modal retrieval based on multi-view features

Hashing techniques have been applied broadly in retrieval tasks due to their low storage requirements and high speed of processing. Many hashing methods based on a single view have been extensively studied for information retrieval. However, the representation capacity of a single view is insufficient and some discriminative information is not captured, which results in limited improvement. In this paper, we employ multiple views to represent images and texts for enriching the feature information. Our framework exploits the complementary information among multiple views to better learn the discriminative compact hash codes. A discrete hashing learning framework that jointly performs classifier learning and subspace learning is proposed to complete multiple search tasks simultaneously. Our framework includes two stages, namely a kernelization process and a quantization process. Kernelization aims to find a common subspace where multi-view features can be fused. The quantization stage is designed to learn discriminative unified hashing codes. Extensive experiments are performed on single-label datasets (WiKi and MMED) and multi-label datasets (MIRFlickr and NUS-WIDE), and the experimental results indicate the superiority of our method compared with the state-of-the-art methods.
Research Challenges and Opportunities in Conducting Quantitative Studies on Large-Scale Agile Methodology

This research note suggests five research challenges when conducting quantitative studies on large-scale agile methodology (LSAM). First, the LSAM empirical literature, which is mainly characterized by qualitative studies primarily focusing on coordination issues, provides limited background. Second, the notion of “large” in LSAM needs to be clarified because the existing research seems to have focused on “very large” or outlier projects. Third, the popular LSAM methods suggest broad and general maxims that may result in difficulty in operationalizing dependent variables, especially in innovation adoption studies. Fourth, the researcher may get overwhelmed when selecting independent variables from the plethora of suggested constructs. Finally, some of the problems associated with large-scale agile are mostly challenges of using conventional agile during a time-period when LSAM had not formally emerged. Researchers should take a balanced approach considering both benefits and challenges of using LSAM and focusing on project-level dependent measures such as success and acceptance.
Segment‐aware dynamic routing for DASH flows over software‐defined networks

Summary Most of the video streaming applications running over the Internet send video data over HTTP and provide an architecture for video clients to adapt video quality during streaming. In HTTP adaptive streaming, a raw video is encoded at various qualities, each encoded video file is divided into small segments, and the clients may change the segment quality by sending requests for segments having different qualities over time. MPEG has standardized dynamic adaptive streaming over HTTP (MPEG‐DASH) due to this tendency. In this work, we focus on DASH over software‐defined networks (SDN), and we dynamically reroute DASH flows by considering the current network capacity, available bandwidth of the paths, and bitrate of the segments in order to provide high quality of experience (QoE) and fairness among DASH clients. Simulations performed under various network conditions show that the proposed study provides higher QoE and fairness compared with the max‐flow routing approach.
Exploring the Potential of Immersive Narrative Scenarios to Identify Design Criteria for our Digital Future(s)

This paper proposes a research design that is centered around narrative scenarios as a means of illustrating human life on the edge of technological singularity. The question is whether narrative scenarios are effective artefacts to trigger an interdisciplinary and transdisciplinary discourse that results in actionable design criteria for the future co-existence of humans and intelligent machines. For this purpose, a systematic participatory approach must be developed and validated that is both plausible and replicable for different domains of human life.
Case-Based Generation of Regulatory Documents and Their Semantic Relatedness

Regulatory documents are required or provided by authorities in many domains. They commonly point out relevant incidents for specific scenarios. For those they have to present suitable preventive and reactive measures. We introduce an approach to connect a case-based description of the incidents structure with a pre-calculated word embedding and describe how to adapt this word embedding to different context. This paper shows how to use case-based methods to retrieve, adapt, and reuse incidents descriptions. Subsequently they are used to generate new regulatory documents via case-based reasoning.
Explicitly exploiting hierarchical features in visual object tracking

A common drawback of convolutional features based trackers is the incapability of distinguishing tracking targets from distractors, even in the presence of distinct color difference. In this paper, we design a robust hybrid tracker that explicitly combines color features with convolutional features. We design our tracker on the basis of fully convolutional Siamese network (SiamFC) to emphasize the performance promotion by introducing color features instead of using a more advanced network architecture. A novel approach to integrate two score maps from different channels is proposed. Techniques include cropping out the ineffective area and denoising via Gaussian smoothing. Experiments conducted on OTB2015 and VOT2018 benchmarks show the superiority of our hybrid tracker over the original SiamFC.
Analyzing Connections Between User Attributes, Images, and Text

This work explores the relationship between a person’s demographic/psychological traits (e.g., gender and personality) and self-identity images and captions. We use a dataset of images and captions provided by N ≈ 1350 individuals, and we automatically extract features from both the images and captions. We identify several visual and textual properties that show reliable relationships with individual differences between participants. The automated techniques presented here allow us to draw interesting conclusions from our data that would be difficult to identify manually, and these techniques are extensible to other large datasets. Additionally, we consider the task of predicting gender and personality using both single modality features and multimodal features. We show that a multimodal predictive approach outperforms purely visual methods and purely textual methods. We believe that our work on the relationship between user characteristics and user data has relevance in online settings, where users upload billions of images each day.
Adaptive Image Enhancement for Automatic Complicated G-band Chromosome Number Determination

The light microscopic image of the chromosome is one of the sources to diagnose genetic disorders. Chromosome counting is the first step for diagnosing genetic abnormalities. However, in the case of complicated chromosome pattern in images, it still needs improvement due to its complication and poor image quality. Moreover, every chromosome images are different in contrast and brightness. Therefore, in order to achieve a better performance in chromosome counting, the complicated chromosome images need to be specially enhanced. This paper proposes a technique called Adaptive Complicated Chromosome Image Enhancement (ACCIE) using the determining threshold value method, and the dynamically determining intensity adjustment method. The proposed method helps to generate a proper chromosome skeletonization and yields 86.81% for complicated chromosome number determination.
Design and Development of Ground Control System for Tethered UAV

This paper designs and develops a tethered UAV ground control system based on Qt environment. Firstly, the framework and main functions of the tethered UAV ground control system are introduced. Ground control system can show the information about the flight status, mode, location and surroundings of UAV in real time through data display module, map control module and aerial image module. Ground control system and flight control system communicate by MAVLink protocol and the location of UAV can be observed in the map control module. Tethered UAV transmits information to the ground station by cable, so it has a faster transmission rate and higher quality of image. Therefore, this paper specially designs the transmission and processing of airborne HD video, and adopts KCF (Kernelized Correlation Filters) tracking algorithm to identify and track moving targets. The ground control system has also realized high-speed real-time serial communication. A four-axis eight-paddle tethered UAV is used as the platform and a tracking experiment is carried out. The results show that the ground control system can display information correctly and control tethered UAV to track a moving target.
Quantum-based Rivest–Shamir–Adleman (RSA) approach for digital forensic reports

Cloud computing is a model that permits usage of a distributed resource for cloud users using the pay-as-you-use method. It offers many advantages to users and companies, in terms of various resources and applications as a service. In spite of the existence of these advantages, there are a few limitations that place constraints on the utilization of a cloud computing environment. Security is an important concern in a cloud computing environment as it probes various security attacks. Therefore, in this work, a novel quantum-based Rivest–Shamir–Adleman (RSA) model is proposed for encryption of forensic reports during storage or data sharing on clouds. To evaluate the effectiveness of the proposed approach, a suitable simulation environment is designed for a multi-cloud environment. Experimental results reveal the proposed approach can efficiently encrypt and store data on multiple clouds without introducing potential overheads. Therefore, the proposed approach is more efficient for real-time applications.
Research on Image Caption Method Based on Mixed Image Features

With the continuous development of deep learning in the field of image caption, the effect of it has improved. However, the traditional method for feature extraction is based on the whole picture, ignoring the local features and the relationships of global and local features. Another problem is that the text description is broad and not targeted. Considering the above problems, we propose a new method based on mixed image features. The method uses an improved ResNet to extract global features. Local features are extracted using a deep RetinaNet. The global and the local features of the image are merged by an attention mechanism, which are mapped into embedding vectors. To obtain the mapping relationship between images and descriptions, a long short-term memory (LSTM) based on an attention mechanism is used as a language generation model. Image features and semantic features are combined to generate content description of images.