Reviewerly/exHarmony huggingface dataset:
The Role of the CFO of an Industrial Company: An Analysis of the Impact of Blockchain Technology

This qualitative multiple case study explores the influence of blockchain technology on the chief financial officer (CFO) of an industrial company. Due to the advancing digitalization of business sectors and increasing competitive pressures, industrial companies are forced to promote their own digital transformation to sustain on the market. Here, the literature regards the CFO as a key corporate function to induce digitization initiatives within organizations. The blockchain technology, due to its features of transparency, immutability and cryptography combined with its ability to coordinate data flows of e.g., the Internet of Things (IoT) or Artificial Intelligence (AI), constitutes a suitable instrument for the CFO to meet the requirements of Industry 4.0. This paper provides a contribution to address existing research gaps regarding the application side of blockchain technology. Thus, the objective of this work is to provide corporate financial functions, such as the CFO of an industrial company, with an understanding of the extent to which blockchain technology can be used for the role-specific responsibilities. Therefore, the underlying qualitative study explores the influence of blockchain technology on the CFO-function of an industrial company. Thus, intending to address a research gap on the application side, it asks (1) What is the impact of blockchain technology on the financial as well strategic role of the CFO? (2) What is the impact of blockchain technology in convergence with the Machine Economy on the key performance indicators (KPIs) of the CFO? (3) What is the impact of blockchain-enabled integrated business ecosystems on the role of the CFO? Based on a review of literature, semi-structured expert interviews were conducted with 23 participants. Analysis of the responses demonstrated a considerable impact of blockchain technology on the CFO-function. The results indicate improvements of business processes in regard to efficiency and automation, a relocation of the CFO’s strategic role, improvements of CFO-relevant KPIs through integrating machines into payment networks as well as the emergence of integrated business ecosystems facilitating new forms of inter-organizational collaboration. Necessary prerequisites for adoption include digital competences of the CFO, appropriate organizational structures, digital currencies and identities on the blockchain, a change of the competitive mindset as well as standardized platforms with a neutral governance.
A novel path planning methodology for automated valet parking based on directional graph search and geometry curve

The paper presents a novel path planning methodology based on the directional graph search and the geometry curve for the Automated Valet Parking (AVP) system. The whole path planning methodology is divided into three parts including the global path planning, the path coordination strategy and the parking path planning. Firstly, the global path planning is triggered to find a path from the parking slot entrance to the rough location of the assigned parking spot. A novel directional Hybrid A* algorithm is proposed to generate the global path efficiently without redundant searches, such as the dead end. Afterwards, the path coordination strategy gives a transitional path to connect the end node of the global path to the parking planning start node. The transitional path is composed of geometry curves including arcs and line segments based on the optimal parking start node. Finally, the parking path planning generates a parking path to guide the vehicle from parking start node to the parking space. A modified C-type vertical parking path planning algorithm is utilized to generate the parking path, offering flexibility for choosing the parking start node. Simulation results based on Matlab and PreScan show that it takes less time for the proposed path planning algorithm to generate a feasible path for the AVP system compared with the general planning algorithm. The novel AVP path planning algorithm also has the potential for practical use.
A Robust Vehicle Detection Scheme for Intelligent Traffic Surveillance Systems in Smart Cities

Accurately obtaining road vehicle information is important in intelligent traffic surveillance systems for smart cities. Especially smart vehicle detection is recognized as the critical research issue of intelligent traffic surveillance systems. In this paper, a robust real-time vehicle detection method for the system is proposed. The method combines background subtraction model MOG2(Mixture of Gaussians) with a modified SqueezeNet model (H-SqueezeNet). The MOG2 model is utilized to create scale-insensitive Region of Interest (RoIs) from video frames. H-SqueezeNet is then proposed to accurately identify vehicle category. The effectiveness of the method was verified in CDnet2014 dataset, UA-DETRAC dataset and video data from a traffic intersection in Suzhou, China. The experiment results show that the method can achieves excellent detection accuracy in traffic surveillance systems, and achieve an average detection speed of 39.1 FPS.
Non-disruptive collagen characterization in clinical histopathology using cross-modality image synthesis

Abstract The importance of fibrillar collagen topology and organization in disease progression and prognostication in different types of cancer has been characterized extensively in many research studies. These explorations have either used specialized imaging approaches, such as specific stains (e.g., picrosirius red), or advanced and costly imaging modalities (e.g., second harmonic generation imaging (SHG)) that are not currently in the clinical workflow. To facilitate the analysis of stromal biomarkers in clinical workflows, it would be ideal to have technical approaches that can characterize fibrillar collagen on standard H&amp;E stained slides produced during routine diagnostic work. Here, we present a machine learning-based stromal collagen image synthesis algorithm that can be incorporated into existing H&amp;E-based histopathology workflow. Specifically, this solution applies a convolutional neural network (CNN) directly onto clinically standard H&amp;E bright field images to extract information about collagen fiber arrangement and alignment, without requiring additional specialized imaging stains, systems or equipment.
Computer-aided teaching mode of oral English intelligent learning based on speech recognition and network assistance

The purpose of this paper is to use English specific syllables and prosodic features in spoken speech data to carry out English spoken recognition, and to explore effective methods for the design and application of English speech detection and automatic recognition systems. The method proposed by this study is a combination of SVM_FF based classifier, SVM_IER based classifier and syllable classifier. Compared with the method based on the combination of other phonological characteristics such as phonological rate, intensity, formant and energy statistics and pronunciation rate, and the syllable-based classifier based on specific syllable training, a better recognition rate is obtained. In addition, this study conducts simulation experiments on the proposed English recognition and identification method based on specific syllables and prosodic features and analyzes the experimental results. The result found that the recognition performance of the English spoken recognition system constructed by this study is significantly better than the traditional model.
Virtual machine placement strategy using cluster-based genetic algorithm

The problem of virtual machine (VM) live migration in self-driving systems targets reallocating resources among VMs running different self-driving services for load balance. It is of great importance to enable a running VM to be moved to another physical machine (host) seamlessly. Due to the nature of self-driving applications, it is important to select an optimal set of hosts to place VMs within an interactive time. The VM placement problem can be formalized as a bin packing problem, which is proved to be NP-hard. To solve the problem, we develop a cluster-based genetic algorithm that outputs an approximation result of the bin pack problem. In particular, our proposed algorithm clusters the population of current generation and selects individuals from different groups with reduced crossover operations. The number of crossover operations is directly related to the algorithm efficiency. We use the run-time features to evaluate the preference of VMs on hardware resources, which is utilized to generate initial solutions and avoid overload. Experimental results show that our approach is able to outperform the tradition genetic algorithm regarding both accuracy and efficiency.
A Supervised Filter Feature Selection method for mixed data based on Spectral Feature Selection and Information-theory redundancy analysis

Spectral analysis and Information-theory are two powerful and successful frameworks for feature selection in supervised classification problems. However, most of the methods developed under these frameworks have been introduced for handling exclusively numerical or non- numerical data. In this paper, we propose a supervised filter feature selection method that combines Spectral Feature Selection and Information-theory based redundancy analysis for selecting relevant and non-redundant features in supervised mixed datasets; i.e., datasets where the objects are described simultaneously by both, numerical and non-numerical features. To demonstrate the effectiveness of our proposed supervised filter feature selection method, we conducted several experiments on 40 public real-world datasets. Additionally, we compare our method against relevant state-of-the-art supervised filter methods for numerical, non-numerical, and mixed data. From this comparison, our method, in general, obtains better results than the results obtained by the other evaluated filter feature selection methods.
This PIN Can Be Easily Guessed: Analyzing the Security of Smartphone Unlock PINs

We provide the first comprehensive study of user-chosen 4- and 6-digit PINs (n = 1220) collected on smartphones with participants being explicitly primed for device unlocking. We find that against a throttled attacker (with 10, 30, or 100 guesses, matching the smartphone unlock setting), using 6-digit PINs instead of 4-digit PINs provides little to no increase in security, and surprisingly may even decrease security. We also study the effects of blacklists, where a set of "easy to guess" PINs is disallowed during selection. Two such blacklists are in use today by iOS, for 4-digits (274 PINs) as well as 6-digits (2910 PINs). We extracted both blacklists compared them with four other blacklists, including a small 4-digit (27 PINs), a large 4-digit (2740 PINs), and two placebo blacklists for 4- and 6-digit PINs that always excluded the first-choice PIN. We find that relatively small blacklists in use today by iOS offer little or no benefit against a throttled guessing attack. Security gains are only observed when the blacklists are much larger, which in turn comes at the cost of increased user frustration. Our analysis suggests that a blacklist at about 10 % of the PIN space may provide the best balance between usability and security.
Semantically-guided low-light image enhancement

Recently, extensive research efforts have been made on low-light image enhancement. Many novel models have been proposed, such as the ones based on the Retinex theory, multiple exposure fusion, and deep neural networks. However, current models do not directly consider the semantic information in the modeling process. As a result, they tend to introduce more artifacts, such as boosted noises and unnatural visual appearances. To address this issue, we propose a fusion-based low-light enhancement model that explicitly harnesses the scene semantics into the enhancement process. In constructing the fusion map, the image regions with a specific semantic category is firstly extracted via semantic segmentation. Then, they are further combined and refined jointly with an illumination-aware map estimated from the scene illumination. Guided by the semantic information, our model is able to intentionally enhance a part of the dark regions, which therefore generates enhanced results with more natural appearances and less artifacts. In experiments, we first validate our model with some empirical studies, including parameter sensitivity and segmentation error tolerance. Then we compare our model with several state-of-the-art low-light enhancement methods, which further shows the effectiveness and advantage of our model.
Black-box adversarial sample generation based on differential evolution

Deep Neural Networks (DNNs) are being used in various daily tasks such as object detection, speech processing, and machine translation. However, it is known that DNNs suffer from robustness problems — perturbed inputs called adversarial samples leading to misbehaviors of DNNs. In this paper, we propose a black-box technique called Black-box Momentum Iterative Fast Gradient Sign Method (BMI-FGSM) to test the robustness of DNN models. The technique does not require any knowledge of the structure or weights of the target DNN. Compared to existing white-box testing techniques that require accessing model internal information such as gradients, our technique approximates gradients through Differential Evolution and uses approximated gradients to construct adversarial samples. Experimental results show that our technique can achieve 100% success in generating adversarial samples to trigger misclassification, and over 95% success in generating samples to trigger misclassification to a specific target output label. It also demonstrates better perturbation distance and better transferability. Compared to the state-of-the-art black-box technique, our technique is more efficient. Furthermore, we conduct testing on the commercial Aliyun API and successfully trigger its misbehavior within a limited number of queries, demonstrating the feasibility of real-world black-box attack.
Rldish: Edge-Assisted QoE Optimization of HTTP Live Streaming with Reinforcement Learning

Recent years have seen a rapidly increasing traffic demand for HTTP-based high-quality live video streaming. The surging traffic demand, as well as the real-time property of live videos, make it challenging for content delivery networks (CDNs) to guarantee the Quality-of-Experiences (QoE) of viewers. The initial video segment (IVS) of live streaming plays an important role in the QoE of live viewers, particularly when users require fast join time and smooth view experience. State-of-the-art research on this regard estimates network throughput for each viewer and thus may incur a large overhead that offsets the benefit. To tackle the problem, we propose Rldish, a scheme deployed at the edge CDN server, to dynamically select a suitable IVS for new live viewers based on Reinforcement Learning (RL). Rldish is transparent to both the client and the streaming server. It collects the real-time QoE observations from the edge without any client-side assistance, then uses these QoE observations as real-time rewards in RL. We deploy Rldish as a virtualized network function (VNF) in a real HTTP cache server, and evaluate its performance using streaming servers distributed over the world. Our experiments show that Rldish improves the state- of-the-art IVS selection scheme w.r.t. the average QoE of live viewers by up to 22%.
A Comparison of Unidirectional and Bidirectional LSTM Networks for Human Activity Recognition

Human activity recognition targets identifying different classes of human movements using data gathered from various types of sensors. Deep learning approaches, such as Recurrent Neural Networks, are gaining interest in the classification of human activities using time series data. Long-Short Term Memory is a recurrent neural network approach that is well suited for the classification of time series data where it handles the vanishing gradient and the long-term dependency problems efficiently. In this paper, we compare the human activity recognition accuracy of the unidirectional and bidirectional Long-Short Term Memory models on two different datasets that represent accelerometer data. The results show that the bidirectional approach slightly enhances the recognition quality over the unidirectional approach. However, the bidirectional approach spends more time during the training, which may hinder its applicability on large datasets.
Efficient Certificateless Multi-Copy Integrity Auditing Scheme Supporting Data Dynamics

To improve data availability and durability, cloud users would like to store multiple copies of their original files at servers. The multi-copy auditing technique is proposed to provide users with the assurance that multiple copies are actually stored in the cloud. However, most multi-replica solutions rely on Public Key Infrastructure (PKI), which entails massive overhead of certificate computation and management. In this article, we propose an efficient multi-copy dynamic integrity auditing scheme by employing certificateless signatures (named MDSS), which gets rid of expensive certificate management overhead and avoids the key escrow problem in identity-based signatures. Specifically, we improve the classic Merkle Hash Tree (MHT) to achieve batch updates for multi-copy storage, which allows the communication overhead incurred for dynamics to be independent of the replica number. To meet the flexible storage requirement, we propose a variable replica number storage strategy, allowing users to determine the replica number for each block. Based on the fact that auditors may frame Cloud Storage Servers (CSSs), we use signature verification to prevent malicious auditors from framing honest CSSs. Finally, security analysis proves that our proposal is secure in the random oracle model. Analysis and simulation results show that our proposal is more efficient than current state-of-the-art schemes.
Community answer generation based on knowledge graph

Community Question Answering (CQA) has become an indispensable way for modern people to share and acquire knowledge. It allows users to ask questions, which will be answered by experienced users enthusiastically. By recording user operation logs, CQA has accumulated a large amount of valuable and complex data. However, askers must wait (usually for a long time) until other expert users answer their questions on social platforms. This will seriously affect the user experience. In this paper, we propose a Community Answer Generation method based on the Knowledge Graph, called CAGKG, to generate natural language answers automatically. Firstly, we extract the core phrases of posts to represent their semantics relations. Then, we model the user's knowledge background based on their action records. Finally, we query knowledge entities in a knowledge graph based on user background and question semantics, then convert them into natural language answers. Besides, we proposed a Phrase-based Answers Semantic Similarity Evaluation indicator, called PASSE, which focuses on the semantic similarity between texts instead of literal matching. To the best of our knowledge, it is the first work that utilizes the user knowledge and text semantics to improve the performance of CQA. Experiments on four real datasets (Stack Overflow, Super User, Mathematics, and Quora) show that CAGKG is superior to the state-of-the-art question answering frameworks. Compared with other answer evaluation indicators, PASSE is a promising indicator for evaluating semantic similarity.
Aspect-Based Fashion Recommendation With Attention Mechanism

With the rapid growth of fashion e-commerce, fashion recommendation has become a main digital marketing tool that is built on customer reviews and ratings. Online review is a powerful source for understanding users’ shopping experiences, preferences and feedbacks on product/item performances, and thus is useful for enhancing personalized recommendations for future purchases. However, most extant fashion recommendation methods lack effective frameworks to integrate local and global aspect representations extracted from customers’ ratings and reviews. In this paper, we proposed an aspect-based fashion recommendation model with attention mechanism (AFRAM) to predict customer ratings based on online reviews of fashion products. This model can extract latent aspect features about users and items separately through two parallel paths of convolutional neural networks (CNN), long short-term memory networks (LSTM), and attention mechanisms. One path processes user reviews and the other copes with item reviews. On each path, CNN and LSTM are both coupled with an attention mechanism to capture local aspect features and global aspect features respectively, which are combined through a mutual operation module. The mutual operations on both paths can enhance the generalization of the AFRAM model. The extracted features from the two paths are further merged to predict users’ ratings. Real-world customer reviews and ratings collected from two renowned business websites were used to train and test AFRAM. The experiment results demonstrate that AFRAM is more effective in customer rating predictions, as compared to several state-of-the-art fashion recommenders.
Arbitrarily-Oriented Text Detection in Low Light Natural Scene Images

Text detection in low light natural scene images is challenging due to poor image quality and low contrast. Unlike most existing methods that focus on well-lit (normally daylight) images, the proposed method considers much darker natural scene images. For this task, our method first integrates spatial and frequency domain features through fusion to enhance fine details in the image. Next, we use Maximally Stable Extremal Regions (MSER) for detecting text candidates from the enhanced images. We then introduce Cloud of Line Distribution (COLD) features, which capture the distribution of pixels of text candidates in the polar domain. The extracted features are sent to a Convolution Neural Network (CNN) to correct the bounding boxes for arbitrarily oriented text lines by removing false positives. Experiments are conducted on a dataset of low light images to evaluate the proposed enhancement step. The results show our approach is more effective compared to existing methods in terms of standard quality measures, namely, BRISQE, NIQE and PIQE. In addition, experimental results on a variety of standard benchmark datasets, namely, ICDAR 2013, ICDAR 2015, SVT, Total-Text, ICDAR 2017-MLT and CTW1500, show that the proposed approach not only produces better results for low light images, at the same time it is also competitive for daylight images.
Application of deep learning and cloud data platform in college teaching quality evaluation

In this paper, the author introduces the theory of fuzzy mathematics into the evaluation of higher education. By determining the set of evaluation factors and comments, the author constructs the relevant mathematical model and processes the data, thus turning the evaluation problem into the multiplication problem of the fuzzy matrix. Deep learning is a very active branch of machine learning research in recent years. By increasing the depth and breadth of the model, i.e. increasing the number of operations from the input end to the output end and the number of channels of the model, the scale of parameters of the model is increased, so that the model has the ability to express complex functions. It is appropriate to use deep learning in teaching quality evaluation. The simulation results show that the deep learning model is very effective in dealing with data diversity and extracting complex implicit rules. It can effectively model experts’ professional knowledge and experience. Deep neural network has powerful expressive ability, and can effectively extract the deep-seated laws affecting the teaching quality. It can be used as an assistant technology for the evaluation of teaching quality in Colleges.