Reviewerly/exHarmony huggingface dataset:
A Proposal of Architecture Framework and Performance Indicator Derivation Model for Digitalization of Quality Management System

Manufacturers of pharmaceuticals and medical devices are accountable for the quality and stable supply of their health-related products. In order to guarantee the provision of products that meet quality requirements, they have established and are operating quality management systems. On the other hand, as the risk of unstable supply increases due to pandemics and outsourcing of supply chains, there is a need to strengthen the monitoring capabilities for early detection of quality problems and countermeasures. Recently, with the advancement of digital technology, IoT, automation and block chain technologies are expected to be used in the field of quality management. Alternatively, quality management systems must continuously maintain their performance in the process of change. However, no method has been established to systematically promote the digitalization of quality management systems and to evaluate the performance of the transition process. In this paper, we propose an architecture framework and a model for deriving performance indicators to promote the digitalization of quality management systems and to evaluate the performance of the transition process. Issues and future plans in this field are also discussed.
Violations of the Leggett–Garg inequality for coherent and cat states

We show that in some cases the coherent state can have a larger violation of the Leggett-Garg inequality (LGI) than the cat state by numerical calculations. To achieve this result, we consider the LGI of the cavity mode weakly coupled to a zero-temperature environment as a practical instance of the physical system. We assume that the bosonic mode undergoes dissipation because of an interaction with the environment but is not affected by dephasing. Solving the master equation exactly, we derive an explicit form of the violation of the inequality for both systems prepared initially in the coherent state $|\alpha\rangle$ and the cat state $(|\alpha\rangle+|-\alpha\rangle)$. For the evaluation of the inequality, we choose the displaced parity operators characterized by a complex number $\beta$. We look for the optimum parameter $\beta$ that lets the upper bound of the inequality be maximum numerically. Contrary to our expectations, the coherent state occasionally exhibits quantum quality more strongly than the cat state for the upper bound of the violation of the LGI in a specific range of three equally spaced measurement times (spacing $\tau$). Moreover, as we let $\tau$ approach zero, the optimized parameter $\beta$ diverges and the LGI reveals intense singularity.
The Relationship Between Ethical leadership and Teachers Continuance Organizational Commitment

The current study aims to investigate the relationship between the behavior of ethical leaders EL and their continuance organizational commitment COC, and also to discover the mediational impact of teachers' loyalty TL on their relationships at the international schools in Jeddah, Kingdom of Saudi Arabia (KSA). The study employed a quantitative approach as follows: a formatted questionnaire was distributed to a random sample, of which 372 valid questionnaires were returned to the researchers. In addition to examining the research hypotheses, several statistical methods were used to analyse the compiled data. The study showed that when the supervisor displays positive behaviors and a moral leadership style, this has leverage on the employees’ performance. The study found that there is a statistically important engagement relevance between ethical leadership EL and teachers’ loyalty TL. Moreover, the study showed that teachers' loyalty facilitated the association between EL and COC. The current research recommended that it was essential to increase the standard of ethical leadership EL for managers, to positively influence COC and to enhance TL.
Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension

Task requirements (TRs) writing is an important question type in Key English Test and Preliminary English Test. A TR writing question may include multiple requirements and a high-quality essay must respond to each requirement thoroughly and accurately. However, the limited teacher resources prevent students from getting detailed grading instantly. The majority of existing automatic essay scoring systems focus on giving a holistic score but rarely provide reasons to support it. In this paper, we proposed an end-to-end framework based on machine reading comprehension (MRC) to address this problem to some extent. The framework not only detects whether an essay responds to a requirement question, but clearly marks where the essay answers the question. Our framework consists of three modules: question normalization module, ELECTRA based MRC module and response locating module. We extensively explore state-of-the-art MRC methods. Our approach achieves 0.93 accuracy score and 0.85 F1 score on a real-world educational dataset. To encourage reproducible results, we make our code publicly available at https://github.com/aied2021TRMRC/AIED_2021_TRMRC_code .
Fisheye Image Correction Based on Three-Dimensional Control Field

Abstract The fisheye image has severe distortions, which is not in line with human visual habits and brings inconvenience to its application. This paper classifies them into spherical structural distortion and optical distortion, and proposes a fisheye image correction method based on 3D control field. First, the spherical transformation radius and the optical center of the fisheye image are accurately solved, and the fisheye image is corrected by combining the spherical perspective projection; then, the distortion model of the fisheye camera is introduced into the DLT model to calibrate the optical distortion, and the results are used to recorrect the image. This method has been experimentally proven to be fast and effective.
Research on Key Technology of Integral Translation and Rotation of the Large-Diameter Shield and Steel Sleeve in the Underwater Tunnel of the Karnaphuli River, Bangladesh

Abstract The construction of the underwater tunnel of the Karnaphuli River in Bangladesh boasts the characteristics such as short time limit, small working shaft space and a large weight of the shield tunneling machine and etc.. Moreover, the translation and rotation of the shield tunneling machine are the key link of the entire construction, which also takes a longer time and is more difficult to achieve. The existing rotating technology of a shield tunneling machine and its applicable conditions were analyzed to conquer the difficulties like the tight time limit and the high mechanical level of disassembling and assembling of the shield tunneling machine in this project. The technology of integral translation and rotation of the large-diameter steel sleeve of the shield and the shield tunneling machine was proposed according to its characteristics and the present situation of confined working space. The technology mainly includes the design and application of the new steel ball base, the preparation of the working shaft space and its base, the leveling and translation of the shield tunneling machine, the translation of the shield machine, and its rotation at 180° in situ. The sliding friction between the sleeve base and the bottom plate of the working shaft is turned into rolling friction by the new steel ball base, which can effectively reduce the resistance of the translation and rotation of the steel sleeve. In a word, the working efficiency can be highly improved and the construction period can be shortened.
“One vs All” Classifier Analysis for Multi-label Movie Genre Classification Using Document Embedding

The classification of movie genres from their synopses has attracted the attention of many researchers. Indeed, synopses are a source of relevant information that contributes to determinate movie genre. The automation of this classification process is very useful in several applications, such recommendation systems. Moreover, movies can belong simultaneously to several genres (drama, action, comedy, horror), which reflects a typical problem of multi-label classification (MLC). In this article, we use a powerful representation of film synthesis via a document integration technique Doc2vec in the multi-label classification context. The technique used in our experience is One Vs All, which is a transformation approach; it creates a model for each label through a kernel classifier. We have chosen to use three different classifiers: logistic regression, SVM and ANN. The results of our experimental study show that the best accuracies are obtained using ANN model.
On a class of modal Θ-valent convolutional codes

The notions of mΘ codes, mΘ linear codes and mΘ cyclic codes are defined in [4]; [5]; [6]. In this note, we plan to define a notion of mΘ convolutional code on the mΘ field .
DCNet: Dual-Task Cycle Network for End-to-End Image Dehazing

Single image dehazing is an important technology in the field of computer vision. In this paper, we propose an image dehazing via dual learning strategy, named dual-task cycle network (DCNet). The core of DCNet is a dual learning framework, which consists of two tasks: the dehazing task and the haze generation task. The dehazing task completes the image dehazing, while the haze generation task achieves the restoration from the dehazed image to the haze image and can form a cycle to provide additional supervision. Our method uses the duality between each task as a constraint to learn and train two tasks jointly, so that the effects of the dehazing model can be improved. Since the haze generation process does not depend on clear images, the DCNet can satisfy the requirements for limited supervision. Extensive experiments demonstrate that our DCNet performs favorably on haze removal.
MProve+: Privacy Enhancing Proof of Reserves Protocol for Monero

Proof of reserves protocols enable cryptocurrency exchanges to prove solvency, i.e. prove that they have enough reserves to meet their liabilities towards their customers. MProve (EuroS&PW, 2019) was the first proof of reserves protocol for Monero which provided some privacy to the exchanges' addresses. As the key images and the addresses are inherently linked in the MProve proof, an observer could easily recognize the exchange-owned address when a transaction spending from it appears on the blockchain. This is detrimental for an exchange's privacy and becomes a natural reason for exchanges to not adopt MProve. To this end, we propose MProve+, a Bulletproofs-based (S&P, 2018) NIZK protocol, which unlinks the key images and the addresses, thus alleviating the drawback of MProve. Furthermore, MProve+ presents a promising alternative to MProve due to an order of magnitude smaller proof sizes along with practical proof generation and verification times.
Decision Support for Integrated Management of Local-Level Adaptation to Climate Changes: The Case of Serbia

Abstract Projected climate changes will additionally increase the already significant risk of natural hazard-related disasters in Serbia and the west Balkan region as a whole. Serbia is about to introduce the strategy for climate change adaptation and mitigation. However, a national decision-support system for implementation of the climate change law and strategy is yet to be developed. This study contributes to the implementation of adaptation policies at subnational levels by development of a decision-support model for local-level management of the climate change adaptation process. The study explores the potential for synergetic application of multicriteria decision making analysis and probabilistic reasoning methods by focusing on Bayesian networks, analytical hierarchy processes, and geographic information systems for selection of priority adaptation measures. The study was based on the formation of causal chains, which enable linking management decisions and socioeconomic or biophysical consequences into articulated sequences of conditional relationships. A model was tested in the forestry sector, and it clearly pointed out development of an early warning system and planning of water intake basins as priority adaptation measures. Since the results are shown as a probability distribution for each alternative solutions, the model can assist decision makers with prompt evaluation of various scenarios.
SeekDoc: Seeking eligible doctors from electronic health record

<abstract> With the development of online medical service platform, patients can find more medical information resources and obtain better medical treatment. However, it is difficult for patients to discover the most suitable doctors from the complex information resources. Therefore, the analysis and mining of Electronic Health Record(EHR) is very important for patients' timely and accurate treatment. Discovering the most suitable doctor is actually predicting the exact performance of the doctor for a specific disease. We believe that "a curative/bad treatment is likely to be caused by a good/bad doctor, and a good/bad doctor has a higher/lower evaluation by the patient(s)". In this paper, we propose a novel approach named <italic>SeekDoc</italic>, which is to seek the most effective doctor for a specific disease. Specifically, we build a doctor-disease heterogeneous information network and collect patients reviews and rating records for doctors. Then, we embed the comprehensive comment data for doctors and the constructed heterogeneous information network. Next, we use the autoencoder mechanism to learn the embedded features, which is an effective learning algorithm for constructing the latent feature representation in an unsupervised manner. After this learning, the latent features are input into the extreme gradient boosting (XGBoost) algorithm to improve their detection capabilities. Finally, extensive experiments show that our method can effectively and efficiently predict the doctor's experience score for specific diseases and has good performance compared with other algorithms. </abstract>
SocialSDN: Design and Implementation of a Secure Internet Protocol Tunnel Between Social Connections

End-to-end encrypted (E2EE) network services can be classified into 1) network services that provide native endto-end encryption and 2) non-encrypted services transported through secure tunnels. While the first solution of native E2EE applications lacks generality and standardization, the second option of secure tunnels shows itself to be a promising solution, yet the current state-of-the-art still possesses several drawbacks. Primarily, the current state-of-the-art for establishing a secure tunnel for arbitrary IP traffic between two or more users requires significant technical expertise. Secondly, due to side-channel effects, the current state-of-the-art for cryptographically protected network tunnels may leak sensitive information through traffic pattern analysis. Lastly, the current state-of-the-art for this type of networking lacks elegance and convenience and therefore users often settle for less secure non-E2EE services. In this paper, we present SocialSDN which utilizes concepts from social networking and software-defined networking to build a tool which addresses many of the issues holding back mass adoption of E2EE network services.
Mean field approximation for solving QUBO problems

The Quadratic Unconstrained Binary Optimization (QUBO) problem is NP-hard. Some exact methods like the Branch-and-Bound algorithm are suitable for small problems. Some approximations like stochastic simulated annealing for discrete variables or mean-field annealing for continuous variables exist for larger ones, and quantum computers based on the quantum adiabatic annealing principle have also been developed. Here we show that the mean-field approximation of the quantum adiabatic annealing leads to equations similar to those of thermal mean-field annealing. However, a new type of sigmoid function replaces the thermal one. The new mean-field quantum adiabatic annealing can replicate the best-known cut values on some of the popular benchmark Maximum Cut problems.
Various Deep Learning Approaches in Electronic Health Records

There is a large amount of data stored in electronic health records (EHR). EHR planned to store restaurant data and management assignments that were accommodating. Although these documents were used by other experts in clinical informatics undertakings. Early, the locale of the scientist noticed the developments of deep understanding, which applied exceptionally to EHR. We review deep learning concepts, advanced angles related to EHR in this paper. In this paper we will see the available approaches to deep learning and how they work with EHR.
Analysis of power distribution network operation without power cut based on actual engineering data

Abstract Distribution Network operation without power cut has gradually become the trend of the future distribution network construction, the investment in distribution network has increased year by year, carrying out the study on the analysis of distribution network operation without power cut, analyzing the benefit of distribution network operation without power cut, it is of great significance to provide suggestions for the future distribution network operation cost lean management. Based on the technical analysis of non-outage operation in distribution network, combined with the social benefits of non-outage operation, this paper gives some suggestions on strengthening the management of non-outage operation.
One-Stage vs Two-Stage Deep Learning Method for Bone Abnormality Detection

This paper introduces an automatic reliable bone abnormality detection using two different classification approaches. The introduced method considers the seven extremity upper bones namely shoulder, humerus, forearm, elbow, wrist, hand, and finger. For this purpose, two different classification approaches are considered by utilizing Inception pre-trained model. The images are first enhanced by utilizing adaptive histogram equalization. Thereafter, the enhanced images are fed into the classification step. The first classification approach is a one-stage approach where the model takes x-ray images as input and outputs both the bone type and whether it is normal or not. While the second approach is a two-stage hierarchical approach where the x-ray images are fed into the first stage to classify the bone type and then fed into the second stage to detect the bone abnormality. The average sensitivity achieved is 61.53% and 70.04% and the average specificity achieved is 84.22% and 74.76% for one-stage and two-stage hierarchical approaches, respectively. All the experiments were carried out using MURA dataset.
Compositional Video Synthesis with Action Graphs

Videos of actions are complex signals, containing rich compositional structure. Current video generation models are limited in their ability to generate such videos. To address this challenge, we introduce a generative model (AG2Vid) that can be conditioned on an Action Graph, a structure that naturally represents the dynamics of actions and interactions between objects. Our AG2Vid model disentangles appearance and position features, allowing for more accurate generation. AG2Vid is evaluated on the CATER and Something-Something datasets and outperforms other baselines. Finally, we show how Action Graphs can be used for generating novel compositions of actions.
Multi-resolution Odd Sketch for Mining Jaccard Similarities between Dynamic Streaming Sets

Estimating similarity between streaming sets is a fundamental problem with many Internet applications, such as evaluating user similarity in social networks and analyzing similarity of IP hosts' behaviors in communication networks. For a “streaming” data set, its elements arrive in a streaming fashion, and we have only limited memory to process its element stream. To meet the size constraint of high-speed memory, this data set must be stored as a summary called `sketch'. Then, in the distributed scenario, the sketches of all data sets can be efficiently transferred to a central server to calculate the Jaccard similarity between each pair of sets. To balance between memory cost and similarity evaluation accuracy, many sketching methods have been proposed, such as MinHash, virtual odd sketch (VOS) and MaxLogHash. However, both MinHash and MaxLogHash fail to deal with fully dynamic streaming sets that allow the deletion of elements. Although VOS partially solves the deletion problem by adopting the odd sketch structure and enhance it with a physical-virtual structure, its similarity evaluation accuracy will degrade when handling small streaming sets. In this paper, we propose a multi-resolution odd sketch (MROS), which allows more accurate similarity estimation with less memory consumption. Its design is to encode a streaming set into multiple layers of odd sketches with exponentially reducing sampling probabilities. We conduct both experiments and analysis to evaluate our method. Results show that the estimation accuracy of our MROS outperforms existing works, e.g., MinHash and VOS.
AMEBA: An Adaptive Approach to the Black-Box Evasion of Machine Learning Models

Machine learning models are vulnerable to evasion attacks, where the attacker starts from a correctly classified instance and perturbs it so as to induce a misclassification. In the black-box setting where the attacker only has query access to the target model, traditional attack strategies exploit a property known as transferability, i.e., the empirical observation that evasion attacks often generalize across different models. The attacker can thus rely on the following two-step attack strategy: (i) query the target model to learn how to train a surrogate model approximating it; and (ii) craft evasion attacks against the surrogate model, hoping that they "transfer" to the target model. This attack strategy is sub-optimal, because it assumes a strict separation of the two steps and under-approximates the possible actions that a real attacker might take. In this work we propose AMEBA, the first adaptive approach to the black-box evasion of machine learning models. AMEBA builds on a well-known optimization problem, known as Multi-Armed Bandit, to infer the best alternation of actions spent for surrogate model training and evasion attack crafting. We experimentally show on public datasets that AMEBA outperforms traditional two-step attack strategies.
The Minimal Ramification Problem for Rational Function Fields over Finite Fields

Abstract We study the minimal number of ramified primes in Galois extensions of rational function fields over finite fields with prescribed finite Galois group. In particular, we obtain a general conjecture in analogy with the well studied case of number fields, which we establish for abelian, symmetric, and alternating groups in many cases.