Reviewerly/exHarmony huggingface dataset:
BERT-Based Detection of Sexual Harassment in Dialogues

It tends to become a trend of booking transportation through network equipment with the further integration of the Internet into people's life, and online car-hailing platforms have sprung up. However, a new social crisis has also come with it. Because of the need for platform expansion, most online ride-hailing drivers have not undergone strict professional ethics reviews, which increases the risk of passengers taking the car. Primarily, female users are more susceptible to abuse and harassment and even persecution by drivers due to their disadvantaged position. Unfortunately, this phenomenon is happening every day and even getting worse. Regarding this aspect of supervision, it is difficult for relevant departments to have a more direct management plan. However, it is difficult for relevant departments to give a more natural and effective management plan. Therefore, ensuring the safety of passengers (predominantly female passengers) using online car-hailing becomes particularly important. In the Chinese field, few people try to improve this problem from the perspective of natural language. This work expects to use natural language technology to evaluate the driver's language and determine the degree of potential danger and criminal tendency, thus protecting the passenger and providing evidence for the judicial authorities. We first collected many dialogues between drivers and passengers, then used back translation to expand the corpus. Finally, we adopted various BERT-based model methods to compare and analyze the performance of different variants.
A Model Randomization Approach to Statistical Parameter Privacy

In this article, we study a privacy filter design problem for a sequence of sensor measurements whose joint probability density function (p.d.f.) depends on a private parameter. To ensure parameter privacy, we propose a filter design framework which consists of two components: a randomizer and a nonlinear transformation. The randomizer takes the private parameter as input and randomly generates a pseudo parameter. The nonlinear mapping transforms the measurements such that the joint p.d.f. of the filter's output depends on the pseudo parameter rather than the private parameter. It also ensures that the joint p.d.f. of the filter's output belongs to the same family of distributions as that of the measurements. The design of the randomizer is formulated as an optimization problem subject to a privacy constraint, in terms of mutual information, and it is shown that the optimal randomizer is the solution of a convex optimization problem. Using information-theoretic inequalities, we show that the performance of any estimator of the private parameter, based on the output of the privacy filter, is limited by the privacy constraint. The structure of the nonlinear transformation is studied in the special cases of independent and identically distributed, Markovian, and Gauss-Markov measurements. Our results show that the privacy filter in the Gauss-Markov case can be implemented as two one-step ahead Kalman predictors and a set of minimum mean square error predictors. A numerical example on occupancy privacy in a building automation system illustrates the approach.
Interval Versus Histogram of Symbolic Representation Based One-Class Classifier for Offline Handwritten Signature Verification

This paper proposes a comparison study of using Interval and Histogram of Symbolic Representation (ISR and HSR) based One-Class classifiers, namely OC-ISR and OC-HSR, respectively, applied to the offline signature verification. Usually, symbolic verification models are built straightforward from the feature space. The proposed work explores an alternative approach based on the use of feature-dissimilarities generated from Curvelet Transform (CT) for building the OC-ISR and the OC-HSR classifier. For the OC-ISR classifier, a new weighted membership function is proposed for computing the similarity values between a dissimilarity query vector and a targeted ISR model. The experimental evaluation performed on the well-known public datasets GPDS, CEDAR, and MCYT, reveals the proposed OC-ISR’s superiority over the OC-HSR classifier. Moreover, the proposed verification model based on the OC-ISR classifier outperforms the last similar work reported in the literature on the GPDS-160 dataset by 0.99%, 0.8%, and 0.35% of Average Error Rate (AER) for 5, 8, and 12 reference signatures, respectively.
An Image Stabilization Technique for Long-durational Outdoor Footages Obtained by Visual IoT Systems

Disaster mitigation is a significant issue where modern wireless network systems are expected to play a crucial role. It is believed that Visual IoT is one of the key techniques because of its monitoring abilities of urban and rural areas. The outdoor Visual IoT systems generally transfer a large number of footage every day. To detect information from large-size footage datasets, automatic time subtractions between frames are effective. However, to extract even tiny difference between frames, camera shake causes a serious damage. In this study we first survey long-durational footage transmitted from outdoor cameras installed in a city to examine that the stabilization techniques based on feature keypoints are effective to camera shake. Based on this survey we define a matching index to judge if the stabilization is of use or not for every couple of frames. The index is implemented with help of a camera calibration library in OpenCV using AKAZE feature. We then propose a method to stabilize footage continuously obtained by outdoor Visual IoT systems. Using the matching index, we examine one-day footage to find that stabilization is occasionally not applicable in case when the matching index is relatively larger or smaller than 1 (when matching is complete) or the number of matching pairs using AKAZE keypoints are too few. According to the results we set a threshold value of the matching index. We finally perform this technique to footage recorded on a couple of strongly windy days. The efficiency is numerically and visually confirmed on each footage successfully.
Few-Shot Tumor Detection via Feature Reweighting and Knowledge Transferring

Tumor detection is a fundamental and difficult task for computer aided diagnose system. Due to the morphology of tumors varies greatly, it is difficult to train a general tumor detector for all kinds of tumors. Existing tumor detection method always train a special tumor detector in a supervision way. However, training a supervised tumor detector always needs many annotated samples. It is difficult and costly to obtain annotated samples in medical image domain, especially for rare tumor. To overcome these problems, we address the few-shot tumor detection with limited annotated samples. In this paper, we present a few-shot tumor detection method, which extracts a latent task representation from few supervisions, and optimize the architecture of the detector end-to-end for efficient and accurate few-shot object detection. Our method can transfer to new type of tumor without further optimization and quickly update when given few more samples. We reported the results for three type of tumors detection from one annotation per class and show motivation to two other kinds of tumor detection. Our approach reweights features across space for new class tumor. The result show that our method has an acceptable accuracy and transportability.
Development of programs for effective scientific and technical activity of universities

The paper discusses approaches to the formation of an optimal program for improving scientific and technical activities (STA) efficiency of universities using modern approaches of organizational systems management, project management, and active systems theories. The successive stages of scientific and technical activity, which represent research and development work, design and development work, and product manufacturing based on their results, are considered. Graph theory methods are used to represent the structure of scientific and technical projects, modeling related processes, and to find optimal solutions in the field of scientific and technical activities of universities. The search for optimal solutions in the field of universities’ STA is based on the application of dichotomous programming methods. The tasks of constructing the dependence of the project implementation effect on its resource availability are set. The described technology can be generalized to complex design structures. The paper describes the problem of coordinated program planning related to the presence of two sources of universities’ scientific and technical activities formation – external orders and internal proposals. As the main issue of targeted and operational management of resources in the formation of the university's STA program, the main priority mechanisms for their distribution were discussed.
An Attempt for Wordnet Construction for Odia Language

Wordnet is an effective and powerful tool used in natural language processing (NLP). It is also beneficial for retrieving information for the words with similar meanings, i.e., semantic processing. It consists of a database which contains words of related vocabulary of a language called lexical database. The words are grouped into synsets. Wordnet is very useful as it has a variety of technological applications in different languages. This motivated many researchers for construction of wordnet in different languages. Creation of wordnet for the low-resource languages like Odia becomes time-consuming and expensive if created from scratch. Thus, wordnets can be developed by implementing the format of the other wordnet. The words are translated into the destination language that is associated with the particular synsets. In this paper, an attempt has been made for implementing this by using the “Expansion Approach.” The main advantage in this method is that the resulting wordnet in the destination language is aligned to the source wordnet and the interlingual index (ILI). The main purpose for constructing a complete wordnet is to create a synonym set for a low-resource language like Odia.
New ways of speech skills training in online Chinese teaching based on Internet

Under the influence of the new coronavirus epidemic, TCFL has been greatly impacted. Thanks to the development of Internet technology, many schools and institutions turn to online teaching, online live teaching, online course customization, mixed multiple teaching and other online teaching modes are widely used. This paper focuses on the necessity and problems of Chinese language skills training for foreign students under the online teaching mode, and discusses how to carry out language skills training in online Chinese teaching from four aspects of listening, speaking, reading and writing. Some suggestions are put forward, such as changing the way of classroom practice and strengthening students' self-training, using modern science and technology, network resources and online learning platform to provide more and more comprehensive teaching services for Chinese learners, and setting up incentive mechanism to mobilize students' enthusiasm. This paper hopes to provide some new teaching ideas, teaching methods and teaching approaches for online Chinese language skills training in the new era.
Boosting the Competitiveness of Organizations With the Use of Software Engineering

Software has become the core of organizations in different domains because the capacity of their products, systems, and services have an increasing dependence on software. This fact highlights the research challenges to be covered by computer science, especially in the software engineering (SE) area. On the one way, SE is in charge of covering all the aspects related to the software development process from the early stages of software development until its maintenance and therefore is closely related to the software quality. On the other hand, SE is in charge of providing engineers able to provide technological-base solutions to solve industrial problems. This chapter provides a research work path focused on helping software development organizations to change to a continuous software improvement culture impacting both their software development process highlighting the human factor training needs. Results show that the implementation of best practices could be easily implemented if adequate support is provided.
Where Digital Meets Physical: A Conceptual Framework for Exploring Aesthetic Potential of Mixed Reality Interface

This paper aims to conceptually explore the aesthetic potential phenomenon of a mixed reality interface. The research is intended to create a novel conceptual framework that includes characteristics of mixed reality interface and approaches to studying the aesthetic potential phenomenon. The framework is based on the synthesis of knowledge and integration of human-computer interaction concepts and methodologies from design, philosophy, psychology, and media culture. This paper offers a new expanded view of the aesthetic potential phenomenon. This includes seeing the aesthetic potential in the way the mixed reality interface manifests itself as a meaningful part of our lifeworld. Furthermore, the next research steps are outlined under a new perspective of studying the interface and its future.
Medical Consultation System based on Python Web crawler

With the development of the times and the continuous progress of society, the Internet has become the most representative of today's society. Medical data as an important part of network data, with the rapid development of the network also presents the trend of explosion. It is difficult for users to get useful data resources, which is a serious drawback of the Internet today. Although the traditional search engine can return a great deal of information to the user, the user must spend a great deal of time to obtain the information that is valid to itself. According to the background of big data era and the requirement of practical application, this paper gives some shallow opinions on the Web Crawler, introduces the importance of Web Crawler, and the system crawls medical data through Scrapy frame. The system mainly realizes the crawling and the data storage of the doctor information in the specific area and the designated department.
Virtual Kernel Discriminative Dictionary Learning With Weighted KNN for Video Analysis

Recently Kernel-Based Discriminative Dictionary (KDDL) for Video Semantic Content Analysis (VSCA) has become very popular research area, particularly in Human Computer Interactions and Computer Vision decades. Nonetheless, the existing KDDL approaches based on reconstruction error classification, coupled with sparse coefficients do not fully consider discrimination, which is essential for classification performance between video samples, despite their numerous successes. In addition, the size of video samples, an important parameter in kernel-based approaches is mostly ignored. To further improve the accuracy of video semantic classification, a VSC classification approach based on Sparse Coefficient Vector and a Virtual Kernel-based Weighted KNN is proposed in this paper. In the proposed approach, a loss function that integrates reconstruction error and discrimination is put forward. The experimental results show that this method effectively improves recognition and classification accuracy for VSCA compared with some state-of-the-art baseline approaches.
Fuzzy Recognition of Color and Its Application in Image Retrieval

In view of the shortcomings of traditional local binary pattern (LBP) in solving the problems of noise, rotation and scale change at the same time, this paper proposes a local binary algorithm with scale invariant principal direction adaptive threshold to construct the multi-scale space of the original image; in order to improve the rotation invariance and anti noise ability, a binary pattern with principal direction adaptive threshold is proposed Taking the mean value of dslbp features of different scale images as two representative texture database and cloth texture database, the experimental results show that the algorithm can solve the problems of noise, rotation and multi-scale, and has better retrieval accuracy.
An Ensemble Learning Method on Mammogram Data for Breast Cancer Prediction—Comparative Study

Cancer remains a serious problem for people nowadays because of its largest death rate in the world. The biggest rate is full of the absence of adequate early detection of cancer. Breast cancer carcinoma is one of the main killer illness among ladies in the world, and the commonly diagnosed non-skin cancer in women. Early detection of this deadly disease can considerably increase the possibility of successful treatment. The traditional way of cancer diagnosis primarily depends on the doctor’s or technician’s experience to identify the abnormalities in our human body. Bosom malignancy happens once the cell tissues of the breast become irregular and wildly divided. By investigating, the mammogram information can help doctors in recognizing disease cases at a whole lot sooner stage and accordingly can altogether improve the capability of patient treatment. It is as yet a test to anticipate the forecast of disease patients, due to its high heterogeneity and multifaceted nature. This study focuses on the forecast of breast cancer from mammogram pictures and assesses the precision of various CNN models.
Temporal Attention and Spatial Difference Net for Action Recognition

On existing large action recognition datasets, especially complex action datasets, existing methods still make this field challenging due to video background noise and difficulties in temporal modeling. To alleviate this issue, this paper proposes a new model, called Temporal Attention and Spatial Difference Net (TASDN), with the main objective of capturing multi-scale motion information. The core idea of TASDN is to obtain temporal information by spatial difference module (SDM) and temporal attention module​ (TAM). SDM obtains spatial features similar to optical flow in two stream model based on spatial difference, while Tam captures temporal features based on attention. Specifically, TASDN uses multi-rate video sampling to build a multi-level network. TASDN achieves 51.9% top1 accuracy on the Something-SomethingV1 dataset, which focuses on temporal modeling. In addition, TASDN also excelled on the Something-SomethingV2 and Kinetics-400 data, achieving top1 accuracy of 63.1% and 78.6%, respectively.
Applying Advanced Wireless Network Cluster-Tree Topology to Optimize Covid-19 Sanitary Passport Blockchain-Based Security in a Constrained IoT Platform

Our work revolves around creating and developing a medical platform that acts on the citizen's right to access a public space by differentiating between vaccinated and those who have done the PCR test to avoid the fast spread of the covid-19 virus. In this scope, three algorithms were applied to complete this platform: the first one serves to verify the citizens who are vaccinated, the second allows to see and check the validity of the PCR test for people who are not vaccinated, and, finally, the third algorithm serves to check the temperature and the identity of citizens. This last algorithm is applied when the citizen is not vaccinated or does not have the PCR test. For this project, we used four IoT nodes to run our platform in real time: a Passive infrared (PIR) client node, a temperature sensor, a RFID tag, and a camera client node. We also used artificial intelligence for facial recognition, which will allow us to verify the citizen's identity. We applied the Constrained Application Protocol to ensure a fluid communication between the platform's nodes, the integrity, and the confidentiality of the data shared. We secured payloads with two encryption algorithms which are Advanced Encryption Standard (AES)-Secure Hash Algorithm (SHA) 256-bit and Rivest Shamir Adleman (RSA)-SHA256. The security of the database's information is also an important aspect; thus, the use of the AT2 blockchain will allow us to strengthen the security of the database against any network attack. As for the network topology, we have opted for the cluster-tree topology, and that is after a study that showed us it is the best in terms of execution time, memory occupation, and energy consumption and even for the reliability of the communication.
An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.
Using Facial Recognition Tools for Health Assessment

The number of applications for facial recognition technology is increasing due to the improvement in image quality, artificial intelligence, and computer processing power that has occurred during the last decades. Algorithms can be used to convert facial anthropometric landmarks into a computer representation, which can be used to help identify nonverbal information about an individual's health status. This article discusses the potential ways a facial recognition tool can perform a health assessment. Because facial attributes may be considered biometric data, clinicians should be informed about the clinical, ethical, and legal issues associated with its use.
Automata-Based Approach to Manage Self-Adaptive Component-Based Architectures

Dynamic software architectures in changing environments need human supervision to continue operation in all conditions. These changes may come from the software architecture or its environment. Such software architecture is required to monitor itself and its environment, detect changes, decide how to react, and act to perform such changes. Enhancing the software architecture with the ability to self-adapt offers a very interesting way forward. This work presents an automata based approach to manage self-adaptive component-based architecture. This approach allows software architecture to adjust itself to changes during runtime and without stopping its execution. It helps the maintenance of the consistency and the coherence of software architecture before, during and after adaptation. The proposed approach is based mainly on the use of the meta-model, automata concept, XML language and UPPAAL model checking tool for the verification of the software consistency before enacting adaptation.