Reviewerly/exHarmony huggingface dataset:
Hate Speech Detection using Machine Learning

A lot of methods have already been created for the automation of hate speech detection online. There are two elements to this process: identifying the qualities that these terms utilize to target a certain group and classifying textual material as hate or non-hate speech. Due to time restraints, research efforts are initiated on the latter issue in this project. For this reason, detecting hate speech is a more challenging endeavor, as our research of the language used in typical datasets reveals that hate speech lacks distinctive, discriminatory characteristics. Deep neural network topologies are very useful for capturing the meaning of hate speech and are thus proposed as feature extractors. Data from social media sites such as Twitter are used to test the effectiveness of these procedures, and they reveal a 6 percentage point improvement in macro-average F1 or a 9 percent improvement for content that has been labeled as hateful, respectively.
Deep Fourier Ranking Quantization for Semi-Supervised Image Retrieval

To reduce the extreme label dependence of supervised product quantization methods, the semi-supervised paradigm usually employs massive unlabeled data to assist in regularizing deep networks, thereby improving model performance. However, the existing method focuses on the overall distribution consistency between unlabeled data and class prototypes, while ignoring subtle individual variances between unlabeled instances. Therefore, the local neighborhood structure is not fully explored, which will cause the model to easily overfit in the training set. In this paper, we introduce a new Fourier perspective to alleviate this issue by exploring the semantic relations between unlabeled instances in a self-supervised manner. Specifically, based on Fourier Transform, we first design a Phase Mixing (PM) strategy, which can manipulate the mixing area and values of the phase component between two images to control the proportion of semantic information. In this way, we can construct multi-level similarity neighbors naturally for unlabeled data. Then, a ranking quantization loss is formulated to perceive multi-level semantic variances in neighbor instances, which improves the robustness and generalization of the model. Extensive experiments in three different semi-supervised settings show that our method outperforms existing state-of-the-art methods by averaged 3.95% improvement on four datasets.
Chatbots at Digital Workplaces – A Grounded-Theory Approach for Surveying Application Areas and Objectives

Abstract Background: Chatbots are currently on the rise as more and more researchers tackle this topic from different perspectives. Simultaneously, workplaces and ways of working are increasingly changing in the context of digitalization. However, despite the promised benefits, the changes still show problems that should be tackled more purposefully by chatbots. Application areas and underlying objectives of a chatbot application at digital workplaces especially have not been researched yet. Method: To solve the existing problems and close the research gap, we did a qualitative empirical study based on the grounded-theory process. Therefore, we interviewed 29 experts in a cross-section of different industry sectors and sizes. The experts work in the information systems domain or have profound knowledge of (future) workplace design, especially regarding chatbots. Results: We identified three fundamental usage scenarios of chatbots in seven possible application areas. As a result of this, we found both divisional and cross-divisional application areas at workplaces. Furthermore, we detected fifteen underlying objectives of a chatbot operation, which can be categorized from direct over mid-level to indirect ones. We show dependencies between them, as well. Conclusions: Our results prove the applicability of chatbots in workplace settings. The chatbot operation seems especially fruitful in the support or the self-service domain, where it provides information, carries out processes, or captures process-related data. Additionally, automation, workload reduction, and cost reduction are the fundamental objectives of chatbots in workplace scenarios. With this study, we contribute to the scientific knowledge base by providing knowledge from practice for future research approaches and closing the outlined research gap. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/3/ Recommended Citation Meyer von Wolff, Raphael; Hobert, Sebastian; Masuch, Kristin; and Schumann, Matthias (2020) "Chatbots at Digital Workplaces – A Grounded-Theory Approach for Surveying Application Areas and Objectives," Pacific Asia Journal of the Association for Information Systems: Vol. 12: Iss. 2, Article 3. DOI: 10.17705/1pais.12203 Available at: https://aisel.aisnet.org/pajais/vol12/iss2/3
Designing and implementing smart glass technology for emergency medical services: a sociotechnical perspective

Abstract Objective This study aims to investigate key considerations and critical factors that influence the implementation and adoption of smart glasses in fast-paced medical settings such as emergency medical services (EMS). Materials and Methods We employed a sociotechnical theoretical framework and conducted a set of participatory design workshops with 15 EMS providers to elicit their opinions and concerns about using smart glasses in real practice. Results Smart glasses were recognized as a useful tool to improve EMS workflow given their hands-free nature and capability of processing and capturing various patient data. Out of the 8 dimensions of the sociotechnical model, we found that hardware and software, human-computer interface, workflow, and external rules and regulations were cited as the major factors that could influence the adoption of this novel technology. EMS participants highlighted several key requirements for the successful implementation of smart glasses in the EMS context, such as durable devices, easy-to-use and minimal interface design, seamless integration with existing systems and workflow, and secure data management. Discussion Applications of the sociotechnical model allowed us to identify a range of factors, including not only technical aspects, but also social, organizational, and human factors, that impact the implementation and uptake of smart glasses in EMS. Our work informs design implications for smart glass applications to fulfill EMS providers’ needs. Conclusion The successful implementation of smart glasses in EMS and other dynamic healthcare settings needs careful consideration of sociotechnical issues and close collaboration between different stakeholders.
Imaginary-time evolution with quantum nondemolition measurements: Multiqubit interactions via measurement nonlinearities

We show that quantum nondemolition (QND) measurements can be used to realize measurement-based imaginary-time evolution. In our proposed scheme, repeated weak QND measurements are used to estimate the energy of a given Hamiltonian. Based on this estimated energy, adaptive unitary operations are applied such that only the targeted energy eigenstates are fixed points of the evolution. In this way, the system is deterministically driven towards the desired state. The nonlinear nature of the QND measurement, which allows for producing interactions between systems, is explicitly derived in terms of measurement operators. We show that for suitable interaction times, single-qubit QND Hamiltonians can be converted to effective multiqubit imaginary time operations. We illustrate our techniques with the example of preparing a four-qubit cluster state, which is prepared using only collective single-qubit QND measurements and single-qubit adaptive operations.
Development of Digital Evidence Collector and File Classification System with K-Means Algorithm

Digital forensic is a branch of forensic science that focuses on research on the usual storage media that electronic users use such as hard disks, flash drives or other devices used on computers. The output is called digital evidence. The purpose of doing digital forensics is to find an evidence that can be used in the investigation of a case, until the evidence becomes valid and could be used as evidence in court. In this research, we implement a system of digital evidence collection, recovery, and file classification application. The classification was done by K-Means clustering algorithm. The system could detect the storage media, duplicate the content, and classify the output using K-Means algorithm. It can help a forensic examiner in the collection, examination, analysis, and reporting phase in accordance with NIST SP 800–86. The application also can assist investigators in managing files in the storage media as digital evidence so that outputs are obtained in accordance with applicable law.
Automatic classification of breast lesions usingTransfer Learning

Breast lesions are one of the most common types of lesions among women in Brazil and worldwide, accounting forabout 28% of new cases each year. These lesions may have Benignor Malignant behaviors. In this work, a computational method-ology for image classification was developed to differentiate malignant and benign lesions breast, aiming at low computational cost and good efficiency. In our approach, different Convolutional Neural Networks architectures and several classifiers were tested. Transfer Learning was employed to deal with the limitation of the small number of images in the database, reaching an accuracy of 81.73%, a sensitivity of 85.66%, a specificity of 78.40%, Kappa of 0.63 and ROC curve of 0.82. Finally, it is believed that theproposed methodology can integrate a CAD tool acting as patient screening or providing a second opinion to the specialist.
A Survey on Privacy-Preserving Outsourced Data on Cloud with Multiple Data Providers

Deep Learning has become the most common technique used in different fields like pattern recognition, weather prediction, medical diagnosis, banking sectors, aerospace and defense, data mining applications, image processing, speech recognition, cancer analysis, etc. The training of the model demands an enormous amount of data that is gathered from various data providers and sent to the cloud because they have limited storage and less computational resources. Cloud trains the model with the help of the gathered data. Also, data providers have genuine and confidential data that emerges the privacy issues, so to preserve privacy, the data is sent in the encrypted form and cannot be exposed to anyone and must be secured also. This paper focuses on the pros and cons of the different algorithms and techniques used for machine and deep learning models used for privacy preservation.
Noise-response Analysis for Rapid Detection of Backdoors in Deep Neural Networks.

The pervasiveness of deep neural networks (DNNs) in technology, matched with the ubiquity of cloud-based training and transfer learning, is giving rise to a new frontier for cybersecurity whereby `structural malware' is manifest as compromised weights and activation pathways for unsecure DNNs. In particular, DNNs can be designed to have backdoors in which an adversary can easily and reliably fool a classifier by adding to any image a pattern of pixels called a trigger. Since DNNs are black-box algorithms, it is generally difficult to detect a backdoor or any other type of structural malware. To efficiently provide a reliable signal for the absence/presence of backdoors, we propose a rapid feature-generation step in which we study how DNNs respond to noise-infused images with varying noise intensity. This results in titration curves, which are a type of `fingerprinting' for DNNs. We find that DNNs with backdoors are more sensitive to input noise and respond in a characteristic way that reveals the backdoor and where it leads (i.e,. its target). Our empirical results demonstrate that we can accurately detect a backdoor with high confidence orders-of-magnitude faster than existing approaches (i.e., seconds versus hours). Our method also yields a titration-score that can automate the detection of compromised DNNs, whereas existing backdoor-detection strategies are not automated.
Deep Active Learning with Simulated Rationales for Text Classification

Neural networks have become a preferred tool for text classification tasks, demonstrating state of the art performances when trained on a large set of labeled data. However, in an early active learning setup, the scarcity of the ground-truth labels available severely penalizes the generalization capability of the neural network. In order to overcome such limitations, in this paper, we introduce a new learning strategy, which consist of inserting in the early stages of the learning process some additional, local and salient knowledge, presented under the form of simulated, human like rationales. We show how such knowledge can be automatically extracted from documents by analyzing the class activation maps of a convolutional neural network. The experimental results obtained demonstrate that the exploitation of such rationales permits to significantly speed-up the learning process, with a spectacular increase of the accuracy rates, starting from a very reduced number of documents (10–20).
Self-supervision on Unlabelled or Data for Multi-person 2D/3D Human Pose Estimation

2D/3D human pose estimation is needed to develop novel intelligent tools for the operating room that can analyze and support the clinical activities. The lack of annotated data and the complexity of state-of-the-art pose estimation approaches limit, however, the deployment of such techniques inside the OR. In this work, we propose to use knowledge distillation in a teacher/student framework to harness the knowledge present in a large-scale non-annotated dataset and in an accurate but complex multi-stage teacher network to train a lightweight network for joint 2D/3D pose estimation. The teacher network also exploits the unlabeled data to generate both hard and soft labels useful in improving the student predictions. The easily deployable network trained using this effective self-supervision strategy performs on par with the teacher network on \emph{MVOR+}, an extension of the public MVOR dataset where all persons have been fully annotated, thus providing a viable solution for real-time 2D/3D human pose estimation in the OR.
A Survey Of Credit Card Fraud Detection Use Machine Learning

Credit cards have become an important part of the day, and they are being used in areas of payment for a range of sales or transfers from mobile or payment through electronic stores, and their frequent use has increased the rate of fraud and penetration in credit cards. Many technologies were introduced to reduce fraud and all the techniques were doing the same task, but each one has its advantages and disadvantages. In this paper, after researching and looking at the difficulties of highlighting and detecting fraud and credit card fraud, we strive to retrieve, review and mention the arrival of Yemen techniques for detecting fraud in credit cards. The characteristics, whether good or bad, are mentioned for fraud detection and comparison methods. The techniques are mentioned in two fraud detection forms, either supervised or unsupervised. We cited the findings of the experiments they had done.
PENGARUH METODE VISIT HOME DAN POLA BIMBINGAN ORANG TUA TERHADAP MOTIVASI BELAJAR SISWA SEKOLAH DASAR DI ERA PANDEMI COVID-19

This study aims to know the effect of visit home methods and parental guidance patterns of student learning motivation in the pandemic era. This research was conducted at SD Negeri 060931 Medan Amplas District. The research sample was students in grade 5 which consisted of 2 classes, V-A 25 students and V-B 25 students. Research data collection using a questionnaire. Hypothesis testing is tested with two-way Anava. The results showed that: (1) There was a significant difference between the learning motivation of students who were taught using the visit home method and the learning motivation of students taught by the online method (F count = 23.476; sig. 0.000 <0.05). Meanwhile, the average learning motivation of students taught using the visit home method was 87.383, while the average learning motivation of students taught using the online method was 80.317; (2) The motivation to learn students who are guided by their parents intensively is higher than the motivation to learn from students who are rarely guided by their parents (F count = 7.673; sig. 0.008 <0.05); and (3) There is an interaction between learning methods and parental guidance patterns in influencing student learning motivation during learning in the pandemic era (F count = 13.003; sig. 0.000 <0.05).
Everlasting security of quantum key distribution with 1K-DWCDM and quadratic hash

Quantum key distribution (QKD) offers a very strong property called everlasting security, which says if authentication is unbroken during the execution of QKD, the generated key remains information-theoretically secure indefinitely. For this purpose, we propose the use of certain universal hashing based MACs for use in QKD, which are fast, very efficient with key material, and are shown to be highly secure. Universal hash functions are ubiquitous in computer science with many applications ranging from quantum key distribution and information security to data structures and parallel computing. In QKD, they are used at least for authentication, error correction, and privacy amplification. Using results from Cohen [Duke Math. J., 1954], we also construct some new families of $\varepsilon$-almost-$\Delta$-universal hash function families which have much better collision bounds than the well-known Polynomial Hash. Then we propose a general method for converting any such family to an $\varepsilon$-almost-strongly universal hash function family, which makes them useful in a wide range of applications, including authentication in QKD.
Adversarially-Trained Deep Nets Transfer Better

Transfer learning has emerged as a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains. This process consists of taking a neural network pre-trained on a large feature-rich source dataset, freezing the early layers that encode essential generic image properties, and then fine-tuning the last few layers in order to capture specific information related to the target situation. This approach is particularly useful when only limited or weakly labeled data are available for the new task. In this work, we demonstrate that adversarially-trained models transfer better than non-adversarially-trained models, especially if only limited data are available for the new domain task. Further, we observe that adversarial training biases the learnt representations to retaining shapes, as opposed to textures, which impacts the transferability of the source models. Finally, through the lens of influence functions, we discover that transferred adversarially-trained models contain more human-identifiable semantic information, which explains -- at least partly -- why adversarially-trained models transfer better.
Improving Human Decision-Making with Machine Learning

A key aspect of human intelligence is their ability to convey their knowledge to others in succinct forms. However, despite their predictive power, current machine learning models are largely blackboxes, making it difficult for humans to extract useful insights. Focusing on sequential decision-making, we design a novel machine learning algorithm that conveys its insights to humans in the form of interpretable
A Review on WMSN (Wireless Medical Sensor Networks) for Health Monitoring Systems

Wireless Medical Sensor (WMSN) networks have become a great technology to allow for the best kind of applications. Recent developments at WMSN have helped to create a comprehensive health monitoring concept in both home and hospital care settings. Current technological advances in sensors, integrated dynamic circuits, and wireless connectivity have allowed the event of smaller, heavier, less expensive, and clever body sensors. These modules are able to hear, process, and communicate with one or more important signals. In addition, they will be used on local wireless networks (WPANs) or wireless nerve networks (WBSNs) to monitor health. Many studies are being conducted and/or ongoing to develop flexible, reliable, secure, realistic, and effective WBSNs for health applications. The importance of wireless nerve networks in health surveillance is constantly evolving, due to the growing need for safety and security in cities. The rapid development of wireless technology has significantly aided the advancement of monitoring systems that have been developed through the combination of wireless sensors. The wireless health monitoring introduces emerging innovations with significant advantages when compare to conventional communication networks, which has the advantages of lowering the cost of installation and maintenance while increasing the effectiveness of the health monitoring system in the field of medicine.
A new multistable jerk system with Hopf bifurcations, its electronic circuit simulation and an application to image encryption

In this work, we announce a new 3-D jerk system and show that it is chaotic and dissipative with the calculation of the Lyapunov exponents of the system. By performing a detailed bifurcation analysis, we observe that the new jerk system exhibits Hopf bifurcations. It is also shown that the new jerk system exhibits multistability behaviour with two coexisting chaotic attractors. An electronic circuit simulation of the jerk system is built using Multisim. Finally, we design a new approach to image encryption as a cryptographic application of our chaotic jerk system. The simulation outcomes prove the efficiency of the proposed encryption scheme with high security.
Generality Explained

What explains the truth of a universal generalization? Two types of explanation can be distinguished. While an ‘instance-based explanation’ proceeds via some or all instances of the generalization, a ‘generic explanation’ is independent of the instances, relying instead on completely general facts about the properties or operations involved in the generalization. This intuitive distinction is analyzed by means of a truthmaker semantics, which also sheds light on the correct logic of quantification. On the most natural version of the semantics, this analysis vindicates some claims made—without a proper defense—by Michael Dummett, Solomon Feferman, and others. Where instance-based explanations are freely available, classical logic is shown to be warranted. By contrast, intuitionistic logic (or slightly more) remains warranted regardless of what explanations are available.