Reviewerly/exHarmony huggingface dataset:
Defending against attacks tailored to transfer learning via feature distancing

Transfer learning is preferable for training a deep neural network with a small training dataset by leveraging a pre-trained teacher model. However, transfer learning opens a door for new attacks that generate adversarial examples using the pre-trained teacher model. In this paper, we propose a novel method called feature distancing to defend against adversarial attacks tailored to transfer learning. The method aims to train a student model with a distinct feature representation from the teacher model. We generate adversarial examples of the mimic attack with the teacher model, and the examples are used to train the student model. We use triplet loss to put the mimic attack examples close to their source images and far from their target images in the feature space of the student model. The proposed method is evaluated on three different transfer learning tasks with diverse attack configurations. It is the only method that achieves high “robust accuracy” and high “test accuracy” on every task we evaluate. • Transfer learning suffers from two test-time attacks tailored to transfer learning. • The attacks exploit the similarity of weights between teacher and student models. • Existing defenses are not proper to a small training dataset of transfer learning. • Combining triplet and classification losses can learn a robust student model.
Ciljevi europskih politika obrazovanja odraslih u vrijeme pandemije COVID -19

The article analyzes the changes in the goals of adult education policies in the European educational area during the COVID -19 pandemic. Different forms of policy changes, incremental and radical changes of goals are analyzed. Elements of historical institutionalism, discursive institutionalism and elements of rational choise policy (optimal action within the given possibilities) are used. From the analysis of documents and available analyzes, it was concluded that the dominant discourse of lifelong learning in the knowledge society has remained in the context of significant economic, demographic, health, technological and climate challenges. It is shown how the key instruments of educational policies fit into this discourse. The introduction of micro-qualifications, an individual learning account and strengthening of digital and green skills is especially presented. As the instruments are not neutral, they also contain the goals that adult education in Europe must achieve.
Ultrahigh-dynamic-range wavefront sensor based on absolute double-slit interferometry

This Letter reports a new, to the best of our knowledge, technique for the quality testing of steep optical samples by introducing an absolute interferometry method based on a double-slit interference experiment. We determine the quality of the sample with an ultrahigh-dynamic-range wavefront sensor by determining the deformation of the central fringe of the double-slit interferometer recorded for two different separations of the slits. The transmission function of the double slit is implemented on an amplitude spatial light modulator. Therefore, the slits' location can be easily displaced over the entire area of the sample's wavefront. We applied the proposed method on two samples: a microscope slide and a conventional ophthalmic lens, and maximum absolute phase variations of 0.33 and 26.7 rad were measured, respectively. Our estimation shows that an absolute phase variation of about 700 rad can be measured with this method.
Characteristics of Financial Crimes in China

In the era of globalization, financial crimes in China present new characteristics, such as cases’ types being diversified and relatively concentrated, crimes’ estimates being of high value, cases of financial crime involving a large number of victims and Internet financial crimes standing out, financial professionals having a higher proportion of committing crime and joint crime, criminal means renovating ceaselessly, and also present fraudulence, concealment, internationalism and other characteristics. Financial crimes result from many factors, such as economic reasons, legislative reasons, criminal justice reasons, financial supervision reasons, criminals’ reasons, victims’ reasons, financial globalization reasons and so on. Prevention and control of financial crimes shall adhere to the principle of comprehensive treatment, and follow the criminal policy of tempering justice with mercy, including perfecting the financial legislation, strengthening the function of criminal justice, promoting the reform of financial system, increasing financial supervision efficiency, constructing ethics in financial market, improving professional ethics of financial workers, enhancing consumers’ awareness of financial risk prevention and strengthening international cooperation, etc.
Sabotage of Production and Operation in the Network Era from the Perspective of Criminal Law

Internet Plus in the Internet era has realized the combination of the Internet and traditional industries, shaped the cyberspace, and created industries such as e-commerce. In recent years, with the development of e-commerce such as online shopping, online car-hailing, and online group purchase, some perpetrators have used the non-face-to face characteristics of online transactions to arbitrarily “click farming” and “improve credibility”. Meanwhile, the “reverse click farming” and “malicious speculation” have also appeared from time to time. Either “click farming” or “reverse click farming”, their essences are the same, that is, network fictitious transaction behavior. Scholars have different opinions on how to characterize such online fictitious transactions and whether they can be treated as crimes. This paper will discuss the above questions based on case study and illustrates the legislative evolution and controversy over the legal interest of sabotaging production and operation in China.
DevReplay: Linter that generates regular expressions for repeating code changes

Coding rules automatically exclude language-specific anti-patterns in the source code. However, developers still need to validate project-specific anti-patterns manually. We present a novel static analysis tool DevReplay that generates coding rules as regular expressions from real-time source code editing. The generated regular expressions automatically repeat the same changes to the source code. Hence, users can share their own coding rules with inter-project members to keep source code consistency. As a field study, we submitted patches to the most popular open-source projects implemented by different languages, and project reviewers accepted 80% (8 of 10) of our patches.
Online User Authentication System Using Keystroke Dynamics

Nowadays, people become more connected to the internet using their mobile devices. They tend to use their critical and sensitive data among many applications. These applications provide security via user authentication. Authentication by passwords is a reliable and efficient access control procedure, but it is not sufficient. Additional procedures are needed to enhance the security of these applications. Keystroke dynamics (KSD) is one of the common behavioral based systems. KSD rhythm uses combinations of timing and non-timing features that are extracted and processed from several devices. This work presents a novel authentication approach based on two factors: password and KSD. Also, it presents extensive comparative analysis conducted between authentication systems based on KSDs. It proposes a prototype for a keyboard in order to collect timing and non-timing information from KSDs. Hence, the proposed approach uses timing and several non-timing features. These features have a demonstrated significant role for improving the performance measures of KSD behavioral authentication systems. Several experiments have been done and show acceptable level in performance measures as a second authentication factor. The approach has been tested using multiple classifiers. When Random Forest classifier has been used, the approach reached 0% error rate with 100% accuracy for classification.
Using Machine Learning Classifiers and A Virtual Voice Assistant for Common Tasks, An Employee Performance Evaluation Model is Used

This study focuses on a conversational voice assistant for businesses that facilitates staff productivity and efficiency gains by speeding up routine, time-consuming operations. By using straightforward voice commands, they can manage conference rooms, arrange meetings, and adjust work hours thanks to speech recognition and TTS technologies. The necessity of employee performance analysis is also taken into account in this study, and this is done with the aid of machine learning classification models, specifically L1 and L2 Regularization, Random Forest, and Gradient Boost. The experimental results reveal that the Random Forest Classification Algorithm yields the highest accuracy, F1 Score, Train and Test AUC-ROC, with values of 97.08 percent, 98.27 percent, 99.82 percent, and 98.41 percent, respectively.
O contributo do profissional da informação na curadoria digital

The information professional is gaining more and more space in the current information society. Information and communication technologies appear to facilitate the performance of work in the digital environment. With the increasing amount of information, either digitized or born digital, the care of digital objects has become urgent. Digital curation appears as an alternative in the care of digital objects, ensuring that they have a longer life and can be used. It is believed that activities related to digital curation have required constant updates and other competencies for the information professional. Therefore, we intend to know: what are the information professional's practices that contribute to digital curatorship? General Objective: To identify which types of information professionals perform activities related to digital curatorship. Specific objectives: to highlight the literatures that deal with the information professional in digital curatorship; to verify the necessary competences for the information professional to act as digital curator; to specify the similar and different aspects in the typologies of the information professional. Results: Most digital curators are librarian, archivist or museologist professionals, with master's degrees in the areas of librarianship, records management, archivists or in information science. Few specialize or have master's degrees in digital curation or data curation. Some of the activities that the digital curator performs include the following services: creating metadata; accessing, collecting and manipulating content; discarding; maintaining resources; developing policies and managing data plans and procedures for digital content. Conclusion: Digital curation is important to ensure the long life and integrity of the digital object, so organizations adapt to their reality, life cycle models to work with these digital objects. The specialized training for digital curators is directly linked to the areas of Librarianship and Information Science. This research highlights the type of information professional: the librarian, who most performs activities related to digital curation.
LPS-ORAM: Perfectly Secure Oblivious RAM with Logarithmic Bandwidth Overhead

Oblivious Random Access Machine (ORAM) is a cryptographic tool used to obfuscate the access pattern. In this paper, we focus on perfect security of ORAM. A perfectly secure ORAM is an ORAM that can resist against an adversary with unlimited computing power, and the failure probability of ORAM is zero rather than negligible. Since all existing perfectly secure single-server ORAM solutions require at least sublinear worst-case bandwidth overhead, we pose a natural and open question: can we construct a perfectly secure single-server ORAM with logarithmic worst-case bandwidth overhead? In this paper, we propose the first tree-based perfectly secure ORAM scheme, named LPS-ORAM. To meet the requirements of perfectly secure ORAM, two techniques are presented. One technique is dynamic remapping associated with a mutable scope, and the other is dynamically balanced eviction. Their combined effect allows the root bucket to never fill up while maintaining its statistical security in tree-based ORAM. In the worst case, our solution achieves logarithmic bandwidth overhead. Therefore, our solution answers the open question in the affirmative. In terms of overhead for temporary storage on the client side, compared with the latest perfectly secure ORAM solution, our solution is reduced from sublinear to logarithmic, and even if the server storage overhead scales lightly, it is still at the same level of quantity as the state of the art. Finally, the evaluation results show that our LPS-ORAM has a significant advantage in terms of bandwidth overhead and overhead for temporary storage on the client side.
A comprehensive survey on sentiment analysis: Challenges and future insights

With the rise of social networks, people now express their sentiments more frequently and comfortably through their social media activities on different events, person, and every little thing surrounding them. This generates a lot of unstructured data; billions of users post tweets every day as a daily regime on Twitter itself. This has given rise to many texts classification and analysis tasks, Sentiment Analysis (SA) being one of them. Through SA, it is conferred whether the users have negative or positive orientations in their opinions; the results of this task are significantly useful for decision-makers in various fields. This paper presents various facets of SA, like the process followed in SA, levels, approaches, and sentences considered in SA. Aspects such as growth, techniques, the share of various platforms, and SA pipeline are also covered in this paper. At last, we have highlighted some major challenges in order to define future directions.
Detecting the modality of a medical image using visual and textual features

Knowing the modality of a medical image is crucial in understanding the characteristics of the image. Therefore, it is important to classify medical images as per their modality. The image and its accompanying text caption contain information that could help in identifying the modality of a given medical image. This work proposes an approach for medical image modality classification using visual and textual features. The proposed approach uses convolutional neural networks to extract visual features from a medical image. Word embeddings obtained from biomedical word2vec models are used to generate textual features from the image captions. Support vector machine based classifiers are then used to classify medical images using these features. We propose to use the late fusion approach to combine visual and textual features. The proposed approach performs better than the state-of-the-art methods.
KOMUNIKASI ANTARBUDAYA MAHASISWA PESERTA PROGRAM INDONESIAN INTERNATIONAL STUDENT MOBILITY AWARDS (IISMA) DI RUSIA

Indonesian students who take part in the Indonesian International Student Mobility Awards program in Russia may encounter challenges when interacting with local residents due to the relatively foreign culture, including language. This study was conducted to describe the facts that occurred in the process of intercultural communication during Indonesian students participating in the program in Russia. The method used in this research is a qualitative approach with case study research methods. The researcher chose to use a case study because the researcher wanted to conduct an in-depth search of programs, events and process activities for one or more individuals who were limited by time and activity. Furthermore, the researchers collected complete information using a variety of data collection procedures over a sufficient period of time. The results of this study indicate a tendency for the contribution of conceptual filters according to Gudykunst Kim's cultural communication model which includes, culture, socio-culture, psycho-culture, and environment that affect the interaction process of program participants while in Russia. Indonesian students participating in the Indonesian International Student Mobility Awards program who choose Russia as their destination country are advised in advance to take a basic level Russian language course in Indonesia.
Reasoning with principles

The issue of classification of legal norms is much debated in legal theory. One of the most recurring topics in this area is the distinction between two types of legal norms: rules and principles. The papers presents a formal model of principle-based reasoning. The model presented in the paper provides formal grounds for decision making including principles. The model is based on the goal- and value-based reasoning mechanism, which introduces a framework allowing for autonomous goal-setting based on values that should be promoted. • Comprehensive discussion of the concept of legal principle in decision making. • A novel proposal of formalization of legal principles. • A novel proposal of formalization of reasoning with legal principles. • An argumentation framework allowing for decision making in a legal environment. • Decision making in situations with a lack of satisfactory options.
Video super-resolution with fused local and nonlocal feature

Video super-resolution (VSR) is a typical ill-posed problem aiming to increase spatial resolution of a video. To explore interframe information between adjacent frames, optical flow and deformable convolution are commonly used for temporal dependency modeling. However, they lack the ability to use nonlocal spatial information. We propose the dual-branch feature extraction network for VSR. Our method consists of feature extraction, feature fusion and refinement, and frame reconstruction. Specifically, the feature extraction is designed to explore both local and nonlocal information of input frames. For the local feature extraction branch, we use deformable convolutions to eliminate motion between frames. For the nonlocal feature extraction branch, we employ a nonlocal module to gather relevant features. We concatenate these extracted features on channel dimension for progressive feature fusion and refinement. The frame reconstruction part is responsible for increasing spatial resolution and reconstruction. Experimental results demonstrate the effectiveness of the proposed method by extensive quantitative and qualitative evaluations in public benchmark datasets.
Image denoising using block matching and convolutional neural network

Image noise removal (denoising) is a significant preprocessing step in image processing research domain. In past decades, various noise removal algorithms have been developed, and they are categorized into conventional and deep learning denoising methods. Most recently, deep learning-based denoising methods outperform the conventional methods. However, deep learning methods have the limitations of requiring large training sample images, difficult to train the deep convolution neural network (DeCNN), and the majority of the convolutional neural network suffers from the performance saturation. Hence, using combination of block matching (BM) and dilated convolutional neural network (DCNN), a unique method for reducing noise in still images is proposed. In this approach, initially, existing algorithm used to preprocess the image. Then, BM technique uses a sliding window size of a four-by-four block and moves it across the image to select for blocks that are similar in the image. The matching blocks are highly correlated with one another. The matching blocks are then fed to the deep DCNN to remove noise and obtain a better noiseless image. Finally, the noiseless image performances are evaluated using standard metrics. From the experimental results, it is observed that the proposed BM and deep DCNN yielded substantially better results than existing state-of-the-art methods to remove the image’s noise.
Toward Enhancing Room Layout Estimation by Feature Pyramid Networks

Abstract As a fundamental part of indoor scene understanding, the research of indoor room layout estimation has attracted much attention recently. The task is to predict the structure of a room from a single image. In this paper, we illustrate that this task can be well solved even without sophisticated post-processing program, by adopting Feature Pyramid Networks (FPN) to solve this problem with adaptive changes. The proposed model employs two strategies to deliver quality output. First, it can predicts the coarse positions of key points correctly by preserving the order of these key points in the data augmentation stage. Then the coordinate of each corner point is refined by moving each corner point to its nearest image boundary as output. Our method has demonstrated great performance on the benchmark LSUN dataset on both processing efficiency and accuracy. Compared with the state-of-the-art end-to-end method, our method is two times faster at processing speed (32 ms) than its speed (86 ms), with 0.71% lower key point error and 0.2% higher pixel error respectively. Besides, the advanced two-step method is only 0.02% better than our result on key point error. Both the high efficiency and accuracy make our method a good choice for some real-time room layout estimation tasks.
RL2: A Call for Simultaneous Representation Learning and Rule Learning for Graph Streams

Heterogeneous graph streams are very common in the applications today. Although representation learning has advantages in prediction accuracy, it is inherently deficient in the abilities to interpret or to reason well. It has long been realized as far back as in 1990 by Marvin Minsky that connectionist networks and symbolic rules should co-exist in a system and overcome the deficiencies of each other. The goal of this paper is to show that it is feasible to simultaneously and efficiently perform representation learning (for connectionist networks) and rule learning spontaneously out of the same online training process for graph streams. We devise such a system called RL$^2$, and show, both analytically and empirically, that it is highly efficient and responsive for graph streams, and produces good results for both representation learning and rule learning in terms of prediction accuracy and returning top-quality rules for interpretation and building dynamic Bayesian networks.
A Model-Agnostic Approach to Differentially Private Topic Mining

Topic mining extracts patterns and insights from text data (e.g., documents, emails and product reviews), which can be used in various applications such as intent detection. However, topic mining can result in severe privacy threats to the users who have contributed to the text corpus since they can be re-identified from the text data with certain background knowledge. To our best knowledge, we propose the first differentially private topic mining technique (namely TopicDP) which injects well-calibrated Gaussian noise into the matrix output of any topic mining algorithm to ensure differential privacy and good utility. Specifically, we smoothen the sensitivity for the Gaussian mechanism via sensitivity sampling, which addresses the major challenges resulted from the high sensitivity in topic mining for differential privacy. Furthermore, we theoretically prove the differential privacy guarantee under the Rényi differential privacy mechanism and the utility error bounds of TopicDP. Finally, we conduct extensive experiments on two real-word text datasets (Enron email and Amazon Reviews), and the experimental results demonstrate that TopicDP is a model-agnostic framework that can generate better privacy preserving performance for topic mining as compared against other differential privacy mechanisms.