Reviewerly/exHarmony huggingface dataset:
A Relational Gradient Descent Algorithm For Support Vector Machine Training

Previous chapter Next chapter Full AccessProceedings Symposium on Algorithmic Principles of Computer Systems (APOCS)A Relational Gradient Descent Algorithm For Support Vector Machine TrainingMahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley, Kirk Pruhs, and Alireza SamadianMahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley, Kirk Pruhs, and Alireza Samadianpp.100 - 113Chapter DOI:https://doi.org/10.1137/1.9781611976489.8PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We consider gradient descent like algorithms for Support Vector Machine (SVM) training when the data is in relational form. For relational data the gradient of the SVM objective cannot be efficiently computed by known techniques as it suffers from the “subtraction problem”. We first show that the subtraction problem cannot be surmounted by showing that computing any constant approximation of the gradient of the SVM objective function is #P-hard, even for acyclic joins. However, we circumvent the subtraction problem by restricting our attention to stable instances, which intuitively are instances where a nearly optimal solution remains nearly optimal if the points are perturbed slightly. We give an efficient algorithm that computes a “pseudo-gradient” that guarantees convergence for stable instances at a rate comparable to that achieved by using the actual gradient. We believe that our results suggest that this sort of stability analysis would likely yield useful insight in the context of designing algorithms on relational data for other learning problems in which the subtraction problem arises. Previous chapter Next chapter RelatedDetails Published:2021eISBN:978-1-61197-648-9 https://doi.org/10.1137/1.9781611976489Book Series Name:ProceedingsBook Code:APOSC21Book Pages:ii + 158
Selective Feature Compression for Efficient Activity Recognition Inference

Most action recognition solutions rely on dense sampling to precisely cover the informative temporal clip. Extensively searching temporal region is expensive for a real-world application. In this work, we focus on improving the inference efficiency of current action recognition backbones on trimmed videos, and illustrate that an action model can accurately classify an action with a single pass over the video unlike the multi-clip sampling common with SOTA by learning to drop non-informative features. We present Selective Feature Compression (SFC), an action recognition inference strategy that greatly increases model inference efficiency without compromising accuracy. Different from previous works that compress kernel size and decrease the channel dimension, we propose to compress features along the spatio-temporal dimensions without the need to change backbone parameters. Our experiments on Kinetics-400, UCF101 and ActivityNet show that SFC is able to reduce inference speed by 6-7x and memory usage by 5-6x compared with the commonly used 30 crop dense sampling procedure, while also slightly improving Top1 Accuracy. We perform thorough quantitative and qualitative evaluation and show how our SFC learns to attend to important video regions for the task of action recognition.
Applying the Naive Bayes Algorithm to Predict the Student Final Grade

The teaching and learning process of the Faculty of Engineering of Universitas Muhammadiyah Yogyakarta has used e-learning intensively. One of the benchmarks in determining students’ final grade is to take the values in e-learning. This study aims to predict students’ final grades by utilizing the data mining process and the Naive Bayes algorithm. This study provides students and lecturers information to enhance the teaching and learning process to improve students’ final grades and maintain satisfactory final grades until the lecture is complete. The research began with the literature study, data collection, data selection, data cleaning, data transformation and implementation with rapidminer and conclusion drawing. Based on the prediction of students’ final grades, one course obtained many unsatisfactory grades with an accuracy rate of 93.75%. Thus, the higher the accuracy value, the closer the predicted final value to the actual value.
Information Hiding Using Quantum Image Processing State of Art Review

The bottleneck of digital image processing field narrows down to the memory consumption and the processing speed problems, which can be resolved by performing image processing in quantum state. In this Internet era, all the information is exchanged or transferred through the Web of things, which necessitates maintaining the security of transmitted data. A variety of techniques are available to perform secret communication. A quantum steganography scheme is introduced to conceal a quantum secret message or image into a quantum cover image. For embedding of secret data into the quantum cover, many algorithms like LSB Qubits, QUALPI are available. This paper thrashes out on the subject of secret data transmission using quantum image steganography.
Autonomy and Attachment in Relationships with Parents in the Context of Students’ Real Self-Determination

Objectives. Studying the relationship of autonomy and attachment in relationships with parents with real self-determination in students. Background. The growing world globalization, intercultural contradictions, confrontation within society seriously complicate the path of self-determination of modern youth. At the same time, due to technical progress and digitalization of the communication and education environment, there are significant transformations in the interaction of people in almost all spheres, including in the family. Therefore, the question of studying the most important aspects in the relationship of young people with their parents for real self-determination is especially acute. Study design. The work studied the indicators of real self-determination, attachment to parents and autonomy among students. The presence of a relationship between these parameters was established by calculating the Spearman correlation coefficient. Participants. 60 college students aged 18 to 21. Measurements. Methodology “Scale of adult attachment for close relationships”; autonomy questionnaire (authors O.A. Karabanova and N.N. Poskrebysheva); questionnaire “ORS” (author D.A. Krasilo). Results. An inverse relationship of indicators of real self-determination of students with the level of intimacy and the level of anxiety (scales of attachment to parents) was established. A direct relationship was revealed between the indicators of real self-determination and the level of autonomy. Conclusions. Proximity and anxiety (components of attachment) have a significant inverse relationship with the level of real self-determination in students. There are no significant connections between reliability (a component of attachment) and real self-determination in the sample under study. Emotional autonomy, cognitive autonomy, behavioral autonomy, and value autonomy have significant positive correlations with the level of real self-determination in students.
<b>deepregression</b>: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encompasses (1) a modular neural network building system based on the deep learning library TensorFlow for the fusion of various statistical and deep learning approaches, (2) an orthogonalization cell to allow for an interpretable combination of different subnetworks, as well as (3) pre-processing steps necessary to set up such models. The software package allows to define models in a user-friendly manner via a formula interface that is inspired by classical statistical model frameworks such as mgcv. The package's modular design and functionality provides a unique resource for both scalable estimation of complex statistical models and the combination of approaches from deep learning and statistics. This allows for state-of-the-art predictive performance while simultaneously retaining the indispensable interpretability of classical statistical models.
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

In the last few years, deep learning classifiers have shown promising results in image-based medical diagnosis. However, interpreting the outputs of these models remains a challenge. In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i.e. the location of a lesion. Alternatively, segmentation or detection models can be trained with pixel-wise annotations indicating the locations of malignant lesions. Unfortunately, acquiring such labels is labor-intensive and requires medical expertise. To overcome this difficulty, weakly-supervised localization can be utilized. These methods allow neural network classifiers to output saliency maps highlighting the regions of the input most relevant to the classification task (e.g. malignant lesions in mammograms) using only image-level labels (e.g. whether the patient has cancer or not) during training. When applied to high-resolution images, existing methods produce low-resolution saliency maps. This is problematic in applications in which suspicious lesions are small in relation to the image size. In this work, we introduce a novel neural network architecture to perform weakly-supervised segmentation of high-resolution images. The proposed model selects regions of interest via coarse-level localization, and then performs fine-grained segmentation of those regions. We apply this model to breast cancer diagnosis with screening mammography, and validate it on a large clinically-realistic dataset. Measured by Dice similarity score, our approach outperforms existing methods by a large margin in terms of localization performance of benign and malignant lesions, relatively improving the performance by 39.6% and 20.0%, respectively. Code and the weights of some of the models are available at this https URL
The Ways to Come Over the Institutional Traps of Higher Education and Science Spere

The present paper is devoted to the Russian ineffective higher education and science institutions functioning study and analysis. We consider such institutions to be institutional traps, which are identified as sustainable inefficient rules and practices with the negative impact for education and science sphere reforms and transformations. The lack of the methodology base for regular institutional monitoring is one of the reasonable issues to institutional traps study. Such institutional monitoring should consist of qualitative and quantitative studies to find out the higher education and science sphere representatives points of view on the reforms results. To find out the solution and to improve the research methodology we used in-depth interview surveys and focus group discussions with lecturers and scientists of the Southern Federal University. This methodology let us to improve the institutional traps study and to design the way to come over the difficulties and barriers considering the ideas and experience of the sphere representatives. The responders involved in the present research were suggested by the six following institutional traps to discuss: the metric's trap, the trap of raising level of bureaucracy, the trap of shortage of financing, the quality of education decline trap, the trap of staff capacity and the electronization and digitalization trap.
Sockpuppet Detection in Social Network Based on Adaptive Multi-source Features

Social media, such as Facebook and Twitter, has become an indispensable part of the Internet. These online social platforms facilitate the information exchange between people. At the same time, some users create multiple accounts on the same social media, pretend to be others, and publish articles or comments through different accounts to influence online public opinion. We refer to multiple accounts created by the same user named as sockpuppet accounts. In the past, people's research on sockpuppet detection was mostly based on verbal features or non-verbal features. These kinds of methods have their unique advantages and drawbacks, most of them ignore the connection between the two methods and study separately. For this purpose, we propose an adaptive multi-source feature fusion method, which not only considers the importance of text in identifying the sockpuppet, but also considers the characteristics of users’ online behavior which is not easy to disguise. The experimental results on real datasets show that our method has a better detection rate than compared with competitive algorithms.
Unsupervised Part Discovery by Unsupervised Disentanglement

We address the problem of discovering part segmentations of articulated objects without supervision. In contrast to keypoints, part segmentations provide information about part localizations on the level of individual pixels. Capturing both locations and semantics, they are an attractive target for supervised learning approaches. However, large annotation costs limit the scalability of supervised algorithms to other object categories than humans. Unsupervised approaches potentially allow to use much more data at a lower cost. Most existing unsupervised approaches focus on learning abstract representations to be refined with supervision into the final representation. Our approach leverages a generative model consisting of two disentangled representations for an object’s shape and appearance and a latent variable for the part segmentation. From a single image, the trained model infers a semantic part segmentation map. In experiments, we compare our approach to previous state-of-the-art approaches and observe significant gains in segmentation accuracy and shape consistency (Code available at https://compvis.github.io/unsupervised-part-segmentation ). Our work demonstrates the feasibility to discover semantic part segmentations without supervision.
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound.

We analyze a family of supervised learning algorithms based on sample compression schemes that are stable, in the sense that removing points from the training set which were not selected for the compression set does not alter the resulting classifier. We use this technique to derive a variety of novel or improved data-dependent generalization bounds for several learning algorithms. In particular, we prove a new margin bound for SVM, removing a log factor. The new bound is provably optimal. This resolves a long-standing open question about the PAC margin bounds achievable by SVM.
The principles of unique identification of library documents in automatic proximity identification systems

The author examines prospects for applying the full-range standard functionalities of automatic proximity identification systems. He discusses their performance capabilities for unique identification of library documents in the library information systems of different scaling plateau; using standards library standard identifiers in non-library information systems; the functionality of automatic proximity identification systems for library automation. The RFID technology plays the key role in developing automatic proximity identification. The library application features are examined from the viewpoint of harmonization with international and RF standards of automatic proximity identification systems. Developing the Internet of things concept gives into a new communication environment emerging based on the automatic proximity identification. This technology's standard capabilities may significantly expand the functionality of library automation. Integration of library information systems with global automatic identification systems is on agenda which is evidenced by the logic of development of information systems and library RFID. The article is written within the framework of the State Order № 730000F.99.1.BV09АА00006.
Is Your Language Model Ready for Dense Representation Fine-tuning?

Pre-trained language models (LM) have become go-to text representation encoders. Prior research used deep LMs to encode text sequences such as sentences and passages into single dense vector representations. These dense representations have been used in efficient text comparison and embedding-based retrieval. However, dense encoders suffer in low resource situations. Many techniques have been developed to solve this problem. Despite their success, not much is known about why this happens. This paper shows that one cause lies in the readiness of the LM to expose its knowledge through dense representation in fine-tuning, which we term Optimization Readiness. To validate the theory, we present Condenser, a general pre-training architecture based on Transformer LMs, to improve dense optimization readiness. We show that fine-tuning from Condenser significantly improves performance for small and/or noisy training sets.
Photovoltaic Power Forecasting Methods

The rapid growth in grid penetration of photovoltaic (PV) calls for more accurate methods to forecast the performance and reliability of PV. Several methods have been proposed to forecast the PV power generation at different temporal horizons. In this chapter the different methods used in PV power forecasting are described with an example on their applications and related uncertainty. The methods discussed include physical, heuristic, statistical and machine learning methods. When benchmarked, it is shown that physical method showed the highest uncertainties compared to other methods. In the chapter, the effect of degradation on lifetime PV power and energy forecast is also assessed using linear and non-linear degradation scenarios. It is shown that the relative difference in lifetime yield prediction is over 5% between linear and non-linear scenarios.
Impact of Social Networking and Technology on Knowledge Sharing among Undergraduate Students

Effective and inclusive Knowledge Sharing is an integral part of successful and practical university learning. This study examines the factors impacting Knowledge sharing among students as there is a lack of studies conducted among students in the context of middle east countries. A survey was carried out among undergraduate students at Cihan University-Erbil, Iraq and questionnaires consisted of two main sections were used to collect the data. Demographic information was collected in the first section, and the second section contained six factors: File-sharing, Perceived Enjoyment, Perceived Reciprocal Benefit, Information and Communication Technology, Technology Availability, and Knowledge Sharing. Out of 150 questionnaires distributed, 114 were valid for analysis representing a 76% response rate and the data were analyzed using Smart-PLS 3.2.9. The results indicated that Files-sharing, Perceived Enjoyment, Perceived Reciprocal Benefit, and Information and Communication Technology positively impacted Knowledge Sharing among students. However, Technology Availability did not affect Knowledge Sharing among students. The results implied that universities should encourage Knowledge Sharing between students by designing course activities and assessment that incorporate Files-sharing and Information and Communication Technology by fostering enjoyment and creating awareness of Knowledge Sharing’s reciprocal benefits.
Students’ views on learning French online with Microsoft Teams® during Covid-19 pandemic

Technological developments have made online learning tools become a part of educational process. The Covid-19 pandemic has made them a necessity. Learning a foreign language with the help of e-learning software is a debatable subject of 21st century. Microsoft Teams® is one of this software and understanding its effect on learning a foreign language is worth studying. Thus, this study aims to understand students’ views on learning French online with Microsoft Teams. The design of this study is the case study. The participants were 23 students learning French as a foreign language. The data were gathered through semi-structured interview form and the data were analyzed through content analysis. The results of the study reveal three themes on this subject. These themes are the reality of distance learning, the user friendliness of Microsoft Teams, and learning a foreign language online. With these themes, this study tries to compose a deeper understanding about learning a foreign language with a popular software.
Educational Reforms in the Context of Contemporary Civilizational Changes

Analysis of educational system seems to be the most reasonable when the key points are reformation and modernization of education, since the mentioned processes are the response to transformations in adjacent social spheres caused in their turn by objective changes, which may include civilizational, transnational changes in education at national level. This article is devoted to the estimation of educational reforms in the context of contemporary civilizational changes. The research aimed to systematize the features of development of reforms in the field of education, to develop measures on coordination of activities among main participants in educational process, to determine its main features in the context of civilizational changes. It has been established that the key point is to detect balance between autonomous right of a university to render education and scientific studies for economy and society, and responsibility of state for assurance of quality, accountability, integrity, and availability of education. It has been proved that the education sector needs investment to improve learning and research conditions. Another issue is implementation of new forms of financing, including on competitive basis, to support interest and interaction between universities and business environment. It has been determined that new trends in cooperation should not only concern the development of innovations and commercialization but also attract employers to cooperation aiming at development of new or updating the existing learning programs.
Regulatory Approaches of Cross-border Data Flow in the Big Data Era: China’s Choice

Abstract Cross-border data flow is characterized by multi-direction and rapid dynamic gridding in the big data era. Accordingly, these characteristics necessitate effective regulation for crossborder data flows. The US and the European Union (EU) follow their respective regulatory models for cross-border data flows by considering each one’s economic, political, and historical traditions. The “geographically based” policy of data flows, characterized as internal and external, is the basic position adopted by the EU on cross-border data flows. Although the US regulatory path favors (in principle) the free flow of data across borders, this country actually adopts data protectionism for data involving critical technologies and security areas. Given the political system differences among China, the EU, and the US, China can learn from the experiences of the US and the EU, actively seek value balance between data regulation and free flow, construct its own regulatory model, and establish a diversified data management system. Lastly, China should also actively participate in the international regulation competition on cross-border data flows, thereby giving the country a voice in the international arena.