Reviewerly/exHarmony huggingface dataset:
Dual-Axis Solar Tracking System

For maximum power output through any PV panel module, it is necessary to adjust the PV panel in such a way that the solar radiation falls perpendicularly to the panel. Since the solar position varies with time and date throughout the year, for the optimum power output, the panel should not be set fixed. To perfectly track the solar position throughout the year, dual-axis controllable tracking system is needed to be design. This study focuses on the controlling of dual-axis solar tracking system. The main aim is to maximize the power efficiency of the photovoltaic module, by adjusting the angle in order to maintain the perpendicular angle between the sun and the PV module. This system introduces two motors with some electronic sensors connected at different positions for PV module adjustments. Further, this method is simulated and it is seen to be advantageous. This simulation gives reliable performance of the system.
Growing Concern During the COVID-19 Pandemic: Data Privacy

As of late December 2019, coronavirus disease-2019 (COVID-19) has emerged in Wuhan, China, known as COVID-19 that adversely affects many countries in the world. 1 According to a report published on February 11, 2020 by the Chinese Center for Disease Control and Prevention, it took 30 days for the disease to spread from Hubei to all over the country. 2 The World Health Organization soon announced this disease as a public health problem at a global level. 3
Akatemiasampo-portaali ja -datapalvelu henkilöiden ja henkilöryhmien historialliseen tutkimukseen

AcademySampo – Academic people in Finland 1640–1899 is a portal and a Linked Open Data service on the Semantic Web. AcademySampo contains richly interlinked open data about all people that have got academic education in Finland in 1640–1899. The system is targeted to researchers and the general public for biographical and prosopographical research. This review gives an overview on how AcademySampo can be utilized in practise with its novel digital humanities tools included in the portal and by using the data service via APIs.
Alternative Scholarly Publishing: A Commercial Publisher's Perspective

A lot has changed during that time. A great deal of progress has been made in the world of serials during those two decades, with changes occurring in publishing, libraries, technologies, research and scholarly communication. Journals, as people know them, came into existence about 350 years ago. The balance among these four functions and the way librarians and publishers handle these functions will vary at different times, depending upon changes in technology, the pressure to publish, the responsibility for archiving and preservation, etc. In the print world the publishing process may have seemed a more difficult thing, but in fact it was easier. The issue of archiving is a great challenge, as this landscape is changing. In the print world, the areas of responsibility were clearly delineated but that’s not true in the electronic environment. In the current structure, self-monitoring is achieved largely through the peer-review process.
Effective content-based pattern predicted text mining using PSE model

The main importance of pattern searching engine (PSE) model provides solution for the applications, which involves pattern-based mining and find connections between patterns (e.g., emotions) and affective terms by categorising the text in the content under examination. It discovers patterns of word-use and how to connect documents that shared similar patterns. PSE model uses both theme-based examination and idea-based investigation, those can foresee the normal example by utilising semantic-based natural seeking model which interface words with comparative implications and recognise employments of words with different implications in a viable and speedy way.
Linked Data for the Perplexed Librarian,

As the authors of Linked data for the perplexed librarian point out, the roots of linked data go back at least as far as 1989. This was the year that information scientist and current Director of t...
Situated Temporal Planning Using Deadline-aware Metareasoning

We address the problem of situated temporal planning, in which an agent's plan can depend on scheduled exogenous events, and thus it becomes important to take the passage of time into account during the planning process. Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associated metareasoning problem. Although even the simplified version of the metareasoning problem is NP-hard, we provide a pseudo-polynomial time optimal solution to the case with known deadlines. We leverage intuitions emerging from this case to provide a fast greedy scheme that significantly improves upon previous schemes even for the case of unknown deadlines. Finally, we show how this new method can be applied inside a practical situated temporal planner. An empirical evaluation suggests that the new planner provides state-of-the-art results on problems where external deadlines play a significant role.
Pooling Acceleration in the DaVinci Architecture Using Im2col and Col2im Instructions

Image-to-column (Im2col) and column-to-image (Col2im) are data transformations extensively used to map convolution to matrix multiplication. These transformations rearrange the inputs of convolution to avoid its strided memory access pattern, thus providing a friendlier data layout for CPUs and GPUs. In artificial intelligence (AI) accelerators, these transformations allow convolution to be computed in matrix-multiplier units. Implemented in software, however, they impose a significant overhead that must be compensated by the efficiency gains of matrix multipliers. DaVinci is an AI accelerator architecture that introduces instructions to optimize Im2col and Col2im. Another core layer of convolutional neural networks that presents a strided memory access pattern is pooling. This paper explores the specialized Im2col and Col2im instructions to accelerate pooling layers in DaVinci. An experimental evaluation reveals that the proposed pooling implementations can yield speedups of up to 5.8 times compared to a baseline that does not use these specialized instructions. The speedups follow from an improved memory layout in the inputs of pooling, as this layout leads to better utilization of the vector processing unit in DaVinci.
Hyperspectral Image Denoising via Nonconvex Logarithmic Penalty

Hyperspectral images (HSIs) can help deliver more reliable representations of real scenes than traditional images and enhance the performance of many computer vision tasks. However, in real cases, an HSI is often degraded by a mixture of various types of noise, including Gaussian noise and impulse noise. In this paper, we propose a logarithmic nonconvex regularization model for HSI mixed noise removal. The logarithmic penalty function can approximate the tensor fibered rank more accurately and treats singular values differently. An alternating direction method of multipliers (ADMM) is also presented to solve the optimization problem, and each subproblem within ADMM is proven to have a closed-form solution. The experimental results demonstrate the effectiveness of the proposed method.
Content Moderation as Surveillance

Technology platforms are the new governments, and content moderation is the new law, or so goes a common refrain. And as platforms increasingly turn toward new, automated mechanisms of enforcing their rules, the apparent power of the private sector seems only to grow. Yet beneath the surface lies a web of complex relationships between public and private authorities that call into question whether platforms truly possess such unilateral power. Law enforcement and police are exerting influence over platform content rules, giving governments a louder voice in supposedly “private” decisions. At the same time, law enforcement avails itself of the affordances of social media in detecting, investigating, and preventing crime. This Article, prepared for a symposium dedicated to Joel Reidenberg’s germinal article Lex Informatica, untangles the relationship between content moderation and surveillance. Building on Reidenberg’s fundamental insights regarding the relationships between rules imposed by legal regimes and those imposed by technological design, the Article first traces how content moderation rules intersect with law enforcement, including through formal demands for information, informal relationships between platforms and law enforcement agencies, and the impact of end-to-end encryption. Second, it critically assesses the degree to which government involvement in content moderation actually tempers platform power. Rather than effective oversight and checking of private power, it contends, the emergent arrangements between platforms and law enforcement institutions foster mutual embeddedness and the entrenchment of private authority within public governance.
Improved Face Recognition With Fractal-Based Texture Analysis

Fractals are useful to uniquely represent texture in the human face, which serves as an equivalent of human vision. FaceNet, calculating face descriptors of a person, has been observed to perform with setbacks when several factors of occlusion are present. This paper proposes a new methodology that exploits the self-similar patterns in a person's face to highlight and enhance regions of high texture in a facial image. The system maps the original image into a representation in the pre-processing stage of computer vision. This representation when fed as an input to the FaceNet CNN optimizes the face embedding generated. An SVM classifier separates the hard positive examples from the hard negative examples during classification. The model is trained using YouTube Faces DB as primary dataset and for validation; a custom dataset is designed to verify a person's identity despite the presence of secondary factors such as expressions and forgery. The proposed model attained an overall accuracy of 96.73% with the YouTube Faces DB, and a notable reduction in the false positive rates is observed.
Optimal Quantization using Scaled Codebook

We study the problem of quantizing N sorted, scalar datapoints with a fixed codebook containing K entries that are allowed to be rescaled. The problem is defined as finding the optimal scaling factor α and the datapoint assignments into the α-scaled codebook to minimize the squared error between original and quantized points. Previously, the globally optimal algorithms for this problem were derived only for certain codebooks (binary and ternary) or under the assumption of certain distributions (Gaussian, Laplacian). By studying the properties of the optimal quantizer, we derive an $\mathcal{O}\left( {NK\log K} \right)$ algorithm that is guaranteed to find the optimal quantization parameters for any fixed codebook regardless of data distribution. We apply our algorithm to synthetic and real-world neural network quantization problems and demonstrate the effectiveness of our approach.
Compute functional analysis leveraging the IAAS private cloud computing service model in packstack development

Analyzing compute functions by utilizing the IAAS model for private cloud computing services in packstack development is one of the large-scale data storage solutions. Problems that often occur when implementing various applications are the increased need for server resources, the monitoring process, performance efficiency, time constraints in building servers and upgrading hardware. These problems have an impact on long server downtime. The development of private cloud computing technology could become a solution to the problem. This research employed Openstack and Packstack by applying one server controller node and two servers compute nodes. Server administration with IAAS and self-service approaches made scalability testing simpler and time-efficient. The resizing of the virtual server (instance) that has been carried out in a running condition shows that the measurement of the overhead value in private cloud computing is more optimal with a downtime of 16 seconds.
Automated vehicle detection and classification methods

Recognition, Localization and Classification of objects, especially vehicles, have been an area of immense research in Computer Vision (CV). Automated Vehicle Detection and Classification on real world traffic and over various terrains has found its use in autonomous cars, intelligent parking systems, automated petrol systems, intelligent vehicle tracking systems, security systems and many more CV based applications. There has been a considerable evolution in the methods adopted before to detect and classify vehicles in real world scenario to those being employed today. Vehicle Detection and Classification (VDC), today, involves prediction of the coordinate-based location of particular category of vehicles in a given input image by means of bounding boxes using Deep Learning and Neural Networks (NNs). Feature calculation carried out by Deep learning-based vehicle detectors are more in use due to its better performance, ease and higher accuracy compared to the earlier employed machine learning techniques involving SVMs and Regression to identify patterns in vehicle frames. Background Detection methods and use of Gaussian Mixture Models for detection and classification are among the oldest attempts made in this problem. This paper reviews some of the crucial methods, by highlighting the different works done so far by researchers across the globe, to process real-time traffic images and videos to eliminate the need for manual surveillance and thus gradually introducing the idea of smart traffic management tools. This paper further points to the history of CV in VDC problem along with the future possibilities.
ADEPT: An Adjective-Dependent Plausibility Task

A false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors.While correctly interpreting and assessing the effects of such adjective-noun pairs (e.g., false key) on the plausibility of given events (e.g., opening doors) underpins many natural language understanding tasks, doing so often requires a significant degree of world knowledge and common-sense reasoning.We introduce ADEPT -a large-scale semantic plausibility task consisting of over 16 thousand sentences that are paired with slightly modified versions obtained by adding an adjective to a noun.Overall, we find that while the task appears easier for human judges (85% accuracy), it proves more difficult for transformer-based models like RoBERTa (71% accuracy).Our experiments also show that neither the adjective itself nor its taxonomic class suffice in determining the correct plausibility judgement, emphasizing the importance of endowing automatic natural language understanding systems with more context sensitivity and commonsense reasoning.
IDEAS OF THE BASIC SCHOOL STUDENTS ABOUT THE SOCIO-PROFESSIONAL STRUCTURE AS AN ELEMENT OF THEIR READINESS FOR PROFESSIONAL SELF-DETERMINATION (A CASE STUDY OF THE PERM REGION)

Professional self-determination of students, its economic, pedagogical and psychological prerequisites are becoming increasingly relevant subjects for research in Russian science. However, the issues related to the social prerequisites of this process remain much less studied: the students’ ideas about the socioprofessional structure of the society, the adequacy of their ideas about the content of jobs in professional areas they might choose. As a theoretical basis of the study, the author considers the psychological and sociological theory of self-determination (self-determination theory), which focuses on the autonomy of the individual in various fields of activity, including educational and labor. The author notes the importance of taking into account the objective factors of professional choice considered in Soviet works on this issue as well as in classical articles of the structural functionalism representatives. The empirical base of the research is the materials of diagnostic surveys conducted in the Perm region since 2017 to assess readiness of basic school students for professional self-determination. The author analyzes the indicators characterizing the students’ ideas about the objective conditions of professional selfdetermination concerning two areas: the adequacy of students’ ideas about the socio-professional structure of the society and various ways of considering the significance of particular factors in choosing the future path by students. The survey allows us to identify a number of problems regarding the adequacy of students’ ideas about the socio-professional structure of the society: despite the wide range of different activities that students are able to name and evaluate in terms of their relevance in the region, to a considerable extent basic school graduates do not possess a clear idea of the content of jobs as well as the conditions of professional activity.
Learning Prototypical Functions for Physical Artifacts

Humans create things for a reason.Ancient people created spears for hunting, knives for cutting meat, pots for preparing food, etc.The prototypical function of a physical artifact is a kind of commonsense knowledge that we rely on to understand natural language.For example, if someone says "She borrowed the book" then you would assume that she intends to read the book, or if someone asks "Can I use your knife?" then you would assume that they need to cut something.In this paper, we introduce a new NLP task of learning the prototypical uses for human-made physical objects.We use frames from FrameNet to represent a set of common functions for objects, and describe a manually annotated data set of physical objects labeled with their prototypical function.We also present experimental results for this task, including BERT-based models that use language model predictions from masked patterns as well as artifact sense definitions from WordNet and frame definitions from FrameNet.
Shape-Pose Ambiguity in Learning 3D Reconstruction from Images

Learning single-image 3D reconstruction with only 2D images supervision is a promising research topic. The main challenge in image-supervised 3D reconstruction is the shape-pose ambiguity, which means a 2D supervision can be explained by an erroneous 3D shape from an erroneous pose. It will introduce high uncertainty and mislead the learning process. Existed works rely on multi-view images or pose-aware annotations to resolve the ambiguity. In this paper, we propose to resolve the ambiguity without extra pose-aware labels or annotations. Our training data is single-view images from the same object category. To overcome the shape-pose ambiguity, we introduce a pose-independent GAN to learn the category-specific shape manifold from the image collections. With the learned shape space, we resolve the shape-pose ambiguity in original images by training a pseudo pose regressor. Finally, we learn a reconstruction network with both the common re-projection loss and a pose-independent discrimination loss, making the results plausible from all views. Through experiments on synthetic and real image datasets, we demonstrate that our method can perform comparably to existing methods while not requiring any extra pose-aware annotations, making it more applicable and adaptable.
Entity Name Recognition in Job Postings and Resumes

Searching for jobs online on popular websites is extremely useful in serving as an important tool for job seekers and employers for many years. Despite their valuable benefit in linking employers' needs to potential employees, job search websites' search and filtering technologies struggle to keep up with innovations. As a solution to the aforementioned problems, a system has been considered to determine both the abilities of the candidate and the skills required from the job posting. For this purpose, a system has been implemented that removes the skills of the candidate from the resume and the desired skills for the job from the job advertisement. Entity name recognition and fastText text classification methods are used to extract information from the relevant texts.
Engage the Public: Poll Question Generation for Social Media Posts

This paper presents a novel task to generate poll questions for social media posts.It offers an easy way to hear the voice from the public and learn from their feelings to important social topics.While most related work tackles formally-written texts (e.g., exam papers), we generate poll questions for short and colloquial social media messages exhibiting severe data sparsity.To deal with that, we propose to encode user comments and discover latent topics therein as contexts.They are then incorporated into a sequence-to-sequence (S2S) architecture for question generation and its extension with dual decoders to additionally yield poll choices (answers).For experiments, we collect a large-scale Chinese dataset from Sina Weibo containing over 20K polls.The results show that our model outperforms the popular S2S models without exploiting topics from comments and the dual decoder design can further benefit the prediction of both questions and answers.Human evaluations further exhibit our superiority in yielding high-quality polls helpful to draw user engagements.
LineageBA: A Fast, Exact and Scalable Graph Generation for the Barabási-Albert Model

The Barabási-Albert(BA) model plays an important role in many domains since it can generate a scale-free graph having the degree exponents that real graphs have. However, due to the dependency among the edges generated at different time steps, the exact generation methods support only a single thread, and the parallel generation methods generate a graph only approximately. There is no method that can generate a large-scale graph following the BA model strictly using multiple threads. We propose a fast, exact, and scalable graph generation method called LineageBA that solves the above issue. We propose the concept of lineage relationship for reducing memory usage significantly and the detection of hash collisions for parallelizing the graph generation. Through extensive experiments, we have shown that LineageBA significantly outperforms the state-of-the-art BA graph generation methods and easily generates 2.5 trillion edges within four hours using a small cluster of PCs.
An Entity-Aware Adversarial Domain Adaptation Network for Cross-Domain Named Entity Recognition (Student Abstract)

Existing methods for named entity recognition (NER) are critically relied on the amount of labeled data. However, these methods suffer from performance decline in a new domain which is fully-unlabeled. To handle the situation, we propose an entity-aware adversarial domain adaptation network, which utilizes the labeled data from source domain and then adapts to unlabeled target domain. We first apply adversarial training to reduce the distribution gap between different domains. Furthermore, we introduce an entity-aware attention to guide adversarial to achieve the alignment of entity features. The experimental results show that our model outperforms the state-of-the-art approaches.