Reviewerly/exHarmony huggingface dataset:
Data-Efficient Information Extraction from Form-Like Documents

Automating information extraction from form-like documents at scale is a pressing need due to its potential impact on automating business workflows across many industries like financial services, insurance, and healthcare. The key challenge is that form-like documents in these business workflows can be laid out in virtually infinitely many ways; hence, a good solution to this problem should generalize to documents with unseen layouts and languages. A solution to this problem requires a holistic understanding of both the textual segments and the visual cues within a document, which is non-trivial. While the natural language processing and computer vision communities are starting to tackle this problem, there has not been much focus on (1) data-efficiency, and (2) ability to generalize across different document types and languages. In this paper, we show that when we have only a small number of labeled documents for training (~50), a straightforward transfer learning approach from a considerably structurally-different larger labeled corpus yields up to a 27 F1 point improvement over simply training on the small corpus in the target domain. We improve on this with a simple multi-domain transfer learning approach, that is currently in production use, and show that this yields up to a further 8 F1 point improvement. We make the case that data efficiency is critical to enable information extraction systems to scale to handle hundreds of different document-types, and learning good representations is critical to accomplishing this.
Revenue Assurance and Fraud Detection for Telecom Operators – Combating Bypass Fraud

2) Phase 2: Analyse& Identify Key activities in this phase includes:  Assess current state of governance, processes and systems  Assess future state of governance, processes and systems  Determine gap between as-is and future state  Identify major areas for revenue leakage and fraud risk  Identify products & services with potential risks for revenue leakage and fraud  Infrastructure design for the RA and FM tool  Sizing the infrastructure requirement for deploying the RA and FM  Define transformation strategy  Review of rules and thresholds to minimize false exceptions and to achieve continuous improvement Key outcomes of this phase include:  Identified revenue leakage risks  Gap analysis 3) Phase 3: Design &Build Key activities in this phase includes:  Design the right-fit system for the current operations  Design the business architecture for the planned future state operations  Design the data flow requirements as per the planned architecture  Assess key vendor/partner processes and interfaces  Define the logic and the rules in the system that will be deployed  Building the right statistical model  Building the adaptive learning algorithm for the operations on a test environment  Testing the statistical model and algorithm on a test environment  Finalize implementation plans in discussion with the key stakeholders Key outcomes of this phase include:  Model Building  High-level implementation and transformation plan  RA and FM infrastructure identification 4) Phase 4: Implement &Operate This is the steady-state phase of the project.Key activities in this phase includes:  Model deployment on the identified infrastructure  Adaptive learning algorithm modified and deployed  Execute and monitor implementation  Revising of the logic and rules of the system on an on-going basis  Testing of the re-configured rules are working as per plan  Continuous improvement based on periodic reviews Key outcomes of this phase include:  Fully implemented and tested RAFD Management system deployed
IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation

Outlier detection is one of the most popular and continuously rising topics in the data mining field due to its crucial academic value and extensive industrial applications. Among different settings, unsupervised outlier detection is the most challenging and practical one, which attracts tremendous efforts from diverse perspectives. In this paper, we consider the score-based outlier detection category and point out that the performance of current outlier detection algorithms might be further boosted by score propagation. Specifically, we propose Infinite Propagation of Outlier Factor (iPOF) algorithm, an extremely and excitingly simple outlier detection booster via infinite propagation. By employing score-based outlier detectors for initialization, iPOF updates each data point's outlier score by averaging the outlier factors of its nearest common neighbors. Extensive experimental results on numerous datasets in various domains demonstrate the effectiveness and efficiency of iPOF significantly over several classical and recent state-of-the-art methods. We also provide the parameter analysis on the number of neighbors, the unique parameter in iPOF, and different initial outlier detectors for general validation. It is worthy to note that iPOF brings in positive improvements ranging from 2% to 46% on the average level, and in some cases, iPOF boosts the performance over 3000% over the original outlier detection algorithm.
SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)

Deep Neural Network (DNN) watermarking is a method for provenance verification of DNN models. Watermarking should be robust against watermark removal attacks that derive a surrogate model that evades provenance verification. Many watermarking schemes that claim robustness have been proposed, but their robustness is only validated in isolation against a relatively small set of attacks. There is no systematic, empirical evaluation of these claims against a common, comprehensive set of removal attacks. This uncertainty about a watermarking scheme's robustness causes difficulty to trust their deployment in practice. In this paper, we evaluate whether recently proposed watermarking schemes that claim robustness are robust against a large set of removal attacks. We survey methods from the literature that (i) are known removal attacks, (ii) derive surrogate models but have not been evaluated as removal attacks, and (iii) novel removal attacks. Weight shifting and smooth retraining are novel removal attacks adapted to the DNN watermarking schemes surveyed in this paper. We propose taxonomies for watermarking schemes and removal attacks. Our empirical evaluation includes an ablation study over sets of parameters for each attack and watermarking scheme on the CIFAR-10 and ImageNet datasets. Surprisingly, none of the surveyed watermarking schemes is robust in practice. We find that schemes fail to withstand adaptive attacks and known methods for deriving surrogate models that have not been evaluated as removal attacks. This points to intrinsic flaws in how robustness is currently evaluated. We show that watermarking schemes need to be evaluated against a more extensive set of removal attacks with a more realistic adversary model. Our source code and a complete dataset of evaluation results are publicly available, which allows to independently verify our conclusions.
La Percepción del Espacio en Realidad Virtual, correlación entre posibilidades de acción y presencia espacial

Purpose:  To explore the correlation between two concepts in the study of embodied perception in Virtual Reality (VR): affordances and spatial presence. Methodological design:  It involved the review of literature on spatial presence and affordances in the fields of phenomenology and cognitive neuroscience, and an experiment with 30 participants using the Spatial Presence Experience Scale (SPES) proposed by Hartmann  et al.  (2016). Each participant tested 5 VR simulations and answered 5 questionnaires based on the SPES. This study proposes a model for the classification of types and subtypes of affordances in virtual reality. Results:  The comparison of means between scores showed a positive correlation between possibilities for action and intensity of spatial presence. Data suggests motor affordances (navigational, goal-oriented and handle grasp types) intensify spatial presence. Frequency of videogame use was found unrelated to the intensity of spatial presence, but it showed an increase in user’s dexterity and the ease of perceiving affordance in virtual environments. Research limitations:  The scope of this research is limited to describing the correlation between affordances and intensity of spatial presence in VR.  Findings:   These confirmed the hypothesis: more possibility types of affordances in VR result in a higher intensity of spatial presence.
Elaborative Rehearsal for Zero-shot Action Recognition

The growing number of action classes has posed a new challenge for video understanding, making Zero-Shot Action Recognition (ZSAR) a thriving direction. The ZSAR task aims to recognize target (unseen) actions without training examples by leveraging semantic representations to bridge seen and unseen actions. However, due to the complexity and diversity of actions, it remains challenging to semantically represent action classes and transfer knowledge from seen data. In this work, we propose an ER-enhanced ZSAR model inspired by an effective human memory technique Elaborative Rehearsal (ER), which involves elaborating a new concept and relating it to known concepts. Specifically, we expand each action class as an Elaborative Description (ED) sentence, which is more discriminative than a class name and less costly than manual-defined attributes. Besides directly aligning class semantics with videos, we incorporate objects from the video as Elaborative Concepts (EC) to improve video semantics and generalization from seen actions to unseen actions. Our ER-enhanced ZSAR model achieves state-of-the-art results on three existing benchmarks. Moreover, we propose a new ZSAR evaluation protocol on the Kinetics dataset to overcome limitations of current benchmarks and demonstrate the first case where ZSAR performance is comparable to few-shot learning baselines on this more realistic setting. We will release our codes and collected EDs at https://github.com/DeLightCMU/ElaborativeRehearsal.
Amazon CEO Jeff Bezos claps back at girlfriend's brother, alleges extortion attempt

Amazon founder Jeff Bezos slammed Michael Sanchez – the brother of his girlfriend, Lauren Sanchez – for allegedly attempting to extort the billionaire by suing him for defamation.
City Branding Implementation as an Effort of Increasing Tourism Performance

Pelabuhan Perikanan Nusantara (PPN) Pelabuhanratu is one of the fishing ports that is the center of capture fisheries activities in the Southern Waters of West Java, PPN Pelabuhanratu is a selected area as a fishery management center for the economic development of fishery businesses developed with integrity by the government. Aquaculture is the responsibility of Pelabuhanratu PPN starting from hatchery to product development. Good utilization, distribution, and processing of fishery products are needed to fulfill these responsibilities, but these efforts cannot be carried out because the data processing is still simple, and the PPN Pelabuhanratu data is complex so that the required information has not been fulfilled. To improve the data processing and analysis process, you can take advantage of Business Intelligence, with Business intelligence PPN Pelabuhanratu can obtain a lot of and diverse information. This study using the Online Analytical Processing (OLAP) method as data visualization. The information generated is in the form of information on fish production based on fish species in a certain period, information on fish distribution in a certain period, and information on the value of fish quality in a certain period. All of this information is presented in graphical form to make it easier to read the information.
INCREASING THE STUDENTS’ SKILL IN READING NARRATIVE TEXT BY USING KNOW-WANT-LEARNED STRATEGY AT THE EIGHT GRADE STUDENTS OF JUNIOR HIGH SCHOOL 3 PALEMBANG

The aim of this research was to increase the students’ skill in reading narrative text by using Know-Want-Learned strategy at the eight grade students of Junior High School 3 Palembang. This research of this study was conducted by using classroom action research. The data used in this research are the result the pre-test to know the improvement of students in reading narrative text. The mean of the pre-test was 64,5. The mean of the first cycle was 84,85, it indicated that the scores and the mean in first cycle were better than the pre-test. The percentage of students who got point 75 or more there was also grew up. In the pre-test, there was 10 students (25%) who passed or got score up to 75. In the post-test of cycle I the students who got point 75 or more there was 40 (100%) and the increasing was about 75%. In other words, the students’ skill in reading narrative text improved and became well in the first meeting to the next meeting. For the hyphothesis testing used t-test formula from the compilation, it could be seen that coefficient of it could be seen that coefficient of tobserved (16,72) &gt; t table (0,316). Thus, alternative hyphothesis (Ha) could be received. Based on the research, It could be get that using Know-Want-Learned (KWL) Strategy could increase the students’ skill in reading narrative text.
Hans Trading Sdn Bhd : Threat Into Thread / Hanna Humaira Mokhtar ...[et al.]

Hans Trading Sdn Bhd offers threads made out of recycled plastics to our customers providing a greener approach to the textile and clothing industry. We identified our customers as clothing companies whom are searching for alternative and new materials to produce their products. With the variety of our products, we can supplying to our customers new and fashionable materials to work with. Hans Trading Sdn also specializes in exporting products so our international customers can rest easy as we can deliver our products to them efficiently
Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing.

Contrastive predictive coding (CPC) aims to learn representations of speech by distinguishing future observations from a set of negative examples. Previous work has shown that linear classifiers trained on CPC features can accurately predict speaker and phone labels. However, it is unclear how the features actually capture speaker and phonetic information, and whether it is possible to normalize out the irrelevant details (depending on the downstream task). In this paper, we first show that the per-utterance mean of CPC features captures speaker information to a large extent. Concretely, we find that comparing means performs well on a speaker verification task. Next, probing experiments show that standardizing the features effectively removes speaker information. Based on this observation, we propose a speaker normalization step to improve acoustic unit discovery using K-means clustering of CPC features. Finally, we show that a language model trained on the resulting units achieves some of the best results in the ZeroSpeech2021~Challenge.
A "Searchable" Space with Routes for Querying Scientific Information

Users searching for scientific information are confronted by a hidden face of searchable space: their own selection of items, which could help map their navigation on recorded search routes, is not open to consultation, and remains either concealed or even unavailable. At best, users benefit from recommendations but, as data on their own choices are not shared, networked information for global navigation remains nebulous. This position paper tackles the following research question: How could users searching for scientific information benefit from each other's search sessions? Our answer comprises two steps. First, using examples from our own data, we look at the characteristics of user behavior, approached here mainly via structures of collaborative personalized search. The second step proposes a mapping of recorded search sessions: for similar queries, search sessions are modeled by sets of typical user/item pairs in networks we call eco-systems of queries. These ecosystems connect search sessions and are open to navigation for users. Final output is threefold: 1) visualization of interconnection between search sessions, 2) localization of the search session that best suits a user's needs, 3) navigation on alternative routes between search sessions selecting alternative paths to answers. The conclusion raises questions about typical uses of this new kind of a documentary object, exploiting functionalities originally adapted from a bipartite graph.
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation

Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain. We propose a novel approach named T-SVDNet to address the task of Multi-source Domain Adaptation (MDA), which is featured by incorporating Tensor Singular Value Decomposition (T-SVD) into a neural network's training pipeline. Overall, high-order correlations among multiple domains and categories are fully explored so as to better bridge the domain gap. Specifically, we impose Tensor-Low-Rank (TLR) constraint on a tensor obtained by stacking up a group of prototypical similarity matrices, aiming at capturing consistent data structure across different domains. Furthermore, to avoid negative transfer brought by noisy source data, we propose a novel uncertainty-aware weighting strategy to adaptively assign weights to different source domains and samples based on the result of uncertainty estimation. Extensive experiments conducted on public benchmarks demonstrate the superiority of our model in addressing the task of MDA compared to state-of-the-art methods.
The Importance of Remote Sensing Technology and its use in the Study of Environmental Systems (Detection of Groundwater, Model)

The main objective of this research know is to evaluate the ability remote sensingtechniques to detect ground water in the study area.To achieve this goal, Americansatellite imagery (Landsat-7) was used to cover the study area.And use of the digital image processor program (ERDAS IMAGINE)(EarthResources Data Analysis System)in order to improve aerial image and the signatureof the real coordinates of the area and classified according to the nature of the region,then analyze this image by comparing them with geophysical methods used to identifygroundwater wells.This analysis of the ability of remote sensing techniques and digital image processinghas proven to identify groundwater sites depending on the study and classification ofthe surface crust covering the area, which provides an effective tool for exploringgroundwater.
Cross-Domain Recommendation Approach Based on Topic Modeling and Ontology

Single-domain recommendation systems lack in cross-diversity of items. For a sparse user, it is very difficult to recommend item. But we can utilize the knowledge of other domains to recommend an item to that sparse (unknown user preferences) user. This can be possible with the help of cross-domain recommendation systems. After watching movies, listening to songs or reading books, user reviews them with a specific rating. This data which is generated by a user can be used for recommendation purpose. By using topic modeling techniques like Latent Dirichlet Allocation (LDA) and ontology methods, genres can be extracted from reviews. By passing user reviews to the model, a pattern of words can be obtained which further be mapped to genres by implementing ontological profile approach. Dictionary for the genres can be obtained by crawling similar type of words related to a genre. Book dataset and movie lens dataset are used for our implementation purpose. Evaluated result gives better mean precision in comparison with existing approach based on semantic clustering.
Building a Student-to-Workforce Pipeline for 21st Century Cloud Industry Careers

Dade College (MDC).She has helped MDC secure over $3 million in federal funding the past three years for STEM and emerging technology education programs as well as a collaborative high tech learning hub, Cloud Computing Center, with the aim of
A Study on the First Grade Students’ Understanding of Personal Pronouns in English Reading Texts at SMPN 1 Singingi

The problem of this research was to know how well the students’ of SMPN 1 Singingi in Understanding personal pronouns in a text at SMPN 1 Singingi is. The research was conducted at SPMN 1 Singingi from Februari 3rd to Februari 27th, 2021. The population of this research was 40 students of two classes and researcher took 20 students as the sample. This research was quantitaive- descriptive research. Researcher used fill-in-the-blank text test to collect the data. In analyzing the data, researcher collected and classified each item of the test and multiplied them by standardizing the scores that had been determined before. The next step was counted the percentage of the level of the students’ ability in using personal pronouns. From the research findings, researcher found that the test was gained 1545 from total scores of all the students who joined the test. The average of students' scores was 77.25 and the sum of the total scores was 85%. It means that most of the students did well on the test and understood personal pronouns in reading text.
Personalised Recommender Engine Usinga Probablistic Model

The world of e-commerce and e-business has opened many horizons to explore customers on web. Consumers are expecting businesses to approach and please them with their expectations. This has given rise to recommender systems. Many of the recommender systems are generalized in nature which are often based on market stratum and user predictions. However, extensive research is being carried out in providing personalized recommendations using association rules, customer segmentation, social media ontologies and demographics. There are many issues in the implementation of these systems. This paper discusses diverse recommender approaches proposed in the past with a comparative study and gap analysis. It also proposes a Hybrid Personalized Recommender system by using a probabilistic model.
국방 암호장비 유지보수 발전방안 연구

Despite the rapid development and changes in the IT environment due to the advent of the 4th industrial revolution, the military’s defense coded equipment maintenance system has been operating in the same system as the existing system without any improvement until now. As a result, various problems have arisen in the maintenance work of cryptographic equipment. In addition, due to the repeated development of the defense information system, it is now difficult to perform the maintenance work of cryptographic equipment without improving the maintenance system of cryptographic equipment. Therefore, in order to convert the defense coded equipment maintenance system into a maintenance system, this study uses a deductive method to prove the validity of the variable after selecting variables that affect the defense coded equipment maintenance system through interviews with a group of experts related to encryption equipment maintenance. Therefore, a plan was proposed to solve the defense code equipment maintenance work through an agency in charge of maintenance and secure the efficiency, expertise, and security of the encryption equipment maintenance work.