Reviewerly/exHarmony huggingface dataset:
The Simplest Multi-key Linearly Homomorphic Signature Scheme.

We consider the problem of outsourcing computation on data authenticated by different users. Our aim is to describe and implement the simplest possible solution to provide data integrity in cloud-based scenarios. Concretely, our multi-key linearly homomorphic signature scheme (\({\mathsf {mklhs}}\)) allows users to upload signed data on a server, and at any later point in time any third party can query the server to compute a linear combination of data authenticated by different users and check the correctness of the returned result. Our construction generalizes Boneh et al.’s linearly homomorphic signature scheme (PKC’09 [7]) to the multi-key setting and relies on basic tools of pairing-based cryptography. Compared to existing multi-key homomorphic signature schemes, our \({\mathsf {mklhs}}\) is a conceptually simple and elegant direct construction, which trades-off privacy for efficiency. The simplicity of our approach leads us to a very efficient construction that enjoys significantly shorter signatures and higher performance than previous proposals. Finally, we implement \({\mathsf {mklhs}}\) using two different pairing-friendly curves at the 128-bit security level, a Barreto-Lynn-Scott curve and a Barreto-Naehrig curve. Our benchmarks illustrate interesting performance trade-offs between these parameters, involving the cost of exponentiation and hashing in pairing groups. We provide a discussion on such trade-offs that can be useful to other implementers of pairing-based protocols.
Artificial Intelligence Fairness in the Context of Accessibility Research on Intelligent Systems for People who are Deaf or Hard of Hearing

We discuss issues of Artificial Intelligence (AI) fairness for people with disabilities, with examples drawn from our research on human-computer interaction (HCI) for AI-based systems for people who are Deaf or Hard of Hearing (DHH). In particular, we discuss the need for inclusion of data from people with disabilities in training sets, the lack of interpretability of AI systems, ethical responsibilities of access technology researchers and companies, the need for appropriate evaluation metrics for AI-based access technologies (to determine if they are ready to be deployed and if they can be trusted by users), and the ways in which AI systems influence human behavior and influence the set of abilities needed by users to successfully interact with computing systems.
Revealing Backdoors, Post-Training, in DNN Classifiers via Novel Inference on Optimized Perturbations Inducing Group Misclassification

Recently, a special type of data poisoning (DP) attack targeting Deep Neural Network (DNN) classifiers, known as a backdoor, was proposed. These attacks do not seek to degrade classification accuracy, but rather to have the classifier learn to classify to a target class whenever the backdoor pattern is present in a test example. Launching backdoor attacks does not require knowledge of the classifier or its training process - it only needs the ability to poison the training set with (a sufficient number of) exemplars containing a sufficiently strong backdoor pattern (labeled with the target class). Here we address post-training detection of backdoor attacks in DNN image classifiers, seldom considered in existing works, wherein the defender does not have access to the poisoned training set, but only to the trained classifier itself, as well as to clean examples from the classification domain. This is an important scenario because a trained classifier may be the basis of e.g. a phone app that will be shared with many users. Detecting backdoors post-training may thus reveal a widespread attack. We propose a purely unsupervised anomaly detection (AD) defense against imperceptible backdoor attacks that: i) detects whether the trained DNN has been backdoor-attacked; ii) infers the source and target classes involved in a detected attack; iii) we even demonstrate it is possible to accurately estimate the backdoor pattern. We test our AD approach, in comparison with alternative defenses, for several backdoor patterns, data sets, and attack settings and demonstrate its favorability. Our defense essentially requires setting a single hyperparameter (the detection threshold), which can e.g. be chosen to fix the system's false positive rate.
ANALISIS KEBUTUHAN PENGEMBANGAN BAHAN AJAR BERBASIS KONTEKSTUAL BERDASARKAN KEANEKARAGAMAN MIKROALGA DI PULAU TUNDA

Teaching materials are one of the important components that support learning activities to achieve learning goals. Teaching materials will be meaningful if they are related to real life contexts and utilize local potential in the surrounding environment. This study aims to analyze the need for developing contextual-based teaching materials based on microalgae diversity on Tunda Island. The research method used is descriptive method with the number of research subjects 5 high school biology teachers in Serang. The instruments used were questionnaires for teaching material needs for teachers and analysis instruments for preparing LKS. The results of the teacher questionnaire state that 20% is very important and 80% is important in making contextual based teaching materials based on Banten's local biodiversity. Based on the results of the analysis it can be concluded that it is necessary to develop teaching materials in the form of LKS on the concept of protists for contextual microalgae material to make it easier for students to recognize the local biodiversity of Banten, Tunda Island.
Two subtypes of lung cancer classification from histopathology images based on deep learning

Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most common forms of lung cancer. These two subtypes of lung cancer are usually classified by visual inspection clinically. Our aim is to design an effective strategy based on convolutional neural networks to classify histopathology slides of these two types of lung cancer. With augmentation of the histopathology slides, different classifiers were trained and three ensemble learning methods were compared in this project. Finally, we determined which training strategy that produced the best result. In the case of limited samples, we find that the combination of transfer learning and ensemble learning greatly improves the classification accuracy for whole-slide images of lung tissue. The optimal strategy achieved 94.2% accuracy with 120 training cases and 86.3% accuracy with 80 independent test cases. We consider this comprehensive strategy remarkable in solving the classification problem with the different kinds of lung cancer. (Less)
Coin_flipper at eHealth-KD Challenge 2019: Voting LSTMs for Key Phrases and Semantic Relation Identification Applied to Spanish eHealth Texts.

This paper describes our approach presented for the eHealth-KD 2019 challenge. Our participation was aimed at testing how far we could go using generic tools for Text-Processing but, at the same time, using common optimization techniques in the field of Data Mining. The architecture proposed for both tasks of the challenge is a standard stacked 2-layer bi-LSTM. The main particularities of our approach are: (a) The use of a surrogate function of F1 as loss function to close the gap between the minimization function and the evaluation metric, and (b) The generation of an ensemble of models for generating predictions by majority vote. Our system ranked second with an F1 score of 62.18% in the main task by a narrow margin with the winner that scored 63.94%.
중국의 반(反)접근 · 지역거부 도전과 미국의 군사적 응전 :공해전투에서 다중영역전투까지

As China has become a dominant politico-military power, regional security order in East Asia, which heavily relied on hegemony of the U.S., is facing a growing strategic challenges. Especially China`s evolving military capabilities for Anti-access/Area Denial (A2/AD), which can discourage the U.S. military power for intervention toward East Asia, are heightening such concerns. In response, the U.S. sought to develop AirSea Battle concept, which pursued to destroy China`s A2/AD threat by naval and aerospace power. However the idea of AirSea Battle was criticized for possible risk of escalating politico-military conflicts between the U.S. and China into a full-scale war. Later, Multi-domain Battle concept, pursued by the U.S. Army since 2016, is getting attention for emphasizing “combat power projection from land into maritime and aerial domain to overcome A2/AD challenges from China. Korea also needs to closely study the utility of Multi-domain Battle 1) to cope with the possibility of military contest with China; and 2) to prepare restructuring of allied defense posture with the U.S., including reshaping missions and force structure of the U.S. troops in Korea, in the future.
LibGuides: Music: Essential Resources

We designed this research guide for students in the music program at Gonzaga. You'll find the materials, search tools, and other resources you need to start your research. Please contact your librarian if you need any assistance.
Perancangan Sistem Informasi Pendaftaran Peserta Didik Baru Berbasis Web pada SMK Kosgoro Kota Bogor

Abstract: New Student Admission is one process that exists in educational institutions such as schools that are useful for screening prospective students who are selected according to the criteria determined by the school to become students. In general, the process of admitting new students is done through the stages of registration, selection tests and announcements of new student admissions. So far, Kosgoro Vocational School has registered new student participants in a manual and not computerized way, causing time constraints and more costs for prospective students. In developing this application using the waterfall method includes the stages of determining software requirements, program design and testing of applications that have been made, In terms of making this website using Codeigniter and PHP so that the web display is more attractive and more efficient in accepting new students and providing convenience to prospective students to be able to register online and administrative staff can use it and see the latest developments in new student registration.&#x0D; &#x0D; Keywords: New student registration, website, registration system
Vectorial shearing cascaded Sagnac interferometric wavefront sensor

We propose a highly stable and single-shot wavefront sensor using vectorial shearing interferometry. We design it using a pair of cascaded Sagnac interferometers that allow spatial frequency multiplexing in the interferogram in order to detect the vector gradients simultaneously and independently. The scheme avoids wavelength dispersion in sheared copies as compared to the vectorial shearing interferometer schemes based on diffractive optical elements. Capability to tune the amount of shear merely through the axial displacement of the imaging sensor, and with the added advantage of adjustable resolution controlled by the spatial frequency introduced, the near common-path geometry opens up its applications in optical testing even in a vibration sensitive environment.
Multi-branch Input Structure for Pyramid Scene Parsing Network

Deep convolutional neural networks have been widely researched and have made outstanding achievements in the field of target recognition and image segmentation in recent years. In this paper, we propose a multi-branch input method for aggregating feature information at the early convolutional layer. At present, the popular neural network model uses single branch down-sampling to obtain feature maps, while we built four-branches structure with different dimension channels to segment the original image. Then, in the fusion unit, four feature maps are fused point by point and transmitted to the next convolution layer. Experiments show that the multi-branch input structure mentioned above can improves the system performance and saves the training time. In the experiment, we use the ADE20K dataset and the Cityscapes dataset, both of which are considered as high quality semantically segmented datasets.
Generalised Gradient Vector Flow for Content-Aware Image Resizing

Image retargeting is devoted to preserve the visual content of images with a proper resizing, removing vertical and/or horizontal paths of pixels which contain low semantic information. In this paper, a method based on the Generalised Gradient Vector Flow (GGVF) is presented. The GGVF formulation allows the balancing of the smoothing term and data term of the flow by proper parameter tuning. The proposed approach has been tested by considering a data set of 1000 images and varying the percentage of resizing from 10% to 50% and for different values of the aim involved parameter K. Results show that our algorithm better preserves the important information compared to GVF and Seam Carving approaches. Preliminary results show an underlying relation between parameter K and the percentage of resizing has been also exploited.
DIVERSIFYING THE PREDICTIONS IN THE RECOMMENDER SYSTEMS

In pursuance of building a recommender system, the existing collaborative filtering model often fails to provide a diversified list of recommendation to the end user. Most of the existing models target accuracy and thus fails in avoiding the uniformity dullness from the recommended list. In our paper, we have made use of imputation technique to cut-off sparsity and employed graph-based algorithm to generate a diversified list of recommendations in order to prevent the aforementioned problem of over specialisation. A substantial coverage evaluation on MovieLens dataset demonstrates the fruitfulness of our proposed graph-based model.
Workshop pembuatan perangkat pengajaran Kurikulum 2013 dan penguatan konsep dasar Matematika pada Kelompok Kerja Guru SD

The Curriculum of 2013 (K-13) requires the teaching for every subject at an integrated level, known as thematic teaching, which means that on one theme may link several subjects to that particular subject. After evaluating the implementation of K-13, the revised edition provides exceptions for teaching mathematics. The main problem faced by Math teachers is their understanding of basic concepts in mathematics still low, there are still various materials that they do not know and they often difficult to solve particular math problems. This program aims to help upper class teachers in the 7th cluster of Ujung district in Pare-Pare in understanding the basic concepts of Mathematics, helping to solve problems that are considered difficult and Math modules that are difficult to teach. This program is an intensive workshop for teaching Math. The method used in this workshop is interactive coaching. The trainer and the participants are working together to solve Math problems and the participants also equipped with independent tasks which indicate their progress in knowledge and understanding of basic concept of Mathematics.
The Effectiveness of Reading for Pleasure on Students’ Vocabulary Acquisition (Quasi Experimental Research at the Seventh Grade of MTsN 1 Pandeglang)

Fitri Febriani. 152301902. 2019.The Effectiveness of Reading for Pleasure onStudents’ Vocabulary Acquisition (Quasi Experimental Research at the SeventhGrade of MTsN 1 Pandeglang).This research investigates the effectiveness of reading for pleasure in vocabularyacquisition. This research was to find out whether teaching vocabulary by usingreading for pleasure activity can increase students’ vocabulary acquisition. Thisresearch used one group pre-test and post-test design in quantitative researchmethod. The instrument used in this research was tests (pre-test and post-test). Thisresearch involves 66 students as sample among regular seventh grade of MTsN 1Pandeglang in academic year 2018/ 2019. The last result showed that t-observation“to” that the writer gor from the calculation is higher than t-table “tt” both at thedegree of significant 5% and 1 %. Significant 5% to = 3,71 and tt = 1,99 that meansto > tt or 3,71 > 1,99 and significant 1% to = 3,71 and tt = 2,65 that means to > tt or3,71 > 2,65. In addition, the result of the observation showed that the students moremotivated in learning English, especially in vocabulary by using reading forpleasure activity. Based on the result above, it can be concluded that reading forpleasure activity effectively can be used in teaching vocabulary acquisition. Theresult of this research shows that students’ vocabulary acquisition by using readingfor pleasure activity achieve better score than those who do not use it.Key words: vocabulary acquisition, reading for pleasure. quantitative research
Setting Up Your Environment

Virtual environment or virtualization is not mandatory for the experienced ethical hacker. As an experienced ethical hacker, you can run Kali Linux as your main system and do the hacking using mainly a terminal with the help of a programming language such as Python, or you can use selected tools like Metasploit. However, for beginners, virtualization is compulsory.
Distance-Preserving Graph Embeddings from Random Neural Features.

We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks. The embedding naturally deals with graph isomorphism and preserves the metric structure of the graph domain, in probability. In addition to being an explicit embedding method, it also allows us to efficiently and effectively approximate graph metric distances (as well as complete kernel functions); a criterion to select the embedding dimension trading off the approximation accuracy with the computational cost is also provided. GRNF can be used within traditional processing methods or as a training-free input layer of a graph neural network. The theoretical guarantees that accompany GRNF ensure that the considered graph distance is metric, hence allowing to distinguish any pair of non-isomorphic graphs.
IML - An Image Manipulation Language

Several image manipulation tools support the use of at least one general scripting language (e.g., Python, JavaScript), for task automation. But, users of such tools usually do not have much experience or skill with these (or often any) programming languages, which represents a barrier for the use of such languages when automating a task. With this in mind, we present IML, a work-in-progress, domain-specific language designed for easy and clear image manipulation. Besides describing the basic constructs and operations of this language, we compared a simple IML program with equivalent implementations in the languages currently supported by the popular image manipulation tool GIMP. This illustrates how IML might make the image editing automation process simpler, easier to learn, and more straightforward.
Neural Rule Grounding for Low-Resource Relation Extraction.

While deep neural models have gained successes on information extraction tasks, they become less reliable when the amount of labeled data is limited. In this paper, we study relation extraction (RE) under low-resource setting, where only some (hand-built) labeling rules are provided for learning a neural model over a large, unlabeled corpus. To overcome the low-coverage issue of current bootstrapping methods (i.e., hard grounding of rules), we propose a Neural Rule Grounding (REGD) framework for jointly learning a relation extraction module (with flexible neural architecture) and a sentence-rule soft matching module. The soft matching module extends the coverage of rules on semantically similar instances and augments the learning on unlabeled corpus. Experiments on two public datasets demonstrate the effectiveness of REGD when compared with both rule-based and semi-supervised baselines. Additionally, the learned soft matching module is able to predict on new relations with unseen rules, and can provide interpretation on matching results.
Cluster based convolution process on Big Data in Privacy preserving datamining

The main intension of this paper is to privacy preserving-aware over big data in clouds using KNN and MapReduce framework. This paper consists of three phases such as, MapReduce phase, clustering the map reduced data and evaluation phase. In MapReduce phase, we are splitting the input data after the splitting process we are including a k-means clustering algorithm to cluster the map reduced data. Then, we are performing a convolution process to the dataset and create a new matrix. Once it is over, the privacy-persevering framework over big data in cloud systems is performed based on the evaluation base. In evaluation module, deduplication is performed with the aid of the KNN algorithm. In this phase using the KNN technique to check the duplication of data based on the threshold. Thus the non-duplicated data's are stored in cloud database, which is improving the utility of the privacy data.
Recognition of Human Activities in Daubechies Complex Wavelet Domain

Recognition of accurate human activities is a challenging research problem in video surveillance problem of computer vision research. The task of recognizing activities of human from video sequence exhibits more challenges because of real time processing of data. In this paper, we have proposed a method for recognition of human activities based on Daubechies complex wavelet transform (DCxWT). Better edge representation and approximate shift invariant properties of DCxWT over the other real valued wavelet transform motivates us to utilize properties of DCxWT in recognition of human activities. The multi-class SVM is used for classifying the recognized human activities. The proposed method is compared with other state-of-the-art method, on various standard publicly available dataset, in terms of different quantitative performance measures. We found that the proposed method has better recognition accuracy in comparison to other state-of-the-art methods.
Look and Modify: Modification Networks for Image Captioning

Attention-based neural encoder-decoder frameworks have been widely used for image captioning. Many of these frameworks deploy their full focus on generating the caption from scratch by relying solely on the image features or the object detection regional features. In this paper, we introduce a novel framework that learns to existing captions from a given framework by modeling the residual information, where at each timestep the model learns to keep, remove or add to the existing caption allowing the model to fully focus on what to modify rather than on what to predict. We evaluate our method on the COCO dataset, trained on top of several image captioning frameworks and show that our model successfully modifies captions yielding better ones with better evaluation scores.