Reviewerly/exHarmony huggingface dataset:
Recommendation Based on Users’ Long-Term and Short-Term Interests with Attention

Recommendations based on user behavior sequences are becoming more and more common. Some studies consider user behavior sequences as interests directly, ignoring the mining and representation of implicit features. However, user behaviors contain a lot of information, such as consumption habits and dynamic preferences. In order to better locate user interests, this paper proposes a Bi-GRU neural network with attention to model user’s long-term historical preferences and short-term consumption motivations. First, a Bi-GRU network is established to solve the long-term dependence problem in sequences, and attention mechanism is introduced to capture user interest changes related to the target item. Then, user’s short-term interaction trajectory based on self-attention is modeled to distinguish the importance of each potential feature. Finally, combined with long-term and short-term interests, the next behavior is predicted. We conducted extensive experiments on Amazon and MovieLens datasets. The experimental results demonstrate that the proposed model outperforms current state-of-the-art models in Recall and NDCG indicators. Especially in MovieLens dataset, compared with other RNN-based models, our proposed model improved at least 2.32% at Recall@20, which verifies the effectiveness of modeling long-term and short-term interest of users, respectively.
DEVELOPMENT OF INFORMATION CULTURE OF STUDENTS OF HUMANITARIAN SPECIALITIES

Issues related to information culture evoke a natural interest of Ukrainian and foreign researchers. In addition to purely technical aspects and the development of competency characteristics, the problem of the development of students’ information culture and their personal characteristics is acquiring particular relevance. Virtual space has significant advantages in terms of the speed of information distribution, communication opportunities, and the exchange of scientific data. At the same time, threats are evident, in particular the growth of cybercrime, aggression, dependence, and manipulation. This is particularly obvious in the youth environment. The creation of national elite in modern universities, the openness of the information space, globalization, and the growth of competition in the labor market require operational information for specification of educational policy and adoption of optimal managerial decisions. It becomes increasingly clear that there is a contradiction between the low level of information and analytical skills of students and the high level of requirements for the organization of work with computer technology; sufficient level of theoretical and practical knowledge and low level of skills necessary for work with information technologies; sufficient level of professional training and low level of readiness to use computer technologies in educational activities. Information culture is considered as one of the facets of the universal culture associated with the social nature of man and is the product of its various creative abilities. The need for original specialized courses, which allow quick response to new challenges and improve the quality of professional training of students is constantly increasing. The article outlines the preconditions for improving students' information culture in view of the experience of foreign countries and Ukraine.
Analisis Kemampuan Literasi Sains Siswa SMP pada Materi Sirkulasi Darah

Science literacy is the ability to use scientific knowledge to identify questions, explain scientific phenomena that occur using contextual facts. The circulation theme is one of the themes of contextual learning that is very closely related to everyday life. This study aims to determine the scientific literacy skills of students in blood circulation material at the junior high school level. The research method used was descriptive qualitative with scientific literacy instruments in the form of tests with research subjects 38 students of SMP N 4 Belik class VIIID academic year 2018/2019. The results of this study indicate that students' literacy skills in the category are low. This can be seen from the aspect of identifying valid scientific opinions (58%), understanding the design elements of the research findings (24%), being able to solve questions based on scientific phenomena (32%), understanding and interpreting basic statistics (21%) and students able to make inferences, predictions, and draw conclusions based on data (39%). The value obtained by students is divided into four categories of values obtained by students, which are very low, low, sufficient, and high based on the results of research, the average literacy skills of students in the low category. The conclusion of this study is that the science literacy skills of junior high school students are low on aspects of understanding and interpreting basic statistics. Keywords: Science Literacy, Contextual, Blood Circulation
Antonym-Synonym Classification Based on New Sub-Space Embeddings

Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed from the distilled sub-spaces along with some word level features to distinguish antonyms from synonyms. Experimental results show that the proposed model outperforms existing research on antonym synonym distinction in both speed and performance.
Recurrent neural networks with controlled elements in restoring frame flows

Introduction: Various interfering influences raise pressing problems of promptly restoring the flow of distorted frames,remembering about the background and dynamics of the event measurement laws. The traditional methods of recovering flows ofdistorted frames do not fully take into account the peculiarities of this process. Purpose: Exploring the possibilities of recurrent neuralnetworks with controlled elements for restoring frame flows. Results: It is proposed to evaluate the potential of a recurrent neuralnetwork with controlled elements by the number of successful options for restoring a distorted sequence of frames. Evaluation of thecapabilities of such neural networks according to the introduced indicator showed their strong dependence on the type of networkstructure and settings. Recurrent neural networks with spiral structures of layers work better. As the number of the turns in the helixgrows, the network capabilities also grow. Enhancing the capacity of a network to restore distorted frame flows is feasible if we replaceunipolar functions of the synapse weights by bipolar ones. A significant increase in the capabilities of the neural networks under studyis possible by controlling the neuron excitation thresholds in order to provide sequential rather than parallel elimination of variouserrors. In contrast to the conventional neural networks, recurrent neural networks with controlled elements can adapt to changes in№ 5, 2019 ИНФОРМАЦИОННОУПРАВЛЯЮЩИЕ СИСТЕМЫ 17ОБРАБОТКА ИНФОРМАЦИИ И УПРАВЛЕНИЕthe laws inherent in frame flows, and implement controlled associative signal processing. Experiments have shown that these neuralnetworks can use associative connections for taking into account deep current experience in signal processing, and be successfully usedfor restoring distorted frame flows.
Efficient Semantic Segmentation Using Spatio-Channel Dilated Convolutions

There has been an increasing interest in reducing the computational cost to develop efficient deep convolutional neural networks (DCNN) for real-time semantic segmentation.In this paper, we introduce an efficient convolution method, Spatio-Channel dilated convolution (SCDC) which is composed of structured sparse kernels based on the principle of split-transform-merge.Specifically, it employs the kernels whose shapes are dilated, not only in spatial domain, but also in channel domain, using a channel sampling approach.Based on SCDC, we propose an efficient convolutional module named Efficient Spatio-Channel dilated convolution (ESC).With ESC modules, we further propose ESCNet based on ESPNet architecture which is one of the state-of-the-art real-time semantic segmentation network that can be easily deployed on edge devices.We evaluated our ESCNet on the Cityscapes dataset and obtained competitive results, with a good trade-off between accuracy and computational cost.The proposed ESCNet achieves 61.5 % mean intersection over union (IoU) with only 196 K network parameters, and processes high resolution images at a rate of 164 frames per second (FPS) on a standard Titan Xp GPU.Various experimental results show that our method is reasonably accurate, light, and fast.
Market2Dish

In order to help people develop healthy eating habits, we present a personalized health-aware food recommendation system, calledMarket2Dish. Market2Dish could recognize the ingredients in the micro-videos taken from the market, characterize the health conditions of users from their social media accounts, and ultimately recommend users with the personalized healthy foods. Specifically, we employ a word-class interaction based text classification model to learn the fine-grained similarity between sparse health features on the social media platforms and pre-defined health concepts, and then a category-aware hierarchical memory network based recommender is introduced to learn the user-recipe interactions for better food recommendations. Moreover, we demonstrate this system as an online app for real-time interactions with users.
Implementation of AES using biometric

Mobile Adhoc network is the most advanced emerging technology in the field of wireless communication. MANETs mainly have the capacity of self-forming, self-healing, enabling peer to peer communication between the nodes, without relying on any centralized network architecture. MANETs are made applicable mainly to military applications, rescue operations and home networking. Practically, MANET could be attacked by several ways using multiple methods. Research on MANET emphasizes on data security issues, as the Adhoc network does not befit security mechanism associated with static networks. This paper focuses mainly on data security techniques incorporated in MANET. Also this paper proposes an implementation of Advanced Encryption Standard using biometric key for MANETs. AES implementation includes, the design of most robust Substitution-Box implementation which defines a nonlinear behavior and mitigates malicious attacks, with an extended security definition. The key for AES is generated using most reliable, robust and precise biometric processing. In this paper, the input message is encrypted by AES powered by secured nonlinear S-box using finger print biometric feature and is decrypted using the reverse process.
Platform Urbanism, Creativity, and the New Educational Futurism

Abstract This essay examines speculative narratives reflective of Silicon Valley and corporate technology culture that project creative scenarios and sociotechnical futures for cities and citizens, whereby learning and creativity become global imperatives to defer future risk within a new digital urbanism. Taking inspiration from Fredric Jameson's transhistorical dialectics, Alexander J. Means assembles a cognitive map of emerging sociotechnical projections of urbanity and education through three frames — solutionism, collaborationism, and techno‐realism — linking each to specific conceptualizations of creativity and learning. Means argues that these projections are subsumed within an ontology of the present, and they therefore fail to index the complexities that drive historical and technological change, particularly the deep structure of power and conflict immanent to late capitalism as a world‐historical system. Such an ontology is ultimately rooted in imaginative displacement, a futurism without futurity, where time is rendered a static feedback loop of present systems and processes.
Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY

Convergence between high-performance computing (HPC) and big data analytics (BDA) is currently an established research area that has spawned new opportunities for unifying the platform layer and data abstractions in these ecosystems. This work presents an architectural model that enables the interoperability of established BDA and HPC execution models, reflecting the key design features that interest both the HPC and BDA communities, and including an abstract data collection and operational model that generates a unified interface for hybrid applications. This architecture can be implemented in different ways depending on the process- and data-centric platforms of choice and the mechanisms put in place to effectively meet the requirements of the architecture. The Spark-DIY platform is introduced in the paper as a prototype implementation of the architecture proposed. It preserves the interfaces and execution environment of the popular BDA platform Apache Spark, making it compatible with any Spark-based application and tool, while providing efficient communication and kernel execution via DIY, a powerful communication pattern library built on top of MPI. Later, Spark-DIY is analyzed in terms of performance by building a representative use case from the hydrogeology domain, EnKF-HGS. This application is a clear example of how current HPC simulations are evolving toward hybrid HPC-BDA applications, integrating HPC simulations within a BDA environment.
Memory Disaggregation: Research Problems and Opportunities

Memory usage imbalance has been consistently observed in many virtualized Clouds and production datacenters. Such temporal memory utilization variance is a major root cause for excessive paging and thrashing on virtual servers even though there are sufficient idle memory on the same node or in the Cloud cluster. Memory disaggregation is an emerging research and development endeavor towards addressing these memory usage imbalance problems. This paper first defines and characterizes the concept of memory disaggregation, and discusses the demands and challenges of efficient memory disaggregation in cloud datacenters. It then examines some promising research issues, design choices and directions to overcome some of the challenges posed by memory disaggregation. Specifically, it proposes two major new research challenges and solution directions for enabling elastic, on-demand disaggregated memory orchestration: (1) virtual server memory and node level memory co-design and (2) local memory and remote memory co-design. A brief description of two ongoing research projects is provided for both solution directions. The paper ends with a brief discussion of other advanced and emerging memory and storage technologies and potential opportunities for memory disaggregation.
Optimizing Joins in a Map-Reduce for Data Storage and Retrieval Performance Analysis of Query Processing in HDFS for Big Data

Big Data challenges prompting by the fast sending of investigation of information internationally.New excellent presentation systems are presently required to process a regularly expanding volume of information from informational collections.With information immovably close by and with the capacity given by Big Data Technologies to viably store and examine this information, we can discover answers to these inquiries and work to enhance each part of our conduct.The Hadoop structure is an open-source execution of the Map Reduce (MR) figuring model that is picking up force for Big Data investigation in smart matrix applications.For the most part, different DB2 languages turn into a critical application territory for Hadoop by utilizing Query handling.The outcomes demonstrate a hard belief on the measure of reducers and IO execution of the group, which describes the regular assessment that MR is IO-bound.These outcomes can look at the execution conduct of various languages and fill in as a reason for understanding the impact of design parameters on the last execution.
RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation

Following navigation instructions in natural language requires a composition of language, action, and knowledge of the environment.Knowledge of the environment may be provided via visual sensors or as a symbolic world representation referred to as a map.Here we introduce the Realistic Urban Navigation (RUN) task, aimed at interpreting navigation instructions based on a real, dense, urban map.Using Amazon Mechanical Turk, we collected a dataset of 2515 instructions aligned with actual routes over three regions of Manhattan.We propose a strong baseline for the task and empirically investigate which aspects of the neural architecture are important for the RUN success.Our results empirically show that entity abstraction, attention over words and worlds, and a constantly updating world-state, significantly contribute to task accuracy.
Thin and Thick Cloud Removal on Remote Sensing Image by Conditional Generative Adversarial Network

Cloud removal is an essential step to enhance the quality of cloud-covered remote sensing image. In recent years, conditional Generative Adversarial Network (cGAN) yields promising improvement in plentiful image-to-image translation tasks. In this paper, we propose a novel objective function to upgrade the structural similarity index based on cGAN. We discover that ImageGAN is effective to focus on global information for thick cloud-covered images and Patch-GAN has fewer parameters while maintaining outstanding performance for thin cloud-covered remote sensing images in the experiments. Experimental results demonstrate that our method achieves remarkable performance in both PSNR, SSIM and visual effect on cloud-covered remote sensing images especially thin cloud-covered images.
Scalable Knowledge Graph Construction from Text Collections

We present a scalable, open-source platform that "distills" a potentially large text collection into a knowledge graph.Our platform takes documents stored in Apache Solr and scales out the Stanford CoreNLP toolkit via Apache Spark integration to extract mentions and relations that are then ingested into the Neo4j graph database.The raw knowledge graph is then enriched with facts extracted from an external knowledge graph.The complete product can be manipulated by various applications using Neo4j's native Cypher query language: We present a subgraph-matching approach to align extracted relations with external facts and show that fact verification, locating textual support for asserted facts, detecting inconsistent and missing facts, and extracting distantly-supervised training data can all be performed within the same framework.
Acorns: A Framework for Accelerating Deep Neural Networks with Input Sparsity

Deep neural networks have been employed in a broad range of applications, including face detection, natural language processing, and autonomous driving. Yet, the neural networks with the capability to tackle real-world problems are intrinsically expensive in computation, hindering the usage of these models. Sparsity in the input data of neural networks provides an optimizing opportunity. However, harnessing the potential performance improvement on modern CPU faces challenges raised by sparse computations of the neural network, such as cache-unfriendly memory accesses and efficient sparse kernel implementation. In this paper, we propose Acorns, a framework to accelerate deep neural networks with input sparsity. In Acorns, sparse input data is organized into our designed sparse data layout, which allows memory-friendly access for kernels in neural networks and opens the door for many performance-critical optimizations. Upon that, Acorns generates efficient sparse kernels for operators in neural networks from kernel templates, which combine directions that express specific optimizing transformations to be performed, and straightforward code that describes the computation. Comprehensive evaluations demonstrate Acorns can outperform state-of-the-art baselines by significant speedups. On the real-world detection task in autonomous driving, Acorns demonstrates 1.8-22.6× performance improvement over baselines. Specifically, the generated programs achieve 1.8-2.4× speedups over Intel MKL-DNN, 3.0-8.8× speedups over TensorFlow, and 11.1-13.2× speedups over Intel MKL-Sparse.
Lexical Quantile-Based Text Complexity Measure

This paper introduces a new approach to estimating the text document complexity.Common readability indices are based on average length of sentences and words.In contrast to these methods, we propose to count the number of rare words occurring abnormally often in the document.We use the reference corpus of texts and the quantile approach in order to determine what words are rare, and what frequencies are abnormal.We construct a general text complexity model, which can be adjusted for the specific task, and introduce two special models.The experimental design is based on a set of thematically similar pairs of Wikipedia articles, labeled using crowdsourcing.The experiments demonstrate the competitiveness of the proposed approach.
Cost-effective Resource Provisioning for Spark Workloads

Spark is one of the prevalent big data analytical platforms. Configuring proper resource provision for Spark jobs is challenging but essential for organizations to save time, achieve high resource utilization, and remain cost-effective. In this paper, we study the challenge of determining the proper parameter values that meet the performance requirements of workloads while minimizing both resource cost and resource utilization time. We propose a simulation-based cost model to predict the performance of jobs accurately. We achieve low-cost training by taking advantage of simulation framework, i.e., Monte Carlo (MC) simulation, which uses a small amount of data and resources to make a reliable prediction for larger datasets and clusters. The salient feature of our method is that it allows us to invest low training cost while obtaining an accurate prediction. Through experiments with six benchmark workloads, we demonstrate that the cost model yields less than 7% error on average prediction accuracy and the recommendation achieves up to 5x resource cost saving.
Representing Movie Characters in Dialogues

We introduce a new embedding model to represent movie characters and their interactions in a dialogue by encoding in the same representation the language used by these characters as well as information about the other participants in the dialogue.We evaluate the performance of these new character embeddings on two tasks: (1) character relatedness, using a dataset we introduce consisting of a dense character interaction matrix for 4,761 unique character pairs over 22 hours of dialogue from eighteen movies; and (2) character relation classification, for fine-and coarse-grained relations, as well as sentiment relations.Our experiments show that our model significantly outperforms the traditional Word2Vec continuous bag-of-words and skip-gram models, demonstrating the effectiveness of the character embeddings we introduce.We further show how these embeddings can be used in conjunction with a visual question answering system to improve over previous results.