Reviewerly/exHarmony huggingface dataset:
VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search

Numerous online stock image libraries offer high quality yet copyright free images for use in marketing campaigns. To assist advertisers in navigating such third party libraries, we study the problem of automatically fetching relevant ad images given the ad text (via a short textual query for images). Motivated by our observations in logged data on ad image search queries (given ad text), we formulate a keyword extraction problem, where a keyword extracted from the ad text (or its augmented version) serves as the ad image query. In this context, we propose VisualTextRank: an unsupervised method to (i) augment input ad text using semantically similar ads, and (ii) extract the image query from the augmented ad text. VisualTextRank builds on prior work on graph based context extraction (biased TextRank in particular) by leveraging both the text and image of similar ads for better keyword extraction, and using advertiser category specific biasing with sentence-BERT embeddings. Using data collected from the Verizon Media Native (Yahoo Gemini) ad platform's stock image search feature for onboarding advertisers, we demonstrate the superiority of VisualTextRank compared to competitive keyword extraction baselines (including an 11% accuracy lift over biased TextRank). For the case when the stock image library is restricted to English queries, we show the effectiveness of VisualTextRank on multilingual ads (translated to English) while leveraging semantically similar English ads. Online tests with a simplified version of VisualTextRank led to a 28.7% increase in the usage of stock image search, and a 41.6% increase in the advertiser onboarding rate in the Verizon Media Native ad platform.
Estimating pose from pressure data for smart beds with deep image-based pose estimators

In-bed pose estimation has shown value in fields such as hospital patient monitoring, sleep studies, and smart homes. In this paper, we explore different strategies for detecting body pose from highly ambiguous pressure data, with the aid of pre-existing pose estimators. We examine the performance of pre-trained pose estimators by using them either directly or by re-training them on two pressure datasets. We also explore other strategies utilizing a learnable pre-processing domain adaptation step, which transforms the vague pressure maps to a representation closer to the expected input space of common purpose pose estimation modules. Accordingly, we used a fully convolutional network with multiple scales to provide the pose-specific characteristics of the pressure maps to the pre-trained pose estimation module. Our complete analysis of different approaches shows that the combination of learnable pre-processing module along with re-training pre-existing image-based pose estimators on the pressure data is able to overcome issues such as highly vague pressure points to achieve very high pose estimation accuracy.
FRaGenLP: A Generator of Random Linear Programming Problems for Cluster Computing Systems

The article presents and evaluates a scalable FRaGenLP algorithm for generating random linear programming problems of large dimension n on cluster computing systems. To ensure the consistency of the problem and the boundedness of the feasible region, the constraint system includes $$2n+1$$ standard inequalities, called support inequalities. New random inequalities are generated and added to the system in a manner that ensures the consistency of the constraints. Furthermore, the algorithm uses two likeness metrics to prevent the addition of a new random inequality that is similar to one already present in the constraint system. The algorithm also rejects random inequalities that cannot affect the solution of the linear programming problem bounded by the support inequalities. The parallel implementation of the FRaGenLP algorithm is performed in C++ through the parallel BSF-skeleton, which encapsulates all aspects related to the MPI-based parallelization of the program. We provide the results of large-scale computational experiments on a cluster computing system to study the scalability of the FRaGenLP algorithm.
Smart integration of sensors, computer vision and knowledge representation for intelligent monitoring and verbal human-computer interaction

The details presented in this article revolve around a sophisticated monitoring framework equipped with knowledge representation and computer vision capabilities, that aims to provide innovative solutions and support services in the healthcare sector, with a focus on clinical and non-clinical rehabilitation and care environments for people with mobility problems. In contemporary pervasive systems most modern virtual agents have specific reactions when interacting with humans and usually lack extended dialogue and cognitive competences. The presented tool aims to provide natural human-computer multi-modal interaction via exploitation of state-of-the-art technologies in computer vision, speech recognition and synthesis, knowledge representation, sensor data analysis, and by leveraging prior clinical knowledge and patient history through an intelligent, ontology-driven, dialogue manager with reasoning capabilities, which can also access a web search and retrieval engine module. The framework's main contribution lies in its versatility to combine different technologies, while its inherent capability to monitor patient behaviour allows doctors and caregivers to spend less time collecting patient-related information and focus on healthcare. Moreover, by capitalising on voice, sensor and camera data, it may bolster patients' confidence levels and encourage them to naturally interact with the virtual agent, drastically improving their moral during a recuperation process.
Research on the Application of Computer Multimedia Technology in English Education and Teaching

Abstract Under the background of world multi-polarization, economic globalization, and education internationalization, the world is changing rapidly, from which computer multimedia technology stands out. With the vigorous advancement of educational information, the process of vigorously advocating innovative education, the modern educational technology In particular, the widespread use of computer multimedia in English classrooms has become a necessity, bringing a qualitative leap in English teaching. Respectively from listening, speaking, reading and writing, the visualization of the in-depth study of English teaching. The computer can process text, sound, and images. Therefore, it is not difficult to form a teaching system with both audio, video, pictures and texts. This not only breaks the traditional teaching methods, but also conforms to the students’ cognitive laws and promotes the development of students’ diversified thinking. Multimedia teaching, as well as vivid, intuitive, visual, novel, large capacity, more information, and fast efficiency, has effectively improved the quality and effect of classroom teaching, and has been favored by teachers. Multimedia-assisted teaching has increasingly become a major teaching method. means. Therefore, the importance of computer multimedia technology in English teaching application research is obvious [1].
Setting the Scope for a New Agile Assessment Model: Results of an Empirical Study

Abstract Agile software development methods have been increasingly adopted by many organizations at different organizational levels. Whether named agile adoption, agile transition, agile transformation, digital transformation or new ways of working, the success of embracing this change process mostly remains uncertain. This is primarily because there are many ways of evaluating success. Based on the existing agile assessment models, we developed a model of principles with associated practice clusters that serves as a core for a new agile assessment model that is capable of assessing agile organizations at different scale. Towards our ultimate goal to establish a lightweight, context-sensitive agile maturity model, we validated our initial findings in an expert interview study to identify improvement points, and ensure the at hand model’s applicability, coherence and relevance. The results of the interview study show that the structure as well as the content of our assessment model fits with the experts’ expectations and experience.
Practical and Private (Deep) Learning without Sampling or Shuffling

We consider training models with differential privacy (DP) using mini-batch gradients. The existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD), requires privacy amplification by sampling or shuffling to obtain the best privacy/accuracy/computation trade-offs. Unfortunately, the precise requirements on exact sampling and shuffling can be hard to obtain in important practical scenarios, particularly federated learning (FL). We design and analyze a DP variant of Follow-The-Regularized-Leader (DP-FTRL) that compares favorably (both theoretically and empirically) to amplified DP-SGD, while allowing for much more flexible data access patterns. DP-FTRL does not use any form of privacy amplification. The code is available at https://github.com/google-research/federated/tree/master/dp_ftrl and https://github.com/google-research/DP-FTRL .
Discuss the Application Research of Computer Virtual Reality Technology in College Student Management

Abstract With the development of technology, computer virtualization technology has been widely used in all walks of life, and it has greatly promoted the development of various industries. Of course, it also played a great role in promoting education and teaching management. This paper mainly analyzes the specific application of computer virtual technology in teaching management.
Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training

Much recent research has been dedicated to improving the efficiency of training and inference for image classification. This effort has commonly focused on explicitly improving theoretical efficiency, often measured as ImageNet validation accuracy per FLOP. These theoretical savings have, however, proven challenging to achieve in practice, particularly on high-performance training accelerators. In this work, we focus on improving the practical efficiency of the state-of-the-art EfficientNet models on a new class of accelerator, the Graphcore IPU. We do this by extending this family of models in the following ways: (i) generalising depthwise convolutions to group convolutions; (ii) adding proxy-normalized activations to match batch normalization performance with batch-independent statistics; (iii) reducing compute by lowering the training resolution and inexpensively fine-tuning at higher resolution. We find that these three methods improve the practical efficiency for both training and inference. Code available at https://github.com/graphcore/graphcore-research/tree/main/Making_EfficientNet_More_Efficient .
SumMerge

Deep Neural Network (DNN) inference efficiency is a key concern across the myriad of domains now relying on Deep Learning. A recent promising direction to speed-up inference is to exploit \emph{weight repetition}. The key observation is that due to DNN quantization schemes---which attempt to reduce DNN storage requirements by reducing the number of bits needed to represent each weight---the same weight is bound to repeat many times within and across filters. This enables a weight-repetition aware inference kernel to factorize and memoize out common sub-computations, reducing arithmetic per inference while still maintaining the compression benefits of quantization. Yet, significant challenges remain. For instance, weight repetition introduces significant irregularity in the inference operation and hence (up to this point) has required custom hardware accelerators to derive net benefit.
DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy

In this paper, we consider privacy-preserving update strategies for secure outsourced growing databases. Such databases allow appendonly data updates on the outsourced data structure while analysis is ongoing. Despite a plethora of solutions to securely outsource database computation, existing techniques do not consider the information that can be leaked via update patterns. To address this problem, we design a novel secure outsourced database framework for growing data, DP-Sync, which interoperate with a large class of existing encrypted databases and supports efficient updates while providing differentially-private guarantees for any single update. We demonstrate DP-Sync's practical feasibility in terms of performance and accuracy with extensive empirical evaluations on real world datasets.
Self-Actualization Level and Adaptation Potential as Personality Coping Resources at the Stage of Self-Realization

The article raises a problem of strengthening personal resources when facing difficult life situations at the stage of self-realization. The latter refers to the period of personality develop-ment from the age of 20 to 55. At this stage, the adaptation potential, the level of self-actualization; and proactive coping behavior are highlighted as personality resources. Proac-tive coping behavior is considered to be an important factor in the personality’s self-efficacy, while the other two constructs are its predictors. Proactive coping is revealed as a set of strate-gies that launches preparatory work to prevent stress exposure, the purposeful development of which is an independent copying resource. Presumably, at the stage of self-realization, a person seeks self-actualization, the maxi-mum manifestation of his abilities, based on existing experience in opposing adverse envi-ronmental influences. The study involved 155 people aged from 21 to 55. The main method of data collection is psychological testing, implemented via the following methodological com-plex: Self-actualization test (E. Shostrom, adapted by Yu.E. Aleshina, L.Ya. Gozman, M.V. Zagika, M.V. Kroz); Losses and acquisitions of personal resources (N. Vodopyanova, M. Stein); Proactive coping behavior (L. Aspinwall, R. Schwarzer and S. Taubert, adapted by E. Starchenkova). Statistical data processing method is the regression analysis. The results of the regression analysis proved the influence of self-actualization and adaptive potential on the proactive coping behavior of an individual at the stage of self-realization. The choice of each strategy is determined by predictors (from 2 to 6). The greatest contribution belongs to the indicators of self-actualization scales (9 out of 14). Value orientations, spontaneity, synergy, cognitive needs and creativity stayed outside the influence on proactive coping. The connec-tions between the proactive coping strategies and the components of self-actualization are contradictory and ambiguous (most of them - 14 out of 22 - have the opposite character). The explanation is largely determined by the traits of the collectivist culture, the values of which are shared by adult members of the society. Adaptation potential was revealed to have a con-nection only in relation to the strategy of preventive coping. Self-esteem, sociability and the ability to find the optimal balance in the system of “internal-external support” should be attributed to the key predictors of proactive coping at the stage of self-realization.
Quantum crosstalk cancellation for fast entangling gates and improved multi-qubit performance

Quantum computers built with superconducting artificial atoms already stretch the limits of their classical counterparts. While the lowest energy states of these artificial atoms serve as the qubit basis, the higher levels are responsible for both a host of attractive gate schemes as well as generating undesired interactions. In particular, when coupling these atoms to generate entanglement, the higher levels cause shifts in the computational levels that leads to unwanted $ZZ$ quantum crosstalk. Here, we present a novel technique to manipulate the energy levels and mitigate this crosstalk via a simultaneous AC Stark effect on coupled qubits. This breaks a fundamental deadlock between qubit-qubit coupling and crosstalk, leading to a 90ns CNOT with a gate error of (0.19 $\pm$ 0.02) $\%$ and the demonstration of a novel CZ gate with fixed-coupling single-junction transmon qubits. Furthermore, we show a definitive improvement in circuit performance with crosstalk cancellation over seven qubits, demonstrating the scalability of the technique. This work paves the way for superconducting hardware with faster gates and greatly improved multi-qubit circuit fidelities.
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models

Recent work showed that there could be a large gap between the classical uniform convergence bound and the actual test error of zero-training-error predictors (interpolators) such as deep neural networks. To better understand this gap, we study the uniform convergence in the nonlinear random feature model and perform a precise theoretical analysis on how uniform convergence depends on the sample size and the number of parameters. We derive and prove analytical expressions for three quantities in this model: 1) classical uniform convergence over norm balls, 2) uniform convergence over interpolators in the norm ball (recently proposed by Zhou et al. (2020)), and 3) the risk of minimum norm interpolator. We show that, in the setting where the classical uniform convergence bound is vacuous (diverges to $\infty$), uniform convergence over the interpolators still gives a non-trivial bound of the test error of interpolating solutions. We also showcase a different setting where classical uniform convergence bound is non-vacuous, but uniform convergence over interpolators can give an improved sample complexity guarantee. Our result provides a first exact comparison between the test errors and uniform convergence bounds for interpolators beyond simple linear models.
Meningkatkan Keterampilan Menulis Deskripsi dengan Model Picture and Picture di SDN 3 Bangkleyan Kabupaten Blora

The purpose of this study was to improve teacher skills, increase student activity, and improve student learning outcomes in writing descriptions through the application of the Picture and Picture model. This classroom action research was carried out in 3 cycles, each cycle consisting of 2 meetings. The subjects of this study were the teacher and 18 second grade students of SDN 3 Bangkleyan. The research data in the form of quantitative data and qualitative data were analyzed using descriptive analysis techniques. From the results of the study it can be concluded that: (1) the skills of teachers in the first cycle got an average score of 29 with good criteria and in the second cycle got an average score of 31 with good criteria, the third cycle got an average score of 35 with very good criteria. (2) student activities in cycle I obtained an average score of 24 with sufficient criteria, in cycle II obtained an average score of 27 with good criteria, and in cycle III obtained an average score of 30 with good criteria. (3) student learning outcomes in the form of descriptive writing skills at the end of the first cycle obtained an average value of 65.5 and classical learning completeness 50%, in the second cycle an average value of 69 and classical learning completeness of 69.4% cycle III got an average score of 79 and classical learning completeness of 83.3%.
Concerto: Dynamic Processor Scaling for Distributed Data Systems with Replication

A surge of interest in data-intensive computing has led to a drastic increase in the demand for data centers. Given this growing popularity, data centers are becoming a primary contributor to the increased consumption of energy worldwide. To mitigate this problem, this paper revisits DVFS (Dynamic Voltage Frequency Scaling), a well-known technique to reduce the energy usage of processors, from the viewpoint of distributed systems. Distributed data systems typically adopt a replication facility to provide high availability and short latency. In this type of architecture, the replicas are maintained in an asynchronous manner, while the master synchronously operates via user requests. Based on this relaxation constraint of replica, we present a novel DVFS technique called Concerto, which intentionally scales down the frequency of processors operating for the replicas. This mechanism can achieve considerable energy savings without an increase in the user-perceived latency. We implemented Concerto on Redis 6.0.1, a commercial-level distributed key-value store, demonstrating that all associated performance issues were resolved. To prevent a delay in read queries assigned to the replicas, we offload the independent part of the read operation to the fast-running thread. We also empirically demonstrate that the decreased performance of the replica does not cause an increase of the replication lag because the inherent load unbalance between the master and replica hides the increased latency of the replica. Performance evaluations with micro and real-world benchmarks show that Redis saves 32% on average and up to 51% of energy with Concerto under various workloads, with minor performance losses in the replicas. Despite numerous studies of the energy saving in data centers, to the best of our best knowledge, Concerto is the first approach that considers clock-speed scaling at the aggregate level, exploiting heterogeneous performance constraints across data nodes.
TEACHERS' CITIZENSHIP BEHAVIOR AS A PREDICTOR OF TEACHING PERFORMANCE: EVIDENCE FROM PAKISTANI CONTEXT

Purpose of the study: The current study investigated the correlation between teachers' organizational citizenship behavior and teaching performance at the primary level in the Lahore division of Punjab province, Pakistan.&#x0D; Methodology: Researchers structured the descriptive and quantitative research, directed the positivistic paradigm, and obeyed the correlational design. The sample was selected on a random sampling technique named multi-stage sampling. Two hundred ninety subjects comprising male and female were selected from primary school. Two closed-ended tools were applied to collect the data. Both research instruments were pilot tested to ensure validity and reliability. Inferential statistical analysis techniques such as Pearson r, regression analysis, independent t-test, and one-way ANOVA were run for data analysis.&#x0D; Principal Findings: The results revealed a moderate statistically significant positive correlation between teachers' organizational citizenship behavior and teaching performance.&#x0D; Applications of this study: The current study is helpful in how teaching performance can be enhanced at the primary level in the Lahore division of Punjab province, Pakistan.&#x0D; Novelty/Originality of this study: for the first time, the association between organizational citizenship behavior and teaching performance at the primary level is examined in an emerging country like Pakistan.