Reviewerly/exHarmony huggingface dataset:
Generating description with multi-feature and saliency maps of image

Automatically generating the description of an image is a task that connects computer vision and natural language processing. It has gained more and more attention in the field of artificial intelligence. In this paper, we present a model that generates description for images based on RNN (recurrent neural network) with multi-feature weighted by object attention to represent images. We use LSTM (long short term memory), which is a RNN model, to translate multi-feature of images to text. Most existing methods use single CNN (convolution neural network) trained on ImageNet to extract image features which mainly focuses on objects in images. However, the context in the scene is also informative to image captioning. So we incorporate the scene feature extracted with CNN trained on Places205. We evaluate our model on MSCOCO dataset based on standard metrics. Experiments show that multi-feature performs better than single feature. In addition, the saliency weight on images emphasizes the salient objects in images as the subject in image descriptions. The results show that our model performs better than several state-of-the-art methods on image captioning.
Android application for ringing and accessing device remotely using SMS

Now a day’s android applications are making our life much easier and happier. We always ensure that device that contains all applications should be always with us and secure anytime anywhere. In this journal I developed an android application to find your mobile phone using other devices when you lost it nearby, using text message and unique code generated on both devices. The application is designed in such a way that we can ring phone even when it is on silent mode. For developing this application I proposed a method, I simply divided code modules of Android functions, so that we can able to invoke each module of android device separately to ring, vibrate and other function. Each android module will take the device administrator permission to modify android function using application.
The influence of vocabulary mastery and reading comprehension of argumentation writing skills

This study aims to determine the effect of variable vocabulary and reading comprehension skills of argument essay writing. Method used is the method of survey with a sample of students of 40 students were taken by random sampling technique from Tunas High School Jaka Sampurna in Bekasi. Data collection using the validated test instrument. Regression equation Y ̂=1,079+0,385X_1 wich means that if the students vocabulary mastery is ignoed, then the ability to write arguments for 1,079. Every adding one point to the vocabulary it will add the ability to write the argument of 0,385 point. Regression equation Y ̂=1,079+0,355X_2 whic means that if reading comprehension is ignored, then the argument of 1,079 skill writing, meaning the reading comprehension is ignored then the ability to write arguments. For each additional 1,079 points in reading comprehension, reading comprehension will add 0,353 points. It can be concluden that the vocabulary and reading comprehension together affect 23,4% of the writing skills of argumentation percent of the value. (koef determinate R2 = 0,234 with the value of the test F = 5,665.
Mining Big data streams using Business analytics tools: A bird

Big data evolves as the prominent field in modern computing era. Big data analytics and its impact on extracting business intelligence is becoming indispensable for plethora of applications. The non-proprietary software revolution paved the way for illustrious evolution of tools like Weka, rapid miner, orange and R. Traditional data mining techniques hardly adapts to the requirements of rapid data analysis. The data stream processing algorithms that handle multitude of data endow with greater challenge in real time. Big data mining requires further improvisation in traditional tools to address the challenges of massive data processing. This paper highlights the importance of data stream mining and explores two important open source frameworks, namely massive online analysis (MOA) and scalable advanced massive online analysis (SAMOA). The implications of both the tools augurs well for further deliberations in big data research community. Business information system (BIS) models can reach unprecedented heights with the proliferation of these business analytics tools.
Applying for a Grant

TEA publishes grant applications electronically through theeGrants system and on paper. The process an applicant must follow to apply forfunds is different for eGrants and paper applications. eGrantApplications: An eGrant is an onlinegrant application that is published through eGrants, TEA’s electro
State Assessments for English Language Learners Archive

Student Assessment Home  | Contact Student Assessment | Printing PDFs This webpage contains archived information on state assessments for English language learners (ELLs) and includes resources for Linguistically Accommodated Testing (LAT), the Texas English Language Proficiency Assessment System (TELPAS), a
Three-qubit separable states of length ten with unique decompositions

We construct one-parameter families of three-qubit separable states with length ten, which is strictly greater than the whole dimension eight. These states are located on the boundary of the convex set of all separable states, but they are in the interior of the convex set of all states with positive partial transposes. They are also decomposed into the convex sum of ten pure product states in a unique way.
TELPAS Statewide Summary Reports

Student Assessment Home  | Contact Student Assessment | Printing PDFs The Texas English Language Proficiency Assessment System (TELPAS) summary reports are listed below. For detailed information about these reports, refer to the Interpreting Assessment Reports Guide . For reports from other testing programs
2018–2019 School Transformation Fund - Planning

The following applicants havebeen preliminarily selected to receive an award for the 2018–2019 SchoolTransformation Fund - Planning (STF - Planning) grant program, pendingsuccessful completion of fiscal and programmatic review and negotiation. The purpose of the STF -Planning grant is to increase
Annotated Example SDP for WebRTC

The Web Real Time Communications (WebRTC) family of protocols definesmechanism for direct interactive rich communication using audio,video, and data between two peers' web browsers. With in the WebRTCframework, the Session Description Protocol (SDP) is used fornegotiating session capabilities between the peers. Such a negotiationhappens based on the SDP offer/answer exchange mechanism Thisdocument provides an informational reference in describing the role ofSDP and the offer/answer exchange mechanism for the most common WebRTCuse cases. This document makes no changes to the SDP offer/answerexchange mechanism.
A rule-based approach for multi-perspective adaptation of service-based systems

A key feature of any business process (BP) implemented as service-based system (SBS) is adherence to compliance. BP workflows are frequently prone to faults or anomalies of various kinds namely, functional, temporal, structural, regulatory or technological updates. If faults are not handled in a timely manner, it may result in violation of service level agreements (SLAs). Therefore it is crucial for any BP to be flexible enough to adapt to changes of various categories. Current adaptation approaches try to adapt from either data (interface) mismatches from service perspective, temporal requirements from business perspective or resource constraints from the infrastructure perspective. We propose an adaptation framework which is capable of recovering from data, control-flow, temporal and infrastructure faults. The prototypical implementation using rule based approach is applied on a laboratory simulation.
Multi-domain Service Function Chaining with ALTO

Currently, Service Function Chaining (SFC) that span domains withdifferent technology, administration or ownership are being defined bythe SFC WG. This document focuses on how the Application Layer TrafficOptimization (ALTO) protocol can be used to advertise and discoverabstract topology, resource and service information from differentdomains, and then compute inter-domain service function paths. Anotherimportant concern of this draft is to initiate a discussion (ALTO, SFCas well as other WGs) regarding if, how, and under what conditionsALTO can be useful to improve the multi-domain SFC process.
LibGuides: Collection Management: Collection Management

Collection Management is an ongoing process that we conduct in order to ensure our materials support the needs of the MSOE Community.
High Amplification Scales Handling Frequency Content and Novel Gradient Sharpening Procedures

In this paper, a method for adaptive pure interpolation (PI) in the frequency domain, with gradient auto-regularization, is proposed. The input image is transformed into the frequency domain and convolved with the Fourier transform (FT) of a 2D sampling array (interpolation kernel) of initial size L rows by M columns. The inverse Fourier transform (IFT) is applied to the output coefficients and the edges are detected and counted. To get a denser kernel, the sampling array is interpolated in the frequency domain and convolved again with the transform coefficients of the original image of low resolution and transformed back into the spatial domain. The process is repeated until a maximum number of edges is reached in the output image, indicating that a locally optimal magnification factor has been attained. In a next step a maximum ascend&#x2010;descend gradient auto-regularization method is designed and the edges are sharpened. For the gradient management, a new strategy is proposed, referred to as the natural bi-directional gradient field (NBGF). It uses a natural following of a pair of directional and orthogonal gradient fields. Finally we find a novel procedure based on the observation of auto-regularized gradient management (ARG) in which a direct and compact mathematical proposition is applied effectively. A development of series of derivatives constructs directly the gradient field without parameters of experimental initialization. The proposed procedure is comparable to novel algorithms reported in the state of the art with good results for high scales of amplification.
Communication-rounds tradeoffs for common randomness and secret key generation

We study the role of interaction in the Common Randomness Generation (CRG) and Secret Key Generation (SKG) problems. In the CRG problem, two players, Alice and Bob, respectively get samples X1, X2, . . . and Y1, Y2, . . . with the pairs (X1, Y1), (X2, Y2), . . . being drawn independently from some known probability distribution μ. They wish to communicate so as to agree on L bits of randomness. The SKG problem is the restriction of the CRG problem to the case where the key is required to be close to random even to an eavesdropper who can listen to their communication (but does not have access to the inputs of Alice and Bob). In this work, we study the relationship between the amount of communication and the number of rounds of interaction in both the CRG and the SKG problems. Specifically, we construct a family of distributions μ = μr,n,L, parametrized by integers r, n and L, such that for every r there exists a constant b = b(r) for which CRG (respectively SKG) is feasible when (Xi, Yi) ~ μr,n,L with r + 1 rounds of communication, each consisting of O(log n) bits, but when restricted to r/2 − 2 rounds of interaction, the total communication must exceed Ω(n/ logb(n)) bits. Prior to our work no separations were known for r ≥ 2.
Image De-noising Based on a Local Optimal Balance between Data Fidelity and Output Smoothness

This paper addresses image denoising problem based on minimization of an appropriate energy function. This energy function consists of data fidelity term and targeted smoothness term. In this paper, a local optimal balance between these two terms is considered. This strategy leads to image invariant denoising and also preserves edges simultaneously. Experimental results verify the performance of this approach.
Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos

We present the outcomes of a recent large-scale subjective study of Mobile Cloud Gaming Video Quality Assessment (MCG-VQA) on a diverse set of gaming videos. Rapid advancements in cloud services, faster video encoding technologies, and increased access to high-speed, low-latency wireless internet have all contributed to the exponential growth of the Mobile Cloud Gaming industry. Consequently, the development of methods to assess the quality of real-time video feeds to end-users of cloud gaming platforms has become increasingly important. However, due to the lack of a large-scale public Mobile Cloud Gaming Video dataset containing a diverse set of distorted videos with corresponding subjective scores, there has been limited work on the development of MCG-VQA models. Towards accelerating progress towards these goals, we created a new dataset, named the LIVE-Meta Mobile Cloud Gaming (LIVE-Meta-MCG) video quality database, composed of 600 landscape and portrait gaming videos, on which we collected 14,400 subjective quality ratings from an in-lab subjective study. Additionally, to demonstrate the usefulness of the new resource, we benchmarked multiple state-of-the-art VQA algorithms on the database. The new database will be made publicly available on our website: \url{https://live.ece.utexas.edu/research/LIVE-Meta-Mobile-Cloud-Gaming/index.html}
Evaluation of pre-service teachers’ digital skills and ICT competencies in context of the demands of the 21st century

21st century has seen the emergence of digital skills as a crucial ability with profound effects on employment, education, and social relationships, among other facets of life. The best talents to teach are the subject of study by those who establish educational policies. The goal of this study is to examine the relationship between information and communication technology competency (ICTC) and digital abilities. It focuses on which of the digital skills sub-dimensions has a greater impact on ICTC and how this impact varies by country, age, and gender. This study employed a quantitative research design to evaluate the association between ICTC and various digital skills. The sample group consisted of 620 university students from Russia and Kazakhstan. Digital skills were measured using a scale adapted for the Russian context, while ICTC scale was first adapted for the Russian context following language validity steps. The study also conducted path analysis to determine the impact of ICTC on digital skill dimensions and multigroup path analysis to assess effects according to different independent variables. The study found that certain dimensions of digital skills, specifically “use of digital tools”, “communication of digital content”, and “creation of digital content”, significantly influence ICTC. However, “management of digital content”, “digital security”, and “digital empathy” dimensions did not show a significant effect. The impact of these dimensions varied significantly across different demographic groups, suggesting that digital skills and ICTC development may differ among these groups. The findings highlight the need for tailored strategies to develop and promote digital skills and ICTC, considering these demographic differences.
Automatic Skill-Oriented Question Generation and Recommendation for Intelligent Job Interviews

Job interviews are the most widely accepted method for companies to select suitable candidates, and a critical challenge is finding the right questions to ask job candidates. Moreover, there is a lack of integrated tools for automatically generating interview questions and recommending the right questions to interviewers. To this end, in this paper, we propose an intelligent system for assisting job interviews, namely, DuerQues. To build this system, we first investigate how to automatically generate skill-oriented interview questions in a scalable way by learning external knowledge from online knowledge-sharing communities. Along this line, we develop a novel distantly supervised skill entity recognition method to identify skill entities from large-scale search queries and web page titles with less need for human annotation. Additionally, we propose a neural generative model for generating skill-oriented interview questions. In particular, we introduce a data-driven solution to create high-quality training instances and design a learning algorithm to improve the performance of question generation. Furthermore, we exploit click-through data from query logs and design a recommender system for recommending suitable questions to interviewers. Specifically, we introduce a graph-enhanced algorithm to efficiently recommend suitable questions given a set of queried skills. Finally, extensive experiments on real-world datasets demonstrate the effectiveness of our DuerQues system in terms of the quality of generated skill-oriented questions and the performance of question recommendation.
Big data analytics techniques and their impacts on reducing information asymmetry: Evidence from Jordan

This study aimed to demonstrate the impact of big data analytics techniques on reducing information asymmetry in industrial companies listed on the Amman Stock Exchange from the point of view of workers in Jordanian financial intermediation companies. Two approaches have been adopted to achieve the target of this research. The first approach is the analytical descriptive approach through a survey to collect primary data that measures the elements of the independent variable related to big data analytics techniques (Volume, Velocity, Variety, and Veracity). The second approach is an applied approach that measures the dependent variable of information asymmetry based on the financial statements of industrial companies listed on Amman Stock Exchange for the period (2015-2021). The statistical program (SPSS) has been used to analyze data and test the hypotheses through multiple regression testing. Based on the results of the statistical analysis of the data and the opinions of the research community, it was found that the huge volume of big data has become difficult to process using traditional data processing applications. Furthermore, there is a statistically significant relationship between big data analytics techniques and the reduction of information asymmetry from the point of view of employees in intermediation firms in Jordan. Consequently, it is necessary for those in charge of the industrial companies listed on the Amman Stock Exchange to develop modern techniques capable of analyzing big data with high efficiency. It can also assist in providing target groups including investors, stakeholders, and other beneficiaries with reliable and efficient data required to make rational decisions, as well as to reduce the risks of information asymmetry.
HGR-ViT: Hand Gesture Recognition with Vision Transformer

Hand gesture recognition (HGR) is a crucial area of research that enhances communication by overcoming language barriers and facilitating human-computer interaction. Although previous works in HGR have employed deep neural networks, they fail to encode the orientation and position of the hand in the image. To address this issue, this paper proposes HGR-ViT, a Vision Transformer (ViT) model with an attention mechanism for hand gesture recognition. Given a hand gesture image, it is first split into fixed size patches. Positional embedding is added to these embeddings to form learnable vectors that capture the positional information of the hand patches. The resulting sequence of vectors are then served as the input to a standard Transformer encoder to obtain the hand gesture representation. A multilayer perceptron head is added to the output of the encoder to classify the hand gesture to the correct class. The proposed HGR-ViT obtains an accuracy of 99.98%, 99.36% and 99.85% for the American Sign Language (ASL) dataset, ASL with Digits dataset, and National University of Singapore (NUS) hand gesture dataset, respectively.
The ability of Segmenting Anything Model (SAM) to segment ultrasound images

Accurate ultrasound (US) image segmentation is important for disease screening, diagnosis, and prognosis assessment. However, US images typically have shadow artifacts and ambiguous boundaries that affect US segmentation. Recently, Segmenting Anything Model (SAM) from Meta AI has demonstrated remarkable potential in a wide range of applications. The purpose of this paper was to conduct an initial evaluation of the ability for SAM to segment US images, particularly in the event of shadow artifacts and ambiguous boundaries. We evaluated SAM's performance on three US datasets of different tissues, including multi-structure cardiac tissue, thyroid nodules, and the fetal head. Results indicated that SAM generally performs well with US images with clear tissue structures, but it has limited performance in the event of shadow artifacts and ambiguous boundaries. Thus, creating an improved SAM that considers the characteristics of US images is significant for automatic and accurate US segmentation.
Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles

Attack Ensemble (AE), which combines multiple attacks together, provides a reliable way to evaluate adversarial robustness. In practice, AEs are often constructed and tuned by human experts, which however tends to be sub-optimal and time-consuming. In this work, we present AutoAE, a conceptually simple approach for automatically constructing AEs. In brief, AutoAE repeatedly adds the attack and its iteration steps to the ensemble that maximizes ensemble improvement per additional iteration consumed. We show theoretically that AutoAE yields AEs provably within a constant factor of the optimal for a given defense. We then use AutoAE to construct two AEs for l∞ and l2 attacks, and apply them without any tuning or adaptation to 45 top adversarial defenses on the RobustBench leaderboard. In all except one cases we achieve equal or better (often the latter) robustness evaluation than existing AEs, and notably, in 29 cases we achieve better robustness evaluation than the best known one. Such performance of AutoAE shows itself as a reliable evaluation protocol for adversarial robustness, which further indicates the huge potential of automatic AE construction. Code is available at https://github.com/LeegerPENG/AutoAE.