Reviewerly/exHarmony huggingface dataset:
Resource optimization using predictive virtual machine consolidation approach in cloud environment

The Proliferation of on-demand usage-based IT services, as well as the diverse range of cloud users, have led to the establishment of energy-hungry hefty cloud data centers. Therefore, cloud service providers are striving to reduce energy consumption for cost-saving and environmental sustainability issues of data centers. In this direction, Virtual Machine (VM) consolidated is a widely used approach to optimize hardware resources at cost of performance degradation due to unnecessary migrations. Hence, the motivation of the proposed approach is to minimize energy consumption while maintaining the performance of cloud data centers. This leads to a reduction in the overall cost and an increase in the reliability of cloud service providers. To achieve this goal Predictive Virtual Machine Consolidation (PVMC) algorithm is proposed using the exponential smoothing moving average (ESMA) method. In the proposed algorithm, the ratio of deviation to utilization is calculated for VM selection and placement. migrating the high CPU using VMs or we can restrict steady resource-consuming VMs from migration. The outcomes of the proposed algorithm are validated on computer-based simulation under a dynamic workload and a variable number of VMs (1–290). The experimental results show an improvement in the mean threshing index (40%, 45%) and instruction energy ratio (15%, 17%) over the existing policies. Hence, the proposed algorithm could be used in real-world data centers for reducing energy consumption while maintaining low service level agreement violations.
A Method for IR Measurement of Large Scale Roughened Surfaces in Hypersonic Flow

This paper presents a methodology for adapting the Infrared Thermography (IRT) diagnostic technique for use on large scale rough surfaces. Situations where more established methods of surface heat transfer measurement, such as thin film gauges (TFG) and calorimeter gauges, are typically employed are met with a considerable challenge when applied to rough walls. In order for the gauges to provide meaningful data, their surface must possess the same topography as the surrounding roughness, which adds to the complexity and expense of gauge construction. Thus, a benefit of IRT over gauge methods is IRT’s ability to provide data in a non-intrusive manner that is easily adapted to any roughness geometry, reducing the necessity for a multitude of different gauges. Furthermore, IRT produces spatially continuous heat transfer data over a given surface area, providing insight into the heat flux augmentation that rough walls experience. This methodology also solves the general problem that arises from the change in directional emissivity that accompanies the varying surface height of rough walls. As this is a consequence of 3D effects, the 2D image produced by IRT does not inherently account for this and so an emissivity map must be evaluated and given as an input. The methodology in this paper is then applied to an idealised two-dimensional roughness pattern and a three-dimensional pattern that represents an ablated surface of HEEET material developed by NASA, showcasing the Stanton number enhancement maps and emissivity maps produced.
Twins Voice Verification And Speaker Identification

The similarity in twins voice has become a serious matter of research recently. The present work is a preliminary study of exploratory character describing the similarities of voices of twins and to give the accuracy by prediction. Speaker identification is a task of identifying persons voice accuracy from their voices. Recently, deep learning has dramatically revolutionized speaker identification. However, there is lack of comprehensive reviews on the exciting progress. Siamese Neural Network (twin neural network) is used in this work. The neural network helps in the speaker identification. In prior works of speaker identification, discriminative neural networks, utterance-level evaluation speaker representations and the accuracy of the voice are usually calculated. In this model the frame-level evaluation representation is mapped into enrollment feature domain and an utterance-level evaluation-enrollment joint vector for final similarity measure is further generated. Feature learning, attention mechanism, and metric learning are jointly optimized using an end- to-end loss function.
Memory Protection Keys: Facts, Key Extension Perspectives, and Discussions

Memory Protection Keys (MPK) offers per-thread memory protection with an affordable overhead, prompting many new studies. With protection key extension, MPK provides more fine-grained protection and better functionality. MPK can be an attractive option for memory protection in industry.
Bandwidth Prediction in Low-Latency Media Transport

Designing a robust bandwidth prediction algorithm for low-latency media transport that can quickly adapt to varying network conditions is challenging. In this paper, we present the working principles of a hybrid bandwidth predictor (termed BoB, Bang-on-Bandwidth) we developed recently for real-time communications and discuss its use with the new Media-over-QUIC (MOQ) protocol proposals.
Research on Text Classification Technology Integrating Contrastive Learning and Adversarial Training

Text classification is an important task in natural language processing. Although the current text classification model can achieve good classification accuracy, it still has the shortcomings of poor data expansion consistency and inability to learn noise-invariant representations. The text classification model cannot effectively resist disturbance, the generalization ability is limited, and the prediction distribution of similar samples is inconsistent, and the performance of the text classification model is seriously affected. Aiming at the above problems, a text classification based on contrastive adversarial training (TCCA) model is proposed. Combining contrastive learning and adversarial training to construct a new loss function optimization model enables the model to learn the noise-invariant representation while classifying correctly, and then uses the prior distribution to adjust the model prediction results to improve the accuracy of the classification task. The TCCA model is experimented on THUCNews, iFLYTEK, TNEWS datasets. Compared with other text classification models, the TCCA model can effectively improve the accuracy of text classification tasks.
Fine-Tuning Vision Encoder–Decoder Transformers for Handwriting Text Recognition on Historical Documents

Handwritten text recognition (HTR) has seen significant advancements in recent years, mainly due to the incorporation of deep learning techniques. One area of HTR that has garnered particular interest is the transcription of historical documents, as there is a vast amount of records available that have yet to be processed, potentially resulting in a loss of information due to deterioration. Currently, the most widely used HTR approach is to train convolutional recurrent neural networks (CRNN) with connectionist temporal classification loss. Additionally, language models based on n-grams are often utilized in conjunction with CRNNs. While transformer models have revolutionized natural language processing, they have yet to be widely adopted in the context of HTR for historical documents. In this paper, we propose a new approach for HTR on historical documents that involves fine-tuning pre-trained transformer models, specifically vision encoder–decoder models. This approach presents several challenges, including the limited availability of large amounts of training data for specific HTR tasks. We explore various strategies for initializing and training transformer models and present a model that outperforms existing state-of-the-art methods on three different datasets. Specifically, our proposed model achieves a word error rate of 6.9% on the ICFHR 2014 Bentham dataset, 14.5% on the ICFHR 2016 Ratsprotokolle dataset, and 17.3% on the Saint Gall dataset.
LISP EID Anonymity

This specification will describe how ephemeral LISP EIDs can be usedto create source anonymity. The idea makes use of frequently changingEIDs much like how a credit-card system uses a different credit-cardnumbers for each transaction.
PENGARUH NILAI BUDAYA DALAM NOVEL YANG BERJUDUL “TENGGELAMNYA KAPAL VAN DER WIJCK” TERHADAP PEMBELAJARAN BAHASA INDONESIA

The novel is a literary work in the form of prose. There are many titles novel that has been print, one of them novel sinking Van der Wijk. Novel research novel tengelamnya ship Van der Wijk aims to describe the story of the novel is may affect the reader students SMPN 1 padalarang. This research was performed using the qualitative methods and techniques research include the stage of data collection, data analysis, the presentation of the data and withdrawal conclusion. Source of data obtained the form of the primary data (tengelamnya ship Van der wijck) and secondary from other sources. Research has a crucial role in providing feedback to readers. From the results showed that the aspects of educational value sinking Van der wijcksebanyak 57.3%, this indicates that readers novel sinking Van der Wijk give appreciation to refer to the contents of the story very reasonable. The second aspect of the figure of figures tengelamnya ship Van der wijck with the result is 50,6% indicating that the respondents average like to figure novel sinking Van der wijck, while aspects of the third called aspect of the events that occurred with the result are 40.66%. Of the three aspects can be concluded that the novel sinking Van der Wijk quite interesting.
이라크 전쟁의 전쟁양상 변화 원인에 대한 고찰 - 저항의지와 정치적 행위자의 결합을 중심으로 -

The Iraq war was the one in which both conventional war, which was a national war, and non-conventional war, which was a war with non-state at the same time. The coalition, centered on the United States, conducted a highly effective conventional warfare using weapons systems combined with state-of-the-art science and technology. However, contrary to expectation, the insurgency began to be organized and the warfare changed into a counterinsurgency phase. As can be seen in the Donbass war in 2014, it is hard to imagine that a war between nations will not take place in the modern era where there are 195 nations around the world. However, the characteristics of war can change during the In this sense, the war in Iraq, which changed the characteristics of war, has many implications. Therefore, this study focuses on what causes of warfare have changed in Iraq during the war. To prove this, I have illuminated will, actors and the during the Iraq War using Clausewitz`s as a framework of analysis. The will of the Iraqi people to resist and the political changed during the After the conventional warfare, the Iraqi army was forced to dissolve, but new resistance came to the surface and the war came to a new phase. This study proved through the marvelous trinity that the war pattern changed during the Iraq And the cause of the change is that when the resistance will of the people is combined with the political actors, it shows the nature of the war completely. In order to minimize the resistance of the enemy countries, it is important to establish security order after the end of the conventional warfare and to take measures to prevent the emergence of political actors.
Active Scene Learning.

Sketch recognition allows natural and efficient interaction in pen-based interfaces. A key obstacle to building accurate sketch recognizers has been the difficulty of creating large amounts of annotated training data. Several authors have attempted to address this issue by creating synthetic data, and by building tools that support efficient annotation. Two prominent sets of approaches stand out from the rest of the crowd. They use interim classifiers trained with a small set of labeled data to aid the labeling of the remainder of the data. The first set of approaches uses a classifier trained with a partially labeled dataset to automatically label unlabeled instances. The others, based on active learning, save annotation effort by giving priority to labeling informative data instances. The former is sub-optimal since it doesn't prioritize the order of labeling to favor informative instances, while the latter makes the strong assumption that unlabeled data comes in an already segmented form (i.e. the ink in the training data is already assembled into groups forming isolated object instances). In this paper, we propose an active learning framework that combines the strengths of these methods, while addressing their weaknesses. In particular, we propose two methods for deciding how batches of unsegmented sketch scenes should be labeled. The first method, scene-wise selection, assesses the informativeness of each drawing (sketch scene) as a whole, and asks the user to annotate all objects in the drawing. The latter, segment-wise selection, attempts more precise targeting to locate informative fragments of drawings for user labeling. We show that both selection schemes outperform random selection. Furthermore, we demonstrate that precise targeting yields superior performance. Overall, our approach allows reaching top accuracy figures with up to 30% savings in annotation cost.
Filtering Point Targets via Online Learning of Motion Models

Filtering point targets in highly cluttered and noisy data frames can be very challenging, especially for complex target motions. Fixed motion models can fail to provide accurate predictions, while learning based algorithm can be difficult to design (due to the variable number of targets), slow to train and dependent on separate train/test steps. To address these issues, this paper proposes a multi-target filtering algorithm which learns the motion models, on the fly, using a recurrent neural network with a long short-term memory architecture, as a regression block. The target state predictions are then corrected using a novel data association algorithm, with a low computational complexity. The proposed algorithm is evaluated over synthetic and real point target filtering scenarios, demonstrating a remarkable performance over highly cluttered data sequences.
Assembling nanocrystal superstructures

At the nanoscale, electronic materials can develop size-dependent properties due to quantum-confinement effects. This is the case for semiconductor nanocrystals, or quantum dots (QDs). In these materials, the confinement of charge carriers leads to a size-dependent electronic bandgap; consequently, the light absorption and emission properties of QDs are tunable over a wide spectral range by simply changing their size. Furthermore, QDs are synthesized as a colloidal dispersion, making their use compatible with inexpensive and efficient roll-to-roll printing technologies. The unique photophysics of QDs, together with their ease of fabrication and processability, make them promising candidates as building blocks for novel optoelectronic devices, such as solar cells, photodetectors, light emitting diodes, and lasers. However, to be used as active layer in a device, QDs must be assembled to form macroscopic solids. In this thesis, we propose novel approaches to drive the assembly of QDs into programmable artificial solids with morphology-defined properties. By exploiting an emulsion-templated approach, we direct the assembly of QDs into spherical crystals of nanocrystals, or supercrystals. These mesoscopic objects feature enhanced light-matter interaction, increasing the potential of QDs for applications. Furthermore, we show that QDs crystallize similarly to hard spheres, when taking into account the role of surface ligands. To explore further the role of these surface-bound molecules, we use colloidal nanoplatelets as a model system and show that surface repassivation can result in either structural damage or effective reconstruction depending on experimental conditions. Finally, we demonstrate the use of a novel colloidal interaction, the critical Casimir effect, to drive the growth of QD superstructures of morphologies varying from 3D fractals to 2D epitaxial monolayers.
다케다 가라쿠리연극과 시계기술 —일본의 근대화와 기술의 대중화—

In the Edo period, not only the big performances such as kabuki and Joruri, but also a variety of performances, various street performances such as “misemono” were overflowed in temporary houses, small theaters etc.Among them, misemono using Karakuri dolls attracted a great deal of popularity due to the characteristics of street performances that should attract a lot of viewers in a short time. Karakuri is a term used to refer to a kind of automata driven by yarn or mechanical devices, popularized by Takeda Omi''s Karakuri plays.Omi was able to produce Karakuri dolls because European missionaries have brought the iron cannon and mechanical watches, the products of the most advanced Western science and technology, were introduced, at the end of the Sengoku Period of Japan.The clock technology that was brought to Japan was used in the Wadokei and karakuri dolls with the unauthorized method.That was because of Peace in the Edo period led to the development of ordinary people''s culture, and the Edo shogunate forbade military technology development.After the decline of the Takeda Karakuri Drama in the mid 18th century, the Karakuri performances attracted popularity by the performers. However, Unlike the western automata that emphasized accuracy, the Japanese Karakuri used in the performances gave more importance to intimacy.This Japanese attitude has led to a human-shaped atom. In addition, the clock technology used in Karakuri has made a great contribution to the acceptance of Western technology.
An Improved Reversible Data Hiding in Encrypted Images using Parametric Binary Tree Labeling

This work proposes an improved reversible data hiding scheme in encrypted images using parametric binary tree labeling(IPBTL-RDHEI), which takes advantage of the spatial correlation in the entire original image but not in small image blocks to reserve room for hiding data. Then the original image is encrypted with an encryption key and the parametric binary tree is used to label encrypted pixels into two different categories. Finally, one of the two categories of encrypted pixels can embed secret information by bit replacement. According to the experimental results, compared with several state-of-the-art methods, the proposed IPBTL-RDHEI method achieves higher embedding rate and outperforms the competitors. Due to the reversibility of IPBTL-RDHEI, the original plaintext image and the secret information can be restored and extracted losslessly and separately.
3D Trajectory Reconstruction Using Color-Based Optical Flow and Stereo Vision

Automatic trajectory estimation of a moving object in a video is one of the most active research areas of computer vision, which finds many practical applications, such as development of sport playing robots, predicting trajectory for avoiding obstacle collision, automatic navigation of driverless vehicles, monitoring target hitting, etc. However, most of the work reported in literature only considers monocular videos. Due to the availability of low price stereo cameras, many applications take their advantages by incorporating depth information. In this paper, the 3D trajectory of a primary-color (red or green or blue) object is estimated using color-based optical flow and stereo vision. The purpose of using stereo vision is to gain depth information for generating 3D trajectory. The system has been tested on many stereo videos and experimental results are quite accurate. Besides, the low computation time required for finding depth of the tracked path makes it suitable for real time applications.
Evaluasi Kesesuaian Lahan untuk Wilayah Permukiman di Kecamatan Kartasura Kabupaten Sukoharjo

Land suitability for residential areas is used to determine the level of land suitability for settlements based on physical parameter parameters in each land unit. The objectives of this study are (1) To find out the land suitability class for residential areas in Kartasura Sub-District, (2) Evaluate the development of residential areas in Kartasura District based on existing land suitability classes.The research method used in this research is the survey survey method directly in the field and laboratory analysis, while the sampling method uses the cluster random sampling method which is based on land units. The land units in the research location are 12, namely F1IIIGkSw, F1IIIRkP, F1IIIRkSw, F1IIRkP, F1IIRkSw, F1IVGkP, F1IVGkSw, F1IVRkP, F1IVRkSw, F1VGkP, F1VGkSw and F1VRkP. The analysis technique used in this study is overlay, scoring and dignity. The physical parameters observed and laboratory tests carried out in this study were the flooding duration, flooding conditions, soil texture, amount and depth of grooves, groundwater depth and rock weathering levels.Based on field observations and laboratory tests of physical parameters in the land unit. Land suitability in Kartasura District is divided into three classes, namely S1 (Very Appropriate), S2 (Appropriate) and S3 (Moderate). Class S1 has an area of 114.58 ha which is found in F1IIIRkSw land units. Class S2 has an area of 1064.16 ha which is found in land units F1IIIGkSw, F1IIIRkP, F1IIRkSw, F1IVGkP, F1IVGkSw, F1IVRkSw, and F1VRkP. The S3 class has an area of 786.52 ha which is located in the land unit F1IIRkP, F1IVRkP, F1VGkP,F1VGkSw. Keywords: Land Suitability, Settlement, Land Unit, Conformity Class
Computational Studies of Structure and Surface Reactivity in Metal Nanoclusters

Metal nanoparticles exhibit physical and chemical properties unique to their length scale that have the potential to shape next generation technologies. The large structural and chemical space that determines nanoparticle properties requires feedback between computational and experimental studies to drive the discovery of both new architectures and target properties of these systems. This dissertation describes the application and development of theoretical methods to study metal nanoparticle electronic structure, developing new structure-property relationships and new concepts that govern nanoparticle behavior while connecting theoretical insight with laboratory observations.In Chapter 1, the dissertation is introduced by detailing how the connection between geometry and electronic structure has shaped the way we teach and understand chemistry across length scale, and projects these concepts onto the 1-3 nm length scale where traditional descriptors of electronic structure break down. In Chapter 2, the optoelectronic impact of alloying Cu with a Au nanocluster is studied, revealing how atomic descriptors and position of the heteroatom determine optical absorption in [Au25(SR)18] , providing an easily accessible experimental readout of electronic structure. Building on hypotheses tested in Chapter 2, Chapter 3 explores the size dependence of Cu/Au alloying. Here, the atom concentration and composition architecture are key parameters predicted to drive the emergence of plasmonic behavior in Au144-xCux(SR)60 nanocluster.In addition to the composition dependence of nanoparticle properties, the surface chemistry is known to dominate overall nanocluster electronic structure. In Chapter 4, both the type and specific molecular descriptors of the ligand were shown to impact the total magnetic moment of Co13 and Co55 model nanoclusters. Chapter 5 extends these concepts of surface chemistry to address the size dependent evolution of the ligand mediated magnetic properties in CoN nanoclusters, demonstrating how energy level alignment and orbital symmetry contribute to size dependent trends. Finally, Chapter 6 describes a reduced scaling computational method that improves the approximation of mean field excited state energy predictions, increasing the size and complexity of systems that can be treated with high accuracy.
Here’s How To Undo A Sent Email In Office365!

Today’s blog will give you an important (but often overlooked) Outlook tip that will help you recall or get a second chance to make changes