Reviewerly/exHarmony huggingface dataset:
Size-based scheduling vs fairness for datacenter flows

Contrary to the conclusions of a recent body of work where approximate shortest remaining processing time first (SRPT) flow scheduling is advocated for datacenter networks, this paper aims to demonstrate that per-flow fairness remains a preferable objective. We evaluate abstract queuing models by analysis and simulation to illustrate the non-optimality of SRPT under the reasonable assumptions that datacenter flows occur in batches and bursts and not, as usually assumed, individually at the instants of a Poisson process. Results for these models have significant implications for the design of bandwidth sharing strategies for datacenter networks. In particular, we propose a novel "virtual fair scheduling" algorithm that enforces fairness between batches and is arguably simple enough to be implemented in high speed devices.
Sojourn Time Minimization of Successful Jobs

Due to a growing interest in deep learning applications [5], compute-intensive and long-running (hours to days) training jobs have become a significant component of datacenter workloads. A large fraction of these jobs is often exploratory, with the goal of determining the best model structure (e.g., the number of layers and channels in a convolutional neural network), hyperparameters (e.g., the learning rate), and data augmentation strategies for the target application. Notably, training jobs are often terminated early if their learning metrics (e.g., training and validation accuracy) are not converging, with only a few completing successfully. For this motivating application, we consider the problem of scheduling a set of jobs that can be terminated at predetermined checkpoints with known probabilities estimated from historical data. We prove that, in order to minimize the time to complete the first K successful jobs on a single server, optimal scheduling does not require preemption (even when preemption overhead is negligible) and provide an optimal policy; advantages of this policy are quantified through simulation. Related Work. While job scheduling has been investigated extensively in many scenarios (see [6] and [2] for a survey of recent result), most policies require that the cost of waiting times of each job be known at scheduling time; in contrast, in our setting the scheduler does not know which job will be the K-th successful job, and sojourn times of subsequent jobs do not contribute to the target metric. For example, [4, 3] minimize makespan (i.e., the time to complete all jobs) for known execution times and waiting time costs; similarly, Gittins index [1] and SR rank [7] minimize expected sojourn time of all jobs, i.e., both successfully completed jobs and jobs terminated early. Unfortunately, scheduling policies not distinguishing between these two types of jobs may favor jobs where the next stage is short and leads to early termination with high probability, which is an undesirable outcome in our applications of interest.
SWAN

Wide-area stream analytics is commonly being used to extract operational or business insights from the data issued from multiple distant datacenters. However, timely processing of such data streams is challenging because wide-area network (WAN) bandwidth is scarce and varies widely across both different geo-locations (i.e., spatially) and points of time (i.e., temporally). Stream analytics desirable under a WAN setup requires the consideration of path diversity and the associated bandwidth from data source to sink when performing operator task placement for the query execution plan. It also has to enable fast adaptation to dynamic resource conditions, e.g., changes in network bandwidth, to keep the query execution stable.
CHMM Object Detection Based on Polygon Contour Features by PSM

Since the conventional split-merge algorithm is sensitive to the object scale variance and splitting starting point, a piecewise split-merge polygon-approximation method is proposed to extract the object contour features. Specifically, the contour corner is used as the starting point for the contour piecewise approximation to reduce the sensitivity of the contour segment for the starting point; then, the split-merge algorithm is used to implement the polygon approximation for each contour segment. Both the distance ratio and the arc length ratio instead of the distance error are used as the iterative stop condition to improve the robustness to the object scale variance. Both the angle and length as two features describe the shape of the contour polygon; they have a strong coupling relationship since they affect each other along the contour order relationship. To improve the description correction of the contour, these two features are combined to construct a Coupled Hidden Markov Model to detect the object by calculating the probability of the contour feature. The proposed algorithm is validated on ETHZ Shape Classes and INRIA Horses standard datasets. Compared with other contour-based object-detection algorithms, the proposed algorithm reduces the feature number and improves the object-detection rate.
Optimizing half precision Winograd convolution on ARM many-core processors

Convolutional Neural Networks (CNNs) are widely used in real world applications, e.g, computer vision. Winograd based convolution is usually applied due to its low computation complexity. For the underling hardware, ARM many-core CPUs, by their price performance, are favored by cloud providers like Amazon Web Services (AWS). However, existing Winograd convolution implementations for ARM architecture are mostly optimized for mobile devices, and usually cannot fully utilize hardware resources of many-core processors. In this paper, we propose HAWC, an optimized half precision floating-point (FP16) Winograd convolution implementation for ARM many-core processors. HAWC employs a series of optimization methods, which are suitable for ARM NEON architecture, and assembles them as an entire solution to improve performance. Our evaluation shows that HAWC achieves on average 10.74X and up to 27.56X speedup on representative convolution layers over state-of-the-art solutions.
Trust, collegiality, and communication between teachers and librarians to support student learning

This paper investigates the trust, collegiality, and communication between teachers and librarians to support student learning at three senior high schools in Surabaya, Indonesia. The conceptual research framework uses Montiel-Overall's Model B Cooperation/Partnerships from the Teacher and Librarian Collaboration (TLC) model. The research approach is descriptive/quantitative and is based on 36 respondents consisting of 31 teachers and 5 librarians. The findings indicate that regarding trust, the indicator most depicted is benevolence. In terms of collegiality, teachers, and librarians care for each other, and in the communication aspect, teachers and librarians often communicate with each other.
Attitudes of Teachers to Using Information and Communication Technology in Teaching – Advantages and Obstacles

This paper analyses the attitudes of teachers to using information and communication technology in teaching. The study was conducted on a sample of 269 teachers in the Republic of Serbia. The purpose of this research is to look at the intensity of ICT use in teaching, as well as to identify the benefits and obstacles that teachers face when using this technology. The study was based on a descriptive research methodology. Surveying and scaling techniques were used to measure the attitudes of teachers. It was concluded that teachers often use ICT in teaching. The greatest advantage of ICT use, as seen by teachers, is improved quality of teaching, a possibility to deliver interesting lessons and quick and easy access to information. The greatest obstacles emphasized by teachers were underdeveloped competencies of teachers, limited access to ICT in schools and insufficient trust in the use of new technologies.
Engaging educators in the ideation of scenarios for cross-reality game-based learning experiences

Abstract Cross-reality media technology creates alternate reality experiences in which the physical and the virtual world are interconnected and influence each other through a network of sensors and actuators. Despite technological advances, the landscape of cross-reality technology as an enabler of alternate reality educational experiences has not been explored yet. The technical expertise required to set up and program such mixed environments is too high to engage the problem owners (i.e. educational experts) in the design process and, hence, user-driven innovation remains challenging. In this paper we explore the co-creation of cross-reality experiences for educational games. We created a no-programming toolkit that provides a visual language and interface abstractions to quickly build prototypes of cross-reality interactions. The toolkit supports experience prototyping and allows designers to coproduce, with educational experts, meaningful scenarios while they create, try out and reconfigure their prototypes. We report on a workshop with 36 educators where the toolkit was used to ideate cross-reality games for education. We discuss use cases of game-based learning applications developed by the participants that follow different pedagogical strategies and combine different physical and virtual spaces and times. We outline implications for the design of cross-reality interactions in educational settings that trigger further research and technological developments.
Bringing order to chaos: Research on Easy Swedish

Abstract Despite the early establishment of Easy Language in Sweden in the 1960s and a growing interest in producing Easy Language materials, linguistic research on Easy Swedish remains scarce. This literature review aims to describe how Easy Swedish has been understood in previous research, bringing together a wide range of perspectives and approaches. Applying a meta-narrative method, we investigate terms and definitions, descriptions of the target group, and ideologies, discourses, and values that justify Easy Language. Our results show an exclusive focus on written language and use of the term lättläst (Easy to Read), and a lack of a universal definition of the term. The results also show general agreement on the heterogeneity of the target group. Controversy arises from different perspectives on reading as well as the breadth of the concept, and the conceptualisation of Easy Swedish varies depending on the aims, discipline, and studied material.
Sistem Pakar Akurasi dalam Mengidentifikasi Penyakit Gingivitis pada Gigi Manusia dengan Metode Naive Bayes

Gingivitis is a condition where the gums become inflamed due to a bacterial infection, causing the gums to swell. Gingivitis if treated too late will trigger more dangerous dental disease. Minimize knowledge about gingivitis and limited time to consult with experts so that people pay less attention to dental and oral health which can indicate gingivitis. The purpose of this study was to determine accuracy in identifying gingivitis disease using the Naive Bayes method, which can help the public to find out information about gingivitis, so a system with experts was built. Expert System is knowledge from experts that is entered into a computer or system that can be used for consultation. The data used in this study were 24 symptom data and 5 types of disease data sourced from interviews with an expert. The experts used in this study were dentists at the Rahmatan Lil Alamin Clinic. The data can be obtained from the results of the medical records of patients who perform examinations with dentists. The data processed in this study is knowledge about the symptoms and types of gingivitis in the teeth obtained from an expert. The result of the test is that the largest value of the total probability is found in the type of gum disease Puberty with a value of 0.000106795855. This research can help the community to conduct consultations easily, find out the symptoms early so that people do not have to go to the hospital with a long distance.
Automated software effort estimation for agile development system by heuristically improved hybrid learning

Summary In the process of modern software development, an important role is played by software effort estimation. The failure or success of the projects is mostly based on the schedule outcomes and effort estimation accuracy. The effort estimation problem still remains as a challenging issue because of the limitations of various standard measures for forecasting the efforts in the plan‐driven software development. This article emphasizes on novel software effort estimation framework using heuristically improved hybrid learning model. This article proposes heuristically improved hybrid learning (HI‐HL) with deep belief network and artificial neural network for software effort estimation. The weight optimization strategy of DBN and ANN to attain highly accurate estimation. Here, the weight optimization is performed by integrating two standard optimization algorithms such as forest optimization algorithm and moth‐flame optimization, so called as solution index‐based forest moth flame optimization with the purpose of solving the fitness function concerning the “Magnitude of Relative Error and Mean Absolute Error (MAE).” From the table results, for dataset 1, the SMAPE of SI‐FMFO‐HI‐HL is correspondingly secured 60.75%, 34.26%, 56.77%, and 61.44% improved than ELM, LSTM, DNN, and fuzzy. The simulation findings indicated that the recommended estimation model outperformed the baseline approaches on the three publically available datasets.
METODE RAPID APPLICATION DEVELOPMENT DALAM PERANCANGAN SISTEM INFORMASI BOOKING SERVICE KENDARAAN

ABSTRACT&#x0D; &#x0D; Providing sales, maintenance, repair and car parts services, the management system at Honda Megatama Kapuk, y where the system already uses information management but not yet ada Booking service booking system that uses a web-based application. The web-based application aims to make it easier for customers to book vehicle services. With this application, an appropriate method is needed in designing the information system. The Rapid Application Development (RAD) method was chosen as a rapid design method with 5 steps of business modeling, data modeling, application design, and application deployment, testing. This method of RAD has the advantage of reducingthe overall development time so that it is suitable for the information system needed by the urgent company si. The results of the design of the booking servise application information system which is designed to have a saving time to make it easier for customers are proven by the test results with the System Usability Scale (SUS) method toget a value of 75 which is between the B-C (good-exellent) graphs. It can be concluded that the vehicle service booking application can provide convenience for admins, owners to control system management and customers for vehicle booking services.&#x0D; &#x0D; Keywords: Booking, Vehicle Servise, Method, Rapid Application Development.
Quantum Tree Search with Qiskit

We indicate the quantum tree search qiskit implementation by popular examples from symbolical artificial intelligence, the 3-puzzle, 8-puzzle and the ABC blocks world. Qiskit is an open-source software development kit (SDK) for working with quantum computers at the level of circuits and algorithms from IBM. The objects are represented by symbols and adjectives. Two principles are presented. Either the position description (adjective) is fixed and the class descriptors moves (is changed) or, in the reverse interpretation, the class descriptor is fixed and the position descriptor (adjective) moves (is changed). We indicate how to decompose the permutation operator that executes the rules by the two principles. We demonstrate that the the branching factor is reduced by Grover’s amplification to the square root of the average branching factor and not to the maximal branching factor as previously assumed.
Extending Fuzzy Linguistic Logic Programming with Negation †

Fuzzy linguistic logic programming (FLLP) is a framework for representation and reasoning with linguistically expressed human knowledge. In this paper, we extend FLLP by allowing negative literals to appear in rule bodies, resulting in normal logic programs. We study the stable model semantics and well-founded semantics of such programs and their relation. The two kinds of semantics are adapted from those of classical ones based on the Gelfond–Lifschitz transformation and van Gelder’s alternating fixpoint approach, respectively. To our knowledge, until now, there has been no work on the well-founded semantics of normal programs in any fuzzy logic programming (FLP) framework based on Vojtáš’s FLP. Moreover, the relation between the two kinds of semantics is usually studied using a bilattice setting of the truth domain. However, our truth domains do not possess a complete knowledge-ordering lattice and, thus, do not have a bilattice structure. The two kinds of semantics possess properties similar to those of the classical case. Every stable model contains the well-founded (partial) model, and the well-founded total model coincides with the unique stable model, but not vice versa. Since the well-founded semantics is closely related to the stable model semantics, it can help compute stable models more efficiently.
Reading sound: textual value devices in gallery sound

As sound practices continue to evolve in the gallery arts, conceptual texts create value for audiences who have been trained to see but who are still learning to listen. Based on 105 semi-structured interviews and four years of ethnographic observation, this paper finds that, contrary to the reliance on hearing in music or vision in the other gallery arts, gallery sound has had to rely, paradoxically, on reading audiences. After a look at models of cultural-economic valuation, this study finds that sound artists tend to rely on written language to value their works, and these textual value devices appear on a variety of interfaces. Artist statements are communicated through online platforms, handouts, didactic panels, and grant applications as instances of economic agencement – rendering the aesthetic economic – for works that otherwise might be mistaken for music or visual art. Respondents explained this reliance on text in terms of art world isomorphism, as a response to technical maladaptation, and as a tool for sensory learning. The resulting hierarchies of aesthetic value have implications for the relationship between sensory perception and conceptual understanding throughout the gallery arts and beyond.
The Pairwise Similarity Partitioning Algorithm: A Method for Unsupervised Partitioning of Geoscientific and Other Datasets Using Arbitrary Similarity Metrics

Abstract A simple yet flexible and robust algorithm is described for fully partitioning an arbitrary dataset into compact, nonoverlapping groups or classes, sorted by size, based entirely on a pairwise similarity matrix and a user-specified similarity threshold. Unlike many clustering algorithms, there is no assumption that natural clusters exist in the dataset, although clusters, when present, may be preferentially assigned to one or more classes. The method also does not require data objects to be compared within any coordinate system but rather permits the user to define pairwise similarity using almost any conceivable criterion. The method therefore lends itself to certain geoscientific applications for which conventional clustering methods are unsuited, including two nontrivial and distinctly different datasets presented as examples. In addition to identifying large classes containing numerous similar dataset members, it is also well suited for isolating rare or anomalous members of a dataset. The method is inductive in that prototypes identified in representative subset of a larger dataset can be used to classify the remainder.
Area, Time and Energy Efficient Multicore Hardware Accelerators for Extended Merkle Signature Scheme

This paper addresses a barrier that prevents the timely adoption of post-quantum signature algorithms, such as the eXtended Merkle Signature Scheme (XMSS), due to its lack of fast, cost-effective and energy-efficient hardware accelerators. Two new architectures that use more than one hash core are proposed for the first time to significantly reduce the latency of two bottleneck XMSS operations, namely key generation and signature generation, for which the speed of existing hardware accelerators is still apparently inadequate. The first proposed multi-core design uses block RAM and a simplified data flow to maximize the use of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> hash cores concurrently in three major sequential stages of computation, i. e., Winternitz One-time Signature (WOTS), L-tree and Merkle tree. The second proposed multi-core design adds a dedicated hash core for tree hashing in the L-tree and Merkle tree while keeping the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> hash cores solely for chain hashing in WOTS. The dedicated hash core leapfrogs between the L-tree and Merkle tree and computes concurrently with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> hash cores to keep the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p+1$ </tex-math></inline-formula> hash cores active most of the time while minimizing the storage requirement and energy consumption. Both designs are implemented on a 28 nm ATRIX-7 FPGA chip. Experimental results show that both proposed accelerators with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p=8$ </tex-math></inline-formula> operate at a much faster speed and consume significantly less hardware resources and energy than all existing XMSS accelerators. Specifically, they are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 8\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 6\times $ </tex-math></inline-formula> faster than the fastest reported design in key generation and signature generation operations, respectively.
Wireless Federated Learning With Dynamic Quantization and Bandwidth Adaptation

Quantization has been increasingly proposed in wireless federated learning (FL) to compress transmission data, which therefore significantly reduces the training latency. In this letter, we further reduce the training latency by introducing dynamic quantization and bandwidth adaptation. Our insights are twofold: (1) without introducing any information loss, the quantization bit length can be dynamically adjusted according to the magnitude of weight updates (i.e., differentials), which vary in different devices and training iterations, and (2) bandwidth allocation can further adapt to the varying sizes of transmission data to reduce the communication latency of straggler devices. We mathematically prove the convergence of dynamic quantization and formalize the optimization problem of bandwidth allocation. The evaluation results demonstrate that while preserving the test accuracy, our techniques can reduce over 50% training latency in both i.i.d. and non-i.i.d. datasets compared to previous work.