Miracle-dz/UniFAR-FTUs huggingface dataset:
Workload-aware budget compensation scheduling for NVMe solid state drives

Recently, solid state drives (SSDs) are replacing hard disk drives (HDDs) in datacenter storage systems in order to reduce power consumption and improve I/O performance. Additionally, in order to mitigate the performance bottleneck at I/O interface between host and SSD, the PCIe-leveraging NVMe SSD is emerging for data center SSDs. The NVMe interface supports the I/O virtualization mechanism called single root I/O virtualization (SR-IOV), which is a device self-virtualization technique for supporting direct paths from virtual machines (VMs) to I/O devices. Multiple virtual machines can share an SR-IOV-supporting physical device without intervention of virtual machine monitor. SR-IOV-supporting SSD should provide a device-level scheduler which can schedule the requests from multiple VMs considering performance isolation and fairness. In this paper, we propose a workload-aware budget compensation scheduling algorithm for the device-level request scheduler. To guarantee the performance isolation, the device-level scheduler estimates the contribution on the garbage collection (GC) cost of each virtual machine in the SSD device. Based on the estimated GC contributions, the budget of each VM is compensated for performance isolation. We experimented the effects of the proposed technique with an SSD simulator. The experiments showed that the scheduler can guarantee the performance isolation when multiple VMs share an NVMe SSD with different workloads.
Scale-independent Spatio-temporal Statistical Shape Representations for 3D Human Action Recognition

Since depth measuring devices for real-world scenarios became available in the recent past, the use of 3d data now comes more in focus of human action recognition. We propose a scheme for representing human actions in 3d, which is designed to be invariant with respect to the actor’s scale, rotation, and translation. Our approach employs Principal Component Analysis (PCA) as an exemplary technique from the domain of manifold learning. To distinguish actions regarding their execution speed, we include temporal information into our modeling scheme. Experiments performed on the CMU Motion Capture dataset shows promising recognition rates as well as its robustness with respect to noise and incorrect detection of landmarks.
Two Attacks on a Two-Factor User Authentication in Wireless Sensor Networks

Recently, a secure two-factor user authentication scheme for wireless sensor networks (WSN) was proposed by Das. The proposed scheme can resist many logged in users with the same login identity, stolen-verifier, guessing, impersonation and replay threats. In this paper we study the two-factor user authentication scheme for WSN and find that the scheme is insecure against the masquerade attacks. With our masquerade attacks, an attacker can masquerade as any legal user to login the gateway node without knowing the user's password or can masquerade as a gateway node to communicate with the legal user at anytime.
Modeling the Intra Household Behavioral Interaction

Quantitative models in marketing typically focus on the household as the unit of analysis while ignoring the individual family members' behavior and behavioral interactions among household members. However, knowledge of such intra-household behavioral interaction enables marketers to target their communications more effectively. In this paper, we propose a modeling framework to capture the intra-household behavioral interaction based on family members' actual consumption behavior over time. We develop a model to capture multiple agents' (more than two individuals') simultaneous choice decisions over more than two choice alternatives. This is extremely difficult with other previously developed modeling approaches. We apply the proposed model to a context of family member's television viewing, and simultaneously model whether TV is on, which type of programs is playing and which family member(s) is(are) watching. The proposed model allows us to estimate the individual's intrinsic preference and the extrinsic preference from a joint consumption with other members. These estimates allow us to test several alternative group decision-making heuristics that may operate in those joint consumption occasions and conduct managerially useful counterfactual simulations.
Computational Caricatures: Probing the Game Design Process with AI

We propose the creation of computational caricatures as a design research practice that aims to advance understanding of the game design process and to develop the reusable technology for design automation. Computational caricatures capture and exaggerate statements about the game design process in the form of computational systems (i.e. software and hardware). In comparison with empirical interviews of game designers, arguments from established design theory, and the creation of neutral simulations of the design process, computational caricatures provide more direct access to inquiry and insight about design. Further, they tangibly demonstrate architectures and subsystems for a new generation of human-assisting design support systems and adaptive games that embed aspects of automated design in their runtime processes. In this paper, we frame the idea of computational caricature, review several existing design automation prototypes through the lens of caricature, and call for more design research to be done following this practice.
Predicting Last-Touch References under Optimal Replacement

Effective cache replacement is becoming an increasingly important issue in cache hierarchy design as large set-associative caches are widely used in high-performance systems. This paper proposes a novel approach to approximate the decisions made by an optimal replacement algorithm (OPT) using last-touch prediction. The central idea is to identify, via prediction, the final reference to a cache block before the block would be evicted under OPT—the “OPT last touch”. Given perfect prediction, replacing the referenced block immediately after each OPT last touch would give optimal replacement behavior. This paper evaluates the feasibility of this approach by studying, at a fundamental level, the predictability of OPT last-touch references, and the applicability of these predictions to improving replacement decisions. We show that trace-based predictors, previously proposed for LRU lasttouch prediction, can predict OPT last touches as well. We enhance these predictors using future information, but find that their performance degrades significantly as cache size and associativity increase. We introduce a new class of predictors based on last-touch history, which significantly outperform trace-based predictors on large set-associative secondary caches. Across eight SPEC CPU2000 benchmarks on a 1MB 16-way associative secondary cache, an idealized history-based OPT last-touch predictor can potentially eliminate 39% of LRU misses—eliminating 63% of the gap between LRU and OPT.
A Probabilistic Approach to Optical Flow based Super-Resolution

This paper deals with the computation of a single super-resolution image from a set of low-resolution images, where the motion fields are not constrained to be parametric. In our approach, the inversion process, in which the super-resolved image is inferred from the input data, is interleaved with the computation of a set of dense optical flow fields. The case of arbitrary motion presents several significant challenges. First of all, the super-resolution setting dictates that the optic flow computations must be very precise. Furthermore, we have to consider the possibility that certain parts of the scene, which are visible in the super-resolved image, are occluded in some of the input images. Such occlusions must be identified and dealt with in the restoration process. We propose a Bayesian approach to tackle these problems. In this framework, the input images are regarded as sub-sampled and noisy versions of the unknown high-quality image. Also, the input data is considered incomplete, in the sense that we do not know which pixels from the evolving super-resolution image are occluded in particular images from the input set. This will be modeled by introducing so-called visibility maps, which are treated as hidden variables. We describe an EM-algorithm, which iterates between estimating values for the hidden quantities, and optimizing the flow-fields and the super-resolution image. The approach is illustrated with a synthetic and a challenging real-world example.
Determining Attitudinal Beliefs about Controversial Advertising

This study aims to determine the beliefs about advertising of two controversial products, namely political parties and sexual diseases prevention. The theory of reasoned action was utilized as underlying premise to elicit beliefs about controversial advertising in order to understand the formation of attitude towards the advertising. A qualitative approach by means of personal interview was adopted to explore the relatively unknown phenomenon. Subsequently, 43 interviews were conducted in Sarawak and the transcripts were then content-analyzed by multiple coders. The findings show that there are 11 statements of belief for the advertising of political parties and 6 statements for the advertising of sexual diseases prevention. Although both types of advertising are primarily considered as a source of information, their descriptions are largely different. While the advertising of political parties is mainly believed to be giving misleading information and for personal interest, the advertising of sexual diseases prevention is regarded as a form of education when executed appropriately. Therefore, the former must be carried out with care to avoid negative responses, and the latter should be done fittingly. This articulates the underlying consideration that there are different beliefs by type of advertising, and about what might create controversy.
Populating a framework for Readability Analysis Word frequency = word difficulty

This paper discusses a computational approach to readability that is expected to lead eventually towards a new and configurable metric for text readability. Our research involves the elaboration, implementation and evaluation of an 8-part framework that requires consideration of both textual and cognitive factors such as language, vocabulary, background knowledge, motivation and cognitive load. We will discuss our work to date that has examined the limitations of current measures of readability and made consideration for how the wider textual and cognitive phenomena may be accounted for. This includes techniques for statistical and linguistic approaches to terminology extraction, approaches to lexical and grammatical simplification and considerations of plain language. Our interest in readability is motivated towards making semantic content more readily available and, as a consequence, improving quality of documents, There are already indications that this work will contribute to a British Standard for readability.
Vitamin D-insufficiency

Background: Vitamin D insufficiency is increasingly recognized as an important risk factor in the pathogenesis of falls and fractures and may increase the risk of other diseases. The aim of this study was to obtain information about the vitamin D supply from a representative cohort of the German population. Methods: 264 General practitioners participated in the DeViD-Trial (D-Vitamin in Deutschland) by taking blood samples from their consenting daily ambulant patients regardless of the actual reason for consultation. In these blood samples vitamin D [25(OH)D] and other related parameters were measured at a central laboratory. The patients filled in a simple questionnaire (i.e., age, sex, etc.). The trial was performed between February 26 and May 25, 2007. Results: Laboratory and personal data were documented for 1,343 individuals (615 men, 728 women). The age distribution ranged from 20 to 99 y, the mean age of the whole cohort was 57.6 y (men 58.2, women 57.2). The mean 25-OH-D-value for the whole cohort was 16.2 ng/ml (range: 6.0 to 66.8, median 14.1 ng/ml). Ten percent of the patients had 25(OH)D-values below 7 ng/ml, 65% below 20 ng/ml and 92% showed values below 30 ng/ml. In the more recent literature, 25(OH)D values below 30 ng/ml are regarded as sub-optimal for bone, muscle and general health. Correspondingly it can be stated that in this representative population there is a high prevalence of moderate to severe vitamin D-insufficiency regardless of young or old age
Optimal energy management of grid-connected photovoltaic micro-grid

In accordance with the energy optimization management of grid-connected PhotoVoltaic (PV) micro-grid, this paper firstly forecasts the output of PV power based on Least Squares Support Vector Machine (LS-SVM) to resolve the randomness of distributed PV power generation. Then a Micro-grid Energy Management (MEM) model that treats the lowest total cost of electricity in micro-grid as the objective function is set up, which takes the power balance, the upper and lower power of units, the unit commitment status, PV as well as the grid real-time bidding pricing and so on into account. Next a Modified Artificial Fish School (MAFSA) algorithm, which adds dynamic parameters (changeable visual distance, variable moving step) and Student's t-distribution probability operator to enhance the global optimization ability and the convergence accuracy in the mid-to-late iteration, is introduced to economically allocate all the micro powers in micro-grid. Finally, through an example analysis, the results show that the proposed MEM model is feasible and has a certain reference value for MEM.
Debug all your code: portable mixed-environment debugging

Programmers build large-scale systems with multiple languages to reuse legacy code and leverage languages best suited to their problems. For instance, the same program may use Java for ease-of-programming and C to interface with the operating system. These programs pose significant debugging challenges, because programmers need to understand and control code across languages, which may execute in different environments. Unfortunately, traditional multilingual debuggers require a single execution environment. This paper presents a novel composition approach to building portable mixed-environment debuggers, in which an intermediate agent interposes on language transitions, controlling and reusing single-environment debuggers. We implement debugger composition in Blink, a debugger for Java, C, and the Jeannie programming language. We show that Blink is (1) relatively simple: it requires modest amounts of new code; (2) portable: it supports multiple Java Virtual Machines, C compilers, operating systems, and component debuggers; and (3) powerful: composition eases debugging, while supporting new mixed-language expression evaluation and Java Native Interface (JNI) bug diagnostics. In real-world case studies, we show that language-interface errors require single-environment debuggers to restart execution multiple times, whereas Blink directly diagnoses them with one execution. We also describe extensions for other mixed-environments to show debugger composition will generalize.
West Nile Virus Encephalitis and Myocarditis in Wolf and Dog

In the third season (2002) of the West Nile virus epidemic in the United States, two canids (wolf and dog) were diagnosed with West Nile virus encephalitis and myocarditis with similarities to known affected species (humans, horses, and birds). The West Nile virus infections were confirmed by immunohistochemistry and polymerase chain reaction.
Working Memory Replay Prioritizes Weakly Attended Events

Abstract One view of working memory posits that maintaining a series of events requires their sequential and equal mnemonic replay. Another view is that the content of working memory maintenance is prioritized by attention. We decoded the dynamics for retaining a sequence of items using magnetoencephalography, wherein participants encoded sequences of three stimuli depicting a face, a manufactured object, or a natural item and maintained them in working memory for 5000 ms. Memory for sequence position and stimulus details were probed at the end of the maintenance period. Decoding of brain activity revealed that one of the three stimuli dominated maintenance independent of its sequence position or category; and memory was enhanced for the selectively replayed stimulus. Analysis of event-related responses during the encoding of the sequence showed that the selectively replayed stimuli were determined by the degree of attention at encoding. The selectively replayed stimuli had the weakest initial encoding indexed by weaker visual attention signals at encoding. These findings do not rule out sequential mnemonic replay but reveal that attention influences the content of working memory maintenance by prioritizing replay of weakly encoded events. We propose that the prioritization of weakly encoded stimuli protects them from interference during the maintenance period, whereas the more strongly encoded stimuli can be retrieved from long-term memory at the end of the delay period.
The Most Persistent Soft-Clique in a Set of Sampled Graphs

When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. However, it turns out that the inconsistencies introduced when different graph instances have different edge sets pose a serious challenge. In this work we address this challenge for the problem of finding maximum weighted cliques. We introduce the concept of most persistent soft-clique. This is subset of vertices, that 1) is almost fully or at least densely connected, 2) occurs in all or almost all graph instances, and 3) has the maximum weight. We present a measure of clique-ness, that essentially counts the number of edge missing to make a subset of vertices into a clique. With this measure, we show that the problem of finding the most persistent soft-clique problem can be cast either as: a) a max-min two person game optimization problem, or b) a min-min soft margin optimization problem. Both formulations lead to the same solution when using a partial Lagrangian method to solve the optimization problems. By experiments on synthetic data and on real social network data we show that the proposed method is able to reliably find soft cliques in graph data, even if that is distorted by random noise or unreliable observations.
Lossless source coding for a Heegard-Berger problem with two sources and degraded reconstruction sets

This work investigates the problem of lossless compression of two arbitrarily correlated memoryless sources in an instance of Heegard-Berger problem with degraded reconstruction sets. Specifically, one of the sources is to be reproduced losslessly by two separate decoders, and the other one is to be reproduced losslessly by only one of the two decoders. The decoders observe side information sequences that are arbitrarily correlated among them, and with the sources. We establish a single-letter characterization of the optimal compression rate of this model. We also discuss several examples, and highlight the utility of an appropriate joint compression of the two sources for it to be optimal.
Novel Compact UWB Bandpass Filters Design With Cross-Coupling Between $\lambda / 4$  Short-Circuited Stubs

Two novel, compact, and highly frequency selective ultra-wideband (UWB) bandpass filters (BPFs) based on an improved model are presented in this letter. The improved model contains four short-circuited stubs connected by transmission lines. A cross-coupling between the first and last short-circuited stubs is introduced to obtain new pairs of transmission zeros on both sides of the passband. The relationship between the coupling factor and the position of the transmission zeros was analyzed and good agreement was obtained between the analysis and model simulation results. Based on the model, two UWB BPFs are designed with 51% (3.1 to 5.2) GHz and 108% (3 to 10) GHz relative bandwidth and small size of 15 mm × 15 mm and 19 mm × 14 mm, respectively. The insertion loss of the (3.1 to 5.2) GHz filter is less than 0.4 dB in passband and the other one is less than 3 dB. The simulation and measurement results agree well.
Creatively Constructing a Community of Learners.

This action research study investigated first-graders' attitudes toward being a community of learners as a result of their involvement in constructing their classroom environment. The children's attitudes throughout the year were measured through student surveys, parent surveys, student reflections, teacher reflective journal entries, and informal reading inventories. Even though the experimental group's perceptions of helping, closeness, collaboration, and student influence decreased after their involvement in the intervention, these perceptions were still higher than those measured at the beginning of the year. These students' attitudes pertaining to positive interpersonal relationships never lessened but grew steadily stronger throughout the year. The results also indicated that the experimental group perceived itself more as a community of learners at the end of the year than did the control group. Involvement in making decisions with classmates, collaborating in learning with peers, and engaging in multi-age activities prompted feelings of community among the children. Authentic opportunities for student-driven learning, decision making, boosting self-confidence, and collaborating emerged. Notable gains in the experimental group's reading achievement were observed. The conclusions paralleled the transactive model of teaching and supported the belief that encouraging students to socially and physically create their learning environment promotes learning while building a sense of community. Limitations of the study, such as student test anxiety, were addressed. Concluding thoughts highlighted insights into how to allow the construction process to flow more smoothly and be more effective for all involved.
Building "problem solving engines" for combinatorial optimization problems

We describe our attempts to build problem solving engines that together cover a large portion of combinatorial optimization problems encountered in real world applications. Our approach is to select a list of standard problems, and develop their solvers as engines. All the engines we developed are based on the idea of local search and metaheuristics. As standard problems, we have chosen so far CSP (constraint satisfaction problem), RCPSP (resource constrained project scheduling problem), GAP (generalized assignment problem), VRP (vehicle routing problem), SCP (set covering problem), MAX-SAT (maximum satisfiability problem), 2PP (2-dimensional packing problem) and others. We outline definitions of these problems, algorithmic contents of engines, and some computational results, putting emphasis on RCPSP and VRP.
ON-LINE IDENTIFICATION OF A ROBOT MANIPULATOR USING NEURAL NETWORK WITH AN ADAPTIVE LEARNING RATE

Abstract This paper proposes an extension of neural network identification capabilities for on-line identification of a nonlinear closed-loop control system. The neural network (NN) is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate. The optimization algorithm is performed at each sample time to compute the optimal control input. The results confirm the effectiveness of the proposed neural network based identification scheme and control architecture.