Miracle-dz/UniFAR-FTUs huggingface dataset:
OptiShare : A Dynamic Channel Sharing Scheme for Power Efficient On-chip Optical Architectures

In this paper we explore a new class of optical networks that try to reduce static power using laser modulation techniques as well as by sharing optical channels. In our proposed scheme, OptiShare, a station can only source power from a segmented tree based power waveguide only if it has one or multiple tokens. A station arbitrates for a token before sending a message, and then releases it. At the end of an epoch, we aggregate usage information from across the network and predict the number of units of power (1 unit = 1 token) that should be supplied in the next epoch. Such an approach helps us conserve static power, and share optical power equitably across stations. OptiShare is 14% faster than a traditional network that does not share waveguides, and consumes 20-30X less energy than systems that do not use laser modulation.
PrefEdge: SSD Prefetcher for Large-Scale Graph Traversal

Mining large graphs has now become an important aspect of multiple diverse applications and a number of computer systems have been proposed to provide runtime support. Recent interest in this area has led to the construction of single machine graph computation systems that use solid state drives (SSDs) to store the graph. This approach reduces the cost and simplifies the implementation of graph algorithms, making computations on large graphs available to the average user. However, SSDs are slower than main memory, and making full use of their bandwidth is crucial for executing graph algorithms in a reasonable amount of time. In this paper, we present PrefEdge, a prefetcher for graph algorithms that parallelises requests to derive maximum throughput from SSDs. PrefEdge combines a judicious distribution of graph state between main memory and SSDs with an innovative read-ahead algorithm to prefetch needed data in parallel. This is in contrast to existing approaches that depend on multi-threading the graph algorithms to saturate available bandwidth. Our experiments on graph algorithms using random access show that PrefEdge not only is capable of maximising the throughput from SSDs but is also able to almost hide the effect of I/O latency. The improvements in runtime for graph algorithms is up to 14× when compared to a single threaded baseline. When compared to multi-threaded implementations, PrefEdge performs up to 80% faster without the program complexity and the programmer effort needed for multi-threaded graph algorithms.
GRSA: Generalized range swap algorithm for the efficient optimization of MRFs

Markov Random Field (MRF) is an important tool and has been widely used in many vision tasks. Thus, the optimization of MRFs is a problem of fundamental importance. Recently, Veskler and Kumar et. al propose the range move algorithms, which are one of the most successful solvers to this problem. However, two problems have limited the applicability of previous range move algorithms: 1) They are limited in the types of energies they can handle (i.e. only truncated convex functions); 2) These algorithms tend to be very slow compared to other graph-cut based algorithms (e.g. α-expansion and αβ-swap). In this paper, we propose a generalized range swap algorithm (GRSA) for efficient optimization of MRFs. To address the first problem, we extend the GRSA to arbitrary semimetric energies by restricting the chosen labels in each move so that the energy is submodular on the chosen subset. Furthermore, to feasibly choose the labels satisfying the submodular condition, we provide a sufficient condition of the submodularity. For the second problem, unlike previous range move algorithms which execute the set of all possible range moves, we dynamically obtain the iterative moves by solving a set cover problem, which greatly reduces the number of moves during the optimization. Experiments show that the GRSA offers a great speedup over previous range swap algorithms, while it obtains competitive solutions.
Designing a minimalist socially aware robotic agent for the home

We present a minimalist social robot that relies on long timeseries of low resolution data such as mechanical vibration, temperature, lighting, sounds and collisions. Our goal is to develop an experimental system for growing socially situated robotic agents whose behavioral repertoire is subsumed by the social order of the space. To get there we are designing robots that use their simple sensors and motion feedback routines to recognize different classes of human activity and then associate to each class a range of appropriate behaviors. We use the Katie Family of robots, built on the iRobot Create platform, an Arduino Uno, and a Raspberry Pi. We describe its sensor abilities and exploratory tests that allow us to develop hypotheses about what objects (sensor data) correspond to something known and observable by a human subject. We use machine learning methods to classify three social scenarios from over a hundred experiments, demonstrating that it is possible to detect social situations with high accuracy, using the low-resolution sensors from our minimalist robot.
Information Flow and Entropy Production in the Kalman-Bucy Filter ∗

We investigate the information theoretic properties of KalmanBucy filters in continuous time, developing notions of information supply, storage and dissipation. By introducing a concept of energy, we develop a physical analogy in which the unobservable signal describes a statistical mechanical system interacting with a heat bath. The abstract ‘universe’ comprising the signal and the heat bath obeys a non-increase law of entropy; however, with the introduction of partial observations, this law can be violated. The Kalman-Bucy filter behaves like a Maxwellian demon in this analogy, returning signal energy to the heat bath without causing entropy increase. This is made possible by the steady supply of new information. The analogy thus provides a quantitative example of Landauer’s Principle, operating in reverse. The filter sets up a stationary non-equilibrium state, in which energy flows around the loop comprising the heat bath, the signal and the filter. We define a rate of interactive entropy production, isolating the statistical mechanics of this flow from the marginal mechanics of the signal and filter. This leads naturally to a dual filtering problem in which information flows are reversed.
Corresponding Authors: Hierarchical Task Representation: Task Files and Response Selection Task-file Representation Task Files Account for Interactions between Discrete Responses

How do we achieve flexible goal-directed behavior when we are continually confronted with an array of stimuli and a variety of potential responses to those stimuli? This fundamental operation is the purview of response selection, the set of cognitive processes that choose (activate) a behavior (response) to a stimulus given one's goals and current situation. Many theories of response selection implicitly or explicitly assume a simple associative mechanism linking stimuli to responses. Botvinick, Braver, Barch, Carter, and Cohen's (2001) conflict-monitoring model is a good example of this. In it, the environment activates stimulus representations, which in turn activate responses associated with those stimuli. Separate conflict-monitoring and control processes bias which stimulus-response association produces an action. This mechanism has been highly productive. In fact, it is difficult to imagine a framework for response selection that does not involve, at some level, stimulus-response associations. However, there is a large body of data that are not readily accommodated by the view that response selection is simply the activation of the most appropriate independent stimulus-response association by environmental and control processes. We surveyed these findings in Hazeltine and Schumacher (2016), where we concluded that conceptualizing response selection as the activation of particular stimulus-response associations fails to capture critical features of behavior relating to the ability to encode abstract relationships across sets of stimuli and responses (see also Rescorla, 1988). An alternative approach, outlined here, is to apply principles from accounts of frontal-lobe function in which hierarchical systems organize behaviors to meet current task goals Much of this theoretical work has focused on how discrete responses are flexibly selected so that action is driven by context, goals, and plans rather than strictly by the current environment. Here, we argue that such mechanisms should not be ignored when considering discrete choice reaction tasks with fixed mappings and minimal planning requirements. Rather, we propose that prefrontal mechanisms described Abstract Human behavior is remarkably complex—even during the performance of relatively simple tasks—yet it is often assumed that learned associations between stimuli and responses provide the representational substrate for action selection. Here, we introduce an alternative framework, called a task file, that includes hierarchical associations between stimulus features, response features, goals, and drives, which may overcome the limitations inherent in the conceptualization of response selection as being based solely on associations between stimuli and responses. We then review evidence from our own experimental research showing that even in the …
The use of Hamburger paragraph and selfregulated learning on student’s writing recount text (A Quasi-experimental study at the tenth grade students of Perguruan Rakyat 2 vocational high school)

Ika Kurniasih. The Use of Hamburger Paragraph and Self-Regulated Learning on Writing Recount Text (A Quasi-Experimental Study at the Tenth Grade Students of Perguruan Rakyat Vocational High School), 2018. The goals of this study were to obtain the empirical evidences on the effect of hamburger paragraph and self-regulated learning on writing recount text at the tenth grade students of Perguruan Rakyat 2 Vocational High School in academic year 2017-2018. The study used quasi-experimental method which employed cluster random sampling. There were two classes taken as sample that each class consists of 30 students. The data were analyzed using Analysis of Variance (ANOVA) and two ways design with treatment by level 2x2. The Findings showed that: 1) there was significant difference of writing recount text score between students who were taught by hamburger paragraph with those that were taught by conventional strategy, 2) there was significant difference between students with high selfregulated learning and the students with low self-regulated learning on their writing score of recount text, 3) there was no interactional effect of hamburger paragraph and self-regulated learning on student’s writing recount text. Thus, the research shows that hamburger paragraph and self-regulated learning had some positive impact on student’s writing recount text.
CONTEXT-AWARE DECISION MAKING SYSTEM FOR MOBILE CLOUD OFFLOADING

In this study, a mobile cloud offloading system has been developed to decide that a process run on the cloud or on the mobile platform. A context-aware decision algorithm has been developed. The low performance and problem of battery consumption of mobile devices have been fundamental challenges on the mobile computing. To overcome this kind of challenges, recent advances towards mobile cloud computing propose a selective mobile-to-cloud offloading service by moving a mobile application from a slow mobile device to a fast server in the cloud during run time. Determine whether a process running on cloud or not is an important issue. Power consumption and time limits are vitally important for decision. In this study we used PowerTutor application which is a dynamic power measurement modelling tool. Another important factor is the process completion time. Calculate the power consumption is very difficult.
Semantic Parsing for Priming Object Detection in RGB-D Scenes

The advancements in robot autonomy and capabilities for carrying out more complex tasks in unstructured indoors environments can be greatly enhanced by endowing existing environment models with semantic information. In this paper we describe an approach for semantic parsing of indoors environments into semantic categories of ground, structure, furniture and props. Instead of striving to categorize all object categories and instances encountered in the environment, this choice of semantic labels separates clearly objects and nonobject categories. We use RGB-D images of indoors environments and formulate the problem of semantic segmentation in the Conditional Random Fields Framework. The appearance and depth information enables us induce the graph structure of the random field, which can be effectively approximated by a tree and to design robust geometric features, which are informative for separation and characterization of different categories. These two choices notably improve the efficiency and performance of the semantic parsing tasks. We carry out the experiments on a NYU V2 dataset and achieve superior performance and the fraction of computational cost.
Comparison of Attitudes of School Administrators and Counselors towards Mandatory Reporting of Suspected Child Sexual Abuse

The aim of this study was to compare the attitudes of Turkish school administrators and school counselors towards the mandatory reporting of suspected child sexual abuse and to determine their main support needs in relation to their reporting obligations. Quantitative and qualitative research methods were used in this study. Both groups had high subscale scores which indicated positive attitude, but although the quantitative results suggested that school administrators had a more positive attitude than school counselors, this result was not confirmed by the qualitative analysis. The school administrators did not know the relevant laws and the school counselors thought they were unclear. Both groups had concerns about the consequences of reporting for themselves and children, but only reporter-related factors such ensuring the security of reporters were identified as preconditions for fulfilling their obligation to report CSA. More child-centered reporting policies should be devised and implemented.
On Bayesian inference for the M/G/1 queue with efficient MCMC sampling

We introduce an efficient MCMC sampling scheme to perform Bayesian inference in the M/G/1 queueing model given only observations of interdeparture times. Our MCMC scheme uses a combination of Gibbs sampling and simple Metropolis updates together with three novel "shift" and "scale" updates. We show that our novel updates improve the speed of sampling considerably, by factors of about 60 to about 180 on a variety of simulated data sets.
Computing core payments in combinatorial auctions

A combinatorial auction is one in which preferences are expressed for combinations or bundles of items, rather than just for individual items. The importance of combinatorial auctions has grown tremendously in the past 15 years, which have seen an explosion in the use of auction mechanisms in both government allocation problems and business-to-business commerce. Among the many applications of combinatorial auction research are auctions for shipping-lanes and other procurement in the private sector, as well as auctions for spectrum licenses by the FCC and the proposed auctioning of airport landing slots by the FAA in the public sector. In each of these environments, the expression of aggregate information allows the bidders to realize synergies (e.g., economies-of-scale or owning complementary items) while the auction mechanism stimulates competition, aiding the seller of items with more competitive prices. Much of the literature on combinatorial auctions has focused on the underlying problem of finding an efficient allocation (called the winner-determination problem) which is usually NP-hard, and the problem that the amount of information necessary to describe a bidder’s preferences for all bundles grows exponentially in the number of items. Proposed solutions to these problems include the description of classes of winner-determination instances which are tractable, and the development of “bidding languages” which can express preferences more efficiently, respectively. The problem of strategic difficulty for bidders is often ignored or down-played, however. In order to assume away the bidders’ problem of determining an opti-
Optimal Point Set Partitioning using Rigid Motion Star Placement

We consider the problem of determining the placement of a star R on a set P of n points in the plane such that a given objective function is maximized. A star R is a set of m rays {r1,. ,rm} in R 2 , emanating from a point p such that the angle between two consecutive rays is 2 m . A cone defined by two consecutive rays is c-occupied if it contains at least c points of P. Our main result is an O(n 3 m 2 ) expected time (and O(n 3 m 2 lognm) deterministic time) algorithm to find the rigid motion placement of R that maximizes the number of c-occupied cones. We then show how this technique can be extended to solve several other optimization problems.
Despondent officer narratives and the ‘post-Ferguson’ effect: exploring law enforcement perspectives and strategies in a southern American state

ABSTRACT The fatal shooting of a young, black, and male citizen named Michael Brown by a white male police officer in Ferguson (MO) generated political and media backlash that continues to erode law enforcement legitimacy today. Law enforcement is sensitive to changes in the public discourse on their profession and has responded in kind. A plethora of discussion has focused on the potential existence of a so-called ‘Ferguson Effect’, whereby de-policing has emerged among officers as a result of concerns about being subjected to negative media scrutiny for racial profiling or excessive use of force, in turn increasing crime rates. Unfortunately, the ‘Ferguson Effect’ is long on anecdotes but short on data. To better understand officers’ attitudes, perspectives, and strategies and the way in which the negative press related to the Ferguson incident interacted with these, in-depth semi-structured interviews with 20 law enforcement officials were supplemented with participant observations of officer deployments in two southern State counties in the United States. Officer confidence, morale, and policing strategies were queried within the post-Ferguson era. The data suggest that there had been an increased conceptual awareness of procedural justice but also a reduction in officer morale and emphasis on proactive policing strategies, and that Ferguson was often drawn upon as a reference point among officers. The implications for these findings are discussed in terms of future police policies and practices within a procedural justice framework that seeks to reduce crime and increase law enforcement legitimacy.
Computational analysis to explore authors’ depiction of characters

This study involves automatically identifying the sociolinguistic characteristics of fictional characters in plays by analyzing their written “speech”. We discuss three binary classification problems: predicting the characters’ gender (male vs. female), age (young vs. old), and socioeconomic standing (upper-middle class vs. lower class). The text corpus used is an annotated collection of August Strindberg and Henrik Ibsen plays, translated into English, which are in the public domain. These playwrights were chosen for their known attention to relevant socioeconomic issues in their work. Linguistic and textual cues are extracted from the characters’ lines (turns) for modeling purposes. We report on the dataset as well as the performance and important features when predicting each of the sociolinguistic characteristics, comparing intra- and inter-author testing.
Characterization of a P-Rex1 gene signature in breast cancer cells

The Rac nucleotide Exchange Factor (Rac-GEF) P-Rex1 is highly expressed in breast cancer, specifically in the luminal subtype, and is an essential mediator of actin cytoskeleton reorganization and cell migratory responses induced by stimulation of ErbB and other tyrosine-kinase receptors. Heregulin (HRG), a growth factor highly expressed in mammary tumors, causes the activation of P-Rex1 and Rac1 in breast cancer cells via ErbB3, leading to a motile response. Since there is limited information about P-Rex1 downstream effectors, we carried out a microarray analysis to identify genes regulated by this Rac-GEF after stimulation of ErbB3 with HRG. In T-47D breast cancer cells, HRG treatment caused major changes in gene expression, including genes associated with motility, adhesion, invasiveness and metastasis. Silencing P-Rex1 expression from T-47D cells using RNAi altered the induction and repression of a subset of HRG-regulated genes, among them genes associated with extracellular matrix organization, migration, and chemotaxis. HRG induction of MMP10 (matrix metalloproteinase 10) was found to be highly sensitive both to P-Rex1 depletion and inhibition of Rac1 function by the GTPase Activating Protein (GAP) β2-chimaerin, suggesting the dependence of the P-Rex1/Rac1 pathway for the induction of genes critical for breast cancer invasiveness. Notably, there is a significant association in the expression of P-Rex1 and MMP10 in human luminal breast cancer, and their co-expression is indicative of poor prognosis.
Random Oracle Model of Information Hiding System

— Random Oracle Model (ROM) is an effective method for measuring the practical security of cryptograph. In this paper, we try to use it into information hiding system (IHS). Because IHS has its own properties, the ROM must be modified if it is used into IHS. Firstly, we fully discuss why and how to modify each part of ROM respectively. The main changes include: 1) Divide the attacks that IHS may be suffered into two phases and divide the attacks of each phase into several kinds. 2) Distinguish Oracles and Black-boxes clearly. 3) Define Oracle and four Black-boxes that IHS used. 4) Propose the formalized adversary model. And 5) Give the definition of judge. Secondly, based on ROM of IHS, the security against known original cover attack (KOCA-KOCA-security) is defined. Then, we give an actual information hiding scheme and prove that it is KOCA-KOCA-secure. Finally, we conclude the paper and propose the open problems of further research.
A Petri net based deadlock prevention policy for flexible manufacturing systems

In this paper we illustrate a compositional method for modeling the concurrent execution of working processes in flexible manufacturing systems (FMS) through a special class of Petri nets that we call S/sup 3/PR. In essence, this class is built from state machines sharing a set of places modeling the availability of system resources. The analysis of S/sup 3/PR leads us to characterize deadlock situations in terms of a zero marking for some structural objects called siphons. In order to prevent the system from deadlocks, we propose a policy for resource allocation based on the addition of new places to the net imposing restrictions that prevent the presence of unmarked siphons (direct cause of deadlocks). Finally we present the application of this technique to a realistic FMS case. >