Miracle-dz/UniFAR-FTUs huggingface dataset:
Rehearsal Model as the Practicum Model in Preparing Professional Pre-service English Teachers

Practicum module in the Faculty of Tarbiyah and Teacher Training at UIN Sunan Ampel Surabaya has not been integrated fully with other modules offered. However, the Faculty offers Microteaching module (PPL1) and Internship module (PPL2) as two practicum modules available. Microteaching module (PPL1) had not trained students for real classroom condition. Therefore, an innovation to improve previous practicum system has been applied through the implementation of Rehearsal Model. The purpose of this study is to find out the implication of Rehearsal Model implementation in preparing professional pre-service teachers. The result shows that students were more ready, more motivated, and that they gained more self-confidence, and they studied harder. It is assumed that Rehearsal Model is a suitable model to be applied in Practicum module. Keywords—Rehearsal Model, Practicum, Microteaching,
Symbol-level synchronization using probability lookup-table for IDS error correction

Insertion/Deletion/Substitution (IDS) error correction will become important for future high-density storage devices, e.g., bit-patterned media. For IDS error correction, concatenated codings of an outer LDPC code and an inner marker code have been proposed. To improve the synchronization capability, marker decoder can employ symbol-level synchronization (SLS) with a large symbol size, while the computational time complexity of the SLS increases exponentially with the symbol size. This paper proposes an SLS using a probability lookup table to reduce the complexity. Probabilities in the table are precomputed for given channel and code parameters, and computationally intensive calculations of the SLS are replaced by table lookups. The proposed method is applicable to both single-path and multipath decodings. The bit error rate (BER) is evaluated for SLS of symbols sizes bS = 1, 2, 4, and 8 bits, and it is confirmed that larger symbol size gives lower BER.
The Influence of Social Hierarchy on Primate Health

Dominance hierarchies occur in numerous social species, and rank within them can greatly influence the quality of life of an animal. In this review, I consider how rank can also influence physiology and health. I first consider whether it is high- or low-ranking animals that are most stressed in a dominance hierarchy; this turns out to vary as a function of the social organization in different species and populations. I then review how the stressful characteristics of social rank have adverse adrenocortical, cardiovascular, reproductive, immunological, and neurobiological consequences. Finally, I consider how these findings apply to the human realm of health, disease, and socioeconomic status.
Epigenetic activation of MGAT3 and corresponding bisecting GlcNAc shortens the survival of cancer patients

Bisecting GlcNAc on N-glycoproteins is described in E-cadherin-, EGF-, Wnt- and integrin- cancer-associated signaling pathways. However, the mechanisms regulating bisecting GlcNAc expression are not clear. Bisecting GlcNAc is attached to N-glycans through beta 1-4 N-acetylglucosaminyl transferase III (MGAT3), which is encoded by two exons flanked by high-density CpG islands. Despite a recently described correlation of MGAT3 and bisecting GlcNAc in ovarian cancer cells, it remains unknown whether DNA methylation is causative for the presence of bisecting GlcNAc. Here, we narrow down the regulatory genomic region and show that reconstitution of MGAT3 expression with 5-Aza coincides with reduced DNA methylation at the MGAT3 transcription start site. The presence of bisecting GlcNAc on released N-glycans was detected by mass spectrometry (LC-ESI-qTOF-MS/MS) in serous ovarian cancer cells upon DNA methyltransferase inhibition. The regulatory impact of DNA methylation on MGAT3 was further evaluated in 18 TCGA cancer types (n = 6118 samples) and the results indicate an improved overall survival in patients with reduced MGAT3 expression, thereby identifying long-term survivors of high-grade serous ovarian cancers (HGSOC). Epigenetic activation of MGAT3 was also confirmed in basal-like breast cancers sharing similar molecular and genetic features with HGSOC. These results provide novel insights into the epigenetic regulation of MGAT3/bisecting GlcNAc and demonstrate the importance of N-glycosylation in cancer progression.
Information Gain vs. State Disturbance in Quantum Theory

The engine that powers quantum cryptography is the principle that there are no physical means for gathering information about the identity of a quantum system’s state (when it is known to be prepared in one of a set of nonorthogonal states) without disturbing the system in a statistically detectable way. This situation is often mistakenly described as a consequence of the “Heisenberg uncertainty principle. ” A more accurate account is that it is a unique feature of quantum phenomena that rests ultimately on the Hilbert space structure of the theory along with the fact that time evolutions for isolated systems are unitary. In this paper we shall explore several aspects of the information–disturbance principle in an attempt to make it firmly quantitative and flesh out its significance for quantum theory as a whole.
Specifying Distributed Adaptation through Software Component Relocation

This paper proposes a theoretical foundation for specifying and reasoning about adaptations based on our middleware system that introduces the relocation of software components to define functions between computers as a basic mechanism for adaptation on distributed systems. It provides a language for specifying adaptations policies. The language is useful to reason about adaptations and can be executed in the middleware system.
A Behavioral Odor Similarity “Space” in Larval Drosophila

To provide a behavior-based estimate of odor similarity in larval Drosophila, we use 4 recognition-type experiments: 1) We train larvae to associate an odor with food and then test whether they would regard another odor as the same as the trained one. 2) We train larvae to associate an odor with food and test whether they prefer the trained odor against a novel nontrained one. 3) We train larvae differentially to associate one odor with food, but not the other one, and test whether they prefer the rewarded against the nonrewarded odor. 4) In an experiment like (3), we test the larvae after a 30-min break. This yields a combined task-independent estimate of perceived difference between odor pairs. Comparing these perceived differences to published measures of physicochemical difference reveals a weak correlation. A notable exception are 3-octanol and benzaldehyde, which are distinct in published accounts of chemical similarity and in terms of their published sensory representation but nevertheless are consistently regarded as the most similar of the 10 odor pairs employed. It thus appears as if at least some aspects of olfactory perception are "computed" in postreceptor circuits on the basis of sensory signals rather than being immediately given by them.
Online Prosocial Behaviors

Prosocial behaviors in the cyber context (i.e., the internet, text messages) can be traced back to when the internet was just a message board, used to share open source software. Following these early investigations of prosocial behaviors, clinicians recognized that the internet might remove barriers to help seeking. Recent investigations have provided support for the internet as a place to seek help among various populations. Prosocial behaviors in the cyber context also have benefits for the givers as well, including health benefits, personal satisfaction, and reputational increases. This chapter draws on multidisciplinary research to review prosocial behaviors in the cyber context.
Model-Driven Refactoring Approaches: A Comparison Criteria

Model-driven engineering, an emerging trend in software engineering, has enabled the application of refactoring to UML models. Due to its growing popularity in the domain of refactoring, a number of approaches to specify models and transformation rules have been proposed in literature. A comparison framework is required by researchers and practitioners to guide them in selecting an appropriate approach suitable to their specific needs and trade-offs. In this paper, we provide a set of suitable criteria to evaluate and compare the various model refactoring approaches that can aid practitioners and researchers in the selection process. The paper also compares the refactoring approaches against the framework.
Real- Time Monocular Face Tracking Using an Active Camera

This paper addresses the problem of facial feature detection and tracking in real-time using a singleactive camera. The variable parameters of the camera (i.e pan, tilt and zoom) are changed adaptively totrack the face of the agent in successive frames and detect the facial features which may be used for facialexpression analysis for surveillance or mesh generation for animation purposes, at a later stage. Ourtracking procedure assumes planar motion of the face. It also detects invalid feature points i.e. thosefeature points which do not correspond to actual facial features, but are outliers. They are subsequentlyabandoned by our procedure in order to extract ‘high level’ information from the face for mesh generationor emotion recognition etc. The performance of the procedure is independent of the velocity of the agentand is robust to velocity changes. The only limitation on the performance of the procedure is imposedby the maximum pan/tilt range of the camera.2
Locomotion of Metamorphic Robotic System Based on Local Information (Extended Abstract)

Motivated by the molecular robotics project of Japan, we investigate the problem of designing a distribution locomotion algorithm for metamorphic robotic system consisting of homogeneous modules. The algorithm, which works on each module, must navigate it so that the entire system moves in a predetermined direction. We present an algorithm that moves the system eastward, provided that an initial configuration is monotone. Then we show the outline of its correctness.
A na ve de-lambing method for speaker identification

This paper addresses the issue of close-set text-independent speaker identification from speech samples recorded over telephone. We have known that the speaker identification performance variability can be attributed to many factors. One major factor is the inherent differences in the recognizability of different speakers. In speaker recognition systems such differences are characterized by the use of animal names for different types of speakers. In this paper we use lambs to refer to those speakers who are particularly easy to imitate in our closeset text-independent speaker identification system. That is, other speakers are much more likely to be recognized as these lamb speakers when they cannot be correctly recognized. Lambs adversely affect our close-set text-independent speaker identification performance a lot. In this paper we describe a naive de-lambing method to deal with these lamb speakers so as to improve our system performance. The speech data of our close-set speaker identification system is from the NIST 1999 Speaker Recognition Evaluation. Our experiments were conducted on 230 male speakers. We tried both testing from same telephone channels and sessions with training and different telephone channels and sessions with training for each speaker. Combined, the method developed in this paper result in a 15% relative improvement on the close-set 45-second training 10-second testing condition.
Every Second Counts: Quantifying the Negative Externalities of Cybercrime via Typosquatting

While we have a good understanding of how cyber crime is perpetrated and the profits of the attackers, the harm experienced by humans is less well understood, and reducing this harm should be the ultimate goal of any security intervention. This paper presents a strategy for quantifying the harm caused by the cyber crime of typo squatting via the novel technique of intent inference. Intent inference allows us to define a new metric for quantifying harm to users, develop a new methodology for identifying typo squatting domain names, and quantify the harm caused by various typo squatting perpetrators. We find that typo squatting costs the typical user 1.3 seconds per typo squatting event over the alternative of receiving a browser error page, and legitimate sites lose approximately 5% of their mistyped traffic over the alternative of an unregistered typo. Although on average perpetrators increase the time it takes a user to find their intended site, many typo squatters actually improve the latency between a typo and its correction, calling into question the necessity of harsh penalties or legal intervention against this flavor of cyber crime.
Learning Vision Algorithms for Real Mobile Robots with Genetic Programming

We present a genetic programming system to evolve vision based obstacle avoidance algorithms. In order to develop autonomous behavior in a mobile robot, our purpose is to design automatically an obstacle avoidance controller adapted to the current context. We first record short sequences where we manually guide the robot to move away from the walls. This set of recorded video images and commands is our learning base. Genetic programming is used as a supervised learning system to generate algorithms that exhibit this corridor centering behavior. We show that the generated algorithms are efficient in the corridor that was used to build the learning base, and that they generalize to some extent when the robot is placed in a visually different corridor. More, the evolution process has produced algorithms that go past a limitation of our system, that is the lack of adequate edge extraction primitives. This is a good indication of the ability of this method to find efficient solutions for different kinds of environments.
Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index

In light of recent research that has begun to examine the link between textual “big data” and social phenomena such as stock price increases, this chapter takes a novel approach to treating news as big data by proposing the intelligent investment decision-making support model based on opinion mining. In an initial prototype experiment, the researchers first built a stock domain-specific sentiment dictionary via natural language processing of online news articles and calculated sentiment scores for the opinions extracted from those stories. In a separate main experiment, the researchers gathered 78,216 online news articles from two different media sources to not only make predictions of actual stock price increases but also to compare the predictive accuracy of articles from different media sources. The study found that opinions that are extracted from the news and treated with proper sentiment analysis can be effective in predicting changes in the stock market.
A Robust Geometric Approach to Room Compensation for Sound Field Rendering

SUMMARY In this manuscript we present a methodology for reducing the impact of the hosting room reflections in sound field rendering applications based on loudspeaker arrays. The problem is formulated in a least-squares sense. Since matrices involved in the problem are ill-conditioned, it is important to devise a suitable technique for the regularisation of the pseudo-inverse. In this work we adopt a truncated SVD method. The trun-cation, in particular, aims at reducing the impact of numerical errors and also errors on the knowledge of the sound speed. We include a wide set of experimental results, which validate the proposed technique.
A framework for analysing the effect of 'change' in legacy code

We propose a sound and practical approach, based on a formal method (known as interval temporal logic), to cope with 'change' and analyse its effect. The approach allows lows to capture a snapshot of system's behaviour over which various interesting properties, such as liveness, timeliness and safety properties, can be validated compositionally. These properties may include invariants that are required to be valid after changes have taken place. We also present and evaluate the design and implementation of a formal tool, AnaTempura, which supports the developed approach. A case study is presented to illustrate our approach and the tool.
Wavelet‐AI equalization and detection for indoor diffuse infrared wireless systems

This paper briefly reviews motivators for indoor diffuse infrared wireless communications and discusses the difficulties in realizing high data rate implementations. We consider a sub‐set of traditional performance enhancing techniques and compare them with the proposed novel wavelet and artificial intelligence (AI) detection mechanism. Results of computer simulations for both the minimum mean square error (MMSE) equalizer and wavelet–AI receivers are presented that indicate the performance of the wavelet–AI receiver is almost indistinguishable from that of the match filter MMSE receiver. Copyright © 2005 John Wiley & Sons, Ltd.
Special Issue: 2002 Franz Edelman Award for Achievement in Operations Research and the Management Sciences: Combinatorial and Quantity-Discount Procurement Auctions Benefit Mars, Incorporated and Its Suppliers

Simple auctions neglect the complex business constraints required by strategic sourcing. The Mars-IBM team created a procurement auction Web site that enables buyers to incorporate complex bid structures (such as bundled all-or-nothing bids and quantity-discounted bids) and business constraints into strategic-sourcing auctions. Outcomes in such auctions must lead to win-win solutions to sustain long-term relationships between procurer and suppliers. These factors are as important or more important than price. The Mars procurement auction Web site supports several alternatives to simple auctions that help match its needs as procurer and the capabilities of suppliers by incorporating optimal bid selection subject to constraints based on business rules in a dynamic environment. The ability to consider geographic, volume, and quality factors helps both parties. Feedback from participant suppliers has highlighted the benefits of time efficiency, transparency, and fairness. Although they reflect just one side of the benefits ledger, the monetary benefits to Mars (a $14 billion company) and to its suppliers are significant.
Improving the representation of legal case texts with information extraction methods

The prohibitive cost of assigning indices to textual cases is a major obstacle for the practical use of AI and Law systems supporting reasoning and arguing with cases. While progress has been made toward extracting certain facts from well-structured case texts or classifying case abstracts under Key Number concepts, these methods still do not suffice for the complexity of indexing concepts in CBR systems. In this paper, we lay out how a better example representation may facilitate classification-based indexing. Our hypotheses are that (1) abstracting from the individual actors and events in cases, (2) capturing actions in multi-word features, and (3) recognizing negation, can lead to a better representation of legal case texts for automatic indexing. We discuss how to implement these techniques with state-of-the-art NLP tools. Preliminary experimental results suggest that a combination of domain-specific knowledge and information extraction techniques can be used to generalize from the examples and derive more powerful features.
The Impact of Case Diagnosis Coverage and Diagnosis Delays on the Effectiveness of Antiviral Strategies in Mitigating Pandemic Influenza A/H1N1 2009

Background Neuraminidase inhibitors were used to reduce the transmission of pandemic influenza A/H1N1 2009 at the early stages of the 2009/2010 pandemic. Policies for diagnosis of influenza for the purposes of antiviral intervention differed markedly between and within countries, leading to differences in the timing and scale of antiviral usage. Methodology/Principal Findings The impact of the percentage of symptomatic infected individuals who were diagnosed, and of delays to diagnosis, for three antiviral intervention strategies (each with and without school closure) were determined using a simulation model of an Australian community. Epidemic characteristics were based on actual data from the A/H1N1 2009 pandemic including reproduction number, serial interval and age-specific infection rate profile. In the absence of intervention an illness attack rate (AR) of 24.5% was determined from an estimated R0 of 1.5; this was reduced to 21%, 16.5% or 13% by treatment-only, treatment plus household prophylaxis, or treatment plus household plus extended prophylaxis antiviral interventions respectively, assuming that diagnosis occurred 24 hours after symptoms arose and that 50% of symptomatic cases were diagnosed. If diagnosis occurred without delay, ARs decreased to 17%, 12.2% or 8.8% respectively. If 90% of symptomatic cases were diagnosed (with a 24 hour delay), ARs decreased to 17.8%, 11.1% and 7.6%, respectively. Conclusion The ability to rapidly diagnose symptomatic cases and to diagnose a high proportion of cases was shown to improve the effectiveness of all three antiviral strategies. For epidemics with R0< = 1.5 our results suggest that when the case diagnosis coverage exceeds ∼70% the size of the antiviral stockpile required to implement the extended prophylactic strategy decreases. The addition of at least four weeks of school closure was found to further reduce cumulative and peak attack rates and the size of the required antiviral stockpile.
A Feedback-based Admission Control Unit for QoS provision of video Transmission over WLANs

The IEEE 802.11e standard intends to enhance the Quality of Service (QoS) by introducing Hybrid Coordination Function Controlled Channel Access (HCCA). In HCCA, the QoS-enabled Station (QSTA) is assigned a Transmission Opportunity (TXOP) based on TS Specification (TSPEC) assigned during traffic setup time. Allocating fixed TXOP is only efficient for scheduling Constant Bit Rate (CBR) applications. However, Variable Bit Rate (VBR) traffics are not adequately supported via this approach, as its usage results in non-deterministic traffic profile. More specifically, this leads to degradation in the performance of the multimedia transmission and reduction in the number of admitted traffics. In this work, we propose an efficient admission control unit, called Feedback-based Admission Control Unit (FACU). The proposed scheme aims at maximizing the utilization of the surplus bandwidth which has never been tested in previous schemes. The FACU exploits piggybacked information containing size of subsequent video frames to increase the number of admitted flows. The proposed method is analytically evaluated using different video sequences. The results show that the FACU maximizes the number of admitted video streams comparable to other proposed techniques without jeopardizing the assigned QoS constraints.