Miracle-dz/UniFAR-FTUs huggingface dataset:
MENINGKATKAN KEMAMPUAN GENERALISASI MATEMATIS MELALUI DISCOVERY LEARNING DAN MODEL PEMBELAJARAN PEER LED GUIDED INQUIRY (PLGI)

Generalisasi matematis diperlukan dalam memahami pelajaran yang diberikan oleh guru. Rendahnya kemampuan generalisasi matematis peserta didik SMPN 17 Bandar Lampung disebabkan karena kebanyakan peserta didik masih mengalami kesulitan dalam menarik suatu kesimpulan dari materi yang telah mereka peroleh dan proses pembelajaran yang kurang melatih aspek generalisasi atau kurang tepatnya model pembelajaran yang digunakan. Tujuan dari penelitian ini adalah untuk mengetahui adakah pengaruhmodel pembelajaran Peer Led Guided Inquiry dan model pembelajaran discovery learning terhadap kemampuan generalisasi matematis.Variabel yang diukur dari penelitian ini adalah kemampuan generalisasi dalam pembelajaran matematika dengan pokok bahasan faktorisasi bentuk aljabar. Penelitian ini merupakan penelitian eksperimental semu ( quasi eksperimental research ), dimana subjek penelitiannya adalah Peserta didik kelas VIII didik SMPN 17 Bandar Lampung. Teknik pegumpulan data yang digunakan adalah tes kemampuan generalisasi matematis. Teknik analisis data menggunakan analisis varians (ANAVA) satu jalan dengan sel tak sama dengan taraf signifikasi α = 0,05dan sebagai persyaratan analisis, yaitu populasi berdistribusi normal dengan model liliefors dan populasi homogen dengan Uji Bartlett . Berdasarkan hasil penelitian dengan menggunakan ANAVA satu jalan dengan sel tak sama dengan taraf signifikasi α = 0,05diperoleh bahwa yakni , maka keputusan ujinya Ho ditolak. Sehingga dapat disimpulkan bahwa terdapat pengaruh model pembelajaran Peer Led Guided Inquiry, model pembelajaran Discovery Learning, model pembelajaran Konvensional terhadap kemampuan generalisasi matematis peserta didik. Pengujian dilanjutkan dengan uji komparansi ganda menggunakan metode Scheffe’ untuk melihat manakah yang memberikan pengaruh yang berarti terhadap kemampuan generalisasi matematis. Hasil perhitungan menunjukkan bahwa dari ketiga perlakuan tersebut, model pembelajaran Discovery Learning yang memberikan pengaruh yang berarti terhadap kemampuan generalisasi matematis.
Deterministic Replay of Multithread Applications Using Virtual Machine

The deterministic replay technology usually is used to reproduce non-deterministic failures, especially the concurrency failures which are hard to debug with cyclic-debugging techniques. Previous techniques either incur large overhead or require custom hardware extensions. They have also suffered from the interference with irrelevant processes. This paper introduces WDRVirt, a new software based replay system that provides deterministic replay for concurrent applications. This paper makes three contributions. First, WDRVirt enforces a deterministic replay for the interleaving of lock acquisitions with low performance overhead. Second, different from the previous works, we customize the lightweight virtual machine execution environment as the container for the target program. WDRVirt replays the container to avoid the interference with other irrelevant processes. At last, WDRVirt is deployed into both virtual machine(VM) and virtual machine manager(VMM) to deal with different types of non-deterministic event. We have implemented this system based on the Xen virtualization platform. Our experiments with real-world benchmarks demonstrate the effectiveness of WDRVirt.
Clustering of SNPs by a Structural EM Algorithm

In population based human genetic studies, unrelated individuals are collected and SNPs are measured. There are several kinds of generative models proposed for modeling the data containing a large number of SNPs loci according to the characters of human genome. However, such models can only deal with ordered loci. In this paper, we try to model the same data without using the order information. Firstly, we present a clustering model for SNPs by modifying the multi-block model used in GERBIL. It is a two-layer Bayesian network with multiple latent variables. It does not use the order information of the loci. Secondly, we solve the model by employing a structural EM algorithm combined with simulated annealing mechanism. A real data set was analyzed by the model. The results show that the SNPs can be clustered effectively. Such a model is potentially useful for clustering distantly correlated SNPs loci.
Structural Determinants of Transmission at Large Hippocampal Mossy Fiber Synapses

Synapses are the key elements for signal processing and plasticity in the brain. To determine the structural factors underlying the unique functional properties of the hippocampal mossy fiber synapse, the complete quantitative geometry was investigated, using electron microscopy of serial ultrathin sections followed by computer-assisted three-dimensional reconstruction. In particular, parameters relevant for transmitter release and synaptic plasticity were examined. Two membrane specializations were found: active zones (AZs), transmitter release sites, and puncta adherentia, putative adhesion complexes. Individual boutons had, on average, 25 AZs (range, 7–45) that varied in shape and size (mean, 0.1 μm2; range, 0.07–0.17 μm2). The mean distance between individual AZs was 0.45 μm. Mossy fiber boutons and their target structures were mostly ensheathed by astrocytes, but fine glial processes never reached the active zones. Two structural factors are likely to promote synaptic cross talk: the short distance between AZs and the absence of fine glial processes at AZs. Thus, synaptic cross talk may contribute to the efficacy of hippocampal mossy fiber synapses. On average, a bouton contained 20,400 synaptic vesicles; ∼900 vesicles were located within 60 nm from the active zone, ∼4400 between 60 and 200 nm, and the remaining beyond 200 nm, suggesting large readily releasable, recycling, and reserve pools. The organization of the different pools may be a key structural correlate of presynaptic plasticity at this synapse. Thus, the mossy fiber bouton differs fundamentally in structure and function from the calyx of Held and other central synapses.
Marginable Bandwidth Reservation

With up-coming Quality of Service (QoS) requirements raised by a wide range of communication-intensive, real-time multimedia applications, resource reservation is one of the key issues we have to deal with. Resource ReSerVation Protocol (RSVP) is a well-known signaling protocol for bandwidth reservation in the Internet. However, there are still some open problems in the Internet resource reservation research. One of them is the New Killer Reservation Problem (NKR Problem) which will be described later. In this paper, we study the NKR problem and answer two questions: (1) under what scenarios does NKR problem become a serious problem; and (2) How can we solve this problem. We introduce the Marginable Bandwidth Reservation Protocol (MBR) as a solution to the NKR problem. The results show that MBR protocol works well.
Probing the gravity-induced bias with weak lensing: test of analytical results against simulations

Future weak lensing surveys will directly probe the density fluctuation in the Universe. Recent studies have shown how the statistics of the weak lensing convergence field is related to the statistics of collapsed objects. Extending earlier analytical results on the probability distribution function of the convergence field, we show that the bias associated with the convergence field can be directly related to the bias associated with the statistics of underlying overdense objects. This will provide us with a direct method to study the gravity-induced bias in galaxy clustering. Based on our analytical results, which use the hierarchical Ansatz for non-linear clustering, we study how such a bias depends on the smoothing angle and the source redshift. We compare our analytical results with ray-tracing experiments through N-body simulations of four different realistic cosmological scenarios, and find a very good match. Our study shows that the bias in the convergence map strongly depends on the background geometry and hence can help us in distinguishing different cosmological models in addition to improving our understanding of the gravity-induced bias in galaxy clustering.
Bile Salt-Stimulated Lipase Plays an Unexpected Role in Arthritis Development in Rodents

Objective The present study aimed to explore the hypothesis that bile salt-stimulated lipase (BSSL), in addition to being a key enzyme in dietary fat digestion during early infancy, plays an important role in inflammation, notably arthritis. Methods Collagen-induced arthritis (CIA) and pristane-induced arthritis (PIA) in rodents are commonly used experimental models that reproduce many of the pathogenic mechanisms of human rheumatoid arthritis, i.e. increased cellular infiltration, synovial hyperplasia, pannus formation, and erosion of cartilage and bone in the distal joints. We used the CIA model to compare the response in BSSL wild type (BSSL-WT) mice with BSSL-deficient ‘knock-out’ (BSSL-KO) and BSSL-heterozygous (BSSL-HET) littermates. We also investigated if intraperitoneal injection of BSSL-neutralizing antibodies affected the development or severity of CIA and PIA in mice and rats, respectively. Results In two consecutive studies, we found that BSSL-KO male mice, in contrast to BSSL-WT littermates, were significantly protected from developing arthritis. We also found that BSSL-HET mice were less prone to develop disease compared to BSSL-WT mice, but not as resistant as BSSL-KO mice, suggesting a gene-dose effect. Moreover, we found that BSSL-neutralizing antibody injection reduced both the incidence and severity of CIA and PIA in rodents. Conclusion Our data strongly support BSSL as a key player in the inflammatory process, at least in rodents. It also suggests the possibility that BSSL-neutralizing agents could serve as a therapeutic model to reduce the inflammatory response in humans.
A Game Perspective of Refinement of Component Models

Previous works on formal development for component-based systems usually equate refinement relations as behaviors containment. This setting facilitates verifying safety properties, but can’t capture the intuition that a refined component should more easily react to the environment and is not convenient from a point view of design. To address this issue, we argue in favor of defining refinement of component models in terms of alternating simulation, which is a relation on game models with a game-theoretical interpretation. We investigate the refinement relations of component models in the framework of rCOS, which is a formal development method for component systems. We propose the refinement relations of rCOS contract models and specification models respectively and further prove they are alternating simulations. Finally, we define the composition operator on specifications and show the alternating refinement is compositional with respect to this operator.
Generalized Binary Representation for the Nonbinary LDPC Code With Decoder Design

In this paper, we consider the performance-optimized nonbinary low-density parity check code over general linear group, i.e., C̅. A new methodology for constructing the binary representation [generalized binary representation (GBR)] of C̅ is proposed, which can be optimized with regard to both degree distributions and girth. As to the decoding of the GBR, we develop a low-complexity hybrid parallel decoding process. It is shown that the decoding performance of the GBR under the proposed binary decoding process could closely approach the decoding performance of its mother code C̅ under nonbinary belief propagation decoding. A simple code optimization algorithm for the GBR is also provided. Simulations show the comparative results and justify the advantages of the proposed constructions.
Navigating , Recognising and Describing Urban Spaces With Vision and Laser

This paper describes a body of work aimed at extending the rea ch of mobile navigation and mapping. We describe how running topological and metric mapping and pos e estimation processes concurrently, using vision and laser ranging, has produced a full six-degree-of-freed om outdoor navigation system. It is capable of producing intricate 3D maps over many kilometers and in real time. We co nsider issues concerning the intrinsic quality of the built maps and describe our progress towards adding sema ntic l bels to maps via scene de-construction and labeling. We show how our choices of representation, infere ce methods and use of both topological and metric techniques naturally allow us to fuse maps built from multip le sessions with no need for manual frame alignment or data association.
CADS: A Collaborative Adaptive Data Sharing Platform 1

management tools like Microsoft's SharePoint allow users of an application domain to share documents a nd tag them in an ad-hoc way. Similarly, Google Base allows use rs to define attributes for their objects or choose from predefi ned templates. This ad-hoc or predefined annotation of the shared data incurs problems like schema explosion or inadequate data a nnotation, which in turn lead to poor search and analysis capa bilities. We propose CADS, a Collaborative Adaptive Data Sharing platform, where the information demand of the community-e.g., query workload-is exploited to annotate the data at insertion-time. A key novelty of CADS is that it learns with time t he most important data attributes of the application, and u ses this knowledge to guide the data insertion and querying. In this position paper, we present the challenges and preli minary design ideas for building a CADS platform. We use the appl ication of CADS on the Business Continuity Information Network (BCIN) of South Florida as a motivating example.
Image Retrieval Algorithm Based on Color Block Histogram

To further enhance the efficiency of color histogram - based image retrieval algorithm, this paper presents a new algorithm which looks for maximum color lump, extracts its local histogram, and integrates with the original histogram. The algorithm adds spatial distribution characteristics of color while retaining the original algorithm, thus reducing absolute dependence on the color for retrieval results. The simulation results show that this algorithm has better precision and recall and has especially remarkable effect for image retrieval of apparent color lump.
Dense, Unspecific Connectivity of Neocortical Parvalbumin-Positive Interneurons: A Canonical Microcircuit for Inhibition?

GABAergic interneurons play a major role in the function of the mammalian neocortex, but their circuit connectivity is still poorly understood. We used two-photon RuBi-Glutamate uncaging to optically map how the largest population of cortical interneurons, the parvalbumin-positive cells (PV+), are connected to pyramidal cells (PCs) in mouse neocortex. We found locally dense connectivity from PV+ interneurons onto PCs across cortical areas and layers. In many experiments, all nearby PV+ cells were connected to every local PC sampled. In agreement with this, we found no evidence for connection specificity, as PV+ interneurons contacted PC pairs similarly regardless of whether they were synaptically connected or not. We conclude that the microcircuit architecture for PV+ interneurons, and probably neocortical inhibition in general, is an unspecific, densely homogenous matrix covering all nearby pyramidal cells.
Let Latency Guide You: Towards Characterization of Cloud Application Performance

Public cloud infrastructures provide flexible hosting for web application providers, but the rented virtual machines (VMs) often offer unpredictable performance to the deployed applications. Understanding cloud performance is challenging for application providers, as clouds provide limited information that would help them have expectations about their application performance. In this paper we present a technique to measure the performance of cloud applications, based on observations of the application latency. We treat the cloud application as a black box, making no assumption about the underlying platform. From our measurements, we can observe the varying performance provided by the different VM profiles across well-known commercial cloud platforms. We also identify a trade-off between the responsiveness and the load of the measured servers, which can help application providers in their deployment and provisioning.
A social-based meta-multidisciplinary framework to motivate the creation of a new generation of electronics 2.0 systems

In a previous work, we published multidisciplinary experiences on teaching the design of electronics 2.0 systems for social change. In this paper we present the lasts changes done in our framework. The most important change currently is the inclusion of a new course from the business creation and development academic program. Previous experiences have been considered only computer-related engineering and industrial design students. This new step presents improvements in our previous process. Since the new included academic program has the objective to graduate students with an enterprise, the integration of our previous experience and this new academic program is a win-win junction to achieve that goal. The main objective is to create a new generation of electronics products that adds social capital to the society through its use. This approach to promote the entrepreneurship spirit in our undergraduate students can be considered as pragmatic since students from electronic engineering and industrial design are not following a formal entrepreneur education. We will present the results of surveys that take into account several institutional criteria.
An empirical assessment of completeness in UML designs

Delivering high quality software in an economic way requires advanced control over the software development process and the product in all stages of its life-cycle. The use of metrics as means of control and improvement plays an important role in software engineering. Interviews with industrial software engineers identified incompleteness of UML designs as a potential problem for subsequent stages of development. In this paper we propose a definition of completeness of a UML model and present a set of rules to assess model completeness. We report results from industrial case studies to assess the level of completeness in practice. The amount of completeness and consistency rule violations was very high. Different case studies showed large variations in conformance to and violations of rules.
Consumer Choice of Private Label or National Brand: The case of organic and non-organic milk

We use a two-stage, sample selection model to investigate organic milk purchases using Neilsen’s Homescan data. In the first stage, households decide on a weekly basis to buy mainly organic milk or non-organic milk. Results from this stage show that higher income, better education, having children at home, and several other demographic and marketing variables have a positive effect on organic choice. In the second stage, consumers then choose to buy mainly private label milk or national brand milk conditional on their first-stage choice. Most demographic and marketing variables are found to affect the organic and non-organic private label decision in the same way. However, our results show that a few factors, such as marriage status and children, affect the private label decision differently for organic and non-organic milk customers.
A novel click-free interaction technique for large-screen interfaces

We developed a novel click-free interface for laser-pointer-based systems with wall-sized screens. The technique includes two moving operations: “crossing,” which uses four edges as a crossing target to execute a command, and “encircling,” which selects objects by drawing a circle around them. This paper describes the proposed interface, its implementation, and an experiment conducted to test the technique.
Oil Price Forecasting Based on Particle Swarm Neural Network

Petroleum is one of the indispensable energy for development of world economy and politics. Oil price is affected by the situation of economy and diplomacy. The hybrid training algorithm is combined with the improved particle swarm optimization and BP algorithm, the improved PSO-BP ANN model is developed trained by the hybrid algorithm based on improved PSO and BP algorithm. According to problems of petroleum price prediction and the feasibility of petroleum price prediction model, the improved BP model for petroleum price prediction is proposed. It is shown that the proposed model is feasible and reliable to predict the petroleum price. Compared with conventional PSO-BP algorithm, the proposed algorithm has better accuracy and correlation.
Risk of skeletal related events among elderly prostate cancer patients by site of metastasis at diagnosis

The purpose of this study was to estimate the risk of developing skeletal‐related events (SREs) based on site of metastasis at diagnosis and identify other predictors of developing SREs among metastatic prostate cancer patients. We conducted a retrospective cohort study using linked SEER (Surveillance, Epidemiology, and End Results) and Medicare data and identified men over the age of 65 with incident metastatic prostate cancer diagnosed during 2005–2009. SREs included radiation (RAD), pathological fractures (PF), bone surgery (BS), and spinal cord compression (SCC). The association between site of metastasis at diagnosis and SRE was examined using a Cox proportional hazards model that accounts for death as a competing risk. Among 4404 men (median age: 79 years) with incident metastatic prostate cancer, 44% experienced SREs at a median of 9.6 months post diagnosis. Compared to bone metastasis only, our model showed that patients were significantly less likely to develop SREs if they had LN‐only metastasis at diagnosis (Sub‐Hazard Ratio [SHR] 0.56; 95% Confidence Interval [CI]: 0.43–0.72) or unknown site of metastasis (SHR: 0.79; CI: 0.64–0.97). Other predictors of reduced SRE risk were age 80+ years (SHR: 0.83; CI: 0.75–0.91), non‐Hispanic Black (SHR: 0.77; CI: 0.65–0.90), or being diagnosed in year 2009 (SHR: 0.85; CI: 0.72–0.99). Patients were significantly more likely to develop SREs if they received androgen deprivation therapy (SHR: 1.73; CI: 1.48–2.02) or had Gleason score 8–10 disease (SHR: 0.79; CI: 0.64–0.97). Compared to patients who present with bone metastasis only at diagnosis, patients presenting with other metastatic sites have similar risk of developing SREs, with the exception of those presenting with lymph node only metastasis who have a significantly reduced risk of SREs.