Miracle-dz/UniFAR-FTUs huggingface dataset:
Pedagogical Discourse: Connecting Students to Past Discussions and Peer Mentors within an Online Discussion Board

The goal of the Pedagogical Discourse project is to develop instructional tools that will help students and instructors use discussion boards more effectively, with an emphasis on automatically assessing discussion activities and building tools for promoting student discussion participation and learning. In this paper, we present a two related participation and learning scaffolding tools that exploit natural language processing and information retrieval techniques. The PedaBot tool is designed to aid student knowledge acquisition and promote reflection about course topics by connecting related discussions from a knowledge base of past discussions to the current discussion thread. The MentorMatch tool aims at promoting student participation using student mentors, i.e., course peers with a relatively good understanding of a particular topic. The system identifies students who often provide answers on a given topic and encourages classmates to invite mentors to participate in related discussions. Both tools have been integrated into a live discussion board that is used by an undergraduate computer science course. This paper describes our approaches to applying information retrieval and natural language processing techniques in the development of the tools and presents initial results from instrumentation and survey.
Notice of RetractionRelationship analysis of debt financing and company scale

This paper summarizes references of debt financing structure, chooses 32 food companies from Shanghai and Shenzhen Securities Exchanges as the study objects and tests the liability item affections to company scale. The result of those tests manifest that short term loan, accounts payable, and employee payable have significant positive correlation with business scale. In addition, due to the undeveloped exchange market, corporation bond in China is another potential element to scale expanding which would lead to the further study.
Efficient Distributed Data Mining using Intelligent Agents

—Data Mining is the process of extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases. Agents are defined as software or hardware entities that perform some set of tasks on behalf of users with some degree of autonomy. In order to work for somebody as an assistant, an agent has to include a certain amount of intelligence, which is the ability to choose among various courses of action, plan, communicate, adapt to changes in the environment, and learn from experience. In general, an intelligent agent can be described as consisting of a sensing element that can receive events, a recognizer or classifier that determines which event occurred, a set of logic ranging from hard-coded programs to rule-based inference, and a mechanism for taking action. In several steps through knowledge discovery, which include data preparation, mining model selection and application, and output analysis, intelligent agent paradigm can be used to automate the individual tasks. In the experiment setup, we discover association rules in a distributed database using intelligent agents. We apply an original approach for effective distributed mining association rules: loose-couple incremental methods. We compare the results obtained with the similar work done in the field. I. INTELLIGENT AGENTS N recent years, distributed artificial intelligence developed and diversified, as it is a research field that merges concepts and results from many disciplines, such as psychology, sociology and economy. Its interdisciplinary nature makes it difficult to established a unanimously accepted definition, but generally distributed artificial intelligence refers to "the study, construction, and application of multiagent systems, that is, systems in which several interacting, intelligent agents pursue some set of goals or perform some set of tasks. " [1]. This development was facilitated by the progress accomplished in computer science. Multi-tasking operating systems, communicating processes, distributed computing, The prototype of GIS web system for data mining using intelligent agents ". Cristian Aflori is a lecturer at the " Gh. and object oriented programming languages supported the design, implementation and deployment of agent-based systems. Most classical artificial intelligence systems are statical, their architecture is predefined, while agent-based systems dynamically modify in time. Distributed artificial intellience studies the issues related to the design of distributed, interactive systems. An autonomous agent can be considered a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its …
Bisection and Shift Camera Parameters Tuning for Stabilizing Image Acquisition

Ever-changing shooting conditions, such as altered ambient lighting, visual angle and live scene, impact image quality remarkably. Tuning camera parameters may relieve unwanted impacts to some extent, but generally the tuning is a trial and error process, which is boring and time-consuming, especially when being carried out manually, due to the numerous possible value and combination of many different parameters therein. In this paper, we propose a human-simulated camera parameters tuning method called Bisection and Shift Camera Parameters Tuning (BSCPT), which keeps tracking varied shooting conditions and implements a smart and swift parameters tuning for digital cameras so as to make the image quality as stable as possible. The experiments show that BSCPT remedies adverse conditions effectively, reaching optimal parameters setting in real-time image acquisition.
Communications and Sensing of Illumination Contributions in a Power LED Lighting System

In recent years, LED technology emerged as a prime candidate for the future illumination light source, due to high energy efficiency and long life time. In addition, LEDs offer a superior flexibility in terms of colors and shapes, which leads to a potentially infinite variety of available light patterns. In order to create these patterns via easy user interaction, we need to sense the local light contribution of each LED. This measurement could be enabled through tagging of the light of each LED with unique embedded IDs. To this end, we propose a new modulation and multiple access scheme, named as code- time division multiple access - pulse position modulation (CTDMA- PPM): a form of PPM which is keyed according to a spreading sequence, and in which the duty cycle is subject to pulse width modulation (PWM) according to the required lighting setting. Our scheme considers illumination constraints in addition to the communication requirements and, to our best knowledge, it has not been addressed by other optical modulation methods. Based on the proposed modulation method and multiple access schemes, we develop a system structure, which includes illumination sources, a sensor receiver and a control system. Illumination sources illuminate the environment and transmit information, simultaneously. According to our theoretical analysis, this system structure could support a number of luminaries equal to the size of the CDMA codebook times the dimming range.
Switched Interleaving Techniques with Limited Feedback for Interference Mitigation in DS-CDMA Systems

In this paper we propose a novel switched-interleaving algorithm based on limited feedback for both uplink and downlink DS-CDMA systems. The proposed switched chip-interleaving DS-CDMA scheme requires the cooperation between the transmitter and the receiver, and a feedback channel sending the index of the interleaver to be used. The transmit chip-interleaver is chosen by the receiver from a codebook of interleaving matrices known to both the receiver and the transmitter and the codebook index is sent back using a limited number of bits. In order to design the codebook, we consider a number of different chip patterns by using random interleavers, block interleavers and a proposed frequently selected patterns method (FSP). The best interleaving patterns are chosen by the selection functions of the received signal to interference plus noise ratio (SINR) for both downlink and uplink systems. Since the selection function needs to determine the best interleaver based on the channel state information, it is necessary to predict reliably the channel state information for typical delay values. We present symbol-based and block-based linear minimum mean squared error (MMSE) receivers for interference suppression. Simulation results show that our proposed algorithm achieves significantly better performance than the conventional DS-CDMA (C-CDMA) systems and the existing chip-interleaving, linear precoding and adaptive spreading techniques.
Unifying Bayesian networks and IMM filtering for improved multiple model estimation

Multiple model filtering has become an important concept for various applications such as maneuvering target tracking or road vehicle positioning. Algorithms like the Interacting Multiple Model (IMM) filter allow an adaption of the filter bandwidth to different motion patterns of the target. In general, the individual probabilities of each model are derived from the estimation itself and the incorporated measurements. In this paper, an approach to exploit additional uncertain knowledge for multiple model filtering is presented. This method is modeling the additional information in a Meta Model using a Bayesian network. Thus, two important concepts of information fusion are unified to a holistic approach for multiple model filtering. The proposed method is demonstrated on the example of maneuver recognition for road vehicles.
Stability and Change in Self-Esteem During the Transition to Parenthood

The present longitudinal study used data from 187 newlywed couples to examine the impact of the birth of the first child on self-esteem over the course of the first 5 years of marriage. Results suggest that the birth of the first child is associated with changes in parents’ (especially mothers’) self-esteem. For the average parent, these changes were negative with sudden declines in self-esteem in the year after childbirth and continuing gradual decreases throughout the remaining years of the study. A comparison group of couples who did not have children during the research period showed no changes in self-esteem, suggesting that the results seen in the parent sample may indeed be due to the birth of the first child. Discussion focuses on the implications of the results for theory and research on the development of the self-esteem.
The Pathogenesis of Saffold Virus in AG129 Mice and the Effects of Its Truncated L Protein in the Central Nervous System

Saffold Virus (SAFV) is a human cardiovirus that has been suggested to cause severe infection of the central nervous system (CNS). Compared to a similar virus, Theiler’s murine encephalomyelitis virus (TMEV), SAFV has a truncated Leader (L) protein, a protein essential in the establishment of persistent CNS infections. In this study, we generated a chimeric SAFV by replacing the L protein of SAFV with that of TMEV. We then compared the replication in cell cultures and pathogenesis in a mouse model. We showed that both SAFV and chimeric SAFV are able to infect Vero and Neuro2a cells well, but only chimeric SAFV was able to infect RAW264.7. We then showed that mice lacking IFN-α/β and IFN-γ receptors provide a good animal model for SAFV infection, and further identified the locality of the infection to the ventral horn of the spine and several locations in the brain. Lastly, we showed that neither SAFV nor chimeric SAFV causes persistence in this model. Overall, our results provide a strong basis on which the mechanisms underlying Saffold virus induced neuropathogenesis can be further studied and, hence, facilitating new information about its pathogenesis.
An Active Networks Simulation Environment

The steady increase in size and complexity of communication networks, coupled with growing needs and demands, has motivated the development of active networks. Active networking techniques embed computational capabilities into conventional networks, thereby massively increasing the complexity and customization of the computations that are performed with a network. One of the most effective techniques for studying and analyzing active networks is computer-based simulation, but customized and flexible tools are required to ease effective use of simulations. In an endeavor to address these diverse issues, an Active Networks Simulation Environment (ANSE) has been developed. ANSE provides an environment for modeling, parallel simulation, and Web-based simulation of active networks. This paper presents the issues involved in the design and development of ANSE and the results obtained from the experiments conducted to evaluate the effectiveness of ANSE. Studies indicate that ANSE provides an effective environment for modeling and simulation of large-scale active networks.
Efﬁcient Data Distribution in a Web Server Farm

High-performance Web sites rely on Web server “farms”—hundreds of computers serving the same content—for scalability, reliability, and low-latency access to Internet content. Deploying these scalable farms typically requires the power of distributed or clustered file systems. Building Web server farms on file systems complements hierarchical proxy caching.1 Proxy caching replicates Web content throughout the Internet, thereby reducing latency from network delays and off-loading traffic from the primary servers. Web server farms scale resources at a single site, reducing latency from queuing delays. Both technologies are essential when building a high-performance infrastructure for content delivery. In this article, we present a cache consistency model and locking protocol customized for file systems that are used as scalable infrastructure for Web server farms. The protocol takes advantage of the Web’s relaxed consistency semantics to reduce latencies and network overhead. Our hybrid approach preserves strong consistency for concurrent write sharing with time-based consistency and push caching for readers (Web servers). Using simulation, we compare our approach to the Andrew file system and the sequential consistency file system protocols we propose to replace.
An Integer DCT and Affine Transformation Based Image Steganography Method

This paper proposed an image steganography method based on integer DCT and affine transformation. Integer DCT is an appropriate domain for steganography because it is invertible and lossless, but the change of the DCT coefficients will damage its Laplacian-shape-like distribution. To ensure the security of the method, we use affine transformation to recover the Laplacian-shape-like distribution of the integer DCT coefficients. Experimental results demonstrate that the proposed method can ensure the stego image visually and statistically undetectable even with large payload.
Low-Power and Reliable Clock Network Design for Through-Silicon Via (TSV) Based 3D ICs

This paper focuses on low-power and low-slew clock network design and analysis for through-silicon via (TSV) based three-dimensional stacked ICs (3D ICs). First, we investigate the impact of the TSV count and the TSV resistance-capacitance (RC) parasitics on clock power consumption. Several techniques are introduced to reduce the clock power consumption and slew of the 3D clock distribution network. We analyze how these design factors affect the overall wire length, clock power, slew, and skew in 3D clock network design. Second, we develop a two-step 3D clock tree synthesis method: 1) 3D abstract tree generation based on the three-dimensional method of means and medians (3D-MMM) algorithm; 2) buffering and embedding based on the slew-aware deferred-merge buffering and embedding (sDMBE) algorithm. We also extend the 3D-MMM method (3D-MMM-ext) to determine the optimal number of TSVs to be used in the 3D clock tree so that the overall power consumption is minimized. Related SPICE simulation indicates that: 1) a 3D clock network that uses multiple TSVs significantly reduces the clock power compared with the single-TSV case, 2) as the TSV capacitance increases, the power savings of a multiple-TSV clock network decreases, and 3) our 3D-MMM-ext method finds a close-to-optimal design point in the “TSV count versus power consumption” tradeoff curve very efficiently.
The study of algorithms for Multi-VMs bandwidth guarantee in datacenters

With the development of the server virtualization technology and the rapid growth of network applications, datacenters have received significant attention because of its cost-effective and promising for large date storage, HPC, cloud computing and diversified network services and applications. Similar to the CPU and memory resource, the network should be shared by co-located tenants in a manner of proportionally to their payments, and meanwhile the minimum bandwidth guarantee for VMs and the utilization of network is also should be taken into consideration. In this paper, on the basis of existing researches, we introduce the algorithms of Multi-VMs bandwidth guarantee. The preliminary simulation results show that we can deploy more VMs with a small probability of congestion. If the VM sends large amounts of data, the algorithm in the paper would have a better performance in network utilization and transmission time.
Data Driven Energy Efficiency in Buildings

Buildings across the world contribute significantly to the overall energy consumption and are thus stakeholders in grid operations. Towards the development of a smart grid, utilities and governments across the world are encouraging smart meter deployments. High resolution (often at every 15 minutes) data from these smart meters can be used to understand and optimize energy consumptions in buildings. In addition to smart meters, buildings are also increasingly managed with Building Management Systems (BMS) which control different sub-systems such as lighting and heating, ventilation, and air conditioning (HVAC). With the advent of these smart meters, increased usage of BMS and easy availability and widespread installation of ambient sensors, there is a deluge of building energy data. This data has been leveraged for a variety of applications such as demand response, appliance fault detection and optimizing HVAC schedules. Beyond the traditional use of such data sets, they can be put to effective use towards making buildings smarter and hence driving every possible bit of energy efficiency. Effective use of this data entails several critical areas from sensing to decision making and participatory involvement of occupants. Picking from wide literature in building energy efficiency, we identify five crust areas (also referred to as 5 Is) for realizing data driven energy efficiency in buildings : i) instrument optimally; ii) interconnect sub-systems; iii) inferred decision making; iv) involve occupants and v) intelligent operations. We classify prior work as per these 5 Is and dis-cuss challenges, opportunities and applications across them. Building upon these 5 Is we discuss a well studied problem in building energy efficiency -non-intrusive load monitoring (NILM) and how research in this area spans across the 5 Is.
Cross-Sectional Volatility of Individual Income Processes ∗

There is large variation in individuals’ earnings within narrowly-defined labor markets even after controlling for age and education. Moreover, a considerable part of this variation is due to transitory labor income shocks. However, each year a small fraction of the labor force moves between different local markets. Therefore, estimation of structural models that assume that transitory earnings component is responsible to the gross labor mobility yields very high moving costs. In this paper, I demonstrate that ignoring positive correlation of labor income shocks across space, when in fact it is present, introduces a large upward bias into the estimates of moving costs. First, I construct a multi-factor dynamic Roy’s model of inter-sectoral mobility by explicitly allowing for: 1) both net and gross mobility, and 2) correlation of transitory earnings across space. Second, I estimate the model using micro-data on individual level mobility patterns and earnings differences between movers and stayers. I find that the cross-sectional volatility of the idiosyncratic labor income shocks is five times higher for college-educated workers than for high school educated workers. The SMM estimation reveals that within each age-and-education group, individuals with lower unobserved skills have lower moving costs and move more frequently than those with higher unobserved skills. These findings cast doubt on the role of credit constraint in explaining mobility differences across schooling levels.
Relating embedding and coloring properties of snarks

In 1969, Grunbaum conjectured that snarks do not have polyhedral embeddings into orientable surfaces. We define the defect of a graph and use it to study embeddings of superpositions of cubic graphs into orientable surfaces. Superposition was introduced by Kochol to construct snarks with arbitrary large girth. It is shown that snarks constructed by Kochol do not have polyhedral embeddings into orientable surfaces. For each k = 2 we construct infinitely many snarks with defect precisely k . We then relate the defect with the resistance r ( G ) of a cubic graph G which is the size of a minimum color class of a 4-edge-coloring of G . These results are then extended to deal with some weaker versions of the Grunbaum Conjecture.
In vitro evaluation of adhesion and aggregation abilities of four potential probiotic strains isolated from guppy (Poecilia reticulata)

The aim of this work was to study the probiotic-related characteristics of four strains of bacteria isolated from the normal flora of the guppy, Poecilia reticulata. In vitro results showed that the strains, namely, MBTU_PB1, MBTU_PB2, MBTU_PB3 and MBTU_PB4 had higher adhesion abilities than the tested indicator strains. However, an association between the cell-surface hydrophobicity and the ability to adhere to the intestinal mucus was not observed for these strains. Further, the selected strains were strongly autoaggregating (autoaggregation percentage ≥ 80) and also showed strain-specific coaggregation abilities with the tested indicator strains.
Design of system-level simulator architecture for underwater acoustic communications and networking

The development on the underwater communication network for sea observation is on the rise due to increasing importance of its applications such as oceanography, marine commercial operation and military surveillance. In this paper, we design the system-level simulator (SLS) architecture for the underwater acoustic communications which can be the basis for the design model of future application-specific underwater network deployment.
Link prediction in graphs with autoregressive features

In the paper, we consider the problem of link prediction in time-evolving graphs. We assume that certain graph features, such as the node degree, follow a vector autoregressive (VAR) model and we propose to use this information to improve the accuracy of prediction. Our strategy involves a joint optimization procedure over the space of adjacency matrices and VAR matrices which takes into account both sparsity and low rank properties of the matrices. Oracle inequalities are derived and illustrate the trade-offs in the choice of smoothing parameters when modeling the joint effect of sparsity and low rank property. The estimate is computed efficiently using proximal methods through a generalized forward-backward agorithm.