Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimum Decision Rule for Pattern Recognition

The concept of rejection is extended to that of class-selective rejection. That is, when an input pattern cannot be reliably assigned to one of the N classes in a N -class problem, it is assigned to a subset of classes that are most likely to issue the pattern, instead of simply rejecting the pattern. First, a new optimality criterion is appropriately de ned to accommodate the newly introduced decision outcomes. Then, a new decision rule is derived and its optimality proven. Various upper-bounds on error rate are obtained. Finally, some examples are provided to illustrate the di erences between the optimum rule and other heuristic rules, such as top-n ranking. CR
Multiple Sclerosis Increases Fracture Risk: A Meta-Analysis

PURPOSE The association between multiple sclerosis (MS) and fracture risk has been reported, but results of previous studies remain controversial and ambiguous. To assess the association between MS and fracture risk, a meta-analysis was performed. METHOD Based on comprehensive searches of the PubMed, Embase, and Web of Science, we identified outcome data from all articles estimating the association between MS and fracture risk. The pooled risk ratios (RRs) with 95% confidence intervals (CIs) were calculated. RESULTS A significant association between MS and fracture risk was found. This result remained statistically significant when the adjusted RRs were combined. Subgroup analysis stratified by the site of fracture suggested significant associations between MS and tibia fracture risk, femur fracture risk, hip fracture risk, pelvis fracture risk, vertebrae fracture risk, and humerus fracture risk. In the subgroup analysis by gender, female MS patients had increased fracture risk. When stratified by history of drug use, use of antidepressants, hypnotics/anxiolytics, anticonvulsants, and glucocorticoids increased the risk of fracture risk in MS patients. CONCLUSIONS This meta-analysis demonstrated that MS was significantly associated with fracture risk.
Energy-efficient reporting scheme for cooperative spectrum sensing

Cooperative spectrum sensing (CSS) has been proposed in order to improve the reliability of the spectrum occupancy decision in cognitive radio networks. There are two well-known schemes for cooperation among cognitive users (CUs), soft scheme (SS) and hard scheme (HS). In SS the exact local sensing result is reported to a Fusion Center (FC), usually by quantizing it with a large number of bits. In contrast, only one bit is used in HS to convey the local sensing result to the FC. The difference in the number of reported bits results in high accuracy in SS and low energy consumption in HS. In this paper, we propose a novel scheme that gets benefits from both schemes, where the proposed scheme uses only one bit, as in HS, and provides high accuracy, almost as in SS. In the proposed scheme, each CU reports only one bit on a time slot related to its actual sensing results. At the FC, the arrival time of a single bit represents the sensing result of the reporting CU. Simulation results show that our proposal outperforms both HS and SS in terms of detection accuracy and energy efficiency.
Supporting low-cost video-on-demand in heterogeneous peer-to-peer networks

We propose a novel low-cost video-on-demand architecture for heterogeneous peer-to-peer networks. The key idea of this architecture is to aggregate peers' storage and bandwidths to facilitate on-demand video streaming. To achieve this goal, we split published videos into segments and distribute them to different peers. When watching a video, a peer searches the corresponding segments, and then aggregates bandwidths from multiple supplying peers to stream the video. Instead of relying on the powerful servers/proxies, this architecture exploits the often-underutilized peers' resources, which makes it cost-effective. We demonstrate the effectiveness of this proposed architecture through extensive simulation experiments on large, Internet-like topologies.
Interpretation of emotional body language displayed by robots

In order for robots to be socially accepted and generate empathy they must display emotions. For robots such as Nao, body language is the best medium available, as they do not have the ability to display facial expressions. Displaying emotional body language that can be interpreted whilst interacting with the robot should greatly improve its acceptance. This research investigates the creation of an "Affect Space" [1] for the generation of emotional body language that could be displayed by robots. An Affect Space is generated by "blending" (i.e. interpolating between) different emotional expressions to create new ones. An Affect Space for body language based on the Circumplex Model of emotions [2] has been created. The experiment reported in this paper investigated the perception of specific key poses from the Affect Space. The results suggest that this Affect Space for body expressions can be used to improve the expressiveness of humanoid robots. In addition, early results of a pilot study are described. It revealed that the context helps human subjects improve their recognition rate during a human-robot imitation game, and in turn this recognition leads to better outcome of the interactions.
Affect Interpretation in Metaphorical and Simile Phenomena and Multithreading Dialogue Context

The detection of complex emotions and value judgments from open-ended text-based multithreaded dialogue and diverse figurative expressions is a challenging but inspiring research topic. In order to explore this line of research, previously we have developed an affect inspired AI agent embedded in an improvisational virtual environment interacting with human users. The human players are encouraged to be creative at their role-play under the improvisation of loose scenarios. The AI agent is capable of detecting 25 affective states from users’ open-ended improvisational input and proposing appropriate responses to stimulate the improvisation.
The Living Systems Technology Suite: An Autonomous Middleware for Autonomic Computing

This paper presents the Living Systemsreg Technology Suite, LS/TS, a middleware based on autonomous software agents and autonomic computing principles. Specifically, the paper describes the autonomic principles built into LS/TS, and the features that follow from them. These features are described within the context of a general taxonomy of autonomic systems, elaborating on the relationship between middleware and application levels. Lastly, the paper addresses the problem of system self-representation, leveraging LS/TS support for ontology and semantic agent communication
Interaction with host SGS3 is required for suppression of RNA silencing by tomato yellow leaf curl virus V2 protein

The V2 protein of tomato yellow leaf curl geminivirus (TYLCV) functions as an RNA-silencing suppressor that counteracts the innate immune response of the host plant. The host-cell target of V2, however, remains unknown. Here we show that V2 interacts directly with SlSGS3, the tomato homolog of the Arabidopsis SGS3 protein (AtSGS3), which is known to be involved in the RNA-silencing pathway. SlSGS3 genetically complemented an AtSGS3 mutation and restored RNA silencing, indicating that SlSGS3 is indeed a functional homolog of AtSGS3. A point mutant of V2 that is unable to bind SlSGS3 also lost its ability to suppress RNA silencing, suggesting a correlation between the V2–SlSGS3 interaction in planta and the suppressor activity of V2.
An Analysis of Machine Learning Algorithms for Condensing Reverse Engineered Class Diagrams

There is a range of techniques available to reverse engineer software designs from source code. However, these approaches generate highly detailed representations. The condensing of reverse engineered representations into more high-level design information would enhance the understandability of reverse engineered diagrams. This paper describes an automated approach for condensing reverse engineered diagrams into diagrams that look as if they are constructed as forward designed UML models. To this end, we propose a machine learning approach. The training set of this approach consists of a set of forward designed UML class diagrams and reverse engineered class diagrams (for the same system). Based on this training set, the method 'learns' to select the key classes for inclusion in the class diagrams. In this paper, we study a set of nine classification algorithms from the machine learning community and evaluate which algorithms perform best for predicting the key classes in a class diagram.
Non-invasive support for personalized navigation of Websites

The growing availability of information on the Web has raised a challenging problem: can a Web-based information system tailor itself to different user requirements with the ultimate goal of personalizing and improving the user's experience in accessing the contents of a Web site? This work proposes a new strategy for Web site personalization based on the exploitation of both user browsing interests and content similarities among Web pages. The outcome is the delivery of page recommendations which are strictly related to visitors' navigational purposes and their actual location within the hyperspace of the Web site. Our approach has been effectively used for developing a non-invasive system which allows Web users to navigate through potentially interesting pages without having a basic knowledge of the structure of a Web site.
Active Learning and Experimental Design with SVMs

In this paper, we consider active learning as a procedure of iteratively performing two steps: rst, we train a classier based on labeled and unlabeled data. Second, we query labels of some data points. The rst part is achieved mainly by standard classiers such as SVM and logistic regression. We develop additional techniques when there are very few labeled data. These techniques help to obtain good classiers in the early stage of the active learning procedure. In the second part, based on SVM or logistic regression decision values, we propose a framework to exibly select points for query. We nd that selecting points with various distances to the decision boundary is important, but including more points close to the decision boundary further improves the performance. Our experiments are conducted on the data sets of Causality Active Learning Challenge. With measurements of Area Under Curve (AUC) and Area under the Learning Curve (ALC), we nd suitable methods for dierent data sets.
SimNUMA: Simulating NUMA-Architecture Multiprocessor Systems Efficiently

Non-uniform memory access (NUMA) architecture is widely used in high-end servers and computing systems due to its scalability. In recent years, the number of processor cores in NUMA systems increases rapidly with the development of multi-core processors. Along with the growing of system scales, simulation of NUMA systems becomes a challenge to traditional general-purpose simulators by reason of their low simulation performance. This paper presents SimNUMA, an execution-driven full-system simulator dedicated for NUMA systems. In the design of SimNUMA, to improve simulation performance significantly, the same type of processor with the target machine is used in the host system, and a new method to capture remote-memory accesses efficiently is proposed, in addition, parallel simulation is used to achieve scalability and improve performance. The modeling and simulation of interconnection networks are also supported. The simulator is tested in accuracy, scalability and performance, results show that the simulation slowdown is rather satisfying. Finally, the paper gives simulation experiments for different scales of target NUMA systems.
Emotional Arousal, Blood Glucose Levels, and Memory Modulation: Three Laboratory Exercises in Cognitive Neuroscience

The relationships between emotional arousal and cognition in humans represent an important field in cognitive neuroscience. Studies examining the characteristics of emotion-induced memory enhancement and the mechanisms through which these effects occur are becoming increasingly common. This article describes three affordable laboratory exercises of relevance to the growing interest in this field. Specifically, Experiment one reviews a protocol for examining memory, hypermnesia, reminiscence, and primacy/recency effects for emotional and neutral words. Experiments two and three provide opportunities to examine the relationships between blood glucose level and memory for either a list of pictures or the spatial location of pictures. Each laboratory exercise contains a certain amount of flexibility and is malleable to the specific needs of the instructor. For example, the use of blood glucose monitoring may be of value to a variety of different exercises examining stress and/or emotional arousal and the stimuli used in each of the protocols may be varied, creating opportunities for a number of different novel exercises. A series of questions have been provided at the end of each exercise in order to help stimulate inclass discussion. The potential application of this line of research in cognitive neuroscience is conveyed through a list of references where glucose has been used to attenuate cognitive deficits in Alzheimer’s disease, age-related cognitive decline, and other neuropsychological conditions.
A Message Passing Mechanism for Reconfigurable Communications Components Interconnected by Network-On-Chip Fabric

One promising solution for interconnecting the reconfigurable communications components in a E2R terminal is by network-on-chip fabric. In line with this direction, this paper proposes a message passing mechanism between the reconfigurable communications components in a terminal, in particular the process of injecting and ejecting messages into and out from the network-on-chip fabric. The proposed message passing system and procedures are important for the on chip operating environment: like supporting switching, routing functions.
A versatile threshold logic gate

The main drawback of Capacitive Threshold Logic (CTL) gates relates to the threshold valid programming. Because each gate may require a different reference voltage to set its threshold it is practically impossible to use this approach to build circuits with more than a couple of gates. In this paper we propose a new scheme, the Capacitor Programmable CTL (CP-CTL), which alleviate the CTL gate programming issue. The CP-CTL implementation of a 32-bit adder as also presented.
Digital Enablement: Social Media in Empowering the Grassroot Environmental Movement of Malaysia

This study examines the use of information and communication technologies (ICT) in mobilizing grassroot environmental movements. In recent years, the rise of social media has allowed the community to assume the role of driver without relying on central and formal leadership in people movements. Yet how this new form of technology enables grassroot organizing has remained theoretically obscure. This study provides an in-depth interpretive case study into an environmental movement in Malaysia, where people have rallied against a rare earth refinery plant that is feared would have posed environmental and health risks. By adopting empowerment as a theoretical lens, this paper focuses on understanding how social media empowers the community for grassroot mobilization from different dimensions, thus allowing a grassroot-driven environmental movement. The findings contribute to the literature of ICT-enabled sustainability and digitally empowered grassroot organizing.
VMM-Based Real-Time Embedded System

In modern real-time embedded systems, to utilize the versatile services of a general kernel while without sacrificing the real-time performance is a great challenge. In the previous work, the services and the real-time kernel run in the same environment without sufficient protection mechanisms, which always leads to the system unreliability. We propose the jMoni architecture, which offers a novel solution to meet this mixed requirement. jMoni serves as a virtual machine monitor (VMM) that provides isolated execution environments, called jMoni virtual machine (VM), for concurrently running real-time kernels and general kernels on a single real machine. jMoni VM is assigned a time-priority property to meet multi-level time requirement.
Comparing Chan-Vese model and convex Chan-Vese model

Chan-Vese model, i.e., active contours without edges, is a successful variational model for segmentation. However, it is also a non-convex model, which means that quality of the segmentation result depends on the choice of initialization. An approximating model, in which the energy function is convex, ensures that there is a unique optimal segmentation and the model is then called convex Chan-Vese model. In this paper, we discuss Chan-Vese model and convex Chan-Vese model thoroughly and point out that Convex Chan-Vese model is outstanding. The conclusion is based on the analysis and comparison of experimental data.
Comparison of voltage security constrained optimal power flow techniques

This paper compares two different optimal power flow (OPF) formulations that consider voltage security in power systems. The techniques are both based on multi-objective optimization methodologies, so that operating costs and losses can be minimized while maximizing the "distance" to voltage collapse. The techniques are described in detail and compared to study their similarities, as well as advantages and disadvantages. The comparisons are based on the results obtained by applying these two methods to a modified version of the 118-bus IEEE test system.
A two-step identification method for human-robot interaction in assistive environments

Integrating robotic platforms in smart home environments can improve the monitoring quality of daily activities. In this study, we explore a scenario where a robot provides a service to the users, which in our case is delivering a cup of coffee. The users place their order via an application, which at the same time captures a short video from their upper-body and their face to obtain information about their identity and to recognize them during the delivery phase. The proposed approach comprises three distinct steps. At a first step the robot detects groups of people, then it captures information from their faces and their upper body and measures the distance with the probe and identifies the person with the higher probability. Finally it approaches this person, performs an additional identification and delivers the cup of coffee. Through real-time preliminary tests under different illumination conditions, we verified that the robot can execute the task with high accuracy.
Construction of portable and flexible solar panel using light harvesting system

Today harvesting and using solar energy serves as a very good addition to the existing energy resources. Various kinds of solar panels are available in markets. But most of them are made up of metals and are rigid. Construction of these panels is also very tedious. Solar panel constructed here is novel since the polymer used renders it flexibility and portability.The base of this work revolves around the natural phenomenon that electron excited in biological light harvesting system, i.e. Photosystem embedded in thylakoid is finally accepted by NADP out of the thylakoid membrane.Leveraging this phenomenon,we replace this NADP by the artificial electron acceptor.Thus electron flowing through ETC will get accepted by this conducting polymer and complete the electrical circuit to generate electrical energy.One more important feature of this solar panel is, it is very easy to construct and dispose after use. Along with being flexible and portable, it is also cost effective. In future, such a solar panel can be used ubiquitously and with ease.
Applying real options theory to value flexibilities in groundwater remediation: an economic method to identify the optimal remediation strategy

Remediation investment projects are commonly evaluated based on traditional NPV (net present value) method. The traditional NPV method, however, is often misleading because it does not take into account the uncertainty of the future and the flexibilities the manager has in terms of adjusting the remediation strategy on demand. Typical imbedded real options during remediation projects include deferring, stopping and switching: deferring means to watch and to investigate (such as done in monitored natural attenuation, MNA); stopping means that the site manager can stop the remediation once the given target is met; switching means to replace a technology in operation by another technology/option that may become more appropriate in the future. Since these contingent management options are not considered by traditional NPV method, remediation strategies offering ample scope of flexibilities can be easily undervalued and, thus, the decisions made based on the traditional NPV method can be wrong. This study introduces a new approach for optimal remediation strategy making applying real options theory. MNA, pump-and-treat (P&T) and a permeable reactive barrier (PRB) are considered in this study as example technologies to demonstrate the approach. The remediation time frame is divided into a number of management periods, in which available options may be exercised. Introducing the concentration of the contaminant as the underlying asset, exercise of the options is triggered by the actual level of contamination compared to the given threshold levels. Uncertainty in concentration level is quantified with Monte Carlo simulations. The real options analysis provides the expected values of the alternative strategies. These strategies will be ranked based on their expected values. A hypothetical case is taken to demonstrate the approach and the sensitivity of the results to the changes of parameters is investigated. It is shown that this new approach is capable of identifying the optimal remediation strategy in terms of cost and effectiveness. It is an improvement compared with traditional economic decision-making techniques. The results suggest that real options theory is particularly appropriate to value remediation strategies with flexibility facing future uncertainties, thus having the potential to significantly improve remediation decision making. It is demonstrated that the optimal decision is very much depending on underlying conditions with respect to target and regulation levels, site conditions, economic assumptions, technologies’ effectiveness and their uncertainties. Voluntarily postponing MNA and applying more active remedial technology instead is recommended for projects where high economic value of cleaned land calls for high effectiveness.
Random Graphs and the Probabilistic Method

These notes accompany a lecture given in the summer of 2006 at the Center for Combinatorics at Nankai University. They are provided as a reference (and especially bibliography) for students new to the probabilistic method. The goal in these notes is twofold. First we wish to demonstrate the basics of the probabilistic method, covering both the expectation and second moment applications. Second, I wish to introduce the study of random graphs and their connection to the probabilistic method. The next lecture on “Sharp Concentration Phenomena” will delve more deeply into the topic of random graphs. Finally, let me mention that none of this material is original, and much of it can be found in Alon and Spencer’s well written book [1].