Miracle-dz/UniFAR-FTUs huggingface dataset:
A truly safely moving robot has to know what injury it may cause

Enabling robots to safely interact with humans is an essential goal of robotics research. The developments achieved over the last years in mechanical design and control made it possible to have active cooperation between humans and robots in rather complex situations. In these terms, safe behavior of the robot even under worst-case situations is crucial and forms also a basis for higher level decisional aspects. In order to quantify what safe behavior really means, the definition of injury, as well as understanding its general dynamics are essential. This insight can then be applied to design and control robots such that injury due to robot-human impacts is explicitly taken into account. In this paper we approach the problem from a medical injury analysis point of view in order to formulate the relation between robot mass, velocity, impact geometry, and resulting injury qualified in medical terms. We transform these insights into processable representations and propose a motion supervisor that utilizes injury knowledge for generating safe robot motions. The algorithm takes into account the reflected inertia, velocity, and geometry at possible impact locations. The proposed framework forms a basis for generating truly safe velocity bounds that explicitely consider the dynamic properties of the manipulator and human injury.
Modelling Corruption in a Cobb-Douglas Production Function Framework*

In this paper, we extend the Solow growth model to include corruption as a determinant of the multifactor productivity using a Cobb-Douglas production function framework. In addition to the classical components of any growth model (output, labor, capital), we incorporate corruption as a determinant of government expenditure, investment and foreign aid. It is proposed that output and growth are influenced by the level of corruption. This model is to be tested empirically to trace the corruptive behaviour in Lebanon based on the available time series data.
Analysis of Demand Control Policies using an Agent-based Multi-layer Power System Model

This paper studies how the communication network affects the power utilization and fairness in a simplified power system model, composed by three coupled layers: physical, communication and regulatory. Using an agent-based approach, we build a scenario where individuals may cooperate (by removing a load) or not (by keeping their loads or adding one more). The agent decision reflects its desire of maximizing the delivered power based on its internal state, its global state perception, a randomized selfishness related to its willingness to follow demand-side control requests, and the state information received from its neighbors in the communication network. Our focus is to understand how the network topology and errors in the communication layer affect the agents' behavior, reflected in the power utilization and fairness for different demand-side policies. Our results show that for optimal power utilization and fairness a global knowledge about the system is needed. We show that close to optimal results can be archived with either a demand control signal or a global pricing for energy.
Coprime factor model reduction for continuous-time uncertain systems

The paper considers the problem of coprime factor model reduction for a class of continuous-time uncertain systems with structured norm bounded uncertainty. The proposed method is applicable to the uncertain systems which may be robustly unstable, overcoming the robust stability restriction in the balanced truncation approach. A systematic approach is presented to construct a contractive coprime factor for the underlying uncertain system, based on the use of LMIs. This enables the balanced truncation to be applied to the contractive coprime factor to obtain the reduced uncertain system. Error bound on the L2-induced norm of the resulting coprime factor is derived.
Research on MPPT Technique in Photovoltaic Power Generation System

Maximum power point tracking (MPPT) is the core technique in photovoltaic power generaton system to improve energy utilization. Major research direciotns of MPPT are focused on model pattern and input-output paramerters pattern. This paper introduces the superiority and inferiority for MPPT in two kind of patterns. Morever, a new MPPT method in model pattern base on ant colony optimization algorithm(ACO) and an optimized fuzzy tracking method in input-output paramerters pattern are also proposed, which are proved to tracking the maximum power point (MPP)effectively from experimental results. Finally, combined with advantages of the above tracking methods, a set of integral tracking strategy is put forward to accurately and timely track MPP under different conditions.
An Exploration of Mobile Shape-Changing Textures

This paper describes the development and evaluation of Undulating Covers (UnCovers), mobile interfaces that can change their surface texture to transmit information. The Pin Array UnCover incorporates sinusoidal ridges controlled by servomotors, which can change their amplitude and granularity. The Mylar UnCover is a more organic interface that exploits the buckling properties of Mylar, using muscle wires, to change the texture granularity. The prototype development process and evaluations show that very low frequency texture changes, using amplitude or granularity, can be distinguished with high levels of accuracy. Since small changes are perceptible, it is possible to incorporate such interfaces into mobile devices without drastically increasing their size or actuation requirements. Finally, ratings from participants indicate that UnCovers would be appropriate for attention-grabbing, or caring and supportive interpersonal messages.
Genetic Algorithms and Explicit Search Statistics

The genetic algorithm (GA) is a heuristic search procedure based on mechanisms abstracted from population genetics. In a previous paper [Baluja & Caruana, 1995], we showed that much simpler algorithms, such as hillclimbing and Population-Based Incremental Learning (PBIL), perform comparably to GAs on an optimization problem custom designed to benefit from the GA's operators. This paper extends these results in two directions. First, in a large-scale empirical comparison of problems that have been reported in GA literature, we show that on many problems, simpler algorithms can perform significantly better than GAs. Second, we describe when crossover is useful, and show how it can be incorporated into PBIL.
PDLIM 5 gene polymorphisms and short term antidepressant response in Chinese major depressive disorders

Several investigations have suggested that PDLIM5 plays a key role in the pathophysiology of major depressive disorder (MDD), and that PDLIM5 might be a therapeutic target for the action of antidepressant. In this study, we sought to investigate whether variations of PDLIM5 gene sequence could predict response to antidepressants in MDD patients. We selected 3 SNPs (rs10008257, rs2433320, 2452600) of PDLIM5 gene, and performed an association analysis of PDLIM5 and the efficacy of fluoxetine treatment in 185 Han Chinese MDD patients. The results show that the rs2433320 of PDLIM5 gene are associated with fluoxetine therapeutic response in MDD patients (X2 = 8.2960, df = 2, P = 0.0145) after correction with the Bonferroni multiple test, the HAMD score of the GG genotype group was significantly lower than that of the AA and AG genotype group at 1, 2, 4 and 6 weeks. The results support the idea that the PDLIM5 gene is likely to be involved in the antidepressant response in MDD.
The Additive Effects of Online Values Clarification and Goal-Setting Training on Measures of a Weight Management System

Obesity in the United States is an urgent issue that the nation must address. Current estimations indicate that overweight and obesity trends are increasing, resulting in more individuals who are overweight or obese and increases in morbidity and mortality. Therefore, it is important to research strategies to make weight management more effective. One approach is to add components to already successful weight management programs in order to boost their long-term effectiveness. The literature indicates that a combination of goal setting and values clarification training can have a significant effect on long-term behavior change. The current research presents the results of adding a goal setting and values clarification training component to an already successful weight management program.
A ligand-binding pocket in the dengue virus envelope glycoprotein

Dengue virus is an emerging global health threat. Its major envelope glycoprotein, E, mediates viral attachment and entry by membrane fusion. A crystal structure of the soluble ectodomain of E from dengue virus type 2 reveals a hydrophobic pocket lined by residues that influence the pH threshold for fusion. The pocket, which accepts a hydrophobic ligand, opens and closes through a conformational shift in a β-hairpin at the interface between two domains. These features point to a structural pathway for the fusion-activating transition and suggest a strategy for finding small-molecule inhibitors of dengue and other flaviviruses.
Block cipher and stream cipher algorithm performance comparison in a personal VPN gateway

A virtual private network (VPN) extends a private network across a public network, such as the Internet. The purpose of this research study is to implemented ATHS3 stream cipher and VEA block cipher algorithm as the addition of TLS ciphersuites in an SBC Raspberry Pi Model B+ as a personal VPN gateway. It consists three main processes, namely the initialization process, key generation process, and encryption process to secure data that are sent through the VPN. The personal VPN Gateway will obtain cryptographic algorithms, TLS ciphersuites supply from OpenSSL library, and VEA algorithm implementation ciphersuites as the addition of TLS ciphersuites. Implementation of VEA algorithm source code will be made in both of the OpenSSL library and the cryptographic library. This comparative study will be conducted by implementing not only a stream algorithm, but also block cipher algorithm on the same VPN gateway and analyze its performances in order to get the most efficient algorithm to secure data that are sent through the VPN. Not only data transfer rate, but also memory usage measurement in SBC Raspberry Pi Model B+ as personal VPN gateway. The personal VPN gateway works as a bridge between the user's PC and network to secure its communication to internal resources, so data that is transmitted is prevented from attacks. The result of performances analysis will recommend the most efficient cipher algorithm, data transfer rate and memory usage to be implemented in an SBC Raspberry Pi Model B+ as a personal VPN gateway on OpenVPN.
Variability in the Benzodiazepine Response of Serotonin 5-HT1A Receptor Null Mice Displaying Anxiety-Like Phenotype: Evidence for Genetic Modifiers in the 5-HT-Mediated Regulation of GABAA Receptors

Benzodiazepines (BZs) acting as modulators of GABAA receptors (GABAARs) are an important group of drugs for the treatment of anxiety disorders. However, a large inter-individual variation in BZ sensitivity occurs in the human population with some anxiety disorder patients exhibiting diminished sensitivity to BZ and reduced density of GABAARs. The mechanism underlying BZ treatment resistance is not known, and it is not possible to predict whether an anxiety patient will respond to BZ. 5-Hydroxytryptamine1A receptor (5-HT1AR) null mice (R-/-) on the Swiss-Webster (SW) background reproduce several features of BZ-resistant anxiety; they exhibit anxiety-related behaviors, do not respond to BZ, have reduced BZ binding, and have decreased expression of the major GABAAR subunits α1 and α2. Here, we show that R-/- mice on the C57Bl6 (B6) background also have anxiety phenotype, but they respond to BZ and have normal GABAAR subunit expression. This indicates that the 5-HT1AR-mediated regulation of GABAAR α subunit expression is subject to genetic modification. Hybrid SW/B6-R-/- mice also exhibit BZ-resistant anxiety, suggesting that SW mice carry a genetic modifier, which mediates the effect of the 5-HT1AR on the expression of GABAARα subunits. In addition, we show that this genetic interaction in SW mice operates early in postnatal life to influence the expression of GABAAR α subunits at the transcriptional level. These data indicate that BZ-resistant anxiety results from a developmental arrest of GABAAR expression in SW-R-/- mice, and a similar mechanism may be responsible for the BZ insensitivity of some anxiety patients.
Gnafuy: A Framework for Ubiquitous Mobile Computation

Along with the clipping evolution of technology, the bud of smartphone has germinated and sprang up in this decade. The increasing popularity and the improving computation and communication power of smart mobile devices facilitates shared economics of ubiquitous mobile computation. We present the design and implementation of Gnafuy, a framework utilizing crowd-smartphones to fulfill ubiquitous distributed computation, offering a novel crowd-based computation service platform and programmable APIs for developers to take leverage of the spare capacity of smartphones among the world. We discuss programming models of parallel computation, detail how tasks can be deployed on massive smartphones via mobile applications, and propose a server side mechanism to increase the probability of successful delivery and detect the corrupt results from the viciously slaves. The demonstration on google scholar paper reference construction solicits smartphone users to solve the image recognition challenged by reCAPTCHA, showing the unique advantage of leveraging crowd-sourcing ability to distributed mobile computation.
Intrinsic and Extrinsic Motivations Affecting Impulse-Buying Tendency in Mobile Shopping

I investigated whether or not the components of the technology acceptance model (TAM), such as cognitive and affective factors, could predict impulse-buying tendency and shopping attitude in a mobile shopping environment. Participants were 234 consumers who had experience of mobile shopping. I conducted this survey using an online survey system provided by a research company. Results showed that cognitive absorption as an affective factor (intrinsic motivation) was related positively to cognitive factors (extrinsic motivations). In addition, cognitive absorption had a significantly positive influence on impulse-buying tendency. Among cognitive factors of the TAM, perceived usefulness affected impulse-buying tendency positively; however, perceived ease of use did not significantly influence impulse-buying tendency. Lastly, I found impulse-buying tendency affected mobile shopping attitude positively. My results suggest that showing an impulse-buying tendency in a mobile shopping environment has a positive impact on shopping attitude.
A proxy mobile IP based layer-3 handover scheme for mobile WiMAX based wireless mesh networks

In this paper, we present a proxy mobile IP based layer-3 handover scheme for mobile WiMAX based wireless mesh networks. Mobile WiMAX provides two types of layer-3 handover schemes: MIP based handover and PMIP based handover. MIP based handover incures the long handover latency because mobile nodes generate a lot of handover messages in wireless area. On the other hand, PMIP based handover decreases the handover latency by reducing the number of handover messages, because a mobile node does not participate in handover procedure. However, conventional PMIP based handover still has latency overhead, because of message exchanges between PBU and PBA after completing the layer-2 handover. Hence, we propose a new layer-3 handover method that does not bring about packet losses while achieving lower handover latency than conventional layer-3 handover scheme. Simulation results show that the proposed scheme achieves loss-free and low handover latency during the layer-3 handover.
An instance-based approach for pinpointing answers in chinese question answering

In this paper, we propose an instance-based approach for pinpointing answers in Chinese question answering (QA). We take the view that, for a particular class, the strategy of answering new questions can be learned from the already solved ones. To confirm the feasibility of this new approach, we use question answer pairs (QA pair) as our learning instances and maximum entropy model (MEM) as a machine learning (ML) technique. The experiment conducted on the class-LOC_COUNTRY and OBJ_LANGUAGE achieves good performance
CMCCR: Classification Based on Multiple Class-Correlation Rules

Previous studies show that associative classification achieves higher classification accuracy than traditional classification approaches. However, associative classification suffers from two major deficiencies and thus sometimes has lower classification accuracy. First, it often generates a large number of rules when the minimum support is set to be low. It is not only time consuming to discover so many itemsets, but also very difficult to select high quality rules from a huge set of rules. Second, the association measure confidence does not reflect correlation relationships between itemsets and the class label. To deal these problems, in this paper, we propose a new classification approach called classification based on multiple class-correlation rules (CMCCR). First, we use both support and all-confidence to mine not only frequent but also mutually associated itemsets. This method not only saves the running time but as well considerably reduces the number of itemsets generated, and thus sharply decreases the number of class-correlation rules produced. Second, we use a new correlation measure correlation confidence to discover class-correlation rules. The measure correlation confidence has two bounds: - 1and 1. It is easy to control the correlation degree of the class-correlation rules found. Finally, we use multiple class-correlation rules and average correlation degree to measure the combined effect of group rules. Experimental results on the mushroom data set show that CMCCR has higher and more stable accuracy than associative classification and decision tree method.
PacketBench: a tool for workload characterization of network processing

Network processing is becoming an increasingly important paradigm as the Internet moves towards an architecture with more complex functionality inside the network. Modern routers not only forward packets, but also process headers and payloads to implement a variety of functions related to security, performance, and customization. It is important to get a detailed understanding of the workloads associated with this processing in order to be able to develop efficient network processing engines. We present a tool called PacketBench, which provides a framework for implementing network processing applications and obtaining an extensive set of workload characteristics. PacketBench provides the support functions to handle various packet traces and manage packet memory. For statistics collection, PacketBench provides the ability to derive a number of microarchitectural and networking related metrics. We present the results of such measurements for four different networking applications ranging from simple packet forwarding to complex packet payload encryption. The results show that such workload analysis has a range of uses from network processor design to application optimization.
Document Image Clean-up and Binarization

Image binarization is a diicult task for documents with text over textured or shaded backgrounds, poor contrast, and/or considerable noise. Current optical character recognition (OCR) and document analysis technology do not handle such documents well. We have developed a simple yet eeective algorithm for document image clean-up and bina-rization. The algorithm consists of two basic steps. In the rst step, the input image is smoothed using a low-pass (Gaussian) lter. The smoothing operation enhances the text relative to any background texture. This is because background texture normally has higher frequency than text does. The smoothing operation also removes speckle noise. In the second step, the intensity histogram of the smoothed image is computed and a threshold automatically selected as follows. For black text, the rst peak of the histogram corresponds to text. Thresholding the image at the value of the valley between the rst and second peaks of the histogram binarizes the image well. In order to reliably identify the valley, the histogram is smoothed by a low-pass lter before the threshold is computed. The algorithm has been applied to some 50 images from a wide variety of sources: digitized video frames, photos, newspapers, advertisements in magazines or sales yers, personal checks, etc. There are 21820 characters and 4406 words in these images. 91% of the characters and 86% of the words are successfully cleaned up and binarized. A commercial OCR was applied to the binarized text when it consisted of fonts which were OCR recognizable. The recognition rate was 84% for the characters and 77% for the words. Any opinions, ndings and conclusions or recommendations expressed in this material are the author(s) and do not necessarily reeect those of the sponsors.
Piglets Learn to Use Combined Human-Given Visual and Auditory Signals to Find a Hidden Reward in an Object Choice Task

Although animals rarely use only one sense to communicate, few studies have investigated the use of combinations of different signals between animals and humans. This study assessed for the first time the spontaneous reactions of piglets to human pointing gestures and voice in an object-choice task with a reward. Piglets (Sus scrofa domestica) mainly use auditory signals–individually or in combination with other signals—to communicate with their conspecifics. Their wide hearing range (42 Hz to 40.5 kHz) fits the range of human vocalisations (40 Hz to 1.5 kHz), which may induce sensitivity to the human voice. However, only their ability to use visual signals from humans, especially pointing gestures, has been assessed to date. The current study investigated the effects of signal type (visual, auditory and combined visual and auditory) and piglet experience on the piglets’ ability to locate a hidden food reward over successive tests. Piglets did not find the hidden reward at first presentation, regardless of the signal type given. However, they subsequently learned to use a combination of auditory and visual signals (human voice and static or dynamic pointing gestures) to successfully locate the reward in later tests. This learning process may result either from repeated presentations of the combination of static gestures and auditory signals over successive tests, or from transitioning from static to dynamic pointing gestures, again over successive tests. Furthermore, piglets increased their chance of locating the reward either if they did not go straight to a bowl after entering the test area or if they stared at the experimenter before visiting it. Piglets were not able to use the voice direction alone, indicating that a combination of signals (pointing and voice direction) is necessary. Improving our communication with animals requires adapting to their individual sensitivity to human-given signals.