Miracle-dz/UniFAR-FTUs huggingface dataset:
Issues and Trends in the Journal of Positive Behavior Interventions: Severe Problem Behavior and Severe Disabilities

Positive behavior support (PBS) has undergone substantial changes since its formation in the mid to late 1980s. In its initial iteration, PBS was described largely as an approach and technology for addressing the extreme problem behaviors of individuals with severe developmental disabilities using assessment-based procedures that relied on constructive and respectful strategies and eschewed the use of aversive stimuli (Horner et al., 1990). Over the following decades, PBS expanded rapidly in terms of applicable populations and contexts and, especially, in its adoption of classrooms, schools and other group contexts as units of analysis and intervention (Dunlap, Sailor, Horner, & Sugai, 2009; Kincaid et al., 2016; Sugai et al., 2000). Without question, PBS has grown impressively in its stature, constituencies, and impact. However, it is important to note that severe problem behaviors, such as aggression and selfinjury, and severe developmental disabilities have ceased to be a prominent focus of PBS. The Journal of Positive Behavior Interventions (JPBI) was created in 1999 to serve as the principal organ of perspectives and innovations in PBS research and practice (Dunlap & Koegel, 1999). The extent to which trends in JPBI publications mirror trends in PBS is a worthy question and Clarke, Zakszeski, and Kern (this issue) present data that help to provide answers. In this commentary, we respond to the editors’ request by addressing issues related to trends in PBS/JPBI and offering opinions related to values and the future of both PBS and JPBI.
FHL2 Silencing Reduces Wnt Signaling and Osteosarcoma Tumorigenesis In Vitro and In Vivo

Background The molecular mechanisms that are involved in the growth and invasiveness of osteosarcoma, an aggressive and invasive primary bone tumor, are not fully understood. The transcriptional co-factor FHL2 (four and a half LIM domains protein 2) acts as an oncoprotein or as a tumor suppressor depending on the tissue context. In this study, we investigated the role of FHL2 in tumorigenesis in osteosarcoma model. Methodology/Principal Findings Western blot analyses showed that FHL2 is expressed above normal in most human and murine osteosarcoma cells. Tissue microarray analysis revealed that FHL2 protein expression is high in human osteosarcoma and correlates with osteosarcoma aggressiveness. In murine osteosarcoma cells, FHL2 silencing using shRNA decreased canonical Wnt/β-catenin signaling and reduced the expression of Wnt responsive genes as well as of the key Wnt molecules Wnt5a and Wnt10b. This effect resulted in inhibition of osteosarcoma cell proliferation, invasion and migration in vitro. Using xenograft experiments, we showed that FHL2 silencing markedly reduced tumor growth and lung metastasis occurence in mice. The anti-oncogenic effect of FHL2 silencing in vivo was associated with reduced cell proliferation and decreased Wnt signaling in the tumors. Conclusion/Significance Our findings demonstrate that FHL2 acts as an oncogene in osteosarcoma cells and contributes to tumorigenesis through Wnt signaling. More importantly, FHL2 depletion greatly reduces tumor cell growth and metastasis, which raises the potential therapeutic interest of targeting FHL2 to efficiently impact primary bone tumors.
ATTENTIONAL TUNNELING AND TASK MANAGEMENT

This paper discusses attentional tunneling as one cause of breakdowns in task management. The phenomenon is defined, and empirical evidence is then reviewed to show the conditions in which the phenomenon is created by head up display location, compelling 3D displays, fault management, and automation induced complacency. Statistical and methodological issues are reviewed regarding the generalization of the phenomenon in the laboratory to real world mishaps.
Recursive Unscented Kalman Filtering based SLAM using a Large Number of Noisy Observations

Simultaneous Localization and Map Building (SLAM) is one of the fundamental problems in robot navigation. The Extended Kalman Filter (EKF), which is widely adopted in SLAM approaches, requires extensive computation. The conventional particle filter also needs intense computation to cover a high dimensional state space with particles. This paper proposes an efficient SLAM method based on the recursive unscented Kalman filtering in an environment including a large number of landmarks. The posterior probability distributions of the robot pose and the landmark locations are represented by their marginal Gaussian probability distributions. In particular, the posterior probability distribution of the robot pose is calculated recursively. Each landmark location is updated with the recursively updated robot pose. The proposed method reduces filtering dimensions and computational complexity significantly, and has produced very encouraging results for navigation experiments with noisy multiple simultaneous observations.
Alignment of Software Product Quality Goals in Two Outsourcing Relationships

Issues with software product quality are commonly reported when organisations engage in outsourcing relationships. To address this issue, value-based software engineering literature emphasises the need for all success-critical stakeholder groups to work towards a mutually agreed goal. Aim: This paper presents a case study that aims to compare and contrast the priority two groups place on software product quality -- stakeholders internal to the development organisation, and stakeholders from outsourcing relationships. Method: A model of software product quality was developed and used for this study based on ISO 9126 standard. Questionnaires were collected from 38 representatives of the two key stakeholder groups, in which each person rates the relative importance of aspects of software product quality using the hierarchical cumulative voting (HCV) technique. The results of these two groups were then analysed and compared. Results: The results show the stakeholders priorities to be a merging of the priorities from both the software development organsiation, and the firm providing the outsourced services. Further, stakeholders from outsourced relationships had greater difficulty define an ideal future balance of software product qualities. Conclusions: One of the keys to success when outsourcing is to ensure both the internal and external groups understand the needs of each other -- and ensure they can work towards a sufficiently compatible goal. It may be necessary to change the way work is outsourced to align the goals of both firms to be compatible.
Measurement of static constraints imposed by a human hand on a grasped object

This paper proposes a method of directly measuring static constraints imposed by a human hand on a grasped object. Based on the analysis of the demonstration data of the human grasp, the static constraints are expressed as a combination of frictional force and normal force generated by the human hand. The static constraints are an important property to be mapped to robotic hands in the programming-by-demonstration. Measured static constraints are to be generated to robotic hands to establish the stable grasp. In the experiments, we have successfully measured the static constraints appeared in various different grasps used in the daily human life.
Improving Spam Detection Using Neural Networks Trained by Memetic Algorithm

In this paper we train an Artificial Neural Network (ANN) using Memetic Algorithm (MA) and evaluate its performance on the UCI spambase dataset. The Memetic algorithm incorporates the local search capacity of Simulated Annealing (SA) and the global search capability of Genetic Algorithm (GA) to optimize the parameters of the ANN. The performance of the MA is compared with traditional GA in training the ANN. We further explore the different parameters, mechanisms and architectures used to optimize the performance of the network and attain a practical balance between the global genetic algorithm and the local search technique. Classification using ANN trained by MA yielded better results on the spambase dataset compared with other algorithms reported in literature.
An Interpretive Review of Psychoanalytic Literature on Empathy in the Therapeutic Relationship

This dissertation is an interpretive review of psychoanalytic literature on empathy within the therapeutic relationship. The creative science of hermeneutics is used to explore the history of empathy and its function within the therapeutic relationship. A critical analysis of the literature provides the platform for assessing present arguments for the importance of empathy in the therapy relationship. This analysis identified three important areas neglected in the research. These are the wider context of the evolution of empathy, the role of culture and consideration of the client’s experience. A discussion of these areas concludes this dissertation. E M P A T H Y I N T H E T H E R A P E U T I C R E L A T I O N S H I P | 3
Indoor Location Search Based on Subarea Fingerprinting and Curve Fitting

Indoor localization has become a hot topic thanks to the fast development of wireless local area networks. The basic localization scheme can be divided into two approaches: fingerprint-based and lateration-based. In this paper, we propose a novel localization method that combines the fingerprint method and lateration method. Fingerprint-based method is first used for sub area determination and lateration method is then used to estimate the mobile coordinate. Simulation results show that our proposed method is quite effective, and can achieve localization improvement of 34% and 71%, compared with the traditional fingerprint method and a peer lateration method.
Foreword to the Special Issue on Machine Learning for Remote Sensing Data Processing

The twenty-seven articles in this special issue is a follow-up to special sessions organized at WHISPERS conferences. Such sessions drew unexpectedly large attendance, signaling the interest and need for a focused platform to exchange knowledge at the intersection of machine learning and remote sensing. The collection of papers in this special issue presents a comprehensive sample of the latest trends in the design of machine learning algorithms for geospatial data. It covers a wide spectrum of remote sensing applications and presents new solutions to answer to the call of the new generation of sensors, covering the electromagnetic range from optical to microwave data.
A Novel Protein, Luman/CREB3 Reruitment Factor, Inhibits Luman Activation of the Unfolded Protein Response

ABSTRACT Luman/CREB3 (also called LZIP) is an endoplasmic reticulum (ER)-bound cellular transcription factor. It has been implicated in the mammalian unfolded protein response (UPR), as well as herpes simplex virus reactivation from latency in sensory neurons. Here, we report the identification of a novel Luman recruitment factor (LRF). Like Luman, LRF is a UPR-responsive basic-region leucine zipper protein that is prone to proteasomal degradation. Being a highly unstable protein, LRF interacts with Luman through the leucine zipper region and promotes Luman degradation. LRF was found to recruit the nuclear form of Luman to discrete nuclear foci, which overlap with the nuclear receptor coactivator GRIP1 bodies, and repress the transactivation activity of Luman. Compared to LRF+/+ mouse embryonic fibroblast (MEF) cells, the levels of CHOP, EDEM, and Herp were elevated in LRF−/− MEF cells. We propose that LRF is a negative regulator of the UPR. For Luman, it may represent another level of regulation following Luman proteolytic cleavage on the ER and nuclear translocation. In addition to inducing rapid Luman turnover, LRF may repress the transactivation potential of Luman by sequestering it in the LRF nuclear bodies away from key cofactors (such as HCF-1) that are required for transcriptional activation.
Does One-Against-All or One-Against-One Improve the Performance of Multiclass Classifications?

One-against-all and one-against-one are two popular methodologies for reducing multiclass classification problems into a set of binary classifications. In this paper, we are interested in the performance of both one-against-all and one-against-one for classification algorithms, such as decision tree, naïve bayes, support vector machine, and logistic regression. Since both one-against-all and one-against-one work like creating a classification committee, they are expected to improve the performance of classification algorithms. However, our experimental results surprisingly show that one-against-all worsens the performance of the algorithms on most datasets. One-against-one helps, but performs worse than the same iterations of bagging these algorithms. Thus, we conclude that both one-against-all and one-against-one should not be used for the algorithms that can perform multiclass classifications directly. Bagging is better approach for improving their performance.
Human identification using WiFi signal

WiFi devices are now pervasive in our environment. Recent research has demonstrated that it is possible to sense the perturbations created by human motion in the WiFi spectrum to identify basic activities, gestures and even keystrokes. In this demo, we address the yet unsolved problem of human identification using WiFi spectrum sensing. We present WiFi-ID, a device-free system that uses off-the-shelf equipment to uniquely identify individuals as they walk through urban spaces that are filled with WiFi transmissions. Our system exploits the fact that each individual has a unique walking style which causes unique disturbances in the WiFi signals. WiFi-ID analyses the Channel State Information and extracts unique features that allow us to identify individuals. We will demonstrate a functioning prototype of our system and use conference attendees as test subjects.
Ordinal Patterns, Entropy, and EEG

In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-world data and, especially, of electroencephalogram (EEG) data. We apply already known (empirical permutation entropy, ordinal pattern distributions) and new (empirical conditional entropy of ordinal patterns, robust to noise empirical permutation entropy) methods for measuring complexity, segmentation and classification of time series.
The law of Verdoorn: evidence from Greek disaggregated manufacturing time series data

This paper tests the validity of Verdoorn's law in Greek manufacturing. Through the Generalised Method of Moments (GMM) methodology, estimates of the Verdoorn law in aggregated and disaggregated manufacturing Greek data are obtained in order to explain disparities in income and growth among Greek manufacturing sectors. The results provided evidence that increasing returns to scale with certain, albeit low, substitutability possibilities between capital and labour are present in Greek manufacturing groups.
A Nonlinear Variational Approach to Motion-Corrected Reconstruction of Density Images

The aim of this paper is to establish a nonlinear variational approach to the reconstruction of moving density images from indirect dynamic measurements. Our approach is to model the dynamics as a hyperelastic deformation of an initial density including preservation of mass. Consequently we derive a variational regularization model for the reconstruction, which - besides the usual data fidelity and total variation regularization of the images - also includes a motion constraint and a hyperelastic regularization energy. Under suitable assumptions we prove the existence of a minimizer, which relies on the concept of weak diffeomorphisms for the motion. Moreover, we study natural parameter asymptotics and regularizing properties of the variational model. Finally, we develop a computational solution method based on alternating minimization and splitting techniques, with a particular focus on dynamic PET. The potential improvements of our approach compared to conventional reconstruction techniques are investigated in appropriately designed examples.
An Automatic Tag Recommendation Algorithm for Micro-blogging Users

Online social networks such as Sina Weibo micro-blogging website allow user to annotate himself (or herself) using tags, which describe the characteristics of the user. Many researches have been done on photos, films, commodities but rare researches on users. In this paper, we try to recommend tags for users who can communicate directly with other users. Our method is based on interactive relations between users and is low cost. We present and evaluate tag recommendation method to support the user self-annotation task by recommending a set of tags for users. Experiment evaluations on real and large Sina Weibo dataset which contains more than 140 million users and distributed processing framework Hadoop shows that our method can effectively recommend relevant tags to users.
Exploiting Sublanguage and Domain Characteristics in a Bootstrapping Approach to Lexicon and Ontology Creation

It is very costly to build up lexical resources and domain ontologies. Especially when confronted with a new application domain lexical gaps and a poor coverage of domain concepts are a problem for the successful exploitation of natural language document analysis systems that need and exploit such knowledge sources. In this paper we report about ongoing experiments with `bootstrapping techniques' for lexicon and ontology creation.
Digital sliding mode control using low cost embedded solution

The implementation of sliding mode control (SMC) for small DC motors is detailed in this paper. Short discussion on sliding mode control theory is made over the case of DC motor modeled under assumptions of load torque as uncertainty. The practical implementation uses the MSP430F2012 controller facilities. Under these considerations, the main result is the fast control loop very efficient for a wide range of DC motor operating conditions. Simulation results and test results using sliding mode control are also presented.
Video Inpainting for Moving Object Removal Using Patch Sparsity

The process of removing and reconstructing the specific area in video is known as video Inpainting. Most of the automatic techniques of video Inpainting are computationally intensive and unable to repair large holes. To overcome this problem, exemplar based video Inpainting method is extended by incorporating the Sparsity of natural image patches using background registration technique is proposed in this paper.
System Level Diagnostics over the PMC Model

System level diagnostics is targeted to complex computer systems. The basis of this diagnostics is defined by formal diagnostic models. The paper presents diagnostic process over the symmetric diagnostics model. A new Boolean syndrome decoding algorithms have been developed and implemented for the one-step diagnostics of t-diagnosable regular systems over this model. New Boolean expres- sions for the system model were defined for regular sys- tems based on simplification of test syndrome decoding. The developed algorithms were tested on several examples of computer architectures and the results were compared with existing and published another syndrome decoding algorithms. The presented algorithms consume less time for larger computer systems in comparison with current algorithms.
Supporting the Sink Mobility: a Case Study for Wireless Sensor Networks

This paper deals with a system level design solution to support information gathering in the presence of a mobile querying node that experiences frequent disconnections from a Wireless Sensor Network (WSN), an application scenario that embraces the area of Intelligent Transportation Systems (ITS). The proposed scheme basically relies on the network capability of autonomously adapting to sink mobility in order to properly deliver the requested data. The system is defined as far as its functional elements and related communications protocols, together with validating its effectiveness by means of a simulative study. In particular, the performance in terms of delivering latency and packet delivery ratio has been investigated for several network topologies, architecture and mobility pattern, always highlighting a remarkable quality of provided services together with robustness with regard to operative conditions.
The Effects of Pair-wise and Higher-order Correlations on the Firing Rate of a Postsynaptic Neuron

Coincident firing of neurons projecting to a common target cell is likely to raise the probability of firing of this postsynaptic cell. Therefore, synchronized firing constitutes a significant event for postsynaptic neurons and is likely to play a role in neuronal information processing. Physiological data on synchronized firing in cortical networks are based primarily on paired recordings and cross-correlation analysis. However, pair-wise correlations among all inputs onto a postsynaptic neuron do not uniquely determine the distribution of simultaneous postsynaptic events. We develop a framework in order to calculate the amount of synchronous firing that, based on maximum entropy, should exist in a homogeneous neural network in which the neurons have known pair-wise correlations and higher-order structure is absent. According to the distribution of maximal entropy, synchronous events in which a large proportion of the neurons participates should exist even in the case of weak pair-wise correlations. Network simulations also exhibit these highly synchronous events in the case of weak pair-wise correlations. If such a group of neurons provides input to a common postsynaptic target, these network bursts may enhance the impact of this input, especially in the case of a high postsynaptic threshold. The proportion of neurons participating in synchronous bursts can be approximated by our method under restricted conditions. When these conditions are not fulfilled, the spike trains have less than maximal entropy, which is indicative of the presence of higher-order structure. In this situation, the degree of synchronicity cannot be derived from the pair-wise correlations.
On the complexity of using Handelman relaxation for solving polynomial optimization problems

This paper investigates the computational complexity involved when using Handelman relaxation method for solving multivariable polynomial optimization problems. In particular, the relationship between the degree of the Handelman representation of non-negative polynomial function over convex polytopic set and the number of decision variables required to prove its existence through linear programming method is characterized. A simulation result to help illustrate the computational complexity and rate of convergence of Handelman relaxation method for solving multivariable polynomial optimization problem is also presented.