Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing Domain-Specific Language for Wireless Sensor Network application development

Wireless Sensor Networks (WSNs) have a potential for a wide scope of uses in the real-world environment. However, WSN application development is considered a challenging and complex task because of the complexity in low-level details, which a developer must deal with. Besides most of the current application development is done using node-centric, low-level languages such as C. In order to make the WSN technology more ubiquitous, application development complexity needs to be reduced, and development efficiency increased. This paper describes SenNet language, which is a new approach to WSN application development using a Domain-Specific Language (DSL). SenNet empowers application developers by allowing them to focus on modelling the application logic rather than the programming and low-level technical details. The new approach gives the ability to write applications using multi-abstraction levels (i.e. network, group, and node-level). Furthermore, evaluation results show that SenNet reduces the cognitive effort needed for learning WSN application development in addition to the time required to write the application by using automated code generation from abstracted language commands.
Active learning technique for biomedical named entity extraction

One difficulty with machine learning for information extraction is the high cost of collecting labeled examples. Active Learning can make more efficient use of the learner's time by asking them to label only instances that are most useful for the trainer. In random sampling approach, unlabeled data is selected for annotation at random and thus can't yield the desired results. In contrast, active learning selects the useful data from a huge pool of unlabeled data for the classifier. The strategies used often classify the corpus tokens (or, data points) under wrong classes. The classifier is confused between two categories if the token is located near the margin. We develop a method for solving this problem and show that it favorably results in the increased performance. Our approach is based on the supervised machine learner, Conditional Random Field (CRF). The proposed approach is applied for solving the problem of named entity extraction from biomedical domain. Results show that proposed active learning based technique indeed improves the performance of the system.
Teaching a Humanoid Robot to Recognize and Reproduce Social Cues

In a robot programming by demonstration framework, several demonstrations of a task are required to generalize and reproduce the task under different circumstances. To teach a task to the robot, explicit pointers are required to signal the start/end of a demonstration and to switch between the learning/reproduction phases. Coordination of the learning system can be achieved by adding social cues to the interaction process. Here, we propose to use an imitation game to teach a humanoid robot to recognize communicative gestures, which then serve as social signals in a pointing-at-objects scenario. The system is based on hidden Markov models (HMMs) and use motion sensors to track the user's gestures
A Case Study of iPad Implementation in One Rural Elementary School

The study explored the implementation of a 1:1 iPad initiative with a focus on leadership. The distributed leadership model provided the framework using semistructured interviews as the primary source of data collection. Using the distributed leadership model, the researcher examined the role of school leaders. Four emerging themes were identified: time, access, technical support, and professional development. Results showed that successful implementation of a 1:1 iPad program requires advanced planning including time to obtain new skills and to research and explore the vast potential of the iPad in an environment where technical assistance is available within reasonable time constraints.
An event-based model for interactive live TV shows

Over the years some models for multimedia spatio-temporal synchronization has been proposed. In most of these models, a known drawback is that they generally abstract the media content and consider only start and end events content presentation. Thus, it is not possible to specify synchronization relations involving "within" media content events. Highlights in a live TV show are examples of these events. In such cases, it may be expected that highlights "may occur", but it is not possible to determine "when". In order to solve this drawback, this article presents an approach for modeling live Interactive TV shows that support the definition of synchronization constraints related to events not known at authoring time. These events are structured in objects, using an object oriented paradigm, and broadcasted to TV receivers as way to assure the synchronization relations
A Supply Chain Network Design Model for Deteriorating Items

We consider a supply chain network design problem for deteriorating items, involving a single supplier, some potential distribution centers and multiple retailers. This problem is to determine how many distribution centers to set up, where to locate them, how to serve the retailers using these distribution centers, and to determine the optimal inventory policies for the whole supply chain network. The goal is to minimize the total deterioration, inventory, transportation and facilities location cost. We formulate this problem as a nonlinear mix-integer programming model. This model is a NP-Hard problem, so we present a Lagrangian relaxation solution algorithm and a Genetic algorithm to solve our model respectively. Finally, numerical examples are given to illustrate the application of these two algorithms, and our computational results show that Lagrangian relaxation algorithm is more efficient for our problem.
An On-demand Multi-Path Interest Forwarding strategy for content retrievals in CCN

Content Centric Networking (CCN) is a new paradigm in networking and a future Internet architecture. Performance evaluations show that conventional CCN forwarding strategies which use replication of Interests (standard) or the shortest path (best-face) do not perform well under high bandwidth requirements and loaded networks. We have designed and evaluated the performance of an On-demand Multi-Path Interest Forwarding (OMP-IF) strategy which identifies a set of paths based on the disjointness of paths to content locations. Then, the discovered paths are used simultaneously to distribute (split) Interests based on the characteristics of the paths. We have evaluated OMP-IF strategy using a simulator with a large scale network scenario and a realistic traffic generation model. The results show improved performance in CCN networks considering download time, load balancing, content hit ratios, and others.
Supporting Precise Manual-handling Task using Visuo-haptic Interaction

Precise manual handling skills are necessary to create art and to paint models. However, these skills are difficult to learn. Some research has approached this issue using mechanical devices. However, mechanical systems have high costs and limit the user's degree-of-freedom. In our research, we propose a system using visuo-haptics to support accurate work without using any mechanical devices. We considered the principle that when a visuo-haptic force is generated on a user's hand in the opposite direction of a target path, the user moves her/his hand to the right direction reflexively to repel the force. Based on this idea, we created a system that can modify users' hand movement by showing a dummy hand using a mixed reality display, which supports precise manual-handling tasks. To demonstrate this, we performed experiments conducted with a video see-through system that uses a head mounted display (HMD). The results showed that an expansion of the deviation between the target route and the actual hand position improved accuracy up to 50%. We also saw a tendency for a lager expansion to give the most improvement in quality, but slow down working speed at the same time. According to experimental results, we find that a gain of about 2.5 gives an ideal balance between the working precision and the drawing speed.
Molecular Diagnosis of Plant Viruses

Makkouk, K.M. and S.G. Kumari. 2006. Molecular Diagnosis of Plant Viruses. Arab J. Pl. Prot. 24: 135-138. Proper virus identification is always the key in developing appropriate practical solutions to manage plant virus diseases. Recent advances in biotechnology and molecular biology have played a significant role in the development of rapid, specific and sensitive diagnostic tests. The use of ELISA, by employing either polyclonal or monoclonal antibodies, was a significant step in adding sensitivity and precision to virus detection. The development of the tissue-blot immunoassay (TBIA), as a variant of ELISA, greatly simplified testing, reduced the cost and permitted virus testing at locations where facilities are limited or even absent. Immuno Chromatographic Assay (ICA), is another ELISA variant which added speed to virus identification, where results can be obtained within 10-15 minutes, as compared to 2-3 hours for TBIA. However, ICA is more expensive than TBIA. The development of nucleic acid-based tools was another new dimension of virus detection. The most common among these techniques are cDNA hybridization and polymerase chain reaction (PCR). In addition, PCR can be used as a confirmatory test for TBIA, where processed blots can be cut individually and tested by PCR. This proved to work well with both DNA and RNA plant viruses. Furthermore, unprocessed plant tissue blots on nitrocellulose membrane represent a good sample for PCR amplification. PCR products can also be used for cloning and subsequent sequencing which is extremely useful for identification of new viruses or virus strains.
Face Recognition in Challenging Situations

A great deal of previous research has demonstrated that face recognition is unreliable for unfamiliar faces and reliable for familiar faces. However, such findings typically came from tasks that used ‘cooperative’ images, where there was no deliberate attempt to alter apparent identity. In applied settings, images are often far more challenging in nature. For example multiple images of the same identity may appear to be different identities, due to either incidental changes in appearance (such as age or style related change, or differences in images capture) or deliberate changes (evading own identity through disguise). At the same time, images of different identities may look like the same person, due to either incidental changes (natural similarities in appearance), or deliberate changes (attempts to impersonate someone else, such as in the case of identity fraud). Thus, past studies may have underestimated the applied problem. In this thesis I examine face recognition performance for these challenging image scenarios and test whether the familiarity advantage extends to these situations. I found that face recognition was indeed even poorer for challenging images than previously found using cooperative images. Familiar viewers were still better than unfamiliar viewers, yet familiarity did not bring performance to ceiling level for challenging images as it had done in the cooperative tasks in the past. I investigated several ways of improving performance, including image manipulations, exploiting perceptual constancy, crowd analysis of identity judgments, and viewing by super-recognisers. This thesis provides interesting insights into theory regarding what it is that familiar viewers are learning when they are becoming familiar with a face. It also has important practical implications; both for improving performance in challenging situations and for understanding deliberate disguise.
Cognitive Reflection Predicts Decision Quality in Individual and Strategic Decisions

Cognitive reflection has been shown to be an important trait which is correlated with the propensity to: take risks, delay gratification, and form accurate beliefs about others’ behavior. However, previous research has not cleanly identified whether reflective thinkers make ‘better’ decisions than intuitive thinkers, since inferences of decision quality are confounded by inferences regarding risk preferences, time preferences, and beliefs. We directly test for differences in decision quality between reflective thinkers and intuitive thinkers in both individual and strategic decisions using a design which makes it possible to objectively rank risky and strategic choices, independent of one’s attitudes toward risk or one’s beliefs about the strategic sophistication or altruism of other decision makers. Employing a lottery choice task involving a dominant and a dominated alternative, and implementing multiple rounds of a second price auction, we find that the tendency to cognitively reflect has strong predictive power across domains (reasoning tasks, choices between lotteries, bidding behavior in auctions), and across time (as the tasks were administered on separate dates). In particular, the same subjects who engaged in reflective thinking on simple reasoning problems were also more likely to choose optimally in the lottery choice task and to bid closer to the dominant strategy equilibrium in second price auctions. We also find that experience helps to narrow the gap in performance between reflective and intuitive thinkers.
Testing the Quantal Response Hypothesis

This paper develops a formal test for consistency of players' behavior in a series of games with the quantal response equilibrium (QRE). The test exploits a characterization of the equilibrium choice probabilities in a QRE as the gradient of a convex function, which thus satisfies the cyclic monotonicity inequalities. Our testing procedure utilizes recent econometric results for moment inequality models. We assess the performance of the test using both Monte Carlo simulation and lab experimental data from a series of generalized matching pennies games. Our experimental findings are consistent with the literature: the joint hypothesis of QRE, risk neutrality and player role homogeneity is rejected in the pooled data, but cannot be rejected in the individual data for over half of the subjects. By considering subsets of cycle monotonicity inequalities, our approach also highlights the nature of QRE consistency violations.
A full upper limb robotic exoskeleton for reaching and grasping rehabilitation triggered by MI-BCI

In this paper we propose a full upper limb exoskeleton for motor rehabilitation of reaching, grasping and releasing in post-stroke patients. The presented system takes into account the hand pre-shaping for object affordability and it is driven by patient's intentional control through a self-paced asynchronous Motor Imagery based Brain Computer Interface (MI-BCI). The developed antropomorphic eight DoFs exoskeleton (two DoFs for the hand, two for the wrist and four for the arm) allows full support of the manipulation activity at the level of single upper limb joint. In this study, we show the feasibility of the proposed system through experimental rehabilitation sessions conducted with three chronic post-stroke patients. Results show the potential of the proposed system for being introduced in a rehabilitation protocol.
Learning to Heal from Those Who Know! The “Lived” Experience of a Peer Support Program for Adult Survivors of Childhood Trauma

ABSTRACT This study explored the dual phenomenon of experiencing a peer support residential trauma program from the perspective of (a) a client, and (b) a facilitator. It sought both positive and negative subjective interpretations. Participants were former clients and current facilitators of the program. Data were collected through a focus group, and analyzed using interpretative phenomenological analysis. One superordinate theme, altruistic growth, overarched 2 subordinate themes. The first, modeling through respect, included subthemes of respect and hope and empowerment. The second subordinate theme, reciprocal model of care, encapsulated personal challenges such as facing the mirror, old demons, and burnout and breaking points. Conversely, participants mused on these challenges as opportunities for further healing through opportunities for self-nurturing and gratitude. Implications for treatment of adult survivors of childhood trauma are discussed. For facilitators, the program was seen as offering further personal reflection and a giving forward that was interpreted as altruistic growth.
Design of Unified Power Quality Conditioner ( UPQC ) to Improve the Power Quality Problems by Using PQ Theory

This paper presents a Design of a Unified Power Quality conditioner (UPQC) to improve the power quality problems by using P-Q theory. The design of UPQC connected to three phase four wire system (3P4W). The neutral of series transformer used in the fourth wire for the 3P4W system. The neutral current that may flow toward transformer neutral point is compensated by using a four-leg voltage source inverter topology for shunt part. The series transformer neutral will be at virtual zero potential during all operating conditions. In this simulation we observe the power quality problems such as unbalanced voltage and current, harmonics by connecting non linear load to 3P4W system with Unified Power Quality conditioner. And also calculating the THD (1.46%) and active power and reactive power. A new control strategy is proposed to the control algorithm for series APF is based on unit vector template generation to compensate the current unbalance present in the load currents by expanding the concept of single phase PQ theory. The P-Q theory applied for balanced three phase system. And also be used for each phase of unbalanced system independently.
Vibration-based damage identification in composite circular plates using polar discrete wavelet transform

Non-destructive damage identification is one of the most important problems in maintenance and reliability of composite structures. The methods applied for damage identification should be sensitive for external and internal damages and provide their precise localization. One of the promising methods for the damage identification is the wavelet transform applied to modal shapes of investigated objects. If the investigated object has circular geometry it is suitable to adapt the algorithm of damage identification to polar coordinate system. The paper presented an application of polar wavelet transform in order to detect and identify various types of damages occurred in composite structures with circular geometry. Several (external and internal) types of damages were considered in the presented study. Basing on comparative study the most adequate polar wavelets for damage identification problems were selected.
A discrete computational model of sensorimotor contingencies for object perception and control of behavior

According to Sensorimotor Contingency Theory (SCT), visual awareness in humans emerges from the specific properties of the relation between the actions an agent performs on an object and the resulting changes in sensory stimulation. The main consequence of this approach is that perception is based not only on information coming from the sensory system, but requires knowledge about the actions that caused this input. For the development of autonomous artificial agents the conclusion is that consideration of the actions, that cause changes in sensory measurements, could result in a more human-like performance in object recognition and manipulation than ever more sophisticated analyses of the sensory signal in isolation, an approach that has not been fully explored yet. We present the first results of a modeling study elucidating computational mechanisms implied by adopting SCT for robot control, and demonstrate the concept in two artificial agents. The model is given in abstract, probabilistic terms that lead to straightforward implementations on a computer, but also allow for a neurophysiological grounding. After demonstrating the emergence of object-following behavior in a computer simulation of the model, we present results on object perception in a real robot controlled by the model. We show how the model accounts for aspects of the robot's embodiment, and discuss the role of memory, behavior, and value systems with respect to SCT as a cognitive control architecture.
An Opportunistic Two Transceiver Based MAC Protocol for Cognitive Radio Networks

Enormous growth of wireless applications makes the spectrum scarcity problem worse day by day. Moreover, traditional fixed spectrum assignment policy causes serious spectrum underutilization problem. However, Cognitive Radio that enables opportunity based spectrum access for unlicensed users has recently been recognized as a heartening solution for spectrum scarcity and underutilization problem. Medium access control protocol is the key enabler or coordinator for this opportunity based channel access. However, recent studies show that in distributed cognitive radio networks MAC protocol suffers from severe performance degradation due to common control channel selection (CCC) problem, the multichannel hidden terminal problem (MHTP). In this paper, we propose a MAC protocol that eliminates CCC selection problem. Here, unlicensed users use dedicated band owned by them as a common control channel. Every unlicensed user equipped with two transceivers. One is control radio that helps SU’s to get updated control information and other is data radio. This clearly cuts down the number of collisions with other secondary and primary users in data transmission phase. To obtain the accurate sensing result, we propose cooperative sensing for idle channel detection. In this paper, we develop an analytical model to analyze important performance metrics. Simulation results show the effectiveness of our system.
Energy flow control system based on neural compensator in the feedback path for autonomous energy source

In this paper an artificial neural network, which realizes a nonlinear adaptive control algorithm, has been applied in a control system of variable speed generating system. The speed is adjusted automatically as a function of load power demand. The controller employs a single layer neural network to estimate the unknown plant nonlinearities online. Optimization of the controller is difficult because the plant is nonlinear and no stationary. Furthermore, it deals with the situation where the plant becomes uncontrollable without any restrictive assumptions. In contrast to previous work [1] on the same subject, the number of neural networks has been reduced to only one network. The number of the neurons in a network structure as well as choosing certain design parameters was specified a priori. The computer test results have been presented to show performance of proposed neural controller.