Miracle-dz/UniFAR-FTUs huggingface dataset:
The plot thickens: supporting pre-service teachers in authentic use of cooperative learning through the Storypath instructional approach

Abstract Pre-service teachers typically find it challenging to implement cooperative learning authentically in schools given the complexities of classroom environments. Curriculum integration also is demanding because it requires combining research-informed pedagogies and best practices to promote academic and social learning. This article describes how Storypath, a narrative approach to organising the curriculum, assists novice teachers in skilfully implementing cooperative learning and meaningfully integrating it with other best practices. It begins by describing cooperative learning’s central role in Seattle University’s Master in Teaching (MIT) programme, along with elements foundational to cooperative work. It continues by explaining how Storypath scaffolds pre-service teachers’ capacity to successfully facilitate cooperative learning, then provides a specific example focused on environmental sustainability. The example illustrates how Storypath naturally creates positive interdependence among participants, thereby nurturing compelling successful cooperative experiences in creating the story. The article concludes with reflections on impact from MIT graduates.
Research on Color-Based Image Retrieval and Implement of the System

To quickly index and retrieval the image of interest, the content-based image retrieval arose as a promising technique. Some CBIR algorithms based on color features are proposed and this paper also presents the design framework of a CBIR system and a prototype system implemented by author. Some test experiments are conducted on a fire image database with good precision and recall.
Modulation of vaccine-induced immune responses to hepatitis C virus in rhesus macaques by altering priming before adenovirus boosting.

BACKGROUND Preventive and therapeutic vaccine strategies aimed at controlling hepatitis C virus (HCV) infection should mimic the immune responses observed in patients who control or clear HCV, specifically T helper (Th) type 1 and CD8+ cell responses to multiple antigens, including nonstructural protein (NS) 3. Given the experience with human immunodeficiency virus, the best candidates for this are based on DNA prime, pox, or adenovirus boost regimens. METHODS In rhesus macaques, we compared NS3-expressing DNA prime and adenovirus boost strategy with 2 alternative priming approaches aimed at modifying Th1 and CD8+ responses: DNA adjuvanted with interleukin (IL)-2- and -12-encoding plasmids or Semliki Forest virus (SFV). RESULTS All prime-boost regimens elicited NS3-specific B and T cell responses in rhesus macaques, including CD8+ responses. SFV priming induced higher lymphoproliferation and longer Th1 memory responses. The use of IL-2- and IL-12-expressing vectors resulted in reduced Th2 and antibody responses, which led to increased Th1 skewing but not to an increase in the magnitude of the IFN- gamma and CD8+ responses. CONCLUSIONS All strategies induced Th1 cellular responses to HCV NS3, with fine modulations depending on the different priming approaches. When they are developed for more HCV antigens, these strategies could be beneficial in therapeutic vaccine approaches.
Energy analysis of compressed sensing in wireless multi media sensor network

— A Wireless Sensor Network (WSN) consists of several sensor nodes deployed in inaccessible areas for monitoring temperature, pressure, vibration, sound, motion etc. A WSN is used for variety of applications such as military, civil, industrial automation, medical, home automation, fleet monitoring, habitat monitoring, preventing theft etc. The availability of inexpensive hardware such as CMOS cameras and microphones has led to the development of Wireless Multimedia Sensor Networks (WMSN) which is used for image and video applications. In case of video applications the captured data will be too large if transmitted as such so it has to be compressed before transmission. Compression in traditional video encoding makes use of motion estimation and motion compensation techniques which requires intensive operations that lead to significant energy consumption and also the storage required is high. This drawback can be addressed by Compressed sensing, an emerging technique that directly obtains the desired samples, thereby reducing the energy consumption, storage capacity and bandwidth used in the network. It is used for reconstructing a signal from the M<<N measurements obtained from sparse or compressible signals, where N is the number of samples required for Nyquist sampling. Compressed sensing can overcome the drawbacks of traditional video encoders by simultaneously sensing and compressing the data at low complexity. The original signal can be recovered from measurements using basis pursuit and greedy algorithms. The objective of this paper is to implement a video compressed sensing framework using Gaussian measurement matrix and reconstruct it using Orthogonal Matching Pursuit algorithm and further transmission energy is analysed for the video compressed sensing framework .
The role of religious leaders in anti-Apartheid mobilisation: implications for violence prevention in contemporary South Africa

ABSTRACT This study examines how South African religious leaders mobilised spiritual capacity and religious assets to resist and fight against state and structural violence during the 1970s and 1980s. Document analysis and qualitative in-depth interviews were conducted with six key informants who were directly or indirectly involved in an interfaith Peace March in 1989 in Cape Town. Our analysis was aimed at identifying and understanding the factors, which enabled individuals from diverse religious backgrounds to work together to combat the Apartheid state and structural violence, drawing out possible roles for interfaith leadership within the prevention of direct violence in South Africa. The analysis shows that interfaith leadership was enacted as five kinds of performances: of safety in sacred spaces, solidarity and community, non-violence, voice and representation, and humanitarian relief and healing. These performances relied on specific religious assets, spiritual capacity and psychological and interpersonal qualities characteristic of transformative leaders. In conclusion, we offer some suggestions for how interfaith leadership alongside other social actors may contribute to the prevention of interpersonal violence.
Phonation Contrasts across Languages

This study compared the contrastive phonation types of four languages—Gujarati (modal vs. breathy), Hmong (modal vs. breathy vs. creaky), Mazatec (modal vs. breathy vs. creaky), and Yi (tense vs. lax)—on several acoustic measures, within and across languages. For Gujarati, Hmong, and Yi, two electroglottographic (EGG) measures were also compared; a Contact Quotient measure distinguished the within-language phonation types in all three languages. While several acoustic measures distinguished phonation types within each language, only H1*-H2* did so in all four languages. However, when each within-language phonation category was then compared across languages, each category was found to differ from language to language on multiple acoustic measures, e.g. breathy in Hmong is distinct from breathy in Gujarati. This unexpected result suggests that language/speaker differences in voice quality are larger than phonation category differences. This suggestion finds support in a Multi-Dimensional Scaling analysis of the acoustic measures. A three-dimensional space turns out to mostly distinguish the languages, less so the phonations. The phonation categories do not form clusters in this space across languages as might have been expected, but they do occupy separate regions along the third dimension of the space, a dimension correlated especially with H1*-H2*. Thus H1*-H2* is again seen to be the most important measure of phonation contrasts across languages. Copyright Information: All rights reserved unless otherwise indicated. Contact the author or original publisher for any necessary permissions. eScholarship is not the copyright owner for deposited works. Learn more at http://www.escholarship. org/help_copyright. html#reuse Phonation Contrasts Across Languages P. Keating, C. Esposito, M. Garellek, S. Khan, J. Kuang UCLA; Macalester College; Brown University (keating@humnet.ucla.edu)
AMCL based map fusion for multi-robot SLAM with heterogenous sensors

This paper proposes an efficient adaptive Monte Carlo Localization (AMCL) based approach to align the occupancy grid maps built by a multi-robot system. Map alignment plays an important role for the map fusion of multi-robot simultaneous localization and mapping (SLAM), especially for the SLAM with heterogenous sensors. Two robots equipped with a laser and Kinect respectively are executing FastSLAM 2.0 in the same environment but at different starting point; the motion and measurement information is recorded with time-stamps. To merge the maps built by different robots, one robot is first relocated in the map built by the other robot by using the recorded motion sequences and measurement information. With the relocation result, the transformation matrix between the two different maps is the calculated; the matrix is further used as the initial relative pose information for ICP process to obtain precise alignment result. Experiments are finally performed to demonstrate the effectiveness of the proposed approach. Index Terms - AMCL; map fusion; multi-robot SLAM; heterogenous sensors.
Recursive OpenIE

This paper proposes an extension to the OpenIE paradigm, to allow the expression of recursive relations, and presents a fully implemented extension to the EXEMPLAR system, uncreatively referred to as Recursive-EXEMPLAR, to extract recursive n-ary relations automatically. A rule-based approach is shown to achieve very high accuracy. Attempts at the automatic learning of rules from hand-labelled data proved to be problematic, because I find that my labellings are inconsistent and contradictory, and automatic labeling methods are too compute intensive for the project timescale. recall.
Wireless Sensor Networks:A case study for Energy Efficient Environmental Monitoring

Energy efficiency is a key issue for wireless sensor networks, since sensors nodes can often be powered by non-renewable batteries. In this paper, we examine four MAC protocols in terms of energy consumption, throughput and energy efficiency. A forest fire detection application has been simulated using the well-known ns-2 in order to fully evaluate these protocols.
Proposing Forgiveness Therapy for those in Prison: An Intervention Strategy for Reducing Anger and Promoting Psychological Health

We propose a novel approach to corrections, both in maximum security prisons for adults and juvenile detention centers for adolescents. It is our hypothesis that unjust treatment from others can lead to inner pain, which can lead to anger. Unresolved anger can deepen and linger, turning to what we call excessive anger, compromising one’s psychological health and behavior. Excessive anger can turn to rage (very intense, potentially violent anger) which can fuel crime, a lack of cooperation within the prison system, and increased recidivism rates. When the excessive anger is caused by unjust behavior from others, prior to a person’s crime, conviction, and imprisonment, then we can reduce and even eliminate the excessive anger through the empirically-verified treatment of Forgiveness Therapy. It is rare, in our experience, for prison rehabilitation to have those in prison look backward, toward the injustices that happened to them prior to the crime. When they are given the chance to do this, they have the opportunity, perhaps for the first time, to confront the injustice and to heal from the pain and anger resulting from the unjust actions against them that might have happened years ago, even in childhood. Forgiveness Therapy may be one of the few existing mental health approaches which offer the opportunity to be free of excessive anger, perhaps for the first time in the person’s life. Research on Forgiveness Therapy with an imprisoned sample of youth in Korea is described as are two individual cases of men within a maximum security facility.
Parental Involvement and Students’ Academic Achievements: A Quantitative Study

This study aims at to find out whether parental involvement affects student academic scores and whether or not children’s self-efficacy mediates between parental involvement and students’ scores. The candidate explanatory variable Parental involvement (PI) is found statistically significantly contributing towards the outcome variable Students’ score (SS); these results help us to accept hypothesis that there is a relationship between parental involvement and their children’s academic achievement ( here students’ score). The contributions of PI variable has decreased from c1 =107.251 (Step I) to c/1 = 92.875 (Step III); however, the value of latter c/1 has not become statistically insignificant, suggesting that, instead of complete mediation, there occurs partial mediation. Mediating variable self-efficacy (SE) itself is statistically significant, reinforcing that variable SE fulfills the condition of being a mediator, and meanwhile the contribution of PI variable is still statistically significant, SE variable is so partially mediating; these results help us to accept hypothesis that self-efficacy (SE) of the students plays mediating role between the Parental involvement and students’ academic scores. Computations for direct and indirect mediation effects provide estimates of parental involvement exert 86.61 percent direct effect and 13.39 percent indirect (meditation) effect through mediator self-efficacy. Sobel test suggests that mediation effects of 13.39 percent are small but statistically significant.
Unsupervised Spatial Data Mining for Smart Homes

This paper presents a novel unsupervised spatial data mining model especially adapted for activity recognition inside a smart home. The goal of our research is to have a scalable, simple to implement model that could enable to recognize the resident's Activities of Daily Living (ADLs). Our algorithm exploits ubiquitous sensors and passive RFID technology to achieve the learning and the recognition. The RFID is used to track all objects in the smart home in real-time. An algorithm then extracts qualitative spatial features from the positions dataset. Finally a clustering is performed with an adapted version of the Flocking algorithm. Our experimental results are very encouraging with a classification rate ranging from 85% to 93%.
Psychophysiological responses to robot training in different recovery phases after stroke

Psychophysiological responses have become a valuable tool in human-robot interaction since they provide an objective estimate of the user's psychological state. Unfortunately, their usefulness in rehabilitation robotics is uncertain since they are influenced by both physical activity and pathological conditions such as stroke. We performed psychophysiological measurements in subacute and chronic stroke patients as well as healthy controls during a reaching and grasping exercise task performed in a multimodal virtual environment. Furthermore, we evaluated the differences in kinematic and static parameters between the three groups of subjects. The results of the observed kinematic and static evaluation parameters showed significant differences when different assistive modes enabled the subject to focus on a particular function of the exercise, like reaching or grasping, or coordinated actions that combine reaching and grasping, reflecting the motor abilities of the individual. The analysis of psychophysiological responses suggests that both chronic and subacute stroke subjects have weaker psychophysiological responses than healthy subjects, though the responses of chronic patients have recovered somewhat. This certainly indicates that further studies are needed before psychophysiological responses can be used in clinical practice.
FAULT TOLERANT SCHEDULING STRATEGY FOR COMPUTATIONAL GRID ENVIRONMENT

Computational grids have the potential for solving large-scale scientific applications using heterogeneous and geographically distributed resources. In addition to the challenges of managing and scheduling these applications, reliability challenges arise because of the unreliable nature of grid infrastructure. Two major problems that are critical to the effective utilization of computational resources are efficient scheduling of jobs and providing fault tolerance in a reliable manner. This paper addresses these problems by combining the checkpoint replication based fault tolerance mechanism with Minimum Total Time to Release (MTTR) job scheduling algorithm. TTR includes the service time of the job, waiting time in the queue, transfer of input and output data to and from the resource. The MTTR algorithm minimizes the TTR by selecting a computational resource based on job requirements, job characteristics and hardware features of the resources. The fault tolerance mechanism used here sets the job checkpoints based on the resource failure rate. If resource failure occurs, the job is restarted from its last successful state using a checkpoint file from another grid resource. A critical aspect for an automatic recovery is the availability of checkpoint files. A strategy to increase the availability of checkpoints is replication. Replica Resource Selection Algorithm (RRSA) is proposed to provide Checkpoint Replication Service (CRS). Globus Tool Kit is used as the grid middleware to set up a grid environment and evaluate the performance of the proposed approach. The monitoring tools Ganglia and NWS (Network Weather Service) are used to gather hardware and network details respectively. The experimental results demonstrate that, the proposed approach effectively schedule the grid jobs with fault tolerant way thereby reduces TTR of the jobs submitted in the grid. Also, it increases the percentage of jobs completed within specified deadline and making the grid trustworthy.
Phishing Email Filtering Techniques A Survey

The most interesting species of Internet fraud is Phishing. Email Phishing is a vulnerable activity which is referred as E-mail fraud, includes web link or form and Asks for confidential information such as password, account details. The email will be classified as phishing email and legitimate email by various phishing email filter techniques based on their functional activities. Various Anti phishing Mechanisms and tools are used for user’s protection against this fraudulent act by using heuristics method and machine learning algorithm by (SVM) support vector machine classifier. The phishing problem is highly effective and no single solution exists to mitigate all the vulnerabilities effectively. This survey relies on recently developed anti phishing mechanisms and tools.
Metode Tamyiz (Sebuah Formulasi Teori Nahwu Shorof Quantum)

To be able to read and write and translate the Arabic writing, then the required mastery of nahwu shorof adequate. constraints faced by students during this time is the difficulty of formulating theories and nahwu shorof shorof yellow book with an easy way of learning. These constraints are: must learn to read the book and nahwu shorof; must learn to translate the book; must learn to understand the theory of the book; must learn to apply thetheory of the book is on the yellow book, even on a particular book should memorize nadhom. Tamyiz method is innovation and new breakthroughs in quantum nahwu shorof learning. With the basic assumption that a small child can, a small ever can. Submission of material is so fun from easy to difficult, so students do not feel pressured, even students without the burden of memorizing and translating the Qur'anic verses with ease. And Tamyiz this method is really a method that is able to deliver the students and those studying this method can quickly translate the Qur'an. Untuk dapat membaca dan menulis serta menterjemahkan tulisan yang berbahasa Arab, maka dibutuhkan penguasaan ilmu nahwu shorof yang memadai. kendala yang dihadapi santri selama ini adalah sulitnya memformulasikan teori nahwu dan shorof dengan cara pembelajaran yang mudah. Kendala tersebut adalah : harus belajar membaca kitab nahwu dan shorof; harus belajar menerjemahkan kitab tersebut; harus belajar memahami teori kitab tersebut; harus belajar mengaplikasikan teori kitab tersebut pada kitab kuning, bahkan pada kitab tertentu harus menghafal nadhom. Metode Tamyiz merupakan inovasi dan terobosan baru dalam pembelajaran nahwu shorof quantum. Dengan asumsi dasar bahwa anak kecil saja bisa, yang pernah kecil pasti bisa. Penyampaian materi begitu menyenangkan dari yang mudah ke yang sulit, sehingga santri tidak merasa tertekan, bahkan santri tanpa beban menghafal dan menterjemahkan ayat-ayat Qur‟an dengan mudah. Dan metode Tamyiz ini benar-benar sebuah metode yang mampu mengantarkan para santri dan mereka yang belajar metode ini dapat menterjemahkan Qur‟an dengan cepat.
Does the Quality of Training Programs Matter? Evidence from Bidding Processes Data

This paper estimates the effect of training quality on labor-market earnings using a Peruvian non-experimental training program, PROJOVEN, which targets disadvantaged youths aged 16 to 24 years. The identification of causal effects is possible because of two attractive features in the data. First, the selection of training courses is based on public bidding processes that assign standardized scores to multiple proxies for quality. Second, the program`s evaluation framework allows for the identification and comparison of individuals in the treatment and comparison groups six, 12, and 18 months after the program. Using difference-in-differences kernel matching methods, we find that individuals attending high-quality training courses have higher average and marginal treatment impacts. The external validity of our estimates was assessed by using five different calls of this program over a nine-year period.
Clustering of web users using the tensor decomposed models

We propose to use the Tensor Space Modeling (TSM) to represent and analyze the user’s web log data that consists of multiple interests and spans across multiple dimensions. Further we propose to use the decomposition factors of the Tensors for clustering the users based on similarity of search behaviour. Preliminary results show that the proposed method outperforms the traditional Vector Space Model (VSM) based clustering.
GENETICALLY MODIFIED FOODS, TRADE, AND DEVELOPING COUNTRIES

This paper analyzes price, production and trade consequences of changing consumer preferences regarding the use of genetically modified organisms (GMOs) in food production. The analytical framework used is an empirical global general equilibrium model, in which the entire food processing chain - from primary crops through livestock feed to processed foods - is segregated into genetically modified (GM) and non-GM lines of production. This model is used to analyze the implications of widespread use of genetically engineered crops in some regions whilst consumers in Western Europe and High- income Asia adopt a critical attitude toward GM foods. Two different representations of consumer preference changes are illustrated: (1) a change in price sensitivity: i.e. consumer demand is less sensitive to a decline in the price of GM foods relative to non-GM varieties, and (2) a structural demand shift: for a given price ratio consumers simply demand less of the GM variety relative to the non-GM variety. This analysis finds that developing countries adjust their trade patterns in response to preference changes in important trading partner countries. Non-GM varieties are diverted to GM-critical regions while GM varieties are sold to countries in which consumers are not sensitive to GM content. Furthermore, the development of segregated GM and non-GM food creates a potential niche market for producers if the non-GM characteristic can in fact be preserved and verified throughout the marketing system at reasonable costs.