Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a dual-arm robotic system for gesture imitation

Research in robotics has been playing an important role in human-robot interaction field. This interaction has evolved in several areas such as speech recognition, walking, gesture imitation, exploring and cooperative work. Imitation learning has several advantages over conventional programming methods because it allows the transfer of new skills to the robot through a more natural interaction. The work aims to implement a dual-arm manipulation system able to reproduce human gestures in real-time. The robotic arms are fixed to a mechanical structure similar to the human torso developed for this purpose. The demonstrations are obtained from a human motion capture system based on the Kinect sensor. The captured movements are reproduced in a two Cyton Gamma 1500 robotic arms assuming physical constraints and workspace limits, as well as avoiding self-collisions and singular configurations. The kinematics study of the robot arms provides the basis for the implementation of kinematics control algorithms. The software development is supported by the Robot Operating System (ROS) framework following the philosophy of modular and open-ended development. Several experimental tests are conducted to validate the proposed solutions and to evaluate the system’s performance in different situations, including those related with joints physical limits, workspace limits, collisions and singularity avoidance.
Quantifying myocardial fibrosis on murine model using ImageJ

Quantification of histological images is a task often required in biomedical research. Many of the methods used are time-consuming, difficult to replicate and subjective. This work aimed to describe and validate a more objective, reproducible and easy to perform methodology to measure murine heart fibrosis, using ImageJ software. For this matter, a macro was created and validated for its intra and inter-observational reproducibility. No significant differences were found between the results obtained manually and with the macro (p> 0.05). Regarding the interobserver variability, the macro proved to be a less variable method by presenting higher correlation coefficients between observers when compared with the manual measurement. With these results, we were able to define and describe a methodology that outputs objective and replicable results in a less laborious way.
Pronouns Traces of Grammar Transformation Version of Government and Binding

A problem concerned to be examined in this study was how the description of personal pronoun was contained in the single sentence. Therefore, this study was conducted aiming at describing personal pronouns in a single sentence and identifying the role of semantic of the personal pronoun in the single sentence. The theory used to study the phenomenon was Trace theory version of Transformation Theory of Government and Binding. This study discovered findings as follows: 1) the base position of the personal pronoun was under the position of NP, VP node; however, with the transfer process, the personal pronoun was under the position of NP, Agr. It was because every visible NP should have a case from its verb; 2) the trace marked by t was the landing site or base position before the pronoun and other elements move; 3) this theory was sufficient to give an idea of the position of the element of aspect and modal as the category of MP in phrase structure.
Graph K-means Based on Leader Identification, Dynamic Game, and Opinion Dynamics

With the explosion of social media networks, many modern applications are concerning about people's connections, which leads to the so-called social computing. An elusive question is to study how opinion communities form and evolve in real-world networks with great individual diversity and complex human connections. In this scenario, the classic K-means technique and its extended versions could not be directly applied, as they largely ignore the relationship among interactive objects. On the other side, traditional community detection approaches in statistical physics would be neither adequate nor fair: they only consider the network topological structure but ignore the heterogeneous-objects’ attributive information. To this end, we attempt to model a realistic social media network as a discrete-time dynamical system, where the opinion matrix and the community structure could mutually affect each other. In this paper, community detection in social media networks is naturally formulated as a multi-objective optimization problem (MOOP), i.e., finding a set of densely connected components with similar opinion vectors. We propose a novel and powerful graph K-means framework, which is composed of three coupled phases in each discrete-time period. Specifically, the first phase uses a fast heuristic approach to identify those opinion leaders who have relatively high local reputation; the second phase adopts a novel dynamic game model to find the locally Pareto-optimal community structure; and the final phase employs a robust opinion dynamics model to simulate the evolution of the opinion matrix. We conduct a series of comprehensive experiments on real-world benchmark networks to validate the performance of GK-means through comparisons with the state-of-the-art graph clustering technologies.
An $E$ -Band Power Amplifier With 26.3% PAE and 24-GHz Bandwidth in 22-nm FinFET CMOS

This paper presents the design and optimization of an <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-band power amplifier (PA) implemented in Intel’s 22FFL FinFET process. Layout optimization and characterization of the PA unit cell yielding optimal millimeter-wave (mmW) performance of the active device are described. A holistic optimization methodology is also presented which co-optimizes efficiency of the active devices and passive networks by incorporating passive losses earlier in the design process. The measured PA achieves a peak gain of 18.6 dB with a 3-dB bandwidth of 62–86 GHz (24 GHz). At 75 GHz, the measured <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {sat}}$ </tex-math></inline-formula>, OP<sub>1dB</sub>, and peak power added efficiency (PAE) are +12.6 dBm, +5.7 dBm, and 26.3%, respectively. The PA can amplify a 6-Gb/s 16-QAM signal at an average <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {out}}$ </tex-math></inline-formula> of +5 dBm with a PAE of 10% and −26-dB EVM(rms) and a 9-Gb/s 64-QAM signal at an average <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {out}}$ </tex-math></inline-formula> of +1.3 dBm with a PAE of 5% and −28.3-dB EVM(rms). The compact layout of the PA yields a core area of 0.054 mm<sup>2</sup>, enabling compact integration.
Research on Character Recognition Technology of New Tai Lue Based on Gabor Feature and SVM

New Tai Lue script which is also known as Simplified Tai Lue, is an alphabet used to write the Tai Lü language used in Xishuangbanna of Yunnan Province in China. In this paper, we present a handwritten character recognition method of New Tai Lue by Support vector machine (SVM) based classifier on Gabor Features. First, some preprocessing approaches such as superfluous points removing, normalization, smoothing, resampling are applied to preprocess the handwritten character images of New Tai Lue. Second, a filter bank consisting of two-dimensional Gabor filters with various scales and orientations is created to extract features from these images. After that, the result images are processed by PCA to do dimension reduction. Finally, SVM is applied as the classifier to perform recognition task. Experimental results conducted on a database of 16200 characters show that the proposed method are effective.
H$^2$—A Hybrid Heterogeneity Aware Resource Orchestrator for Cloud Platforms

Cloud computing services are provided through datacenters that consume significant amount of energy and contribute to global warming. Largely, resources are unused due to low service demand, in principal, wasting energy and resources. Energy could be saved through efficient resource management systems; that is of utmost importance, particularly, when service providers use various kinds of sand-boxing technologies, such as bare-metal, virtual machine and/or containers to provide quality services to users. The lack of a single resource manager creates questions on infrastructure energy efficiency and service performance. In this paper, we investigate the possibility of using a single resource manager to manage datacenter heterogeneous resources, energy, and performance (hence cost) efficiently. Our evaluation suggests that $\sim$30.47% energy could be saved at cost of only 2.14% loss in performance through using a single scheduler instead of multiple schedulers.
The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy

Background Computer-aided detection (CAD) systems might assist endoscopists in the recognition of Barrett's neoplasia. Aim To develop a CAD system using endoscopic images of Barrett's neoplasia. Methods White light endoscopy (WLE) overview images of 40 neoplastic Barrett's lesions and 20 non-dysplastic Barret's oesophagus (NDBO) patients were prospectively collected. Experts delineated all neoplastic images. The overlap area of at least four delineations was labelled as the ‘sweet spot’. The area with at least one delineation was labelled as the ‘soft spot’. The CAD system was trained on colour and texture features. Positive features were taken from the sweet spot and negative features from NDBO images. Performance was evaluated using leave-one-out cross-validation. Outcome parameters were diagnostic accuracy of the CAD system per image, and localization of the expert soft spot by CAD delineation (localization score) and its indication of preferred biopsy location (red-flag indication score). Results Accuracy, sensitivity and specificity for detection were 92, 95 and 85%, respectively. The system localized and red-flagged the soft spot in 100 and 90%, respectively. Conclusion This uniquely trained and validated CAD system detected and localized early Barrett's neoplasia on WLE images with high accuracy. This is an important step towards real-time automated detection of Barrett's neoplasia.
New effective neighborhoods for the permutation flow shop problem

We propose an extension of the Taillard's implementation, which allows to remove efficiently the less well inserted jobs in a permutation. We describe then six new neighborhoods for the permutation flow shop problem. Computational results show clearly that at least three of them are better than the insertion move. Their application into a simple metaheuristic is very effective, since a new upper bound has been found for a hard Taillard's instance.
Exploring the affordances of telepresence robots in foreign language learning

The importance of authentic communicative practices in foreign language (FL) learning has long been recognized. However, most FL learners lack adequate access to authentic communicative environments in the target language. In this article, we propose the use of telepresence robots as a potential solution to bridge this gap. Telepresence robots can be controlled by remote language learners online, enabling them to gain virtual access to authentic environments in the target language and to interact with native speakers in those environments in real time. In this exploratory study, three English learners and a native-speaker of American English participated in a campus tour activity using a telepresence robot. We examined the experience of our participants and the conversational features of their telepresence interactions through analyses of the interview data, field notes, and transcripts of conversations captured on video. Our findings show that telepresence robots have substantial potential for promoting FL learning by providing authentic communicative practice for remote language learners. The findings have useful implications for informing future research design.
Multi-fidelity algorithms for the horizontal alignment problem in road design

Multi-fidelity algorithms for solving the horizontal alignment problem in road design are considered. A multi-fidelity surrogate model is built and quantile regression is used to understand its accuracy at various fidelity levels. Two algorithms are compared: a generalized pattern search algorithm with adaptive precision control, and a trust-region algorithm for unconstrained problems with controlled error. To make a fair comparison, the parameters of each algorithm are tuned on five small roads using performance profiles. Then the algorithms are evaluated on 35 roads, ranging from small to very large roads. The results show that using multi-fidelity surrogates in optimization algorithms provide notable speed-up when compared to single-fidelity algorithms while preserving the quality of solutions (cost error ). On the longest roads, higher speed-up and better accuracy are observed.
WhatsApp as a Site for Meaningful Dialogue.

Smart-phone technology is pervasive across Colombia, even among those of lower economic means. This has greatly changed the face of society and the world of education is no different. Instant messaging services like WhatsApp allow for an extension of the traditional classroom. This study is concerned with how such technology allows learners of a second language a space where true dialogue and meaningful interaction may occur. This report presents progress of an ongoing investigation into how a shared WhatsApp group may be used as a site for dialogue for students of International Relations. The study consists of analysis of the interaction which occurs in the group as well as learner reflection on the benefits of the space. The data generated thus far have demonstrated examples of interaction between participants in their second language on complex themes related to their country and the international community. Participant responses have been positive on the benefits of the space. The study adds further credence to a growing body of work on the benefits of such a space for encouraging discussion, participation and enhancement of learner identity.
Efficient Computation of Smoothed Exponential Maps

Many applications in geometry processing require the computation of local parameterizations on a surface mesh at interactive rates. A popular approach is to compute local exponential maps, i.e. parameterizations that preserve distance and angle to the origin of the map. We extend the computation of geodesic distance by heat diffusion to also determine angular information for the geodesic curves. This approach has two important benefits compared to fast approximate as well as exact forward tracing of the distance function: First, it allows generating smoother maps, avoiding discontinuities. Second, exploiting the factorization of the global Laplace–Beltrami operator of the mesh and using recent localized solution techniques, the computation is more efficient even compared to fast approximate solutions based on Dijkstra's algorithm.
Big Data and Strategy: Theoretical Foundations and New Opportunities

The digitization of products, processes, and business models--and the corresponding explosion of big data--has led to an evolution within business organizations. Reaching far beyond information technology's traditional role in business strategy, the implications of this big data phenomenon are considered through an exploration into what big data is, how it is currently being used by existing firms, and how it factors into strategic thinking. As different organizational approaches have developed toward big data, we use resource-based theory and organizational learning as anchoring perspectives to link this phenomenon with traditional strategic management. We also identify four avenues for future scholarship as the nature of business moves increasingly digital.
Enhancing critical thinking through the science learning on using interactive problem based module

Facing the 21st century, higher order thinking skills are becoming a valuable thing to be learned. This is why learning science has to be designed on using innovative models, approaches, and Media. The R and D research on using ADDIE design was done to develop the interactive problem based module on population and environmental pollution topics. After validation process on the conceptual aspects as well as the learning and techno-pedagogic aspects, the module was then implemented in science learning at secondary school, to enhance critical thinking skills. The research shows that the module is very effective to use as a supporting material in order to enhance critical thinking skills. Based on the students’ response, the media was very helpful to student to learn science as well as to enhance the skill in how to practice IT.
Unsupervised Fine-tuning of Optical Flow for Better Motion Boundary Estimation

Recently, convolutional neural network (CNN) based approaches have proven to be successful in optical flow estimation in the supervised as well as in the unsupervised training paradigms. Supervised training requires large amounts of training data with task specific motion statistics. Usually, synthetic datasets are used for this purpose. Fully unsupervised approaches are usually harder to train and show weaker performance, although they have access to the true data statistics during training. In this paper we exploit a well-performing pretrained model and fine-tune it in an unsupervised way using classical optical flow training objectives to learn the dataset specific statistics. Thus, per dataset training time can be reduced from days to less than 1 minute. Specifically, motion boundaries estimated by gradients in the optical flow field can be greatly improved using the proposed unsupervised fine-tuning.
Three-Dimensional Printed Models for Lateral Skull Base Surgical Training: Anatomy and Simulation of the Transtemporal Approaches

BACKGROUND Three-dimensional (3D) printing holds great potential for lateral skull base surgical training; however, studies evaluating the use of 3D-printed models for simulating transtemporal approaches are lacking. OBJECTIVE To develop and evaluate a 3D-printed model that accurately represents the anatomic relationships, surgical corridor, and surgical working angles achieved with increasingly aggressive temporal bone resection in lateral skull base approaches. METHODS Cadaveric temporal bones underwent thin-slice computerized tomography, and key anatomic landmarks were segmented using 3D imaging software. Corresponding 3D-printed temporal bone models were created, and 4 stages of increasingly aggressive transtemporal approaches were performed (40 total approaches). The surgical exposure and working corridor were analyzed quantitatively, and measures of face validity, content validity, and construct validity in a cohort of 14 participants were assessed. RESULTS Stereotactic measurements of the surgical angle of approach to the mid-clivus, residual bone angle, and 3D-scanned infill volume demonstrated comparable changes in both the 3D temporal bone models and cadaveric specimens based on the increasing stages of transtemporal approaches (PANOVA <.003, <.007, and <.007, respectively), indicating accurate representation of the surgical corridor and working angles in the 3D-printed models. Participant assessment revealed high face validity, content validity, and construct validity. CONCLUSION The 3D-printed temporal bone models highlighting key anatomic structures accurately simulated 4 sequential stages of transtemporal approaches with high face validity, content validity, and construct validity. This strategy may provide a useful educational resource for temporal bone anatomy and training in lateral skull base approaches.
Solving a Hard Instance of Suspicious Behaviour Detection with Sparse Binary Vectors Clustering

In this article we present a study dealing with the problem of detecting a very small subset of suspicious and malicious behaviours represented by sparse binary vectors in a population of individuals significantly larger. The main problem lies in the fact that malicious behaviours, in the case of sparse vectors, are difficult to distinguish from normal behaviours. Despite the fact that vectors are apparently strongly unbalanced, this property cannot be exploited since the objects to classify (behaviours) do not exhibit strongly enough frequencies discrepancy. It is not possible to work on detection directly and it is therefore necessary to go through a preliminary phase of vector partitioning (representing normal or malicious behaviour) to select a reduced subset concentrating with a high probability most of the vectors corresponding to malicious behaviours. We have been working on a set of anonymized real data from terrorism-related cases.
Political Participation Through Social Media: Comparison of Pakistani and Malaysian Youth

This paper aims to investigate the phenomenon of online political participation in a cross cultural context by applying the concept of cognitive engagement theory. The relationship of access to political stuff on social media, political interest, and policy satisfaction with online political participation is studied in the context of Pakistan and Malaysia. The variable of political knowledge is used as a moderator in the study in order to reveal deeper insights. Data were collected from a sample of 504 students from COMSATS University Islamabad and University Utara Malaysia. In Pakistani context, political interest, policy satisfaction, and political knowledge show a positively significant result, whereas online incivility shows a negatively significant outcome. In the context of Malaysia, availability of political stuff online, political interest, policy satisfaction, and political knowledge shows a positively significant impact on online political participation. Political knowledge holds as a moderator in Malaysia but not in Pakistan. This paper has theoretical as well as the practical contribution in developing an understanding about political participation through social media in both Pakistan and Malaysia. Furthermore, cognitive engagement theory is also used on a rare basis, and hence, this paper adds to the generalization of the theory.
An Effective Encoding Method Based on Local Information for 3D Point Cloud Classification

Point cloud is a collection of many unordered points. Deep learning network encounters difficulties in utilizing the local information of point cloud because of its irregular format. This is not conducive to the network to identify the details of the object. Some strategies that design new network structures are used to capture the local information, but they make the networks become complicated. This paper proposes an effective method by encoding points into feature vectors. The local information is represented using neighborhood points that are searched by the $k$ -nearest neighbor method. The neighborhood points are converted into feature elements based on the distance between these points and the encoded point. The feature vector of each point consists of its coordinates and the corresponding feature elements. A simple deep learning network is used to process these feature vectors. The proposed method is applied to ModelNet40 shape classification benchmark. The experimental results show that the classification accuracy of the simple deep learning network is improved by the proposed method.
A List of the Orthoptera of Illinois.-IV

THE following list is an enumeration of all the species of Orthoptera known to me to occur within the State, as well as those species reported by others but not identified by the writer. Names of the last mentioned class are preceded by an asterisk and accompanied by the name of the person upon whose authority they are inserted. If the species is uncommon, one or more localities in which it has been or is reported to have been captured will be given. The earliest date, known to me, of the appearance of each species will be given and unless otherwise stated this date will be understood to be the recorded time of their appearance or capture at Rock Island, Ill.