Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition of Symbolic Gestures Using Depth Information

Symbolic gestures are the hand postures with some conventionalized meanings. They are static gestures that one can perform in a very complex environment containing variations in rotation and scale without using voice. The gestures may be produced in different illumination conditions or occluding background scenarios. Any hand gesture recognition system should find enough discriminative features, such as hand-finger contextual information. However, in existing approaches, depth information of hand fingers that represents finger shapes is utilized in limited capacity to extract discriminative features of fingers. Nevertheless, if we consider finger bending information (i.e., a finger that overlaps palm), extracted from depth map, and use them as local features, static gestures varying ever so slightly can become distinguishable. Our work here corroborated this idea and we have generated depth silhouettes with variation in contrast to achieve more discriminative keypoints. This approach, in turn, improved the recognition accuracy up to 96.84%. We have applied Scale-Invariant Feature Transform (SIFT) algorithm which takes the generated depth silhouettes as input and produces robust feature descriptors as output. These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. We have tested our results with a standard dataset containing 10 symbolic gesture representing 10 numeric symbols (0-9). After that we have verified and compared our results among depth images, binary images, and images consisting of the hand-finger edge information generated from the same dataset. Our results show higher accuracy while applying SIFT features on depth images. Recognizing numeric symbols accurately performed through hand gestures has a huge impact on different Human-Computer Interaction (HCI) applications including augmented reality, virtual reality, and other fields.
Directed Graph Placement for SNN Simulation into a multi-core GALS Architecture

In this paper, we present a methodology for effi-ciently mapping neural networks over a neuromorphic computing architecture. The target architecture is a globally asynchronous locally synchronous (GALS) multi-core designed for simulating spiking neural networks (SNN) in real-time, that is spike timings should be the same as in the human brain. The SNN is implemented as a set of concurrent tasks modelling the behaviour of biological neurons, which are executed on the processing cores and communicate through spikes travelling on a network-on-chip. The problem of neuron-to-core mapping is relevant as a non-efficient allocation may impact real-time and reliability of the neural network execution. We designed a task placement pipeline capable of analysing the network of neurons and producing a placement configuration that enables a reduction of communication between computational nodes. The neuron-to-core mapping problem has been formalised as a problem of minimisation of synaptic elongation. Intuitively, this metric represents the cumulative distance that spikes generated by neurons running on a specific core have to travel to reach their destination core. The proposed placement methodology allows using different techniques to solve the problem. In this work Spectral Analysis, Multilevel Static Mapping, and Simulated Annealing were compared evaluating the overall post-placement synaptic elongation. Results point out that mapping solutions taking into account the directionality of the SNN provide a better placement and quantify this impact. Between all techniques considered only the Simulated Annealing was able to overcome an improvement of 25% compared to a random placement.
A Gap Analysis of Noncognitive Constructs in Evaluation Instruments Designed for Computing Education

A growing body of evidence indicates that there is a deep effect of noncognitive factors on academic achievement and learning. In this study, we analyzed a set of 31 evaluation instruments designed to measure noncognitive constructs (e.g., self-efficacy, confidence, motivation) within computing education. Using the Lee and Shute framework, we assigned each of the 115 unique constructs found in the instruments into one of the four components (Student Engagement, Learning Strategies, School Climate, Social-familial Influences) and their subcomponents to determine which constructs are most frequently measured. We found that the majority of constructs were designed to measure Student Engagement (Affect and Cognition) and School Climate (Teacher Variables). Constructs measuring Learning Strategies and Social-Familial Influences (e.g., homework strategies, peer influences) occur the least. This study may enable further discussions of what noncognitive factors are/are not currently being measured within the computing education community.
Non-Programming Activities for Engagement with Foundational Concepts in Introductory Programming

Programming-in a text-based or visual language-is a complex activity that novices find difficult to learn. In this paper, we present an experience report on how novel activities designed for early conceptual exploration can support middle school students' engagement with and learning of foundational programming concepts beginning programmers often struggle with, specifically, variables, expressions (Boolean, arithmetic, relational), loops, and abstraction. We drew on mathematics education research on dynamic representations to design and develop a set of four non-programming, (open access) web-based, interactive activities and micro-worlds and two unplugged activities as part of a suite of activities that can be embedded in a typical introductory programming curriculum to introduce learners to the target concepts before they encounter them in programming. This report describes the activities as well as our experience with their use as part of an introductory computer science course in three diverse, urban, middle school classrooms in the US
Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities

The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data from our cities. In this paper, we investigate a novel way of analyzing data from social sciences studies by employing machine learning and data science techniques. This enables us to maximize the insight gained from this type of studies by fusing both objective (sensor information) and subjective data (direct input from the users). The pilot study is concerned with better understanding the interactions between citizens and urban green spaces. A field experiment was carried out in Sheffield, U.K., involving 1870 participants for two different time periods (7 and 30 days). With the help of a smartphone app, both objective and subjective data were collected. Location tracking was recorded as people entered any of the publicly accessible green spaces. This was complemented by textual and photographic information that users could insert spontaneously or when prompted (when entering a green space). By employing data science and machine learning techniques, we identify the main features observed by the citizens through both text and images. Furthermore, we analyze the time spent by people in parks as well as the top interaction areas. This paper allows us to gain an overview of certain patterns and the behavior of the citizens within their surroundings and it proves the capabilities of integrating technology into large-scale social studies.
Visible/Infrared Combined 3D Reconstruction Scheme Based on Nonrigid Registration of Multi-Modality Images With Mixed Features

When traditional 3D reconstruction techniques were used to reconstruct a scene with hidden and disguised heat source targets, the reconstructed scene could not contain these targets; thus, we could not recognize them. The disadvantages of such problems are particularly acute in the military and remote-sensing areas. For this application problem, the authors proposed a visible/infrared combined 3D reconstruction scheme. The 3D scene containing thermal radiation information could be reconstructed by fusing the data from RGB optical and infrared images combined with computer vision passive optical 3D scene reconstruction technique. Meanwhile, the authors proposed the nonrigid registration of multi-modality images with mixed features to solve the problem that the registration algorithm, which is widely used in traditional passive reconstruction technology, cannot accurately match visible and infrared images. Registration accuracy was improved by approximately 40%, experimental results showed that the visible/infrared combined 3D reconstruction scenes retain the visual reality of the traditional 3D reconstruction, and the hidden targets are highlighted in the scenes, which is conducive to the detection and recognition of interesting targets.
First Things First: Providing Metacognitive Scaffolding for Interpreting Problem Prompts

When solving programming problems, novices are often not aware of where they are in the problem-solving process. For instance, students who misinterpret the problem prompt will most likely not form a valid conceptual model of the task and fail to make progress towards a working solution. Avoiding such errors, and recovering from them once they occur, requires metacognitive skills that enable students to reflect on their problem-solving processes. For these reasons, developing metacognitive awareness is crucially important for novice students. Previous research has shown that explicitly teaching key steps of programming problem-solving, and having students reflect on where they are in the problem-solving process, can help students complete future programming assignments. Such metacognitive awareness training can be done through personal tutoring, but can be difficult to implement without a high ratio of instructors to students. We explore a more scalable approach, making use of an automated assessment tool, and conduct a controlled experiment to see whether scaffolding the problem-solving process would increase metacognitive awareness and improve student performance. We collected all code submissions by students in both control and experimental groups, as well as data from direct observation using a think-aloud protocol. We found that students who received the intervention showed a higher degree of understanding of the problem prompt and were more likely to complete the programming task successfully.
Large-Margin Regularized Softmax Cross-Entropy Loss

Softmax cross-entropy loss with L2 regularization is commonly adopted in the machine learning and neural network community. Considering that the traditional softmax cross-entropy loss simply focuses on fitting or classifying the training data accurately but does not explicitly encourage a large decision margin for classification, some loss functions are proposed to improve the generalization performance by solving the problem. However, these loss functions enhance the difficulty of model optimization. In addition, inspired by regularized logistic regression, where the regularized term is responsible for adjusting the width of decision margin, which can be seen as an approximation of support vector machine, we proposed a large-margin regularization method for softmax cross-entropy loss. The advantages of the proposed loss are twofold as follows: the first is the generalization performance improvement, and the second is easy optimization. The experimental results on three small-sample datasets show that our regularization method achieves good performance and outperforms the existing popular regularization methods of neural networks.
Performance Comparison of a Parallel Recommender Algorithm Across Three Hadoop-Based Frameworks

One of the challenges our society faces is the ever increasing amount of data. Among existing platforms that address the system requirements, Hadoop is a framework widely used to store and analyze “big data”. On the human side, one of the aids to finding the things people really want is recommendation systems. This paper evaluates highly scalable parallel algorithms for recommendation systems with application to very large data sets. A particular goal is to evaluate an open source Java message passing library for parallel computing called MPJ Express, which has been integrated with Hadoop. As a demonstration we use MPJ Express to implement collaborative filtering on various data sets using the algorithm ALSWR (Alternating-Least-Squares with Weighted-λ-Regularization). We benchmark the performance and demonstrate parallel speedup on Movielens and Yahoo Music data sets, comparing our results with two other frameworks: Mahout and Spark. Our results indicate that MPJ Express implementation of ALSWR has very competitive performance and scalability in comparison with the two other frameworks.
Mobile Phones Hematophagous Diptera Surveillance in the field using Deep Learning and Wing Interference Patterns

Real-time monitoring of hematophagous diptera (such as mosquitoes) populations in the field is a crucial challenge to foresee vaccination campaigns and to restrain potential diseases spreading. However, current methods heavily rely on costly DNA extraction which is destructive, costly, time consuming and requires experts. The contributions of this work are: 1) the usage of a new type of imaging, named Wing Interference Patterns (WIPs), which is non-destructive and easier to produce during in the field experiments; 2) a deep learning architecture which is optimized for very low computation cost, memory usage and a short inference time; 3) the use of a dataset of more than 50 medically important species of hematophagous diptera with more than 3000 images of WIPs. With these contributions, we demonstrate that WIPs are an excellent medium to automatically recognize a large amount of hematophagous diptera species with very high accuracy and low computational cost convolutional neural network.
SleepBot: encouraging sleep hygiene using an intelligent chatbot

Maintaining good sleep hygiene is a constant challenge in modern lives. Sleep habits are hard to monitor and record, especially when most sleep monitoring programs overlook the necessity of calculating user input. This input is vital in order to change poor sleeping patterns, as it is difficult to identify the source of an individual's problems. Sleep tracking software also struggle with a lack of user transparency and interactivity leading individuals to mistrust the results these applications generate or otherwise not feel like the insights are actionable. To explore these issues, we designed an interventional chat bot to mediate information collection and interaction between end user and sleep monitoring technology. The SleepBot prompts users with simple questions that attempt to elicit insight into larger problems that contribute to poor sleep and help craft successful sleep hygiene behaviors. Text messaging based interaction eases the process as it is similar to talking with a friend, making for a unique environment in which the user is able to share personal data comfortably.
What to Expect and What to Focus on in SQL Query Teaching

In the process of learning a new computer language, writing erroneous statements is part of the learning experience. However, some errors persist throughout the query writing process and are never corrected. Structured Query Language (SQL) consists of a number of different concepts such as expressions, joins, grouping and ordering, all of which by nature invite different possible errors in the query writing process. Furthermore, some of these errors are relatively easy for a student to fix when compared to others. Using a data set from three student cohorts with the total of 744 students, we set out to explore which types of errors are persistent, i.e., more likely to be left uncorrected by the students. Additionally, based on the results, we contemplate which types of errors different query concepts seem to invite. The results show that syntax and semantic errors are less likely to persist than logical errors and complications. We expect that the results will help us understand which kind of errors students struggle with, and e.g., help teachers generate or choose more appropriate data for students to use when learning SQL.
Online Mob Programming: Effective Collaborative Project-Based Learning

This work presents a new paradigm for collaborative project-based computer science education called Online Mob Programming (OMP). OMP is adapted from the industrial practice of Mob Programming, where groups of developers work on the same problem, at the same time, in the same place. OMP was designed and implemented as a technique where a group of 4-6 students collaborate online through a structured process for solving programming tasks. In OMP, students rotate through clearly defined roles to collectively contribute towards a solution to a programming challenge. These roles require students to brainstorm potential solutions, decide on a path forward, and implement the correct course of action respectively. OMP was investigated in the context of a 6-week free online course on Cloud Computing. During the course, students participated in four intelligent conversational agent-coordinated OMP sessions. By instrumenting the online development environment, all student code revisions and chat logs were collected in addition to qualitative data from questionnaires. Analyses show evidence of success in terms of students following the structure of OMP and further investigations into differences in mob behavior based on the size, and problem outcome provide pedagogically valuable insights and a path toward building OMP into the computer science education curriculum.
Automated User Authentication in Wireless Public Key Infrastructure for Mobile Devices Using Aadhar Card

Identification and authentication play a significant role in security controls. The manual process of user identification and authentication is time-consuming and tedious. The proposed research work aims to overcome this drawback and achieve user identification and authentication through an automated method using the Aadhar card. Aadhar project is developed by the Unique Identification Authority of India by merging biometrics and digitization. The details are stored in the form of a quick response code along with a 12-digit Aadhar card number. A model is built to capture the information from the Aadhar card. The mobile phones are used to obtain the data from the Aadhar card. The user validation is through two-step process using One Time Password and email address. The wireless public key infrastructure issues a digital certificate to the valid user. The proposed authentication and symmetric key exchange algorithm are formally verified and analyzed using automated validation of Internet security protocols and applications. The result of simulation proves that the proposed scheme is secure and safe. The experimental results show that time consumption for user authentication is acceptable. The acceptance of the proposed automated user identification and authentication model is authorized using a modified technology acceptance model. The result proves that there exists a strong relationship between the perceived usefulness of items and the Attribute Of Use (AOU). It also exhibits that the association between the AOU and user satisfaction is influential. The model demonstrates that the association between the Perceived Ease Of Use and attribute towards use is weak. The influence of exogenous variables over endogenous variables exhibits that the proposed model outperforms the traditional manual system and the target users are inclined towards it. The proposed model also finds its application in services which requires user authentication.
On the Effect of Question Ordering on Performance and Confidence in Computer Science Examinations

Most computer science examinations tend to start with the easiest questions and progress towards the more difficult material. Whether this is because of the highly scaffolded nature of the course, an attempt to 'ease students in', or simply by convention, is unclear. However, there is a great deal of data from the psychology literature to suggest that human perception of the difficulty or discomfort of a task is disproportionately affected by the last part of the task completed. Therefore, is it possible that by structuring our exams in an easy-to-hard fashion, we are causing students to perceive the test as more difficult than it actually is? Could changing the question order allow us to change students' perception of their own achievement? What effect could this have on actual performance? This paper attempts to answer these questions by randomly assigning students to write exams ordered either easy-to-hard (referred as 'Easy-Difficult') or hard-to-easy ('Difficult-Easy), then ask them to predict their marks on per-question basis. We find that the question ordering has a small but not statistically significant effect on the performance, and virtually no effect on predicted marks when treating the entire class as one unstratified sample. However, the effect was significant for certain subgroups created via stratification. In particular, swapping the order of the questions may have hurt the performance of international students, but significantly raised both the performance and confidence of female students.
Getting virtually personal: chatbots who actively listen to you and infer your personality

We build conversational agents to serve as AI interviewers who engage a user in a one-on-one, text-based conversation. Our live demos showcase two special skills of an AI interviewer: (a) ability to actively listen to a user during an interview-responding to complex and diverse user input empathetically; and (b) the ability to automatically infer the user's Big 5 personality traits from the interview. We provide an overview of the technologies that enable these two abilities and real-world applications of such AI interviewers.
Low Detection Limit Time-Correlated Single Photon Counting Lifetime Analytical System for Point-of-Care Applications

Photon-counting analysis plays a key role in many areas, such as biology, chemistry, and medicine. In this paper, we present an integrated time-correlated single photon counting (TCSPC) lifetime analytical system with a complete signal path—from fluorophore excitation, emission detection, to lifetime extraction. The time-to-digital module of the proposed TCSPC system achieves a root-mean-square differential non-linearity of 4% of the least significant bit and a full width at half maximum temporal resolution from 121 to 145 ps within the 500-ns full-scale range. To evaluate the lifetime extraction and detection limit of the proposed TCSPC system, a wide variety of samples, such as fluorescein in water, coumarin 6 in dimethyl sulfoxide, and rhodamine 6G in water, each prepared in 14 concentrations from 0.5 nM (nanomolar, <inline-formula> <tex-math notation="LaTeX">$10^{-9}$ </tex-math></inline-formula> mol/L) to <inline-formula> <tex-math notation="LaTeX">$25~\mu \text{M}$ </tex-math></inline-formula> (micromolar, <inline-formula> <tex-math notation="LaTeX">$10^{-6}$ </tex-math></inline-formula> mol/L), are tested. With the optimized hardware and firmware design, the proposed TCSPC system can accurately extract the fluorescence lifetime of fluorescein, coumarin 6, and rhodamine 6G down to the concentration of 1, 1, and 2.5 nM, respectively, significantly outperforming similar fluorescence lifetime analysis systems.