Reviewerly/exHarmony huggingface dataset:
Drawing From Social Media to Inspire Increasingly Playful and Social Drone Futures

In this pictorial, we explore the potential of social media to help inspire ideas for future drone design applications that support playful and social experiences. Drawing from a Situated Play Design approach [3], we turn to social media posts to identify recurring playful and social instances of drone use in social settings. We present the results of collecting 143 posts found on Instagram, TikTok, and YouTube, from which we identified a non-exhaustive.list.of.drone-based.play.potentials, i.e. existing ways in which people already appropriate drones to playfully augment social situations. We present these play potentials as potentially.valuable and inherently situated intermediate-level knowledge with generative power. We argue they might inspire the design of future.drone.technologies.and.experiences in Human-Drone Interaction (HDI), in directions that increasingly respond to people's desires for play and social connection.
A reconfigurable and compact hardware architecture of CLEFIA block cipher with multi-configuration

In this paper, a reconfigurable architecture of CLEFIA block cipher which supports multi-configuration is proposed. Moreover, different generalized Feistel networks are integrated into a novel folding structure, which not only increases the circuit flexibility but also reduces the hardware overhead. Besides, the processes of generating intermediate key and encryption share the same circuit by time-division multiplexing technology to reduce the hardware resource consumption further. The proposed CLEFIA design is implemented in Artix-7 XC7A35 T FPGA board. Measurement results show that it can realize all the three CLEFIA configurations of key size, which are 128/192/256-bit, with a few resources of 1725 look-up-tables and 663 registers. At a clock frequency of 147 MHz, it also reaches high throughputs of 990 Mbps, 818 Mbps, and 696 Mbps in the three configurations respectively.
Sentiment Analysis of Arabic Tweets about Violence Against Women using Machine Learning

Social Media platforms, such as Twitter became a significant pulse in smart societies that are shaping our communities by sensitizing people's information and perceptions across living areas over space and time. Social media sentiment analysis helps in recognizing people's emotions and attitudes and helps in assessing various public issues, such as, women's rights and violence against women. In this paper, we used the sentence based sentiment analysis to study the notion of women's rights. We collected Arabic dialect tweets from the whole Arab world as data via a Twitter API, then we cleaned the data to use it in the classification step. We have examined different types of traditional classification algorithms namely, Support Vector Machine, K-Nearest-Neighbour, Decision Trees, and Naive Bayes. Then, we compared these results with deep learning results. Finally, we compared the classification results using the precision, recall and accuracy measurements. We found that the Support Vector Machine algorithm gained the best results, while the Naive Bayes was the worst. We also noticed that there is an increasing attention to women's rights in the Arab world.
Attention-based model for predicting question relatedness on Stack Overflow

Stack Overflow is one of the most popular Programming Community-based Question Answering (PCQA) websites that has attracted more and more users in recent years. When users raise or inquire questions in Stack Overflow, providing related questions can help them solve problems. Although there are many approaches based on deep learning that can automatically predict the relatedness between questions, those approaches are limited since interaction information between two questions may be lost. In this paper, we adopt the deep learning technique, propose an Attention-based Sentence pair Interaction Model (ASIM) to predict the relatedness between questions on Stack Overflow automatically. We adopt the attention mechanism to capture the semantic interaction information between the questions. Besides, we have pre-trained and released word embeddings specific to the software engineering domain for this task, which may also help other related tasks. The experiment results demonstrate that ASIM has made significant improvement over the baseline approaches in Precision, Recall, and Micro-F1 evaluation metrics, achieving state-of-the-art performance in this task. Our model also performs well in the duplicate question detection task of AskUbuntu, which is a similar but different task, proving its generalization and robustness.
KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning

Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning new tasks, which is known as catastrophic forgetting. To learn new task without forgetting, recently, the mask-based learning method (e.g. piggyback [10]) is proposed to address this issue by learning only a binary element-wise mask, while keeping the backbone model fixed. However, the binary mask has limited modeling capacity for new tasks. A more recent work [5] proposes a compress-grow-based method (CPG) to achieve better accuracy for new tasks by partially training backbone model, but with order-higher training cost, which makes it infeasible to be deployed into popular state-of-the-art edge-/mobile-learning. The primary goal of this work is to simultaneously achieve fast and high-accuracy multi task adaption in continual learning setting. Thus motivated, we propose a new training method called Kernelwise Soft Mask (KSM), which learns a kernel-wise hybrid binary and real-value soft mask for each task. Such a hybrid mask can be viewed as a superposition of a binary mask and a properly scaled real-value tensor, which offers a richer representation capability without low-level kernel support to meet the objective of low hardware overhead. We validate KSM on multiple benchmark datasets against recent state-of-the-art methods (e.g. Piggyback, Packnet, CPG, etc.), which shows good improvement in both accuracy and training cost.
DevOps Research-Based Teaching Using Qualitative Research and Inter-Coder Agreement

DevOps is becoming a main competency required by the software industry. However, academic institutions have been slow to provide DevOps training in software engineering (SE) curricula. One reason for this is the fact that the problems addressed by DevOps may be hard to understand to students who have not previously worked in the industry or on projects of meaningful size and complexity. This paper shows an experience that integrates DevOps in SE curricula through research-based teaching (RBT). We aim to expose students to the problems that have led companies to adopt DevOps by researching and analyzing real cases of companies, thereby placing students at the center of learning. The contribution of this work is to innovate the application of RBT in software engineering by showing that the RBT approach is, at least, as good as the traditional approach and that it also leads to some extra benefits. This innovative solution has been implemented by using (i) qualitative analysis, specifically coding techniques, to discover knowledge and (ii) inter-coder agreement (ICA), specifically Krippendorff’s <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> coefficients, to measure the extent of students’ learning. These techniques allow teachers to determine whether students’ learning in the subject is homogeneous and to analyze disagreements among students during their analysis. This approach provides teachers with new tools (Krippendorff’s <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> coefficients) to identify those concepts that are less understood by students and to evaluate whether improvements in the research instruments (e.g., the codebook used in the qualitative analysis) also generate improvements in the students’ agreement. This RBT experience shows evidence that can be used to assess whether a similar experience and the use of ICA could be applied in similar learning contexts with similar research contexts.
On-Chain and Off-Chain Collaborative Management System Based on Consortium Blockchain

The blockchain system can provide a trust infrastructure for sharing data among untrusted parties. However, storing the original shared data directly on the blockchain is not suitable for large-scale data sharing scenarios. Therefore, we designed a data sharing system architecture in which data hashing and response records are stored on the blockchain and the original data is stored in the off-chain database. This architecture can alleviate the system overload and protect privacy problems to a certain extent. This paper proposes a three-tier system structure to ensure the function of the network. Subsequently, formulate request rules, deploy smart contracts, and build a platform based on the alliance chain. Finally, the system functions and performance are analyzed and compared through experiments. The results show that the system can realize efficient and transparent information sharing while satisfying on-chain and off-chain collaborative management, and the system has certain advantages in function, overall performance and throughput performance.
DDPG-Based Energy-Efficient Flow Scheduling Algorithm in Software-Defined Data Centers

With the rapid development of data centers, the energy consumption brought by more and more data centers cannot be underestimated. How to intelligently manage software-defined data center networks to reduce network energy consumption and improve network performance is becoming an important research subject. In this paper, for the flows with deadline requirements, we study how to design the rate-variable flow scheduling scheme to realize energy-saving and minimize the mean completion time (MCT) of flows based on meeting the deadline requirement. The flow scheduling optimization problem can be modeled as a Markov decision process (MDP). To cope with a large solution space, we design a DDPG-EEFS algorithm to find the optimal scheduling scheme for flows. The simulation result reveals that the DDPG-EEFS algorithm only trains part of the states and gets a good energy-saving effect and network performance. When the traffic intensity is small, the transmission time performance can be improved by sacrificing a little energy efficiency.
Speed benchmarking of genetic programming frameworks

Genetic Programming (GP) is known to suffer from the burden of being computationally expensive by design. While, over the years, many techniques have been developed to mitigate this issue, data vectorization, in particular, is arguably still the most attractive strategy due to the parallel nature of GP. In this work, we employ a series of benchmarks meant to compare both the performance and evolution capabilities of different vectorized and iterative implementation approaches across several existing frameworks. Namely, TensorGP, a novel open-source engine written in Python, is shown to greatly benefit from the TensorFlow library to accelerate the domain evaluation phase in GP. The presented performance benchmarks demonstrate that the TensorGP engine manages to pull ahead, with relative speedups above two orders of magnitude for problems with a higher number of fitness cases. Additionally, as a consequence of being able to compute larger domains, we argue that TensorGP performance gains aid the discovery of more accurate candidate solutions.
WRTRe: Weighted relative position transformer for joint entity and relation extraction

Entity and relation extraction is a critical task of information extraction in natural language processing. With fast developments of deep learning, this area has attracted great research attention. In spite of these achievements, however, due to the limited feature extraction ability of previous models, extracting overlapping and multiple relation triplets from a sentence is still an enormous challenge. Aim to this issue, here we propose a sequence-to-sequence method, which includes a weighted relative position Transformer encoder to flexibly capture the semantic relationship between entities. To prove the effectiveness of this suggested method, we conduct experiments on two publicly available datasets NYT24 and NYT29. The experimental results show that the proposed approach outperforms previous methods and achieves state-of-the-art performance. Such a framework may shed novel light into knowledge graph construction under complex situations and its potential applications.
Three Birds with One Stone: Multi-Task Temporal Action Detection via Recycling Temporal Annotations

Temporal action detection on unconstrained videos has seen significant research progress in recent years. Deep learning has achieved enormous success in this direction. However, collecting large-scale temporal detection datasets to ensuring promising performance in the real-world is a laborious, impractical and time consuming process. Accordingly, we present a novel improved temporal action localization model that is better able to take advantage of limited labeled data available. Specifically, we design two auxiliary tasks by reconstructing the available label information and then facilitate the learning of the temporal action detection model. Each task generates their supervision signal by recycling the original annotations, and are jointly trained with the temporal action detection model in a multi-task learning fashion. Note that the proposed approach can be pluggable to any region proposal based temporal action detection models. We conduct extensive experiments on three benchmark datasets, namely THUMOS’14 [15], Charades [35] and ActivityNet [14]. Our experimental results confirm the effectiveness of the proposed model.
Measuring students' contributions in software development projects using Git metrics

Many courses in the software engineering area are centered around team-based project development. Evaluating these projects is a challenge due to the difficulty of measuring individual student contributions versus team contributions. The adoption of distributed version control systems like Git enables the measurement of students' and teams' contributions to the project. In this work, we analyze the contributions within five software development projects from undergraduate courses that used project-based learning. For this, we generate visualizations of aggregated Git metrics using inequality indexes and inter-decile ratios, which offer insights into the practices and processes followed by students and teams throughout the project development. This approach allowed us to identify both inequality among students' contributions and development processes with a non-steady pace, rendering a useful feedback tool for instructors and students during the development of the project. Further studies can be conducted to assess the complexity and value of students' contributions by analyzing their source code commits and other software artifacts.
METAL: Explorations into Sharing 3D Educational Content across Augmented Reality Headsets and Light Field Displays

Augmented Reality and Virtual Reality become increasingly popular in scientific visualization especially for education where they can support collaborative scientific visualization experiences in the classroom. However, the inherent limitations of head-mounted AR and VR tools are stemming the popularization of these existing content sharing tools. Instead of sharing 3D educational content between AR/VR headsets, this paper proposes a novel prototype Mixed rEaliTy shAring pLatform (METAL) to allow for 3D educational content to be shared between a Microsoft HoloLens 2 and multiple Looking Glass displays which are a type of Light Field (Multi-view Autostereoscopic) display. This platform allows one teacher to use a HoloLens to manipulate and share different 3D contents with multiple student groups via the network, thus each student group can observe the synchronized 3D educational content with autostereoscopic experiences. Therefore, this proposed prototype enables a low-cost one-to-multiple 3D content sharing experience that allows the intuitive 3D model interaction and seamless communication between the students and the teacher.
The impact of semantics on aspect level opinion mining

Recently, many users prefer online shopping to purchase items from the web. Shopping websites allow customers to submit comments and provide their feedback for the purchased products. Opinion mining and sentiment analysis are used to analyze products’ comments to help sellers and purchasers decide to buy products or not. However, the nature of online comments affects the performance of the opinion mining process because they may contain negation words or unrelated aspects to the product. To address these problems, a semantic-based aspect level opinion mining (SALOM) model is proposed. The SALOM extracts the product aspects based on the semantic similarity and classifies the comments. The proposed model considers the negation words and other types of product aspects such as aspects’ synonyms, hyponyms, and hypernyms to improve the accuracy of classification. Three different datasets are used to evaluate the proposed SALOM. The experimental results are promising in terms of Precision, Recall, and F-measure. The performance reaches 94.8% precision, 93% recall, and 92.6% f-measure.
Unified Questioner Transformer for Descriptive Question Generation in Goal-Oriented Visual Dialogue

Building an interactive artificial intelligence that can ask questions about the real world is one of the biggest challenges for vision and language problems. In particular, goal-oriented visual dialogue, where the aim of the agent is to seek information by asking questions during a turn-taking dialogue, has been gaining scholarly attention recently. While several existing models based on the GuessWhat?! dataset [10] have been proposed, the Questioner typically asks simple category-based questions or absolute spatial questions. This might be problematic for complex scenes where the objects share attributes, or in cases where descriptive questions are required to distinguish objects. In this paper, we propose a novel Questioner architecture, called Unified Questioner Transformer (UniQer), for descriptive question generation with referring expressions. In addition, we build a goal-oriented visual dialogue task called CLEVR Ask. It synthesizes complex scenes that require the Questioner to generate descriptive questions. We train our model with two variants of CLEVR Ask datasets. The results of the quantitative and qualitative evaluations show that UniQer outperforms the baseline.
Two-Stream Learning-Based Compressive Sensing Network With High-Frequency Compensation for Effective Image Denoising

This paper presents a two-stream learning-based compressive sensing network with a high-frequency compensation module (TSLCSNet) that betters restores the detailed components of an image during the image denoising process.The proposed two-stream network consists of a compressive sensing network (CSN) and a high-frequency compensation network (HCN).CSN restores the main structure of the image, while HCN adds the detail that is not obtainable from the CSN.To improve the performance of the proposed model, we add an incoherence loss function to the total loss function.We also employ an octave convolution to allow the two-stream network to communicate in order to extract less redundant and more compressive features.Representative experimental results show the superiority of the proposed TSLCSNet and TSLCSNet+ compared to state-of-the-art methods for the removal of synthetic and real noise.
Detecting Software Vulnerabilities Using Neural Networks

As software vulnerabilities remain prevalent, automatically detecting software vulnerabilities is crucial for software security. Recently neural networks have been shown to be a promising tool in detecting software vulnerabilities. In this paper, we use neural networks trained with program slices, which extract the syntax and semantic characteristics of the source code of programs, to detect software vulnerabilities in C/C++ programs. To achieve a strong prediction model, we combine different types of program slices and optimize different types of neural networks. Our result shows that combining different types of characteristics of source code and using a balanced ratio of vulnerable program slices and non-vulnerable program slices a balanced accuracy in predicting both vulnerable code and non-vulnerable code. Among different neural networks, BGRU performs the best in detecting software vulnerabilities with an accuracy of 94.89%.
Mobile Information System of English Teaching Ability Based on Big Data Fuzzy K-Means Clustering

The implication of mobile English teaching is that English teachers and students use mobile devices for English teaching and communication at the same time. In order to accurately evaluate language interpretation skills, it is necessary to construct a mobile information system sampling model of the restrictive factors of language interpretation skills. Then, the nonlinear information fusion method is combined with the time series cognition method to make a statistical cognition of language interpretation skills. The parameter of language interpretation skills constraint is a set of nonlinear time series. To this end, this paper studies the language interpretation skills mobile information system, proposes language interpretation skills, and constructs the constraint parameters of the language interpretation skills evaluation and cognition using an indicator cognition model. The quantitative recursive cognition method analyzes the language interpretation ability evaluation model and the entropy feature of language interpretation ability and extracts the constraint feature information. The combination of large-scale data information fusion and K-means clustering algorithms provides indexing and integration of index parameters for language interpreting skills. On this basis, the corresponding allocation scheme of teaching resources is formulated to realize the assessment of language interpretation skills. The experimental results of related big data clustering algorithms show that the English teaching method proposed in this paper is highly effective, and the evaluation accuracy and teaching resource utilization rate have been increased by 5% and 6%, respectively.
Soft Cross Entropy Loss and Bottleneck Tri-Cost Volume For Efficient Stereo Depth Prediction

Real-time, robust, and accurate stereo depth-prediction algorithms deliver cutting-edge performance in applications ranging from autonomous driving to augmented reality. Many state-of-the-art approaches produce subpixel error and subsecond runtimes on commodity hardware, but improving even these remains an area of active research. We focus on improving accuracy and efficiency in stereo-based depth prediction by contributing two generic techniques to improve performance and runtime. First, we propose encoding the ground truth disparity as a discrete distribution that can be trained via cross-entropy loss. Specifically, we use the minimum variance and unbiased ‘Soft’ encoding, where two adjacent bins are weighted so the expected value is ground truth. We demonstrate that training with cross entropy loss using this encoding decreases error rate by 10% on synthetic and LIDAR datasets over the more popular regression losses such as Huber and MAE. Second, we propose a bottleneck tri-cost volume composed of the sum of absolute difference of the features as well as two reference channels. Replacing the standard 64-channel concatenation popular in state-of-the-art networks with this 3-channel cost-volume maintains metric performance and can reduce runtime by over 22% on PSM-Net architectures.