Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Social Media for Emotion Classification

Social media consists of various kinds of emotions and sentiments of its users in the form of electronic media. To analyze the reactions or sentiments of the users on a certain post is also a challenging task. Our project aims to automate this task of analyzing the reactions and the posts and generate a report based on the outcome. A custom social media platform is proposed that would give the ability to the users to perform activities such as post, like, comment, and share. The users can be enrolled in different groups such as business or university. This platform would have the ability to perform sentiment analysis on all the activities of the users in a group and create a report according to their reactions and their posts on the platform. Every activity would be rated based on the sentiments behind it on a scale of -1 and +1, 0 is a neutral sentiment. The appropriate admin would receive the reports which can be used for further actions. The analysis would be performed on various factors such as the response of other users on a post. According to the user's posts, its home page would display other relevant posts. The analysis would facilitate the decision-making process for the admins (around other activities on the platform) and also help detect any need to give special attention to any user in a group, such as a student coping up with depression. Also, the proposed platform can be used to share content on other social media platforms as well. As a result, the users would have a single platform with the ability to do a lot more than any existing social media platform facilitates.
Clustering of unknown protocol messages based on format comparison

As a solution to detect and analyse unknown or proprietary protocols, Protocol Reverse Engineering(PRE) has been developed swiftly in recent years. In this field, message clustering aimed at protocol format serves as a fundamental solution for differentiating of unknown protocol messages. This paper works on the problem of format-oriented message clustering of unknown protocols, including messages from proprietary or non-cooperative network environments with their specifications unknown. By introducing basic rules of ABNF, we define Token Format Distance (TFD) and Message Format Distance (MFD) to represent format similarity of tokens and messages, and introduce Jaccard Distance and an optimized sequence alignment algorithm (MFD measurement) to compute them. Then, a distance matrix is built by MFD and we feed it to DBSCAN algorithm to cluster unknown protocol messages into classes with different formats. In this process, we design an unsupervised clustering strategy with Silhouette Coefficient and Dunn Index applied to parameter selecting of DBSCAN. In experiment on two datasets, the harmonic average v-measures of homogeneity and completeness on result clusters are both above 0.91, with fmis and coverages no less than 0.97. Together with iqr of v-measure and fmi bellow 0.1 and 0.03 separately in boxplot analyses, this method is proved to have remarkable validity and stability. Comprehensive analyses and comparisons on these indexes also show considerable advantages of our method over previous work.
On Binary Quantizer For Maximizing Mutual Information

We consider a channel with a binary input X being corrupted by a continuous-valued noise that results in a continuous-valued output Y . An optimal binary quantizer is used to quantize the continuous-valued output Y to the final binary output Z to maximize the mutual information I(X; Z). We show that when the ratio of the channel conditional density r(y) = P (Y =y|X=0)/P (Y =y|X=1) is a strictly increasing or decreasing function of y, then a quantizer having a single threshold can maximize mutual information. Furthermore, we show that an optimal quantizer (possibly with multiple thresholds) is the one with the thresholding vector whose elements are all the solutions of r(y) = r* for some constant r* > 0. In addition, we also characterize necessary conditions using fixed point theorem for the optimality and uniqueness of a quantizer. Based on these conditions, we propose an efficient procedure for determining all locally optimal quantizers, and thus, a globally optimal quantizer can be found. Our results also confirm some previous results using alternative elementary proofs.
Explaining VQA predictions using visual grounding and a knowledge base

In this work, we focus on the Visual Question Answering (VQA) task, where a model must answer a question based on an image, and the VQA-Explanations task, where an explanation is produced to support the answer. We introduce an interpretable model capable of pointing out and consuming information from a novel Knowledge Base (KB) composed of real-world relationships between objects, along with labels mined from available region descriptions and object annotations. Furthermore, this model provides a visual and textual explanations to complement the KB visualization. The use of a KB brings two important consequences: enhance predictions and improve interpretability. We achieve this by introducing a mechanism that can extract relevant information from this KB, and can point out the relations better suited for predicting the answer. A supervised attention map is generated over the KB to select the relevant relationships from it for each question-image pair. Moreover, we add image attention supervision on the explanations module to generate better visual and textual explanations. We quantitatively show that the predicted answers improve when using the KB; similarly, explanations improve with this and when adding image attention supervision. Also, we qualitatively show that the KB attention helps to improve interpretability and enhance explanations. Overall, the results support the benefits of having multiple tasks to enhance the interpretability and performance of the model.
A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI

Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer’s disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, complex shape, low contrast and discontinuous boundaries. The active contour model (ACM) with a statistical shape prior is robust. However, it is difficult to build a shape prior that is general enough to cover all possible shapes of the HC and that suffers the problems of complicated registration of the shape prior and the target object and of low efficiency. In this paper, we propose a semi-automatic model that combines a deep belief network (DBN) and the lattice Boltzmann (LB) method for the segmentation of HC. The training process of DBN consists of unsupervised bottom-up training and supervised training of a top restricted Boltzmann machine (RBM). Given an input image, the trained DBN is utilized to infer the patient-specific shape prior of the HC. The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation. We used a subset of OASIS-1 as the training set and the preliminary release of EADC-ADNI as the testing set. The segmentation results of our method have good correlation and consistency with the manual segmentation results.
Using sensing technologies to explain children's self-representation in motion-based educational games

Motion-Based Touchless Games (MBTG) are being investigated as a promising interaction paradigm in children's learning experiences. Within these games, children's digital persona (i.e, avatar), enables them to efficiently communicate their motion-based interactivity. However, the role of children's Avatar Self-Representation (ASR) in educational MBTG is rather under-explored. We present an in-situ within subjects study where 46 children, aged 8--12, played three MBTG with different ASRs. Each avatar had varying visual similarity and movement congruity (synchronisation of movement in digital and physical spaces) to the child. We automatically and continuously monitored children's experiences using sensing technology (eye-trackers, facial video, wristband data, and Kinect skeleton data). This allowed us to understand how children experience the different ASRs, by providing insights into their affective and behavioural processes. The results showed that ASRs have an effect on children's stress, arousal, fatigue, movement, visual inspection (focus) and cognitive load. By exploring the relationship between children's degree of self-representation and their affective and behavioural states, our findings help shape the design of future educational MBTG for children, and emphasises the need for additional studies to investigate how ASRs impacts children's behavioural, interaction, cognitive and learning processes.
A MapReduce C4.5 Decision Tree Algorithm Based on Fuzzy Rule-Based System

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Finding Longest Common Subsequences: New anytime A<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e16706" altimg="si725.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:math>search results

The Longest Common Subsequence (LCS) problem aims at finding a longest string that is a subsequence of each string from a given set of input strings. This problem has applications, in particular, in the context of bioinformatics, where strings represent DNA or protein sequences. Existing approaches include numerous heuristics, but only a few exact approaches, limited to rather small problem instances. Adopting various aspects from leading heuristics for the LCS, we first propose an exact A∗ search approach, which performs well in comparison to earlier exact approaches in the context of small instances. On the basis of A∗ search we then develop two hybrid A∗–based algorithms in which classical A∗ iterations are alternated with beam search and anytime column search, respectively. A key feature to guide the heuristic search in these approaches is the usage of an approximate expected length calculation for the LCS of uniform random strings. Even for large problem instances these anytime A∗ variants yield reasonable solutions early during the search and improve on them over time. Moreover, they terminate with proven optimality if enough time and memory is given. Furthermore, they yield upper bounds and, thus, quality guarantees when terminated early. We comprehensively evaluate the proposed methods using most of the available benchmark sets from the literature and compare to the current state-of-the-art methods. In particular, our algorithms are able to obtain new best results for 82 out of 117 instance groups. Moreover, in most cases they also provide significantly smaller optimality gaps than other anytime algorithms.
Using Natural Version Redundancy of FPGA Projects in Area of Critical Applications

The paper focuses on the role of version redundancy, which increases with the expansion of critical applications and the tightening of their functional safety requirements. International standards governing these requirements have already fixed the importance of version redundancy to counter common cause failures in safety-related systems. The problem of hidden faults inherent in these systems is equally acute, undermining confidence in the fault tolerance of circuit solutions important for functional safety. We suggest that this problem be seen as a growth problem where the system rises to the level of diversification in modes of operation by dividing them into normal and emergency and checkability circuits, but components continue to be stamped at a lower level of replication. Then the solution to the problem is to raise the components to the level of the system by developing their version redundancy. We demonstrate this development for FPGA components with LUT-oriented architecture and offer a method to improve the FPGA-ready project by natural version redundancy of its program code. The method generates a set of program code versions and selects versions that improve the checkability of circuits with respect to constant faults and the trustworthiness of results, respectively, in normal and emergency mode to reduce a set of hidden faults. The method is illustrated by an iterative array multiplier implemented in an FPGA project. Versions are generated, evaluated, and selected using the program developed for this.
A Monolithic Forward-View MEMS Laser Scanner With Decoupled Raster Scanning and Enlarged Scanning Angle for Micro LiDAR Applications

This paper reports the design, fabrication, and characterization of a forward-view optical scanner realized by two vertical 2-axis MEMS mirrors integrated on a Silicon Optical Bench (SiOB). The mirror plate sizes of the first and second MEMS mirrors are 0.6×0.7 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 1.2×1.4 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. With the second MEMS mirror larger, the optical beam will not be truncated even during large-angle dynamic scanning. This dual-mirror configuration also enables multiple scanning modes, e.g., an enlarged 2D field of view (FoV) by combining the scans of both mirrors, and a decoupled raster scanning by actuating the two mirrors separately. Experiments show that the enlarged 2D FoV reached 28°(h)×31°(v) and the FoV of the decoupled raster scanning reached 38°(h)×8°(v). The measured first resonances of the two mirrors were 2.09 kHz and 0.85 kHz, respectively. The overall size of the MEMS scanner was only 5.3 × 5.8 × 3.0 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , and its weight was 20 mg. This 2-axis forward optical scanner is much smaller and lighter than other two-mirror scanning modules, giving it a great potential for applications in miniature LiDAR for micro-robotics.
Human Action Recognition Algorithm Based on Improved ResNet and Skeletal Keypoints in Single Image

Human action recognition is an important part for computers to understand the behavior of people in pictures or videos. In a single image, there is no context information for recognition, so its accuracy still needs to be greatly improved. In this paper, a single-image human action recognition method based on improved ResNet and skeletal keypoints is proposed, and the accuracy is improved by several methods. We improved the backbone network ResNet-50 and CPN to a certain extent and constructed a multitask network to suit the human action recognition task, which not only improves the accuracy but also balances the total number of parameters and solves the problem of large network and slow operation. In this paper, the improvement methods of ResNet-50, CPN, and whole network are tested, respectively. The results show that the single-image human action recognition based on improved ResNet and skeletal keypoints can accurately identify human action in the case of different human movements, different background light, and occlusion. Compared with the original network and the main human action recognition algorithms, the accuracy of our method has its certain advantages.
Starting Big: The Effect of Unit Size on Language Learning in Children and Adults

Abstract Multiword units play an important role in language learning and use. It was proposed that learning from such units can facilitate mastery of certain grammatical relations, and that children and adults differ in their use of multiword units during learning, contributing to their varying language-learning trajectories. Accordingly, adults learn gender agreement better when encouraged to learn from multiword units. Previous work has not examined two core predictions of this proposal: (1) that children also benefit from initial exposure to multiword units, and (2) that their learning patterns reflect a greater reliance on multiword units compared to adults. We test both predictions using an artificial-language. As predicted, both children and adults benefit from early exposure to multiword units. In addition, when exposed to unsegmented input – adults show better learning of nouns compared to article-noun pairings, but children do not, a pattern consistent with adults’ predicted tendency to focus less on multiword units.
IPANM: Incentive Public Auditing Scheme for Non-Manager Groups in Clouds

Cloud storage services give users a great facility in data management such as data collection, storage and sharing, but also bring some potential security hazards. An utmost importance is how to ensure the integrity of data files stored in the cloud, particular for user groups without trusted managers. Existing literature focuses on integrity checking for groups with managers who have lots of permissions. To overcome the shortage of public auditing for non-manager user groups in clouds, we develop a novel framework IPANM that integrates <inline-formula><tex-math notation="LaTeX">$(t,n)$</tex-math></inline-formula> threshold technology, blinding technology, and incentive mechanism to realize an incentive privacy-preserving public auditing scheme. In IPANM, the data integrity is guaranteed by our <inline-formula><tex-math notation="LaTeX">$(t,n)$</tex-math></inline-formula> threshold signature based public auditing and the data privacy during public auditing is protected by the blinding technology. The generation of signatures can be accelerated by our blockchain-aided incentive mechanism that mobilizes the initiative of signers in the signature generation by rewarding the contributed signers. We formally prove the security of our IPANM and conduct numerical analysis and evaluation study to validate its high efficiency. The experimental results demonstrate that IPANM has lower overheads of storage, communication, and computation as compared to the state-of-the-art technique IAID-PDP and NPP.
C4 model in a Software Engineering subject to ease the comprehension of UML and the software

Software engineering provides the competences and skills to design and develop robust, secure and efficient applications that solve real problems. Students have to develop their abstract thinking to find solutions taking into account not only technical development, but economic and social impact. In previous years, different changes have been introduced in the teaching methods with significant outcomes. However, students are still facing difficulties with one of the core contents of the subject, UML. For this reason, the present work aims to introduce C4 model as a complement of the existing UML diagrams. This proposal uses the two first levels of the C4 model to complement the requirements elicitation process, traditionally based only on use cases, to let students start the design of their systems without going into greater technical details.
Insights in Students’ Problems during UML Modeling

UML (Unified Modeling Language) is the current de facto as well as de jure standard (ISO/IEC 19505:2012) notation to visualize models in software development. UML provides essential guidelines and rules to visualize and understand complex software systems. This is the reason why it has become part of curricula for software engineering courses at many universities worldwide. It is well known, however, that UML is hard to grasp for novices, mainly due to its complexity. In order to tackle the problem of teaching UML to novice students appropriately, it is inevitable to understand their needs and problems much better than we do now. This paper presents empirical insights into students’ problems when developing common UML diagrams. Identified problems are generalized, giving rise to a problem catalogue that is derived from our empirical findings, thus establishing a basis for addressing these problems through focused learning arrangements.
An Interactive Network for End-to-End Review Helpfulness Modeling

Abstract Review helpfulness prediction aims to prioritize online reviews by quality. Existing methods largely combine review texts and star ratings for helpfulness prediction. However, star ratings are used in a way that has either little representation capacity or limited interaction with review texts. As a result, rating information has yet to be fully exploited during the combination. This paper aims to overcome the two drawbacks. A deep interactive architecture is proposed to learn the text–rating interaction (TRI) for helpfulness modeling. TRI enlarges the representation capacity of star ratings while enhancing the influence of rating information on review texts. TRI is evaluated on six real-world domains of the Amazon 5-Core dataset. Extensive experiments demonstrate that TRI can better predict review helpfulness and beat the state of the art. Ablation studies and qualitative analysis are provided to further understand model behaviors and the learned parameters.
Team‐external coordination in large‐scale software development projects

Abstract High‐quality work is said to depend on team abilities. However, teams working in large‐scale projects often do not have all expertise to complete their tasks, which are also highly interdependent. Therefore, teams need to rely on coordination with other teams, experts, and supporting roles. In this paper, we explore teams' coordination needs and evaluate the impact of the satisfaction of these needs on team performance. We conducted an embedded multicase study with nine teams in two projects in two companies. We collected qualitative data through nine focus groups and 19 interviews and quantitative data using a questionnaire with 49 members from the studied teams. Our results suggest that project‐, team‐, and task‐related characteristics impact teams' coordination needs. Even in the same project, teams may have different expertise and work coordination needs. We found that the satisfaction of these needs seems to influence teams' performance, although our results are inconclusive and yield a closer look in future research. On the basis of our findings, we recommend the companies to cultivate a networking culture and support teams external coordination with other teams and experts, paying attention to their needs, for example, driven by a lack of experience or increased work complexity.
A content analysis of voice-based apps on the market for early literacy development

Voice-based applications powered by conversational agents can potentially support young children's literacy development in informal settings. Yet, to realize such potential, designers must consider young users' typical communication and learning patterns. In this paper, we developed a framework of seven design dimensions across three aspects (i.e., learning content and goals, interactions and gamifications, and conversation design) that could influence the educational benefits young children receive from voice-based apps. We then used this framework to conduct a content analysis of 535 literacy-focused voice apps on the market to examine whether the prevalent design features of these apps meet the unique needs of young learners. Lastly, we discussed the implications of current design tendencies with the aim of encouraging future voice-based app designers to bridge the gap between research and practice.