Reviewerly/exHarmony huggingface dataset:
Opening the Black Box: Exploring Automated Speaking Evaluation

The rapid advances in speech processing and machine learning technologies have attracted language testers’ strong interest in developing automated speaking assessment in which candidate responses are scored by computer algorithms rather than trained human examiners. Despite its increasing popularity, automatic evaluation of spoken language is still shrouded in mystery and technical jargon, often resembling an opaque “black box” that transforms candidate speech to scores in a matter of minutes. Our chapter explicitly problematizes this lack of transparency around test score interpretation and use and asks the following questions: What do automatically derived scores actually mean? What are the speaking constructs underlying them? What are some common problems encountered in automated assessment of speaking? And how can test users evaluate the suitability of automated speaking assessment for their proposed test uses? In addressing these questions, the purpose of our chapter is to explore the benefits, problems, and caveats associated with automated speaking assessment touching on key theoretical discussions on construct representation and score interpretation as well as practical issues such as the infrastructure necessary for capturing high-quality audio and the difficulties associated with acquiring training data. We hope to promote assessment literacy by providing the necessary guidance for users to critically engage with automated speaking assessment, pose the right questions to test developers, and ultimately make informed decisions regarding the fitness for purpose of automated assessment solutions for their specific learning and assessment contexts.
Data Preprocessing Techniques in Web Usage Mining: A Literature Review

With the massive growth of Web, there is a huge volume of dynamic, distributed, heterogeneous, structured, unstructured, semi-structured, and high dimensional data available on Web. Apart from content and structural information of Website, server logs are also considered as a valuable source of information. Web usage mining is a class of Web mining where usersâ€™ navigation behavior is analyzed from Web server log. It is divided into three phases: Data preprocessing, pattern discovery, and pattern evaluation. Among them, data preprocessing is considered as a time-consuming and complex phase in Web usage mining process due to huge and noisy nature of log data. This article present a review and critical analysis of sequential techniques applied in data preprocessing of Web server log with emphasis on sub-phases such as data cleaning, user identification, and session identification. Moreover this article also includes the survey of techniques applied for server log analytics using Big Data technologies such as Hadoop MapReduce and Spark framework. This article would be helpful for researchers to find issues related to data cleaning, user identification, and session identification phases of Web usage mining process.
Source-Based Writing in Secondary School

This chapter examines the instructional intervention aimed at enhancing source-based compare-contrast writing in the secondary school students. Conceptually, it relies on the schema theory as a cognitive basis for integrated reading-writing instruction. The theory asserts that writing and reading both generate meaning using similar cognitive processes and types of knowledge: meta-knowledge of reading and writing strategies in relation to communicative goals, domain and textual knowledge, procedural knowledge that involves integrating writing and processing information while reading the text. Methodologically, the instruction is based on read-write cycle and was carried out in a secondary public school of Kyiv, with the 10th-grade students being the participant (n=22). The general hypothesis about a positive impact of read-write cycle instruction is partially confirmed in the research, which is an indication of the necessity to give further insight into the issue.
Medical Image Tampering Detection: A New Dataset and Baseline

The recent advances in algorithmic photo-editing and the vulnerability of hospitals to cyberattacks raises the concern about the tampering of medical images. This paper introduces a new large scale dataset of tampered Computed Tomography (CT) scans generated by different methods, LuNoTim-CT dataset, which can serve as the most comprehensive testbed for comparative studies of data security in healthcare. We further propose a deep learning-based framework, ConnectionNet, to automatically detect if a medical image is tampered. The proposed ConnectionNet is able to handle small tampered regions and achieves promising results and can be used as the baseline for studies of medical image tampering detection.
Uncertainty-aware Active Learning for Optimal Bayesian Classifier

For pool-based active learning, in each iteration a candidate training sample is chosen for labeling by optimizing an acquisition function. In Bayesian classification, expected Loss Reduction~(ELR) methods maximize the expected reduction in the classification error given a new labeled candidate based on a one-step-look-ahead strategy. ELR is the optimal strategy with a single query; however, since such myopic strategies cannot identify the long-term effect of a query on the classification error, ELR may get stuck before reaching the optimal classifier. In this paper, inspired by the mean objective cost of uncertainty (MOCU), a metric quantifying the uncertainty directly affecting the classification error, we propose an acquisition function based on a weighted form of MOCU. Similar to ELR, the proposed method focuses on the reduction of the uncertainty that pertains to the classification error. But unlike any other existing scheme, it provides the critical advantage that the resulting Bayesian active learning algorithm guarantees convergence to the optimal classifier of the true model. We demonstrate its performance with both synthetic and real-world datasets.
Learning Ball-Balancing Robot through Deep Reinforcement Learning

The ball-balancing robot (ballbot) is a good platform to test the effectiveness of a balancing controller. Considering balancing control, conventional model-based feedback control methods have been widely used. However, contacts and collisions are difficult to model, and often lead to failure in balancing control, especially when the ballbot tilts a large angle. To explore the maximum initial tilting angle of the ballbot, the balancing control is interpreted as a recovery task using Reinforcement Learning (RL). RL is a powerful technique for systems that are difficult to model, because it allows an agent to learn policy by interacting with the environment. In this paper, by combining the conventional feedback controller with the RL method, a compound controller is proposed. We show the effectiveness of the compound controller by training an agent to successfully perform a recovery task involving contacts and collisions. Simulation results demonstrate that using the compound controller, the ballbot can keep balance under a larger set of initial tilting angles, compared to the conventional model-based controller.
Multi-Task Deep Learning for Depth-based Person Perception in Mobile Robotics

Efficient and robust person perception is one of the most basic skills a mobile robot must have to ensure intuitive human-machine interaction. In addition to person detection, this also includes estimating various attributes, like posture or body orientation, in order to achieve user-adaptive behavior. However, given limited computing and battery capabilities on a mobile robot, it is inefficient to solve all perception tasks separately, especially when using computationally expensive deep neural networks. Therefore, we propose a multi-task system for person perception, comprising of a fast, depth- based region proposal and an efficient, lightweight deep neural network. Using a single network forward pass, the system simultaneously detects persons, classifies their body postures, and estimates the upper body orientations while retaining almost the same computation time as a single-task network. We describe how to handle a real-world multi-task scenario and conduct an extensive series of experiments in order to compare various network architectures and task weightings. We further show that multi-task learning improves the networks' performance compared to their single-task baselines. For training and evaluation, we combine an existing dataset for orientation estimation and a new, self-recorded dataset, consisting of more than 235,000 depth patches that is made publicly available to the research community.
LVQ: A Lightweight Verifiable Query Approach for Transaction History in Bitcoin

In the Bitcoin system, transaction history of an address can be useful in many scenarios, such as the balance calculation and behavior analysis. However, it is non-trivial for a common user who runs a light node to fetch historical transactions, since it only stores headers without any transaction details. It usually has to request a full node who stores the complete data. Validation of these query results is critical and mainly involves two aspects: correctness and completeness. The former can be implemented via Merkle branch easily, while the latter is quite difficult in the Bitcoin protocol. To enable the completeness validation, a strawman design is proposed, which simply includes the BF (Bloom filter) in the headers. However, since the size of BF is about KB, light nodes in the strawman will suffer from the incremental storage burden. What's worse, an integrated block must be transmitted when BF cannot work, resulting in large network overhead. In this paper, we propose LVQ, the first lightweight verifiable query approach that reduces the storage requirement and network overhead at the same time. To be specific, by only storing the hash of BF in headers, LVQ keeps data stored by light nodes being little. Besides, LVQ introduces a novel BMT (BF integrated Merkle Tree) structure for lightweight query, which can eliminate the communication costs of query results by merging the multiple successive BFs. Furthermore, when BF cannot work, a lightweight proof by SMT (Sorted Merkle Tree) is exploited to further reduce the network overhead. The security analysis confirms LVQ's ability to enable both correctness and completeness validation. In addition, the experimental results demonstrate its lightweight.
Forensic Analysis of Dating Applications on Android and iOS Devices

Dating application use is on the rise, and with it comes the need to better understand what data can be recovered to assist in an investigation. While using these dating applications, people send countless messages (including pictures and videos) without ever considering exactly what data is being sent within that message. In this project, we conduct a forensic analysis of five popular dating applications (Her, Hily, Hinge, OkCupid, and Plenty of Fish (POF)) that are available on both Android and iOS devices. We also determined what forensically relevant data can be recovered from dating applications on both Android and iOS. Specifically, we determined what data can be recovered about the sender from the receiver's phone. Secondly, we identified any privacy concerns that result due to the recoverable data and discuss their implications for users. Lastly, we detailed the investigative process that should be followed and presented the locations of any relevant data to aid digital forensics investigators during an Investigation.
Assessment and selection of management consultants: A comparative cognitive study between small- and large-scale companies

This paper explores alternative ways of evaluating and ultimately hiring a generalist consultancy for short- and medium-term projects, and how this is shaped by the size of the clients’ company. What buying factors do managers of small and large firms have in mind when hiring consultants for their projects? Instead of employing surveys incorporating predefined buying factors based on what the researchers think would be important, a cognitive mapping methodology - the repertory grid technique - is applied to investigate the underlying phenomena. Using the repertory grid technique to draw mental models is a novel research technique within the supply management field. Recent behavioral supply chain management research found the behavior of managers to be critically influenced by mental models: the lenses through which managers perceive, simplify and interpret the world. Results show differences between the buying factors used in small- and large-scale companies: managers of large companies are more ‘reputation and outcome-oriented’ and managers of small businesses are more concerned with ‘how the services are rendered’. However, both constituents stressed the importance of expertise, quality and the relationship between the client and the management consultancy, although the character of such a relationship differed between small- and large-scale firms. Results also point out criteria that managers of small and large companies employ to discriminate between good and poor performing consultancies and to judge service delivery. The practical implications for both companies hiring consultancies and the consultancy companies are discussed. For consultancies, our findings can help tailoring their efforts to a differing clientele when marketing and selling their services. For companies hiring consultancies, fixating too much on particular buying factors may lead to biased decisions.
Multi-objective soft subspace clustering in the composite kernel space

Conventional subspace clustering algorithms group the data samples by optimizing the objective function which aggregates different clustering criteria using the linear combination. However, the performance is sensitive to the user-defined coefficients. Besides, the widely used Euclidean distance metric falls short of handling the linear indivisible problems. Some composite kernel metrics are proposed to overcome this drawback, but it is still difficult to determine the proper weight of base kernels. To address these problems, a novel multi-objective soft subspace clustering model is proposed. The novel model simultaneously optimizes three clustering criteria without setting coefficients. The distance between data points is measured in a composite kernel space. The weight of base kernels is optimized by a multi-objective evolutionary algorithm. A decomposition-based local search strategy is developed to enhance the performance of the proposed algorithm. The experimental results indicate that the proposed algorithm can achieve better solutions.
Rating-boosted abstractive review summarization with neural personalized generation

In this paper, we study abstractive summarization for product reviews in the recommender systems, which aims to generate condensed text for online reviews. The summary generation is not only relevant with the content of the review itself but should be fully aware of the intrinsic features of the corresponding user and product, i.e., personalization, which are helpful to identify the saliency information in the reviews. Therefore, we propose a Rating-boosted Abstractive Review Summarization with personalized generation (RARS). In our approach, we first propose a neural review-level attention model to effectively learn user preference embedding and product characteristic embedding from their history reviews. Then, we design a personalized decoder to generate the personalized summary, which utilizes the representations of the user and the product to calculate saliency scores for words in the input review to guide the summary generation process. In addition, the rating information can explicitly indicate the sentiment opinion, hence we jointly optimize the summary generation and rating prediction through a multi-task framework, where the two tasks inherently share user preference embedding and product characteristics embedding. Extensive experiments on four datasets show that our model can effectively improve the performance of both review summarization and rating prediction.
Monte Carlo Tree Search as an intelligent search tool in structural design problems

Abstract Monte Carlo Tree Search (MCTS) is a search technique that in the last decade emerged as a major breakthrough for Artificial Intelligence applications regarding board- and video-games. In 2016, AlphaGo, an MCTS-based software agent, outperformed the human world champion of the board game Go. This game was for long considered almost infeasible for machines, due to its immense search space and the need for a long-term strategy. Since this historical success, MCTS is considered as an effective new approach for many other scientific and technical problems. Interestingly, civil structural engineering, as a discipline, offers many tasks whose solution may benefit from intelligent search and in particular from adopting MCTS as a search tool. In this work, we show how MCTS can be adapted to search for suitable solutions of a structural engineering design problem. The problem consists of choosing the load-bearing elements in a reference reinforced concrete structure, so to achieve a set of specific dynamic characteristics. In the paper, we report the results obtained by applying both a plain and a hybrid version of single-agent MCTS. The hybrid approach consists of an integration of both MCTS and classic Genetic Algorithm (GA), the latter also serving as a term of comparison for the results. The study’s outcomes may open new perspectives for the adoption of MCTS as a design tool for civil engineers.
Automatic Labelling of Malay Cyberbullying Twitter Corpus using Combinations of Sentiment, Emotion and Toxicity Polarities

Automatic labelling is essential in large corpuses. Engaging in human experts to label can be challenging. Semantic understanding can differ from one labeler to another based on individual's language ability. Platforms such as AmazonTurk are not able to ensure the quality of annotations in every domain. Extensive steps such as qualification and counter checking of labels may be implemented which will increase the cost of data annotation. Thus, the higher quality of labelled data expected, the greater the cost that needs to be expended. This scenario is made worse when the language is of low resource where in this work is the Malay language. Malay is a language used mostly in Malaysia, Indonesia, Singapore and Brunei. Unlike English which has large resources to tap into the semantics of sentences, making automatic labelling faster to mature, resources in Malay language are still limited. Further compounded is the use of social media data where the text is short, unnormalized and the inherent presence of code switching. The availability of qualified native Malay labelers is also scarce. To overcome this, we devised a method to automatically label a total of 219,444 Malay tweets by using a combination of sentiment, emotion and toxicity polarities. We extend the work from Arslan et al. who proposed the use of sentiment and emotion to identify cyberbullying text. Our work added toxicity polarity in the context of automatic labelling of cyberbully tweets in Malay. We were able to employ 5 experts with formal degrees in Malay language to label our training set. We applied this method to Malay cyberbullying corpus to determine “bully” and “not bully” labels. We have tested our method on 54,867 manually labelled data and achieved high accuracy.
Facial Emotion Prediction through Action Units and Deep Learning

With the recent advancements in deep learning techniques, attention has been given to training and testing facial emotions through highly complex deep learning systems. In this paper we apply machine learning techniques which require less resources to produce comparable results for emotion prediction. As the underlying technique for the emotion prediction in this research is based on clinically recognized Facial Action Coding System (FACS), a further analysis is given on the contribution of each of the Action Units (AUs) for the predicted emotion. This analysis would complement, strengthen and be a main resource for addressing many different health issues related to facial muscle movements.
Speech Scrambler Based on Discrete Cosine Transform and Novel Seven-Dimension Hyper Chaotic System

Abstract Because of their inherent features related to cryptography, the hyperchaotic system has strong potential in security systems. In this paper, a new cryptography scheme for speech scrambling has been proposed which gives it more security level. Also, it introduced a novel seven dimensions (7D) nonlinear hyperchaotic system in third-order which has twelve positive parameters. A chaotic behavior for the proposed novel system is analyzed. The hyperchaotic system is demonstrated that beside it appropriates for speech scrambler. PRNG (Pseudo Random Number Generated) is created based on the proposed 7D hyperchaotic result. The proposed scheme depends on this novel hyperchaotic system as PRNG to generate a random keystream bit. The input speech signal in this work is first squeezed by discrete cosine transformation (DCT) to minimize the residual intelligibility (R. I.). Increasing the number of system parameters accomplished a high key space, therefore, improves the security to resist several cryptographic attacks. The scheme is measured using various tests like. Peak Signal-to-Noise Ratio (PSNR), Signal to noise ratio (SNR), percentage of Difference (P. Diff.), Segmental Signal to Noise Ratio Measure (SSNR), Crest factor measure (CF), Correlation coefficient Measure (R_C), root mean square measure (RMS), histogram, key sensitivity, spectrogram, and waveform The results of simulation and comparison prove that the algorithm can achieve better performance in encryption that means the scheme is stronger and highly secure against various types of attacks than many recent similar audio encryption schemes. Simulated and implemented the result using Mathematica and MATLAB programs to simulation results, provided qualitatively, and figures.
Relating human physiology content to COVID-19: a strategy to keep students in touch with physiology in times of social distance due to pandemic

In 2020 universities had to quickly implement remote education alternatives as a result of the social distancing due to the COVID-19 pandemic. To keep students engaged with the university, we implemented a teaching-learning model that relates physiology contents to the COVID-19 pandemic using online educational platforms. A 1-mo web course was proposed for health sciences students from the Federal University of Pampa. It included synchronous meetings twice a week and asynchronous activities using scientific articles, case studies, and interactive online tools. The students approved the methodology developed, assessing it as dynamic and innovative. They reported that the activity helped to better understand the relations between COVID-19 and physiological systems. The web course also contributed to the identification of reliable sources of news and stimulated the sharing of scientific content with their families. We concluded that the use of online platforms contextualizing the physiology content considering current events helps students in learning human physiology and improves their abilities to apply this information to their daily life, in this specific case, regarding the COVID-19 pandemic.