Reviewerly/exHarmony huggingface dataset:
Toward Effective Courseware at Scale: Investigating Automatically Generated Questions as Formative Practice

Courseware is a comprehensive learning environment that engages students in a learning by doing approach while also giving instructors data-driven insights on their class, providing a scalable solution for many instructional models. However, courseware-and the volume of formative questions required to make it effective-is time-consuming and expensive to create. By using artificial intelligence for automatic question generation, we can reduce the time and cost of developing formative questions in courseware. However, it is critical that automatically generated (AG) questions have a level of quality on par with human-authored (HA) questions in order to be confident in their usage at scale. Therefore, our research question is: are student interactions with AG questions equivalent to HA questions with respect to engagement, difficulty, and persistence metrics? This paper evaluates data for AG and HA questions that students used as formative practice in their university Communication course. Analysis of AG and HA questions shows that our first generation of AG questions perform equally well as HA questions in multiple important respects.
Automatic Generation of Machine Learning Synthetic Data Using ROS

Data labeling is a time intensive process. As such, many data scientists use various tools to aid in the data generation and labeling process. While these tools help automate labeling, many still require user interaction throughout the process. Additionally, most target only a few network frameworks. Any researchers exploring multiple frameworks must find additional tools or write conversion scripts. This paper presents an automated tool for generating synthetic data in arbitrary network formats. It uses Robot Operating System (ROS) and Gazebo, which are common tools in the robotics community. Through ROS paradigms, it allows extensive user customization of the simulation environment and data generation process. Additionally, a plugin-like framework allows the development of arbitrary data format writers without the need to change the main body of code. Using this tool, the authors were able to generate an arbitrarily large image dataset for three unique training formats using approximately 15 min of user setup time and a variable amount of hands-off run time, depending on the dataset size. The source code for this data generation tool is available at https://github.com/Navy-RISE-Lab/nn_data_collection
Learning from Imbalanced Data Streams Based on Over-Sampling and Instance Selection

Learning from imbalanced data streams is one of the challenges for classification algorithms and learning classifiers. The goal of the paper is to propose and validate a new approach for learning from data streams. However, the paper references a problem of class-imbalanced data. In this paper, a hybrid approach for changing the class distribution towards a more balanced data using the over-sampling and instance selection techniques is discussed. The proposed approach assumes that classifiers are induced from incoming blocks of instances, called data chunks. These data chunks consist of incoming instances from different classes and a balance between them is obtained through the hybrid approach. These data chunks are next used to induce classifier ensembles. The proposed approach is validated experimentally using several selected benchmark datasets and the computational experiment results are presented and discussed. The results of the computational experiment show that the proposed approach for eliminating class imbalance in data streams can help increase the performance of online learning algorithms.
Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty

While classic control theory offers state of the art solutions in many problem scenarios, it is often desired to improve beyond the structure of such solutions and surpass their limitations. To this end, residual policy learning (RPL) offers a formulation to improve existing controllers with reinforcement learning (RL) by learning an additive "residual" to the output of a given controller. However, the applicability of such an approach highly depends on the structure of the controller. Often, internal feedback signals of the controller limit an RL algorithm to adequately change the policy and, hence, learn the task. We propose a new formulation that addresses these limitations by also modifying the feedback signals to the controller with an RL policy and show superior performance of our approach on a contact-rich peg-insertion task under position and orientation uncertainty. In addition, we use a recent Cartesian impedance control architecture as the control framework which can be available to us as a black-box while assuming no knowledge about its input/output structure, and show the difficulties of standard RPL. Furthermore, we introduce an adaptive curriculum for the given task to gradually increase the task difficulty in terms of position and orientation uncertainty. A video showing the results can be found at https://youtu.be/SAZm_Krze7U.
Automatic Gland Segmentation for Detection of CRC Using Enhanced SegNet Neural Network

Colorectal cancer (CRC) is a sort of cancer that begins in the rectum or large intestine, generally showing more impact on adults. It generally starts as tiny, non-cancerous (benign) lumps of cells called polyps that form on the colon's inward lining. Detection of colorectal cancers in early stages can reduce the risk of mortality rate. Detection of polyps through screening tests may fail because of the length of the rectum and the tiny size of the polyps. Computer-based biomedical imagining might help the radiologists sift through the information and analyse clinical pathology images better. The majority of research focuses on gland segmentation using CNN architectures, but accuracy is still challenging. In this work, encoder-decoder-based deep learning model was proposed for morphological colon gland segmentation. This research of enhanced SegNet architecture improved the system's overall performance and, when tested on the standard benchmark Warwick-QU dataset for colonoscopy images. We trained the system by the training dataset consisting of 7225 images, and evaluation of the model is done using a test dataset divided into two parts: A (60 images), B (20 images). The results show that the proposed model is comparable to all the existing models regarding the same dataset and attain segmentation accuracy of 0.924 on Part A, 0.861 on Part B. The shape similarity of 83.145 on Part A, 88.642 on Part B, is considered adequate compared with the existing methods.
Image Captioning with Inherent Sentiment

We propose a new task called sentimental image captioning which aims to generate captions with the inherent sentiment reflected by the image. Compared with the stylized image captioning task that requires a predefined style independent of the image, our new task can automatically analyze the inherent sentiment tendency within the image. With this in mind, we propose an Inherent Sentiment Image Captioning (InSenti-Cap) method that first extracts the content and sentiment information from the image, and then fuses these information into the sentimental sentence generation via an attention mechanism. To effectively train the proposed model using the pairs of image and factual caption in existing captioning dataset and the extra sentiment corpus, we propose a two-stage training strategy that involves a sentimental regularization and a sentimental reward to enable the model to generate fluent and relevant sentences with inherent sentimental styles. Experiments demonstrate the effectiveness of our method.
Priority-Based Selection of Individuals in Memetic Algorithms for Distributed Data-Intensive Web Service Compositions

In distributed computing, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Web Service Composition (WSC)</i> leads to the effective reuse of existing services and produces added value. WSC must fulfil functional requirements and optimise <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quality of Service (QoS)</i> attributes, simultaneously. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Memetic Algorithms (MAs)</i> are promising for automatically composing numerous Web services to satisfy the above requirements. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data-intensive Web services</i> focus on providing and updating data with a significant volume of data operation and exchange. However, current composition approaches have ignored the impact of data communication and the distribution of services, which significantly affect the performance when applied to the challenging <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Distributed Data-intensive Web Service Composition (DDWSC)</i> problem. Although recent approaches have revealed the usefulness of local search, they have completely overlooked the question of preferring appropriate composition solutions for local search. To address this research issue, we propose a priority-based selection method for the local search that can be consistently integrated with any MA for DDWSC. This enables us to develop state-of-the-art algorithms for DDWSC by explicitly considering the problem-specific, population and solution-related information for choosing a solution. Extensive experimental evaluation using benchmark datasets shows that our proposed method significantly outperforms several recently proposed methods.
Data visualisation in continuous integration and delivery: Information needs, challenges, and recommendations

Several operations, ranging from regular code updates to compiling, building, testing, and distribution to customers, are consolidated in continuous integration and delivery. Professionals seek additional information to complete the mission at hand during these tasks. Developers who devote a large amount of time and effort to finding such information may become distracted from their work. We will better understand the processes, procedures, and resources used to deliver a quality product on time by defining the types of information that software professionals seek. A deeper understanding of software practitioners' information needs has many advantages, including remaining competitive, growing knowledge of issues that can stymie a timely update, and creating a visualisation tool to assist practitioners in addressing their information needs. This is an extension of a previous work done by the authors. The authors conducted a multiple-case holistic study with six different companies (38 unique participants) to identify information needs in continuous integration and delivery. This study attempts to capture the importance, frequency, required effort (e.g. sequence of actions required to collect information), current approach to handling, and associated stakeholders with respect to identified needs. 27 information needs associated with different stakeholders (i.e. developers, testers, project managers, release team, and compliance authority) were identified. The identified needs were categorised as testing, code & commit, confidence, bug, and artefacts. Apart from identifying information needs, practitioners face several challenges in developing visualisation tools. Thus, 8 challenges that were faced by the practitioners to develop/maintain visualisation tools for the software team were identified. The recommendations from practitioners who are experts in developing, maintaining, and providing visualisation services to the software team were listed.
Ranking of Educational Institutions Based on User Priorities Using AHP-PROMETHEE Approach

Today, education plays a major role in bringing values to the coming generations. With right to education taken up seriously by the government, there is a vast increase in the number of students taking up various courses and degrees. To cater to these needs, several educational institutions have also come up with the intention of providing a variety of courses to the students. However, as the numbers increase, the need to assess these institutions also increases in order to help students decide the institution of their choice and also for the institutions to better themselves. To accomplish evaluation of such institutions, a number of ranking systems were established with a fixed set of criteria. However, a ranking system which would cater to the needs of a student individually was never developed. This paper aims to develop a ranking system, which ranks the educational institutions based on the needs of an individual, using analytical hierarchy process and the multi-criteria decision-making method PROMETHEE. In order to make this system automized, the data required for this process has been retrieved using the process of web crawling. Web crawlers are automated scripts that are used to browse the World Wide Web in a systematic manner. This work will be useful for parents and students to find institutions based on their set of preferences.
Mobile Learning Grounded on Universal Instructional Design Principles on the Shift to Emergency Online Education

The onset of COVID-19 transmissions enforced school closures worldwide to restrict health threats particularly to students and teachers. Educational responses are developed to pursue the goals of education amid the COVID-19 outbreak. Among these educational responses is the integration of technology in acquiring knowledge through Mobile Learning or M-learning. This article presents the benefits, universal design principles, and strategies of Mobile Learning for the purpose of convenient and accessible learning through the use of mobile phones and ubiquitous technologies. Nonetheless, gaps in curriculum planning, course contents, and technological expertise among instructors need to be addressed to reinforce the implementation of effective Mobile Learning.
PnP-DRL: A Plug-and-Play Deep Reinforcement Learning Approach for Experience-Driven Networking

While Deep Reinforcement Learning has emerged as a de facto approach to many complex experience-driven networking problems, it remains challenging to deploy DRL into real systems. Due to the random exploration or half-trained deep neural networks during the online training process, the DRL agent may make unexpected decisions, which may lead to system performance degradation or even system crash. In this paper, we propose PnP-DRL, an offline-trained, plug and play DRL solution, to leverage the batch reinforcement learning approach to learn the best control policy from pre-collected transition samples without interacting with the system. After being trained without interaction with systems, our Plug and Play DRL agent will start working seamlessly, without additional exploration or possible disruption of the running systems. We implement and evaluate our PnP-DRL solution on a prevalent experience-driven networking problem, Dynamic Adaptive Streaming over HTTP (DASH). Extensive experimental results manifest that 1) The existing batch reinforcement learning method has its limits; 2) Our approach PnP-DRL significantly outperforms classical adaptive bitrate algorithms in average user Quality of Experience (QoE); 3) PnP-DRL, unlike the state-of-the-art online DRL methods, can be off and running without learning gaps, while achieving comparable performances.
Limitations of imitation: Lessons from another Bitcoin copycat

We find strong evidence that imitation to the leader in an uncertain environment provides short-term early success without enhancing long-term survival. Using a unique setting in cryptocurrencies where Bitcoin has been the centre focus, we define copycats as cryptocurrencies that have a name similar to Bitcoin. Our results show copycats earn higher returns in the first day and first week of trading but have a lower survival rate comparing to non-copycats and are robust to alternative definitions of copycat and an instrumental variable. Our results highlight that in an uncertain market created by a novel technology, the name of an organization may be a strong signal to investors when other signals are unclear.
Design of Performance Evaluation System for Transformation of Patent Achievements in Colleges and Universities Based on AHP

In order to improve the performance rating ability of university patent achievements transformation, a performance rating model of university patent achievements transformation is proposed based on AHP. Establish a big data analysis model of university patent achievement transformation performance, analyze the constraint parameters of university patent achievement transformation performance rating by using quantitative recursive analysis method, extract the fuzzy association rule feature quantity of university patent achievement transformation performance big data, and make optimization decision and comprehensive evaluation of university patent achievement transformation performance rating by using the methods of performance reliability transformation model comprehensive evaluation and fuzzy decision. The expert system analysis model of university patent achievement transformation performance rating is established, and the analytic hierarchy process (AHP) is used to adaptively optimize the university patent achievement transformation performance rating. The maximum likelihood estimation and fuzzy multi-parameter joint feature analysis are used to realize the optimization design of the university patent achievement transformation performance rating model. Combined with embedded B/S framework, the performance rating system of university patent achievement transformation is designed, and the information processing module of university patent achievement transformation performance rating system is designed by ISA/EISA framework. The simulation results show that this method has higher accuracy and better convergence, and improves the performance rating efficiency of university patent achievements transformation.
A Novel Attention Enhanced Residual-In-Residual Dense Network for Text Image Super-Resolution

Natural scene text images captured by handheld devices usually cause low resolution (LR) problems, thus making sub-sequent detection and recognition tasks more challenging. To address this problem, LR text images are generally super-resolution (SR) processed first. In this paper, we propose a novel low-resolution text image super-resolution method. This method adopts the residual-in-residual dense network (RRDN) to extract deeper high-frequency features than the residual dense network (RDN). Then, enhances the spatial and channel features with an attention mechanism. According to the characteristics of the text, we added gradient loss to adversarial learning. Experiments show that our method performs well in both qualitative and quantitative aspects of the latest public text image super-resolution dataset. Similarly, the proposed super-resolution method for text images of natural scenes also achieves the latest results.
On the symmetry of the quadratic assignment problem through elementary landscape decomposition

When designing meta-heuristic strategies to optimize the quadratic assignment problem (QAP), it is important to take into account the specific characteristics of the instance to be solved. One of the characteristics that has been pointed out as having the potential to affect the performance of optimization algorithms is the symmetry of the distance and flow matrices that form the QAP.
Security Threats and Privacy Issues in Cloud Data

The quick advancement of Web-based applications has led to a huge amount of information being scanned and gathered for business examination or scholarly research purposes, which may disregard individual protection. Organizations, industries and individuals data are at stake. In this paper, utilizing on the Web Personal Health Record (PHR) as contextual analysis, first demonstrate the need of inquiry ability approval that lessens the security introduction coming about because of the list items, and build up a versatile structure for authorized private keyword Search (APKS) over encoded cloud information. This particular model proposes two novel answers for APKS given on-going cryptographic crude, hierarchical predicate encryption (HPE). Our answers empower efficient multi-dimensional watchword looks with a run question, permit assignment and renouncement of hunt abilities. Additionally, the proposed system improves the question protection which conceals clients’ inquiry watchwords against the server. Actualize our plan on an advanced workstation, and exploratory outcomes exhibit its appropriateness for reasonable use. Privacy has seen advancement lately as information mining of the datasets in a dispersed huge information condition has turned into a successful worldwide business which is none other than data management or data analytics which ensures the security of data.
Central Bank Digital Currency Can Lead to the Collapse of Cryptocurrency

Cryptocurrencies have become popular. Economic agents use cryptocurrency such as bitcoins to make payments and it pose a threat to fiat currency. Central banks have begun to respond to this threat. They realize that they need to join the race to offer a digital currency and dominate the digital currency landscape which can lead to the collapse of most private digital currencies that are not issued by a central bank or a monetary authority. In this paper, I show how the issuance of a central bank digital currency can lead to the collapse of private digital currencies such as bitcoin. I argue that central banks will leverage on their monetary powers, and the trust that citizens have in government-backed money. This may give central banks strong incentives to issue a central bank digital currency. The issuance of a central bank digital currency can erode trust in cryptocurrencies, and lead to lack of trust in cryptocurrency, thereby leading to the collapse of cryptocurrencies although not immediately.
Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters

Neural network based recommendation models are widely used to power many internet-scale applications including product recommendation and feed ranking. As the models become more complex and more training data is required during training, improving the training scalability of these recommendation models becomes an urgent need. However, improving the scalability without sacrificing the model quality is challenging. In this paper, we conduct an in-depth analysis of the scalability bottleneck in existing training architecture on large scale CPU clusters. Based on these observations, we propose a new training architecture called Hierarchical Training, which exploits both data parallelism and model parallelism for the neural network part of the model within a group. We implement hierarchical training with a two-layer design: a tagging system that decides the operator placement and a net transformation system that materializes the training plans, and integrate hierarchical training into existing training stack. We propose several optimizations to improve the scalability of hierarchical training including model architecture optimization, communication compression, and various system-level improvements. Extensive experiments at massive scale demonstrate that hierarchical training can speed up distributed recommendation model training by 1.9x without model quality drop.