Reviewerly/exHarmony huggingface dataset:
Machine Learning-based Predictive Analytics of Student Academic Performance in STEM Education

Machine Learning (ML) is expected, in the near future, to provide various venues and effective tools to improve education in general, and Science-Technology-Engineering- Mathematics (STEM) education in particular. The Gartner Analytics Ascendancy Model requires the use of four types of data analytics to be considered comprehensive: descriptive, diagnostic, predictive and prescriptive data analytics. This paper presents the outcomes of a research and development project at Bradley University (Peoria, IL, USA) aimed at the setup and benchmarking of eight ML algorithms for predictive learning analytics, specifically, a prediction of student academic performance in a course. The analyzed and tested ML algorithms include linear regression, logistic regression, k- nearest neighbor classification, naïve Bayes classification, artificial neural network regression and classification, decision tree classification, random forest classification, and support vector machine classification. Based on the obtained accuracy of the analyzed and tested ML algorithms, we have formulated a set of recommendations for faculty and practitioners in terms of selection, setup and utilization of ML algorithms in predictive analytics in STEM education. We also performed formative and summative surveys of undergraduate and graduate students in Computer Science and Computer Information Systems courses to understand their opinion about utilization of ML-based predictive analytics in education; a summary of obtained student feedback is presented in this paper.
A New Hybrid Image Encryption Algorithm Based on 2D-CA, FSM-DNA Rule Generator, and FSBI

Image encryption is an efficient and vital way to protect classified and secret images.With the advancement of the processing power of the computer, AES, DES, or chaotic series type just alike image encryption schemes are not as secure as before.Therefore, in this paper, we present a new hybrid image encryption method for protecting secret and imperative images by employing logistic sine system (LSS) together with two-dimensional cellular automata and FSM-based DNA rule generator.The secure hash (SHA-256) algorithm is used to generate a secret key and to compute initial values for the LSS.In our proposed method, there are three stages and each stage has its own rule.After the scrambling process, the first stage is the Feistel structure-based bit inversion (FSBI) to change the pixels' value.The second stage is 2D-CA with Moore neighborhood structure-based local rules.The third is DNA conversion based on finite-state machine (FSM-DNA) rule generator.The proposed encryption scheme is robust against the well-known attacks, such as statistical attacks, brute force attacks, differential attacks, and pixel correlation attacks, and also possesses strong key sensitivity.The results show that our three-layer hybrid image encryption technique is robust against many well-known attacks and can be applied directly to all types of classified gray-scale images to make them more secure from such cryptography attacks.
Quantum homomorphic encryption scheme with flexible number of evaluator based on (k, n)-threshold quantum state sharing

Quantum homomorphic encryption provides the ability to perform calculations on encrypted data without decrypting them. The number of evaluators in most previous schemes is 1, and in other schemes it is n. In this paper, we propose a novel quantum homomorphic encryption scheme with flexible number of evaluator. One of our main contributions is that the number of evaluators is d (k ⩽ d ⩽ n), here n ≥ 2. These evaluators are allowed to alternately complete the evaluation of single-qubit unitary operations on the shared encrypted sequence. Other work is that for n = 1, we also give the quantum homomorphic encryption scheme that one evaluator can complete the evaluation. Then, for (3, 5)-case and (2, 2)-case, we give two examples (i.e. example-I and example-II) to illustrate the flexibility of evaluators. Finally, it is shown that the scheme is secure from analyzing the private key, plaintext sequence and encrypted sequence.
A Robust Image Zero-watermarking using Convolutional Neural Networks

In the image zero-watermarking techniques, a watermark sequence is not physically embedded into the host image but has a logical linkage with the host image. This property of zero-watermarking is desirable for some kinds of images in which a minimum distortion may cause serious detection or diagnostic errors, such as medical images and remote sensing images. In this paper, we propose a robust zero-watermarking algorithm based on the Convolutional Neural Networks (CNN) and deep learning algorithm, in which robust inherent features of image is generated by the CNN, and it is combined with the owner's watermark sequence using XOR operation. The experimental results show the watermark robustness against several attacks and common image processing.
Two Hybrid End-Effector Posture-Maintaining and Obstacle-Limits Avoidance Schemes for Redundant Robot Manipulators

To fulfill path tracking tasks with the end-effector posture controlled in a complex environment, maintaining the robot manipulator end-effector posture and avoiding obstacles are two important issues needed to be considered. In this paper, two hybrid end-effector posture-maintaining and obstacle-limits avoidance (hybrid PM-OLA) schemes are proposed and investigated for motion planning of redundant robot manipulators, which are based on the quadratic programming (QP) framework. The end-effector posture-maintaining, obstacle-avoidance, and the joint-angular-limits are formulated as an equality constraint, inequality constraint, and bound constraint into the QP problem. With these hybrid PM-OLA schemes, the robot manipulator can avoid the obstacle and joint physical limits when executing end-effector tasks. The hybrid PM-OLA schemes are finally transformed into linear variational inequalities and solved by a recurrent neural network. Computer simulations and physical experiments substantiate the effectiveness, accuracy, safety, and the practicability of the proposed hybrid PM-OLA schemes. Comparisons with other schemes show that the proposed hybrid PM-OLA schemes are more suitable for applications.
Comparison of Lung Cancer Detection Algorithms

Lung cancer is a kind of difficult to diagnose and dangerous cancer. It commonly causes death both men and women so fast accurate analysis of nodules is more important for treatment. Various methods have been used for detecting cancer in early stages. In this paper, machine learning methods compared while detect lung cancer nodule. We applied Principal Component Analysis, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Decision Trees and Artificial Neural Networks machine learning methods to detect anomaly. We compared all methods both after preprocessing and without preprocessing. The experimental results show that Artificial Neural Networks gives the best result with 82,43% accuracy after image processing and Decision Tree gives the best result with 93,24% accuracy without image processing.
A flexible rule for evidential combination in Dempster–Shafer theory of evidence

Dempster’s combination rule in Dempster–Shafer theory of evidence is widely used to combine multiple pieces of evidence. However, when the evidence is severely conflicting, the result could be counter-intuitive. Thus, many alternative combination rules have been proposed to address this issue. Nevertheless, the existing ones sometimes behave not very well. This may be because they do not hold some essential properties. To this end, this paper firstly identifies some of the important properties. Then, following the cues from these properties, we propose a novel evidential combination rule as a remediation of Dempster’s combination rule in Dempster–Shafertheory. Our new rule is based on the concept of complete conflict (we introduced in this paper), Dempster’s combination rule, and the concept of evidence weight. Moreover, we illustrate the effectiveness of our new rule by using it to successfully resolve well-known Zadeh’s counter-example, which is against Dempster’s combination rule. Finally, we confirm the advantages of our method over the existing methods through some examples.
Physical Orienteering Problem for Unmanned Aerial Vehicle Data Collection Planning in Environments With Obstacles

This letter concerns a variant of the orienteering problem (OP) that arises from multi-goal data collection scenarios where a robot with a limited travel budget is requested to visit given target locations in an environment with obstacles. We call the introduced OP variant the physical OP (POP). The POP sets out to determine a feasible, collision-free, path that maximizes collected reward from a subset of the target locations and does not exceed the given travel budget. The problem combines motion planning and combinatorial optimization to visit multiple target locations. The proposed solution to the POP is based on the variable neighborhood search (VNS) method combined with the asymptotically optimal sampling-based probabilistic roadmap (PRM*) method. The VNS-PRM* uses initial low-dense roadmap that is continuously expanded during the VNSbased POP optimization to shorten paths of the promising solutions and, thus, allows maximizing the sum of the collected rewards. The computational results support the feasibility of the proposed approach by a fast determination of high-quality solutions. Moreover, an experimental verification demonstrates the applicability of the proposed VNS-PRM* approach for data collection planning for an unmanned aerial vehicle in an urban-like environment with obstacles.
You Invaded my Tracking Space! Using Augmented Virtuality for Spotting Passersby in Room-Scale Virtual Reality

With the proliferation of room-scale Virtual Reality (VR), more and more users install a VR system in their homes. When users are in VR, they are usually completely immersed in their application. However, sometimes passersby invade these tracking spaces and walk up to users that are currently immersed in VR to try and interact with them. As this either scares the user in VR or breaks the user's immersion, research has yet to find a way to seamlessly represent physical passersby in virtual worlds. In this paper, we propose and evaluate three different ways to represent physical passersby in a Virtual Environment using Augmented Virtuality. The representations encompass showing a Pointcloud, showing a 3D-Model, and showing an Image Overlay of the passerby. Our results show that while an Image Overlay and a 3D-Model are the fastest representations to spot passersby, the 3D-Model and the Pointcloud representations were the most accurate.
Negativity of Quasiprobability Distributions as a Measure of Nonclassicality

We demonstrate that the negative volume of any $s$-paramatrized quasiprobability, including the Glauber-Sudashan $P$-function, can be consistently defined and forms a continuous hierarchy of nonclassicality measures that are linear optical monotones. These measures therefore belong to an operational resource theory of nonclassicality based on linear optical operations. The negativity of the Glauber-Sudashan $P$-function in particular can be shown to have an operational interpretation as the robustness of nonclassicality. We then introduce an approximate linear optical monotone, and show that this nonclassicality quantifier is computable and is able to identify the nonclassicality of nearly all nonclassical states.
Automatic Whole Heart Segmentation Using a Two-Stage U-Net Framework and an Adaptive Threshold Window

Whole heart segmentation is an important medical imaging method used to enable clinical applications. However, automatic segmentation of the heart is still a challenging task due to the complexity and particularity of medical images, especially when the heart is segmented into substructures. In this study, we present a training strategy that relies on a two-stage U-Net framework and an adaptive threshold window to automatically segment a whole heart and its substructures. The two-stage U-Net framework consists of a region of interest (ROI) detection of the whole heart and accurate segmentation of the heart substructures. The adaptive threshold window method is used to remove the noisy parts of the data while preserving the anatomical relationships between local regions. Experiments were performed on a dataset from the MM-WHS Challenge 2017. The proposed approach resulted in a high segmentation accuracy with a 79.3% and 95.5% Dice similarity coefficient for the whole heart and ascending aorta segmentation, respectively, using limited GPU computing resources and small amounts of annotated data. The full implementation and configuration files in this paper are available at https://github.com/liut969/Whole-Heart-Segmentation.
On the use of virtual reality in software visualization: The case of the city metaphor

Background: Researchers have been exploring 3D representations for visualizing software. Among these representations, one of the most popular is the city metaphor, which represents a target object-oriented system as a virtual city. Recently, this metaphor has been also implemented in interactive software visualization tools that use virtual reality in an immersive 3D environment medium. Aims: We assessed the city metaphor displayed on a standard computer screen and in an immersive virtual reality with respect to the support provided in the comprehension of Java software systems. Method: We conducted a controlled experiment where we asked the participants to fulfill program comprehension tasks with the support of (i) an integrated development environment (Eclipse) with a plugin for gathering code metrics and identifying bad smells; and (ii) a visualization tool of the city metaphor displayed on a standard computer screen and in an immersive virtual reality. Results: The use of the city metaphor displayed on a standard computer screen and in an immersive virtual reality significantly improved the correctness of the solutions to program comprehension tasks with respect to Eclipse. Moreover, when carrying out these tasks, the participants using the city metaphor displayed in an immersive virtual reality were significantly faster than those visualizing with the city metaphor on a standard computer screen. Conclusions: Virtual reality is a viable means for software visualization.
Three-Stream Network With Bidirectional Self-Attention for Action Recognition in Extreme Low Resolution Videos

This letter presents a novel three-stream network for action recognition in extreme low resolution (LR) videos. In contrast to the existing networks, the new network uses the trajectory-spatial network, which is robust against visual distortion, instead of the pose information to complement the two-stream network. Also, the new three-stream network is combined with the inflated 3D ConvNet (I3D) model pre-trained on kinetics to produce more discriminative spatio-temporal features in blurred LR videos. Moreover, a bidirectional self-attention network is aggregated with the three-stream network to further manifest various temporal dependence among the spatio-temporal features. A new fusion strategy is devised as well to integrate the information from the three different modalities. Simulations show that the new architecture outperforms the main state-of-the-art extreme LR action recognition methods on the HMDB-51 and IXMAS datasets.
Argument Search

We report on the first user study on assessing argument relevance. Based on a search among more than 300,000 arguments, four standard retrieval models are compared on 40 topics for 20 controversial issues: every issue has one topic with a biased stance and another neutral one. Following TREC, the top results of the different models on a topic were pooled and relevance-judged by one assessor per topic. The assessors also judged the arguments' rhetorical, logical, and dialectical quality, the results of which were cross-referenced with the relevance judgments. Furthermore, the assessors were asked for their personal opinion, and whether it matched the predefined stance of a topic. Among other results, we find that Terrier's implementations of DirichletLM and DPH are on par, significantly outperforming TFIDF and BM25. The judgments of relevance and quality hardly correlate, giving rise to a more diverse set of ranking criteria than relevance alone. We did not measure a significant bias of assessors when their stance is at odds with a topic's stance.
A Fast, Scalable, and Reliable Deghosting Method for Extreme Exposure Fusion

HDR fusion of extreme exposure images with complex camera and object motion is a challenging task. Existing patch-based optimization techniques generate noisy and/or blurry results with undesirable artifacts for difficult scenarios. Additionally, they are computationally intensive and have high execution times. Recently proposed CNN-based methods offer fast alternatives, but still fail to generate artifact-free results for extreme exposure images. Furthermore, they do not scale to an arbitrary number of input images. To address these issues, we propose a simple, yet effective CNN-based multi-exposure image fusion method that produces artifact-free HDR images. Our method is fast, and scales to an arbitrary number of input images. Additionally, we prepare a large dataset of 582 varying exposure images with corresponding deghosted HDR images to train our model. We test the efficacy of our algorithm on publicly available datasets, and achieve significant improvements over existing state-of-the-art methods. Through experimental results, we demonstrate that our method produces artifact-free results, and offers a speed-up of around 54× over existing state-of-the-art HDR fusion methods.
Language models and fusion for authorship attribution

We deal with the task of authorship attribution, i.e. identifying the author of an unknown document, proposing the use of Part Of Speech (POS) tags as features for language modeling. The experimentation is carried out on corpora untypical for the task, i.e., with documents edited by non-professional writers, such as movie reviews or tweets. The former corpus is homogeneous with respect to the topic making the task more challenging, The latter corpus, puts language models into a framework of a continuously and fast evolving language, unique and noisy writing style, and limited length of social media messages. While we find that language models based on POS tags are competitive in only one of the corpora (movie reviews), they generally provide efficiency benefits and robustness against data sparsity. Furthermore, we experiment with model fusion, where language models based on different modalities are combined. By linearly combining three language models, based on characters, words, and POS trigrams, respectively, we achieve the best generalization accuracy of 96% on movie reviews, while the combination of language models based on characters and POS trigrams provides 54% accuracy on the Twitter corpus. In fusion, POS language models are proven essential effective components.
RouteChain: Towards Blockchain-based Secure and Efficient BGP Routing

Routing on the Internet is defined among autonomous systems (ASes) based on a weak trust model where it is assumed that ASes are honest. While this trust model strengthens the connectivity among ASes, it results in an attack surface which is exploited by malicious entities to hijacking routing paths. One such attack is known as the BGP prefix hijacking, in which a malicious AS broadcasts IP prefixes that belong to a target AS, thereby hijacking its traffic. In this paper, we proposeRouteChain: a blockchain-based secure BGP routing system that counters BGP hijacking and maintains a consistent view of the Internet routing paths. Towards that, we leverage provenance assurance and tamper-proof properties of blockchains to augment trust among ASes. We group ASes based on their geographical (network) proximity and construct a bihierarchical blockchain model that detects false prefixes prior to their spread over the Internet. We validate strengths of our design by simulations and show its effectiveness by drawing a case study with the Youtube hijacking of 2008. Our proposed scheme is a standalone service that can be incrementally deployed without the need of a central authority.
Noise Contrastive Estimation for One-Class Collaborative Filtering

Previous highly scalable One-Class Collaborative Filtering (OC-CF) methods such as Projected Linear Recommendation (PLRec) have advocated using fast randomized SVD to embed items into a latent space, followed by linear regression methods to learn personalized recommendation models per user. However, naive SVD embedding methods often exhibit a strong popularity bias that prevents them from accurately embedding less popular items, which is exacerbated by the extreme sparsity of implicit feedback matrices in the OC-CF setting. To address this deficiency, we leverage insights from Noise Contrastive Estimation (NCE) to derive a closed-form, efficiently computable "depopularized" embedding. We show that NCE item embeddings combined with a personalized user model from PLRec produces superior recommendations that adequately account for popularity bias. Further analysis of the popularity distribution of recommended items demonstrates that NCE-PLRec uniformly distributes recommendations over the popularity spectrum while other methods exhibit distinct biases towards specific popularity subranges. Empirically, NCE-PLRec produces highly competitive performance with run-times an order of magnitude faster than existing state-of-the-art approaches for OC-CF.
Improving Vendor-managed Inventory Strategy Based on Internet of Things (IoT) Applications and Blockchain Technology

Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers/vendors manage the inventory of retailers and take responsibility for making decisions related to the timing and extent of inventory replenishment. There exist several prerequisites for a successful VMI strategy implementation like information sharing, trust, systems integration and long-term collaboration. However, in nowadays logistics flows, complex processes, high number of participants and complex distribution channels impede the successful adoption of VMI strategies. In this paper, we propose a new interaction framework based on smart contracts and blockchain for governing the relationship between the vendor and the buyer. A use-case VMI scenario is presented along with a functional smart contract. Tests performed using a local private blockchain illustrate the applicability of the proposed architecture along with the significant benefits for each participant.
Enhancing the Academic Library Experience with Chatbots: An Exploration of Research and Implications for Practice

This paper explores the potential of using chatbots to improve the academic research experience for university students with a literature-based discussion reflecting on a prototype being developed at the University of Technology Sydney (UTS). The paper proposes that information professionals need to adapt emerging technologies such as chatbots to innovate, improve and support library services. Designing a positive experience for the user is essential to ensure that such technological solutions are sustainable. In this exploratory paper, we argue that it is important that librarians engage with the conversational design of the library chatbot in collaboration with the technology developers in order to make it useful, friendly, trustworthy, and customisable for university students.