Reviewerly/exHarmony huggingface dataset:
Machine Learning for Quantum Hardware Performance Assessment

The development of near-term quantum computers, referred to as Noisy Intermediate-Scale Quantum (NISQ) computers, has progressed rapidly in the past few years resulting in several quantum computers which vary in their underlying technology and physical constraints. The performance of these computers also varies from one quantum algorithm to another. To enable efficient selection of the quantum computer that provides the highest output fidelity for a given application, an accurate noise modeling of each quantum hardware is required. However, noise modeling for a given application is a complex problem because of the unknown interaction between the quantum circuit parameters and the noise parameters of NISQ devices. We propose the use of Machine Learning (ML) to model the performance of different quantum computers at the application level. The ML models predict the output fidelity of the quantum application executed on different quantum computers given their publicly available physical constraints. We use a diverse training dataset to cover the key features for application-level benchmarking of the quantum hardware. Our results obtained from different superconducting quantum devices show that our proposed ML models enable cost-effective quantum computer selection for different quantum applications with different fidelity metrics.
Eliciting Cooperative Persuasive Dialogue by Multimodal Emotional Robot

Using emotional expressions is an effective dialogue technique in human–human dialogue. Introducing such techniques to human–robot interaction might improve their effectiveness to encourage the cooperative dialogue manner of system users. However, most of the existing research on emotional agent systems was based on the Wizard-of-Oz (WOZ) method to verify the abilities of interactive interfaces. In this paper, we build an autonomous dialogue robot that uses emotional expressions for eliciting the cooperative dialogue manner of users. The robot uses both verbal and multimodal expressions as well as emotional speech and emotional gestures in interactions. Our dialogue experiments showed that positive emotional expressions are the most efficient strategy for facilitating cooperative dialogues with users. Moreover, using negative emotional expressions is also an effective strategy in some dialogue contexts. We also investigated several modalities to emphasize the robot's emotional expression abilities.
Efficient Deep Super-Resolution of Voxelized Point Cloud in Geometry Compression

Point cloud compression is an essential task for practical applications using point clouds. Most of the previous approaches rely on octree compression which involves voxelization in the coding itself. Distortions derived from voxelization can be reduced without increasing the bitrate by postprocessing. In this article, we propose a super-resolution method for a decoded voxelized point cloud as a postprocessing step in the geometry compression. The proposed method increases the resolution of the voxelized point cloud by predicting the occupancy of higher resolution voxels than those used to compress the original point cloud. For efficient prediction, we propose a deep neural network for super-resolution based on sparse convolution. It can be highly efficient even for a large point cloud since the network applies convolution only to nonempty space. The proposed method predicts the occupancies represented by continuous values for each point and estimates the binary occupancies through a thresholding procedure. We design a dynamic threshold to ensure that at least one of all voxels is predicted to be occupied in order to prevent the generation of regions with missing points. We also introduce an occupancy prediction method to address the sparsity of high-resolution occupied voxels. Experiments on the outdoor and indoor datasets demonstrate the effectiveness of the proposed method.
Project-sized scaffolding for software engineering courses

Students can often graduate with a degree in computer science without working with legacy code bases, yet when they join the workforce they will almost certainly work on an existing project with thousands, if not millions of lines of existing code. In order to give students a realistic experience without overwhelming them, we added scaffolding to an existing open source project and used it in our third year software engineering course. We asked students to complete a series of 5 tasks, from bug fixing to feature addition, with this scaffolded project. Our scaffolding consisted of enhanced documentation, demonstration videos, compilation videos, enhanced task descriptions, and hints for task completion. After running this course project we collected feedback via a survey (n=87) and a small focus group (n=7). We found that students appreciated the realistic experience, but that they recommend further scaffolding, especially within source code, to better balance between difficulty and learning.
Application of Hyperledger Blockchain to Reduce Information Asymmetries in the Used Car Market

The used car market has long been an example of a market rife with information asymmetry between sellers and buyers. Since most consumers have little experience and knowledge in buying cars, they rely on the historical vehicle documents provided only by car dealers, which might be insufficient to make pre-purchase judgments. To receive more information about events that occurred in the vehicle's past, buyers need to spend time collecting other related documents from different sources. The whole process is time-consuming and leads to quality uncertainties causing market inefficiency. Such a problem can be alleviated by blockchain technology by using nodes of a computer network to record the historical information of a car, where the chain of data cannot be falsified, creating transparent, verified, and easy access to all documents. Accordingly, we propose a Hyperledger-based approach and simulate the acquisition time of historical vehicle data to illustrate the blockchain application to reduce information asymmetries in the used car market. In Hyperledger Fabric, all business network transactions are recorded on the smart contracts, allowing the records to coexist among the participants, including dealers, maintenance plants, motor vehicle offices, police offices, and buyers. This blockchain technology application mitigates information asymmetries between buyers and sellers, guarantees the integrity and transparency of data, and shortens the time obtaining historical car information.
Machine Learning-Based Scene Classification Using Thepade’s SBTC, LBP, and GLCM

Photographs have become a necessary part of human life. Images are being captured all across the world. One of the significant issues in scene classification is that the photographs are taken in a variety of locations. There are many diverse scenes in these locations, such as an airport, a restaurant, a cafeteria, and a forest. Indoor scene classification and outdoor scene classification are the most common types of scene categorization. Scene classification is challenging, which poses issues in research and image processing. Image classification becomes challenging because of the numerous distinct items present in various sceneries, causing a machine to become confused about the scene’s categories. The current challenge with scene classification results in low-classification accuracy. The object comes in various shapes and sizes and can be found in various locations, which is one of the reasons for the scene classification’s poor accuracy. We will perform feature extraction for pattern recognition and multiple machine learning models for image categorization using the intel image classification dataset. Thepade’s SBTC n-ary feature extraction approach for RGB and LUV color plane models and fusion LBP and GLCM is presented in this study. When the three individual feature extraction methods are compared with the fusion of Thepade’s SBTC and GLCM with RGB color plane, the fusion of Thepade’s SBTC and GLCM with RGB color plane yielded greater accuracy.
A Principal Component Analysis, Sampling and Classifier strategies for dealing with concerns of class imbalance in datasets with a ratio greater than five

This research looks at how the imbalance ratio and classifier affect how well various resampling techniques work when dealing with imbalanced data sets. The research examines the effects on learning when previously imbalanced data is changed into fictitiously balanced class distributions using a variety of resampling strategies. When data sets are significantly uneven, analyses utilising attribute selection, four different classifiers, and six performance evaluation criteria demonstrate that over-sampling (95 percent accuracy) the minority class routinely outperforms under-sampling (77 percent accuracy) the majority class.
WebMILE

In recent years, we have seen the success of network representation learning (NRL) methods in diverse domains ranging from computational chemistry to drug discovery and from social network analysis to bioinformatics algorithms. However, each such NRL method is typically prototyped in a programming environment familiar to the developer. Moreover, such methods rarely scale out to large-scale networks or graphs. Such restrictions are problematic to domain scientists or end-users who want to scale a particular NRL method-of-interest on large graphs from their specific domain. In this work, we present a novel system, WebMILE to democratize this process. WebMILE can scale an unsupervised network embedding method written in the user's preferred programming language on large graphs. It provides an easy-to-use Graphical User Interface (GUI) for the end-user. The user provides the necessary input (embedding method file, graph, required packages information) through a simple GUI, and WebMILE executes the input network embedding method on the given input graph. WebMILE leverages a pioneering multi-level method, MILE (alternatively DistMILE if the user has access to a cluster), that can scale a network embedding method on large graphs. The language agnosticity is achieved through a simple Docker interface. In this demonstration, we will showcase how a domain scientist or end-user can utilize WebMILE to rapidly prototype and learn node embeddings of a large graph in a flexible and efficient manner - ensuring the twin goals of high productivity and high performance.
Prototype of Robot Movement Navigation System Using Pixy Camera (CMUCAM 5)

Technological advances support the development of tools used in the industrial sector, one of which is the Automated Guided Vehicle (AGV). AGV can be used to deliver goods automatically. This AGV can be made using visual sensors, one of which is the Pixy camera. This AGV robot can be used to follow the motion of objects. In addition to the Pixy camera as a visual sensor, the Arduino Mega 2560 is also used as a microcontroller, and the L298N motor driver shield with two DC motors. The distance and speed of the robot are also determined so that it can move in the direction the object is going. Manoeuvring is needed on a robot with better sharpness and ability, so Mamdani fuzzy logic control is used as a method. Tests are carried out using the Turn value or Position and Area or the distance between the camera sensor and the object. The experimental simulation was carried out with a case study of Turn value of 0.6 and Area of 6000. Testing was carried out using simulation software and Arduino IDE which were then calculated manually for comparison. The results obtained in testing with the simulation software are 20.80 for the right DC motor and 85.60 for the left DC motor, with the Arduino IDE software the values are 21.11 for the right DC motor and 85.20 for the left DC motor, and the manual calculation produces a value of 21.09 for the right DC motor and 85.04 for the left DC motor. From the resulting data, from the error rate is 98.51% for the right DC motor and 99.53% for the left DC motor.
Design and sizing of solar PV system for the laboratory building at the Technical College of Engineering/Mosul and simulation with the PV*SOL program

Solar energy is the most important source of renewable energies that turn light or radiation into electrical power. In Iraq, solar stations are now being installed in homes, farms and small markets. In this paper, the design and sizing of the solar PV system for laboratories building in Technical college of Engineering/Mosul to cover its electrical power requirements then simulations this work with the PV*SOL program (3D) selection was used. All solar PV system componetes were determined and similar appeared to the mathmatical calculations.
Post Quantum Cryptography: Comparison between RSA and McEliece

Cryptography is one of the techniques used to secure the process of communication in the presence of an adversarial party. Public key cryptosystem allows the process of encryption and decryption to use two different keys. The current de-facto algorithm for public key cryptosystem is the RSA. However, this cryptosystem is now considered insecure in the presence of quantum computing. The insecurity of this cryptosystem comes from an algorithm that can efficiently solve the Integer Factorization Problem, which has been the strength of RSA cryptosystem. Shor's algorithm can factor composite integers into its prime factors using a quantum computer in polynomial time. This research will give an insight on an alternative public key cryptosystem that is quantum resistant. The result of this research shows that while the RSA has better average time than the McEliece cryptosystem, McEliece cryptosystem has better cryptographic strength when compared to RSA.
An Enhanced Key-utterance Interactive Model with Decouped Auxiliary Tasks for Multi-party Dialogue Reading Comprehension

Multi-party dialogue machine reading comprehension (MRC) is more challenging than plain text MRC because it involves multiple speakers, more complex information flow interaction, and discourse structure. Previously most researchers focus on decoupling the speaker-aware and utterance-aware information to overcome such difficulties. Based on this, the self- and pseudo-self-supervised prediction auxiliary tasks on speakers and key-utterance are proposed. However, the information interaction among key-utterance, question, and dialogue context was ignored in these works, and there should also be a constraint between the two additional tasks. Herein, we proposed an enhanced key-utterance interaction model. It takes the key-utterance predicted by auxiliary task as prior information. Moreover, the co-attention mechanism is used to capture the critical information interaction among dialogue contexts, question, and key-utterance from the two perspectives of question-to-dialogue and dialogue-to-question, respectively. In addition, we introduced minimizing mutual information (MI) between the two auxiliary tasks to prevent mutual interference and overlap of information. Experimental results show that the proposed model achieves significant improvements than the dialogue MRC baseline models in Molweni and FriendsQA datasets.
Triplet transformer network for multi-label document classification

Multi-label document classification is the task of assigning one or more labels to a document, and has become a common task in various businesses. Typically, current state-of-the-art models based on pretrained language models tackle this task without taking the textual information of label names into account, therefore omitting possibly valuable information. We present an approach that leverages this information stored in label names by reformulating the problem of multi label classification into a document similarity problem. To achieve this, we use a triplet transformer network that learns to embed labels and documents into a joint vector space. Our approach is fast at inference, classifying documents by determining the closest and therefore most similar labels. We evaluate our approach on a challenging real-world dataset of a German radio-broadcaster and find that our model provides competitive results compared to other established approaches.
Neural Radiance Fields from Sparse RGB-D Images for High-Quality View Synthesis

The recently proposed neural radiance fields (NeRF) use a continuous function formulated as a multi-layer perceptron (MLP) to model the appearance and geometry of a 3D scene. This enables realistic synthesis of novel views, even for scenes with view dependent appearance. Many follow-up works have since extended NeRFs in different ways. However, a fundamental restriction of the method remains that it requires a large number of images captured from densely placed viewpoints for high-quality synthesis and the quality of the results quickly degrades when the number of captured views is insufficient. To address this problem, we propose a novel NeRF-based framework capable of high-quality view synthesis using only a sparse set of RGB-D images, which can be easily captured using cameras and LiDAR sensors on current consumer devices. First, a geometric proxy of the scene is reconstructed from the captured RGB-D images. Renderings of the reconstructed scene along with precise camera parameters can then be used to pre-train a network. Finally, the network is fine-tuned with a small number of real captured images. We further introduce a patch discriminator to supervise the network under novel views during fine-tuning, as well as a 3D color prior to improve synthesis quality. We demonstrate that our method can generate arbitrary novel views of a 3D scene from as few as 6 RGB-D images. Extensive experiments show the improvements of our method compared with the existing NeRF-based methods, including approaches that also aim to reduce the number of input images.
Active Contour Model for Image Segmentation

Region based active contours algorithms are extensively utilised for image segmentation irrespective of unavailability of the densely annotated large data sets as required in the case of fully supervised deep learning models. However, previous active contours models have certain limitations including false contours appearances when there is in-homogeneity in the image. In our model we combine local and global information in image level set function, proposing a hybrid energy function which helps efficiently evolve contours on image and may assess the significance of the object and surroundings.Bias-correction is used it solve energy of the bias field that takes into consideration the intensity in-homogeneity and the level set functions that indicate a division of the image domain. The proposed model computes its data force using image fitting energy to take out local information from in-homogeneous image and calculates all pixel values by once. Objects having high contrast of different gray level value or more in-homogeneity can be segmented. Results shows that our method is more stable and take less computation time as compared to previous models. Finally the superiority of the proposed models in terms of segmentation efficiency is proved.
New Undersampling Method Based on the kNN Approach

Class imbalance is a common problem in machine learning tasks, which often leads to sub-optimal performance of classifiers, where the classification of a new example is based on minimizing the error rate. Researchers have worked on this problem by developing various methods of resampling or modification of existing classification algorithms, however, there is still a need to look for better solutions that can overcome the limitations of known ones. In this paper, a new undersampling algorithm that is an extension of our previous proposals is proposed. Its main idea is to guarantee an even elimination of majority class objects while focusing on nearest neighbors. Extensive experiments were conducted on artificial and real datasets, and they showed that in many cases, the proposed solution outperformed other tested undersampling techniques.
Configurable-by-Construction Runtime Monitoring

Most modern systems, be it cyber-physical or mere software systems, are highly configurable. The main challenge when dealing with such configurable systems stems from the usually huge number of system variants that can be exponential in the number of configuration options or features. Monitoring systems that react on observations, e.g., sensor data, varying across system configurations or being themselves configurable also face this challenge but have barely been considered in the literature. In this paper, we discuss new aspects for runtime monitoring with variability in the system being monitored as well as the monitor itself. As a first step towards a configurable-by-construction runtime monitoring approach, we introduce configurable monitors from an automata-theoretic and stream-based perspective. For this, we harvest existing work on featured transition systems and present a variability-aware variant of the stream-based specification language Lola.
An Improved Method for Infrared Vehicle and Pedestrian Detection Based on YOLOv5s

The urban road scene at night is complex, and the imaging effect based on visible light at night is insufficient. Aiming at the problems of lack of color information, texture detail, and low spatial resolution of infrared scenes, we propose an improved infrared detection model based on YOLOv5s. The experimental data applies infrared images that are less affected by light conditions. In order to employ the features of the images as much as possible, this experiment combines the features of the DenseNet network and YOLOv5s, replacing the Bottleneck module in the C3 module of YOLOv5s with a custom Denseblock module to derive a custom C3-Dense module. In this paper, the C3-Dense module is a substitute for the C3 partly in the backbone network to improve the feature extraction capability of the network; in order to improve the detection capability of small targets, the SE-Net module is added to the backbone network. The simplified BiFPN structure replaces the original PAN structure, which enhances the ability of the network to extract features for different resolutions. Train and test using the FLIR infrared pedestrian and vehicle dataset. The results of our experiment show that the improved model has improved recall, precision, and mean Average Precision (mAP) for pedestrian and vehicle target detection in infrared images. The recall and precision of pedestrian detection increase by 4.24% and 5.01%; for vehicle detection, that is 1.51% and 3.23%, and the mAP is increased by 3.49%.