Miracle-dz/UniFAR-FTUs huggingface dataset:
Methodology to Determine Important-Points Location for Automated Lumbar Spine Stenosis Diagnosis Procedure

Chronic Lower Back Pain (CLBP) is one of the major types of pain that is affecting many people around the world. Lumbar Spine Stenosis (LSS), a major cause of CLBP, requires experienced neuroradiologists to detect and diagnose. It has been reported that the number of MRI examinations around the world is increasing but the number of specialist neuroradiologists to examine and analyse them has not. This paper presents a continuation of our methodology to automatically detect the presence of LSS by analyzing lumbar spine MRI images. It details important points location-determination algorithm that can be further processed in the LSS diagnosis procedure. We use the results of our, previously developed, boundary delineation method to supply boundary points to the algorithm. The algorithm is applied to the best cut axial-view images of the intervertebral discs of 515 patients contained in the Lumbar Spine MRI dataset. The results of the important points locations are presented.
Masked RCNN+ fast boosted tree classifier: a combination of deep learning technology for neural networks and classifier for pedestrian detection

The most intriguing advancements brought by deep learning and neural networks is in the field of computer vision. We associate any problem that has an image or camera input to encompass problems within computer vision. Self-driving cars, MRI analysis, Mars exploration rovers, facial recognition systems, object detection and augmented reality are just a few breakthroughs in the field. In this paper we will take a look at ways to improve pedestrian detection by using Convolutional Neural Network (CNN) along with Region Proposal Network (RPN), Masked Region Based Convolutional Neural Network (RCNN) and Fast Boosted Tree Classifier. Our approach effectively recognizes objects in a picture while at the same time generating an excellent segmentation mask for each instance. The technique, called Mask R-CNN, expands Faster R-CNN by including a branch for predicting an item mask in parallel with the current branch for bouncing box recognitionn.
On the use of DGAs in malware: an everlasting competition of detection and evasion

Malware typically makes use of Domain Generation Algorithms (DGAs) as a mechanism to contact their Command and Control server. In recent years, different approaches to automatically detect generated domain names have been proposed, based on machine learning. The first problem that we address is the difficulty to systematically compare these DGA detection algorithms due to the lack of an independent benchmark. The second problem that we investigate is the difficulty for an adversary to circumvent these classifiers when the machine learning models backing these DGA-detectors are known. In this paper we compare two different approaches on the same set of DGAs: classical machine learning using manually engineered features and a 'deep learning' recurrent neural network. We show that the deep learning approach performs consistently better on all of the tested DGAs, with an average classification accuracy of 98.7% versus 93.8% for the manually engineered features. We demonstrate that the deep learning solution yields better results even when only 10,000 malicious samples are available. We also show that one of the dangers of manual feature engineering is that DGAs can adapt their strategy, based on knowledge of the features used to detect them. To demonstrate this, we use the knowledge of the used feature set to design a new DGA which makes the Random Forest classifier powerless with a classification accuracy of 57.3%. The deep learning classifier is also (albeit less) affected, reducing its accuracy to 78.9%.
Experience: Quality Benchmarking of Datasets Used in Software Effort Estimation

Data is a cornerstone of empirical software engineering (ESE) research and practice. Data underpin numerous process and project management activities, including the estimation of development effort and the prediction of the likely location and severity of defects in code. Serious questions have been raised, however, over the quality of the data used in ESE. Data quality problems caused by noise, outliers, and incompleteness have been noted as being especially prevalent. Other quality issues, although also potentially important, have received less attention. In this study, we assess the quality of 13 datasets that have been used extensively in research on software effort estimation. The quality issues considered in this article draw on a taxonomy that we published previously based on a systematic mapping of data quality issues in ESE. Our contributions are as follows: (1) an evaluation of the “fitness for purpose” of these commonly used datasets and (2) an assessment of the utility of the taxonomy in terms of dataset benchmarking. We also propose a template that could be used to both improve the ESE data collection/submission process and to evaluate other such datasets, contributing to enhanced awareness of data quality issues in the ESE community and, in time, the availability and use of higher-quality datasets.
BMP: A Bounded Message Protocol for the IoT

In this paper, we introduce a new communication protocol for machine to machine communication within the Internet of Things (IoT) based on physical distances rather than on addresses. A common challenge with the IoT is to filter the quantity of data that can be collected, especially in centralized solutions. By using distance from the emission point instead of addresses to move messages between objects, this protocol forces the use of decentralized algorithms. It also tackles the problem of the quantity of data by giving each data a time to live, after which messages are deleted by the protocol. The idea is that an object that sends a message should decide who can access it and for how long the data is relevant. We specify the conditions under which such a protocol can be useful and further provide some examples of application that could gain from using this protocol.
Multi-UAVs Cooperative Coverage Reconnaissance with Neural Network and Genetic Algorithm

Aiming at the problem of multi-UAVs cooperative coverage reconnaissance mission planning, a planning method combining neural network and genetic algorithm is proposed. Firstly, the relative position relationship between multiple UAVs, the position relationship between each UAV and the boundary of the target area and the motion performance of each UAV are taken as inputs of the neural network, and the output is rough path of each UAV. Then, the weights and thresholds of neural network are optimized by using genetic algorithm, and the optimal paths of multi-UAVs cooperative regional reconnaissance is solved. The simulation results show that the method can not only enable UAVs to learn reconnaissance rules autonomously, but also plan the cooperative reconnaissance paths of each UAV, achieve effective coverage of the target area, and have good reconnaissance efficiency.
Smart Apps vs. Renovated Low-tech Devices with DIY Assistive Technology: A Case of a Banknote Identifier for Visually Impaired People

In this research, the author analyzes the merits of smart device apps and low-tech solutions as examples of assistive technology with identification of banknotes by the visually impaired. In particular, the focus of this research is on the potential of low-tech devices that are often neglected in exploring emerging technology. Applying these devices to DIY assistive technology using ICT sometimes opens up new possibilities. In the latter part of this paper, the author attempts to illustrate such possibilities by developing a Web-based system that enables users to freely create 3D data of banknote identification plates according to individual needs.
A Review of Changepoint Detection Models

The objective of the change-point detection is to discover the abrupt property changes lying behind the time-series data. In this paper, we firstly summarize the definition and in-depth implication of the changepoint detection. The next stage is to elaborate traditional and some alternative model-based changepoint detection algorithms. Finally, we try to go a bit further in the theory and look into future research directions.
Salient Speech Representations Based on Cloned Networks

We define salient features as features that are shared by signals that are defined as being equivalent by a system designer. The definition allows the designer to contribute qualitative information. We aim to find salient features that are useful as conditioning for generative networks. We extract salient features by jointly training a set of clones of an encoder network. Each network clone receives as input a different signal from a set of equivalent signals. The objective function encourages the network clones to map their input into a set of features that is identical across the clones. It additionally encourages feature independence and, optionally, reconstruction of a desired target signal by a decoder. As an application, we train a system that extracts a time-sequence of feature vectors of speech and uses it as a conditioning of a WaveNet generative system, facilitating both coding and enhancement.
Universal Adversarial Triggers for NLP

Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We deﬁne universal adversarial triggers : input-agnostic sequences of tokens that trigger a model to produce a speciﬁc prediction when concatenated to any input from a dataset. We propose a gradient-guided search over tokens which ﬁnds short trigger sequences (e.g., one word for classiﬁcation and four words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop from 89.94% to 0.55%, 72% of “why” questions in SQuAD to be answered “to kill american people”, and the GPT-2 language model to spew racist output even when conditioned on non-racial con-texts. Furthermore, although the triggers are optimized using white-box access to a speciﬁc model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they conﬁrm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models.
Measurements of Nonequilibrium Interatomic Forces in Photoexcited Bismuth

We determine experimentally the excited-state interatomic forces in photoexcited bismuth. The forces are obtained by a constrained least-squares fit of the excited-state dispersion obtained by femtosecond time-resolved x-ray diffuse scattering to a fifteen-nearest neighbor Born-von Karman model. We find that the observed softening of the zone-center $A_{1g}$ optical mode and transverse acoustic modes with photoexcitation are primarily due to a weakening of three nearest neighbor forces along the bonding direction. This provides a more complete picture of what drives the partial reversal of the Peierls distortion previously observed in photoexcited bismuth.
Continuous Toolpath Planning in Additive Manufacturing

We develop a framework that creates a new polygonal mesh representation of the 3D domain of a layer-by-layer 3D printing job on which we identify single, continuous tool paths covering each connected piece of the domain in every layer. We present a tool path algorithm that traverses each such continuous tool path with no crossovers. The key construction at the heart of our framework is a novel Euler transformation that we introduced recently in a separate manuscript. Our Euler transformation converts a 2-dimensional cell complex K into a new 2-complex K^ such that every vertex in the 1-skeleton G^ of K^ has degree 4. Hence G^ is Eulerian, and an Eulerian tour can be followed to print all edges in a continuous fashion without stops. We start with a mesh K of the union of polygons obtained by projecting all layers to the plane. First we compute its Euler transformation K^. In the slicing step, we clip K^ at each layer i using its polygon to obtain K^_i. We then patch K^_i by adding edges such that any odd-degree nodes created by slicing are transformed to have even degrees again. We print extra support edges in place of any segments left out to ensure there are no edges without support in the next layer above. These support edges maintain the Euler nature of K^_i. Finally, we describe a tree-based search algorithm that builds the continuous tool path by traversing"concentric"cycles in the Euler complex. Our algorithm produces a tool path that avoids material collisions and crossovers, and can be printed in a continuous fashion irrespective of complex geometry or topology of the domain (e.g., holes).
Privacy-Preserved Task Offloading in Mobile Blockchain With Deep Reinforcement Learning

Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the process of executing extensive tasks such as computation-intensive data applications and blockchain mining requires high computational and storage capability of mobile devices, which would hinder blockchain applications in mobile systems. To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their data processing tasks and mining tasks to a nearby MEC server via wireless channels. Specially, we formulate task offloading, user privacy preservation and mining profit as a joint optimization problem which is modelled as a Markov decision process, where our objective is to minimize the long-term system offloading utility and maximize the privacy levels for all blockchain users. We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states, wireless channel qualities between MUs and MEC server and user’s power hash states. To further improve the offloading performances for larger-scale blockchain scenarios, we then develop a deep RL algorithm by using deep Q-network which can efficiently solve large state space without any prior knowledge of the system dynamics. Experiment and simulation results show that the proposed RL-based offloading schemes significantly enhance user privacy, and reduce the energy consumption as well as computation latency with minimum offloading costs in comparison with the benchmark offloading schemes.
Finding Hamiltonian and Longest (s, t)-paths of C-shaped Supergrid Graphs in Linear Time

A graph is called Hamiltonian connected if it contains a Hamiltonian path between any two distinct vertices. In the past, we proved the Hamiltonian path and cycle problems for general supergrid graphs to be NP-complete. However, they are still open for solid supergrid graphs. In this paper, first we will verify the Hamiltonian cycle property of C-shaped supergrid graphs, which are a special case of solid supergrid graphs. Next, we show that C-shaped supergrid graphs are Hamiltonian connected except in a few conditions. For these excluding conditions of Hamiltonian connectivity, we compute their longest paths. Then, we design a linear-time algorithm to solve the longest path problem in these graphs. The Hamiltonian connectivity of C-shaped supergrid graphs can be applied to compute the optimal stitching trace of computer embroidery machines, and construct the minimum printing trace of 3D printers with a C-like component being printed.
Understanding Relative Network Delay inMicro-Energy Harvesting Wireless Networks

Micro-energy harvesting wireless network (MEHWN) enables a perpetual network deployment that cannot be achieved in traditional battery-operated counterparts. Despite its sustainability, end-to-end delay in an MEHWN could be very large, due to the large waiting delay on each hop in the network. In this work, we consider an MEHWN where every node constantly switches between on and off states, due to the limited amount of harvested energy. The network delay of an MEHWN is not well understood because of the energy uncertainty, asynchronized working schedules, and complex network topology in an MEHWN. To close this research gap, we define the relative network delay as the ratio between end-to-end delay and distance. Compared to previous works, we are able to identify a closed-form expression of the lower bound and a tighter upper bound of the relative network delay. The theoretical findings are verified in simulations. Our theoretical analysis deepens the understanding about the interplay of network delay, energy harvesting rate, and node density in an MEHWN.
Analysis of MiniJava programs via translation to ML

MiniJava is a subset of the object-oriented programming language Java. Standard ML is the canonical representative of the ML family of functional programming languages, which includes F# and OCaml. Different program analysis and verification tools and techniques have been developed for both Java-like and ML-like languages. Naturally, the tools developed for a particular language emphasise accurate treatment of language features commonly used in that language. In Java, this means objects with mutable properties and dynamic method dispatch. In ML, this means higher order functions and algebraic datatypes with pattern matching. We propose to translate programs from one language into the other and use the target language's tools for analysis and verification. By doing so, we hope to identify areas for improvement in the target language's tools and suggest techniques, perhaps as used in the source language's tools, that may guide their improvement. More generally, we hope to develop tools for reasoning about programs that are more resilient to changes in the style of code and representation of data. We begin our programme by outlining a translation from MiniJava to ML that uses only the core features of ML; in particular, it avoids the use of ML's mutable references.
C-RAN Virtualization with OpenAirInterface

C-RAN virtualization is a research topic with great interest since it allows to share baseband processing resources.Therefore, in this work, we report the implementation of a virtualized LTE testbed environment of C-RAN by integrating the OpenAirInterface (OAI) with Docker. Using the test bed,we conducted a workload study to understand the computation resource demand of C-RAN software. Virtualization in containers has proven to be effective in creating a functional 4G network which achieves realistic results to facilitate research.
CA-EHN: Commonsense Word Analogy from E-HowNet

Word analogy tasks have tended to be handcrafted, involving permutations of hundreds of words with dozens of relations, mostly morphological relations and named entities. Here, we propose modeling commonsense knowledge down to word-level analogical reasoning. We present CA-EHN, the first commonsense word analogy dataset containing 85K analogies covering 5K words and 6K commonsense relations. This was compiled by leveraging E-HowNet, an ontology that annotates 88K Chinese words with their structured sense definitions and English translations. Experiments show that CA-EHN stands out as a great indicator of how well word representations embed commonsense structures, which is crucial for future end-to-end models to generalize inference beyond training corpora. The dataset is publicly available at \url{this https URL}.
Discrete and Fast Fourier Transform Made Clear

Fast Fourier transform was included in the Top 10 Algorithms of 20th Century by Computing in Science & Engineering. In this paper, we provide a new simple derivation of both the discrete Fourier transform and fast Fourier transform by means of elementary linear algebra. We start the exposition by introducing the convolution product of vectors, represented by a circulant matrix, and derive the discrete Fourier transform as the change of basis matrix that diagonalizes the circulant matrix. We also generalize our approach to derive the Fourier transform on any finite abelian group, where the case of Fourier transform on the Boolean cube is especially important for many applications in theoretical computer science.
Expected path length on random manifolds

Manifold learning seeks a low dimensional representation that faithfully captures the essence of data. Current methods can successfully learn such representations, but do not provide a meaningful set of operations that are associated with the representation. Working towards operational representation learning, we endow the latent space of a large class of generative models with a random Riemannian metric, which provides us with elementary operators. As computational tools are unavailable for random Riemannian manifolds, we study deterministic approximations and derive tight error bounds on expected distances.
Question classification system for health care: a review

When we want to analyze something we can search on Internet. Varied form of questions are available online but we are mostly used medical domain has many advantages as it can help us to answer the questions related to health. Answering System for Questions has gained considerable growth in which Question Classification has a major part. The various classification modes are Rule based, Machine learning and Hybrid. This paper presents the various research work done for classifying the questions for healthcare domain.
Guideline for Academic Support of Student Career Path Using Mining Algorithm

In general, higher education is an important step in preparing a career for students in the future. Graduates should have qualifications that are recognized by both entrepreneurs and society. Therefore, every higher educational institution should make an effort to consider how to assist students' performance. This research aims to analyze the relationships between courses that are likely to produce a future career for students using the Apriori algorithm. The data used in the operation of the association rule was the student's grades from 25 main courses in the field of information technology, Department of Information Technology, Faculty of Science and Technology, Suan Sunandha Rajabhat University. This data was recorded between 2011 and 2019 and stored in the registration and graduate career system. The 14 association rules were determined from the operation by using the Weka 3.8. 3 data mining software, this indicated that there were a few courses in which students could have future careers. Most importantly, the results can contribute to guidelines for the academic support of students' future career.