Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study

Background Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data that can be mined to predict mental health states using machine learning methods. Objective This study aimed to benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We tested on datasets that contain posts labeled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assessed the ability of the methods to prioritize posts that a moderator would identify for immediate response. Methods We used 1588 labeled posts from the Computational Linguistics and Clinical Psychology (CLPsych) 2017 shared task collected from the Reachout.com forum. Posts were represented using lexicon-based tools, including Valence Aware Dictionary and sEntiment Reasoner, Empath, and Linguistic Inquiry and Word Count, and also using pretrained artificial neural network models, including DeepMoji, Universal Sentence Encoder, and Generative Pretrained Transformer-1 (GPT-1). We used Tree-based Optimization Tool and Auto-Sklearn as AutoML tools to generate classifiers to triage the posts. Results The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macroaveraged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from metadata or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. In addition, we have presented visualizations that aid in the understanding of the learned classifiers. Conclusions In this study, we found that transfer learning is an effective strategy for predicting risk with relatively little labeled data and noted that fine-tuning of pretrained language models provides further gains when large amounts of unlabeled text are available.
Executability of Python Snippets in Stack Overflow

Online resources today contain an abundant amount of code snippets for documentation, collaboration, learning, and problem-solving purposes. Their executability in a "plug and play" manner enables us to confirm their quality and use them directly in projects. But, in practice that is often not the case due to several requirements violations or incompleteness. However, it is a difficult task to investigate the executability on a large scale due to different possible errors during the execution. We have developed a scalable framework to investigate this for SOTorrent Python snippets. We found that with minor adjustments, 27.92% of snippets are executable. The executability has not changed significantly over time. The code snippets referenced in GitHub are more likely to be directly executable. But executability does not affect the chances of the answer to be selected as the accepted answer significantly. These properties help us understand and improve the interaction of users with online resources that include code snippets.
Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition

Evolutionary stochastic gradient descent (ESGD) was proposed as a population-based approach that combines the merits of gradient-aware and gradient-free optimization algorithms for superior overall optimization performance. In this paper we investigate a variant of ESGD for optimization of acoustic models for automatic speech recognition (ASR). In this variant, we assume the existence of a well-trained acoustic model and use it as an anchor in the parent population whose good "gene" will propagate in the evolution to the offsprings. We propose an ESGD algorithm leveraging the anchor models such that it guarantees the best fitness of the population will never degrade from the anchor model. Experiments on 50-hour Broadcast News (BN50) and 300-hour Switchboard (SWB300) show that the ESGD with anchors can further improve the loss and ASR performance over the existing well-trained acoustic models.
A Behavior-based Approach Towards Statistics-Preserving Network Trace Anonymization

In modern network measurement research, there exists a clear and demonstrable need for open sharing of large-scale network traffic datasets between organizations. Beyond network measurement, many security-related fields, such as those focused on detecting new exploits or worm outbreaks, stand to benefit given the ability to easily correlate information between several different sources. Currently, the primary factor limiting such sharing is the risk of disclosing private information. While prior anonymization work has focused on traffic content, analysis based on statistical behavior patterns within network traffic has, so far, been under-explored. This thesis proposes a new behavior-based approach towards network trace source-anonymization, motivated by the concept of anonymity-by-crowds, and conditioned on the statistical similarity in host behavior. Novel time-series models for network traffic and kernel metrics for similarity are derived, and the problem is framed such that anonymity and statistics-preservation are congruent objectives in an unsupervised-learning problem. Source-anonymity is connected directly to the group size and homogeneity under this approach, and metrics for these properties are derived. Optimal segmentation of the population into anonymized groups is approximated with a graph-partitioning problem where maximization of this anonymity metric is an intrinsic property of the solution. Algorithms that guarantee a minimum anonymity-set size are presented, as well as novel techniques for behavior visualization and compression. Empirical evaluations on a range of network traffic datasets show significant advantages in both accuracy and runtime over similar solutions.
GPU Pro 2

This book focuses on advanced rendering techniques that run on the DirectX and/or OpenGL run-time with any shader language available. It includes articles on the latest and greatest techniques in real-time rendering, including MLAA, adaptive volumetric shadow maps, light propagation volumes, wrinkle animations, and much more. The book emphasizes techniques for handheld programming to reflect the increased importance of graphics on mobile devices. It covers geometry manipulation, effects in image space, shadows, 3D engine design, GPGPU, and graphics-related tools.
Chinese Word Sense Disambiguation based on Context Expansion

Word Sense Disambiguation (WSD) is one of the key issues in natural language processing. Currently, supervised WSD methods are effective ways to solve the ambiguity problem. However, due to lacking of large-scale training data, they cannot achieve satisfactory results. In this paper, we suppose synonyms for context words that can provide more knowledge for WSD task, and present two different WSD methods based on context expansion. The first method regards Synonyms as topic contextual feature to train Bayesian model. The second method treats context words made up of synonyms as pseudo training data, and then derives the meaning of ambiguous words using the knowledge from both training and pseudo training data. Experimental results show that the second method can significantly improve traditional WSD accuracy by 2.21%. Furthermore, it also outperforms the best system in SemEval-2007.
Augmenting Parametric Synthesis with Learned Timbral Controllers

Feature-based synthesis applies machine learning and signal processing methods to the development of alternative interfaces for controlling parametric synthesis algorithms. One approach, geared toward real-time control, uses low dimensional gestural controllers and learned mappings from control spaces to parameter spaces, making use of an intermediate latent timbre distribution, such that the control space affords a spatially-intuitive arrangement of sonic possibilities. Whereas many existing systems present alternatives to the traditional parametric interfaces, the proposed system explores ways in which feature-based synthesis can augment one-to-one parameter control, made possible by fully invertible mappings between control and parameter spaces.
Statistical optimizations of muscle action potentials based on modeling and analysis of ion channel dynamics

STATISTICAL OPTIMIZATIONS OF MUSCLE ACTION POTENTIALS BASED ON MODELING AND ANALYSIS OF ION CHANNEL DYNAMICS GyuTae Kim Old Dominion University, 2011 Director: Dr. Frederic D. McKenzie An Electromyogram (EMG) is an electrical signal, which is measured from a skeletal muscle during voluntary and involuntary contractions. EMGs are useful in interpreting pathological states of the musculoskeletal system. In particular, EMGs offer valuable information concerning the timing of muscular activity and its relative intensity. Various EMG models have developed with many different purposes from a pure mathematical model to a pattern structure model [17,46]. Sophisticated EMG models are necessary to examine the effects of small changes in muscular morphology and activities [46]. Due to the crucial importance of EMG models, all factors in the model should be precise and accurate. Especially, an intracellular action potential (IAP) model, the starting point of an EMG model, should be precisely generated because of its importance as the main component for an EMG model. Generally, the Rosenfalck IAP model [75,89] has been used because of its computational simplicity [59,72,77]. However, the Rosenfalck IAP model oversimplifies a real IAP, which has been experimentally measured, and it results in mismatching amplitudes and time duration between a real and modeled IAP. This research proposes a mathematical IAP model using a series of modified gamma and erlang probability density functions. The optimization of the proposed IAP model was conducted by several different numerical methods, namely Gauss-Newton, Steepest Descent, and Conjugate Gradient methods. These optimizing methods for the proposed muscle IAP model were validated by applying them to the experimental results of the Hudgkin and Huxley neuron action potential [11]. Due to the similarity in the mechanism of both nerve and muscle IAP generations, the validation shows that the methods and results are reasonably applied and obtained in the proposed muscle model, which for the first time incorporates properties that explain ion channel behavior in IAP generation. This thesis is dedicated to my parents, JungMan Kim and YoungHye Bae, who are my mentors for life. Without their encouragements and sacrifices, I couldn't have finished one of my most important academic careers. I also thank my two sisters and their families for their endless support during my long school years. Finally, I want to thank my wife, SunHee Lee, who always trusts and supports me without any doubts.
The role of radiologic imaging in the diagnosis of adrenal tumors : a literature review

Background and aim. The adrenal tumors are a reasonably common pathology. Radiologic imaging detects an adrenal neoplasm in approximately 4% of the patients incidentally. It is also a valuable tool that assists in determining a diagnosis. Furthermore, it may facilitate the further investigation and management of the lesion. This article aims to assess imaging features that are characteristic of the most common adrenal tumors. Materials and methods. A selective search was carried out for relevant studies concerning radiologic imaging of adrenal adenoma, myelolipoma, pheochromocytoma, carcinoma, metastases, neural crest tumors, lymphoma, hemangioma, lymphangioma, and schwannoma. Medline (PubMed), Cochrane Library, SpringerLink and ScienceDirect databases were used. Results. US is helpful for the screening of masses in the suprarenal region. Nevertheless, its findings have to be verified by CT or MRI. The golden standard of assessing adrenal tumors is native CT. Those lesions which present with probable malignant features should be evaluated using dynamic contrast-enhanced CT. Chemical shift MRI is a feasible alternative, especially in lipomatous masses. PET/CT is recommended for patients with large or indeterminate lesions. Conclusion. Adrenal CT protocol is the most reliable technique in the differentiation of these diverse neoplasms. On the other hand, CS-MRI is indispensable for verifying intratumoral adipose tissue and, consequently, selecting the most appropriate approach to manage the mass. Furthermore, PET/CT permits characterizing indistinctive tumors and their potential metastatic spread.
Harnessing GPU Computing in System-Level Software

As the base of the software stack, system-level software is expected to provide efficient and scalable storage, communication, security and resource management functionalities. However, there are many computationally expensive functionalities at the system level, such as encryption, packet inspection, and error correction. All of these require substantial computing power. What’s more, today’s application workloads have entered gigabyte and terabyte scales, which demand even more computing power. To solve the rapidly increased computing power demand at the system level, this thesis proposes using parallel graphics processing units (GPUs) in system software. GPUs excel at parallel computing, and also have a much faster development trend in parallel performance than central processing units (CPUs). However, system-level software has been originally designed to be latency-oriented. GPUs are designed for long-running computation and large-scale data processing, which are throughput-oriented. Such mismatch makes it difficult to fit the system-level software with the GPUs. This thesis presents generic principles of system-level GPU computing developed during the process of creating our two general frameworks for integrating GPU computing in storage and network packet processing. The principles are generic design techniques and abstractions to deal with common system-level GPU computing challenges. Those principles have been evaluated in concrete cases including storage and network packet processing applications that have been augmented with GPU computing. The significant performance improvement found in the evaluation shows the effectiveness and efficiency of the proposed techniques and abstractions. This thesis also presents a literature survey of the relatively young system-level GPU computing area, to introduce the state of the art in both applications and techniques, and also their future potentials.
Performance Comparison Of AODV, DSR And Cluster Based Routings In Mobile Ad Hoc Networks

MANETs based on ‘‘flat’’ routing schemes cannot perform well when the network size increases, especially in face of node mobility as well, due to link and processing overhead. With cluster structure, the processing and spreading of routing information are restricted to partial mobile nodes in the network. Hence, cluster-based routing should be able to provide better scalability solutions for MANETs compared to ‘‘flat’’ routing schemes. Cluster based Routing Protocol (CBRP), maintains 1-hop and 2-hop neighbour information tables which are updated after every hello message interval. Besides explicit ‘‘Hello’’ messages, we propose utilization of data packets and routing packets for updating information tables at each mobile node so that the information tables can be updated more frequently when a mobile node receives packets from others. This addition is named as Modified-CBRP. Simulation results show that Modified-CBRP can achieve satisfying routing performance and outperforms CBRP and other flat routing schemes (DSR and AODV).
Integer Module LWE key exchange and encryption: The three bears (

We propose a new post-quantum key exchange algorithm based on the integer module learning with errors (I-MLWE) problem. Our ThreeBears algorithm is simple and performant, but our main goal is to suggest MLWE over a generalized Mersenne ﬁeld instead of a polynomial ring. We also show how to secure the system against chosen ciphertext attacks so that it can be used for public-key encryption
Notice of Retraction Feature analysis of heart sound based on the improved Hilbert-Huang Transform

In order to analyze the feature of heart sound accurately and effectively, this paper presents a feature analysis approach of heart sound based on the improved Hilbert-Huang Transform after a large number of analysis of heart sounds in time frequency domain. The validity of the proposed method has been verified through Empirical Mode Decomposition (EMD) for a typical vibratory. Calculating and obtaining the characteristic parameter of heart sound by Hilbert spectrum analysis of several cases of normal and abnormal heart sounds. Experimental results show that the presented algorithm is able to identify different heart sounds in time frequency domain, and it also establishes the basis for the classification and recognition of heart sound.
Process simulation support in BPM tools: The case of BPMN

Due to the increasing acceptance of BPM, nowadays BPM tools are extensively used in organizations. Core to BPM are the process modeling languages, of which BPMN is the one that has been receiving most attention these days. Once a business process is described using BPMN, one can use a process simulation approach in order to find its optimized form. In this context, the simulation of business processes, such as those defined in BPMN, appears as an obvious way of improving processes. This paper analyzes the business process modeling and simulation areas, identifying the elements that must be present in the BPMN language in order to allow processes described in BPMN to be simulated. During this analysis a set of existing BPM tools, which support BPMN, are compared regarding their limitations in terms of simulation support.
4 Development of Miniature 125 I-Seeds for the Treatment of Prostate Cancer

Cancer of prostate is one of the very common diseases of ageing men with incidence rises up 40% over the age of 75-80 years. It has also been noticed recently, that incidences of prostate cancers are on increase specially in many developing countries mainly because of long survival time due to better health care facilities available now-a-days and also due to changes in lifestyles of population of these countries. As compared with white men, black men have a 40% higher risk of the disease and twice the rate of death. The mortality due to prostate cancer has steadily declined for a decade, and it decreased by 4% per year between 1999 and 2003. This decrease may be attributable to several factors, including earlier detection of cancer and improved local and possibly systemic treatment [1]. Thanks to the recent advancements in diagnostic tools, more and more patients are being diagnosed with potentially curable localized prostate cancer at a time where radical local treatment is deemed to be appropriate. For disease that is likely confined to the prostate and the immediate surrounding area, surgery, external beam radiation (EBRT) and seed implantation are the primary treatment options. In recent years, seed implantation has become more popular as a treatment option as it is simpler, less traumatic and the duration of relief is comparable. It has been estimated that up to 50% of patients with early stage prostate cancer are now receiving ultra sound guided seed implantation [2].
Hybrid Texture Feature Based Roadside Vegetation Classification

In the proposed technique a novel texture feature based multiple classifier technique is applied to roadside vegetation classification. It is well-known that automation of roadside vegetation classification is one of the important issues emerging strongly in improving the fire risk and road safety. The method proposes a novel texture feature based expert system for vegetation identification as dense and sparse grasses. It consist of five steps, namely image pre-processing, feature extraction, training with multiple classifiers, classification and statistical analysis. Initially, to enhance the input image, histogram equalization is applied. Then, Gray level Run length Matrix (GLRLM)and Compound Local Binary Pattern (CLBP) technique is applied in-order to obtain the texture feature relevant to vegetation in the roadside images.In the training and classification stages, three classifiers have been fused to combine the multiple decisions. The first classifier is support vector machine, the second classifier is artificial neural network and the third classifier is k-Nearest Neighbour (k-NN). The combination of multiple classifiers and fusion of classifiers have received much more attention. The strength of the proposed method is based on new descriptor and the incorporation of the multiple classifiers with majority voting. This method is more efficient and achieves high performance.
On the Use of UAVs for Vessel Inspection Assistance ∗

Vessel maintenance entails periodic visual inspections of internal and external parts of the vessel hull in order to detect the typical defective situations affecting metallic structures, such as cracks, coating breakdown, corrosion, etc. The main goal of the EU-FP7 project MINOAS is the automation of the inspection process, currently undertaken by human surveyors, by means of a fleet of robotic agents. This paper overviews a UAV to be used as part of this fleet, and succinctly describes its control architecture as well as a self-localization solution for this vehicle. Promising experimental results are discussed in the experimental results section. keywords: Vessels maintenance, UAV, visual inspection, visual odometry
A nitrogen-doped electrocatalyst from metal – organic framework-carbon nanotube composite

Replacing precious and nondurable platinum-based catalysts by economical and commercially available materials is a key issue addressed in contemporary fuel cell technology. Carbon-based nanomaterials display great potential to improve fuel tolerance and reduce the cost and stress on metal scalability. However, their relatively low catalytic activity limits the development and application of these catalysts. In this study, we have synthesized a nitrogen-doped carbon electrocatalyst from metal–organic frameworks and carbon nanotube composites, taking advantage of the existing N in the organic linker in the MOFs with more N added through ammonia treatment. The morphology and composition of synthesized catalysts were characterized by SEM, TEM, XPS, and Raman. The derived catalyst exhibited superior catalytic activity than that of commercial Pt-based catalysts. The N enriched carbon catalyst with high surface area, a graphitic carbon skeleton, and a hierarchical porous structure facilitated the mass and charge transfer during electrolysis.