Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrast Level Test-based Methodology for Speed-Up MRI Brain Tumor Detection and Localization Approach

A tumor is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure.A tumor also may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. This paper reviews the processes and techniques used in detecting tumor based on medical imaging results such as mammograms, x-ray computed tomography (x-ray CT) and magnetic resonance imaging (MRI). MRI image which is the Magnetic Resonance Imaging is also could be used for diagnosisand discover which is the type of the human brain that has been tested is normal or abnormal. Moreover, the MRI images give an observation and useful information that will help the doctors and surgeries to avoid some mistakes that will happen during the testing and the diagnosis process. Also, MRI characteristics are used for avoiding human error in manual interpretation of medical content. One of the most popular and useful application that the medical system in need is the MRI brain image classification approach. In this paper, we proposed a new clustering algorithm which relies on the differences between the contrast level of the tumor in the MRI. We depend on new approach for image clustering which is based on the difference between the construct (which is the intensity level) between the tumor region and the whole MRI image. In contrast, other algorithms like k-means, Fuzzy c-means, or probabilistic c-means depend on typical approach which is based on the distance majority between each point (pixel) and its mean. Our work consists some preprocessing steps like smoothed and enhanced the MRI by using enhancement techniques such as Gaussian kernel, median filter, high-pass filter, and Morphological image operation. Mainly, the proposed clustering algorithm in this work uses for segmentation of the image to detect the suspicious region of the tumor in brain MRI image. In this paper feature our results have been compared with the traditional clustering algorithm such as k-means and watershed.
Photoplethysmogram Based Biometric Identification for Twins Incorporating Gender Variability

This study focuses on a Photoplethysmogram (PPG) based biometric identification for twins incorporating gender variability. To the best of our knowledge, little has been said pertaining to this research which identifies twins using PPG signals. PPG device has been widely used due to its advantages such as non-invasive, low cost and small in size which makes it a convenient analytical tool. PPG signals has the capability to ensure the person to be present during the acquisition process which suggest that PPG can provide liveness detection suitable for a biometric system which is not available in other biometric modalities such as fingerprint. A total of four couple of twins which consists of four female and four male subjects in age range between twenty two to thirty years old were used to assess the feasibility of the proposed system. The acquired PPG signals were then processed to remove unwanted noise using low pass filter. After that, multiple cycles of PPG waveforms were extracted and later classified using Radial Basis Function (RBF) and Bayes Network (BN) to categorize the subjects using the discriminant features to calculate and analyze the performance of this system. The outcome also provides a complimentary mechanism to detect twins besides using the current existing methods.
Framework for generating IDS benchmarking Data sets

Benchmarking Intrusion Detection Systems, IDS, is needed for comparing different systems against each other and to determine how good a single system is. For this purpose there is a need to generate test data set, which is based on real network data. To construct data set that contains different attack profiles, special features of attacks are needed to be determined. These can be extracted from analyzing network traffic. The thesis will propose a framework for processing captured network packets and establishing connection records. We look into what features are relevant for IDS systems, by analyzing captured network packets from an academic network and trying to extract different characteristics that constitute attacks. These features can be used to uniquely identify a specific attack from all the connections. The experiment is used to determine characteristics of the constructed data set, and to determine the relevance of the extracted features.
CROSS-LANGUAGE TEXT CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS FROM SCRATCH

Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach.
Application of the Hypervalent Iodine Reagent to the Synthesis of Some Pentasubstituted Aporphine Alkaloids

The oxidative biaryl coupling of various N-substituted 1-benzyltetrahydroisoquinolines to the corresponding aporphines by the hypervalent iodine reagent was studied. The study sheds light on the unifying mechanism of the reaction illustrating the requirement of the P- P coupling via the six-membered transition state as the -initial step. The finding was applied to the synthesis of some pentasubstituted aporphine alkaloids.
Time constants of spin-dependent recombination processes

We present experiments to systematically study the time constants of spin-dependent recombination processes in semiconductors using pulsed electrically detected magnetic resonance (EDMR). The combination of time-programmed optical excitation and pulsed spin manipulation allows us to directly measure the recombination time constants of electrons via localized spin pairs and the time constant of spin pair formation as a function of the optical excitation intensity. Using electron nuclear double resonance, we show that the time constant of spin pair formation is determined by an electron capture process. Based on these time constants we devise a set of rate equations to calculate the current transient after a resonant microwave pulse and compare the results with experimental data. Finally, we critically discuss the effects of different boxcar integration time intervals typically used to analyze pulsed EDMR experiments on the determination of the time constants. The experiments are performed on phosphorus-doped silicon, where EDMR via spin pairs formed by phosphorus donors and Si/SiO2 interface dangling bond defects is detected.
The Effect of FIR Filtering and Spectral Tilt on Speech Recognition with MFCC

In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate for the speaker-independent speech recognition, we study the effect of spectral tilt on the Fourier magnitude spectrum en route to the extraction of MFCC. The effect of FIR filtering on the speech signal on the speech recognition is also investigated in parallel. Evaluation of the proposed methods are performed by two independent ways of the Fisher discriminant objective function and speech recognition test by hidden Markov model with fuzzy vector quantization. From the experiments, the recognition error rate is found to show about 10% relative improvements over the conventional method by an appropriate choice of the tilt factor.
Thermochemical Characterization of Seaborgium Compounds in Gas Adsorption Chromatography

The chemical characterization of the element seaborgium (Z = 106) requires fast experiments such as gas adsorption chromatographic separations in quartz columns with single atoms in a clearly defined chemical state. The maximum separation time for such experiments of only about 10 s is defined by the relatively short half-lives of the currently known longest-lived isotopes 265Sg and 266Sg. To establish optimum experimental parameters, the required thermochemical quantities for chlorides, oxychlorides, and oxides of seaborgium were estimated by extrapolation. On the basis of these results, the stability and volatility of these compounds could be calculated. By use of empirical correlations, the thermochemical constants of the adsorption processes were evaluated and the retention times calculated, taking into account the composition of the reactive carrier gas. The dioxydichloride of seaborgium proved to be the most suitable chemical state regarding its stability, volatility, and retention. Its standard sub.
ADAS: Adaptive Switching between Full-Duplex and MU-MIMO for Wi-Fi Networks

Full-duplex (FD) and multiuser multi-input multioutput (MU-MIMO) approaches have been attracting much attention as core technologies of next-generation Wi-Fi systems, since they have vast potential to improve communication performance. In particular, the system throughput can be significantly increased if both technologies are used in harmony, based on a solid understanding of the characteristics of each technology. To realise this, it is essential and important for a node using both technologies to acquire a priori channel and queue information about the receiving nodes. Unfortunately, it is very challenging to obtain this information in Wi-Fi networks in which there are no separate channels or proper protocols. In this paper, a new MAC protocol for Wi-Fi networks is proposed, called ADAS, which selects the transmission strategy that best matches the given network environment. By fully utilising the conventional CSI acquisition protocol, an ADAS access point (AP) effectively obtains the necessary information and exploits it appropriately, in order to yield higher throughput gains. Through extensive Matlab simulations, the author proves that ADAS maintains high system throughputs for dynamic network changes.
A Study on the Implementation of Indoor Topology Using Image Data

As the need of indoor spatial information has grown, many applications have been developed. Nevertheless, the major representations of indoor spatial information are on the 2D or 3D, recently, the service based on omni-directional image has increased. Current service based on omni-directional image is used just for viewer. To provide various applications which can serve the identifying the attribute of indoor space, query based services and so on, topological data which can define the spatial relationships between spaces is required. For developing diverse applications based on omni-directional image, this study proposes the method to generate IndoorGML data which is the international standard of indoor topological data model. The proposed method is consist of 3 step to generate IndoorGML data; 1) Analysis the core elements to adopt IndoorGML concept to image, 2) Propose the method to identify the element of ‘Space’ which is the core element of IndoorGML concept, 3) Define the connectivity of indoor spaces. The proposed method is implemented at the 6-floor of 21century-building of the University of Seoul to generate IndoorGML data and the demo service is implemented based on the generated data. This study has the significance to propose a method to generate the indoor topological data for the indoor spatial information services based on the IndoorGML.
A Free/Open-Source Morphological Transducer for Western Armenian

We present a free/open-source morphological transducer for Western Armenian, an endangered and low-resource Indo-European language. The transducer has virtually complete coverage of the language’s inflectional morphology. We built the lexicon by scraping online dictionaries. As of submission, the transducer has a lexicon of 75K words. It has over 90% naive coverage on different Western Armenian corpora, and high precision.
Are There Any Body-movement Differences between Women and Men When They Laugh?

Smiling differences between men and women have been studied in psychology. Women smile more than men although the expressiveness of women is not universally more across all facial actions. There are also body movement differences between women and men. For example, more open-body postures were reported for men, but are there any body-movement differences between men and women when they laugh? To investigate this question, we study body-movement signals extracted from recorded laughter videos using a deep learning pose estimation model. Initial results showed a higher Fourier Transform amplitude of thorax and shoulder movements for females while males had a higher Fourier transform amplitude of Elbow movement. The differences were not limited to a small frequency range but covered most of the frequency spectrum. However, further investigations are still needed.
WeCanTalk: A New Multi-language, Multi-modal Resource for Speaker Recognition

The WeCanTalk (WCT) Corpus is a new multi-language, multi-modal resource for speaker recognition. The corpus contains Cantonese, Mandarin and English telephony and video speech data from over 200 multilingual speakers located in Hong Kong. Each speaker contributed at least 10 telephone conversations of 8-10 minutes’ duration collected via a custom telephone platform based in Hong Kong. Speakers also uploaded at least 3 videos in which they were both speaking and visible, along with one selfie image. At least half of the calls and videos for each speaker were in Cantonese, while their remaining recordings featured one or more different languages. Both calls and videos were made in a variety of noise conditions. All speech and video recordings were audited by experienced multilingual annotators for quality including presence of the expected language and for speaker identity. The WeCanTalk Corpus has been used to support the NIST 2021 Speaker Recognition Evaluation and will be published in the LDC catalog.
Synthesis and Explosive Properties of Benzotriazoles

Treatment of nitro-substituted 2-aminodiphenylamines in acetic acid with either nitric acid or nitrous acid yielded nitro-substituted 1-phenylbenzotriazoles, which may be further nitrated with nitric or mixed acid. These materials have been examined as potential energetic materials, with particular reference to their densities, calculated explosive properties and sensitiveness, especially impact sensitivity.
Sentiment Analysis and Topic Modeling for Public Perceptions of Air Travel: COVID Issues and Policy Amendments

Among many industries, air travel is impacted by the COVID pandemic. Airlines and airports rely on public sector information to enforce guidelines for ensuring health and safety of travelers. Such guidelines can be policy amendments or laws during the pandemic. In response to the inception of COVID preventive policies, travelers have exercised freedom of expression via the avenue of online reviews. This avenue facilitates voicing public concern while anonymizing / concealing user identity as needed. It is important to assess opinions on policy amendments to ensure transparency and openness, while also preserving confidentiality and ethics. Hence, this study leverages data science to analyze, with identity protection, the online reviews of airlines and airports since 2017, considering impacts of COVID issues and relevant policy amendments since 2020. Supervised learning with VADER sentiment analysis is deployed to predict changes in opinion from 2017 to date. Unsupervised learning with LDA topic modeling is employed to discover air travelers’ major areas of concern before and after the pandemic. This study reveals that COVID policies have worsened public perceptions of air travel and aroused notable new concerns, affecting economics, environment and health.
Design and Evaluation of the Corpus of Everyday Japanese Conversation

We have constructed the Corpus of Everyday Japanese Conversation (CEJC) and published it in March 2022. The CEJC is designed to contain various kinds of everyday conversations in a balanced manner to capture their diversity. The CEJC features not only audio but also video data to facilitate precise understanding of the mechanism of real-life social behavior. The publication of a large-scale corpus of everyday conversations that includes video data is a new approach. The CEJC contains 200 hours of speech, 577 conversations, about 2.4 million words, and a total of 1675 conversants. In this paper, we present an overview of the corpus, including the recording method and devices, structure of the corpus, formats of video and audio files, transcription, and annotations. We then report some results of the evaluation of the CEJC in terms of conversant and conversation attributes. We show that the CEJC includes a good balance of adult conversants in terms of gender and age, as well as a variety of conversations in terms of conversation forms, places, activities, and numbers of conversants.
RG-improved Prediction for 750 GeV Resonance Production at the LHC

We present a renormalization-group (RG) improved prediction for $750$ GeV heavy state production recently seen as the diphoton excess at the LHC. Due to the universality of QCD feature in the threshold region, our result provides reasonable estimate of the K-factor for most existing leading order predictions, independent of the specific nature of the resonance. We consider specifically the spin-$2$ warped Kaluza-Klein graviton model to highlight our calculation. We performed soft gluon resummation near threshold at next-to-next-to-leading logarithmic (NNLL) accuracy together with resumming enhanced corrections from a towers of $\pi^2$ due to analytic continuation. We find the resummation effects are significant. When matching our resummed result to next-to-leading order calculation, we find our improved predictions exceed the leading fixed-order results by about $56\%$ ($K=1.56$) for the LHC energies at 13 TeV and 14 TeV, and about $63\%$ ($K=1.63$) for 8 TeV.
Systems of Systems with Security

In this report we present two case studies with Systems of Systems modelling. One model illustrates how Cryptographic parameter consistency can be checked using VDMPP for a System of Systems uses encryption to enforce Digital Right Management. The other model shows how a new formalism (CML) tailored specifically to Systems of Systems can express Multi-Party Computation protocol. The idea of using Canetti simulation proofs from Multi-Party computation as a model for refinement of models in CML is presented. Our goal is modest. We do not aim at proving security through refinement but to assists modellers/developers in maintaining security properties during refinement of a concept to designs.
Recurring sets of recurring starspot occultations on exoplanet host Qatar-2

We announce the detection of recurring sets of recurring starspot occultation events in the short-cadence K2 lightcurve of Qatar-2, a K dwarf star transited every 1.34 d by a hot Jupiter. In total we detect 34 individual starspot occultation events, caused by five different starspots, occulted in up to five consecutive transits or after a full stellar rotation. The longest recurring set of recurring starspot occultations spans over three stellar rotations, setting a lower limit for the longest starspot lifetime of 58 d. Starspot analysis provided a robust stellar rotational period measurement of $18.0\pm0.2$ d and indicates that the system is aligned, having a sky-projected obliquity of $0\pm8^{\circ}$. A pronounced rotational modulation in the lightcurve has a period of $18.2\pm1.6$ d, in agreement with the rotational period derived from the starspot occultations. We tentatively detect an ellipsoidal modulation in the phase-curve, with a semi-amplitude of 18 ppm, but cannot exclude the possibility that this is the result of red noise or imperfect removal of the rotational modulation. We detect no transit-timing and transit-duration variations with upper limits of 15 s and 1 min, respectively. We also reject any additional transiting planets with transit depths above 280 ppm in the orbital period region 0.5-30 d.
Development and Evaluation of Speech Recognition for the Welsh Language

This paper reports on ongoing work on developing and evaluating speech recognition models for the Welsh language using data from the Common Voice project and two popular open development kits – HuggingFace wav2vec2 and coqui STT. Activities for ensuring the growth and improvement of the Welsh Common Voice dataset are described. Two applications have been developed – a voice assistant and an online transcription service that allow users and organisations to use the new models in a practical and useful context, but which have also helped source additional test data for better evaluation of recognition accuracy and establishing the optimal selection and configurations of models. Test results suggest that in transcription good accuracy can be achieved for read speech, but further data and research is required for improving recognition results of freely spoken formal and informal speech. Meanwhile a limited domain language model provides excellent accuracy for a voice assistant. All code, data and models produced from this work are freely available.
Breadth-first search for social network graphs on heterogenous platforms

Breadth-First Search (BFS) is the core of many graph analysis algorithms, and it is useful in many problems including social network, computer network analysis, and data organization, but, due to its irregular behavior, its parallel implementation is very challenging. There are several approaches that implement efficient algorithms for BFS in multicore architectures and in Graphics Processors, but an efficient implementation of BFS for heterogeneous systems is even more complicated, as the task of distributing the work among the main cores and the accelerators becomes a big challenge. As part of this work, we have assessed different heterogenous shared-memory architectures (from highend processors to embedded mobile processors, both composed by a multi-core CPU and an integrated GPU) and implemented different approaches to perform BFS. This work introduces three heterogeneous approaches for BFS: Selective, Concurrent, and Async. Contributions of this work includes both the analysis of BFS performance on Heterogenous platforms, as well as in depth analysis of social network graphs and its implications on the BFS algorithm. The results show that BFS is very input dependent, and that the structure of the graph is one of the prime factors to analyze in order to develop good and scalable algorithms. The results also show that heterogenous platforms can provide acceleration to even irregular algorithms, reaching speed-ups of 2.2x in the best case. It is also shown how the different system configurations and capabilities impact the performance and how the shared-memory system can reach bandwidth limitations that prevent performance improvements despite having higher utilization of the resources.