Miracle-dz/UniFAR-FTUs huggingface dataset:
MAT: A simple yet strong baseline for identifying self-admitted technical debt

In the process of software evolution, developers often sacrifice the long-term code quality to satisfy the short-term goals due to specific reasons, which is called technical debt. In particular, self-admitted technical debt (SATD) refers to those that were intentionally introduced and remarked by code comments. Those technical debts reduce the quality of software and increase the cost of subsequent software maintenance. Therefore, it is necessary to find out and resolve these debts in time. Recently, many approaches have been proposed to identify SATD. However, those approaches either have a low accuracy or are complex to implementation in practice. In this paper, we propose a simple unsupervised baseline approach that fuzzily matches task annotation tags (MAT) to identify SATD. MAT does not need any training data to build a prediction model. Instead, MAT only examines whether any of four task tags (i.e. TODO, FIXME, XXX, and HACK) appears in the comments of a target project to identify SATD. In this sense, MAT is a natural baseline approach, which has a good understandability, in SATD identification. In order to evaluate the usefulness of MAT, we use 10 open-source projects to conduct the experiment. The experimental results reveal that MAT has a surprisingly excellent performance for SATD identification compared with the state-of-the-art approaches. As such, we suggest that, in the future SATD identification studies, MAT should be considered as an easy-to-implement baseline to which any new approach should be compared against to demonstrate its usefulness.
MaskedNet: A Pathway for Secure Inference against Power Side-Channel Attacks

Differential Power Analysis (DPA) has been an active area of research for the past two decades to study the attacks for extracting secret information from cryptographic implementations through power measurements and their defenses. Unfortunately, the research on power side-channels have so far predominantly focused on analyzing implementations of ciphers such as AES, DES, RSA, and recently post-quantum cryptography primitives (e.g., lattices). Meanwhile, machine-learning, and in particular deep-learning applications are becoming ubiquitous with several scenarios where the Machine Learning Models are Intellectual Properties requiring confidentiality. The problem of extending side-channel analysis to Machine Learning Model extraction is largely unexplored. This paper extends the DPA framework to neural-network classifiers. First, it shows DPA attacks on classifiers that can extract the secret model parameters such as weights and biases of a neural network. Second, it proposes the first countermeasures against these attacks by augmenting masking. The resulting design uses novel masked components such as masked adder trees for fully-connected layers and masked Rectifier Linear Units for activation functions. On a SAKURA-X FPGA board, experiments show both the insecurity of an unprotected design and the security of our proposed protected design.
Improved Differentially Private Decentralized Source Separation for fMRI Data

Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. In order to leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at a single site. In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting. Conventional approaches to decentralized differentially private algorithms may introduce too much noise due to the typically small sample sizes at each site. We propose a novel protocol that uses correlated noise to remedy this problem. We show that our algorithm outperforms existing approaches on synthetic and real neuroimaging datasets and demonstrate that it can sometimes reach the same level of utility as the corresponding non-private algorithm. This indicates that it is possible to have meaningful utility while preserving privacy.
Modified SPLICE, Extension to Non-Stereo Data and Run-Time Compensation for Noise Robust Speech Recognition

In this paper, a modification to the training process of the po pular SPLICE algorithm has been proposed for noise robust speech r ecognition. The modification is based on feature correlation s, and enables this stereo-based algorithm to improve the perform ance in all noise conditions, especially in unseen cases. Further, modified framework is extended to work for non-stereo datasets w here clean and noisy training utterances, but not stereo counter parts, are required. Finally, an MLLR-based computationally effic ient run-time noise adaptation method in SPLICE framework has be en proposed. The modified SPLICE shows 8.6% absolute improvement over SPLICE in Test C of Aurora-2 database, and 2.93% overall . Non-stereo method shows 10.37% and 6.93% absolute improvements over Aurora-2 and Aurora-4 baseline models respectiv ely. Run-time adaptation shows 9.89% absolute improvement in modified framework as compared to SPLICE for Test C, and 4.96% overall w.r. t. standard MLLR adaptation on HMMs.
Transformer-Transducer: End-to-End Speech Recognition with Self-Attention

We explore options to use Transformer networks in neural transducer for end-to-end speech recognition. Transformer networks use self-attention for sequence modeling and comes with advantages in parallel computation and capturing contexts. We propose 1) using VGGNet with causal convolution to incorporate positional information and reduce frame rate for efficient inference 2) using truncated self-attention to enable streaming for Transformer and reduce computational complexity. All experiments are conducted on the public LibriSpeech corpus. The proposed Transformer-Transducer outperforms neural transducer with LSTM/BLSTM networks and achieved word error rates of 6.37 % on the test-clean set and 15.30 % on the test-other set, while remaining streamable, compact with 45.7M parameters for the entire system, and computationally efficient with complexity of O(T), where T is input sequence length.
Running Head: HASHTAG ANALYSIS OF #SINGAPORE What’s in a Country Name?-Twitter Hashtag Analysis of #singapore

Twitter as a micro-blogging platform rose to instant fame mainly due to its minimalist features that allow seamless communication between users. As the conversations grew thick and faster, a placeholder feature called as Hashtags became important as it captured the themes behind the tweets. Prior studies have investigated the conversation dynamics, interplay with other media platforms and communication patterns between users for specific event-based hashtags such as the #Occupy movement. Commonplace hashtags which are used on a daily basis have been largely ignored due to their seemingly innocuous presence in tweets and also due to the lack of connection with real-world events. However, it can be postulated that utility of these hashtags is the main reason behind their continued usage. This study is aimed at understanding the rationale behind the usage of a particular type of commonplace hashtags:-location hashtags such as country and city name hashtags. Tweets with the hashtag #singapore were extracted for a week’s duration. Manual and automatic tweet classification was performed along with social network analysis, to identify the underlying themes. Seven themes were identified. Findings indicate that the hashtag is prominent in tweets about local events, local news, users’ current location and landmark related information sharing. Users who share content from social media sites such as Instagram make use of the hashtag in a more prominent way when compared to users who post textual content. News agencies, commercial bodies and celebrities make use of the hashtag more than common individuals. Overall, the results show the non-conversational nature of the hashtag. The findings are to be validated with other country names and crossvalidated with hashtag data from other social media platforms.
Spoofing Speaker Verification Systems with Deep Multi-speaker Text-to-speech Synthesis

This paper proposes a deep multi-speaker text-to-speech (TTS) model for spoofing speaker verification (SV) systems. The proposed model employs one network to synthesize time-downsampled mel-spectrograms from text input and another network to convert them to linear-frequency spectrograms, which are further converted to the time domain using the Griffin-Lim algorithm. Both networks are trained separately under the generative adversarial networks (GAN) framework. Spoofing experiments on two state-of-the-art SV systems (i-vectors and Google's GE2E) show that the proposed system can successfully spoof these systems with a high success rate. Spoofing experiments on anti-spoofing systems (i.e., binary classifiers for discriminating real and synthetic speech) also show a high spoof success rate when such anti-spoofing systems' structures are exposed to the proposed TTS system.
Observations of the CO dayglow at 4 . 7 l m on Mars : Variations of temperature and CO mixing

0019-1035/$ see front matter 2013 Elsevier Inc. All rights reserved. http://dx.doi. org/10.1016/j.icarus. 2013.10. 008 ⇑ Address: 6100 Westchester Park Drive #911, College Park, MD 20740, USA. Fax: +1 202 319 4448. E-mail address: vlad.krasn@verizon.net 1 Visiting Astronomer at the Infrared Telescope Facility, which is operated by the University of Hawaii under Cooperative Agreement No. NCC 5-538 with the National Aeronautic and Space Administration, Science Mission Directorate, Planetary Astronomy Program. Vladimir A. Krasnopolsky ⇑,1
Towards Deep Physical Reservoir Computing Through Automatic Task Decomposition And Mapping

Photonic reservoir computing is a promising candidate for low-energy computing at high bandwidths. Despite recent successes, there are bounds to what one can achieve simply by making photonic reservoirs larger. Therefore, a switch from single-reservoir computing to multi-reservoir and even deep physical reservoir computing is desirable. Given that backpropagation can not be used directly to train multi-reservoir systems in our targeted setting, we propose an alternative approach that still uses its power to derive intermediate targets. In this work we report our findings on a conducted experiment to evaluate the general feasibility of our approach by training a network of 3 Echo State Networks to perform the well-known NARMA-10 task using targets derived through backpropagation. Our results indicate that our proposed method is well-suited to train multi-reservoir systems in a efficient way.
Milky Way analogues in MaNGA: multiparameter homogeneity and comparison to the Milky Way

The Milky Way provides an ideal laboratory to test our understanding of galaxy evolution, owing to our ability to observe our Galaxy over fine scales. However, connecting the Galaxy to the wider galaxy population remains difficult, due to the challenges posed by our internal perspective and to the different observational techniques employed. Here, we present a sample of galaxies identified as Milky Way analogues on the basis of their stellar masses and bulge-to-total ratios, observed as part of the Mapping Nearby Galaxies at Apache Point Observatory survey. We analyse the galaxies in terms of their stellar kinematics and populations as well as their ionized gas contents. We find our sample to contain generally young stellar populations in their outskirts. However, we find a wide range of stellar ages in their central regions, and we detect central active galactic nucleus-like or composite-like activity in roughly half of the sample galaxies, with the other half consisting of galaxies with central star-forming emission or emission consistent with old stars. We measure gradients in gas metallicity and stellar metallicity that are generally flatter in physical units than those measured for the Milky Way; however, we find far better agreement with the Milky Way when scaling gradients by galaxies’ disc scale lengths. From this, we argue much of the discrepancy in metallicity gradients to be due to the relative compactness of the Milky Way, with differences in observing perspective also likely to be a factor.
LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have become indispensable for solving machine learning tasks in speech recognition, computer vision, and other areas that involve high-dimensional data. A CNN filters the input feature using a network containing spatial convolution operators with compactly supported stencils. In practice, the input data and the hidden features consist of a large number of channels, which in most CNNs are fully coupled by the convolution operators. This coupling leads to immense computational cost in the training and prediction phase. In this article, we introduce LeanConvNets that are derived by sparsifying fully-coupled operators in existing CNNs. Our goal is to improve the efficiency of CNNs by reducing the number of weights, floating point operations and latency times, with minimal loss of accuracy. Our lean convolution operators involve tuning parameters that controls the trade-off between the network’s accuracy and computational costs. These convolutions can be used in a wide range of existing networks, and we exemplify their use in residual networks (ResNets). Using a range of benchmark problems from image classification and semantic segmentation, we demonstrate that the resulting LeanConvNet’s accuracy is close to state-of-the-art networks while being computationally less expensive. In our tests, the lean versions of ResNet in most cases outperform comparable reduced architectures such as MobileNets and ShuffleNets.
Security of the Internet of Things: Vulnerabilities, Attacks, and Countermeasures

Wireless Sensor Networks (WSNs) constitute one of the most promising third-millennium technologies and have wide range of applications in our surrounding environment. The reason behind the vast adoption of WSNs in various applications is that they have tremendously appealing features, e.g., low production cost, low installation cost, unattended network operation, autonomous and longtime operation. WSNs have started to merge with the Internet of Things (IoT) through the introduction of Internet access capability in sensor nodes and sensing ability in Internet-connected devices. Thereby, the IoT is providing access to huge amount of data, collected by the WSNs, over the Internet. Hence, the security of IoT should start with foremost securing WSNs ahead of the other components. However, owing to the absence of a physical line-of-defense, i.e., there is no dedicated infrastructure such as gateways to watch and observe the flowing information in the network, security of WSNs along with IoT is of a big concern to the scientific community. More specifically, for the application areas in which CIA (confidentiality, integrity, availability) has prime importance, WSNs and emerging IoT technology might constitute an open avenue for the attackers. Besides, recent integration and collaboration of WSNs with IoT will open new challenges and problems in terms of security. Hence, this would be a nightmare for the individuals using these systems as well as the security administrators who are managing those networks. Therefore, a detailed review of security attacks towards WSNs and IoT, along with the techniques for prevention, detection, and mitigation of those attacks are provided in this paper. In this text, attacks are categorized and treated into mainly two parts, most or all types of attacks towards WSNs and IoT are investigated under that umbrella: “Passive Attacks” and “Active Attacks”. Understanding these attacks and their associated defense mechanisms will help paving a secure path towards the proliferation and public acceptance of IoT technology.
Bath Salt Use: A Case Report and Review of the Literature

The new recreational designer drugs known as “bath salts” are synthetic cathinones (e.g., mephedrone, 3,4-methylenedioxypyrovalerone, methylone) that are being abused as stimulants. Bath salts have similar effects as amphetamine, cocaine, and MDMA (3,4-methylenedioxymethamphetamine, also known as ecstasy). Cathinone is a naturally occurring phenylalkylamine alkaloid present in the khat plant that has been used for centuries, but in Western countries, they are primary used by “young clubbers. ” In this review, the pharmacology and neurotoxicity of bath salts will be discussed and the prevalence and pattern of bath salt use will be presented. The U.S. Poison Control Center received an increasing number of calls regarding bath salt intoxication in 2011, which led to an emergency temporary ban in September 2011. Despite the ban, the use of bath salts has continued. Multiple reports have described the clinical features of bath salt intoxication and withdrawal symptoms, as well as the potential for abuse and the development of dependence. There are reports of bath salts causing hallucinations, delirium, and psychosis. For patients presenting with bath salt intoxication, management includes using medications for behavioral control and other symptomatic support. In this review, we will report the use of bath salts in a patient with a history of schizophrenia and comorbid methamphetamine dependence. His clinical course will be discussed. In order to prevent the further abuse of bath salts, we need to encourage the continuing ban on the sale of bath salts and also educate both clinicians and patients about the risks of using such drugs.
A rare case of FR I interaction with a hot X-ray bridge in the A2384 galaxy cluster

Clusters of varying mass ratios can merge and the process significantly disturbs the cluster environments and alters their global properties. Active radio galaxies are another phenomenon that can also affect cluster environments. Radio jets can interact with the intra-cluster medium (ICM) and locally affect its properties. Abell 2384 (hereafter A2384) is a unique system that has a dense, hot X-ray filament or bridge connecting the two unequal mass clusters A2384(N) and A2384(S). The analysis of its morphology suggests that A2384 is a post-merger system where A2384(S) has already interacted with the A2384(N), and as a result hot gas has been stripped over a ∼1 Mpc region between the two bodies. We have obtained its 325 MHz GMRT data, and we detected a peculiar FR I type radio galaxy which is a part of the A2384(S). One of its radio lobes interacts with the hot X-ray bridge and pushes the hot gas in the opposite direction. This results in displacement in the bridge close to A2384(S). Based on Chandra and XMM-Newton X-ray observations, we notice a temperature and entropy enhancement at the radio lobe-X-ray plasma interaction site, which further suggests that the radio lobe is changing thermal plasma properties. We have also studied the radio properties of the FR I radio galaxy, and found that the size and radio luminosity of the interacting north lobe of the FR I galaxy are lower than those of the accompanying south lobe.
THE INFLUENCE OF CHRONIC STRETCH ON THE GASTROCNEMIUS MUSCLES OF ABLE BODIED ADULTS by EMMA LEIGH WALKER

To improve strength, numerous methods have been suggested as alternatives to resistance training. One method of interest is passive chronic stretching. The hypothesis was that chronic stretching will result in increase in muscle mass as compared to strength training and that chronic stretch will increase ankle dorsiflexion angle. Two groups either stretch or strength trained for eight weeks. MRI analysis was used to evaluate gastrocnemius muscle mass before and after training. There were no differences in muscle mass pre and post training in the two groups. Chronic stretch did increase ankle passive dorsiflexion ROM. It can be concluded that chronic stretch did not provide enough stimuli to induce muscle hypertrophy. INDEX WORDS: chronic stretch, gastrocnemius
Statistically significant fits of Hawkes processes to financial data

Most fits of Hawkes processes in financial literature are not statistically significant. Focusing on FX data (EBS limit order book) with 0.1s time resolution, we find that significance is impossible if the event log is incomplete (e.g. best quote changes). Transactions on the other hand can be tracked by analysing the respective volumes of transactions on both sides in each time slice. Assuming a constant exogenous activity rate and using parametric kernels, fits of trade activity satisfy Kolmogorov-Smirnov tests for about an hour, provided that the kernel consists of at least two exponentials; the endogeneity factor does not depend on the time of the day and is about 0.7. Significant fits of a full day can be achieved if one accounts for intra-day variability of exogenous activity, which yields a larger endogeneity factor (0.8). We could not obtain significant fits beyond a single day. Variable seasonalities are major obstacles to fitting FX activity with Hawkes processes accurately and result in larger apparent endogeneity factors and lower statistical significance.
Microsoft Word-eigenmodel_paper_ArXiV_FINAL

A novel stochastic technique is presented to directly model singular vectors and singular values of a multiple input multiple output (MIMO) channel. Thus the components modeled directly in the eigen domain can be adapted to exhibit realistic physical domain behavior when assembled. The model exploits natural paths of eigenmodes, such that a simple Doppler filter generator process can be used. Furthermore, it is possible to directly manipulate the singular vector dynamics in a way that an unrealistic “stress channel” can be modeled in the eigen domain. This is particularly useful for testing the eigenmode channel tracking ability internal to a communication device such as a modem, where impairments in tracking will cause interference between eigenmodes. The model can also facilitate mode tracking testing, as it directly produces tracked (untangled) eigenmodes, providing the narrowest possible singular vector Doppler spectra and consequently lowest required update rates of each eigenmode. This singular vector based model targets testing of the eigen domain functionality of MIMO modems/devices (i.e. an apparatus focus) without the need for including the decomposition stages.