Reviewerly/exHarmony huggingface dataset:
Road Traffic Event Detection Using Twitter Data, Machine Learning, and Apache Spark

Road transportation is the backbone of modern societies, yet it costs annually over a million deaths and trillions of dollars to the global economy. Social media such as Twitter have increasingly become an important source of information in many dimensions of smart societies. Automatic detection of road traffic events using Twitter data mining is one such area of a great many applications and enormous potential, albeit facing major challenges concerning the management and analysis of big data (volume, velocity, variety, and veracity). Various approaches on the subject have been proposed in recent years, but the methods and outcomes are in their infancy. This paper proposes a method for automatic detection of road traffic related events from tweets in the Saudi dialect using machine learning and big data technologies. Firstly, we build and train a classifier using three machine learning algorithms, Naïve Bayes, Support Vector Machine, and logistic regression, to filter tweets into relevant and irrelevant. Subsequently, we train other classifiers to detect multiple types of events including accident, roadwork, road closure, road damage, traffic condition, fire, weather, and social events. The results from the analysis of one million tweets show that our method is able to detect road traffic events, as well as their location and time, automatically, without any prior knowledge of the events. To the best of our knowledge, this is the first work on traffic event detection from Arabic tweets using machine learning and the Apache Spark big data platform.
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning

In this paper, we propose a communication-efficient decen-tralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). Every worker in Q-GADMM communicates only with two neighbors, and updates its model via the group alternating direct method of multiplier (GADMM), thereby ensuring fast convergence while reducing the number of communication rounds. Furthermore, each worker quantizes its model updates before transmissions, thereby decreasing the communication payload sizes. We prove that Q-GADMM converges to the optimal solution for convex loss functions, and numerically show that Q-GADMM yields 7x less communication cost while achieving almost the same accuracy and convergence speed compared to GADMM without quantization.
MarkUs: Drop-in use-after-free prevention for low-level languages

Use-after-free vulnerabilities have plagued software written in low-level languages, such as C and C++, becoming one of the most frequent classes of exploited software bugs.Attackers identify code paths where data is manually freed by the programmer, but later incorrectly reused, and take advantage by reallocating the data to themselves.They then alter the data behind the program's back, using the erroneous reuse to gain control of the application and, potentially, the system.While a variety of techniques have been developed to deal with these vulnerabilities, they often have unacceptably high performance or memory overheads, especially in the worst case.We have designed MarkUs, a memory allocator that prevents this form of attack at low overhead, sufficient for deployment in real software, even under allocation-and memory-intensive scenarios.We prevent use-after-free attacks by quarantining data freed by the programmer and forbidding its reallocation until we are sure that there are no dangling pointers targeting it.To identify these we traverse live-objects accessible from registers and memory, marking those we encounter, to check whether quarantined data is accessible from any currently allocated location.Unlike garbage collection, which is unsafe in C and C++, MarkUs ensures safety by only freeing data that is both quarantined by the programmer and has no identifiable dangling pointers.The information provided by the programmer's allocations and frees further allows us to optimise the process by freeing physical addresses early for large objects, specialising analysis for small objects, and only performing marking when sufficient data is in quarantine.Using MarkUs, we reduce the overheads of temporal safety in low-level languages to 1.1× on average for SPEC CPU2006, with a maximum slowdown of only 2×, vastly improving upon the state-of-the-art.
Supervised Online Diarization with Sample Mean Loss for Multi-Domain Data

Recently, a fully supervised speaker diarization approach was proposed (UIS-RNN) which models speakers using multiple instances of a parameter-sharing recurrent neural network. In this paper we propose qualitative modifications to the model that significantly improve the learning efficiency and the overall diarization performance. In particular, we introduce a novel loss function, we called Sample Mean Loss and we present a better modelling of the speaker turn behaviour, by devising an analytical expression to compute the probability of a new speaker joining the conversation. In addition, we demonstrate that our model can be trained on fixed-length speech segments, removing the need for speaker change information in inference. Using x-vectors as input features, we evaluate our proposed approach on the multi-domain dataset employed in the DIHARD-II challenge: our online method improves with respect to the original UIS-RNN and achieves similar performance to an offline agglomerative clustering baseline using PLDA scoring.
Leveraging EM Side-Channel Information to Detect Rowhammer Attacks

The rowhammer bug belongs to software-induced hardware faults, and has been exploited to form a wide range of powerful rowhammer attacks. Yet, how to effectively detect such attacks remains a challenging problem. In this paper, we propose a novel approach named RADAR (Rowhammer Attack Detection via A Radio) that leverages certain electromagnetic (EM) signals to detect rowhammer attacks. In particular, we have found that there are recognizable hammering-correlated sideband patterns in the spectrum of the DRAM clock signal. As such patterns are inevitable physical side effects of hammering the DRAM, they can "expose" any potential rowhammer attacks including the extremely elusive ones hidden inside encrypted and isolated environments like Intel SGX enclaves. However, the patterns of interest may become unapparent due to the common use of spread-spectrum clocking (SSC) in computer systems. We propose a de-spreading method that can reassemble the hammering-correlated sideband patterns scattered by SSC. Using a common classification technique, we can achieve both effective and robust detection-based defense against rowhammer attacks, as evaluated on a RADAR prototype under various scenarios. In addition, our RADAR does not impose any performance overhead on the protected system. There has been little prior work that uses physical side-channel information to perform rowhammer defenses, and to the best of our knowledge, this is the first investigation on leveraging EM side-channel information for this purpose.
IT Project Failures, Causes and Cures

We have known for decades that IT projects often fail.The usual explanation is that the cause is poor project management, poor cost estimation, poor requirements, etc.But how can we prevent these causes?To answer this question, it is not sufficient to know that there was poor project management, etc. Would it suffice to educate better project managers?And what would we have to teach them that they don't learn today?We have to know what actually took place in the projects, identify the root causes, and find ways to prevent them (cures).This is similar to accident investigations in aviation.This paper reports the results of five accident investigations of large government IT projects in Denmark.We identified 37 root causes and 22 potential cures.Surprisingly, only one of the causes is programming-related.Each project suffered from around 15 of the causes.Twenty-seven of the causes are not reported in the research literature on IT project failures (e.g.surprises with system integration and wrong estimate of human performance).Half of the cures are familiar to developers, but were ignored in the specific project (example: usability test).The rest are unfamiliar (e.g.problem-oriented requirements and monitoring remaining work).
End-To-End Accent Conversion Without Using Native Utterances

Techniques for accent conversion (AC) aim to convert non-native to native accented speech. Conventional AC methods try to convert only the speaker identity of a native speaker's voice to that of the non-native accented target speaker, leaving the underlying content and pronunciations unchanged. This hinders their practical use in real-world applications, because native-accented utterances are required at conversion stage. In this paper, we present an end-to-end framework, which is able to conduct AC from non-native-accented utterances without using any native-accented utterances during online conversion. We achieve this by independently extracting linguistic and speaker representations from non-native accented speech and condition a speech synthesis model on these representations to generate native-accented speech. Experiments on open-source data corpora show that the proposed system can convert Hindi-accented English speech into native American English speech with high naturalness, which is indistinguishable from native-accented recordings in terms of accent.
Learning salient features to prevent model drift for correlation tracking

Correlation Filter (CF) based algorithms play an important role in the field of Visual Object Tracking (VOT) due to their high accuracy and low computational complexity. While existing CF tracking algorithms suffer performance degradation due to inaccurate object modeling. In this paper, we improve the object modeling accuracy in both CF training stage and target detection procedure to preventing the drift problem. Specifically, we propose a multi-model structure for CF trackers to capture the target appearance changes, where different appearance models are trained with specific samples to catch the salient features of the target and reduce the computational cost. Furthermore, a space filter for detection features is designed to suppress the boundary effect under Gaussian motion prior, which contributes to improving the accuracy of position estimation. We deploy our method to three hand-crafted features based CF trackers to perform real-time visual tracking on popular benchmarks. The experimental results demonstrate the efficacy of our proposed scheme and the efficiency of our trackers. In addition, we provide a comprehensive analysis of the proposed method to facilitate application.
Back-And-Forth Prediction for Deep Tensor Compression

Recent AI applications such as Collaborative Intelligence with neural networks involve transferring deep feature tensors between various computing devices. This necessitates tensor compression in order to optimize the usage of bandwidth-constrained channels between devices. In this paper we present a prediction scheme called Back-and-Forth (BaF) prediction, developed for deep feature tensors, which allows us to dramatically reduce tensor size and improve its compressibility. Our experiments with a state-of-the-art object detector demonstrate that the proposed method allows us to significantly reduce the number of bits needed for compressing feature tensors extracted from deep within the model, with negligible degradation of the detection performance and without requiring any retraining of the network weights. We achieve a 62% and 75% reduction in tensor size while keeping the loss in accuracy of the network to less than 1% and 2%, respectively.
An Efficient Dynamic Load Balancing Algorithm for Virtual Machine in Cloud Computing

Nowadays maintaining the load is the most challenging issue for a researcher in a cloud environment. In the IT field, Cloud computing area is a tremendous technology and its usage of computing resources that provided various services over the internet and its charges is based on usage of resources on the cloud. It provides many services to the users which are productive, reliable and low cost. So, nowadays the users of cloud computing are increasing because every organization, government, and education department are moving toward the use of cloud services. Therefore, when many user requests for cloud resources arrive, we can use load balancing technique to fulfill the need of users. Load balancing process is useful to adjust the load on a node (virtual machine) using distributing the load on another underloaded node. The main objective of load balancing is to make the virtual machine balanced which should be not underloaded or overloaded. In this paper, we have proposed a load balancing algorithm by combining two algorithms for balancing the workload over the cloud system. We have used modified honey bee behavior inspired algorithm for priority based tasks and enhanced weighted round-robin algorithm used for non-priority based tasks. The importance of our research work is to improve system performance, better resource utilization and minimum completion time.
Using Vaes and Normalizing Flows for One-Shot Text-To-Speech Synthesis of Expressive Speech

We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second. Specifically, we enhance the disentanglement capabilities of a state-of-the-art sequence-to-sequence based system with a Variational AutoEncoder (VAE) and a Householder Flow. The proposed system provides a 22% KL-divergence reduction while jointly improving perceptual metrics over state-of-the-art. At synthesis time we use one example of expressive style as a reference input to the encoder for generating any text in the desired style. Perceptual MUSHRA evaluations show that we can create a voice with a 9% relative naturalness improvement over standard Neural Text-to-Speech, while also improving the perceived emotional intensity (59 compared to the 55 of neutral speech).
Pseudorandom Black Swans: Cache Attacks on CTR_DRBG

Modern cryptography requires the ability to securely generate pseudorandom numbers. However, despite decades of work on side-channel attacks, there is little discussion of their application to pseudorandom number generators (PRGs). In this work we set out to address this gap, empirically evaluating the side-channel resistance of common PRG implementations.We find that hard-learned lessons about side-channel leakage from encryption primitives have not been applied to PRGs, at all abstraction levels. At the design level, the NIST-recommended CTR_DRBG does not have forward security if an attacker is able to compromise the state (e.g., via a side-channel). At the primitive level, popular implementations of CTR_DRBG such as OpenSSL's FIPS module and NetBSD's kernel use leaky T-table AES as their underlying cipher, enabling cache side-channel attacks. Finally, we find that many implementations make parameter choices that enable an attacker to fully exploit side-channels and recover secret keys from TLS connections.We empirically demonstrate our attack in two scenarios. First, we carry out a cache attack that recovers the private state from vulnerable CTR_DRBG implementations when the TLS client connects to an attacker-controlled server. We then subsequently use the recovered state to compute the client's long-term authentication keys, thereby allowing the attacker to impersonate the client. In the second scenario, we show that an attacker can exploit the high temporal resolution provided by Intel SGX to carry out a blind attack to recover CTR_DRBG's state within three AES encryptions, without viewing output, and thus decrypt passively collected TLS connections from the victim.
Impact of Information and Communication Technology on Economic Growth and Population Health in Malaysia

The paper aims to examine the association between information and communication technology (ICT), economic growth and population health based on health production model in Malaysia.This theoretical health production function is represented as follows: where the output is an individual health outcome, and the inputs are determinants of health, such as income, education, health care costs, medical facilities, the environment, and lifestyle.The development of information and communication technologies are represented as of mobile cellular subscriptions (per 100) and fixed telephone subscriptions (100) using time series data from 1993-2017 from the World Bank database.Using the bound testing technique of cointegration, this study finds that ICT affects population health significantly and positively in the long-and short-run.This is because ICT inclusion improves human health and longevity.Whereas, economic growth has no significant impact on the population"s health both in the short-and long-run.The findings indicate that a weak global economy affects Malaysia"s economic growth and reduces the health expenditure per capita.The results of this study suggest that policymakers must develop policies that improves public health by increasing health literacy, disseminating health information and facilitating medical facilities.This study also suggests that health care systems should to concentrate on digital inclusion.
Convolutional Neural Network for Classification of Histopathology Images for Breast Cancer Detection

One of the most common subtypes of all breast cancers is Invasive Ductal Carcinoma (IDC). Pathologists typically focus on regions which contain IDC to determine whether a patient suffers from breast cancer or not. We make use of publicly available Breast Histopathology Images dataset provided at the Kaggle for classification. In this dataset, images are delineated to extract the exact regions of IDC. This dataset contains 277,524 patches among which 198,738 belong to the negative class and 78,786 belong to the positive class (images with IDC). We present a novel deep convolutional neural network architecture for classification. Our performance in terms of area under the receiver operating characteristic curve for the detection of IDC is 0.94 on a set of 27,553 test images thereby setting a new benchmark for future research efforts.
Human Interaction Recognition Based on Whole-Individual Detection

Human interaction recognition technology is a hot topic in the field of computer vision, and its application prospects are very extensive. At present, there are many difficulties in human interaction recognition such as the spatial complexity of human interaction, the differences in action characteristics at different time periods, and the complexity of interactive action features. The existence of these problems restricts the improvement of recognition accuracy. To investigate the differences in the action characteristics at different time periods, we propose an improved fusion time-phase feature of the Gaussian model to obtain video keyframes and remove the influence of a large amount of redundant information. Regarding the complexity of interactive action features, we propose a multi-feature fusion network algorithm based on parallel Inception and ResNet. This multi-feature fusion network not only reduces the network parameter quantity, but also improves the network performance; it alleviates the network degradation caused by the increase in network depth and obtains higher classification accuracy. For the spatial complexity of human interaction, we combined the whole video features with the individual video features, making full use of the feature information of the interactive video. A human interaction recognition algorithm based on whole–individual detection is proposed, where the whole video contains the global features of both sides of action, and the individual video contains the individual detail features of a single person. Making full use of the feature information of the whole video and individual videos is the main contribution of this paper to the field of human interaction recognition and the experimental results in the UT dataset (UT–interaction dataset) showed that the accuracy of this method was 91.7%.
Detection of Event of Interest for Satellite Video Understanding

Satellite videos provide rich dynamic information of observed scenes at a large spatial and temporal scale and will play an important role in the future space information network. This work devotes to revealing events of interest (EOI) from satellite video scenes by using a two-stream method. In satellite videos, individual frames reflect the static information like the basic scenes where the event was happening, while a sequence of frames determines the motion information. Considering these facts, a novel two-stream EOI detection framework is proposed, where one stream extracts static spatial information of satellite videos by AlexNet, whereas the other stream extracts the motion information using a local trajectories analysis method. First, the whole video scene is segmented into small spatial-temporal patches, where labeling EOI and non-EOI is completed. Next, the trajectories are extracted from 3-D satellite video cubes that are generated from event scene patches. Finally, this trajectory classification process is treated as a weak supervision learning problem and solved by sparse dictionary learning. The experimental results demonstrate that the proposed two-stream method is effective for EOI detection and has a huge potential for satellite video scenes analysis and understanding. The proposed method also outperforms the existing competitive models for video analysis.
Efficient and Secure Attribute-Based Access Control With Identical Sub-Policies Frequently Used in Cloud Storage

Under the assumption of honest-but-curious cloud service provider, various cryptographic techniques have been used to address the issues of data access control and confidentiality in public cloud storage. Among which, attribute-based encryption (ABE) has been shown to be an attractive scheme. Although the technique of ABE brings in various benefits, its onerous overhead should not be ignored. In this article, based on an improved LSSS (linear secret sharing scheme) matrix expression integrated in CP-ABE (Ciphertext-Policy Attribute-Based Encryption) algorithm, we present an efficient and secure attribute-based access control scheme for the scenarios where multiple data are shared and encrypted with frequently used sub-policies. In the scheme, a user can store the parameters about a specific sub-policy in his/her first decryption, which can be reused in the subsequent data decryptions whose embedded access policies include the same sub-policy so as to significantly reduce the computation cost. Our proposed scheme is proved to be semantically secure under chosen plaintext attacks and can well preserve the confidentiality of the data sharing system. Our analysis and experimentation also show that our scheme does significantly reduce the decryption time and while trades in only very little storage overhead, and thus effectively promotes the efficiency.