Reviewerly/exHarmony huggingface dataset:
Information-theoretic Source Code Vulnerability Highlighting

Software vulnerabilities are a crucial and serious concern in the software industry and computer security. A variety of methods have been proposed to detect vulnerabilities in real-world software. Recent methods based on deep learning approaches for automatic feature extraction have improved software vulnerability identification compared with machine learning approaches based on hand-crafted feature extraction. However, these methods can usually only detect software vulnerabilities at a function or program level, which is much less informative because, out of hundreds (thousands) of code statements in a program or function, only a few core statements contribute to a software vulnerability. This requires us to find a way to detect software vulnerabilities at a fine-grained level. In this paper, we propose a novel method based on the concept of mutual information that can help us to detect and isolate software vulnerabilities at a fine-grained level (i.e., several statements that are highly relevant to a software vulnerability that include the core vulnerable statements) in both unsupervised and semi-supervised contexts. We conduct comprehensive experiments on real-world software projects to demonstrate that our proposed method can detect vulnerabilities at a fine-grained level by identifying several statements that mostly contribute to the vulnerability detection decision.
A novel low-order spatiotemporal modeling method for nonlinear distributed parameter systems

Most distributed parameter systems (DPSs) are unknown, including unknown parameter, boundary and even structure, and have a strongly nonlinear spatiotemporal nature. In order to achieve desirable modeling accuracy, the models from the commonly used DPS modeling methods often have a high order, which makes them difficultly used for prediction and control. Here, a novel low-order spatiotemporal least squares support vector machine (LS-SVM) method is proposed for modeling unknown and nonlinear DPSs. Generally, the information of a certain sensor may be represented by information of its neighboring sensors due to the spatial correlation between them. Making use of this feature, a kernel-space based spatial correlation analysis method is developed for deleting redundant spatial kernel functions, from which a low-order model can be achieved and it is without loss of any spatial information. On this basis, a LS-SVM model is constructed to represent the nonlinear temporal dynamics. Integration of the without-redundant spatial kernel functions and the LS-SVM temporal model, a low-order spatiotemporal model is created to reconstruct the spatiotemporal dynamics of the nonlinear DPSs. Additional analysis and proof show that: (1) the proposed method has the same modeling performance with the without-order-reduction spatiotemporal modeling method; and (2) it has better modeling performance than the model with the same order achieved by the without-order-reduction one. Using case studies, the effectiveness of the proposed method and its superior modeling ability compared to several common methods are demonstrated.
Tracking Beyond Detection: Learning a Global Response Map for End-to-End Multi-Object Tracking

Most of the existing Multi-Object Tracking (MOT) approaches follow the Tracking-by-Detection and Data Association paradigm, in which objects are firstly detected and then associated in the tracking process. In recent years, deep neural network has been utilized to obtain more discriminative appearance features for cross-frame association, and noticeable performance improvement has been reported. On the other hand, the Tracking-by-Detection framework is yet not completely end-to-end, which leads to huge computation and limited performance especially in the inference (tracking) process. To address this problem, we present an effective end-to-end deep learning framework which can directly take image-sequence/video as input and output the located and tracked objects of learned types. Specifically, a novel global response network is learned to project multiple objects in the image-sequence/video into a continuous response map, and the trajectory of each tracked object can then be easily picked out. The overall process is similar to how a detector inputs an image and outputs the bounding boxes of each detected object. Experimental results based on the MOT16 and MOT17 benchmarks show that our proposed on-line tracker achieves state-of-the-art performance on several tracking metrics.
Designing VUIs for Social Assistance Robots for People with Dementia

Elderly people and especially people with dementia often experience social isolation and need assistance while performing activities of daily living. We investigate a novel approach to cope with this problem by integrating voice assistants and social assistance robots. Due to the special communication needs of people with mild cognitive impairment, the design of interfaces of such systems is to be based on the particular requirements of the target user group. This paper investigates, how a voice user interface should be designed for elderly users with mild cognitive impairment – such as an early stage of dementia – to provide personalised support throughout activities of daily living. A context and user analysis delivered a set of 11 guidelines for voice user interfaces for people with dementia. For a pilot study we selected those strategies often applied by caregivers in their communication with people with dementia and evaluated the voice user interface among elderly participants and healthcare workers who reported a high feasibility, usefulness and acceptance of the designed system.
Improving surrogate model accuracy for the LCLS-II injector frontend using convolutional neural networks and transfer learning

Machine learning (ML) models of accelerator systems ('surrogate models') are able to provide fast, accurate predictions of accelerator physics phenomena. However, approaches to date typically do not include measured input diagnostics, such as the initial beam distributions, which are critical for accurately representing the beam evolution through the system. In addition, these inputs often vary over time, and models that can account for these changing conditions are needed. As beam time for measurements is often limited, simulations are in some cases needed to provide sufficient training data. These typically represent the designed machine before construction; however, the behavior of the installed components may be quite different due to changes over time or static differences that were not modeled. Therefore, surrogate models that can leverage both simulation and measured data successfully are needed. We introduce an approach based on convolutional neural networks that uses the drive laser distribution and scalar settings as inputs for a photoinjector system model (here, the linac coherent light source II, LCLS-II, injector frontend). The model is able to predict scalar beam parameters and the transverse beam distribution downstream, taking into account the impact of time-varying non-uniformities in the initial transverse laser distribution. We also introduce and evaluate a transfer learning procedure for adapting the surrogate model from the simulation domain to the measurement domain, to account for differences between the two. Applying this approach to our test case results in a model that can predict test sample outputs within a mean absolute percent error of 9%. This is a substantial improvement over the model trained only on simulations, which has an error of 261% when applied to measured data. While we focus on the LCLS-II Injector frontend, these approaches for improving ML-based online modeling of injector systems could be easily adapted to other accelerator facilities.
Establishing a Cybersecurity Home Monitoring System for the Elderly

Many countries have increasingly aging populations. Many of these elderly people live alone and independently, but those suffering from chronic diseases or disabilities are at risk of accidents that will require assistance. Many commercially available homecare systems provide remote monitoring functionality, but these systems require someone on the other end of the remote connection to be paying attention. This raises the need for intelligent home monitoring systems to ensure continuous awareness of user safety. This article proposes a system using beacon technology to assess subject well being based on lack of movement, and automatically activates prepositioned cameras and send an alert to family members or caregivers in response to potentially high-risk activity, such as activating particular kitchen appliances. However, the use of such cameras raises privacy concerns. To improve object readability and privacy, our research uses federated learning to enhance the readability of fast regions with convolutional neural networks features (RCNN). The presented approach stores the personal information of the elderly in the local server, which avoids revealing their home information and secures the data transmission safety by privacy protection. The article has proved the feasibility and practicality by running the experiment; the system can genuinely operate in home health care.
Secure Data Sharing with Confidentiality, Integrity and Access Control in Cloud Environment

Cloud storage is an incipient technology in today's world. Lack of security in cloud environment is one of the primary challenges faced these days. This scenario poses new security issues and it forms the crux of the current work. The current study proposes Secure Interactional Proof System (SIPS) to address this challenge. This methodology has a few key essential components listed herewith to strengthen the security such as authentication, confidentiality, access control, integrity and the group of components such as AVK Scheme (Access List, Verifier and Key Generator). It is challenging for every user to prove their identity to the verifier who maintains the access list. Verification is conducted by following Gulliou-Quisquater protocol which determines the security level of the user in multi-step authentication process. Here, RSA algorithm performs the key generation process while the proposed methodology provides data integrity as well as confidentiality using asymmetric encryption. Various methodological operations such as time consumption have been used as performance evaluators in the proposed SIPS protocol. The proposed solution provides a secure system for firm data sharing in cloud environment with confidentiality, authentication and access control. Stochastic Timed Petri (STPN) Net evaluation tool was used to verify and prove the formal analysis of SIPS methodology. This evidence established the effectiveness of the proposed methodology in secure data sharing in cloud environment.
ExtendedSketch: Fusing Network Traffic for Super Host Identification With a Memory Efficient Sketch

Super host refers to the host that has a high cardinality or exhibits a big change in a network. Facing big-volume network traffic, sketches have been widely applied to identify super hosts in an efficient and accurate way. However, most sketches cannot flexibly balance memory usage and accuracy in host cardinality estimation. Setting an inappropriate counter size for a sketch could either lead to inaccurate host cardinality estimation or cause memory waste. In order to solve this issue, we propose a novel extensible and reversible sketch, named ExtendedSketch, to achieve accurate super host identification with high memory efficiency. The core idea of ExtendedSketch is to monitor low-cardinality hosts with small-sized counters while dynamically extending the size of counters when monitoring high-cardinality hosts by applying an adaptive extension strategy. Such the strategy can adaptively increase counter size according to network traffic status at runtime, which not only ensures the accuracy of high-cardinality host estimation but also avoids unnecessary memory consumption. We perform theoretical analysis and conduct a series of experimental evaluations on ExtendedSketch based on real world network traffic. Experimental results show that under same memory usage, compared to the state-of-the-art, ExtendedSketch achieves <inline-formula><tex-math notation="LaTeX">$1.4{ \sim }7.5$</tex-math></inline-formula> times smaller error rate in estimating host cardinality with <inline-formula><tex-math notation="LaTeX">$1.9{ \sim }26.7$</tex-math></inline-formula> times better accuracy on super host identification and <inline-formula><tex-math notation="LaTeX">$95 {\sim }2^{15}$</tex-math></inline-formula> times faster speed on abnormal address reconstruction. Its advance in accuracy and efficiency demonstrates the practical significance of ExtendedSketch for super host identification.
Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding-Based Deep Learning Techniques

Dictionaries not only are the source of getting meanings of the word but also serve the purpose of comprehending the context in which the words are used. For such purpose, we see a small sentence as an example for the very word in comprehensive book-dictionaries and more recently in online dictionaries. The lexicographers perform a very meticulous activity for the elicitation of Good Dictionary EXamples (GDEX)—a sentence that is best fit in a dictionary for the word’s definition. The rules for the elicitation of GDEX are very strenuous and require a lot of time for committing the manual process. In this regard, this paper focuses on two major tasks, i.e., the development of labelled corpora for top 3K English words through the usage of distant supervision approach and devising a state-of-the-art artificial intelligence-based automated procedure for discriminating Good Dictionary EXamples from the bad ones. The proposed methodology involves a suite of five machine learning (ML) and five word embedding-based deep learning (DL) architectures. A thorough analysis of the results shows that GDEX elicitation can be done by both ML and DL models; however, DL-based models show a trivial improvement of 3.5% over the conventional ML models. We find that the random forests with parts-of-speech information and word2vec-based bidirectional LSTM are the most optimal ML and DL combinations for automated GDEX elicitation; on the test set, these models, respectively, secured a balanced accuracy of 73% and 77%.
ReconfigISP: Reconfigurable Camera Image Processing Pipeline

Image Signal Processor (ISP) is a crucial component in digital cameras that transforms sensor signals into images for us to perceive and understand. Existing ISP designs always adopt a fixed architecture, e.g., several sequential modules connected in a rigid order. Such a fixed ISP architecture may be suboptimal for real-world applications, where camera sensors, scenes and tasks are diverse. In this study, we propose a novel Reconfigurable ISP (ReconfigISP) whose architecture and parameters can be automatically tailored to specific data and tasks. In particular, we implement several ISP modules, and enable back-propagation for each module by training a differentiable proxy, hence allowing us to leverage the popular differentiable neural architecture search and effectively search for the optimal ISP architecture. A proxy tuning mechanism is adopted to maintain the accuracy of proxy networks in all cases. Extensive experiments conducted on image restoration and object detection, with different sensors, light conditions and efficiency constraints, validate the effectiveness of ReconfigISP. Only hundreds of parameters need tuning for every task <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Distributed Big Data Computing for Supporting Predictive Analytics of Service Requests

In the current era of big data, huge volumes of valuable data can be easily generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the initiates of open data also led to the willingness of many government, researchers, and organizations to share their data and make them publicly accessible. An example of open big data is service request data. Analyzing these open big data can be for social good. For instance, by analyzing and mining data on non-emergency city service requests, the city could get an insight about its residents’ demand for services. By taking appropriate actions (e.g., adding more staff and/or services, providing more information regarding city services) could enhance the living condition of city residents. In this paper, we present a distributed big data mining system to analyze and mine big data on these non-emergency city service requests. Evaluation on an open big data from a North American city shows the effectiveness and practicality of our distributed big data system in mining these requests for city services and in supporting predictive analytics.
The Crowd Wisdom for Location Privacy of Crowdsensing Photos

The incorporation of the mobile crowd in visual sensing provides a significant opportunity to explore and understand uncharted physical places. We investigate the gains and losses of the involvement of the crowd wisdom on users' location privacy in photo crowdsensing. For the negative effects, we design a novel crowdsensing photo location inference model, regardless of the robust location protection techniques, by jointly exploiting the visual representation, correlation, and geo-annotation capabilities extracted from the crowd. Compared with existing retrieval-based and model-based location inference techniques, our proposal poses more pernicious threats to location privacy by considering the no-reference-photos situations of crowdsensing. We conduct extensive analyses on the model with four photo datasets and crowdsourcing surveys for geo-annotation. The results indicate that being in a crowd of photos will, unfortunately, increase one's risk to be geo-identified, and highlights that the model can yield a considerable high inference accuracy (48%~70%) and serious privacy exposure (over 80% of users get privacy disclosed) with a small portion of geo-annotated samples. In view of the threats, we further propose an adaptive grouping-based signing model that hides a user's track with the camouflage of a crowd of users. Wherein, ring signature is tailored for crowdsensing to provide indistinguishable while valid identities for every user's submission. We theoretically analyze its adjustable privacy protection capability and develop a prototype to evaluate the effectiveness and performance.
Context-based image explanations for deep neural networks

With the increased use of machine learning in decision-making scenarios, there has been a growing interest in explaining and understanding the outcomes of machine learning models. Despite this growing interest, existing works on interpretability and explanations have been mostly intended for expert users. Explanations for general users have been neglected in many usable and practical applications (e.g., image tagging, caption generation). It is important for non-technical users to understand features and how they affect an instance-specific prediction to satisfy the need for justification. In this paper, we propose a model-agnostic method for generating context-based explanations aiming for general users. We implement partial masking on segmented components to identify the contextual importance of each segment in scene classification tasks. We then generate explanations based on feature importance. We present visual and text-based explanations: (i) saliency map presents the pertinent components with a descriptive textual justification, (ii) visual map with a color bar graph showing the relative importance of each feature for a prediction. Evaluating the explanations using a user study (N = 50), we observed that our proposed explanation method visually outperformed existing gradient and occlusion based methods. Hence, our proposed explanation method could be deployed to explain models' decisions to non-expert users in real-world applications.
Evaluating Impact of Race in Facial Recognition across Machine Learning and Deep Learning Algorithms

The research aims to evaluate the impact of race in facial recognition across two types of algorithms. We give a general insight into facial recognition and discuss four problems related to facial recognition. We review our system design, development, and architectures and give an in-depth evaluation plan for each type of algorithm, dataset, and a look into the software and its architecture. We thoroughly explain the results and findings of our experimentation and provide analysis for the machine learning algorithms and deep learning algorithms. Concluding the investigation, we compare the results of two kinds of algorithms and compare their accuracy, metrics, miss rates, and performances to observe which algorithms mitigate racial bias the most. We evaluate racial bias across five machine learning algorithms and three deep learning algorithms using racially imbalanced and balanced datasets. We evaluate and compare the accuracy and miss rates between all tested algorithms and report that SVC is the superior machine learning algorithm and VGG16 is the best deep learning algorithm based on our experimental study. Our findings conclude the algorithm that mitigates the bias the most is VGG16, and all our deep learning algorithms outperformed their machine learning counterparts.
The Language of Dreams: Application of Linguistics-Based Approaches for the Automated Analysis of Dream Experiences

The study of dreams represents a crucial intersection between philosophical, psychological, neuroscientific, and clinical interests. Importantly, one of the main sources of insight into dreaming activity are the (oral or written) reports provided by dreamers upon awakening from their sleep. Classically, two main types of information are commonly extracted from dream reports: structural and semantic, content-related information. Extracted structural information is typically limited to the simple count of words or sentences in a report. Instead, content analysis usually relies on quantitative scores assigned by two or more (blind) human operators through the use of predefined coding systems. Within this review, we will show that methods borrowed from the field of linguistic analysis, such as graph analysis, dictionary-based content analysis, and distributional semantics approaches, could be used to complement and, in many cases, replace classical measures and scales for the quantitative structural and semantic assessment of dream reports. Importantly, these methods allow the direct (operator-independent) extraction of quantitative information from language data, hence enabling a fully objective and reproducible analysis of conscious experiences occurring during human sleep. Most importantly, these approaches can be partially or fully automatized and may thus be easily applied to the analysis of large datasets.