Reviewerly/exHarmony huggingface dataset:
Trusting Testcases Using Blockchain-Based Repository Approach

Modern vehicles have evolved to support connected and self-driving capabilities. The concepts such as connected driving, cooperative driving, and intelligent transportation systems have resulted in an increase in the connectivity of vehicles and subsequently created new information security risks. The original vehicular ad-hoc network term is now emerged to a new term, Internet of Vehicles (IoV), which is a typical application of symmetry of Internet of Things (IoT). Vehicle manufacturers address some critical issues such as software bugs or security issues through remote updates, and this gives rise to concerns regarding the security of updated components. Moreover, aftermarket units such as those imposed by transportation authorities or insurance companies expose vehicles to high risk. Software testing aims to ensure that software products are reliable and behave as expected. Many commercial and open-source software products undergo formal certifications to increase users’ confidence in their accuracy, reliability, and security. There are different techniques for software certification, including test-based certification. Testcase repositories are available to support software testing and certification, such as the Linux Test Project for Linux kernel testing. Previous studies performed various testing and experimental evaluation of different parts of modern vehicles to assess the security risks. Due to the lack of trusted testcase repositories and a common approach for testing, testing efforts are performed individually. In this paper, we propose a blockchain-based approach for a testcase repository to support test-based software and security testing and overcome the lack of trusted testcase repositories. The novel concept Proof-of-Validation to manage global state is proposed to manage updates to the repository. The initial work in this study considers the LTP test suite as a use case for the testcase repository. This research work is expected to contribute to the further development in including evidence generation for testing verification.
Visual Analysis of Chapbooks Printed in Scotland

Chapbooks were short, cheap printed booklets produced in large quantities in Scotland, England, Ireland, North America and much of Europe between roughly the seventeenth and nineteenth centuries. A form of popular literature containing songs, stories, poems, games, riddles, religious writings and other content designed to appeal to a wide readership, they were frequently illustrated, particularly on their title-pages. This paper describes the visual analysis of such chapbook illustrations. We automatically extract all the illustrations contained in the National Library of Scotland Chapbooks Printed in Scotland dataset, and create a visual search engine to search this dataset using full or part-illustrations as queries. We also cluster these illustrations based on their visual content, and provide keyword-based search of the metadata associated with each publication. The visual search; clustering of illustrations based on visual content; and metadata search features enable researchers to forensically analyse the chapbooks dataset and to discover unnoticed relationships between its elements. We release all annotations and software tools described in this paper to enable reproduction of the results presented and to allow extension of the methodology described to datasets of a similar nature.
Learning An End-to-End Structure for Retrieval in Large-Scale Recommendations

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model, and then use some approximate nearest neighbor (ANN) search algorithm to find top candidates. In this paper, we present Deep Retrieval (DR), to learn a retrievable structure directly with user-item interaction data (e.g. clicks) without resorting to the Euclidean space assumption in ANN algorithms. DR's structure encodes all candidate items into a discrete latent space. Those latent codes for the candidates are model parameters and learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking. Empirically, we first demonstrate that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline on two public datasets. Moreover, we show that, in a live production recommendation system, a deployed DR approach significantly outperforms a well-tuned ANN baseline in terms of engagement metrics. To the best of our knowledge, DR is among the first non-ANN algorithms successfully deployed at the scale of hundreds of millions of items for industrial recommendation systems.
DAHash: Distribution Aware Tuning of Password Hashing Costs

An attacker who breaks into an authentication server and steals all of the cryptographic password hashes is able to mount an offline-brute force attack against each user's password. Offline brute-force attacks against passwords are increasingly commonplace and the danger is amplified by the well documented human tendency to select low-entropy password and/or reuse these passwords across multiple accounts. Moderately hard password hashing functions are often deployed to help protect passwords against offline attacks by increasing the attacker's guessing cost. However, there is a limit to how "hard" one can make the password hash function as authentication servers are resource constrained and must avoid introducing substantial authentication delay. Observing that there is a wide gap in the strength of passwords selected by different users we introduce DAHash (Distribution Aware Password Hashing) a novel mechanism which reduces the number of passwords that an attacker will crack. Our key insight is that a resource-constrained authentication server can dynamically tune the hardness parameters of a password hash function based on the (estimated) strength of the user's password. We introduce a Stackelberg game to model the interaction between a defender (authentication server) and an offline attacker. Our model allows the defender to optimize the parameters of DAHash e.g., specify how much effort is spent in hashing weak/moderate/high strength passwords. We use several large scale password frequency datasets to empirically evaluate the effectiveness of our differentiated cost password hashing mechanism. We find that the defender who uses our mechanism can reduce the fraction of passwords that would be cracked by a rational offline attacker by up to $$15\%$$ .
On the receptive field misalignment in CAM-based visual explanations

Visual explanations aim at providing an understanding of the inner behavior of convolutional neural networks. Naturally, it is necessary to explore whether these methods themselves are reasonable and reliable. In this paper, we focus on Class Activation Mapping (CAM), a type of attractive explanations that has been widely applied to model diagnosis and weakly supervised tasks. Our contribution is two-fold. First, we identify an important but neglected issue that affects the reliability of CAM results: there is a misalignment between the effective receptive field and the implicit receptive field, where the former is determined by the model and the input, and the latter is determined by the upsampling function in CAM. Occlusion experiments are designed to empirically testify to its existence. Second, based on this finding, an adversarial marginal attack is proposed to fool the CAM-based method and the CNN model simultaneously. Experimental results demonstrate that the provided saliency map can be completely changed to another shape by only perturbing the area with 1-pixel width. The prototype code of the method is now available at https://github.com/xpf/CAM-Adversarial-Marginal-Attack.
MANN-KENDALL TEST: TREND ANALYSIS OF TEMPERATURE, RAINFALL AND DISCHARGE OF GHOTKI FEEDER CANAL IN DISTRICT GHOTKI, SINDH, PAKISTAN

Temperature and precipitation variations have huge environmental, socio-economic impacts. This study aims to detect the trend of temperature, precipitation, and discharge from 2000-2020 in the district Ghotki. Mann Kendal test and Sen’s slope were applied by using XLSTAT in MS Excel to investigate the significance of all trends. The results showed that the annual rainfall trend was increased with the highest intensity noted in 2003; 275mm and 2010; 271 mm. The trend in the monsoon season was increased with the highest slope 0.863 by comparing with non-monsoon which showed the slope was 0.642. The annual temperature was increased an average temperature recorded in 2016; 28.5 &amp; 2018; 28 °C. Further, the summer-autumn season’s trend has sharply increased. While the trend of Ghotki feeder discharge was slightly increased in January due to the continuous flow of water and less demand for water during the Rabi season. But in July, the highest discharge was recorded in 2010 due to heavy rainfall and flood situations over the study area. The trend in Kharif was continuously declined due to farmers started sowing sugarcane crops instead of rice and cotton which need less irrigation water. It is concluded that the performance of MK and SS tests was consistent at the verified significance level.
Privacy-preserving location-based traffic density monitoring

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SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining

Privacy-preserving process mining enables the analysis of business processes using event logs, while giving guarantees on the protection of sensitive information on process stakeholders. To this end, existing approaches add noise to the results of queries that extract properties of an event log, such as the frequency distribution of trace variants, for analysis. Noise insertion neglects the semantics of the process, though, and may generate traces not present in the original log. This is problematic. It lowers the utility of the published data and makes noise easily identifiable, as some traces will violate well-known semantic constraints. In this paper, we therefore argue for privacy preservation that incorporates a process’ semantics. For common trace-variant queries, we show how, based on the exponential mechanism, semantic constraints are incorporated to ensure differential privacy of the query result. Experiments demonstrate that our semantics-aware anonymization yields event logs of significantly higher utility than existing approaches.
Design and Empirical Evaluation of a Novel Near-field Interaction Metaphor on Distant Object Manipulation in VR

Manipulating objects in immersive virtual environments has been studied for decades, but is still considered as a challenging problem. Current methods for manipulating distant objects still poses issues such as lack of precision and accuracy, and only a limited set of transformations is supported. In this paper, we proposed three metaphors: a near-field widget-based metaphor, and two near-field metaphors with scaled replica, one is unimanual and the other is bimanual. The widget-based metaphor is an extension of Widgets [20], but supports translation, scaling (anchored scaling) and rotation with multi-level DOF (degree-of-freedom) separation. The near-field metaphors with scaled replica can take advantage of the finer motion control and sharper vision in arm-reach manipulations, and thus increases the manipulation precision. Moreover, manipulating the replica via bounding box’s primitives makes the support of translation, scaling (anchored scaling) and rotation with multi-level DOF separation possible, and also leads to an intuitive interface. The support of multi-level DOF separation may increase the manipulation precision and offer more manipulation flexibility as well. We conducted a between-subjects empirical study with 51 participants to compare the three metaphors in terms of effectiveness measures, user experience and user impression. The findings from this study revealed that the unimanual metaphor with scaled replica (UMSR) yielded the highest efficiency. The widget-based metaphor was slower, and the bimanual metaphor with scaled replica (BMSR) yielded lower movement economy and, in one case, was less accurate. However, the subjective impressions were most favorable in the bimanual metaphor with scaled replica (BMSR). Thus, there may be some discrepancy between perceived comfort and user performance inherent in the near-field bimanual scaled-replica interactions.
Quantum to classical walk transitions tuned by spontaneous emissions

We have realized a quantum walk in momentum space with a rubidium spinor Bose-Einstein condensate by applying a periodic kicking potential as a walk operator and a resonant microwave pulse as a coin toss operator. The generated quantum walks appear to be stable for up to ten steps and then quickly transit to classical walks due to spontaneous emissions induced by laser beams of the walk operator. We investigate these quantum to classical walk transitions by introducing well-controlled spontaneous emissions with an external light source during quantum walks. Our findings demonstrate a scheme to control the robustness of the quantum walks and can also be applied to other cold atom experiments involving spontaneous emissions.
Quantum Geographic Information System (Q-GIS) based study on emerging energy scenario in Hilly Terrain

The present scenario in industrial development, population growth, and improvement in people’s life style throughout the hilly areas has led to increase in primary energy consumption. To a fastest growing economy like India, the primary energy requirements will go up with assured supply of 3–4 times more than consumed at present. In order to reduce the carbon footprints and hence to promote the sustainable energy future, the renewable energy sources are only option to bridge this gap and its popularity is gradually gaining in Eastern Himalayan state of India. The present study deals with a case study on energy used pattern in hilly state of Sikkim where large hydro power projects of total rated capacity of 2150 MW are already been developed and commissioned. But due to resultant consequences of global warming, most of the glaciers on Himalayan belt are melting and may deplete in next 35–40 years of now which is responsible for large hydro power generation. In order to address this cause, government and private organizations have already started taking various steps to identify and develop renewable energy projects based on solar, wind, bio-fuel, micro hydel, etc. at potential sites. This study also accounts with Quantum Geographic Information System (Q-GIS, 2.18.0) based geo-processing model for showing up the highly potential sites for suitably harnessing wind energy at different hub heights as well as solar energy considering its key factors like GHI, DNI, DHI, and PVout for entire region of Sikkim. Based on the present analysis, Wind Power Density (WPD) varies up to 995 W/m2 in windy sites, whereas Solar PVout varies from 2.97 to 4.57 kWp and both sources are dominant at northern parts of Sikkim. This study suggests the valuable details to investors, policymakers, industrialist, and scientist for strategic energy planning to avert the future energy crises in hilly regions.
SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words

Loop closure detection is an essential tool of Simultaneous Localization and Mapping (SLAM) to minimize drift in its localization.Many state-of-the-art loop closure detection (LCD) algorithms use visual Bag-of-Words (vBoW), which is robust against partial occlusions in a scene but cannot perceive the semantics or spatial relationships between feature points.CNN object extraction can address those issues, by providing semantic labels and spatial relationships between objects in a scene.Previous work has mainly focused on replacing vBoW with CNN derived features.In this paper we propose SymbioLCD, a novel ensemble-based LCD that utilizes both CNN-extracted objects and vBoW features for LCD candidate prediction.When used in tandem, the added elements of object semantics and spatial-awareness creates a more robust and symbiotic loop closure detection system.The proposed SymbioLCD uses scale-invariant spatial and semantic matching, Hausdorff distance with temporal constraints, and a Random Forest that utilizes combined information from both CNN-extracted objects and vBoW features for predicting accurate loop closure candidates.Evaluation of the proposed method shows it outperforms other Machine Learning (ML) algorithms -such as SVM, Decision Tree and Neural Network, and demonstrates that there is a strong symbiosis between CNN-extracted object information and vBoW features which assists accurate LCD candidate prediction.Furthermore, it is able to perceive loop closure candidates earlier than state-ofthe-art SLAM algorithms, utilizing added spatial and semantic information from CNN-extracted objects.
Speedo

Structuring cloud applications as collections of interacting fine-grained microservices makes them scalable and affords the flexibility of hot upgrading parts of the application. The current avatar of serverless computing (FaaS) with its dynamic resource allocation and auto-scaling capabilities make it the deployment model of choice for such applications. FaaS platforms operate with user space dispatchers that receive requests over the network and make a dispatch decision to one of multiple workers (usually a container) distributed in the data center. With the granularity of microservices approaching execution times of a few milliseconds combined with loads approaching tens of thousands of requests a second, having a low dispatch latency of less than one millisecond becomes essential to keep up with line rates. When these microservices are part of a workflow making up an application, the orchestrator that coordinates the sequence in which microservices execute also needs to operate with microsecond latency. Our observations reveal that the most significant component of the dispatch/orchestration latency is the time it takes for the request to traverse into and out of the user space from the network. Motivated by the presence of a multitude of low power cores on today's SmartNICs, one approach to keeping up with these high line rates and the stringent latency expectations is to run both the dispatcher and the orchestrator close to the network on a SmartNIC. Doing so will save valuable cycles spent in transferring requests to and back from the user space. The operating characteristics of short-lived ephemeral state and low CPU burst requirements of FaaS dispatcher/orchestrator make them ideal candidates for offloading from the server to the NIC cores. This also brings other benefit of freeing up the server CPU. In this paper, we present Speedo--- a design for offloading of FaaS dispatch and orchestration services to the SmartNIC from the user space. We implemented Speedo on ASIC based Netronome Agilio SmartNICs and our comprehensive evaluation shows that Speedo brings down the dispatch latency from ~150ms to ~140μs at a load of 10K requests per second.
Fair and Robust Classification Under Sample Selection Bias

To address the sample selection bias between the training and test data, previous research works focus on reweighing biased training data to match the test data and then building classification models on the reweighed training data. However, how to achieve fairness in the built classification models is under-explored. In this paper, we propose a framework for robust and fair learning under sample selection bias. Our framework adopts the reweighing estimation approach for bias correction and the minimax robust estimation approach for achieving robustness on prediction accuracy. Moreover, during the minimax optimization, the fairness is achieved under the worst case, which guarantees the model's fairness on test data. We further develop two algorithms to handle sample selection bias when test data is both available and unavailable.
Sentence encoding for Dialogue Act classification

Abstract In this study, we investigate the process of generating single-sentence representations for the purpose of Dialogue Act (DA) classification, including several aspects of text pre-processing and input representation which are often overlooked or underreported within the literature, for example, the number of words to keep in the vocabulary or input sequences. We assess each of these with respect to two DA-labelled corpora, using a range of supervised models, which represent those most frequently applied to the task. Additionally, we compare context-free word embedding models with that of transfer learning via pre-trained language models, including several based on the transformer architecture, such as Bidirectional Encoder Representations from Transformers (BERT) and XLNET, which have thus far not been widely explored for the DA classification task. Our findings indicate that these text pre-processing considerations do have a statistically significant effect on classification accuracy. Notably, we found that viable input sequence lengths, and vocabulary sizes, can be much smaller than is typically used in DA classification experiments, yielding no significant improvements beyond certain thresholds. We also show that in some cases the contextual sentence representations generated by language models do not reliably outperform supervised methods. Though BERT, and its derivative models, do represent a significant improvement over supervised approaches, and much of the previous work on DA classification.
Empowering study of breast cancer data with application of artificial intelligence technology: promises, challenges, and use cases

In healthcare, artificial intelligence (AI) technologies have the potential to create significant value by improving time-sensitive outcomes while lowering error rates for each patient. Diagnostic images, clinical notes, and reports are increasingly generated and stored in electronic medical records. This heterogeneous data presenting us with challenges in data analytics and reusability that is by nature has high complexity, thereby necessitating novel ways to store, manage and process, and reuse big data. This presents an urgent need to develop new, scalable, and expandable AI infrastructure and analytical methods that can enable healthcare providers to access knowledge for individual patients, yielding better decisions and outcomes. In this review article, we briefly discuss the nature of data in breast cancer study and the role of AI for generating "smart data" which offer actionable information that supports the better decision for personalized medicine for individual patients. In our view, the biggest challenge is to create a system that makes data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare outcomes and costs. We highlight some of the challenges in using breast cancer data and propose the need for an AI-driven environment to address them. We illustrate our vision with practical use cases and discuss a path for empowering the study of breast cancer databases with the application of AI and future directions.