Reviewerly/exHarmony huggingface dataset:
Does More Context Help? Effects of Context Window and Application Source on Retrieval Performance

We study the effect of contextual information obtained from a user’s digital trace on Web search performance. Contextual information is modeled using Dirichlet–Hawkes processes (DHP) and used in augmenting Web search queries. The context is captured by monitoring all naturally occurring user behavior using continuous 24/7 recordings of the screen and associating the context with the queries issued by the users. We report a field study in which 13 participants installed a screen recording and digital activity monitoring system on their laptops for 14 days, resulting in data on all Web search queries and the associated context data. A query augmentation (QAug) model was built to expand the original query with semantically related terms. The effects of context window and source were determined by training context models with temporally varying context windows and varying application sources. The context models were then utilized to re-rank the QAug model. We evaluate the context models by using the Web document rankings of the original query as a control condition compared against various experimental conditions: (1) a search context condition in which the context was sourced from search history; (2) a non-search context condition in which the context was sourced from all interactions excluding search history; (3) a comprehensive context condition in which the context was sourced from both search and non-search histories; and (4) an application-specific condition in which the context was sourced from interaction histories captured on a specific application type. Our results indicated that incorporating more contextual information significantly improved Web search rankings as measured by the positions of the documents on which users clicked in the search result pages. The effects and importance of different context windows and application sources, along with different query types are analyzed, and their impact on Web search performance is discussed.
Sub-Bottom Profiler Sonar Image Missing Area Reconstruction Using Multi-Survey Line Patch Group Deep Learning

Air bubbles in seawater always lead to information loss for sub-bottom profiler (SBP) images. This common issue makes it difficult to interpret SBP data. Toward this end, this letter proposes a hybrid method to reconstruct the missing area on an SBP image. This method combines the information of multi-survey line to overcome the disadvantage of using a single survey line that has less observation information. First, based on an improved active contour model (ACM) algorithm and a bottom line match algorithm, the SBP images of adjacent survey lines are aligned, and the image patch group is obtained. Then, a multi-survey line patch group deep learning framework is trained to reconstruct the missing area using the patch group as the input layer. The global–local loss function is employed to optimize the deep learning training model. Both simulated and real experiments have been used to test the effectiveness of the proposed method, and good results have been achieved.
Handwriting Recognition Using Deep Learning

Handwritten figure recognition, whether it has characters or digits, has been a problem for a very long time in the department of design/pattern recognition and their classification. There are traditional techniques that have created Optical Character Recognition (OCR) systems in the 1950s, which were more costly, platform-dependent, and heavy in size. With the active contribution, it becomes possible to develop more efficient techniques. Now, it is also possible to use and contribute at the same time. One of the most prominent modern techniques is focused on segmenting individual handwritten text, i.e., Convolutional Neural Network (CNN), a technique of deep learning. In this experimental analysis, our motive is to spot the impression of the deep learning technique, and predict written digits and characters from 0 to 9 and a/A to z/Z when a set of lakhs of samples of EMNIST are given as an input. The major goal of this paper is to contribute to this topic, providing efficient and steady procedures for the identification of handwritten alpha-numerals. As we all know that everyone has a different form of writing digits and characters, humans will acknowledge simply except for the computer; it's relatively a troublesome task. Therefore, the neural network-based approach comes into the picture, which is a technique based on how humans recognize any imagery. The machine can recognize too and become much more accurate from its experience. The model is trained by using convolutional neural networks. The general system accuracy obtained was 99.40% (digits) and 94.45% (characters).
SQL Injection Attack Detection Framework Based on HTTP Traffic

In view of the characteristics of SQL injection attack under the background of complex HTTP traffic, this paper systematically proposes a framework of SQL injection attack detection based on HTTP traffic, including four modules: data collection, data cleaning, feature representation and model building. The data collection module introduces a variety of channels to obtain data, and the flow cleaning module improves the detection ability of SQL injection attack under the complex traffic environment by reducing the interference of irrelevant information. The feature representation module describes an efficient and easy to obtain feature generation method, that is, lexical features that retain special symbols. The model building module proposed a model building method for detecting arbitrary length Payload and a variable length sequence training method to guarantee efficiency. The detection location covers HTTP request headers, URLs, and POST, providing multi-dimensional protection against SQL injection attacks. In the real network environment, the framework detects SQL injection attacks with low alarm omission rate and low false positive rate.
On Equivariant and Invariant Learning of Object Landmark Representations

Given a collection of images, humans are able to discover landmarks by modeling the shared geometric structure across instances. This idea of geometric equivariance has been widely used for the unsupervised discovery of object landmark representations. In this paper, we develop a simple and effective approach by combining instance-discriminative and spatially-discriminative contrastive learning. We show that when a deep network is trained to be invariant to geometric and photometric transformations, representations emerge from its intermediate layers that are highly predictive of object landmarks. Stacking these across layers in a "hypercolumn" and projecting them using spatially-contrastive learning further improves their performance on matching and few-shot landmark regression tasks. We also present a unified view of existing equivariant and invariant representation learning approaches through the lens of contrastive learning, shedding light on the nature of invariances learned. Experiments on standard benchmarks for landmark learning, as well as a new challenging one we propose, show that the proposed approach surpasses prior state-of-the-art.
Device-Adaptive 2D Gaze Estimation: A Multi-Point Differential Framework

Eye tracking system on mobile devices is important for many interactive applications. However, since models are usually customized with limited types of devices and new devices have totally different physical parameters, it is hard to generalize over unseen devices. In this paper, we present a device-adaptive 2D gaze estimation algorithm based on differential prediction. We reformulate the gaze estimation as a relative position prediction problem between the input image and calibration images, which skips the estimation for camera parameters and makes models easily generalize over devices. To tackle the new challenge, this work proposes a framework which jointly trains a differential prediction module and an aggregation module for ensembling the predictions from multiple calibration points. Experiments show that the framework outperforms baseline models constantly on open datasets with only 3–5 calibration points.
A domain-agnostic ontology for unified metrology data management

Τhe National Physical Laboratory's (NPL) impact on science, economy and well-being largely relies on proper dissemination and exploitation of scientific evidence from data. Measurement data and software that generates it must therefore implement the Findable, Accessible, Interoperable and Reusable principles (FAIR), whilst enabling data traceability, uncertainty, intellectual property protection and compliance with legislation. Semantic web technologies are ideally suited to support such a unified measurement description through deeply ingrained knowledge sharing and data curation capabilities. This paper presents a semantic-based research approach to data and metadata management which aims to unify the organisation of scientific and engineering digital resources across NPL laboratories and engineering services through the development of a domain-agnostic measurement ontology (DAMO). The ontology development is elucidated using a tailored ontology engineering methodology (OEM) applying an agile and iterative approach based on previously published OEMs. Finally, the use of DAMO is demonstrated through three laboratory-based case studies.
On the Interplay between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis

Are end-to-end text-to-speech (TTS) models over-parametrized? To what extent can these models be pruned, and what happens to their synthesis capabilities? This work serves as a starting point to explore pruning both spectrogram prediction networks and vocoders. We thoroughly investigate the tradeoffs between sparsity and its subsequent effects on synthetic speech. Additionally, we explore several aspects of TTS pruning: amount of finetuning data versus sparsity, TTS-Augmentation to utilize unspoken text, and combining knowledge distillation and pruning. Our findings suggest that not only are end-to-end TTS models highly prunable, but also, perhaps surprisingly, pruned TTS models can produce synthetic speech with equal or higher naturalness and intelligibility, with similar prosody. All of our experiments are conducted on publicly available models, and findings in this work are backed by large-scale subjective tests and objective measures. Code and 200 pruned models are made available to facilitate future research on efficiency in TTS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Determining Action Reversibility in STRIPS Using Answer Set and Epistemic Logic Programming

Abstract In the context of planning and reasoning about actions and change, we call an action reversible when its effects can be reverted by applying other actions, returning to the original state. Renewed interest in this area has led to several results in the context of the PDDL language, widely used for describing planning tasks. In this paper, we propose several solutions to the computational problem of deciding the reversibility of an action. In particular, we leverage an existing translation from PDDL to Answer Set Programming (ASP), and then use several different encodings to tackle the problem of action reversibility for the STRIPS fragment of PDDL. For these, we use ASP, as well as Epistemic Logic Programming (ELP), an extension of ASP with epistemic operators, and compare and contrast their strengths and weaknesses.
Argument corpus development and argument component classification: A study in academic Spanish

Abstract Academic writing is a complex task that requires an author to be skilled in argumentation. The goal of an academic author is to communicate ideas clearly and to convince the reader of the veracity of their claims. However, few students in lower level courses are good arguers, as this is a skill that takes time to master. With the aim of contributing to the development of this skill, we introduce a freely available annotated corpus to support research on argumentation in Spanish. To build the corpus, we developed an annotation guide to identify argumentation in paragraphs. The guide also outlines how to annotate segments of sentences as either claims or premises and how to indicate relations (support or attack) between such components. Then, an annotated corpus of 444 sections was created, and an almost perfect interannotator agreement rate was achieved for a number of its components. After its construction, the corpus was used to perform an exploratory analysis, which aimed to identify the amount of argumentation in each section as well as the patterns commonly used for argument identification. Thereafter, experiments were conducted to automatically classify argumentative components using lexical, semantic, discourse, structural, and sentiment features. A Document Occurrence Representation was also tested on the corpus constituting a novel approach in the representation of documents for automatic argument detection. The results of these experiments to automatically classify argumentative components in Spanish are very promising and seem to support the idea that linguistic feature engineering plays a crucial role in text classification.
Developer Relations (DevRel) Roles: an Exploratory Study on Practitioners’ opinions

Amazon, Google, and Facebook are examples of organizations that are investing in Developer Relations (DevRel) teams to support the synergy among developer communities’ expectations and the organization’s goals. The DevRel strategies (e.g., engaging and maturing developer communities) require the understanding of team structure to improve results. Identifying roles is a critical activity because DevRel people must plan, perform and monitor specific strategies to attract, engage, and mature developer communities. This research investigates the DevRel Roles by describing definitions and skills for each role. We synthesize the ”voice” of 116 practitioners from 19 countries with a grey literature review. We discuss nine identified roles regarding definition and skills. The roles include, for example, Developer Programs Engineer, Developer/Technical Advocate, Developer/Technical Evangelist, and Technical Writer. We noticed a lack of standardization of roles in DevRel.
Image Style Transfer Based on DPN-CycleGAN

Image style transfer has always been a hot topic in the field of image generation. Generative Adversarial Network (GAN) is used to realize image style transfer can greatly reduce the workload, and get more fruitful results. The traditional image style transfer algorithm needs to convert between two paired images, but the paired data set used for training is difficult to obtain. In order to avoid being limited by data sets and improve the efficiency of image style transfer, this paper proposes an improved Cycle-Consistent Adversarial Network (DPN-CycleGAN), which uses Dual Path Network (DPN) replaces the Deep Residual Network (ResNet) of the original network generator, and adds Identity Loss on the basis of the original loss. The changes make the network structure more simplified, reduce the computational complexity, improve the network performance to a certain extent, and improve the quality of the image generated by style transfer. Finally, the experimental results show that the SSIM value and PSNR value of the generated images are increased by 3.6% and 9.7% on average, which proves the effectiveness of the DPN-CycleGAN image style transfer algorithm proposed by this paper.
Towards Designerly Data Donation

In-the-wild research allows the HCI community to gain insights into personal behaviour and characteristics. For designers and researchers, this means having access to rich spatiotemporal insights reflecting user's characteristics, behaviours, and needs. However, designerly contexts require contextualized and meaningful data, and collecting it in-the-wild involves a great effort. In addition, ethical implications need to be considered. In this paper, we propose designerly data donation, a participatory approach for data collection in-the-wild, as an effective and ethical way to enable data-centric design processes. We present the potential benefits of designerly data donation around three axes: value gain, data contextualization, and roles and relationships. And we introduce the challenges of designerly data donation at the intersection of HCI, UbiComp, and design.
Game of Coins

The cryptocurrency market is blooming. Tens of new coins emerge every year and their total market cap keeps growing. The research community is trying to keep up by proposing improved mining protocols and attacking existing ones. However, surprisingly as it may sound, most existing works overlook the real-life multi-coin market, by focusing on a system with a single coin. To the best of our knowledge, this paper was the first to consider a system with many coins and strategic miners that are free to choose where to mine. We first formalize the current practice of strategic mining in multi-coin markets as a singleton weighted congestion game and prove that any better-response dynamics in such a game converges to an equilibrium. Then, in our main result, we present a reward design attack that moves the system configuration from any initial equilibrium to a desired one. The attack is executed via temporary manipulation of coin rewards, which leads strategic miners to switch between coins. It applies to any better-response dynamics of the miners. To motivate our attack we show that in any equilibrium there is always at least one miner whose profit is higher in a different one, and thus may benefit from such an attack.
Towards Automating Code-Reuse Attacks Using Synthesized Gadget Chains

In the arms race between binary exploitation techniques and mitigation schemes, code-reuse attacks have been proven indispensable. Typically, one of the initial hurdles is that an attacker cannot execute their own code due to countermeasures such as data execution prevention (DEP, Open image in new window ). While this technique is powerful, the task of finding and correctly chaining gadgets remains cumbersome. Although various methods automating this task have been proposed, they either rely on hard-coded heuristics or make specific assumptions about the gadgets’ semantics. This not only drastically limits the search space but also sacrifices their capability to find valid chains unless specific gadgets can be located. As a result, they often produce no chain or an incorrect chain that crashes the program. In this paper, we present SGC, the first generic approach to identify gadget chains in an automated manner without imposing restrictions on the gadgets or limiting its applicability to specific exploitation scenarios. Instead of using heuristics to find a gadget chain, we offload this task to an SMT solver. More specifically, we build a logical formula that encodes the CPU and memory state at the time when the attacker can divert execution flow to the gadget chain, as well as the attacker’s desired program state that the gadget chain should construct. In combination with a logical encoding of the data flow between gadgets, we query an SMT solver whether a valid gadget chain exists. If successful, the solver provides a proof of existence in the form of a synthesized gadget chain. This way, we remain fully flexible w.r.t. to the gadgets. In empirical tests, we find that the solver often uses all types of control-flow transfer instructions and even gadgets with side effects. Our evaluation shows that SGC successfully finds working gadget chains for real-world exploitation scenarios within minutes, even when all state-of-the-art approaches fail.
Shape Measurement Method of Two-Dimensional Micro-Structures beyond the Diffraction Limit Based on Speckle Interferometry

A technique based on speckle interferometry for observing microstructures beyond the diffraction limit by detecting the spatial phase distribution of scattered light from microstructures has previously been reported. In this study, the development of this technique using a two-dimensional method is discussed. In order to observe general two-dimensional images, development of new technology in several stages is required. A two-dimensional filtering technique to reduce the noise component and a two-dimensional integration path to detect the three-dimensional shape of the surface are described in detail. As a first step toward observing complex two-dimensional structures in the future, it is investigated that directional two-dimensional information such as fibrous materials and micro-linear structures can be visually captured and treated as meaningful two-dimensional structures. As a result, it is shown that it is possible to observe fine two-dimensional letters with a line width of 100 nm, which is beyond the diffraction limit of the objective lens, demonstrating the effectiveness of the observation technique for microstructures by phase detection.
Exploring various emotion-shades for Marathi Sentiment Analysis

People's opinions, attitudes, and emotions can be understood with the help of computational study is known as sentiment analysis or opinion mining. Opinion mining or sentiment analysis extracts subjective information from natural language text. With 90 million fluent speakers worldwide, Marathi ranks as the 4th most spoken language in India and the 15th most globally spoken language. Day by day, the data in the Marathi language is increasing on the Internet. Hence, there is an urge to study opportunities and challenges in Marathi sentiment analysis. This paper reviews the literature on Marathi sentiment analysis to highlight opportunities and challenges in Marathi context-based sentiment analysis for researcher and propose a shaded based Marathi sentiment analysis. This shaded-based sentiment analysis is categorized under semantic-corpus based sentiment classification problem.
Metric-Distortion Bounds Under Limited Information

In this work we study the metric distortion problem in voting theory under a limited amount of ordinal information. Our primary contribution is threefold. First, we consider mechanisms which perform a sequence of pairwise comparisons between candidates. We show that a widely-popular deterministic mechanism employed in most knockout phases yields distortion \(\mathcal {O}(\log m)\) while eliciting only \(m-1\) out of \(\varTheta (m^2)\) possible pairwise comparisons, where m represents the number of candidates. We also provide a matching lower bound on its distortion. In contrast, any mechanism which performs fewer than \(m-1\) pairwise comparisons has unbounded distortion. Moreover, we study the power of deterministic mechanisms under incomplete rankings. Most notably, when every agent provides her k-top preferences we show an upper bound of \(6 m/k + 1\) on the distortion, for any \(k \in \{1, 2, \dots , m\}\), substantially improving over the previous bound of 12m/k recently established by Kempe [, ]. Finally, we are concerned with the sample complexity required to ensure near-optimal distortion with high probability. Our main contribution is to show that a random sample of \(\varTheta (m/\epsilon ^2)\) voters suffices to guarantee distortion \(3 + \epsilon \) with high probability, for any sufficiently small \(\epsilon > 0\). This result is based on analyzing the sensitivity of the deterministic mechanism introduced by Gkatzelis, Halpern, and Shah [].