Reviewerly/exHarmony huggingface dataset:
A Study on Recruitment of Data Analyst Based on Text Mining and Visualization Technology

Abstract Nowadays, the demand for data analysts has increased dramatically. First the job requirements of data analyst are downloaded from online recruitment website ChinaHR.com and zhaopin.com, then the information of the recruitments are analyzed by text mining method such as word segmentation, word frequency statistics and visualization technology. At last we summarized the required skills for data analyst. We found that compared with Python, the company is inclined to use R for data analysis, the recruitment requirements of every type of company are different. Our findings can provide reference for schools and guide individual professionals to assess and advance their competencies.
IVcache

The sharing of last-level cache (LLC) among different CPU cores makes cache vulnerable to side channel attacks. An attacker can get private information about co-running applications (victims) by monitoring their accesses in LLC. Cache side channel attacks can be mitigated by partitioning cache between the victim and attacker. However, previous partition works either make weak assumptions about the attacker's strength or force their security mechanisms and thus overhead to every user on the system, regardless of their security requirement.
Learning Intuitive Physics with Multimodal Generative Models

Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelligent and anticipatory actions. This paper presents a perception framework that fuses visual and tactile feedback to make predictions about the expected motion of objects in dynamic scenes. Visual information captures object properties such as 3D shape and location, while tactile information provides critical cues about interaction forces and resulting object motion when it makes contact with the environment. Utilizing a novel See-Through-your-Skin (STS) sensor that provides high resolution multimodal sensing of contact surfaces, our system captures both the visual appearance and the tactile properties of objects. We interpret the dual stream signals from the sensor using a Multimodal Variational Autoencoder (MVAE), allowing us to capture both modalities of contacting objects and to develop a mapping from visual to tactile interaction and vice-versa. Additionally, the perceptual system can be used to infer the outcome of future physical interactions, which we validate through simulated and real-world experiments in which the resting state of an object is predicted from given initial conditions.
Playing on the Globe: Facilitating virtual communications between Namibian and Finnish learners to co-design an interactive map game

An increasing trend of digitally enhancing learning provides unique opportunities to expand from local to global learning environments. However, current technologies and educational approaches do not adequately support creative communication and learning for primary school learners across the globe. We maintain that it is important to promote agentic engagement amongst learners by involving them in the design of their own digitally enhanced learning environment. In this study, we connected ten learners from a Namibian and a Finnish school in virtual communications to co-design an interactive map game. Following a research through design approach, we share a detailed account of the case study in which we facilitated a distributed co-design process, using Skype, a custom made VR environment, and Microsoft Whiteboard. We reflect on the co-design sessions, focusing on communication and the design concept evolution contributing to distributed co-design methods and techniques not only for the design community but also as an agentic learning process that could be incorporated into formal learning environments. With a paucity of existing empirical work of geographically distributed co-design with children, our case study provides an empirical foundation pointing at critical issues to be further investigated such as multilingual design, social cohesion, facilitation roles, technologies, design breadcrumbs, negotiations and learner’s pride.
Redacting Transactions from Execute-Order-Validate Blockchains

As user privacy gains popularity and attention, and starts to shape relations between users and service providers, blockchain based solutions thrive for ways to relax immutability without sacrificing consistency. This work answers that need and presents the first design for a redactable execute-order-validate blockchain, that grants users with the right to be forgotten. The design is easy to adopt, as we exemplify by implementing it on top of Hyperledger Fabric. It modifies the block structure and extracts user data from the hash-chain without loosening any correctness or liveness criteria. We evaluate our design and show that it provides compliance with only a minimal performance overhead, making it a feasible add-on to any execute-order-validate blockchain system.
Escaping Strict Saddle Points of the Moreau Envelope in Nonsmooth Optimization

Recent work has shown that stochastically perturbed gradient methods can efficiently escape strict saddle points of smooth functions. We extend this body of work to nonsmooth optimization, by analyzing an inexact analogue of a stochastically perturbed gradient method applied to the Moreau envelope. The main conclusion is that a variety of algorithms for nonsmooth optimization can escape strict saddle points of the Moreau envelope at a controlled rate. The main technical insight is that many algorithms applied to the proximal subproblem yield directions that approximate the gradient of the Moreau envelope.
Towards Shaping the Software Lifecycle with Methods and Practices

As software projects are very diverse, each software development process must be adjusted to the needs of the project and the corresponding development team. Frequently, we find different methods and practices combined in a so-called hybrid development method. Research has shown that these hybrid methods evolve over time and are devised based on experience. However, when devising a hybrid method, the methods and practices used should cover the whole software project with its different phases including, among others, project management, requirements analysis, quality management, risk management, and implementation. In this paper, we analyze which methods and practices are used in which phase of a software project. Based on an initial survey with 27 practitioners, we provide a mapping of methods and practices to different project phases and vice versa. Despite the preliminary nature of our study and the small sample size, we observe three remarkable aspects: (1) there are discrepancies between the intended use of methods and practices according to literature and the real use in practice, (2) practices are used more consistently than methods, and (3) parts of the software lifecycle such as maintenance and evolution are hardly covered by widely distributed methods and practices. Consequently, when devising a development process, it is worth a thought whether all phases of the software lifecycle are addressed or not.
SPBERT: an Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs

Knowledge Graph is becoming increasingly popular and necessary during the past years. In order to address the lack of structural information of SPARQL query language, we propose SPBERT, a transformer-based language model pre-trained on massive SPARQL query logs. By incorporating masked language modeling objectives and the word structural objective, SPBERT can learn general-purpose representations in both natural language and SPARQL query language. We investigate how SPBERT and encoder-decoder architecture can be adapted for Knowledge-based QA corpora. We conduct exhaustive experiments on two additional tasks, including SPARQL Query Construction and Answer Verbalization Generation. The experimental results show that SPBERT can obtain promising results, achieving state-of-the-art BLEU scores on several of these tasks.
Going Beyond Empowered Design by Scaffolding Inter-community Engagement

When participatory technological design initiatives are focused on a very specific local context it becomes difficult to scale up their deployment to wider or different contexts. We have therefore extended the notion of empowered design and consequent technology appropriations and show that scaffolding, assistance that fades away, is a viable method to facilitate inter-community engagements with technology. This leads to technology appropriation by communities who did not participate in the original design, and they do this independently of the original academic design team. In this paper we report on our project process over an extended period of time. A collaborator and co-author on this paper is an elder from the original community who is acting to extend and adapt the technology with other, distant, communities from the same ethnic group, the ovaHimba. We particularly focus on the evolving role our collaborator took on over a number of field trips, and how our support, the scaffolding, became less and less important. His perspective is shared through translated statements and integrated into the text after our joint paper discussions.
Optimizing fitness-for-use of differentially private linear queries

In practice, differentially private data releases are designed to support a variety of applications. A data release is fit for use if it meets target accuracy requirements for each application. In this paper, we consider the problem of answering linear queries under differential privacy subject to per-query accuracy constraints. Existing practical frameworks like the matrix mechanism do not provide such fine-grained control (they optimize total error, which allows some query answers to be more accurate than necessary, at the expense of other queries that become no longer useful). Thus, we design a fitness-for-use strategy that adds privacy-preserving Gaussian noise to query answers. The covariance structure of the noise is optimized to meet the fine-grained accuracy requirements while minimizing the cost to privacy.
Model Completeness, Uniform Interpolants and Superposition Calculus

Abstract Uniform interpolants have been largely studied in non-classical propositional logics since the nineties; a successive research line within the automated reasoning community investigated uniform quantifier-free interpolants (sometimes referred to as “covers”) in first-order theories. This further research line is motivated by the fact that uniform interpolants offer an effective solution to tackle quantifier elimination and symbol elimination problems, which are central in model checking infinite state systems. This was first pointed out in ESOP 2008 by Gulwani and Musuvathi, and then by the authors of the present contribution in the context of recent applications to the verification of data-aware processes. In this paper, we show how covers are strictly related to model completions, a well-known topic in model theory. We also investigate the computation of covers within the Superposition Calculus, by adopting a constrained version of the calculus and by defining appropriate settings and reduction strategies. In addition, we show that computing covers is computationally tractable for the fragment of the language used when tackling the verification of data-aware processes. This observation is confirmed by analyzing the preliminary results obtained using the mcmt tool to verify relevant examples of data-aware processes. These examples can be found in the last version of the tool distribution.
Studying the Change Histories of Stack Overflow and GitHub Snippets

Stack Overflow is a popular Q&A forum for soft-ware developers, providing a large number of copyable code snippets. While GitHub is a collaborative development platform, developers often reuse Stack Overflow code in their GitHub projects. These snippets get revised or edited on each platform. In this work, we study Stack Overflow posts and the code snippets that are reused from these posts in GitHub projects. We investigate and compare the change history of SO snippets with the change history of GitHub snippets. We have applied a stratified random sampling when mining 440,000 GitHub projects to create a dataset representing the change history of the reused snippets; this dataset contains 22,900 GitHub projects, 33,765 Stack Overflow references mapped to 4,634 Stack Overflow posts, and a total of 73,322 commits.We analyze the evolution patterns of snippets on each platform, compare key trends and explore the co-change of these snippets. Our results demonstrate that 76% of snippets evolve on Stack Overflow, while only 22% of the reused code snippets evolve in GitHub. Stack Overflow snippets undergo fewer and smaller changes compared to their evolving counterparts on GitHub. The evolution of snippets on both platforms is driven by the original author of the content. Finally, we found that a small percentage of snippets is co-changing across two platforms, while snippets in GitHub and Stack Overflow evolve independently of one another.
Proof-of-Work Cryptocurrencies: Does Mining Technology Undermine Decentralization?

Does the proof-of-work consensus protocol serve its intended purpose of supporting decentralized cryptocurrency mining? To address this question, we develop a game-theoretical model in which miners first invest in hardware to improve the efficiency of their operations and then compete for mining rewards in a rent-seeking game. We show that centralization grows with heterogeneity in mining costs, but hardware capacity constraints prevent the most efficient miners from monopolizing the mining process. Investment leads to a more decentralized network unless larger miners have a significant comparative advantage in acquiring new hardware. Our model generates empirically supported implications: (i) mining centralization is countercyclical with respect to mining reward, and (ii) a change in mining reward leads to a less-than-proportional change in hash rates. This paper was accepted by David Simchi-Levi, Special Section of Management Science: Blockchains and Crypto Economics. Supplemental Material: The data file is available at https://doi.org/10.1287/mnsc.2023.4840 .
Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint

Product search is the most common way for people to satisfy their shopping needs on e-commerce websites. Products are typically annotated with one of several broad categorical tags, such as "Clothing" or "Electronics", as well as finer-grained categories like "Refrigerator" or "TV", both under "Electronics". These tags are used to construct a hierarchy of query categories. Distributions of features such as price and brand popularity vary wildly across query categories. In addition, feature importance for the purpose of CTR/CVR predictions differs from one category to another. In this work, we leverage the Mixture of Expert (MoE) framework to learn a ranking model that specializes for each query category. In particular, our gate network relies solely on the category ids extracted from the user query.While classical MoE's pick expert towers spontaneously for each input example, we explore two techniques to establish more explicit and transparent connections between the experts and query categories. To help differentiate experts on their domain specialties, we introduce a form of adversarial regularization among the expert outputs, forcing them to disagree with one another. As a result, they tend to approach each prediction problem from different angles, rather than copying one another. This is validated by a much stronger clustering effect of the gate output vectors under different categories. In addition, soft gating constraints based on the categorical hierarchy are imposed to help similar products choose similar gate values. and make them more likely to share similar experts. This allows aggregation of training data among smaller sibling categories to overcome data scarcity.Experiments on a learning-to-rank dataset collected from the JD e-commerce search log demonstrate that MoE with these improvements consistently outperforms competing models, in terms of offline metrics and online AB tests.
Enhancing Metric-Based Few-Shot Classification With Weighted Large Margin Nearest Center Loss

Metric-learning-based methods, which attempt to learn a deep embedding space on extremely large episodes, have been successfully applied to few-shot classification problems. In this paper, we propose the adoption of large margin nearest center (LMNC) loss during episodic training to enhance metric-learning-based few-shot classification methods. Loss functions (such as cross-entropy and mean square error) commonly used in episodic training strive to achieve the strict goal that differently labeled examples in the embedding space are separated by an infinite distance. However, the learned embedding space cannot guarantee that this goal will be achieved for every episode sampled from a large number of classes. Instead of an infinite distance, LMNC loss requires only that differently labeled examples be separated by a large margin, which can well relax the strict constraint of the traditional loss functions, easily leading to a discriminative embedding space. Moreover, considering the multilevel similarity between various classes, we alleviate the constraint of a fixed large margin and extend LMNC loss to weighted LMNC (WLMNC) loss, which can effectively take advantage of interclass information, achieving a more separable embedding space with adaptive interclass margins. Experiments on state-of-the-art benchmarks demonstrate that the adoption of LMNC and WLMNC losses can strongly improve the embedding learning performance and classification accuracy of metric-based few-shot classification methods for various few-shot scenarios. In particular, LMNC and WLMNC losses can obtain 1.86% and 2.46% gains in prototypical network on miniImageNet for 5-way 1-shot scenario, respectively.
On the Improvement of Eye Tracking-Based Cognitive Workload Estimation Using Aggregation Functions

Cognitive workload, being a quantitative measure of mental effort, draws significant interest of researchers, as it allows to monitor the state of mental fatigue. Estimation of cognitive workload becomes especially important for job positions requiring outstanding engagement and responsibility, e.g., air-traffic dispatchers, pilots, car or train drivers. Cognitive workload estimation finds its applications also in the field of education material preparation. It allows to monitor the difficulty degree for specific tasks enabling to adjust the level of education materials to typical abilities of students. In this study, we present the results of research conducted with the goal of examining the influence of various fuzzy or non-fuzzy aggregation functions upon the quality of cognitive workload estimation. Various classic machine learning models were successfully applied to the problem. The results of extensive in-depth experiments with over 2000 aggregation operators shows the applicability of the approach based on the aggregation functions. Moreover, the approach based on aggregation process allows for further improvement of classification results. A wide range of aggregation functions is considered and the results suggest that the combination of classical machine learning models and aggregation methods allows to achieve high quality of cognitive workload level recognition preserving low computational cost.
A Deep Learning Approach to Classify and Quantify the Multiple Emotions of Arabic Tweets

In this paper, we introduce both a Multi-Label Classification (MLC) method to determine all the emotions expressed in an Arabic tweet and a Multi-Target Regression (MTR) method to determine the emotions' intensities. MLC involves the prediction of zero or more classes per sample. It is one of the interesting research topics in Natural Language Processing (NLP), especially for the Arabic language due to scarcity of works related to it. MTR is a harder task compared to MLC, but it lends itself as a suitable representation for Emotion Analysis (EA), which is gaining more interest due to the increasing use of social media and the wide range of applications related to it. This work introduces a new study on the use of Deep Learning (DL) techniques for emotions classification and quantification in Arabic tweets.
Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation

Domain adaptation (DA) mitigates the domain shift problem when transferring knowledge from one annotated domain to another similar but different unlabeled domain. However, existing models often utilize one of the ImageNet models as the backbone without exploring others, and fine-tuning or retraining the backbone ImageNet model is also time-consuming. Moreover, pseudo-labeling has been used to improve the performance in the target domain, while how to generate confident pseudo labels and explicitly align domain distributions has not been well addressed. In this paper, we show how to efficiently opt for the best pre-trained features from seventeen well-known ImageNet models in unsupervised DA problems. In addition, we propose a recurrent pseudo-labeling model using the best pre-trained features (termed PRPL) to improve classification performance. To show the effectiveness of PRPL, we evaluate it on three benchmark datasets, Office+Caltech-10, Office31, and Office-Home. Extensive experiments show that our model reduces computation time and boosts the mean accuracy to 98.1%, 92.4%, and 81.2%, respectively, substantially outperforming the state of the art.
Sentimental Analysis on Twitter Data of Political Domain

The arrival of social media has initiated the platform for the public to express their views and to share their emotions. In addition to this, smart phones and mobile communication technologies have emerged and become as a tool to spend more time than earlier on social media to be in touch with their friends. The way of expressing the thoughts, attitude, and feelings is changed drastically due to that short messages and emoticons utilization has been increased through social media. There have been no constraints in using the micro blogging services like Facebook, Twitter, etc. Thus, these messages are the views of people used to describe their behavior. Nowadays political party workers and leaders also started spending more time on Twitter, Facebook, and blogs to be in touch with the public. Hence, political parties or social media campaigners started analyzing for the solutions to utilize the public opinions or views about their own political party. A program is developed which collects the reviews or text posts of people and transfer it to the next module called pre-processing or cleansing. This module is to remove URLs, special symbols, Junk text, stop words, and tokenize the sentences and to perform the stemming process. Word2vec model is used to perform the word embedding to convert the text data into numerical vector format to transfer it to Recurrent Neural Network. The sigmoid function is used as an activation function to classify and polarity of positive and negative sentiments have been evaluated. The effectiveness of the proposed system has been proved through experimental analysis in this paper. This proposed system will be used to understand the winning chances of political party and to analyze the response of the public on particular political decision during election campaign.