Miracle-dz/UniFAR-FTUs huggingface dataset:
Durability and Reliability of EV Batteries under Electric Utility Grid Operations: Path Dependence of Battery Degradation

Vehicle-to-grid (V2G) and Grid-to-vehicle (G2V) strategies are often cited as promising approaches to mitigate the intermittency of renewable energy on electric power grids. However, their impact on vehicle battery degradation have yet to be investigated in detail. Since battery degradation is path dependent, i.e. different usage schedules lead to different degradation mechanisms, it is essential to investigate batteries under realistic V2G and G2V scenarios. The aim of this work is to understand the effect of bidirectional charging on the degradation mechanisms of commercial Li-ion cells used in electric vehicles today. Results showed that an extra V2G step during cycle-aging accelerated capacity loss and degraded the kinetics at the negative electrode. Moreover, for all cycling duty cycles, the loss of active material at the negative electrode was higher than the loss of lithium inventory. This condition could trigger lithium plating and shorten cell lifetimes. In the calendar-aging experiments, state of charge was shown to be an important factor and interacted with temperature to accelerate the loss of active material at the positive electrode and the loss of lithium. It was also found that high state of charge values caused loss of active material at the negative electrode and kinetic limitations.
Auto-FuzzyJoin: Auto-Tune Fuzzy Joins Without Labeled Examples

Fuzzy join is an important database operator that is widely used in practice. So far the research community has focused exclusively on the important problem of efficient fuzzy join algorithms. To-day, practitioners also struggle with a daunting space of parameters to optimize join quality (e.g., distance-function, distance-thresholds, tokenization-options, etc.), where a manual trial-and-error approach has become a major pain-point. This important parameter-tuning problem has thus far received surprisingly little attention from the research community. Motivated by the observation, we study the problem of “auto-tune” suitable fuzzy-join parameters on given input tables, without requiring explicit human input such as labelled training data. We develop an Auto-FuzzyJoin framework that, when given two tables 𝐿 and 𝑅 , and a user-specified target precision 𝜏 (say 0.9), can automatically configure fuzzy-join parameters meeting the precision target 𝜏 in expectation, while maximizing fuzzy-join recall (defined as the number of correctly joined records). We study the hardness of this problem, and develop effective algorithms that are extensively tested on both existing benchmarks as well as a new benchmark of 50 fuzzy-join tasks we created from Wikipedia data. Our evaluation suggests that the proposed Auto-FuzzyJoin significantly outperforms existing unsupervised approaches (e.g., Microsoft Excel and ZeroER). It is surprisingly competitive even against supervised approaches (e.g., Magellan and DeepMatcher) when 50% of ground-truth joins are used as training data. We will release our code and benchmark to facilitate future research.
Sustaining the Life of Wireless Sensor Node with Energy Harvesting

Wireless Sensor Networks (WSN) play a significant role in a number of sensing and monitoring applications. It has also become a key enabling technology for Internet of Things (IoT). Most of the earlier works were focused on extending the battery life by reducing the energy consumption of the wireless node. Availability of small footprint devices capable of ambient energy harvesting has triggered renewed interest in enhancing the performance of WSNs. In this paper we propose a novel method that can be applied to duty cycle based Media Access Control (MAC) protocols in energy harvesting WSNs to sustain the life of the node. Our approach takes into consideration the solar energy available at any instant of time so as to maximize the performance. We have conducted detailed measurements and analysis of solar energy harvested under different conditions. Our results show significant enhancement in throughput achieved while sustaining the life of the node.
Developing a Framework for SME E-Commerce: A UK Perspective

Whilst there has been work on e-commerce development, much of the work has been about large organisations and much has been USA-based. This research focuses on SMEs in the UK, and it considers how much of the existing published literature is applicable in this context. A prototyping approach is being developed to create a framework that will be based on benchmarks and that may be used by SMEs to assist in the creation of successful e-commerce web sites.
Secure the Data by using Image Compression in Multi-Cloud Storage

Cloud storage providers are in ability for trust the data available and accessible that makes physical condition to be secure and running. Organizations bargain storage capacity from the providers to store user data, business, or request data. Clients desires to furnish their data to public cloud servers (PCSs). Recently got security troubles take to solve and aid more client’s improvement their records in multi-cloud Storage. The customers allow saving information in the cloud the use of services supplied by using more than one cloud storage companies (CSPs). It is a hopeful method to enlarge the degree of records availability and confidentiality, as all at once these distinctive CSPs are out of provider at the identical time or collude with each other to extract records of a user. This suggest a survey on the viable protection merits by means of creating use of multiple wonderful clouds at the identical time. In addition, this advice work as merging and splitting standards at some point of storage in cloud environment. In file access, personal keys produced the use of digital key generator. 3DES encryption algorithm generates key in a cipher text format to the users. Towards facts confidentiality, this advocate an assured facts hiding and spitting picture technique in cloud storage. In propose work, essential purpose will be photo compression with mutable statistics hiding approach whilst storing in the real cloud for data hiding & photo compression via the use of Discrete Wavelet Transform (DWT) algorithm.
A Modified Elephant Herd Optimization Algorithm to Solve the Single Machine Scheduling Problems

This study reports the single machine scheduling problem for finding the minimum the total weighted tardiness. As the problem is non-deterministic polynomial-time hard (NP-hard), the problem might not be answered by the exact solution techniques. Henceforth, different heuristics and meta-heuristics were recommended by different practitioners to tackle the problem. A modified elephant herd optimization algorithm (MEHOA) is investigated in the present work to solve the single machine total weighted tardiness scheduling problem (SMTWTSP). The performance of the anticipated approach is studied with the test instances available in the OR library. The outcomes are compared with several different algorithms offered in the literature and indicate the efficacy of the developed methodology.
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling

Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks. However, previous works commonly have difficulty dealing with large-scale dynamic lexicons which often cause excessive matching noise and problems of frequent updates. In this paper, we propose DyLex, a plug-in lexicon incorporation approach for BERT based sequence labeling tasks. Instead of leveraging embeddings of words in the lexicon as in conventional methods, we adopt word-agnostic tag embeddings to avoid re-training the representation while updating the lexicon. Moreover, we employ an effective supervised lexical knowledge denoising method to smooth out matching noise. Finally, we introduce a col-wise attention based knowledge fusion mechanism to guarantee the pluggability of the proposed framework. Experiments on ten datasets of three tasks show that the proposed framework achieves new SOTA, even with very large scale lexicons.
Automated and Explainable Ontology Extension based on Deep Learning: A Case Study in the Chemical Domain

Reference ontologies provide a shared vocabulary and knowledge resource for their domain. Manual construction enables them to maintain a high quality, allowing them to be widely accepted across their community. However, the manual development process does not scale for large domains. We present a new methodology for automatic ontology extension and apply it to the ChEBI ontology, a prominent reference ontology for life sciences chemistry. We trained a Transformer-based deep learning model on the leaf node structures from the ChEBI ontology and the classes to which they belong. The model is then capable of automatically classifying previously unseen chemical structures. The proposed model achieved an overall F1 score of 0.80, an improvement of 6 percentage points over our previous results on the same dataset. Additionally, we demonstrate how visualizing the model's attention weights can help to explain the results by providing insight into how the model made its decisions.
A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches

Computational methods to model political bias in social media involve several challenges due to heterogeneity, high-dimensionality, multiple modalities, and the scale of the data. Most of the current political bias detection methods rely heavily on the manually-labeled ground-truth data for the underlying political bias prediction tasks. Such methods are human-intensive labeling, labels related to only a specific problem, and the inability to determine the near future bias state of a social media conversation. In this work, we address such problems and give machine learning approaches to study political bias in two ideologically diverse social media forums: Gab and Twitter without the availability of human-annotated data. We propose a method to exploit the features of entities on transcripts collected from political speeches in US congress to label political bias of social media posts automatically without any human intervention. With existing machine learning algorithms we achieve the highest accuracy of 70.5% and 65.1% to predict posts on Twitter and Gab data respectively. We also present a machine learning approach that combines features from cascades and text to forecast cascade’s political bias with an accuracy of about 85%.
Neural Unit Test Suggestions

Testing is widely recognized as an important stage of the software development lifecycle. Effective software testing can provide benefits such as documentation, bug finding, and preventing regressions. In particular, unit tests document a unit’s intended functionality. A test oracle , typically expressed as an condition, documents the intended behavior of the unit under a given test prefix. Synthesizing a functional test oracle is a challenging problem, as it has to capture the intended functionality and not the implemented functionality. In this paper, we propose NUTS (Neural Unit Test Suggestions), a unified transformer-based neural approach to infer both exceptional and assertion test oracles based on the context of the focal method. Our approach can handle units with ambiguous or missing documentation, and even units with a missing implementation. We evaluate our approach on both oracle inference accuracy and functional bug-finding. Our technique improves accuracy by 33% over existing oracle inference approaches, achieving 96% overall accuracy on a held out test dataset. Furthermore, we show that when integrated with a automated test generation tool (EvoSuite), our approach finds 54 real world bugs in large-scale Java programs, including 27 bugs that are not found by any other automated testing method in our evaluation.
Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks

Several recent studies on dyadic human-human interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies dedicated to more precise environments such as after sales services or customer satisfaction surveys. In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow. This context leads to multiple challenges that range from exploiting restricted, small and mostly unlabeled datasets to finding and adapting methods for such context. We tackle these challenges by using Few-Shot Learning while making the hypothesis it can serve conversational emotion classification for different languages and sparse labels. We contribute by proposing a variation of Prototypical Networks for sequence labeling in conversation that we name ProtoSeq. We test this method on two datasets with different languages: daily conversations in English and customer service chat conversations in French. When applied to emotion classification in conversations, our method proved to be competitive even when compared to other ones.
Localization with Directional Coordinates

A coordinate system is proposed that replaces the usual three-dimensional Cartesian x, y, z position coordinates, for use in robotic localization applications. Range, azimuth, and elevation measurement models become greatly simplified, and, unlike spherical coordinates, the proposed coordinates do not suffer from the same kinematic singularities and angle wraparound. When compared to Cartesian coordinates, the proposed coordinate system results in a significantly enhanced ability to represent the true distribution of robot positions, ultimately leading to large improvements in state estimation consistency.
Heavy flavor collectivity in small systems

The presence of correlations between particles significantly separated in pseudorapidity in protonproton and proton-nucleus collisions revealed surprises in the early LHC data. Are the physical processes responsible for the observed long-range pseudorapidity correlations and their azimuthal structure the same in small collision systems as in heavy ion collisions? Whereas in the case of heavy ion collisions “flow” is interpreted as generated by initial geometric inhomogeneities, calculations indicate that initial-state momentum correlations are present and could contribute to the observed azimuthal anisotropy in small systems. Probes involving heavy quarks provide us with a unique opportunity to disentangle different quantum chromodynamics effects at the boundary between lowand high-pT interactions, and hence shed light on the origin of flow in small collision systems. A selection of the latest measurements is presented for the flow and production of heavy flavor hadrons and their decay products.
Worldsheet Free Fields, Higher Spin Symmetry and Free ${\cal N}=4$ Super Yang-Mills

By using the free field worldsheet realization described by Gaberdiel and Gopakumar recently, we construct the nontrivial lowest generators of the higher spin superalgebra hs(2, 2|4). They consist of cubic terms between the bilinears of ambitwistor-like fields. We also obtain the worldsheet description for the findings of Sezgin and Sundell twenty years ago given by the familiar oscillator construction. The first order poles of the operator product expansions (OPEs), between the conformal weight-1 generators of Lie superalgebra PSU(2, 2|4) and the above conformal weight-3 generators of hs(2, 2|4), are determined explicitly and the additional generators appear in the worldsheet theory.
Decentralized Wireless Federated Learning With Differential Privacy

This article studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a decentralized wireless federated learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers exchange their privacy preserving data with the analog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies <inline-formula><tex-math notation="LaTeX">$(\epsilon,\delta)$</tex-math></inline-formula>-differential privacy and the privacy budget per worker scales as <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\frac{1}{\sqrt{N}})$</tex-math></inline-formula>, in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\sqrt{\frac{1}{TN}})$</tex-math></inline-formula> as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings.
LODE: Deep Local Deblurring and A New Benchmark

While recent deep deblurring algorithms have achieved remarkable progress, most existing methods focus on the global deblurring problem, where the image blur mostly arises from severe camera shake. We argue that the local blur, which is mostly derived from moving objects with a relatively static background, is prevalent but remains under-explored. In this paper, we first lay the data foundation for local deblurring by constructing, for the first time, a LOcal-DEblur (LODE) dataset consisting of 3,700 real-world captured locally blurred images and their corresponding ground-truth. Then, we propose a novel framework, termed BLur-Aware DEblurring network (BladeNet), which contains three components: the Local Blur Synthesis module generates locally blurred training pairs, the Local Blur Perception module automatically captures the locally blurred region and the Blur-guided Spatial Attention module guides the deblurring network with spatial attention. This framework is flexible such that it can be combined with many existing SotA algorithms. We carry out extensive experiments on REDS and LODE datasets showing that BladeNet improves PSNR by 2.5dB over SotAs for local deblurring while keeping comparable performance for global deblurring. We will publish the dataset and codes.
Asymptotic Causal Inference

We investigate causal inference in the asymptotic regime as the number of variables n→∞ using an information-theoretic framework. We define structural entropy of a causal model in terms of its description complexity measured by the logarithmic growth rate, measured in bits, of all directed acyclic graphs (DAGs) on n variables, parameterized by the edge density d. Structural entropy yields non-intuitive predictions. If we randomly sample a DAG from the space of all models over n variables, as n→∞, in the range d ∈ (0, 1 8 ), almost surely D is a two-layer DAG! Semantic entropy quantifies the reduction in entropy where edges are removed by causal intervention. Semantic causal entropy is defined as the φ-divergence Dφ(P ‖ PS) between the observational distribution P and the interventional distribution PS , where a subset S of edges are intervened on to determine their causal influence. We compare the decomposability properties of semantic entropy for different choices of φ, including φ(t) = t log t (KL-divergence), φ = 12 ( √ t− 1) (squared Hellinger distance), and φ = 12 |t − 1| (total variation distance). We apply our framework to generalize a recently popular bipartite experimental design for studying causal inference on large datasets, where interventions are carried out on one set of variables (e.g., power plants, items in an online store), but outcomes are measured on a disjoint set of variables (residents near power plants, or shoppers). We generalize bipartite designs to k-partite designs, and describe an optimization framework for finding the optimal k-level DAG architecture for any value of d ∈ (0, 12 ). As d increases, a sequence of phase transitions occur over disjoint intervals of d, with deeper DAG architectures emerging as d→ 1 2 . We also give a quantitative bound on the number of samples needed to reliably test for average causal influence for a k-partite design.
Learning Natural Language Generation from Scratch

This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original ap-proach to train conditional language models from scratch by only using reinforcement learning (RL). AsRL methods unsuccessfully scale to large action spaces, we dynamically truncate the vocabulary spaceusing a generic language model. TrufLL thus enables to train a language agent by solely interacting withits environment without any task-specific prior knowledge; it is only guided with a task-agnostic languagemodel. Interestingly, this approach avoids the dependency to labelled datasets and inherently reduces pre-trained policy flaws such as language or exposure biases. We evaluate TrufLL on two visual questiongeneration tasks, for which we report positive results over performance and language metrics, which wethen corroborate with a human evaluation. To our knowledge, it is the first approach that successfullylearns a language generation policy (almost) from scratch.
Extended b-metric-preserving functions

In this paper, we introduce a couple of classes of functions, denoted by DU and EB. We present the relationship between them and other known classes. Also, we show that the elements of the class EB, are amenable and quasi-subadditive functions (Theorem 2.14). Finally, in the Theorem 2.20, we establish that the graphic of these elements is contained in the region proposed by J. Dobos and Z. Piotrowski(see [8]).
Dependency distance minimization predicts compression

Dependency distance minimization (DDm) is a well-established principle of word order. It has been predicted theoretically that DDm implies compression, namely the minimization of word lengths. This is a second order prediction because it links a principle with another principle, rather than a principle and a manifestation as in a first order prediction. Here we test that second order prediction with a parallel collection of treebanks controlling for annotation style with Universal Dependencies and Surface-Syntactic Universal Dependencies. To test it, we use a recently introduced score that has many mathematical and statistical advantages with respect to the widely used sum of dependency distances. We find that the prediction is confirmed by the new score when word lengths are measured in phonemes, independently of the annotation style, but not when word lengths are measured in syllables. In contrast, one of the most widely used scores, i.e. the sum of dependency distances, fails to confirm that prediction, showing the weakness of raw dependency distances for research on word order. Finally, our findings expand the theory of natural communication by linking two distinct levels of organization, namely syntax (word order) and word internal structure.