Reviewerly/exHarmony huggingface dataset:
Invariant Representation Learning for Robust Far-Field Speaker Recognition

The emergence of smart home assistants increased the need for robust Far-Field Speaker Identification models. Speaker Identification enables the assistants to perform personalized tasks. Smart home assistants face very challenging speech conditions, including various room shapes and sizes, various distances of the speaker from the microphone, various types of distractor noises (TV in the background, air conditioner, fridge, babble speech of other speakers, etc.). This paper describes the use of Invariant Representation Learning (IRL) as a method aimed to increase the robustness of Speaker Identification models on Far-Field. We introduce three new versions of IRL: Text-Dependent IRL (TD-IRL), Text Independent IRL (TI-IRL), and Deep Features IRL (DF-IRL). We evaluate the IRL models performance and compare them to the base x-vector model. The various Far-Field scenarios are evaluated using VOiCES dataset - a dataset of simulated Far-Field recordings in four real furnished rooms. TD-IRL and DF-IRL improve the minDCF results on the far-field scenarios by an average of 36%, and TI-IRL improves it by 31% with respect to the baseline model.
Promoting Privacy Considerations in Real-World Projects in Capstone Courses with Ideation Cards

Nearly all software built today impinges upon end-user privacy and needs to comply with relevant regulations. Therefore, there have been increasing calls for integrating considerations of compliance with privacy regulations throughout the software engineering lifecycle. However, software engineers are typically trained in the technical fields and lack sufficient knowledge and support for sociotechnical considerations of privacy. Privacy ideation cards attempt to address this issue by making privacy compliance understandable and actionable for software developers. However, the application of privacy ideation cards in real-world software projects has not yet been systemically investigated. The effectiveness of ideation cards as a pedagogical tool has not yet been examined either. We address these gaps by studying how teams of undergraduate students applied privacy ideation cards in capstone projects that involved building real-world software for industry sponsors. We found that privacy ideation cards fostered greater consideration and understanding of the extent to which the projects aligned with privacy regulations. We identified three main themes from student discussions of privacy compliance: (i) defining personal data; (ii) assigning responsibility for privacy compliance; and (iii) determining and exercising autonomy. The results suggest that application of the cards for real-world projects requires careful consideration of intersecting factors such as the stage at which the cards are used and the autonomy available to the developers. Pedagogically, ideation cards can facilitate low-level cognitive engagement (especially the cognitive processes of meaning construction and interpretation) for specific components within a project. Higher-level cognitive processes were comparatively rare in ideation sessions. These findings provide important insight to help enhance capstone instruction and to improve privacy ideation cards to increase their impact on the privacy properties of the developed software.
Naturally Together: A Systematic Approach for Multi-User Interaction With Natural Interfaces

New technology is moving towards intuitive and natural interaction techniques that are increasingly embedded in human space (e.g., home and office environment) and aims to support multiple users, yet their interfaces do not cover it to the full. Imagine that you have a multi-user device, should it act differently in different situations, people, and group settings? Current Multi-User Interfaces address each of the users as an individual that works independently from others, and there is a lack of understanding of the mechanisms that impact shared usage of these products. Thus we have linked environmental (external) and user-centered (internal) factors to the way users interact with multi-user devices. We analyzed 124 papers that involve multi-user interfaces and created a classification model out of 8 factors. Both the model and factors were validated by a large-scale online study. Our model defines the factors affecting multi-user usage with a single device and leads to a decision on the most important ones in different situations. This paper is the first to identify these factors and to create a set of practical guidelines for designing Multi-User Interfaces.
A Nested Incentive Scheme for Distributed File Sharing Systems

In the distributed file sharing system, a large number of users share bandwidth, upload resources and store them in a decentralized manner, thus offering both an abundant supply of high-quality resources and high-speed download. However, some users only enjoy the convenient service without uploading or sharing, which is called free riding. Free-riding may discourage other honest users. When free-riding users mount to a certain number, the platform may fail to work. The current available incentive mechanisms, such as reciprocal incentive mechanisms and reputation-based incentive mechanisms, which suffer simple incentive models, inability to achieve incentive circulation and dependence on a third-party trusted agency, are unable to completely solve the free-riding problem.In this paper we build a blockchain-based distributed file sharing platform and design a nested incentive scheme for this platform. The proposed nested incentive mechanism achieves the circulation of incentives in the platform and does not rely on any trusted third parties for incentive distribution, thus providing a better solution to free-riding. Our distributed file sharing platform prototype is built on the current mainstream blockchain. Nested incentive scheme experiments on this platform verify the effectiveness and superiority of our incentive scheme in solving the free-riding problem compared to other schemes.
Policy Decomposition: Approximate Optimal Control with Suboptimality Estimates

Numerically computing global policies to optimal control problems for complex dynamical systems is mostly intractable. In consequence, a number of approximation methods have been developed. However, none of the current methods can quantify how much the resulting control underperforms the elusive globally optimal solution. Here we propose policy decomposition, an approximation method with explicit suboptimality estimates. Our method decomposes the optimal control problem into lower-dimensional subproblems, whose optimal solutions are recombined to build a control policy for the entire system. Many such combinations exist, and we introduce the value error and its LQR and DDP estimates to predict the suboptimality of possible combinations and prioritize the ones that minimize it. Using a cart-pole, a 3-link balancing biped and N-link planar manipulators as example systems, we find that the estimates correctly identify the best combinations, yielding control policies in a fraction of the time it takes to compute the optimal control without a notable sacrifice in closed-loop performance. While more research will be needed to find ways of dealing with the combinatorics of policy decomposition, the results suggest this method could be an effective alternative for approximating optimal control in intractable systems.
When Few-Shot Learning Meets Large-Scale Knowledge-Enhanced Pre-training: Alibaba at FewCLUE

With the wide popularity of Pre-trained Language Models (PLMs), it has been a hot research topic to improve the performance of PLMs in the few-shot learning setting. FewCLUE is a new benchmark to evaluate the few-shot learning ability of PLMs over nine challenging Chinese language understanding tasks, which poses significant challenges to the learning process of PLMs with very little training data available. In this paper, we present our solution to FewCLUE tasks by means of large-scale knowledge-enhanced pre-training over massive texts and knowledge triples, together with a new few-shot learning algorithm for downstream tasks. Experimental results show that the generated models achieve the best performance in both limited and unlimited tracks of FewCLUE. Our solution is developed upon the PyTorch version of the EasyTransfer toolkit and will be released to public.
The role of mechanism knowledge in singular causation judgments

Singular causation queries (e.g., "Did Mary's taking contraceptives cause her thrombosis?") are ubiquitous in everyday life and crucial in many professional disciplines, such as medicine or law. Knowledge about general causal regularities is necessary but not sufficient for establishing a singular causation relation because it is possible that co-occurrences consistent with known regularities are in an individual case still just coincidental. Thus, further cues are helpful to establish a singular causation relation. In the present research we focus on information about mechanisms as a potent cue. While previous studies have shown that reasoners consider mechanism information as important when it comes to answering singular causation queries, no formal model has been proposed that explains why this is case. We here present a computational model that explains how causal mechanism information affects singular causation judgments. We also use the model to identify conditions that restrict the utility of mechanism information. We report three experiments testing the implications of our formal analysis. In Experiment 1 we found that reasoners systematically use mechanism information, largely in accordance with our formal model, although we also discovered that some people seem to rely on simpler, computationally less demanding reasoning strategies. The results of Experiments 2 and 3 demonstrate that reasoners have a tentative understanding of the conditions that restrict the utility of causal mechanism information.
A Secure and Efficient Data Sharing Scheme with Outsourced Signcryption and Decryption in Mobile Cloud Computing

This paper proposes a cryptographic-based access control that supports secure and efficient signcryption and decryption feature for data owners and data users in mobile cloud environment. At a core construct of our proposed scheme, a dual encryption based on AES encryption and ciphertext policy attribute-based encryption (CP-ABE) and dual proxy model are integrated. In our model, data owners can encrypt and digitally sign a file by using AES key and their private key stored in the mobile devices respectively. The hash value calculation and CP-ABE encryption are outsourced to the delegated proxy called signcryption proxy. In the similar way, data users can use their mobile devices to decrypt the ciphertext encrypted by the AES key while the computing task of CP-ABE decryption is outsourced to another proxy called decryption proxy. Therefore, most extensive costs for digital signing, encryption, and decryption are offloaded to the proxies located in the cloud. This renders the confidentiality, authenticity, and non-repudiation along with the fine-grained access control for mobile cloud environment where both data owners and data users can use the mobile devices to share and access the data via cloud platform. In addition, we conducted performance evaluation to substantiate that our proposed scheme is efficient in practice.
A brief supplement to ‘The Marlowe Corpus Revisited’ and Phantom Marlowe

Abstract Following the findings of two prior publications this supplement makes use of the General Imposters (GI) method which largely confirms previous results, namely that the Marlowe corpus is stylistically inhomogeneous. The two Tamburlaines match each other in style, but the remaining corpus has hardly any stylistic similarity with them. Instead, there are strong indications of Shakespeare’s involvement in nominal Marlowe plays which becomes most obvious in The Jew of Malta. In terms of methodology, GI turns out to be an additional verification system, at its best when the training set consists of the texts of only two authors.
Query Sense Discovery Approach to Realize the User's Search Intent

The main goal of information retrieval is getting the most relevant documents to a user’s query. So, a search engine must not only understand the meaning of each keyword in the query but also their relative senses in the context of the query. Discovering the query meaning is a comprehensive and evolutionary process; the precise meaning of the query is established as developing the association between concepts. The meaning determination process is modeled by a dynamic system operating in the semantic space of WordNet. To capture the meaning of a user query, the original query is reformulating into candidate queries by combining the concepts and their synonyms. A semantic score characterizing the overall meaning of such queries is calculated, the one with the highest score was used to perform the search. The results confirm that the proposed "Query Sense Discovery" approach provides a significant improvement in several performance measures.
Sedimentological and mineralogical-petrographic characteristics of Miocene evaporitic deposits (SW Erzincan)

This study aimed to find the depositional environment and formation conditions of the gypsum-dominated Miocene succession alternated and intercalated with clastics and carbonates in the southwestern part of the Erzincan Basin. As a result of sedimentological, mineralogical-petrographic investigations, it was determined that the gypsum in this sequence was formed as primary and secondary. Primary lithofacies; nodular anhydrite, selenitic, discoidal, gypsum arenitic secondary lithofacies; It is divided into massive, laminated, banded, nodular, brecciated, satin-spar. The facies repetitions and folds, fractures, cracks and faulting in the sequence showed the effect of tectonism with the water level fluctuations in the basin. In addition, these investigations revealed sedimentary structures such as cross-bedding, ripple undulations, chicken-wire, enterolithic and bacterial-algal structures, parallel laminations in gypsum lithofacies. In addition to these, it was understood that gypsum lithofacies were exposed to diagenetic processes under the influence of a hot-humid and semi-arid climate, organic matter activity, meteoric/underground-water and hydrothermal solution factors, and were subjected to high temperature, pressure, salinity and pH conditions.
Deep Learning Technique for a Identify TE Student System by Face Recognition

In this study, we aim to measure the face recognition algorithm robustness of CiRA CORE software and identify a group of TE student with face recognition using the deep learning technique from CiRA CORE software. The one-shot problem and similar-looking face problem are robust performance tests of CiRA CORE. Thus, the number and properties images of a dataset are considered as the robustness of the CiRA CORE software. Identify TE student experiment; three detected faces that are the maximum number of faces in one frame is a condition of the experiment. The results of identify TE student experiment demonstrate that the system gains ninety percent accuracy for the camouflage test by wearing a cap and achieves one hundred percent accuracy for other camouflage problems and identify TE student test. The result of robustness and identify TE student experiment confirms face recognition algorithm CiRA CORE software is effective and robust for identify TE student system.
Joint Prediction and Association for Deep Feature Multiple Object Tracking

Deep learning (CNN) can significantly improve the accuracy of image recognition with its powerful features, but the low-level network layer also contains important feature information. In order to achieve more stable and efficient tracking in multi-target tracking, this type of deep features will also be used to make the features more expressive by integrating the data from the front and back layers. The deformable convolution is also introduced to overcome the deformation problem caused by the camera motion. And with the increase of time, we predict the position of the target by the motion model, so as to remove the position where the target is impossible to reach in physical space, and further optimize the association before multiple targets. In this paper, we use an end-to-end correlation method to reduce the complexity of the algorithm. We tested it on the open source dataset MOT17 dataset and obtained remarkable results.
Augmented Assembly Work Instruction Knowledge Graph for Adaptive Presentation

The application of Augmented Reality (AR) that present work instruction through visual elements for assembly operation can effectively reduce the cognitive load of operators. Among challenges that prevent AR systems from being widely used in complex assembly operations, the lack of augmented work instruction (AWI) adaptive presentation is an important aspect. This paper propose an augmented assembly work instruction knowledge graph (AWI-KG) for adaptive presentation to solve the problem. The characteristics of AWI are analyzed to abstract the concepts and relationships for domain ontology constructing. And using the domain ontology to extract the entities in assembly manual to establish the AWI-KG. Then, a collaborative filtering method based on AWI-KG is proposed. The multidimensional vectors are used to calculate the adaptive presentation mode for AWI according to the operator's capabilities. The proposed method was applied in an AR system. The results show that the recommended visual elements can effectively adapt to the operator's capabilities.
Towards Human Dependency Elimination: AI Approach to SCA Robustness Assessment

Evaluating the side-channel resistance in practice is a problematic and arduous process. Current certification schemes require to attack the device under test with an ever-growing number of techniques to validate its security. In addition, the success or failure of these techniques strongly depends on the individual implementing them due to the fallible and human intrinsic nature of several steps of this path. To alleviate this problem, we propose a battery of automated (Estimation of Distribution Algorihm(EDA)-based) attacks as a side-channel analysis robustness assessment of an embedded device. To prove our approach, we conduct realistic experiments on two different devices, creating a new dataset (AES_RA) as a part of our contribution. Furthermore, in this context of automation, we propose several novel improvements over current EDA-based attacks, as follows: 1) optimization of the search process by employing two proposed initialization techniques; 2) improvement and analysis of the generalization of the obtained templates; 3) acceleration of the search process by combining EDAs with Principal Component Analysis (PCA). The last contribution also serves as an alternative way of selecting optimal principal components automatically. We support our claims with experiments on AES_RA and a public dataset (ASCAD), showing how our, although fully automated, approach can straightforwardly provide state-of-the-art results.
RASCAL: Robotic Arm for Sherds and Ceramics Automated Locomotion

Ceramics are one of the major sources of information about the past for archaeologists, with a typical archaeological dig unearthing 1000’s of pottery fragments (sherds) each day. However, archaeologists often are not allowed to remove these sherds from their home countries. Therefore, logging data (e.g., mass, color, decoration) in the field is the only way to record valuable information about these sherds. Currently, this laborious process is done manually, using up much of the valuable time at a dig site. This project aims to automate the data collection process, freeing up archaeologists to spend their limited time in the field on other tasks, and to create a large-scale digital database of sherd information, allowing archaeologists to take advantage of new computational tools to make discoveries. The contribution of this paper is an automated system, consisting of several reconfigurable data collection stations and a robotic arm to transport objects between stations, that can rapidly generate a large database of archaeological artifacts. We validate our system in simulation, using high-resolution models of sherds. In other contexts, our system may help to expand the use of automation in materials handling, parts sorting, and more.
CVFCC: CV-Based Framework for Container Consolidation in Cloud Data Centers

As data centers become more energy-intensive, cloud service providers have been faced the problem of reducing the energy consumption of data centers. Currently, the container is becoming increasingly popular as a lightweight packaging technology because of its small size, fast start-up, and low overhead. Therefore, as an effective tool, containers have been widely used deployed directly on the bare metal cloud computing environment. This paper focuses on container consolidation issue with regards to the bare metal deployment model and suggests a new consolidation strategy. We use a coefficient of variation index called CV to reflect the load balancing degree of server resources and use it for container consolidation. Experimental results demonstrate that CVFCC can achieve less energy consumption and fewer migrations.
Security Decision Support in the Control Systems based on Graph Models

An effective response against information security violations in the technical systems remains relevant challenge nowadays, when their number, complexity, and the level of possible losses are growing. The violation can be caused by the set of the intruder's consistent actions. In the area of countermeasure selection for a proactive and reactive response against security violations, there are a large number of techniques. The techniques based on graph models seem to be promising. These models allow representing the set of actions caused the violation. Their advantages include the ability to forecast violations for timely decision-making on the countermeasures, as well as the ability to analyze and consider the coverage of countermeasures in terms of steps caused the violation. The paper proposes and describes a decision support method for responding against information security violations in the technical systems based on the graph models, as well as the developed models, including the countermeasure model and the graph representing the set of actions caused the information security violation.
Multi‐label enhancement manifold learning algorithm for vehicle video

Abstract In this article, we propose a new multi‐label enhancement manifold learning algorithm to solve the vehicle video classification problem. Predicting multiple objects in a traffic video image is a challenging problem. Traditional multi‐label classification methods can solve the problem of simultaneous detection of multiple labels, but cannot handle high‐dimensional streaming video data. Our idea is to use label distribution learning (LDL) to enrich the label space and improve label recognition in the original label space. We use the feature function representing the manifold structure to guide the geometric meaning of the label space and transform the local topology from the feature space to the label space. We first build a label distribution learner. Next, use the LDL model for classification. The similarity between the two distributions is measured by Bayesian divergence, and the label distribution is learned through the maximum entropy model and the objective function of this article is established. Finally, an enhanced label model of the manifold space is established to reduce the dimensionality of the feature matrix generated during the training phase, so that the supervised information in the label manifold can be used in the incremental manifold space to improve the accuracy of feature extraction. Compared to the latest multi‐label learning methods, our multi‐label enhancement manifold learning method has advantages in predicting performance.