Reviewerly/exHarmony huggingface dataset:
Multiple-Instance Learning From Unlabeled Bags With Pairwise Similarity

In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple-instance learning</i> (MIL), each training example is represented by a bag of instances. A training bag is either negative if it contains no positive instances or positive if it has at least one positive instance. Previous MIL methods generally assume that training bags are fully labeled. However, the exact labels of training examples may not be accessible, due to security, confidentiality, and privacy concerns. Fortunately, it could be easier for us to access the pairwise similarity between two bags (indicating whether two bags share the same label or not) and unlabeled bags, as we do not need to know the underlying label of each bag. In this paper, we provide the first attempt to investigate MIL from only similar-dissimilar-unlabeled bags. To solve this new MIL problem, we first propose a strong baseline method that trains an instance-level classifier by employing an unlabeled-unlabeled learning strategy. Then, we also propose to train a bag-level classifier based on a convex formulation and theoretically derive a generalization error bound for this method. Comprehensive experimental results show that our instance-level classifier works well, while our bag-level classifier even has better performance.
GMCNet: A Generative Multi-Resolution Framework for Cardiac Registration

Deformable image registration plays a crucial role in estimating cardiac deformation from a sequence of images. However, existing registration methods primarily process images as pairs instead of processing all images in a sequence together. This study proposes a novel end-to-end learning-free generative multi-resolution convolutional neural network (GMCNet) with the primary focus of registering images in a sequence. Even though learning-based methods have yielded high performance for image registration, their performance depends on their ability to learn information from a large number of samples which are difficult to obtain and might bias the framework to the specific domain of data. The proposed learning-free method eliminates the need for a dedicated training set while exploiting the capabilities of neural networks to achieve accurate deformation fields. Due to its capability of parameter sharing through the architecture, the GMCNet can be used as a groupwise registration as well as pairwise registration. The proposed method was evaluated on three different clinical cardiac magnetic resonance imaging datasets and compared quantitatively against nine other state-of-the-art learning and optimization-based algorithms. The proposed method outperformed other methods in all comparisons and yielded average Dice metric values ranging from 0.85 to 0.88 for the datasets. Different aspects of the GMCNet are also explored by assessing 1) the robustness; 2) performance on pairwise registration; 3) the influence of spatial transformation in a controlled environment; and 4) the impact of different multi-resolution structures. The results demonstrate that using temporal information to estimate the deformation fields leads to more accurate registration results and improved robustness under different noise levels. Moreover, the proposed method does not need images for training, and therefore, its prediction is not domain-specific and can be applied to any sequence of images.
Subversion Resilient Hashing: Efficient Constructions and Modular Proofs for Crooked Indifferentiability

We consider the problem of constructing secure cryptographic hash functions from subverted ideal primitives.Hash functions are used to instantiate Random Oracles in cryptographic protocols.The indifferentiability security notion is a popular tool to certify the structural soundness of a hash design for such instantiations.In CRYPTO 2018, Russell, Tang, Yung, and Zhou introduced the notion of crookedindifferentiability to extend this paradigm even when the underlying primitive of the hashing mode is subverted.They showed that an n-to-n-bit function implemented using Enveloped XOR construction (EXor) with 3n + 1 many independent n-to-n-bit functions and 3n 2 -bit random seed can be proven secure asymptotically in the crooked-indifferentiability setting.Unfortunately, known techniques to prove crookedindifferentiability are extremely complicated, and no practical hashing mode has been analyzed in this setting.-We introduce new techniques to prove crooked-indifferentiability.We establish that upper bounding the subversion probability of a chaining query is sufficient to argue subversion resistance of a standard indifferentiable mode of operation.Our technique links standard indifferentiability and crookedindifferentiability and circumvents the complications of proving the consistency of the simulator in the crooked setting.-We prove crooked-indifferentiability of the sponge construction when the underlying primitive is modelled as an n-to-n-bit random function.Our proofs only require n-bit randomly chosen but fixed IV and do not mandate any independent function requirement.The result naturally extends to the Merkle-Damgård domain extension with prefix-free padding.Our results minimize required randomness and solve the main open problem raised by Russell, Tang, Yung, and Zhou.
Recordings of a Loudspeaker Orchestra With Multichannel Microphone Arrays for the Evaluation of Spatial Audio Methods

For live broadcasting of speech, music, or other audio content, multichannel microphone array recordings of the sound field can be used to render and stream dynamic binaural signals in real time.For a comparative physical and perceptual evaluation of conceptually different binaural rendering techniques, recordings are needed in which all other factors affecting the sound (such as the sound radiation of the sources, the room acoustic environment, and the recording position) are kept constant.To provide such a recording, the sound field of an 18channel loudspeaker orchestra fed by anechoic recordings of a chamber orchestra was captured in two rooms with nine different receivers.In addition, impulse responses were recorded for each sound source and receiver.The anechoic audio signals, the full loudspeaker orchestra recordings, and all measured impulse responses are available with open access in the Spatially Oriented Format for Acoustics (SOFA 2.1, AES69-2022) format.The article presents the recording process and processing chain as well as the structure of the generated database.
Unitarily inequivalent local and global Fourier transforms in multipartite quantum systems

Abstract A multipartite system comprised of n subsystems, each of which is described with ‘local variables’ in $${{\mathbb {Z}}}(d)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Z</mml:mi> <mml:mo>(</mml:mo> <mml:mi>d</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> and with a d -dimensional Hilbert space H ( d ), is considered. Local Fourier transforms in each subsystem are defined and related phase space methods are discussed (displacement operators, Wigner and Weyl functions, etc). A holistic view of the same system might be more appropriate in the case of strong interactions, which uses ‘global variables’ in $${{\mathbb {Z}}}(d^n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Z</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>d</mml:mi> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> and a $$d^n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>d</mml:mi> <mml:mi>n</mml:mi> </mml:msup> </mml:math> -dimensional Hilbert space $$H(d^n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>d</mml:mi> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . A global Fourier transform is then defined and related phase space methods are discussed. The local formalism is compared and contrasted with the global formalism. Depending on the values of d , n the local Fourier transform is unitarily inequivalent or unitarily equivalent to the global Fourier transform. Time evolution of the system in terms of both local and global variables, is discussed. The formalism can be useful in the general area of Fast Fourier transforms.
Effects of Nipa-Nipa Regulation Pond on Flood Control of Tallo River

Abstract The Nipa-Nipa Regulation Ponds are a flood control building for Makassar City and its surroundings due to the overflow of Tallo River downstream with an operating system to accommodate air. Regulation pond is only able to accommodate part of the peak flood discharge which caused Tallo River to overflow. For this reason, an analysis is carried out on how the influence of the Nipa – Nipa Regulation Pond on flood control of the downstream Tallo River is carried out. The analytical method used is a hydraulic analysis with the help of the HEC-RAS application. Based on the results of the 2D HEC – RAS numerical simulation mapped for the downstream area of Nipa - Nipa Regulation Pond, it is obtained that regulation can reduce surface runoff due to the overflow of the Lower Tallo River for Q2 by 183.97 Ha (35.59 %), Q5 271 .85 Ha (37.19 %), Q10 was 249.35 Ha (28.50 %), Q20 was 291.63 Ha (28.18 %), and Q25 was 300.67 Ha (28.08%). From these results, it can also be seen that the current condition of the Nipa – Nipa Regulation Pond is the most effective way to deal with flooding downstream of Tallo River at the five-year return period (Q5).
Flood Analysis of Bila River with Mathematical Models

Abstract The Bila River is one of the rivers in South Sulawesi, especially in Sidrap Regency which almost every year experience river overflows that cause flooding in the area around the river and cause losses to the community trying to analyze the flood of the river Bila with a mathematical model. The method used is to perform hydrological calculations with the help of the HEC-HMS application and simulation of flood discharge with the help of the HEC-RAS application. The results obtained are based on the results of running the HEC-RAS application with a design flood discharge of Q20 yr = 1518.10 m3/s. Recommendations for handling the flood of the Bila. River in the short, medium, and long, namely the addition of top embankments at the point of overflow, installation of control gates on tributaries and construction of retention ponds. Short and medium term amounted to 812.57 Ha and for long term handling amounted to 829.26 Ha.
Improving Conformance of Web Services: A Constraint-based Model-driven Approach

Web services have been widely used to develop complex distributed software systems in the context of Service Oriented Architecture (SOA). As a standard for describing Web services, the Web Service Description Language (WSDL) provides a universal mechanism to describe the service’s functionalities for the service consumers. However, the current WSDL only provides the description of the interfaces to a Web Service without any restrictions or assumptions on how to properly invoke the service, resulting in divergent understanding of the Web service’s behavior between the service developer and service consumer. A particular challenge is how to make explicit the various behavior assumptions and restrictions of a service (for the user), and make sure that the service implementation conforms to them (for the developer). In this article, we propose a constraint-based model-driven approach to improving the behavior conformance of Web services. In our approach, constraints are introduced in an extended WSDL, called CxWSDL, to formally and explicitly express the implicit restrictions and assumptions on the behavior of a Web service, and then the predefined constraints are used to derive test cases in a model-driven manner to test the service implementation’s conformance to its behavior constraints from the user’s perspective. An empirical study involving four real-life Web services was conducted to evaluate the effectiveness of our approach, and four actual inconsistencies were discovered.
Vehicle Tracking Algorithm Based on Deep Learning in Roadside Perspective

Traffic intelligence has become an important part of the development of various countries and the automobile industry. Roadside perception is an important part of the intelligent transportation system, which mainly realizes the effective perception of road environment information by using sensors installed on the roadside. Vehicles are the main road targets in most traffic scenes, so tracking a large number of vehicles is an important subject in the field of roadside perception. Considering the characteristics of vehicle-like rigid targets from the roadside view, a vehicle tracking algorithm based on deep learning was proposed. Firstly, we optimized a DLA-34 network and designed a block-N module, then the channel attention and spatial attention modules were added in the front of the network to improve the overall feature extraction ability and computing efficiency of the network. Next, the joint loss function was designed to improve the intra-class and inter-class discrimination ability of the tracking algorithm, which can better discriminate objects of similar appearance and the color of vehicles, alleviate the IDs problem and improve algorithm robustness and the real-time performance of the tracking algorithm. Finally, the experimental results showed that the method had a good tracking effect for the vehicle tracking task from the roadside perspective and could meet the practical application demands of complex traffic scenes.
Bidirectional Sentence Ordering with Interactive Decoding

Sentence ordering aims at restoring orders of shuffled sentences in a paragraph. Previous methods usually predict orders in a single direction, i.e., from head to tail. However, unidirectional prediction inevitably causes error accumulation, which restricts performance. In this article, we propose a bidirectional ordering method, which predicts orders in both head-to-tail and tail-to-head directions at the same time. In our bidirectional ordering method, two directions can interact with each other and help alleviate the error accumulation problem of ordering. Experiments demonstrate that our method can effectively improve performance of previous models.
Auxiliary Squat Training Method Based on Object Tracking

Background: The deep squat is not only one of the basic movement patterns of the human body, but also a compound movement that can directly train the hip force and has a good exercise effect on the posterior chain muscle groups. However, improper action patterns can affect the quality of action. Research objective: In order to improve training efficiency and reduce sports injuries, a method which can optimize the technology and movements of the deep squat needs to be designed. Methods: The tracking algorithm based on template matching, combined with biomechanical knowledge, was analyzed separately from the sagittal and coronal planes. Emphasis is placed on the analysis of the power chain of force and unbalance. Therefore, two force arms and two angles represent the power chain, and three line segments represent balance on both sides of the body. Results: The performance was more stable during the actual scenario test, and the motion information could be accurately captured and analyzed. Conclusion: This method can obtain the force arm and joint angle of the deep squat movement, and also assist in screening the balance of both sides of the limb. Thus, the pattern and rhythm of the action can be adjusted accordingly.
Topic-Aware Networks for Answer Selection

Answer selection is an essential task in the study of natural language processing, which is involved in many applications such as a dialog system, reading comprehension, and so on. It is a task of selecting the correct answer from a set of given candidates for certain questions. One of the challenging problems for this task is that traditional deep learning model for answer selection lacks real-world background knowledge, which is crucial for answering questions in real-world applications. In this paper, we propose a set of deep learning networks to enhance the traditional answer selection models with topic modeling, so that we could use topic models as external knowledge for the baseline models and improve the performance of the model. Our topic-aware networks (TANs) are specially designed for answer selection task. We proposed a novel method to generate topic embedding for both questions and answers separately. We designed two kinds of TAN models and evaluate our models in two commonly used answer selection datasets. The results verify the advantages of TAN in improving the performance of traditional answer selection deep learning models.
Elements of Continuous Reassessment and Uncertainty Self-awareness: A Narrow Implementation for Face and Facial Expression Recognition

Reflection on one’s thought process and making corrections to it if there exists dissatisfaction in its performance is, perhaps, one of the important traits of intelligence. However, such high-level abstract concepts mandatory for Artificial General Intelligence can be modelled even at the low level of narrow Machine Learning algorithms. Here, we present the self-awareness mechanism emulation in the form of an artificial neural network (ANN) observing patterns in activations of another underlying ANN in a search for indications of the high uncertainty of the underlying ANN and, therefore, the untrustworthiness of its predictions. The underlying ANN is a CNN employed for tasks of face recognition and facial expression. The self-awareness ANN has a memory region where its past performance information is stored, and its learnable parameters are adjusted during the training to optimize the performance. The same memory mechanism is used during the test phase for the continuous reassessment of the learning parameters after each consecutive test run.
The Determinants of the Effectiveness of Open and Distance Learning (ODL) during the COVID -19

The COVID-19 pandemic has caused accounting education to drastically change the teaching and learning methods from the traditional method to the Open and Distance Learning (ODL) method.The switch between the teaching and learning processes of the ODL method has created a problem regarding the effectiveness of the ODL, especially during this COVID-19.Thus, this study is conducted to identify the factors that have an impact on the effectiveness of ODL to ensure the process of ODL is going effectively and efficiently.At the same time the aims of this study are to enhance the stakeholders of higher education institutions in managing the ODL process and make effective decisions towards the effective teaching and learning process.Therefore, two variables are being analyzed, which are the hardware and software provision and interaction.A quantitative approach was used, and data were collected through an online survey using a Google Form.The respondents are full time students enrolled in undergraduate accounting course from Universiti Teknologi Mara (UiTM) located in Malaysia and the sample size is 375 students.The IBM SPSS version 27 and Partial least squares regression (PLS regression) which is WarpPLS version 7.0 was used to analyze the data.The results show that the provision of hardware and software, as well as their interaction, had a significant positive impact on the effectiveness of ODL during COVID-19.Further investigation showed that the hardware and software provision and interaction are the main determinant on the effectiveness of ODL.The findings of this study have significant value for higher education institutions, faculties, lecturers, and students.Future research could be applied to the others higher education institution using the qualitative approach and focuses on the other variables.
Sentiment Analysis on Airline Service Reviews using Data Mining based Classification Techniques

Sentimental analysis is the field where online reviews, opinions, and sentiments from users are available and provide considerable amounts of information about the services, facilities, and status of the service provider in the market. In addition to examining the classification accuracy of standard data mining methods, this research evaluates the sentiments expressed about six social media microblog traveller networking site datasets relevant to Indian airlines. Using standard data mining classifiers, Bayes Net and SVM performed with high accuracy rates. In this paper main analysis of the classification performance of passenger sentiments for six airline services has been performed. However, we found that the Bayes Net performed best accuracy rate using WEKA tool but in case of using Rapid Miner tool SVM has produced the highest accuracy rate for our research among the other common standard classifiers. On the basis of thoroughly favourable service reviews from passengers, Go Air is consistently the airline that is most highly recommended.
Combining Human Parsing with Analytical Feature Extraction and Ranking Schemes for High-Generalization Person Reidentification

Person reidentification (re-ID) has been receiving increasing attention in recent years due to its importance for both science and society. Machine learning (particularly Deep Learning (DL)) has become the main re-ID tool that has allowed to achieve unprecedented accuracy levels on benchmark datasets. However, there is a known problem of poor generalization in respect of DL models. That is, models that are trained to achieve high accuracy on one dataset perform poorly on other ones and require re-training. In order to address this issue, we present a model without trainable parameters. This, in turn, results in a great potential for high generalization. This approach combines a fully analytical feature extraction and similarity ranking scheme with DL-based human parsing wherein human parsing is used to obtain the initial subregion classification. We show that such combination, to a high extent, eliminates the drawbacks of existing analytical methods. In addition, we use interpretable color and texture features that have human-readable similarity measures associated with them. In order to verify the proposed method we conduct experiments on Market1501 and CUHK03 datasets, thus achieving a competitive rank-1 accuracy comparable with that of DL models. Most importantly, we show that our method achieves 63.9% and 93.5% rank-1 cross-domain accuracy when applied to transfer learning tasks, while also being completely re-ID dataset agnostic. We also achieve a cross-domain mean average precision (mAP) that is higher than that of DL models in some experiments. Finally, we discuss the potential ways of adding new features to further improve the model. We also show the advantages of interpretable features for the purposes of constructing human-generated queries from verbal descriptions in order to conduct searches without a query image.
An Accelerated Regularized Chebyshev–Halley Method for Unconstrained Optimization

In machine learning, most models can be transformed into unconstrained optimization problems, so how to solve the unconstrained optimization problem for different objective functions is always a hot issue. In this paper, a class of unconstrained optimization where objection function has [Formula: see text]th-order derivative and Lipschitz continuous simultaneously is studied. To handle such problems, we propose an accelerated regularized Chebyshev–Halley method based on the Accelerated Hybrid Proximal Extragradient (A-HPE) framework. It proves that convergence complexity of the proposed method is [Formula: see text], which is consistent with the lower iteration complexity bound for third-order tensor methods. Numerical experiments on functions in machine learning demonstrate the promising performance of the proposed method.