Reviewerly/exHarmony huggingface dataset:
Learning Object-Action Relations from Bimanual Human Demonstration Using Graph Networks

Recognizing human actions is a vital task for a humanoid robot, especially in domains like programming by demonstration. Previous approaches on action recognition primarily focused on the overall prevalent action being executed, but we argue that bimanual human motion cannot always be described sufficiently with a single action label. We present a system for frame-wise action classification and segmentation in bimanual human demonstrations. The system extracts symbolic spatial object relations from raw RGB-D video data captured from the robot's point of view in order to build graph-based scene representations. To learn object-action relations, a graph network classifier is trained using these representations together with ground truth action labels to predict the action executed by each hand. We evaluated the proposed classifier on a new RGB-D video dataset showing daily action sequences focusing on bimanual manipulation actions. It consists of 6 subjects performing 9 tasks with 10 repetitions each, which leads to 540 video recordings with 2 hours and 18 minutes total playtime and per-hand ground truth action labels for each frame. We show that the classifier is able to reliably identify (action classification macro F1-score of 0.86) the true executed action of each hand within its top 3 predictions on a frame-by-frame basis without prior temporal action segmentation.
Block Randomized Optimization for Adaptive Hypergraph Learning

The high-order relations between the content in social media sharing platforms are frequently modeled by a hypergraph. Either hypergraph Laplacian matrix or the adjacency matrix is a big matrix. Randomized algorithms are used for low-rank factorizations in order to approximately decompose and eventually invert such big matrices fast. Here, block randomized Singular Value Decomposition (SVD) via subspace iteration is integrated within adaptive hypergraph weight estimation for image tagging, as a first approach. Specifically, creating low-rank submatrices along the main diagonal by tessellation permits fast matrix inversions via randomized SVD. Moreover, a second approach is proposed for solving the linear system in the optimization problem of hypergraph learning by employing the conjugate gradient method. Both proposed approaches achieve high accuracy in image tagging measured by F1 score and succeed to reduce the computational requirements of adaptive hypergraph weight estimation.
360-Degree Textures of People in Clothing from a Single Image

In this paper we predict a full 3D avatar of a person from a single image. We infer texture and geometry in the UV-space of the SMPL model using an image-to-image translation method. Given partial texture and segmentation layout maps derived from the input view, our model predicts the complete segmentation map, the complete texture map, and a displacement map. The predicted maps can be applied to the SMPL model in order to naturally generalize to novel poses, shapes, and even new clothing. In order to learn our model in a common UV-space, we non-rigidly register the SMPL model to thousands of 3D scans, effectively encoding textures and geometries as images in correspondence. This turns a difficult 3D inference task into a simpler image-to-image translation one. Results on rendered scans of people and images from the DeepFashion dataset demonstrate that our method can reconstruct plausible 3D avatars from a single image. We further use our model to digitally change pose, shape, swap garments between people and edit clothing. To encourage research in this direction we will make the source code available for research purpose.
End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning

This paper presents our latest investigation on end-to-end automatic speech recognition (ASR) for overlapped speech. We propose to train an end-to-end system conditioned on speaker embeddings and further improved by transfer learning from clean speech. This proposed framework does not require any parallel non-overlapped speech materials and is independent of the number of speakers. Our experimental results on overlapped speech datasets show that joint conditioning on speaker embeddings and transfer learning significantly improves the ASR performance.
Post-Incident Audits on Cyber Insurance Discounts

We introduce a game-theoretic model to investigate the strategic interaction between a cyber insurance policyholder whose premium depends on her self-reported security level and an insurer with the power to audit the security level upon receiving an indemnity claim. Audits can reveal fraudulent (or simply careless) policyholders not following reported security procedures, in which case the insurer can refuse to indemnify the policyholder. However, the insurer has to bear an audit cost even when the policyholders have followed the prescribed security procedures. As audits can be expensive, a key problem insurers face is to devise an auditing strategy to deter policyholders from misrepresenting their security levels to gain a premium discount. This decision-making problem was motivated by conducting interviews with underwriters and reviewing regulatory filings in the U.S.; we discovered that premiums are determined by security posture, yet this is often self-reported and insurers are concerned by whether security procedures are practised as reported by the policyholders. To address this problem, we model this interaction as a Bayesian game of incomplete information and devise optimal auditing strategies for the insurers considering the possibility that the policyholder may misrepresent her security level. To the best of our knowledge, this work is the first theoretical consideration of post-incident claims management in cyber security. Our model captures the trade-off between the incentive to exaggerate security posture during the application process and the possibility of punishment for non-compliance with reported security policies. Simulations demonstrate that common sense techniques are not as efficient at providing effective cyber insurance audit decisions as the ones computed using game theory.
The The Interpersonal Meaning in Trumpâ€™s Victory Speech

This study deals with analyzing language used for communication. It analyzes a victory speech by Donald Trump as the newest president of America from the perspective of SFL especially dealing with the Tenor of the discourse of the speech. In other words, this study is about interpersonal meaning of the speech which can be used to dig up speakerâ€™s attitudes towards what s/he is saying in the speech. In conducting this study, I had two objectives to achieve. First, this study tried to understand the interpersonal meaning of Donald Trumpâ€™s victory speech by looking deeply on the Mood system of the clauses of the speech which means analyzing the two constituents of Mood which are Subjects and Finites of the clauses in the speech. Second, it also tried to uncover Donald Trumpâ€™s attitudes towards Americans by applying the Appraisal theory proposed by Martin. Appraisal offers an ideal analytical framework to systematically identify interpersonal meanings in language. The result of analysis showed that the most subject found in the speech was I â€“ Donald Trump which meant that Trump himself was responsible to what he was saying in the speech. The Finites in the speech were mostly stated in Simple Present Tense which was meant that in delivering his speech, Trump gave the general truth at the moment of the speaking. Since most of the clauses were declarative clauses, Trump was considered to share information in this case he share his vision and mission for future America to the audience. That shared information was further analyzed with the Appraisal theory which led to the conclusion that Trump gave positive attitudes towards Americans. Based on this study, finally we come to an understanding that itâ€™s important to understand any kinds of speech well. One among ways to do it is by analyzing that speech through its interpersonal meaning and its appraisal system. This study is useful for readers, especially English Department students in order to be able to understand the meaning behind a text well.
Modeling Graphs with Vertex Replacement Grammars

One of the principal goals of graph modeling is to capture the building blocks of network data in order to study various physical and natural phenomena. Recent work at the intersection of formal language theory and graph theory has explored the use of graph grammars for graph modeling. However, existing graph grammar formalisms, like Hyperedge Replacement Grammars, can only operate on small tree-like graphs. The present work relaxes this restriction by revising a different graph grammar formalism called Vertex Replacement Grammars (VRGs). We show that a variant of the VRG called Clustering-based Node Replacement Grammar (CNRG) can be efficiently extracted from many hierarchical clusterings of a graph. We show that CNRGs encode a succinct model of the graph, yet faithfully preserves the structure of the original graph. In experiments on large real-world datasets, we show that graphs generated from the CNRG model exhibit a diverse range of properties that are similar to those found in the original networks.
Edge Computing for User-Centric Secure Search on Cloud-Based Encrypted Big Data

Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. This has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can potentially address these concerns by establishing user-centric cloud services and granting data control to the user. Nonetheless, user-side encryption limits the ability to process (e.g., search) encrypted data on the cloud. Accordingly, in this research, we provide a framework that enables processing (in particular, searching) of encrypted multi-organizational (i.e., multi-source) big data without revealing the data to cloud provider. Our framework leverages locality feature of edge computing to offer a user-centric search ability in a real-time manner. In particular, the edge system intelligently predicts the user's search pattern and prunes the multi-source big data search space to reduce the search time. The pruning system is based on efficient sampling from the clustered big dataset on the cloud. For each cluster, the pruning system dynamically samples appropriate number of terms based on the user's search tendency, so that the cluster is optimally represented. We developed a prototype of a user-centric search system and evaluated it against multiple datasets. Experimental results demonstrate 27% improvement in the pruning quality and search accuracy.
Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition

In this paper, we explore various approaches for semi supervised learning in an end to end automatic speech recognition (ASR) framework. The first step in our approach involves training a seed model on the limited amount of labelled data. Additional unlabelled speech data is employed through a data selection mechanism to obtain the best hypothesized output, further used to retrain the seed model. However, uncertainties of the model may not be well captured with a single hypothesis. As opposed to this technique, we apply a dropout mechanism to capture the uncertainty by obtaining multiple hypothesized text transcripts of an speech recording. We assume that the diversity of automatically generated transcripts for an utterance will implicitly increase the reliability of the model. Finally, the data selection process is also applied on these hypothesized transcripts to reduce the uncertainty. Experiments on freely available TEDLIUM corpus and proprietary Adobe's internal dataset show that the proposed approach significantly reduces ASR errors, compared to the baseline model.
The Realization of Deixis in Studentsâ€™ Writing at Sekolah Tinggi Keguruan dan Ilmu Pendidikan Muhammadiyah Pringsewu Lampung

Through the final product of writing, students can communicate their ideas, thoughts, and feelings to the reader. Deixis is one of the branches in pragmatics and one of the writing components that have to be noticed by the students to communicate the meaning in writing. This study is aimed at finding out the realization of deixis in studentsâ€™ writing text at STKIP Muhammadiyah Pringsewu Lampung, and also to explain the difficulties faced by the students and teacher roles to overcome the studentsâ€™ difficulties. The researcher uses descriptive qualitative in analyzing data. There are three methods which are used in this study; they are document, observation, and interview. The results of this study showed that the realization of person deixis is more dominant since the students as the writers position themselves as the main characters of their own stories. Place deixis was realized through demonstrative pronoun, English adverb, and English preposition. The use of the proximal form of place deixis is more dominant rather than the distal form of place deixis. Time deixis was realized through past time, present time, future time, and adverbial of time. Social deixis was also realized through relational social deixis (used in informal situation) and absolute social deixis (used in formal situation). The students used discourse deixis in the form of demonstrative this/that and discourse marker. Further, there were difficulties faced by students in using deixis and the methods that used by the teacher to overcome the studentsâ€™ difficulties. Hence, it is expected that the findings of this study can improve the studentsâ€™ knowledge in pragmatics and deixis in general; especially they will apply appropriate deixis in order to develop their writing abilities.
Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval

In this paper, we present the Hierarchy-of-Visual-Words (HoVW), a novel trademark image retrieval (TIR) method that decomposes images into simpler geometric shapes and defines a descriptor for binary trademark image representation by encoding the hierarchical arrangement of component shapes. The proposed hierarchical organization of visual data stores each component shape as a visual word. It is capable of representing the geometry of individual elements and the topology of the trademark image, making the descriptor robust against linear as well as to some level of nonlinear transformation. Experiments show that HoVW outperforms previous TIR methods on the MPEG-7 CE-1 and MPEG-7 CE-2 image databases.
Heuristic Algorithm for Generalized Function Matching

The problem of generalized function matching can be defined as follows: given a pattern $p=p_1 \cdots p_m$ and a text $t=t_1 \cdots t_n$, find a mapping $f:\Sigma_p\rightarrow\Sigma_t^{*}$ and all text locations $i$ such that $f(p_1) f(p_2) \cdots f(p_m) = t_i \cdots t_j$, a substring of $t$. By modifying the restrictions of the matching function $f$, one can obtain different matching problems, many of which have important applications. When $f:\Sigma_p\rightarrow\Sigma_t$ we are faced with problems found in the well-established field of combinatorial pattern matching. If the single character constraint is lifted and $f:\Sigma_p\rightarrow\Sigma_t^{*}$, we obtain generalized function matching as introduced by Amir and Nor (JDA 2007). If we further constrain $f$ to be injective, then we arrive at generalized parametrized matching as defined by Clifford et al. (SPIRE 2009). There are a number of important applications for pattern matching in computational biology, text editors and data compression, to name a few. Therefore, many efficient algorithms have been developed for a wide variety of specific problems including finding tandem repeats in DNA sequences, optimizing embedded systems by reusing code etc. In this work we present a heuristic algorithm illustrating a practical approach to tackling a variant of generalized function matching where $f:\Sigma_p\rightarrow\Sigma_t^{+}$ and demonstrate its performance on human-produced text as well as random strings.
Private Sequential Function Computation

Consider a system, including a user, $N$ servers, and $K$ basic functions which are known at all of the servers. Using the combination of those basic functions, it is possible to construct a wide class of functions. The user wishes to compute a particular combination of the basic functions, by offloading the computation to $N$ servers, while the servers should not obtain any information about which combination of the basic functions is to be computed. The objective is to minimize the total number of queries asked by the user from the servers to achieve the desired result. As a first step toward this problem, in this paper, we consider the case where the user is interested in a class of functions which are composition of the basic functions, while each basic function appears in the composition exactly once. This means that in this case, to ensure privacy, we only require to hide to the order of the basic functions in the desired composition of the user. We further assume that the basic functions are linear and can be represented by (possibly large-scale) matrices. We call this problem as private sequential function computation. We study the capacity $C$, defined as the supremum of the number of desired computations, normalized by the number of computations done at the servers, subject to the privacy constraint. We prove that $(1-\frac{1}{N})/ (1-\frac{1}{\max(K,N)}) \le C \le 1$. For the achievability, we show that the user can retrieve the desired order of composition, by choosing a proper order of inquiries among different servers, while keeping the order of computations for each server fixed, irrespective of the desired order of composition. In the end, we develop an information-theoretic converse which results in an upper bound on the capacity.
Interview With Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems [Career Profile]

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Prof. Yongduan Song</b> , talks about this reputable publication, his life and favorites.
Quest for the Balance of AI and Privacy [Editor’s Remarks]

The employment of machine learning often requires a large amount of data, including our sensitive and personal information, to train models for various applications. With growing worldwide concerns over data privacy, particularly with the recent introduction of the EU’s General Data Protection Regulation (GDPR), it has become an important topic for AI research and application to implement data privacy-preserving technologies. Researchers or companies that utilize personal data in their AI models face the pressure and difficulty of balancing the advancement of AI and data privacy. In the quest for such a balance, machine learning technologies that conform to some particular aspects of data protection regulated by the GDPR, often combining privacy-preserving technologies such as homomorphic encryption, differential privacy, secure multiparty computation, or federated learning, are used to protect data and models from being accessed by other parties.
A MFCC-CNN BASED VOICE AUTHENTICATION SECURITY

In this paper, a novel architecture is proposed using a convolutional neural network (CNN) and mel frequency cepstral coefficient (MFCC) to identify the speaker in a noisy environment. This architecture is used in a text-independent setting. The most important task in any text-independent speaker identification is the capability of the system to learn features that are useful for classification. We are using a hybrid feature extraction technique using CNN as a feature extractor combined with MFCC as a single set. For classification, we used a deep neural network which shows very promising results in classifying speakers. We made our dataset containing 60 speakers, each speaker has 4 voice samples. Our best hybrid model achieved an accuracy of 87.5%. To verify the effectiveness of this hybrid architecture, we use parameters such as accuracy and precision.
Tourist Spot Recognition Using Machine Learning Algorithms

Tourism plays significant role for enhancing economic potential worldwide. The natural beauty and historical interests of Bangladesh remarked as a major tourist destination for the international tourists. In this study, we target to propose a deep learning-based application to recognize historical interests and tourist spots from an images. Making use of on-device neural engine comes with modern devices makes the application robust and Internet-free user experience. One of the difficult tasks is to collect real images from tourist sites. Our collected images were in different sizes because of using different smartphones. We used following deep learning algorithms—convolution neural network (CNN), support vector machine (SVM), long short-term memory (LSTM), K-nearest neighbor (KNN) and recurrent neural network (RNN). In this proposed framework, tourists can effortlessly detect their targeted places that can boost the tourism sector of Bangladesh. For this regard, convolutional neural network (CNN) achieved best accuracy of 97%.
Archeological research on virtual deaths

Archeologists are trying to learn about the worlds of the past. And it is indeed a very challenging task. Well, how about practicing in the virtual realm? ***** For more information, please visit: https://mindsponge.info/posts/26 *****
Motivate Students to Learning English Using the Kahoot Game

Learning motivation is an impulse that arises from within a person consciously or unconsciously to take action with a specific purpose. Selection of appropriate learning media to consider the needs of students and learning. Kahoot game media is one of the suitable ICT-based media because it is easy to use and is a platform that supports distance learning activities. This study examines the learner's perspective on the motivation to learning English using the Kahoot game. This study uses a quantitative descriptive method. The researcher uses a Google Form questionnaire to find the motivation to learning English using the Kahoot game from the learner's perspective. Respondents were 35 students, the English education study program at IAIN Syekh Nurjati Cirebon in the 2021/2022 academic year. The results shows that in general, there is a positive response that students have the motivation to use the Kahoot game as a medium for learning English. Easy to access, attractive appearance, and is like a game, this is the attraction of Kahoot game to attract interest in learning English. Through the features in the Kahoot game, students admit that there has been an increase in their English skills.
Research on the Teaching Reform of “Inter- Professional Comprehensive Training” Based on the BOPPPS Model under the Background of New Business

With the development of new technologies such as the Internet, cloud computing, artificial intelligence, and big data, new business models and new industrial forms have emerged. The demand for talents is also rapidly increasing across industries. This also puts forward new requirements for the cultivation of business talents and the reform of business teaching mode. “Inter-professional comprehensive training” is a highly applied course. By simulating real job content, business process, business documents, etc., students’ job skills, professional quality, business decision-making ability and innovation ability are cultivated. This paper analyzes the development of inter-professional comprehensive training in Z school, analyzes its existing problems, and proposes, among other things, targeted teaching reform suggestions, so as to provide reference for schools that carry out relevant courses.