Reviewerly/exHarmony huggingface dataset:
Unsupervised Hierarchical Disentanglement for Video Prediction

Video prediction is a complicated task as countless possible future frames exist that are equally plausible. While recent work have made progress in the prediction and generation of future video frames, these work have not attempted to disentangle different features of videos such as an object’s structure and its dynamics. Such a disentanglement would allow one to control these aspects to some extent in the prediction phase, while at the same time maintain the object’s intrinsic properties that are learned as the model’s internal representation. In this work, we propose Ladder Variational Recurrent Neural Networks (LVRNN). We employ a type of ladder autoencoder shown to be effective for feature disentanglement on images and apply it to the Variational Recurrent Neural Network (VRNN) architecture, which has been used for video prediction. We rely on extracted keypoints in each frame to separate the structure from the visual features. We then show how different levels of the ladder network learn to disentangle features and demonstrate that each of these levels can be used for controlling different aspects of future frames such as structure and dynamics. We evaluate our method on the Human3.6M and BAIR robot datasets. We show that our method is able to perform hierarchical disentanglement, yet provide reasonable results compared to similar methods.
Joint Goal for Word Embedding Compression Based on Word Frequency

With the rapid progress in natural language processing many models grow larger. However deploying these models in small devices such as mobile devices requires smaller models. Thus techniques of model compression should be used. We consider the problem of compressing word embedding of these models. Existing methods usually treat all the words as the same. These words often have the same dimension and each word cost the same memory. During our work,first we show the feasibility of further compressing word embedding by considering different impact of each word. The idea comes from the famous Zipf’s law which says few words cover most of the document. We divide the words into groups based on word frequency and encode each group differently. Second we propose a joint goal for automatically choosing the split points between different word groups and determining the size of each group. The joint goal between task performance and model compression ratio solves the problem of how to balance between both goals. By combining both goals into to an explicit formula, it’s easy to find a good solution using an end to end training strategy.It turns to an architecture search problem and we solve it using AutoML methods. The experiment show that the word embedding layer could be further compressed to 22.6% of the baseline. We also get hints on how to choose number of word groups and to balance between the model performance and compression ratio on the experimental result.
IMPLEMENTASI METODE FIFO PADA SISTEM PEMESANAN E-TIKET LOMBA BURUNG BERKICAU BERBASIS WEB

Gantangan Jimjog, which is located in the Ponalan Vegetable Market, Muntilan, Magelang Regency, is one of the most crowded bird competitions in the Magelang area. Gantangan Jimjog is still using the manual system in booking tickets. Bird competition participants must first look for bird competition brochures on social media to view information on the chirping bird competition and place an order for tickets by contacting by telephone and it is possible that more than one participant can order the same hanger number. In this study, a web-based bird competition ticket booking system was made at Jimjog by applying the FIFO method. The FIFO method is applied in determining the queue for ordering race tickets based on the customer who is the first to pay, then the first one is given a hanger number according to what was ordered according to the existing quota. The ticket booking system that has been built can increase the time efficiency and productivity of Gantangan Jimjog and make it easier for participants to get information on the availability of bushel numbers.
Semantic Lexical Error Analysis

The importance of lexis in written translation and how formal lexical errors could affect the quality and clarity of the Saudi English major students’ translation products was detailed in Chap. 4 : Formal Lexical Error Analysis. The formal lexical errors identified were categorised into 17 subtypes and, based on a taxonomy developed specifically for this study, a quantitative analysis was implemented. The chapter provided a detailed explanation of the possible sources of the identified formal lexical errors. The findings indicated that inadequate knowledge of the L2 phonological, morphological, and orthographical aspects and the difference between the linguistic systems of the target and home languages accounted for the occurrence of these lexical errors.
New development trends of “metaverse + psychology” blending virtual and reality

&lt;p align="justify"&gt;Metaverse is a hot topic recently, which has aroused widespread concern from all walks of life. The possible impact of the metaverse on the field of psychology includes creating a new field of panoramic learning process by creating an educational metaverse, creating a more scientific and novel teaching model by creating a MOOC metaverse, breaking the “magic circle” to alleviate interpersonal avoidance and promote individual social behavior, fostering individual self-identity by shaping the metaverse cyberpunk culture, improving the effectiveness of school mental health education by building a metaverse psychological experience center, creating a metaverse psychological intervention system based on traditional behavioral therapy to improve the effectiveness of psychological counseling, etc. However, the emerging technology represented by the metaverse is a double-edged sword. It is not only necessary to pay attention to this opportunity to create a perfect world or to create miracles, but also to guard against potential harm to the development of human society.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
HS$$^2$$N: Heterogeneous Semantics-Syntax Fusion Network for Document-Level Event Factuality Identification

Event factuality identification (EFI) aims to assess the veracity degree to which an event mentioned in a document has happened, and both semantic and syntactic features are crucial for this task. Most of the previous studies only focused on sentence-level event factuality, which may lead to conflicts among mentions of a specific event in a document. Existing studies on document-level EFI (DEFI) are still scarce and mainly focus on semantic features. To address the above issues, we propose a novel Heterogeneous Semantics-Syntax-fused Network (HS $$^2$$ N) for DEFI, which not only integrates both semantic and syntactic information in an efficient way using Biaffine Attention and differentiated alignment method, but also considers both inter-and-intra sentence interaction. Experimental results on the English and Chinese datasets show that our proposed HS $$^2$$ N outperforms the state-of-the-art model.
Garrison: A Novel Watchtower Scheme for Bitcoin

In this paper, we propose Garrison, which is a payment channel with watchtower for Bitcoin. For this scheme, the storage requirements of both channel parties and their watchtower would be $$\mathcal {O}(\log (N))$$ with N being the number of channel updates. Furthermore, using properties of the adaptor signature, Garrison avoids state duplication. It means both parties store the same version of transactions for each state and hence the number of off-chain transactions does not exponentially increase with the number of applications built on top of each other in the channel. Moreover, the new proposal avoids punish-per-output pattern, meaning that all outputs of a revoked state can be claimed using a single revocation transaction. Garrison can be implemented without any update in Bitcoin script.
The Navigation Map-Based Cognitive Architecture—A New Class of Artificial Intelligence

A navigation map-based cognitive architecture uses versions of navigation maps as a store of procedural memories, semantic memories, episodic memories, as well as for working memories. Perceptual inputs are mapped to navigation maps and motor outputs are prepared on navigation maps. The brain-inspired Causal Cognitive Architecture 3 (CCA3) is an example of a navigation map-based cognitive architecture. Newly created or updated single-sensory navigation maps are mapped onto a best matching multi-sensory navigation map. Instinctive and learned primitives, which act as small rules or productions, and which also are stored in modified navigation maps, then operate on the working navigation map, producing an action signal which can be outputted. By feeding back and re-operating on the intermediate results, the architecture can generate causal behavior as well as allow induction by analogy. The list of core operations and properties of the architecture are compared with the basic operations and properties of other approaches towards achieving artificial intelligence. This comparison shows that the navigation map-based cognitive architecture represents a new class of an approach towards artificial intelligence.
ARtisan Bistro: A Cooking Task Environment to Conduct Studies in Augmented Reality

With the increasing popularity of Augmented Reality (AR) and newer emerging technologies for normal consumers there is an in-creasing need to research AR applications in more general everyday settings. ARtisan Bistro is an attempt to create a base layer environment that other researchers can build on. Our effort is to save researchers the time of developing an environment from scratch and focus more on the core part of their research.
Anomaly detection method applied to vehicle monitoring

Anomaly detection is a topic studied in the literature for a large number of applications. As more and more data is collected continuously, numerous algorithms have been developed for anomaly detection in time series. In this paper, we first propose a method for time series anomaly detection that can be used in different fields. This method is then applied in a real case study for the detection of anomalous behavior of hybrid trucks. Finally, the results obtained are analyzed to propose lines of improvement.
Using Dimensionality Reduction Techniques to Understand the Sources of Software Complexity

Despite significant work in the area of software complexity, there are still numerous unanswered questions about the sources and locations of complexity and its relationship to software design and programming language features. In this paper, we attempt to illuminate these questions by applying code-agnostic statistical dimensionality reduction techniques to a large dataset of 3000 popular open source Java programs.We analyze our set of projects to determine key attributes of Java program composition and complexity, using standard metrics from previous work. We apply two proven dimensionality reduction techniques, Principle Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) to explore the relationships between complexity models and program composition. We find support for three primary sources of Java software complexity and note that particular projects are most often associated primarily with one variety. Our results have potential implications for source code analysis and programming language design.
A Trusted Storage System for Digital Object in the Human-Cyber-Physical Environment

The continuous development of the information technology has driven the deep integration of various networks such as the Internet, Internet of Things, and mobile networks, promoting the fusion of human society, information space, and the physical world and the development of the human-cyber-physical (HCP) environment. In the HCP environment, applications usually need to use heterogeneous resources belonging to multiple systems and participants. The Digital Object Architecture (DOA), a data-centric software architecture proposed by Dr. Robert Kahn and aims to enable interoperability across systems, is useful in the HCP environment. HCP resources can be uniformly abstracted and modeled as digital objects (DOs), and all the operations and interactions of HCP resources can be transformed into the DO operations and interactions. There are two challenges of using DOA in the HCP environment, the first is the trust DO governance because of the “digital” nature of DO, making itself easily copied; the second is the dynamic characteristics of HCP resources, making it necessary to store the history state of resource DOs. In this paper, a trusted storage system for resource DOs in the HCP environment is proposed. By using blockchain technology, the change records of DOs can be stored in blockchain nodes, maintained by committee constituted by authoritative participants in the HCP environment. Two algorithms are proposed in the block generation and consensus stage to improve the whole performance of the system. In the block generation stage, the Periodic-Adjusted Merkle (PA-Merkle) tree is proposed and reduces time consumption by 27.8%. In the consensus stage, the Tree Spread PBFT (TS-PBFT) algorithm is proposed and reduces the time consumption by more than 20% under 100 Mbps bandwidth. The overall performance of the system improves by 24.2% in the case of four nodes with 100 Mbps bandwidth.
Compound Label Learning for Affective Image Content Analysis

The single label of an affective image cannot well reflect the emotions underneath, thus converting the single label into compound affective labels better reveals the complex emotions of the image. However, building new datasets on human annotation has high labor costs, and the results are quite subjective. To address this issue, our paper proposes a model for constructing compound affective labels from single-label datasets with a CNN-based model. First, we enhance the ability of image sentiment classification by applying a new classifier loss. Second, we adopt the knowledge distillation model for retaining more sentiment information in image labels. Third, we integrate the attention mechanism within the knowledge distillation to transfer the attention map to the student network for improving its performance. Finally, we attain the compound affective labels from the label probability distribution of the distilled model. The generated image datasets with compound labels can be applied in various fields. They can serve for psychological analysis and evaluation, and provide richer affective references in art design, interactive media, advertising products, etc.
Modeling and simulation of synchronous condenser based on digital twin

With the continuous development of UHV DC transmission technology, the synchronous condenser has become an important supporting equipment in the DC transmission project, and its mechanism modeling and simulation have important practical significance. Based on the digital twin technology, this paper establishes a hybrid symbolic numerical model of synchronous condenser, which can realize model reuse and rapid deployment. First, it analyzes the basic characteristics of the synchronous condenser, and then proposes the digital twin model framework of this article. The digital twin model of the synchronous condenser is developed based on the Andes power system simulation tool. Finally, a simulation case is used to verify the digital twin model of the synchronous condenser. The experimental results show that the digital twin model can realize simulation operation and fault characteristic response simulation.
Construction of online course platform based on open source scheme: taking &amp;quot;Kaiwu Cloud Class&amp;quot; of LZIT as an example

Based on the open source learning system Moodle, we separate the web terminal, database and file resource storage, and then, design the distributed deployment framework and network security scheme. Finally, by using Nginx reverse agent, we realize the load balancing and complet the construction of "Kaiwu cloud class" for Lanzhou Institute of Technology (LZIT). The construction has realized the functions of IPv6/IPv4 dual stack deployment, LDAP unified authentication, online homework review and feedback, and custom test database, etc. The results show that the platform is stable in operation, can meet the needs of online teaching in medium-sized colleges, and has the value of application promotion.
Reorganize Significance of Mt, Ms and Plastic Zone Size Against LMSD Under Plastic Collapse Regime

Abstract Due to the aging of facilities, Oil and Chemical industries in Japan has been longing for using API 579-1/ASME FFS-1 [1] Part 4 and Part 5 assessment over decades. However, most of equipment are subjected to Japanese High Pressure Gas Safety Law so our industry needed to pass through the discussion in a local committee. In the local committee, there was a conflict on the significance of Folias Factor, Mt, and surface correction factor, Ms. The conflict had been a stumbling block against the formal permission to use API 579-1/ASME FFS-1 Part 4 and Part 5 assessment technology. In 2021, throughout the long term effort of cross industry task team led by authors supported by API579-1/ASME Joint Committee on Fitness-For-Service members, the conflict has been solved in the local committee. Effective from April 1st, 2022, Oil and Chemical industry in Japan got a formal approval from the government to use API 579-1/ASME FFS-1 Part 4 and Part 5 assessment for equipment subjected to High Pressure Gas Safety Law. Authors noticed similar conflict on the significance of Folias Factor, Mt, and surface correction factor, Ms, is also found in societies outside of Japan occasionally. Those factors are sometimes referred as “bulging factor” that sounds like those are simple conversion factors between flat plate stress and cylinder stress. However, it is not a whole picture of the factor as discussed in this paper. Therefore, it would be beneficial for future improvement of Fitness-for-Service technology to share our outcomes on the correct significance of Mt and Ms including our Lr discussion on API579-1/ASME FFS-1 Part 9 Crack Like Flaw assessment taking this opportunity. In addition, authors studied the relative relationship between plastic zone length and distance to major stress discontinuity associated with the discussion. This might be beneficial to be shared for a future discussion too. Through this paper, significance of Mt and Ms, including derivation of Ms reinforced by FEM analysis, will be discussed. Also, plastic zone size model that can be used for a future discussion on required Lmsd, distance to major stress discontinuity, will be introduced.
TRQP: Trust-Aware Real-Time QoS Prediction Framework Using Graph-Based Learning

QoS prediction algorithm requires to be real-time to be integrated with most real-time service recommendation or composition algorithms. However, real-time algorithms are prone to compromise on the solution quality to improve their responsiveness, which we aim to address in this paper. The collaborative filtering (CF) technique, the most widely used QoS prediction method, consider the influences of all users/services while predicting the QoS value for a given target user-service pair. However, the presence of untrustworthy users/services, whose QoS invocation patterns are different from the rest, may lead to degradation in prediction accuracy. Moreover, in many cases, the quality of the prediction algorithms often deteriorates to ensure faster responsiveness due to their inability to capture non-linear, higher-order, and complex relationships among user-service QoS data. This paper proposes a trust-aware QoS prediction framework leveraging a novel graph-based learning approach. Our framework (TRQP) is competent enough to identify trustworthy users and services while learning effective feature representation for finding a rich collaborative signal in an end-to-end fashion. Our experiments on the publicly available WS-DREAM-1 dataset show that TRQP is not only eligible as a real-time algorithm but also is well capable of handling various challenges associated with QoS prediction problems (e.g., extracting complex non-linear relationships among QoS data) and outperformed major state-of-the-art methods.
A Comparative Study of BERT-Based Attention Flows Versus Human Attentions on Fill-in-Blank Task

The purpose of this small-scale study is to compare BERT language model’s attention flow—which quantifies the marginal contributions from each word and aggregated word groups towards the fill-in-blank prediction— with human evaluators’ opinions on the same task. Based on a limited number of experiments performed, we have the following findings: (1) Compared with human evaluators, BERT base model pay less attention towards verbs, and more attention towards noun and other word types. That seems to agree with the natural partition hypothesis: nouns predominate over verbs in children’s initial vocabularies because it is easy to understand the meanings of nouns. The premise of such hypothesis is that BERT base model performs like a human child. (2) As sentences become longer and more complex, human evaluators can distinguish the major logic relation and be less distracted by other components in the structure. The attention flow scores calculated using the BERT base model, on the other hand, amortize towards multiple words and word groups as sentences become longer and more complex. (3) Amortized attention flow scores calculated using BERT base model provides a balanced global view towards different types of discourse relations embedded in long and complex sentences. For future works, more examples will be prepared for detailed and rigorous verifications on the findings.
What to Check: Systematic Selection of Transformations for Analyzing Reliability of Machine Vision Components

Machine Vision Components (MVCs) are deployed in safety-critical systems, such as autonomous driving, and their reliability must be checked against scene changes, e.g., rain, that may lead to hazardous situations in the deployment environment. Many scene changes leading to hazardous situations may be hard to reproduce on demand, so existing approaches for MVC reliability analysis use synthetic image transformations to simulate such changes. Therefore, the question of how to select the image transformations to simulate specific hazardous situations is essential to MVC reliability analysis. Yet, this problem has not been addressed by the scientific community so far. In this paper, we propose a framework for mapping between hazardous situations and relevant image transformations using their descriptions. Our framework includes a systematic description mapping process DMaP, a method autoDMaP for automating this process, and coverage metrics measuring how well a list of transformations can simulate a list of hazardous situations. We show the applicability of our framework by mapping hazardous situations from an existing checklist, i.e., CV-HAZOP, to a list of synthetic image transformations from a state-of-the-art transformation library, i.e., Albumentation. As part of evaluation, we conducted an experiment and showed that, compared with the manual, ad-hoc mapping produced by image processing experts, DMaP and autoDMaP resulted in better precision and recall. Additionally, using our new coverage metrics, we found that image transformations considered by state-of-the-art libraries and reliability benchmarks are far from fully simulating the CV-HAZOP hazardous situations, and the MVCs that perform best on these benchmarks have significant reliability gaps against these situations.