Reviewerly/exHarmony huggingface dataset:
The Personalized Customized Framework Built with Implicit Feedback and Features

With the advent of post-web, emerging large-scale IR systems began to be strictly differentiated from simple applications that favored the realization of “Memorization” in the traditional sense, and transformed to predictive tools with the mission of “Generalization”. [1] Feedback from users in the direction of personalized customization in the IR field is a far-reaching example under the influence of this idea. In particular, what this paper wants to focus on is implicit feedback.[2]In this paper, you will see how the development of some other prerequisites in the IR field has profoundly affected the shaping of personalized customized services. Besides, the paper will also show an improved relevance feedback framework for analyzing the personalized services. By constructing the feature table, two types of features are added to the representation process in this paper to improve the accuracy of BM25 algorithm. After DCG calculations., this will ultimately improve the accuracy of the rankings.
Virtual Reality: Confinement and Engulfment; Replacement and Re-placement

This chapter explicates Virtual Reality (VR) as the first instantiation of the post-screen. Specifically, it interrogates VR’s sense of immersion via two vectors in the post-screen’s “forgetting” of screen boundaries – confinement of a viewer’s visual field with restricted viewing devices; and engulfment by being surrounded with large screens. The chapter’s key idea is its alternative expression of VR’s relations of reality as an immersive media form, which it argues shifts from the critical paradigms of replacement to re-placement. Through theoretical critique and readings of various applications of VR, the chapter argues for re-placement as a more ethical and generative space for thinking through VR’s relations of the real. In turn, where and how the actual and the virtual is re-placed informs the very purpose of media itself.
EventKE: Event-Enhanced Knowledge Graph Embedding.

Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference

Sparse Mixture-of-Experts (MoE) has been a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training computation. However, MoE models are prohibitively large and practitioners often resort to methods such as distillation for serving. In this work, we investigate routing strategies at different granularity (token, sentence, task) in MoE models to bypass distillation. Experiments on WMT and a web-scale dataset suggest that task-level routing (task-MoE) enables us to extract smaller, ready-to-deploy sub-networks from large sparse models. On WMT, our task-MoE with 32 experts (533M parameters) outperforms the best performing token-level MoE model (token-MoE) by +1.0 BLEU on average across 30 language pairs. The peak inference throughput is also improved by a factor of 1.9x when we route by tasks instead of tokens. While distilling a token-MoE to a smaller dense model preserves only 32% of the BLEU gains, our sub-network task-MoE, by design, preserves all the gains with the same inference cost as the distilled student model. Finally, when scaling up to 200 language pairs, our 128-expert task-MoE (13B parameters) performs competitively with a token-level counterpart, while improving the peak inference throughput by a factor of 2.6x.
The Potential Utilization of Blockchain Technology

The application of blockchain innovation in E-commerce has a benefit. Add a space be the need for closeness sense in carrying out the transaction process in a security framework is required to preserve the privacy of information. One of the technological marvels is a Blockchain. The application of Blockchain innovation is to supply security for certificate security within the computerized period 4.0 so as not to control by unreliable parties. By utilizing the strategy of issue detailing, inquire about the plan, information, information preparing a introduction, examination a research reports and investigate strategies seven writing ponders. The application of blockchain innovation in an E-commerce service may be a need for closeness sense in carrying out the exchange handle a security framework is required to preserve information secrecy. Numerous things are valuable in Blockchain innovation, counting observing monetary resources, wellbeing records, character, and planning the supply chain. However, many proposals lack criticism and have static thinking. So that it fails to get the main benefits of the blockchain.
Metadata Management in Organizations

This study aims at providing an introductory overview of "metadata management in organizations". It also tries to highlight its role for those interested in data management and governance in organizations. In addition, it aims to identify the benefits, components, challenges, and managers of metadata management in organizations. The inductive approach was used by reviewing the literature on metadata management. Results showed the most important concepts of metadata management, the obvious benefits and the active role that it provides as well as the components of metadata management. Moreover, results indicated that metadata managers and their challenges as well as some techniques to overcome them. The study recommended that organizations should pay attention to managing their data by managing metadata, making use of organizations that are pioneering in this field and seeking assistance from data experts and scientists. Contracting with companies specialized in metadata management. It also recommended qualifying and training the relevant personnel in the organization, and striving to keep pace with recent developments in data management and governance.
VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment

Ontology Alignment is an important research problem applied to various fields such as data integration, data transfer, data preparation, etc. State-of-the-art (SOTA) Ontology Alignment systems typically use naive domain-dependent approaches with handcrafted rules or domain-specific architectures, making them unscalable and inefficient. In this work, we propose VeeAlign, a Deep Learning based model that uses a novel dual-attention mechanism to compute the contextualized representation of a concept which, in turn, is used to discover alignments. By doing this, not only is our approach able to exploit both syntactic and semantic information encoded in ontologies, it is also, by design, flexible and scalable to different domains with minimal effort. We evaluate our model on four different datasets from different domains and languages, and establish its superiority through these results as well as detailed ablation studies. The code and datasets used are available at https://github.com/Remorax/VeeAlign.
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts

Dialogue models trained on human conversations inadvertently learn to generate toxic responses. In addition to producing explicitly offensive utterances, these models can also implicitly insult a group or individual by aligning themselves with an offensive statement. To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations. Specifically, we create ToxiChat, a crowd-annotated dataset of 2,000 Reddit threads and model responses labeled with offensive language and stance. Our analysis reveals that 42% of human responses agree with toxic comments, whereas only 13% agree with safe comments. This undesirable behavior is learned by neural dialogue models, such as DialoGPT, which we show are two times more likely to agree with offensive comments. To enable automatic detection of offensive language, we fine-tuned transformer-based classifiers on ToxiChat that achieve 0.71 F1 for offensive labels and 0.53 Macro-F1 for stance labels. Finally, we quantify the effectiveness of controllable text generation (CTG) methods to mitigate the tendency of neural dialogue models to agree with offensive comments. Compared to the baseline, our best CTG model achieves a 19% reduction in agreement with offensive comments and produces 29% fewer offensive replies. Our work highlights the need for further efforts to characterize and analyze inappropriate behavior in dialogue models, in order to help make them safer.
Classificação e Interpretação de dados do Cadastro Ambiental Rural utilizando técnicas de Aprendizagem de Máquina

The Rural Environmental Registry (CAR) consists of a mandatory public electronic registry for all rural properties in the Brazilian territory, integrates environmental information of the properties, assists the monitoring of them and the fight against deforestation. However, a large number of registrations are carried out erroneously generating inconsistent data, leading these to be canceled and/or to be requested to correct the registration. Performing automatic verification of these records is important to improve the processing of records. This paper proposes an automatic classification method to approve or cancel the CAR registers with interpretation of the classifications performed. For this, four machine learning-based classifiers were tested and the results were evaluated. The model with the best performance was used to interpret the classification using the Local Interpretable Model-agnostic Explanations (LIME) algorithm. The results showed the potential of the method in future real applications.
Design and implementation of the distributed dosimetric system based on the principles of IoT

This paper describes the architecture and components of the distributed information and management system for collecting, processing, storing, and distributing data on a radiometric and dosimetric experiment using the principle of the Internet of Things. Data exchange between elements in the system, as well as the analysis of the received information, involves active application of the ThingSpeak cloud service. Two-way communication with the cloud with a 15-second loop has been implemented. Data are processed in the MATLAB (America) environment, integrated into the cloud. The developed hardware and software solutions demonstrate an increased accuracy of measurements due to the use of promising cadmium telluride (CdZnTe) detectors, modern microcontroller and micro communication technology, and a new algorithm for correcting the dependence of detector sensitivity on radiation energy. Measurement with correction by the method of average charge pulse amplitude is carried out in the energy range from 60 keV to 3 MeV. The resolution of the spectrometric channel is 6.5 % at the peak of 662 keV of full absorption from the reference source, Cesium (Сs – 137). The module for a laboratory sensor network, designed to measure the dose of ionizing radiation, has a built-in spectrometric analog-digital converter, microcontroller control, and a communication unit. Constructing the diagrams demonstrates the operation of the interrupt handler in the form of a series of events occurring when requests arrive from a Web server. The peculiarity of the system is the absence of intermediate devices that make it possible to establish a connection with the Internet. The developed system, equipment, algorithms, and programs are used for experimental studies of radiation and nuclear-physical processes. Elements of the system were useful for remote laboratory work by students
System Identification Based on Completely Connected Neural Networks for Black-Box Modeling of Ship Maneuvers

A system identification method based on completely connected neural networks is proposed to establish the black-box model of ship maneuvers, which can facilitate the design of a ship motion control system. To train and verify the network, a dataset obtained from multiple zigzag and turning circle maneuvers is prepared first. The Adam algorithm that can accelerate the training process is then utilized to determine the optimal parameters of the network. In order to verify the generalization ability of the identified model, predictions of maneuvering motions not involved in the training process are conducted using the identified model. To find the preferable design parameters of the controller, a simulator generated by the neural network is also employed to simulate a maneuver guided by a PD heading controller. The results illustrate good prediction accuracy and strong generalization capability of the model.
HUBUNGAN ANTARA DISIPLIN DENGAN MOTIVASI BELAJAR SISWA KELAS V DI SD NEGERI 2 LOKTABAT UTARA KOTA BANJARBARU

The purpose of this study was to determine whether there is no relationship between learning discipline and learning motivation in grade V at SD Negeri 2 Loktabat Utara, Banjarbaru City. The object of this research is the relationship between learning discipline and learning motivation of fifth grade students at SD Negeri 2 Loktabat Utara, Banjarbaru City. The subjects in this study were all students at the North Loktabat 2 Elementary School, Banjarbaru City. The sample in this study using purposive sampling amounted to 28 students. The data mining tool used was a questionnaire. The data analysis used was correlation technique. Based on the data analysis, it is concluded that there is a relationship between learning discipline and learning motivation in grade V at SDN 2 Loktabat Utara Kota Banjarbaru based on the analysis rcount = 0.926> rt 0.374 at the 5% significance level of N = 28. The researchers stated that are as follows: a) for teachers: become input material for teachers in encouraging students to be more enthusiastic in learning and instilling a disciplined attitude in learning so that learning achievement increases to be better and more effective in motivating classroom student learning V at SDN 2 Loktabat Utara Kota Banjarbaru, 2) the principal should be more motivating teachers to improve the quality of student learning, especially the learning discipline of grade V students at SDN 2 Loktabat Utara Kota Banjarbaru, and 3) to other researchers so that they can conduct research with a focus on problems that exist in school or focus that is different from popu wider lasi.
Adversarial Unsupervised Domain Adaptation for Traffic Anomaly Detection in Convergence Network

With the increasing scale of the network, network security problems are becoming more and more complex. Network traffic anomaly detection can be used as an effective method to provide technical support for network management and ensure the normal operation of the network. In some case, no labeled data is available, traditional machine learning can not learn accurate knowledge and the training of the model can not converge, and it is difficult to achieve good classification performance. To deal with this problem, we propose an unsupervised domain adaptation method based on bi-directional classify-consistent generative adversarial network. The proposed method includes bi-directional cross-domain generators which make full use of two domains and reduce the domain gap with two directions, dual consistent classifiers which complete discrimination and classification at the same time, and class-wise cross-domain alignment which is helpful for improving the classification performance by quantifying the distribution discrepancy between domains and aligning two domains. Experiments on public network traffic datasets indicates that our method is effective and does better than other methods for network traffic anomaly detection with no labeled data.
Big Data Application System of College Students’ Mental Health Based on Mobile Internet Platform

With the continuous maturity and development of Internet technology in our country, a major current development trend is the combination of traditional industries and the Internet. In recent years, in our country’s colleges and universities, the educational problem of the mental health of college students has become more and more prominent. At the same time, the mental health education of college students has increasingly become an urgent topic in the cultivation of talents in colleges and universities. The college students’ mental health big data is a systematic study of a college student’s mental health system based on the Internet platform. The successful design, development and application of the system will promote the informatization of the mental health education of college students in our country’s colleges and universities. Comprehensively improve the healthy development of college students.
Reflections on a Comparative AR Study

A variety of studies exist that design innovative interactions based on augmented reality (AR). Comparing them often involves examining their laboratory studies. However, since AR has a high context dependency (e.g., reference to the real environment), it is important to emphasize that existing literature do not reveal generalizable results regarding how to design AR instructions. Within this paper, we argue that it is time to open up new use cases and test appropriation mechanisms in practice with the help of long-term studies to build a base for the comparison of AR-based interactions. Such a repository built up with long-term studies has the potential to define valid insights for future developments in the AR environment.
Automated Wheelchair with Speech Recognition Module Incorporated with Obstacle Detector to Enable Commotion for Partially Paralyzed People

Automation or Automatic control is an emerging technology that has already found successful application in a variety of different fields, including simulation, training and education. Various control systems are used to operate an equipment. Loss of muscle function in any part of the body or in the whole body is known as Paralysis. It happens when communication between the brain and muscles get interrupted. Either one side or whole of the body can get paralyzed i.e. it can be complete or partial. It can also be localized in one area or it could widespread. If lower portion of the body including both the legs get paralyzed it is called paraplegia. Paralysis of the limbs i.e. the legs and arms is called quadriplegia. So for patients with both forms of paralysis, this paper proposes a wheelchair with speech recognition and safety measures. The main essence behind the project is to control the wheelchair with voice and also keeping the safety measures in place. A dependent user recognition voice system and ultrasonic and infrared sensor system has been integrated in this Wheelchair. The wheelchair's wheels will be controlled by electric motors. The input will be the voice of the patient and given to the speech recognition unit. His Her voice is processed and the necessary changes in the wheels are made. The wheelchair also has an obstacle detection mechanism. And also the distance of the floor from the wheelchair is sensed using proximity sensors so as to avoid accidents such as staircase fall.
Using Convolutional Neural Networks to Detect Compression Algorithms

Machine learning is penetrating various domains virtually, thereby proliferating excellent results. It has also found an outlet in digital forensics, wherein it is becoming the prime driver of computational efficiency. A prominent feature that exhibits the effectiveness of ML algorithms is feature extraction that can be instrumental in the applications for digital forensics. Convolutional neural networks are further used to identify parts of the file. To this end, we observed that the literature does not include sufficient information about the identification of the algorithms used to compress file fragments. With this research, we attempt to address this gap as compression algorithms are beneficial in generating higher entropy comparatively as they make the data more compact. We used a base dataset, compressed every file with various algorithms and designed a model based on that. The used model was accurately able to identify files compressed using compress, lzip and bzip2.
Prototype Sistem Stock Barang Dengan Metode Economic Order Quantity (EOQ)

Many stores still use the traditional way of managing their inventory. From taking notes to using Microsoft Excel. Problems will arise when you want to see the existing stock, the best-selling items and which items have to come out first. So that sometimes they have difficulty in getting information on the stock of goods provided that are not in accordance with existing availability. In this study the author uses the Economic Order Quantity method. The Economic Order Quantity method is a management function in inventory. Where goods must be provided in large quantities because they are sold out is one of the advantages of this method. The results of the analysis of this study are the Design and Build of a Stock of Goods System with a web-based Economic Order Quantity method to make it easier for shop owners to collect inventory data, increase economic value and improve service to customers with always available stock.Keywords: Stock of goods, Economic Order Quantity, Web, Microsoft Excel.
Robust Counterfactual Explanations on Graph Neural Networks

Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition. Most existing methods generate explanations by identifying a subgraph of an input graph that has a strong correlation with the prediction. These explanations are not robust to noise because independently optimizing the correlation for a single input can easily overfit noise. Moreover, they do not align well with human intuition because removing an identified subgraph from an input graph does not necessarily change the prediction result. In this paper, we propose a novel method to generate robust counterfactual explanations on GNNs by explicitly modelling the common decision logic of GNNs on similar input graphs. Our explanations are naturally robust to noise because they are produced from the common decision boundaries of a GNN that govern the predictions of many similar input graphs. The explanations also align well with human intuition because removing the set of edges identified by an explanation from the input graph changes the prediction significantly. Exhaustive experiments on many public datasets demonstrate the superior performance of our method.