Reviewerly/exHarmony huggingface dataset:
Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer

Knowledge Tracing (KT) aims at tracing the evolution of the knowledge states along the learning process of a learner. It has become a crucial task for online learning systems to model the learning process of their users, and further provide their users a personalized learning guidance. However, recent developments in KT based on deep neural networks mostly focus on increasing the accuracy of predicting the next performance of students. We argue that current KT modeling, as well as training paradigm, can lead to models tracing patterns of learner’s learning activities, instead of their evolving knowledge states. In this paper, we propose a new architecture, Diagnostic Transformer (DTransformer), along with a new training paradigm, to tackle this challenge. With DTransformer, we build the architecture from question-level to knowledge-level, explicitly diagnosing learner’s knowledge proficiency from each question mastery states. We also propose a novel training algorithm based on contrastive learning that focuses on maintaining the stability of the knowledge state diagnosis. Through extensive experiments, we will show that with its understanding of knowledge state evolution, DTransformer achieves a better performance prediction accuracy and more stable knowledge state tracing results. We will also show that DTransformer is less sensitive to specific patterns with case study. We open-sourced our code and data at https://github.com/yxonic/DTransformer.
SA-Fusion: Multimodal Fusion Approach for Web-based Human-Computer Interaction in the Wild

Web-based AR technology has broadened human-computer interaction scenes from traditional mechanical devices and flat screens to the real world, resulting in unconstrained environmental challenges such as complex backgrounds, extreme illumination, depth range differences, and hand-object interaction. The previous hand detection and 3D hand pose estimation methods are usually based on single modality such as RGB or depth data, which are not available in some scenarios in unconstrained environments due to the differences between the two modalities. To address this problem, we propose a multimodal fusion approach, named Scene-Adapt Fusion (SA-Fusion), which can fully utilize the complementarity of RGB and depth modalities in web-based HCI tasks. SA-Fusion can be applied in existing hand detection and 3D hand pose estimation frameworks to boost their performance, and can be further integrated into the prototyping AR system to construct a web-based interactive AR application for unconstrained environments. To evaluate the proposed multimodal fusion method, we conduct two user studies on CUG Hand and DexYCB dataset, to demonstrate its effectiveness in terms of accurately detecting hand and estimating 3D hand pose in unconstrained environments and hand-object interaction.
Challenging rules of thumb to redefine flexibility

The recently completed Lindemann Performing Arts Center on the Brown University campus is an exploration in all things flexible. To satisfy the programmatic needs of Brown Arts Initiative, the primary user of this new building, who needed five rooms but only got one, the Main Hall redefined the concept of multiuse hall. All sixsurfaces that define the major acoustically supportive surfaces (ceiling elements, walls, and floors) move to manipulate the otherwise beautifully simple architectural concept into five room configurations—Orchestra, Recital, End Stage Theatre, Experimental Media, and large Flat Floor. A mix of manual and motorized curtains and banners adds still more flexibility. As the paint still dries on the building, this paper will investigate the acoustic challenges, happy accidents, and areas where we might have done with less (or more) in a building that is sometimes heavy handed and sometimes a light touch. The paper will cover topics such as glass as a major reflecting surface, wall buildups that break rules of thumb to produce warm acoustic responses, use of variable acoustic solutions, and ensemble to audience size ratios that challenge conventional wisdom.
Leveraging Model Driven Techniques for Designing Web-GIS Systems

Nowadays, Web-based Geographic Information Systems (Web-GIS) play an essential role in many organizations that provide services to people via the Internet. Although Web-GIS systems are built based on well-known standards and have common features, the problem is that building such systems needs expertise and includes high development costs; hence most organizations do not have enough resources to build their own GIS. To address this problem, we propose a model driven approach that enables the developers to generate a Web-GIS automatically. Our approach includes a metamodel as a domain specific modeling language that covers the main concepts of a Web-GIS, a variety of map controls, and different layer types. We also provide a transformation engine that automatically generates the final code of a Web-GIS from the model designed using our metamodel. To evaluate the proposed approach, we designed a simple system to show that the metamodel covers the main concepts of a Web-GIS and the generated code leads to the implementation of the system without any manual changes.
A Question Summarization Method based-on Deep Learning in Persian Language

To properly answer a question, you must first understand the question. Users typically send more explanations than they need to answer in order to express their question in natural language, which increases the complexity of the question and provides useless side information, which leads to the production of false and unrelated answers and makes it difficult to answer the questions. In this paper, we propose a method for summarizing questions in Persian based on the multilingual pre-trained text-to-text transformer model. To begin, we collected a number of question and question summary pairs from websites for answering religious questions, and after fine-tuning the mT5 model with this dataset, we applied the evaluation criteria of Rouge-1, Rouge-2, and Rouge-L. We examined it using the F-measure and obtained satisfactory results.
Joint Partitioning and Sampling Algorithm for Scaling Graph Neural Network

Graph Neural Network (GNN) has emerged as a popular toolbox for solving complex problems on graph data structures. Graph neural networks use machine learning techniques to learn the vector representations of nodes and/or edges. Learning these representations demands a huge amount of memory and computing power. The traditional shared-memory multiprocessors are insufficient to meet real-world data’s computing requirements; hence, research has gained momentum toward distributed GNN.Scaling the distributed GNN has the following challenges: (1) the input graph needs to be efficiently partitioned, (2) the cost of communication between compute nodes should be reduced, and (3) the sampling strategy should be efficiently chosen to minimize the loss in accuracy. To address these challenges, we propose a joint partitioning and sampling algorithm, which partitions the input graph with weighted METIS and uses a bias sampling strategy to minimize total communication costs.We implemented our approach using the DistDGL framework and evaluated it using several real-world datasets. We observe that our approach (1) shows an average reduction in communication overhead by 53%, (2) requires less partitioning time to partition a graph, (3) shows improved accuracy, (4) shows a speed up of 1.5x on OGB-Arxiv dataset, when compared to the state-of-the-art DistDGL implementation.
Using 3-dimensional rendering programs to visualize 2-dimensional impulse responses of the built environment

As the first notes from a performer are expressed on a newly constructed stage, we acousticians close our eyes and hold our breath. We do this not because our reputation is on the line but rather to focus all our attention on our auditory system. We listen for the impulse response, hearing the loudness, clarity, bass ratio, and profile of the decay. We open our eyes as the performer continues, pairing our visual and auditory senses to gain still more information. We look for those surfaces placed around the stage to support the direct sound. We follow the vibrations into the larger volume and map their path as they bounce around the room and rain down a shower of energy that envelops us in warm sonic embrace. Did we achieve the acoustic signature we designed? We will discuss our methods for mapping out the recording of two-dimensional time and energy in an impulse responses onto three-dimensional space with rendering software in order to analyze and tune our performances spaces.
Deep Simplex Classifier for Maximizing the Margin in Both Euclidean and Angular Spaces

The classification loss functions used in deep neural network classifiers can be grouped into two categories based on maximizing the margin in either Euclidean or angular spaces. Euclidean distances between sample vectors are used during classification for the methods maximizing the margin in Euclidean spaces whereas the Cosine similarity distance is used during the testing stage for the methods maximizing margin in the angular spaces. This paper introduces a novel classification loss that maximizes the margin in both the Euclidean and angular spaces at the same time. This way, the Euclidean and Cosine distances will produce similar and consistent results and complement each other, which will in turn improve the accuracies. The proposed loss function enforces the samples of classes to cluster around the centers that represent them. The centers approximating classes are chosen from the boundary of a hypersphere, and the pairwise distances between class centers are always equivalent. This restriction corresponds to choosing centers from the vertices of a regular simplex. There is not any hyperparameter that must be set by the user in the proposed loss function, therefore the use of the proposed method is extremely easy for classical classification problems. Moreover, since the class samples are compactly clustered around their corresponding means, the proposed classifier is also very suitable for open set recognition problems where test samples can come from the unknown classes that are not seen in the training phase. Experimental studies show that the proposed method achieves the state-of-the-art accuracies on open set recognition despite its simplicity.
Development of scenarios for the use of digital technologies in the design and research activities of university

In the context of scientific and technological progress, the rapid development of information technologies, the problem of training qualified specialists who are able to work effectively in the new information environment is becoming increasingly urgent. Design and research activities of students are one of the main areas that ensure the development and formation of competencies necessary for professional and research work. Within the framework of the digital transformation that is being carried out in Russia, the use of digital technologies at different stages of research and the organization of design and research activities are the most relevant. The paper presents options for using digital tools and services for organizing work, establishing communication, collaborating, planning and accounting for completed tasks within the framework of the research project.
Penerapan Human Machine Interface Berbasis Augmented Reality pada Sistem SCADA Modular Production System

ABSTRAKPenerapan teknologi digital pada sistem SCADA dapat dilakukan dengan menampilkan user interface dengan metode Augmented Reality yaitu suatu teknologi yang menggabungkan suatu objek nyata dan objek virtual secara realtime. Penelitian ini bertujuan untuk mengimplementasikan HMI NodeRED dengan metode pengamatan Augmented Reality pada Sistem SCADA Modular Production Systems (MPS) bagian Testing dan Handling Station. Hasil pengujian menunjukkan sistem berhasil melakukan kontrol dan monitoring plant Testing dan Handling Station melalui Webview Augmented Reality. Nilai rata-rata waktu pengiriman data PLC ke PC pada Testing Station yaitu 109 ms dan rata-rata waktu yang pengiriman data PC ke PLC yaitu 106 ms. Sedangkan untuk Handling Station rata-rata waktu pengiriman data PLC ke PC yaitu 109 ms dan rata-rata waktu pengiriman data PC ke PLC yaitu 105 ms.Kata kunci: Augmented Reality, SCADA, NodeRED, PLC, MPS ABSTRACTThe Implementation of digital technology in the SCADA system can be done by displaying a user interface with the Augmented Reality method, which is a technology that combines a real object and a virtual object in real-time. This study aims to implement HMI NodeRED with the Augmented Reality observation method on the SCADA Modular Production Systems (MPS) for testing and handling stations. The test results showed that the system successfully controlled and monitored the Testing and Handling Station plants through the Augmented Reality Webview.The average value of the time of sending PLC data to PC at the Testing Station is 109 ms and the average time that PC data is sent to the PLC is 106 ms. While for the Handling Station, the average time for sending PLC data to PC is 109 ms and the average time for sending PC data to PLC is 105 ms.Keywords: Augmented Reality, SCADA, NodeRED, PLC, MPS
David Green and the auditory demonstration tapes

In 1976, Dave Green received a grant from the National Science Foundation to create a series of auditory demonstrations, intended for use in experimental psychology courses or labs. He assembled a group of collaborators from the Harvard Laboratory of Psychophysics and elsewhere that ultimately created a set of ten cassette tapes with 20 demonstrations. Demonstrations included critical band, asymmetry of masking, binaural beats, temporal integration, categorical speech perception, combination tones, Shepard tones, and periodicity pitch among others. The demonstrations were well chosen to cope with the limitations of the cassette medium. Ten years later, cassette tapes had been replaced by compact discs, and the Eindhoven group with Houtsma, Rossing, and Wegenaars created a similar set of digital demonstrations. Those recordings are now available from the Acoustical Society of America as the “Auditory Demonstrations IPO-NIU-ASA” CD. This talk will compare those two sets of recorded demonstrations and indicate the influence that the Harvard tapes had on the ASA CDs, what was kept, what was changed, what was improved, and what was not.
Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules

We target the problem of automatically synthesizing proofs of semantic equivalence between two programs made of sequences of statements. We represent programs using abstract syntax trees (AST), where a given set of semantics-preserving rewrite rules can be applied on a specific AST pattern to generate a transformed and semantically equivalent program. In our system, two programs are equivalent if there exists a sequence of application of these rewrite rules that leads to rewriting one program into the other. We propose a neural network architecture based on a transformer model to generate proofs of equivalence between program pairs. The system outputs a sequence of rewrites, and the validity of the sequence is simply checked by verifying it can be applied. If no valid sequence is produced by the neural network, the system reports the programs as non-equivalent, ensuring by design no programs may be incorrectly reported as equivalent. Our system is fully implemented for one single grammar which can represent straight-line programs with function calls and multiple types. To efficiently train the system to generate such sequences, we develop an original incremental training technique, named self-supervised sample selection. We extensively study the effectiveness of this novel training approach on proofs of increasing complexity and length. Our system, <inline-formula><tex-math notation="LaTeX">$\mathsf {S4Eq}$</tex-math></inline-formula> , achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent programs.
Examination of the static and dynamic forces at the termination of a bowed string

In bowed-string instruments, the force exerted by the string on the bridge may be used for analyzing the excitation process. This force is caused by the vibration of the string due to the musician's bowing action. The dynamic component of this force has been investigated in the relevant literature. In this study, focusing on a monochord, an approach is proposed to simultaneously measure both the static and the dynamic components of the force exerted by the string on a rigid support. The use of a robotic arm to excite the string via a linear bowing action permits to measure this force under controlled excitation conditions. The static and dynamic components of the force are analyzed in the transverse plane to the string at several moments of the bow stroke, as well as for selected bowing positions and bow velocities.
Mathematics Behind the RSA Algorithm

Our world is becoming more interconnected through technology, with some notable examples being social media and online payment services. With this higher connection comes the increased risk of information, ranging from harmless text messages to credit card information, being stolen. These risks make cryptography, the field regarding the securing of messages, vital in order to minimize the chance of these data breaches. Essentially, this field is about creating, improving, and implementing various algorithms to encrypt and decrypt messages so only the sender and receiver can see the messages. This paper reviews one such algorithm, the RSA algorithm, and the mathematical concepts behind it.
Digging Deeper - Is Bitcoin Digital Gold? A Mining Perspective

Bitcoin is often labelled digital gold and many studies compare bitcoin and gold prices, returns and volatility. This paper digs deeper and compares the characteristics of bitcoin mining with gold mining to assess claims that bitcoin is digital gold. We identify twenty differences between gold and bitcoin mining. Gold miners locate where gold is present while bitcoin miners locate where cheap electricity is present. Gold mining has large barriers to entry relative to bitcoin mining making it relatively easy to start and abandon a bitcoin mine but much more difficult to start and abandon a gold mine. This is reflected in a greater exposure of gold miners to gold prices and a smaller exposure of bitcoin miners to bitcoin prices. The analysis demonstrates that bitcoin mining is significantly less complex and less risky than gold mining suggesting that the digitalisation of a commodity has clear benefits.
FPGA Implementation of an Adaptive Equalizer with TLBO and PSO Variant

This paper presents the Hardware implementation of two algorithms, Particle Swarm Optimization with Time Varying Inertia Weight and Teaching Learning-Based Optimization, which are applied to an Adaptive Equalizer with a Field Programmable Gate Array. Before the hardware implementation, MATLAB simulation results are also presented with a parameter like mean square error. Error convergence graphs of two algorithms have been observed for signal-to-noise ratios 10, 20, and 30 dB. The frequency response of the channel, equalizer, and of cascaded system is also presented. After MATLAB simulation, the same algorithm is implemented on Field Programmable Gate Array and a complete analysis is presented based on different parameters, resource utilization, execution time, frequency, and power required. Register transfer level views and simulation waveforms are also presented. By overall observation from Software and Hardware results, it is observed that Teaching Learning-Based Optimization algorithm is giving a better performance than Particle Swarm Optimization with Time Varying Inertia Weight algorithm.
Numerical simulation of probabilistic computing to NP-complete number theory problems

Probabilistic computing with p-bits is a powerful, unique paradigm alternative to classical computing and holds experimental advantages over certain forms of quantum computing. Stochastic nanodevices have been experimentally demonstrated to act as artificial neurons in solving certain problems through probabilistic computing. Still, many open questions about the breadth and size of soluble problems remain. We demonstrate the capability of probabilistic computing made of a stochastic nanodevice network in solving likely NP (non-deterministic polynomial time)-complete number theory problems associated with combinatorial optimization, which can be implemented using a network of optical parametric oscillators. These simulation results show robustness across all problems tested, with great potential to scale to solve substantially larger problems.
EVALUATION OF COMPUTER NETWORK SECURITY USING ATTACK UNDIRECTED GEOGRAPHY

To secure a wealth of data traversing the computer network at your fingertips is compulsory.But when attack arises at various parts of the network it is difficult to protect, especially when each incident is investigated separately.Geography is a necessary construct in computer networks.The analytics of geography algorithms and metrics to curate insight from a security problem are a critical method of analysis for computer systems.A geography-based representation is employed to highlight aspects (on a local and global level) of a security problem which are Eigenvalue, eccentricity, clustering coefficient and cliques.Network security model based on attack undirected geography (AUG) is familiarized.First, analysis based upon association rules is presented then the attack threshold value is set from AUG.The probability of an individual attack edge and associated network nodes are computed in order to quantify the security threat.The simulation is exploited to validate that results are effective
An improvement of vehicle and passerby recognition based on YOLO-V3 algorithm

In order to reduce the incidence of traffic accidents, the use of computer vision to identify vehicles and passers-by in the process of driving can achieve the effect of assisting driving. This paper mainly introduces the performance improvement brought by the introduction of the SPP module in YOLO-V3 for object recognition. Model training is performed on the VOC dataset based on YOLO-V3-SPP. Finally, 300 photos were used to test the accuracy of the algorithm. The results show that the recognition accuracy of YOLO-V3-SPP for vehicles and pedestrians can reach 94.19% and 90.68%, and the accuracy of YOLO-V3 is improved by nearly ten under the same equipment. percentage point. The research on this technology can effectively reduce the probability of traffic accidents and provide reference value for the future driving safety warning field.
Research on quality evaluation of user generated content in new media academic community

With the rapid development of digital technology and the popularization of terminal equipment, the new media academic community is booming. The knowledge information in the academic community is mainly generated by the user 's posting and replying, so the quality of academic resources is closely related to the creator 's knowledge level and cultural background. However, the user level is mixed and difficult to evaluate, which makes the academic community user generated content have high redundancy and low quality, and seriously reduces the efficiency of academic users to acquire knowledge. Therefore, this paper uses Word2Vec word embedding model and professional domain dictionary to vectorize and automatically label user-generated content, and then trains the Bi-GRU neural network model to construct the quality evaluation method of user-generated content, which provides a basis for the quality identification and evaluation of user generated content in the new media academic community.