Reviewerly/exHarmony huggingface dataset:
Usability Evaluation of Behind the Screen Interaction

A system (VizSpace) was evaluated that extends the conventional touch table interface by decoupling the display and placing it above the touch surface to create an interaction volume beneath the display. This physically situated setup enables touch and hand interactions beneath the display, allowing users to reach inside with their hands and interact in a 3D virtual workspace. This paper presents an empirical investigation of the performance and usability of such a system. Participants were asked to perform an object translation task that compares 2D drag with 3D grasp interaction techniques whilst also varying the height of the interaction volume. Results suggest that participants completed the task faster and more accurately with the 2D drag interaction mode compared to the 3D grasp interaction mode. More importantly, the size of the interaction volume did not affect task performance but system usability and subjective perception rankings were ranked lower for the least interaction volume with the clearest viewing area. Results suggest that a larger interaction area behind the screen is more important than a clearer viewing area.
“The Woods” AR Game

While loneliness in our real lives is increasingly recognized as having dire physical, mental and emotional consequences, cooperative games have been shown to build empathy and provide positive social impact. In this paper, we present “The Woods”, a local cooperative, mixed-reality game using augmented reality and 4-channel audio spatialization panning that provides players with face-to-face interactions in pursuit of a shared goal. This paper discusses the narrative, mechanical, and sonic components of the game, as well as the players’ experiences. The goal of our team is to develop a narrative-driven AR game that promotes collaborative problem-solving and engages players in an emergent physical and digital experience.
Opportunities and Challenges Application of Desalination Technology for the Clean Water Availability in Bangka Islands

Abstract Bangka Belitung islands is a province with limited clean water due to a lot of tins mine which is not environmentally friendly. on the other hand, Bangka Belitung has a 1200 km coastline more easily to access seawater. for this reason, the desalination technique is used as a way to overcome the limitations of freshwater. In addition to answering the needs for clean water, desalination techniques can also answer the need for salt on the island of Bangka by utilizing brine as waste from the processing of desalination techniques. this study applies the interpretive structural modeling (ISM) method which is based on several criteria such as social, economic, and environmental considerations in assessing the opportunities and constraints of implementing seawater desalination techniques. Result shows of the hierarchical analysis, the main challenges in desalination technique are high operational costs, distribution, and access to water resources and a regulatory framework that has not yet been established. the main opportunities in the desalination development are raw materials (seawater) are easy to access, environmentally friendly technology and support from local government.
Multi-camera Extrinsic Auto-calibration Using Pedestrians in Occluded Environments

In this paper, we propose a novel extrinsic calibration method for camera networks based on a pedestrian who walks on a horizontal surface. Unlike existing methods which require both the feet and head of the person to be visible in all views, our method only assumes that the upper body of the person is visible, which is more realistic in occluded environments. Firstly, we propose a method to calculate the normal of the plane containing all head positions of a single pedestrian. We then propose an easy and accurate method to estimate the 3D positions of a head w.r.t. to each local camera coordinate system. We apply orthogonal procrustes analysis on the 3D head positions to compute relative extrinsic parameters connecting the coordinate systems of cameras in a pairwise fashion. Finally, we refine the extrinsic calibration matrices using a method which minimizes the reprojection error. Experimental results show that the proposed method provides an accurate estimation of the extrinsic parameters.
Using Honeypots to Catch Adversarial Attacks on Neural Networks

Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them. In our work, we explore a new "honeypot'' approach to protect DNN models. We intentionally inject trapdoors, honeypot weaknesses in the classification manifold that attract attackers searching for adversarial examples. Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space. Our defense then identifies attacks by comparing neuron activation signatures of inputs to those of trapdoors.
Symmetric Norm Estimation and Regression on Sliding Windows

The sliding window model generalizes the standard streaming model and often performs better in applications where recent data is more important or more accurate than data that arrived prior to a certain time. We study the problem of approximating symmetric norms (a norm on \(\mathbb {R}^n\) that is invariant under sign-flips and coordinate-wise permutations) in the sliding window model, where only the W most recent updates define the underlying frequency vector. Whereas standard norm estimation algorithms for sliding windows rely on the smooth histogram framework of Braverman and Ostrovsky (FOCS 2007), analyzing the smoothness of general symmetric norms seems to be a challenging obstacle. Instead, we observe that the symmetric norm streaming algorithm of Braverman et al. (STOC 2017) can be reduced to identifying and approximating the frequency of heavy-hitters in a number of substreams. We introduce a heavy-hitter algorithm that gives a \((1+\epsilon )\)-approximation to each of the reported frequencies in the sliding window model, thus obtaining the first algorithm for general symmetric norm estimation in the sliding window model. Our algorithm is a universal sketch that simultaneously approximates all symmetric norms in a parametrizable class and also improves upon the smooth histogram framework for estimating \(L_p\) norms, for a range of large p. Finally, we consider the problem of overconstrained linear regression problem in the case that loss function that is an Orlicz norm, a symmetric norm that can be interpreted as a scale-invariant version of M-estimators. We give the first sublinear space algorithms that produce \((1+\epsilon )\)-approximate solutions to the linear regression problem for loss functions that are Orlicz norms in both the streaming and sliding window models.
Efficient Estimate of Sentence's Representation Based on the Difference Semantics Model

Sentence representation is an important research hotspot in natural language processing (NLP) since it can map the semantics of sentences into semantics vectors, thereby effectively solving complex semantics computing problems. Recently, sentence representations are mainly obtained by indirect means. Specifically, for sentence representations obtained by unsupervised means, they are often calculated by the weighted sum of embeddings of tokens in sentences; for sentence representations obtained by self-supervised or supervised means, they are often derived from intermediate encodings of sentences in prediction tasks. For example, Google's BERT and MUSE respectively use the embedding of [CLS] in the next sentence prediction task and intermediate encodings of sentences in the translation bridge task as sentence representations. In this paper, we use the observed semantics increment feature of sentences to directly model the semantics function of sentences. To be able to use the existing neural network language model to approximate the semantics function, we first implement the first-order Taylor expansion on the semantics function to obtain a difference semantics model and then add it to BERT as a subtask to perform self-supervised fine-tuning. Finally, we get a new sentence representation model S-BERT. S-BERT achieves the state-of-the-art performance on many datasets in Chinese, English, and Vietnamese.
Energy Analyze Tool for Renewable Energy Assited Data Centers

Data centers are estimated to be responsible for 13% of energy consumed in world by 2030. Rapid increase in data storage and server performance requirements are the main causes of increase in energy consumption especially for hyperscale data centers such as in financial and banking industry. However small-scale data centers accounted for 70% of total consumption in 2020, which indicates the energy saving potential due to the old systems and un-specially designed buildings. Unlike standard buildings, data centers have high indoor heat gain, therefore high thermal conductivity in cold climates, low conductivity in hot climates is preferable.Cooling systems analysis, air flow analysis, capacity management and network software have been developed to achieve energy savings in data centers in literature. However, a software that calculates the energy demand by taking into account the building details, location and renewable energy systems installed in the data center is not found. In this study a software is developed to calculate final energy demand of datacenters by taking into account the location, building details, IT devices and renewable energy system. For the verification of developed software, hourly energy consumption data of a data center of a private bank is used.
POTENTIAL APPLICATIONS OF LINGUISTIC STEGANOGRAPHIC TRIGGER-CONTAINERS FOR TEXT WATERMARKING PURPOSES

The paper gives analysis of feasibility of using linguistic steganographic trigger-containers as means of linguistic-based text watermarking. The proposed approah is based on the previous experimental research in the context of Russian native speaker text juncture perception. It was posited that specific minimal text modifications addressed in the paper may be used as means of text watermarking with the aim of tracking the leak of information for the purposes of taking legal actions, enforcing non-disclosure agreements, and testing for internal vulnerabilities. There is analyzed the viability of altering paragraph juncture points in Russian texts and, through the use of the corresponding trigger-containers, usage of this alteration as a means of linguistic watermarking.
Decentralized M-Learning Platform with Trusted Execution Environment

With the rapid growth in E-learning, Mobile learning (M-learning) is one of the most widely used ways in distance education compare to the traditional classroom environment. However, Mobile learning has accentuated the problem of data privacy disclosure and low performance. Blockchain is an important step in smart contracts with peer execution, immutability, and provenance may bring a new level of protection and confidence to M-learning. In combination with Trusted Execution Environment, we proposed a decentralized M-learning platform prototype within TrustZone technology for ARM devices where the learner identity key is encrypted and stored in Hyperledger Fabric with a peer-to-peer network. Our platform resolves the difficulties with privacy data in E-learning and reduced workload. An evaluation demonstrated that It is available to execute encryption algorithms in a trusted execution environment.
Necessary and sufficient conditions of two classes of permutation polynomials

In this paper, two types of permutation polynomials, (x2m+x+δ)s+cx and xr(xq−1+a), are studied. To formulate the necessary and sufficient conditions for the polynomials to be permutation polynomials over finite fields, the structures and properties of the field elements are analyzed. Meanwhile, the number of solutions to some equations over finite fields is investigated. For quadratic equations, the necessary and sufficient conditions are utilized thoroughly to study the permutation polynomials. But for cubic equations, only one direction is investigated by the existing literatures. This paper employs the properties of these equations in full strength to study (x2m+x+δ)s+cx over F23m⁎. For the binomials of the form xr(xq−1+a), two different methods are exploited to formulate the necessary and sufficient conditions over Fq3, and some partial results are obtained for Fqe.
Improving Adversarial Robustness of Detector via Objectness Regularization

Great efforts have been made by researchers for achieving robustness against adversarial examples. However, most of them are confined to image classifiers and only focus on the tiny global adversarial perturbation across the image. In this paper, we are the first to study the robustness of detectors against vanishing adversarial patch, a physically realizable attack method that performs vanishing attacks on detectors. Based on the principle that vanishing patches destroy the objectness feature of attacked images, we propose objectness regularization (OR) to defend against them. By enhancing the objectness of the whole image as well as increasing the objectness discrepancy between the foreground object and the background, our method dramatically improves the detectors’ robustness against vanishing adversarial patches. Compared with other defense strategies, our method is more efficient but robust to adaptive attacks. Another benefit brought by our method is the improvement of recall on hard samples. Experimental results demonstrate that our method can generalize to adversarial patches of different strengths. We reduce the vanishing rate (VR) on YOLOv3 and YOLOv4 under the vanishing attack by 49% and 41% respectively, which is state-of-the-art.
A Bibliometric Analysis of Bitcoin Scientific Production by Most Relevant Keywords

A bibliometric analysis of Bitcoin remains of high importance as it helps to cope with emerging trends. Compared to the existing literature, this paper additionally covers the keywords and analysis of the main topics, provides status-quo information and an insight on the future direction of Bitcoin research. We used VOSviewer to perform a network analysis of the most commonly used keywords in the literature. The results of the network analysis have shown that there are four main cluster groups of keywords in different topics. Also, we used R studio to form a matrix that presented the associations and correlation coefficients between the words most frequently used in combination in abstracts of scientific articles. Bibliography analysis has shown that the main topics dealt with in Bitcoin-related literature in 2020-2021 are volatility, gold, inefficiency, safe-haven, and hedge with a potential future increase of analysis as a safe-haven. By using these results, other researchers could optimize their research by concentrating on a specific topic they are interested in and they can use the relevant keywords we have highlighted for each topic. Furthermore, we have distinguished the most active authors in each field, so that researchers could find potential collaborators in each topic, and we also established the most suitable journals for publishing. Our paper not only optimizes all research processes but also reveals the most relevant topics and identifies future direction.
Securing LSB embedding against structural steganalysis

This work explores the extent to which LSB embedding can be made secure against structural steganalysis through a modification of cover image statistics prior to message embedding. LSB embedding disturbs the statistics of consecutive k-tuples of pixels, and a kth-order structural attack detects hidden messages with lengths in proportion to the size of the imbalance amongst sets of k-tuples. To protect against kth-order structural attacks, cover modifications involve the redistribution of k-tuples among the different sets so that symmetries of the cover image are broken, then repaired through the act of LSB embedding so that the stego image bears the statistics of the original cover. We find this is only feasible for securing against up to 3rd-order attacks since higher-order protections result in virtually zero embedding capacities. To protect against 3rd-order attacks, we perform a redistribution of triplets that also preserves the statistics of pairs. This is done by embedding into only certain pixels of each sextuplet, constraining the maximum embedding rate to be ⩽ 2 / 3 bits per channel. Testing on a variety of image formats, we report best performance for JPEG-compressed images with a mean maximum embedding rate undetectable by 2nd- and 3rd-order attacks of 0.21 bpc.
NFIS: A NMS-free FCOS Method for Instance Segmentation

Instance segmentation for autonomous driving should have a simple process. For dense instance segmentation methods based on FCOS, although they can directly predict instance masks, they output many duplicate predictions. Therefore, they must use non-maximum suppression (NMS) to remove the duplicate predictions and improve the performance. These methods with NMS not only hinder end-to-end training but also make the whole process more complicated. In contrast to these works, we propose a NMS-free FCOS based instance segmentation (NFIS). NFIS achieves comparable performance with NMS. Besides, we propose a context-based encoding module (CBEM) to refine the feature maps. CBEM utilizes the local and global information to refine the parameters of dynamic convolution and improve the classification. The proposed network outperforms most instance segmentation networks on the street scene datasets Cityscapes and Indian Driving Dataset (IDD).
Deformable Spatial Pyramid Pooling for Road Scene Semantic Segmentation

Deformable convolution can handle the geometric transform appearing in the front camera image of the autonomous driving system. However, this method increases the size of the intermediate feature maps because it finds samples referenced by the kernel for each location. Moreover, the feature maps are interpolated to obtain accurate location data. Thus, it is difficult to configure modules due to increased GPU memory usage. In this paper, we propose squeezed deformable convolution robust to geometric changes while optimizing resource usage and a deformable spatial pyramid pooling (DSPP) module. The proposed convolution estimates the same data location as deformable convolution, but it uses squeeze convolution to compress the number of channels in feature processing. Therefore, memory usage and computations are decreased during sampling processing. Furthermore, memory usage can be more optimized for edge networks by removing the interpolation process using integer offsets additionally. The proposed DSPP module reinforces the scale-invariant characteristics by placing squeezed deformable convolutions in parallel and supplying inputs of different sizes. By sharing weights except for the squeeze layer, constraints are added to the sampling process, and the number of effective training samples is increased. The proposed methods are applied to the representative semantic segmentation network, ESPNet V2 and DeepLab V3+ with ResNet-50 to verify the effect. The mean intersection over union (mIoU) of DeepLab V3+ with the DSPP module is increased 1.5% on Cityscapes validation set. Also, ESPNet V2 with it provides 1.4% higher mIoU on Camvid test set. Therefore, the proposed method not only optimizes memory usage and computations but also improves recognition performance with enough margin.
Research Guides: HONR 230 Honors Humanities: Why use library resources?

This guide will help you begin your research paper and/or project and direct you to a variety of resources at the BVU Library.
Peculiarities of teaching the students of the specialty international relations, low and history professional disciplines

The paper highlights the peculiarities of teaching the students of the specialty International Relations, Law and History professionally oriented disciplines when the attention is paid to the study of professional disciplines. After mastering the core materials of the discipline, the students should know all the components of these disciplines and be able to apply their knowledge in practice. We have confirmed by our research that for the modern student to be able to master all knowledge and skills, modern lecturers at their lectures and seminars should change the teaching methods, tending more to the use of interactive methods, which, compared to the traditional ones, are much more efficient for providing the educational process. Among the modern interactive teaching methods, such methods as Circle of Ideas, Aquarium, Microphone, and Role Play, Imitation deserve special attention, and their use promotes students' cognitive abilities, creative thinking, quick decision making, work in groups, thus stimulating the development of communication skills, and mainly the interest of the student in learning. In the conditions of a pandemic (2020-2021), as the experiment showed, it is also possible for teachers to use interactive methods in teaching professional disciplines for students of the above specialties.
MEDIA DESIGN IN THE FORMATION OF INNOVATIVE CULTURAL AND ENTERTAINMENT BUILDINGS

The article is devoted to identifying the design features of innovative cultural and entertainment buildings using media architecture and design tools to develop methodological foundations for their formation. Criteria for differentiation of these objects have been determined. The article presents the main typological characteristics of cultural and entertainment buildings using the means of media architecture and design. The functional components of this type of architectural object and complex have been determined. Modern fundamental approaches and the main trends in urban planning, spatial organization and architectural and planning solutions of innovative cultural and entertainment buildings using media architecture and design are identified. The article analyzes foreign experience in designing innovative cultural and entertainment buildings using media architecture and design. Trends in the formation of their volumetric-spatial structure have been determined. The factors influencing the formation of innovative cultural and entertainment buildings and complexes in the urban environment are identified. The article provides definitions of the concepts of "media architecture" and "media facade". The methods of architectural and design formation of innovative cultural and entertainment buildings using the means of media architecture and design are revealed: the method of multi-functional structure, the method of integration with the environment, the method of using national traditions, the method of complexity, the method of flexibility and communicativeness of space, the method of accessibility and adaptability (setting up a universal space), the reception of stylistic unity. The perspective directions of formation and development trends of innovative architectural objects in the urban environment with the use of media design means are determined.
Power-efficient FPGA implementation of RCM-RW on Digital Images

Reverse contrast mapping (RCM) is an efficient method for watermarking to hide the data (video or audio). The RCM is used for high bit-rate applications also it contains low mathematical complexity. This paper proposed power-efficient hardware implementation of the RCM-RW algorithm by parallel processing architecture. This method takes a fewer number of clock cycles than pipeline architecture, so power consumption less. The RCM-RW hardware implementation has several essential VLSI domain real-time applications, Such as media authentication and copyright protection.