Reviewerly/exHarmony huggingface dataset:
Blockchain technology in the supply chain: learning from emerging ecosystems and industry consortia

Blockchain is a potentially disruptive technology that supports immutability, transparency, and traceability for digital transactions. Blockchain shows promise for tackling several challenges in managing contemporary supply chains. Blockchain technology requires collaboration, which has resulted in the formation of many consortia to advance its adoption. Blockchain consortia are embryonic digital business ecosystems (DBE). We discuss the role of these consortia in addressing key blockchain challenges in business and supply chain applications. Dominant considerations central to a consortium's formation and sustainability are highlighted, including its governance, technical focus, and participation issues. For seven prominent blockchain consortia that encompass supply chain applications, we analyze the type of partnership, scope of activities, objectives and business model. Based on the study, five propositions are presented regarding the nature of blockchain consortia, the drivers for their formation, their collaboration requirements, the impact on relationships, and the influence of industry characteristics on a blockchain consortium's focus and characteristics.
A Unifying Framework for Agency in Hypermedia Environments

One of the emerging trends in engineering multi-agent systems (MASs) is to use the Web as an environment. On the Web, hypermedia is the guiding principle of agent perception and action. Web standards allows agents to have a single uniform interface to their environment, be it real or simulated. Most recent proposals for hypermedia MASs tend, however, to introduce a coupling between agents or between agents and their environment. This paper introduces a framework based on Semantic Web technologies to formalize interactions between agents and a hypermedia environment. Semantic Web technologies and, more specifically Web ontologies, guarantee interoperability on the Web and maximize uncoupling between components. We show how existing ontologies can be used to make existing MAS prototypes fit our framework. Our framework formalizes the guiding principle of agent-environment interactions in hypermedia, state transfer, with respect to a reference formalism originally introduced by Genesereth and Nilsson. We also show the equivalence between the two in the paper, under certain conditions.
Biprojective Almost Perfect Nonlinear Functions

In this paper, we introduce the concept of biprojectivity in studying the cryptographically important almost perfect nonlinear (APN) functions. Although several known families of biprojective APN functions exist in the literature, the concept itself has not been explicitly observed and studied in detail. We give a survey of known APN families and functions that fall into the biprojective setting, then provide a method for finding such families and functions. We finally give two new infinite families of biprojective APN functions <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathsf {CCZ}}$ </tex-math></inline-formula> -inequivalent to any known APN function and study their properties.
Pyramid dilated convolutional neural network for image denoising

Convolutional neural network has been successfully applied to image denoising. In particular, dilated convolution, which expands the network’s receptive field, has been widely used and has achieved good results in image denoising. Losing some image information, a standard network cannot effectively reconstruct tiny image details from noisy images. To solve this problem, we propose a pyramid dilated CNN, which mainly has three pyramid dilated convolutional blocks (PDCBs) and a gated fusion unit (GFU). PDCB uses dilated convolution to expand the network’s receptive field and the pyramid structure to obtain more image details. GFU fuses and enhances the feature maps from different blocks. Experiments demonstrate that the proposed method outperforms the comparative state-of-the-art denoising methods for gray and color images. In addition, the proposed method can effectively deal with real-world noisy images.
Learning Sample Importance for Cross-Scenario Video Temporal Grounding

The task of temporal grounding aims to locate video moment in an untrimmed video, with a given sentence query. This paper for the first time investigates some superficial biases that are specific to the temporal grounding task, and proposes a novel targeted solution. Most alarmingly, we observe that existing temporal ground models heavily rely on some biases (e.g., high preference on frequent concepts or certain temporal intervals) in the visual modal. This leads to inferior performance when generalizing the model in cross-scenario test setting. To this end, we propose a novel method called Debiased Temporal Language Localizer (Debias-TLL) to prevent the model from naively memorizing the biases and enforce it to ground the query sentence based on true inter-modal relationship. Debias-TLL simultaneously trains two models. By our design, a large discrepancy of these two models' predictions when judging a sample reveals higher probability of being a biased sample. Harnessing the informative discrepancy, we devise a data re-weighing scheme for mitigating the data biases. We evaluate the proposed model in cross-scenario temporal grounding, where the train / test data are heterogeneously sourced. Experiments show large-margin superiority of the proposed method in comparison with state-of-the-art competitors.
Calibration method for structure parameters of a binocular stereo vision system equipped with polarizers

For structure parameter calibration of a binocular stereo vision system equipped with polarizers, the optimal calibration polarization angle needs to be determined. There are no corresponding solutions for the determination of the optimal polarization angle of structure parameter calibration. Furthermore, existing research considers the polarization angle that causes the image to possess the lowest brightness (gray value) as the optimal polarization angle. This reduces image contrast, and eventually the texture information of the image is lost, which also affects the accuracy of feature extraction. In this paper, we propose a new calibration method for the structure parameters of a binocular stereo vision system equipped with polarizers. We calculated the pose of the target relative to each camera for different polarization angles. The sum of the object-space errors corresponding to each polarization angle was considered as the evaluation criterion to determine the optimal calibration polarization angle. The calibration of structure parameters was finished using images captured on the premise of the optimal calibration polarization angle. This angle can also be considered as the reference of the polarization angle for measurement. Experiment results show that using the calibration results of our method, the reconstructed length error of a 275×200mm target was less than ±0.052mm, the reconstructed linear displacement error was less than ±0.048mm for the range of 0-30 mm, and the reconstructed rotary angle error was less than ±0.048∘ for the range of-30∘-30∘.
Optimal clustering by merge-based branch-and-bound

&lt;abstract&gt; &lt;p&gt;We present a method to construct optimal clustering via a sequence of merge steps. We formulate the merge-based clustering as a minimum redundancy search tree, and then search the optimal clustering by a branch-and-bound technique. Optimal clustering is found regardless of the objective function used. We also consider two suboptimal polynomial time variants based on the proposed branch-and-bound technique. However, all variants are slow and has merely theoretical interest. We discuss the reasons for the results.&lt;/p&gt; &lt;/abstract&gt;
Adaptive clustering feature matching algorithm based on SIFT and RANSAC

Scale-invariant feature transform (SIFT) algorithm is widely used in the field of image matching. The traditional SIFT does not perform well in terms of computational complexity and matching accuracy in complex scenes. Based on the above problems, an improved SIFT and RANSAC algorithm is proposed in this paper. When selecting extreme points, the original 26 reference points in DOG space are extended to 32 reference points within Manhattan distance 2. By establishing 8 concentric circle neighborhoods with increasing radius in the sampling sub area, the latitude of keypoint descriptor is reduced to 64 dimensions. When performing matching, it first goes through KNN coarse matching, and then uses the score function to calculate the scores of the two clustering result sets, keypoint descriptor Euclidean distance clustering and spatial location clustering, and uses the high score set as input to improve the input parameters and the number of iterations of RANSAC. The simulation results show that the algorithm in this paper can further improve the correct matching rate with good robustness in a variety of scenes compared with the traditional algorithm and some other related algorithms.
Unitail: Detecting, Reading, and Matching in Retail Scene

To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we introduce the United Retail Datasets (Unitail), a large-scale benchmark of basic visual tasks on products that challenges algorithms for detecting, reading, and matching. With 1.8M quadrilateral-shaped instances annotated, the Unitail offers a detection dataset to align product appearance better. Furthermore, it provides a gallery-style OCR dataset containing 1454 product categories, 30k text regions, and 21k transcriptions to enable robust reading on products and motivate enhanced product matching. Besides benchmarking the datasets using various start-of-the-arts, we customize a new detector for product detection and provide a simple OCR-based matching solution that verifies its effectiveness. The Unitail and its evaluation server is publicly available at https://unitedretail.github.io .
Scalable Private Decision Tree Evaluation with Sublinear Communication

Private decision tree evaluation (PDTE) allows a decision tree holder to run a secure protocol with a feature provider. By running the protocol, the feature provider will learn a classification result. Nothing more is revealed to either party. In most existing PDTE protocols, the required communication grows exponentially with the tree's depth d, which is highly inefficient for large trees. This shortcoming motivated us to design a sublinear PDTE protocol with $O(d)$ communication complexity. The core of our construction is a shared oblivious selection (SOS) functionality, allowing two parties to perform a secret-shared oblivious read operation from an array. We provide two SOS protocols, both of which achieve sublinear communication and propose optimizations to further improve their efficiency. Our sublinear PDTE protocol is based on the proposed SOS functionality and we prove its security under a semi-honest adversary. We compare our protocol with the state-of-the-art, in terms of communication and computation, under various network settings. The performance evaluation shows that our protocol is practical and more scalable over large trees than existing solutions.
Pointing Cursor Interaction in Virtual Reality from the Perspective of Distance Perception

In virtual reality, the users cannot interact well or execute tasks efficiently, if the absolute distance is perceived incorrectly, or the pointing cursor is designed unreasonably. Drawing on the Fitts’ law extended for stereo pointing, this paper conducts interactive experiments on the pointing in virtual space. The variables involved in the experiments include type of cursor, absolute distance, object scale, and azimuth angle. A total of six assumptions were put forward: the perception precision of absolute distance and the object pointing velocity are influenced by type of cursor, absolute distance, object scale, and azimuth angle. Based on the experimental results, the object pointing precision and the object pointing duration were analyzed, followed by a discussion about the change law of the perception precision of absolute distance with the object pointing velocity. The experimental results show that, in the VR, both pointing cursor and object scale greatly affect perception precision of absolute distance, and object pointing velocity. The azimuth angle of object and absolute distance merely significantly affect object pointing velocity. The type of cursor and absolute distance exert a bidirectional interactive effect on the perception precision of absolute distance, and object pointing velocity. For the two types of pointing cursors, the object pointing duration has a positive correlation with the coefficient of difficulty, which is in line with the Fitts’ law extended for stereo pointing. The research discloses the factors affecting the perception of absolute distance, and guides the interaction and stereo pointing design of VR.
Juxtaposing Urdu to English Captions of Facebook/ Metaverse: A Neural Machine Translation Study

Facebook/ Metaverse is a world-leading social networking platform that is equally popular in the world that is equally popular globally Due to the diversity of its users, Facebook is dedicated to improving its Machine Translation (MT) to refine the communication process and remove linguistic hurdles for efficient communication. Recently, Metaverse has started Neural Machine Translation (NMT) for the improvement of translation. NMT uses artificial neural networks to predict a more appropriate word choice and sequence. This research investigates the efficiency of Neural Machine Translation (NMT) in translating text from Urdu to the English language. The Oxford Urdu English Dictionary and FARHANG E AASFIA dictionaries have been used as validation parameters in translation. The semantic and syntactic errors have been segregated and categorized with Antoine Berman’s Twelve Deforming Tendencies for rectification. Major findings reveal that the most frequent errors result from transliteration that damages the semantic and syntactic structure of the text.
An Analysis of Pre-service Teachers' Learning Process in Programming Learning

As the importance of computing technology increases, computer science education is being actively implemented around the world. Because computer science education is being introduced into the curriculum, research on how to effectively teach programming (which is the core of automation) is actively underway. Although the importance of block-based programming languages has increased, most studies have focused on text-based programming languages. As interest in programming increases, block-based programming languages will be taught to a variety of audiences. Therefore, this study analyzed Code.org, which provides a development environment for block-based programming; this study then investigated the programming learning process of pre-service teachers, who used Code.org. Sixteen pre-service teachers participated in the study, and their learning processes were uncovered by analyzing their programming results. This suggests that pre-service teachers can learn sequential and necessary repetition without difficulty. However, the pre-service teachers failed to use the repetition block through abstraction. Besides, for While and Until, pre-service teachers did not understand the concept of repeating according to the condition. For Counter, pre-service teachers had difficulty repeating the use of variables. In the condition, pre-service teachers were not able to separate the command, which should be executed when the condition is True and when it is False. For Event, pre-service teachers had no problem utilizing the function, but they were not able to call the function with a parameter. Based on this, it was confirmed that a pre-service teacher can understand the principle of programming development in advance by understanding the abstraction, condition, and variable in the loop statement. In this study, there was a limit to practicing block-based programming language due to the platform’s low scalability. Future research should solve these problems and diversify the research subjects.
The Acoustics of Kabuki Theaters

The study presents a room acoustical investigation of a representative sample of eight Kabuki theaters as the most important public performance venues of pre-modern Japan. Room acoustical parameters according to ISO 3382 were measured for the unoccupied and simulated for the occupied condition. In comparison with European proscenium stage theaters, they have lower room heights in the auditorium, with usually only one upper tier, and no high stage house for movable scenery. The lower volume per seat results in lower reverberation times, The wooden construction and the audience seating arrangement on wooden straw mats on the floor instead of upholstered seats leads to a mostly flat frequency response up to 4 kHz, resulting in an excellent speech intelligibility, as documented by values for definition (D 50 ) and the speech intelligibility index (STI). The acoustical conditions support the dynamic acting space created by pathways extending the stage from the front through the audience to the rear of the auditorium. They allow great contrasts in the perceived acoustical proximity depending on the selected acting position, and support a high degree of immersion of the audience into the dramatic action.
A novel approach to text clustering using shift k-medoid

As the amount of data is growing day by day, there is a need to convert it into some effective manner so as to extract some useful information from huge data. Text mining is used to perform this task. In this paper, text clustering is used to convert the large data into different cluster forms to extract the meaningful information for the purpose of analysis so as to get the summarised data. Three partitioning-based clustering techniques, i.e., k-means, k-means fast and k-medoids are compared, and a new algorithm named shift k-medoid is proposed, which is hybrid of k-medoid and mean shift clustering algorithms. Cosine similarity, correlation coefficient and Jaccard similarity measures are used to check the performance of the algorithms and two measures, i.e., randomised feature and normalised mutual information (NMI) feature are used to test the accuracy of the algorithms. The outcomes demonstrate that the best performance is accomplished by using the proposed algorithm.
Alexa, tell me - A forensic examination of the Amazon Echo Dot 3 rd Generation

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
An authentication scheme for distributed computing environment

In this era of information technology, the protection of personal sensitive information is one of the most challenging tasks which will not allow any human being to escape from browsing, sharing and holding information. In this scenario different service provider offers services for its users to share their personal sensitive information, resources and identity via distributed network. And attackers can easily pick this information from the open network due to lack of cross verification of the legality of peers. Almost no researcher claims with conviction about a foolproof secure authentication scheme for the said environment. In this regard, a robust authentication scheme is presented for the distributed computing environment which not only offers a secure cross verification of user validity but can also protect personal sensitive information and secure sharing of associated resources. The performance and security analysis at the end of this paper shows that the scheme is best among all available in the recent literature. All the narratives show that the scheme can easily be implemented in the recent era for the said purpose.
The Rise and Growth of Digital Payments in India

India stepped into digital India. The digital India program taken up by the Government of India is mainly to transform India into a digitally empowered society. Faceless, paperless and cashless is one of the professed roles of Digital India. According to the Government of India, the digital payments will increase the employment, minimize risk related to cash like robbery, corruption and storage of huge amounts by way of cash and promotes transparency in operations and attains governance. This paper focuses how the transformation has taken place in the payment industry from coins to paper, to plastic money and now to e-wallets.
Construction and validation of micromanagement questionnaire

Micromanagement, in ordinary parlance, is managing things closely and under strict scrutiny. The management style, which involves excessive control and close supervision, could lead to annoying situations and create an atmosphere of mistrust within the organisational settings. Micromanagers seldom give autonomy to their subordinates, dislike feedback, and get involved even in the most intricate of project details. A fair review of literature found there is no scientifically validated questionnaire to measure micromanagement. The present work is a modest effort to fill this gap in literature. By following the rules of the thumps and the stipulations provided by experts in the fields, the authors have scientifically constructed and validated a four factor, 18-item micromanagement questionnaire. Though there is further scope for refining and generalisation across cultures, the questionnaire is an effective scale for the measurement of the construct of micromanagement.
A Framework for Data Security using Cryptography and Image Steganography

Cryptography and steganography are the two research areas which are popular for data confidentiality, data hiding respectively. Steganography hides the data in various multimedia cover files like image, audio and video. This paper handles images for steganography, which have high redundant pixels. In this paper, Lease Significant Bit (LSB) method hides the secret message bits in least significant bits of each pixel. The performance metrics MSE and PSNR are used to check the strength of the proposed algorithm. The proposed algorithm also checks payload capacity, image quality and security of the confidential data by applying cryptography and steganography algorithm and various parameters.