Reviewerly/exHarmony huggingface dataset:
Fingerprint Presence Fraud Detection Using Tight Clustering on Employee’s Presence and Activity Data

Detecting fraud in fingerprint presence poses a unique challenge as we cannot rely on an existing employee's attribute. Furthermore, analyzing using a supervised algorithm cannot handle unlabeled data [1] that generated uniquely for this case. We study the patterns of employee's presence and activity report data and found that fraud action tends to be closely similar to other fraud action. Therefore, we propose a tight clustering method to detect fraud in fingerprint data using DBSCAN (Density-based spatial clustering of applications with noise) algorithm, as tight distance calculation removes non-fraud data because non-fraud data is generated to be unique naturally.
The equivalence between Stein variational gradient descent and black-box  variational inference

We formalize an equivalence between two popular methods for Bayesian inference: Stein variational gradient descent (SVGD) and black-box variational inference (BBVI). In particular, we show that BBVI corresponds precisely to SVGD when the kernel is the neural tangent kernel. Furthermore, we interpret SVGD and BBVI as kernel gradient flows; we do this by leveraging the recent perspective that views SVGD as a gradient flow in the space of probability distributions and showing that BBVI naturally motivates a Riemannian structure on that space. We observe that kernel gradient flow also describes dynamics found in the training of generative adversarial networks (GANs). This work thereby unifies several existing techniques in variational inference and generative modeling and identifies the kernel as a fundamental object governing the behavior of these algorithms, motivating deeper analysis of its properties.
Scholarship in the sandbox: Academic libraries as laboratories, forums, and archives for student work

"Scholarship in the sandbox: Academic libraries as laboratories, forums, and archives for student work." Journal of Access Services, 17(3), pp. 117–118
Automatic Fiber Length Measurements with a Multi-Stencil Fast Marching Method on Microscopy Images

Fiber length has a strong impact on the mechanical properties of composite materials. It is one of the most important quantitative features in characterizing microstructures for understanding the material performance. Studies conducted to determine fiber length distribution have primarily focused on sample preparation and fiber dispersion. However, the subsequent image analysis is frequently performed manually or semi-automatically, which either requires careful sample preparation or manual intervention in the image analysis and processing. In this article, an image processing and analysis method has been developed based on medial axis transformation via the multi-stencil fast marching method for fiber length measurements on acquired microscopy images. The developed method can be implemented fully automatically and without any user induced delays. This method offers high efficiency, sub-pixel accuracy, and excellent statistical representativity.
Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume

Monocular depth estimation has become one of the most studied applications in computer vision, where the most accurate approaches are based on fully supervised learning models. However, the acquisition of accurate and large ground truth data sets to model these fully supervised methods is a major challenge for the further development of the area. Self-supervised methods trained with monocular videos constitute one the most promising approaches to mitigate the challenge mentioned above due to the wide-spread availability of training data. Consequently, they have been intensively studied, where the main ideas explored consist of different types of model architectures, loss functions, and occlusion masks to address non-rigid motion. In this paper, we propose two new ideas to improve self-supervised monocular trained depth estimation: 1) self-attention, and 2) discrete disparity prediction. Compared with the usual localised convolution operation, self-attention can explore a more general contextual information that allows the inference of similar disparity values at non-contiguous regions of the image. Discrete disparity prediction has been shown by fully supervised methods to provide a more robust and sharper depth estimation than the more common continuous disparity prediction, besides enabling the estimation of depth uncertainty. We show that the extension of the state-of-the-art self-supervised monocular trained depth estimator Monodepth2 with these two ideas allows us to design a model that produces the best results in the field in KITTI 2015 and Make3D, closing the gap with respect self-supervised stereo training and fully supervised approaches.
Features of the Development of Modern Educational Space in a High-Tech Society

The article summarizes the features of the development of modern educational space in a high-tech society based on the analysis of domestic and foreign studies. It is shown that the explosive development of digital technologies, mobile Internet, and VR / AR technologies dramatically make digital skills in demand; the boundaries that existed between personal space and the space that was previously called "work space" are being erased. The responsibilities of each employee and their area of responsibility are being seriously expanded into related areas. Routine work is transferred to robots, many new professions are created, and conditions are created to reveal the inner potential of each employee. The structure of the global educational market, which is characterized by high competition, is developing. It is concluded that the key vector in the development of educational technologies is determined by the need to Supplement the existing formats of educational products for teaching digital skills with new approaches based on mobile learning and the use of artificial intelligence. Evaluating the cost of improving the effectiveness of digital skills training identifies a separate problem in terms of ensuring the effectiveness of the training impact. Learning in a digital distance environment requires significantly higher density and fractional evaluation.
Enriching an Explanatory Dictionary with FrameNet and PropBank Corpus Examples

This paper describes ongoing work to extend an online dictionary of Latvian – Tezaurs.lv – with representative semantically annotated corpus examples according to the FrameNet and PropBank methodologies and word sense inventories. Tezaurs.lv is one of the largest open lexical resources for Latvian, combining information from more than 300 legacy dictionaries and other sources. The corpus examples are extracted from Latvian FrameNet and PropBank corpora, which are manually annotated parallel subsets of a balanced text corpus of contemporary Latvian. The proposed approach augments traditional lexicographic information with modern cross-lingually interpretable information and enables analysis of word senses from the perspective of frame semantics, which is substantially different from (complementary to) the traditional approach applied in Latvian lexicography. In cases where FrameNet and PropBank corpus evidence aligns well with the word sense split in legacy dictionaries, the frame-semantically annotated corpus examples supplement the word sense information with clarifying usage examples and commonly used semantic valence patterns. However, the annotated corpus examples often provide evidence of a different sense split, which is often more coarse-grained and, thus, may help dictionary users to cluster and comprehend a fine-grained sense split suggested by the legacy sources. This is particularly relevant in case of frequently used polysemous verbs.
Energy-Aware VM Migration in Cloud Computing

The continuous growth of cloud data centers is accompanied by enormous amounts of energy consumption leading to carbon dioxide (CO2) emissions making the environment unfriendly. Dynamic VM consolidation is an effective policy that can reduce energy consumption in data centers. It reduces the number of servers used complying with Quality of Service (QoS) constraints. This paper presents two-phase approach: First, Power-Aware Placement of VMs provides the least increase in power consumption. Second, a function of CPU utilization and memory utilization with double-threshold policy is used to estimate host current utilization. It chooses VMs for migration based on their CPU and memory utilization, thus reducing the chances of SLA violation and number of migrations as minimum as possible. The energy consumption is optimized by controlling the resource utilization and shifting the idle servers to sleep state. Simulation results depict that our proposed approach significantly reduces energy consumption in dynamic workload scenarios when compared with other algorithms.
User Preference Prediction in a Set of Photos Based on Neural Aggregation Network

In this paper, we focus on the problem of user interests’ classification in visual product recommender systems. We propose a two-stage procedure. At first, the image features are learned by fine-tuning the convolutional neural network, e.g., MobileNet. In the second stage, we use such learnable pooling techniques as neural aggregation network and context gating in order to compute a weighted average of image features. As a result, we can capture the relationships between the products’ images purchased by the same user. We provide an experimental study with the Amazon product dataset. It was shown that our approach achieves an F1-measure of 0.90 for 15 recommendations, which is much higher when compared to 0.66 F1-measure classifications of traditional averaging of the feature vector.
Design of the Tutor Module for an Intelligent Tutoring System (ITS) Based on Science Teachers’ Pedagogical Content Knowledge (PCK)

Pedagogical content knowledge (PCK) is a construct used to represent teacher’s understanding. PCK have used for different purposes, among which are the design of technological tools and curriculum materials. An Intelligent Tutoring Systems (ITS) is a type of Intelligent System, which incorporates AI techniques to know what to teach, who to teach and how to teach individually to each learner. The main module of an ITS is the tutor module. Thus, this study presented the design of the Tutor Module of an ITS using the theoretical assumptions of the METAGOGIC metamodel and the perceptions of the science teachers about their PCK. The research phases developed allowed: collection of the Science Teachers’ Perceptions, content Analysis of these Perceptions and finally the design of Model for the Tutor Module of an ITS based-on Science Teachers PCK using METAGOGIC Metamodel.
Compact Privacy Protocols from Post-quantum and Timed Classical Assumptions

While basic lattice-based primitives like encryption and digital signature schemes are already fairly short, more advanced privacy-preserving protocols (e.g. group signatures) that are believed to be post-quantum secure have outputs of at least several hundred kilobytes. In this paper, we propose a framework for building privacy protocols with significantly smaller parameter sizes whose secrecy is based on post-quantum assumptions, but soundness additionally assumes that some classical assumption, e.g., the discrete logarithm problem (DLP), is hard to break within a short amount of time. The main ingredients of our constructions are statistical zero-knowledge proofs of knowledge for certain relations, whose soundness rely on the hardness of solving the discrete logarithm problem for a fresh DLP instance per proof. This notion has recently been described by the term quantum annoyance. Using such proofs, while also enforcing that they be completed in a fixed amount of time, we then show how to construct privacy-preserving primitives such as (dynamic) group signatures and DAA schemes, where soundness is based on the hardness of the "timed" discrete logarithm problem and SIS. The outputs of our schemes are significantly shorter ($${\approx }30X)$$ than purely lattice-based schemes.
The Synergy Mechanism of Temporal Leadership and Employees’ Synchronization in Innovation Teams

Based on the team coordination theory, this paper constructs a theoretical analysis framework for the synchronous cooperation between temporal leaders and employees, with a view to promoting the research and practice of team collaborative innovation from a time perspective. The study consists of three parts: First, how does temporal leadership promote individuals with different time traits to reach a time consensus, so as to produce efficient team creativity under the moderation of employees’ synchrony preference? Second, how does time cognitive conflict derived from team time consensus inhibit the transformation of time consensus into team creativity by weakening synchrony preference? Third, how does temporal leadership help individuals to adjust the mental accounting of time, activate synchrony preference, and thus eliminate the side-effects derived from time consensus? According to the research findings, temporal diversity（time urgency, pacing style, and time focus）is helpful to meet the requirements of diversity time combinations of innovation tasks（e.g. speed vs. quality; long-term vs. short-term）, but only if a time consensus of teams is formed and individuals have a high synchrony preference. Powerful temporal leadership is beneficial to balance temporal individual differences to form time consensus; while this process impacts upon individuals’ established habits of the time, causes time cognitive conflict, weakens synchrony preference, and thus brings adverse effects on team creativity. Therefore, effective temporal leadership also includes a time motivate dimension, that is, it brings individual time arrangement into the team track by activating individual mental accounting of time, time cognitive conflict is reduced, and synchrony preference is improved. Finally, under the synchronous cooperation between temporal leaders and employees, team time efficiency and team creativity are promoted. In conclusion, this study provides a comprehensive analysis framework for opening up the time paradox” of innovation tasks, and also provides a new research perspective and practical guidance for team collaborative innovation. On the one hand, this study extends the application of the team coordination theory in the field of time management, integrates the explicit and implicit time coordination ways, and constructs a collaborative model of synchronous cooperation between temporal leaders and employees; meanwhile, it expands the connotation and measurement dimension of temporal leadership. On the other hand, the research conclusion suggests that enterprises should pay more attention to the cultivation of temporal leadership, enhance the synchronous cooperation ability of team members, and take temporal heterogeneity into account when setting up heterogeneous teams. In addition, leaders should not only improve their time management skills, but also cultivate their temporal incentive power.
Regularity and variation in Japanese recipes

Abstract This paper investigates the similarities and differences between three sub-registers of Japanese recipe texts: cookbook recipes, online commercial recipes written/edited by professionals, and online user-generated recipes. Past studies on Japanese recipes do not distinguish different sub-registers, and they tend to focus on a single feature. The present study of the sub-registers examines a group of frequently appearing linguistic features and uncovers functional links between observed features and situational characteristics. The comparative perspective contributes to a more comprehensive understanding of Japanese recipe language as well as universal and language-specific aspects of register variation. Shared traits among the three sub-registers are tied to the common topic of cooking and the central purpose of providing easy-to-follow food preparation instructions. Varied linguistic and textual features are motivated by different production circumstances, mediums, and relations among the participants. Professionally edited cookbook and online commercial recipes show a much higher uniformity in their grammatical features than unedited/self-edited user-generated recipes. Online sub-registers share a role of serving as a repository and reference center for numerous recipes and related information. Relationships among writers, readers, and other participants such as publishers and site organizers differ among all three sub-registers, resulting in some unique linguistic patterns.
An Analytical Approach to Customer Sentiments using NLP Techniques and Building a Brand Recommender Based on Popularity Score

Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence that is concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. This field has a great importance in many areas like retail stores and websites, social networking sites like Twitter, Facebook etc.; for understanding the various views or sentiments of the public or the consumers or end users, regarding a particular product or topic.
A Method of Human Motion Feature Extraction and Recognition Based on Motion Capture Device

In recent years, motion capture devices have been widely used in 3D film special effects, animation generation, digital media and other virtual reality fields. The purpose of this paper is to achieve data acquisition, motion feature extraction and recognition of human motion by using motion capture device. First, we use the OptiTrack motion capture device and related data preprocessing methods to collect motion data, and realize the preview of joint point offset data. Then, a human motion feature representation method which combines the collected data with the key frame is designed. For the selection of key frames, we improve the frame subtraction algorithm by adding the second derivative calculation of reconstruction error to achieve the number of key frames automatic determination. In addition, in order to solve the problem of huge amount calculations and error existing in discretization of observation state, we use high-dimensional gaussian function to fit human motion data, and finally apply the method of Gaussian-Mixture Hidden Markov Model (GMM-HMM) for motion recognition. Experiments show that the method has achieved remarkable performances in human motion extraction and recognition.
Identification of Languages in Linked Data: A Diachronic-Diatopic Case Study of French

When modelling linguistic resources as Linked Data, the identification of languages using language tags and language codes is a mandatory task. IETF’s BCP 47 defines the standard for tags, and ISO 639 provides the codes. However, these codes are insufficient for the identification of diatopic variation within a language and, also, for different historical language stages. This weakness hampers the accurate identification of data, which in turn leads to ambiguity when extending, aggregating and re-using this data—a key notion of Linked Open Data and the Semantic Web. We show the limitations of language identification with a case study of French linguistic data from both a diachronic and a diatopic perspective. Our exemplary data derives from dictionaries of Old French, Middle French, and of Modern French dialects, and from a Modern French linguistic atlas. For each exemplar, we propose a solution using the privateuse sub-tag of BCP 47’s language tag, staying within the boundaries of existing standards. Using a predefined pattern for the privateuse sub-tag, the solutions enable a dialect, a patois, in combination with a time period, to be defined and identified. This can lead to shared agreement of language tags that will increase interoperability within the context of Linked Data.
Social Recommender Systems using Collaborative User Network Embedding with Bias

Traditional recommendation algorithms suffer from the problems of cold-start users and data sparsity, which significantly degrade prediction accuracy. This paper proposes a novel method CUNE-bias(Collaborative User Network Embedding with bias) to resolve these issues. Firstly, top-k semantic friends are identified only using information from user-item feedbacks. Then, we incorporate the top-k semantic friends information generated by CUNE into matrix factorization as a regularization term. Finally, the bias, independent of any interactions, is involved in rating to improve accuracy. Experimental results on two datasets demonstrate that the proposed method gets higher accuracy than CUNE-MF and other classical methods.
Graph Data Modelling for Genomic Variants

Genome variant analysis is performed on Variant Call Format (VCF) files. It can take days to process these files for genome analytics due to challenges such as loading the files for each user query and processing them to answer questions of interest. As data sizes grow, timely processing of this data is putting enormous pressure on the computational resources, leading to significant processing delays and may jeopardise the ultimate goal of bringing the genomic discoveries to masses. We believe this problem will not be solved until the underlying data structure to organise and process these files undergoes a transformation. To overcome this problem, we have proposed a graph based system to represent the data in VCF files. This allows the data to be loaded once in a graph model which is then subsequently queried and processed numerous times without any additional computational and data access penalties. This helps reduce data access time by giving a constant time access to any node and addresses performance and scalability challenges that have been a limiting factor for the mass scale adoption of genome analytics. It takes only 2ms to access any data node in our graph model and remains constant for any number of nodes.
Objectness-Aware One-Shot Semantic Segmentation

While deep convolutional neural networks have led to great progress in image semantic segmentation, they typically require collecting a large number of densely-annotated images for training. Moreover, once trained, the model can only make predictions in a pre-defined set of categories. Therefore, few-shot image semantic segmentation has been explored to learn to segment from only a few annotated examples. In this paper, we tackle the challenging one-shot semantic segmentation problem by taking advantage of objectness. In order to capture prior knowledge of object and background, we first train an objectness segmentation module which generalizes well to unseen categories. Then we use the objectness module to predict the objects present in the query image, and train an objectness-aware few-shot segmentation model that takes advantage of both the object information and limited annotations of the unseen category to perform segmentation in the query image. Our method achieves a mIoU score of 57.9% and 22.6% given only one annotated example of an unseen category in PASCAL-5i and COCO-20i, outperforming related baselines overall.