Reviewerly/exHarmony huggingface dataset:
Digital Image and Spectral Analysis of Foreign Currency Notes - A Case Report

The state Tripura is located in the NE-region of India almost surrounded by neighboring foreign country Bangladesh.As per record 856 kms stretches having international borders.Considering the topography, the boarder crimes like circulation of FICN (Fake Indian Currency Note), foreign currency, gold, drugs, explosives, fake passports are frequently encountered in different cases.The boarder is also guarded by the BSF and customs officials to check various illegal activities.In one case, the customs officials detained one person carrying some Bangladesh currency illegally.On interrogation, more information was revealed leading to search of his house.In the course of search, huge amount of Bangladeshi Taka was recovered.The disputed foreign currency was received for forensic examination to establish the genuineness.The various examinations like physical, spectral, microscopic, digital image analysis were conducted comparing different security features with genuine Bangladesh currency notes.The different examinations of foreign currency notes conclusively established to be genuine notes.
Remote Data Auditing and How it May Affect the Chain of Custody in a Cloud Environment

As big data collection continues to grow, more and more organizations are relying on outsourcing their data to cloud-based environments. This includes the federal government and several agencies that depend on maintaining our citizens' secure data. Law enforcement agencies from the national level down to large city police departments are also using the cloud environment to store data. These agencies see this as a method of securing data while saving money by not maintaining the large data centers required to house this information. This data solution presents in own set of problems in that the outsourced data can become untrustworthy due to the lack of control of the data owners. Cloud computing is facing many difficulties, with security being the primary issue. This is because the cloud computing service provider is a separate entity; any data stored in the cloud can be interpreted as giving up control of the data by the primary data owner. [1] Remote data auditing (RDA) is increasingly important when managing data in a cloud environment, especially when organizations have to store their data in a multi-cloud environment. The challenging security threats posed by attempting to maintain the integrity of the data for proper auditing is a trial that was never addressed in the past.
Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions

Similarity functions measure how comparable pairs of elements are, and play a key role in a wide variety of applications, e.g., notions of Individual Fairness abiding by the seminal paradigm of Dwork et al., as well as Clustering problems. However, access to an accurate similarity function should not always be considered guaranteed, and this point was even raised by Dwork et al. For instance, it is reasonable to assume that when the elements to be compared are produced by different distributions, or in other words belong to different ``demographic'' groups, knowledge of their true similarity might be very difficult to obtain. In this work, we present an efficient sampling framework that learns these across-groups similarity functions, using only a limited amount of experts' feedback. We show analytical results with rigorous theoretical bounds, and empirically validate our algorithms via a large suite of experiments.
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective

Few-shot learning models learn representations with limited human annotations, and such a learning paradigm demonstrates practicability in various tasks, e.g., image classification, object detection, etc. However, few-shot object detection methods suffer from an intrinsic defect that the limited training data makes the model cannot sufficiently explore semantic information. To tackle this, we introduce knowledge distillation to the few-shot object detection learning paradigm. We further run a motivating experiment, which demonstrates that in the process of knowledge distillation, the empirical error of the teacher model degenerates the prediction performance of the few-shot object detection model as the student. To understand the reasons behind this phenomenon, we revisit the learning paradigm of knowledge distillation on the few-shot object detection task from the causal theoretic standpoint, and accordingly, develop a Structural Causal Model. Following the theoretical guidance, we propose a backdoor adjustment-based knowledge distillation method for the few-shot object detection task, namely Disentangle and Remerge (D&R), to perform conditional causal intervention toward the corresponding Structural Causal Model. Empirically, the experiments on benchmarks demonstrate that D&R can yield significant performance boosts in few-shot object detection. Code is available at https://github.com/ZYN-1101/DandR.git.
Full Body Video-Based Self-Avatars for Mixed Reality: from E2E System to User Study

In this work we explore the creation of self-avatars through video pass-through in Mixed Reality (MR) applications. We present our end-to-end system, including: custom MR video pass-through implementation on a commercial head mounted display (HMD), our deep learning-based real-time egocentric body segmentation algorithm, and our optimized offloading architecture, to communicate the segmentation server with the HMD. To validate this technology, we designed an immersive VR experience where the user has to walk through a narrow tiles path over an active volcano crater. The study was performed under three body representation conditions: virtual hands, video pass-through with color-based full-body segmentation and video pass-through with deep learning full-body segmentation. This immersive experience was carried out by 30 women and 28 men. To the best of our knowledge, this is the first user study focused on evaluating video-based self-avatars to represent the user in a MR scene. Results showed no significant differences between the different body representations in terms of presence, with moderate improvements in some Embodiment components between the virtual hands and full-body representations. Visual Quality results showed better results from the deep-learning algorithms in terms of the whole body perception and overall segmentation quality. We provide some discussion regarding the use of video-based self-avatars, and some reflections on the evaluation methodology. The proposed E2E solution is in the boundary of the state of the art, so there is still room for improvement before it reaches maturity. However, this solution serves as a crucial starting point for novel MR distributed solutions.
A Two Step Approach for Whole Slide Image Registration

Multi-stain whole-slide-image (WSI) registration is an active field of research. It is unclear, however, how the current WSI registration methods would perform on a real-world data set. AutomatiC Registration Of Breast cAncer Tissue (ACROBAT) challenge is held to verify the performance of the current WSI registration methods by using a new dataset that originates from routine diagnostics to assess real-world applicability. In this report, we present our solution for the ACROBAT challenge. We employ a two-step approach including rigid and non-rigid transforms. The experimental results show that the median 90th percentile is 1,250 um for the validation dataset.
COMMUNICATIVE COMPETENCE’S FORMATION IN LAW ENFORCEMENT ACTIVITY IN THE PROCESS OF EXTRAMURAL MASTERS’ PROFESSIONAL TRAINING

The article analyzes the essence of the concept of "communicative competence", considers the concept of communicative competence as a condition of effective professional activity of law enforcement, identifies components of communicative competence and their role in law enforcement, reveals the main important aspects of communicative competence of future law enforcement officers the process of professional training. It is established that a holistic system of socio-psychological means of developing communication skills contributes to the professional identity of future law enforcement officers, psychological readiness for professional communication activities. Forms and methods of work aimed at the formation of this competence in the study of the course "Communicative competence in law enforcement" for extramural masters. It is emphasized that due to the small amount of time devoted to classroom activities (8 hours), it is necessary to focus on speech (communicative) exercises, use such forms and methods of work as debate, discussion, debate. Writing of creative work on topics related to the future profession of the student / cadet, preparation of project works, etc. take out for self-directed work.
Comparison of unknown unitary channels with multiple uses

Comparison of quantum objects is a task to determine whether two unknown quantum objects are the same or different. It is one of the most basic information processing tasks for learning property of quantum objects, and comparison of quantum states, quantum channels, and quantum measurements have been investigated. In general, repeated uses of quantum objects improve the success probability of comparison. The optimal strategy of pure-state comparison, the comparison of quantum states for the case of multiple copies of each unknown pure state, is known, but the optimal strategy of unitary comparison, the comparison of quantum channels for the case of multiple uses of each unknown unitary channel, was not known due to the complication of the varieties of causal order structures among the uses of each unitary channel. In this paper, we investigate unitary comparison with multiple uses of unitary channels based on the quantum tester formalism. We obtain the optimal minimum-error and the optimal unambiguous strategies of unitary comparison of two unknown $d$-dimensional unitary channels $U_1$ and $U_2$ when $U_1$ can be used $N_1$ times and $U_2$ can be used $N_2$ times for $N_2 \ge (d-1)N_1$. These optimal strategies are implemented by parallel uses of the unitary channels, even though all sequential and adaptive strategies implementable by the quantum circuit model are considered. When the number of the smaller uses of the unitary channels $N_1$ is fixed, the optimal averaged success probability cannot be improved by adding more uses of $U_2$ than $N_2 = (d-1) N_1$. This feature is in contrast to the case of pure-state comparison, where adding more copies of the unknown pure states always improves the optimal averaged success probability. It highlights the difference between corresponding tasks for states and channels, which has been previously shown for quantum discrimination tasks.
ESC-Rules: Explainable, Semantically Constrained Rule Sets

We describe a novel approach to explainable prediction of a continuous variable based on learning fuzzy weighted rules. Our model trains a set of weighted rules to maximise prediction accuracy and minimise an ontology-based 'semantic loss' function including user-specified constraints on the rules that should be learned in order to maximise the explainability of the resulting rule set from a user perspective. This system fuses quantitative sub-symbolic learning with symbolic learning and constraints based on domain knowledge. We illustrate our system on a case study in predicting the outcomes of behavioural interventions for smoking cessation, and show that it outperforms other interpretable approaches, achieving performance close to that of a deep learning model, while offering transparent explainability that is an essential requirement for decision-makers in the health domain.
CADETS’ PSYCHOLOGICAL WELL-BEING DURING TRAINING PERIOD AT MILITARY INSTITUTIONS OF HIGHER EDUCATION

This empirical study describes factors that facilitate/impede the psychological wellbeing of cadets studying at Ukrainian military institutions of higher education. The preference was given to the psycholinguistic method of research, namely the free associative experiment. We argue that the most important factor for cadets’ psychological well-being is self-acceptance. When cadets correlate their self-image with professional military activity they develop healthy self-esteem and they are characterized by positive emotional states. Cadets’ failure to identify themselves with professional military activity leads to the predominance of negative emotional states. Factors such as some opportunities for personal growth and the increases the probabilities of achievement of the vital purposes are characterized by their correlation with positive aspects of cadets’ psychological well-being. A favorable psychological atmosphere in collective is another factor of cadet’s psychological well-being. The exceptions are those cadets who have inflated demands and expectations, or cadets with victim mentality. Factors such as autonomy and environmental management are characterized by their correlation with negative aspects of cadets’ psychological well-being. The high-risk category is cadets who enter the military institutions having ideal vision about military service that does not accord with reality, cadets for whom the decision to study in some military institution is made by their parents against their will, and cadets who join the army under pressure of life circumstances. The lack of personal autonomy and the inability to independently control one’s life can result in health disorders, as indicated by cadet’s emotional states of weariness, despondency, melancholy, hopelessness.
Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer

Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in real-world applications due to its inherent advantage to overcome high-velocity motion blur. However, training the spike depth estimation network holds significant challenges in two aspects: sparse spatial information for dense regression tasks, and difficulties in achieving paired depth labels for temporally intensive spike streams. In this paper, we thus propose a cross-modality cross-domain (BiCross) framework to realize unsupervised spike depth estimation with the help of open-source RGB data. It first transfers cross-modality knowledge from source RGB to mediates simulated source spike data, then realizes cross-domain learning from simulated source spike to target spike data. Specifically, Coarse-to-Fine Knowledge Distillation (CFKD) is introduced to transfer cross-modality knowledge in global and pixel-level in the source domain, which complements sparse spike features by sufficient semantic knowledge of image features. We then propose Uncertainty Guided Teacher-Student (UGTS) method to realize cross-domain learning on spike target domain, ensuring domain-invariant global and pixel-level knowledge of teacher and student model through alignment and uncertainty guided depth selection measurement. To verify the effectiveness of BiCross, we conduct extensive experiments on three scenarios, including Synthetic to Real, Extreme Weather, and Scene Changing. The code and datasets will be released.
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations

Data in many applications follows systems of Ordinary Differential Equations (ODEs). This paper presents a novel algorithmic and symbolic construction for covariance functions of Gaussian Processes (GPs) with realizations strictly following a system of linear homogeneous ODEs with constant coefficients, which we call LODE-GPs. Introducing this strong inductive bias into a GP improves modelling of such data. Using smith normal form algorithms, a symbolic technique, we overcome two current restrictions in the state of the art: (1) the need for certain uniqueness conditions in the set of solutions, typically assumed in classical ODE solvers and their probabilistic counterparts, and (2) the restriction to controllable systems, typically assumed when encoding differential equations in covariance functions. We show the effectiveness of LODE-GPs in a number of experiments, for example learning physically interpretable parameters by maximizing the likelihood.
Need for Design Patterns: Interoperability Issues and Modelling Challenges for Observational Data

Interoperability issues concerning observational data have gained attention in recent times. Automated data integration is important when it comes to the scientific analysis of observational data from different sources. However, it is hampered by various data interoperability issues. We focus exclusively on semantic interoperability issues for observational characteristics. We propose a use-case-driven approach to identify general classes of interoperability issues. In this paper, this is exemplarily done for the use-case of citizen science fireball observations. We derive key concepts for the identified interoperability issues that are generalizable to observational data in other fields of science. These key concepts contain several modeling challenges, and we broadly describe each modeling challenges associated with its interoperability issue. We believe, that addressing these challenges with a set of ontology design patterns will be an effective means for unified semantic modeling, paving the way for a unified approach for resolving interoperability issues in observational data. We demonstrate this with one design pattern, highlighting the importance and need for ontology design patterns for observational data, and leave the remaining patterns to future work. Our paper thus describes interoperability issues along with modeling challenges as a starting point for developing a set of extensible and reusable design patterns.
Meta Objective Guided Disambiguation for Partial Label Learning

Partial label learning (PLL) is a typical weakly supervised learning framework, where each training instance is associated with a candidate label set, among which only one label is valid. To solve PLL problems, typically methods try to perform disambiguation for candidate sets by either using prior knowledge, such as structure information of training data, or refining model outputs in a self-training manner. Unfortunately, these methods often fail to obtain a favorable performance due to the lack of prior information or unreliable predictions in the early stage of model training. In this paper, we propose a novel framework for partial label learning with meta objective guided disambiguation (MoGD), which aims to recover the ground-truth label from candidate labels set by solving a meta objective on a small validation set. Specifically, to alleviate the negative impact of false positive labels, we re-weight each candidate label based on the meta loss on the validation set. Then, the classifier is trained by minimizing the weighted cross entropy loss. The proposed method can be easily implemented by using various deep networks with the ordinary SGD optimizer. Theoretically, we prove the convergence property of meta objective and derive the estimation error bounds of the proposed method. Extensive experiments on various benchmark datasets and real-world PLL datasets demonstrate that the proposed method can achieve competent performance when compared with the state-of-the-art methods.
Exploration and Practice of Examination Reform of Higher Vocational Colleges under the Credit System

The credit system reform is a major driving force for the development of higher vocational colleges, which is conducive to cultivating students' personality development, creating a more relaxed learning environment and promoting the development of technical education in higher vocational colleges. Under this background, this article studies the influencing factors of the credit system reform of higher vocational colleges, analyzes its driving force, searches for the future development path, so as to expand the future development space of vocational colleges, enable higher vocational colleges to cultivate more outstanding qualified talents for the society, and gather successful experience of the credit system reform in China's education field.
The Effect of Communicative Games on Developing Intermediate School Students Pronunciation Performance

This study aims to reveal the effect of communication games on developing intermediate students' pronunciation.To achieve this aim, a quasi-experimental design was used.A pretest and a posttest were used to collect the data.A selected communication games were used to training the students to develop their pronunciation performance.The participants were 68 male students from second grade intermediate school in Baghdad.The results revealed that there is no statistical significance difference between the mean scores of the students in the experimental and control groups due to the teaching method.The researcher suggested to expose students to electronic communication games that provide the sound of the word from a native source .
Practice Research on Mathematics Critical Thinking Ability Cultivation Strategy Based on Flipped Classroom

The method which can be used to develop one of the learning objectives of mathematics is mathematical critical thinking ability by involving students to be active in the learning process. A student who has the ability to think critically is the students that have a systematic way of thinking, awareness in thinking, and have the ability to distinguish a truth from mistakes. In fact, in the learning process students are still given information clearly and directly which created a passive learners. Students also do not have their own knowledge before the learning process begins so that they only expect knowledge from educators. The impact is there are difficulties for students in learning in class due to lack of training time in questions with varying degrees of difficulty. The lack of learning resources that are easy for students to understand is one of the reasons that students lack practice in doing exercises at home to improve their critical thinking skills mathematically. The purpose of this study is to produce a product in the form of a practical learning video to develop students' mathematical critical thinking skills in class XI. The development model used is Model Plomp which consists of three phases, namely the initial investigation phase, the prototype development phase, and the assessment phase. The subjects of this study were educators and students in class XI MIPA SMAN 5 Padang. Based on the results of observations during the learning process, students look active and can solve various questions with the results of work that increases every meeting. The results of interviewing educators and students received a positive response, and based on the questionnaire analysis also obtained practical categories. So that, it can be concluded that flipped classroom practical learning can be used to develop mathematical critical thinking skills of students in class XI.
Bias Analysis of the Coordinating Conjunction “和” Based on HSK Dynamic Composition Corpus

By analyzing and comparing the errors of joint conjunctions in the HSK dynamic composition corpus, this paper analyzes the error phenomenon of the conjunction "和" in the corpus from the perspectives of syntax and semantics, and puts forward teaching suggestions.