Reviewerly/exHarmony huggingface dataset:
Rethinking Preventing Class-Collapsing in Metric Learning With Margin-Based Losses

Metric learning seeks perceptual embeddings where visually similar instances are close and dissimilar instances are apart, but learned representations can be sub-optimal when the distribution of intra-class samples is diverse and distinct sub-clusters are present. Although theoretically with optimal assumptions, margin-based losses such as the triplet loss and margin loss have a diverse family of solutions. We theoretically prove and empirically show that under reasonable noise assumptions, margin-based losses tend to project all samples of a class with various modes onto a single point in the embedding space, resulting in a class collapse that usually renders the space ill-sorted for classification or retrieval. To address this problem, we propose a simple modification to the embedding losses such that each sample selects its nearest same-class counterpart in a batch as the positive element in the tuple. This allows for the presence of multiple sub-clusters within each class. The adaptation can be integrated into a wide range of metric learning losses. The proposed sampling method demonstrates clear benefits on various fine-grained image retrieval datasets over a variety of existing losses; qualitative retrieval results show that samples with similar visual patterns are indeed closer in the embedding space.
Hide Encryption Secret Message in Video Frame Based on MapReduce

The necessity to secure confidential data has arisen, and in order to eliminate the weaknesses in some previous research that hide the data in the video without encryption. This paper proposed a method of video steganography after encrypted data by using Advance Encryption Standard (AES) algorithm then sending it to the other party with high security and embedding rate. In addition facilitating dealing with video frames as large data for the purpose of analyzing, dividing and controlling frames easily, using the MapReduce method. This is done by dividing the video into frames, then, encrypt the message by the (AES) algorithm. And these frames and the encrypted message will be entered into the MapReduce system. Firstly the Master will divide the video frames and the encrypted message into four parts.  Each part will be sent to MapReduse stages, as each part will be processed separately. In the MapReduce stages, some frames will be selected from a series of frames for the purpose to hide the message in them. Then the hiding process will be done using theLeast Significant Bit (LSB) method which uses the stego secret key (. After that, the stego frame will be generated, and so on until the message is completely hidden. Finally, collect all frames from all parts to reconstruct the stego video. The results of this method is efficiency, transparency, robustness, powerful in stego video , high capacity , and high security the attacker cannot detected any suspicious differences in a stego video. This results is obtained through use many measurements PSNR, MSE, Entropy, correlation coefficient.
Families In Wild Multimedia: A Multimodal Database for Recognizing Kinship.

Kinship, a soft biometric detectable in media, is fundamental for a myriad of use-cases. Despite the difficulty of detecting kinship, annual data challenges using still-images have consistently improved performances and attracted new researchers. Now, systems reach performance levels unforeseeable a decade ago, closing in on performances acceptable to deploy in practice. Like other biometric tasks, we expect systems can receive help from other modalities. We hypothesize that adding modalities to FIW, which has only still-images, will improve performance. Thus, to narrow the gap between research and reality and enhance the power of kinship recognition systems, we extend FIW with multimedia (MM) data (i.e., video, audio, and text captions). Specifically, we introduce the first publicly available multi-task MM kinship dataset. To build FIW MM, we developed machinery to automatically collect, annotate, and prepare the data, requiring minimal human input and no financial cost. The proposed MM corpus allows the problem statements to be more realistic template-based protocols. We show significant improvements in all benchmarks with the added modalities. The results highlight edge cases to inspire future research with different areas of improvement. FIW MM supplies the data needed to increase the potential of automated systems to detect kinship in MM. It also allows experts from diverse fields to collaborate in novel ways.
Polynomial Representation Is Tricky: Maliciously Secure Private Set Intersection Revisited.

Private Set Intersection protocols (PSIs) allow parties to compute the intersection of their private sets, such that nothing about the sets’ elements beyond the intersection is revealed. PSIs have a variety of applications, primarily in efficiently supporting data sharing in a privacy-preserving manner. At Eurocrypt 2019, Ghosh and Nilges proposed three efficient PSIs based on the polynomial representation of sets and proved their security against active adversaries. In this work, we show that these three PSIs are susceptible to several serious attacks. The attacks let an adversary (1) learn the correct intersection while making its victim believe that the intersection is empty, (2) learn a certain element of its victim’s set beyond the intersection, and (3) delete multiple elements of its victim’s input set. We explain why the proofs did not identify these attacks and propose a set of mitigations.
Morphable Detector for Object Detection on Demand

Many emerging applications of intelligent robots need to explore and understand new environments, where it is desirable to detect objects of novel classes on the fly with minimum online efforts. This is an object detection on demand (ODOD) task. It is challenging, because it is impossible to annotate a large number of data on the fly, and the embedded systems are usually unable to perform back-propagation which is essential for training. Most existing few-shot detection methods are confronted here as they need extra training. We propose a novel morphable detector (MD), that simply "morphs" some of its changeable parameters online estimated from the few samples, so as to detect novel classes without any extra training. The MD has two sets of parameters, one for the feature embedding and the other for class representation (called "prototypes"). Each class is associated with a hidden prototype to be learned by integrating the visual and semantic embeddings. The learning of the MD is based on the alternate learning of the feature embedding and the prototypes in an EM-like approach which allows the recovery of an unknown prototype from a few samples of a novel class. Once an MD is learned, it is able to use a few samples of a novel class to directly compute its prototype to fulfill the online morphing process. We have shown the superiority of the MD in Pascal, COCO and FSOD datasets. The code is available https://github.com/Zhaoxiangyun/Morphable-Detector.
Digital Technologies In Formation Of Maneuverable Student Identity

The article presents a detailed theoretical analysis of the concepts of "identity", "maneuverability", "mobility" of the personality of a university student. The concept of "identity" is considered in interdisciplinary areas from the standpoint of a personality-oriented, systematic approach. On the basis of this analysis, the concept of “the formation of a flexible identity of university students” was comprehended in the context of the use of digital technologies in higher education. The essence of this concept is determined. It has been established that the agile identity of a university student is an integrative characteristic of a personality, a new quality of a socio-psychological category. The types of pedagogical tools that contribute to the achievement of new educational results have been identified. From the point of view of objective means, these are electronic devices, tools for each educational area. Subjective means include computer and digital technologies addressed to each educational subject. The importance and effectiveness of the use of digital technologies in the university as a condition for the formation of students' identity and a special organization of the educational process are highlighted.
The modern innovative methods of musical pedagogy and the disciplines of musical performance at high school in the context of distance study

The article deals with the topic of how specialist can find and implement methods connected with the musical education. The author writes about methods of musical pedagogy, adopted to the conditions of reality. There is a translation of idea about a great importance of communication between teacher and student. There is an opinion that the most effective method of modern musical pedagogy is a synthesis of classical traditions of study and sources of innovative technologies. The perspectives of distance study are outlined and an assumption is made about what results it can have. The author also shares his own experience connected with the organization the lessons online. The author gives recommendations not only for teachers of musical academies, institutions but also for pedagogues of musical schools and kindergartens. The importance of realization the innovative and the classical methods of musical education is the main question as for as the high musical school and the educational organizations of pre-school work with children.
모바일 기반 언어 학습 도구로서의 인스타그램을 활용한 한국어 교육 방안 연구 -미국 대학 초급 한국어 수업 사례 및 학습자 인식 조사를 중심으로-

This study designed and presented Korean language education activities using Instagram as a Mobile-Assisted Language Learning tool to supplement the limitations of the learning environment of learners at American colleges with limited opportunities to use Korean outside of the classroom. In addition, by examining the contents and results of the awareness survey conducted on learners, the effects and limitations of Korean language education activities using Instagram were reviewed. As a result of conducting Korean language education activities using Instagram during the semester, learners were all positively aware of the areas of interest, interaction, topic, practice, memory, and engagement regarding the Instagram activities. This study also suggested a way of improving the interaction not only among Korean language learners, but also that between Korean language learners and Korean native speakers using Instagram activities. (Yonsei University)
How Computers Play Chess

Electronic computers are completely unlike human chess-players in many respects. The comparison between human and computer play may also be used in reverse, to improve the quality of computer performance. The story of computer chess begins with the development of the machine itself. Shannon proposed what has become the standard outline for a chess program. In order to play chess, the computer must search ahead to find the most promising move. An important extension of the type-B strategy is the use of forward pruning. Despite the difficulties, the very successful Northwestern “Chess” series was distinguished from most of its contemporaries by its use of a relatively elaborate evaluation function. Computer chess can be improved in a variety of ways. Most programs are highly materialistic and can be counted on to make good-looking captures, with a particular emphasis on taking pieces when ahead in order to increase their relative advantage.
Extracting Learner’s Model Variables for Dynamic Grouping System

Collaborative learning is of great help in improving the training of learners in e-learning systems. But the efficiency of collaborative learning depends on the quality of the learning groups formed. The question is therefore: Which variables should be used to group learners in order to reach the most effective collaboration between them? In this paper, we present a literature review of variables describing learner’s model, which can be used for grouping learners in collaborative e-learning systems. Variables are ranged in 11 classes, and possible associations between variables are considered, after extracting frequent patterns from variables using FP-growth algorithm, and generating association rules. The results show that four variables from learner’s are mostly used with association with other variables. These variables are: “Number of accesses to the platform”, “Number of complete activities”, “Platform time” and “Final Grades”.
Incrementalism and paradigms

The regulatory response to the emergence of cryptoassets is incrementalist in nature. The characteristics of incrementalism, its strengths and weaknesses, are considered. The consequences of a fit-to-existing-regulation taxonomy, and the inconsistency of its performance when applied to cryptoassets, suggest it will not assist to foster the new opportunities presented by cryptographic consensus technology. The validity of the incrementalist approach is brought into doubt when considered in light of changed fact patterns and assumptions that no longer apply in the context of cryptoassets. This is a consequence of policy makers organizing their diagnostic and prescriptive enquiries around incompatible frameworks. Thomas Kuhn’s concept of the paradigm is used to explore the collective approach to a financial construct and considers the factors that might lead to regulatory progress.
ELSD: Efficient Line Segment Detector and Descriptor

We present the novel Efficient Line Segment Detector and Descriptor (ELSD) to simultaneously detect line segments and extract their descriptors in an image. Unlike the traditional pipelines that conduct detection and description separately, ELSD utilizes a shared feature extractor for both detection and description, to provide the essential line features to the higher-level tasks like SLAM and image matching in real time. First, we design the one-stage compact model, and propose to use the mid-point, angle and length as the minimal representation of line segment, which also guarantees the center-symmetry. The non-centerness suppression is proposed to filter out the fragmented line segments caused by lines' intersections. The fine offset prediction is designed to refine the mid-point localization. Second, the line descriptor branch is integrated with the detector branch, and the two branches are jointly trained in an end-to-end manner. In the experiments, the proposed ELSD achieves the state-of-the-art performance on the Wireframe dataset and YorkUrban dataset, in both accuracy and efficiency. The line description ability of ELSD also outperforms the previous works on the line matching task.
Anomaly Detection in Multi-Agent Trajectories for Automated Driving

Human drivers can recognise fast abnormal driving situations to avoid accidents. Similar to humans, automated vehicles are supposed to perform anomaly detection. In this work, we propose the spatio-temporal graph auto-encoder for learning normal driving behaviours. Our innovation is the ability to jointly learn multiple trajectories of a dynamic number of agents. To perform anomaly detection, we first estimate a density function of the learned trajectory feature representation and then detect anomalies in low-density regions. Due to the lack of multi-agent trajectory datasets for anomaly detection in automated driving, we introduce our dataset using a driving simulator for normal and abnormal manoeuvres. Our evaluations show that our approach learns the relation between different agents and delivers promising results compared to the related works. The code, simulation and the dataset are publicly available on the project page: this https URL
Context-Aware Text Matching Algorithm for Korean Peninsula Language Knowledge Base Based on Density Clustering

The majority of the traditional methods deal with text matching at the word level which remains uncertain as the text semantic features are ignored. This also leads to the problems of low recall and high space utilization of text matching while the comprehensiveness of matching results is poor. The resultant method, thus, cannot process long text and short text simultaneously. The current study proposes a text matching algorithm for Korean Peninsula language knowledge base based on density clustering. Using the deep multiview semantic document representation model, the semantic vector of the text to be matched is captured for semantic dependency which is utilized to extract the text semantic features. As per the feature extraction outcomes, the text similarity is calculated by subtree matching method, and a semantic classification model based on SWEM and pseudo-twin network is designed for semantic text classification. Finally, the text matching of Korean Peninsula language knowledge base is carried out by applying density clustering algorithm. Experimental results show that the proposed method has high matching recall rate with low space requirements and can effectively match long and short texts concurrently.
RepShard: Reputation-based Sharding Scheme Achieves Linearly Scaling Efficiency and Security Simultaneously.

Sharding technology is becoming a promising candidate to address the scalability issues in blockchain. The key concept behind sharding technology is to partition the network status into multiple distinct smaller committees, each of which handles a disjoint set of transactions to leverage its capability of parallel processing. However, when introducing sharding technology to blockchain, several key challenges need to be resolved, such as security and heterogeneity among the participating nodes. This paper introduces RepShard, a reputation-based blockchain sharding scheme that aims to achieve both linearly scaling efficiency and system security simultaneously. RepShard adopts a two-layer hierarchical chain structure, consisting of a reputation chain and independent transaction chains. Each transaction chain is maintained within its shard to record transactions, while the reputation chain is maintained by all shards to update the reputation score of each participating node. We leverage a novel reputation scheme to record each participating node's integrated and valid contribution to the system, in which we consider the heterogeneity of participating nodes (e.g., computational resources). The reputation score used in sharding and leader election processes maintains the balance and security of each shard. RepShard relies on verifiable relay transactions for cross-shard transactions to ensure consistency between distinct shards. By integrating reputation into the sharding protocol, our scheme can offer both scalability and security at the same time.
Generative Optimization Networks for Memory Efficient Data Generation.

In standard generative deep learning models, such as autoencoders or GANs,the size of the parameter set is proportional to the complexity of thegenerated data distribution. A significant challenge is to deployresource-hungry deep learning models in devices with limited memory to preventsystem upgrade costs. To combat this, we propose a novel framework calledgenerative optimization networks (GON) that is similar to GANs, but does notuse a generator, significantly reducing its memory footprint. GONs use a singlediscriminator network and run optimization in the input space to generate newdata samples, achieving an effective compromise between training time andmemory consumption. GONs are most suited for data generation problems inlimited memory settings. Here we illustrate their use for the problem ofanomaly detection in memory-constrained edge devices arising from attacks orintrusion events. Specifically, we use a GON to calculate areconstruction-based anomaly score for input time-series windows. Experimentson a Raspberry-Pi testbed with two existing and a new suite of datasets showthat our framework gives up to 32% higher detection F1 scores and 58% lowermemory consumption, with only 5% higher training overheads compared to thestate-of-the-art.
LibGuides: Digital Newspaper Subscriptions: Chronicle of Higher Education

This guide provides information for BWU students, faculty, and staff to take advantage of the digital newspaper subscriptions available campus-wide
DeepGaze IIE: Calibrated Prediction in and Out-of-Domain for State-of-the-Art Saliency Modeling

Since 2014 transfer learning has become the key driver for the improvement of spatial saliency prediction; however, with stagnant progress in the last 3-5 years. We conduct a large-scale transfer learning study which tests different ImageNet backbones, always using the same read out architecture and learning protocol adopted from II. By replacing the VGG19 backbone of II with ResNet50 features we improve the performance on saliency prediction from 78% to 85%. However, as we continue to test better ImageNet models as backbones (such as EfficientNetB5) we observe no additional improvement on saliency prediction. By analyzing the backbones further, we find that generalization to other datasets differs substantially, with models being consistently overconfident in their fixation predictions. We show that by combining multiple backbones in a principled manner a good confidence calibration on unseen datasets can be achieved. This new model, DeepGaze IIE, yields a significant leap in benchmark performance in and out-of-domain with a 15 percent point improvement over II to 93% on MIT1003, marking a new state of the art on the MIT/Tuebingen Saliency Benchmark in all available metrics (AUC: 88.3%, sAUC: 79.4%, CC: 82.4%).
EMOJIS AND BRAND SELF-REPRESENTATION: A TEXT ANALYTICS APPROACH

This study investigates the contexts in which emojis occur through co-occurrence, cluster, and association analysis of Airbnb tweets. Findings reveal these positive emojis tend to co-exist with several types of words representing conversation, emotion, activity, and marketing. This study contributes to the textual paralanguage literature in marketing.
Analysis Of Imbalanced Electronic Medical Record Using Classification Approach

Since the last few decades, a class imbalance has been one of the most difficult problems in various fields, consisting of data mining and system studying. In clinical records classification, it regularly faces the imbalanced wide variety of records samples wherein at the least one of the classes constitutes simplest a completely small minority of the information. In the equal time, it represents a difficult problem in maximum of gadget getting to know algorithms. There were many works managing classification of imbalanced dataset. . In medical diagnosis studies, Imbalanced Classification is a not unusual project. For nearly any sickness, a scientific laboratory has greater patients now not having rather than having it. Disease prediction can be implemented to specific domain names including threat control, tailored fitness conversation and decision support structures. Several system studying strategies have been implemented to healthcare data sets for the prediction of destiny fitness care usage such as predicting character costs and disease risks for sufferers. The classification problem for imbalance records is paid greater interest to. So some distance, many giant techniques are proposed and applied to many fields. But more green methods are wished still. In this paper, an expansion of classification methods is compared inside the problem domain class imbalanced clinical information.