Reviewerly/exHarmony huggingface dataset:
A First Step Towards the Development of Yoruba Named Entity Recognition System

The NER task can be considered solved for English and a few other European languages given the available research outputs, tools, resources and applications involving NER for these languages.The scenario is sharply different for Nigerian and most of African languages and hence the motivation for the research reported in this paper.The paper presents an exploration of the potency of some language independent features in the recognition of the mentions of persons, locations and organizations in Yorùbá text in a supervised machine learning set-up.The results are promising but as further investigations revealed, the size of the training corpus is yet an issue that needs to be addressed.
Using of mathematical modelling on binary lessons of mathematics and informatics

Серія «Педагогічні науки» 83 and technical disciplines by the students of the informatics direction of the preparation of the institutions of higher education has been analyzed.Results.In the course of the study, it was found that the main use of the integrative approach to the study of multi-cycle disciplines is the creation of preconditions for the formation of figurative thinking through processes and sub-mechanisms of integration.In the phased formation of concepts, it is necessary to distinguish the enrichment phase, in which, with the help of such submechanisms as associations, analogies and metaphors, integration of heterogeneous knowledge takes place.Originality.It has been shown that the optimal direction of formation of the content of professionally directed academic disciplines is the use of interdisciplinary connections in basic technical disciplines.Disciplines of a technical nature are original integrated courses, which often require scientific correction and didactic substantiation, since they are formed in accordance with practical needs. Conclusion.Implementation of the integrative approach should be carried out through classroom and non-student activity of students, which is reflected in the content of classroom teaching materials, outside classroom activities and tasks, the choice of forms and methods of training.In the method of studying computer systems on the basis of integration with physico-mathematical and general technical disciplines, the methods of stimulation and motivation of educational and cognitive activity of future specialists are dominant.They should be supplemented and enriched with elements of other methods depending on the nature of the educational material.
Video Profile Promosi Dan Informasi Pada SMK Ariya Metta

The development of the media information is evidenced by the number of communications and telecommunications pioneer who compete creates media information either through print media, television, even internet radio is very easy to reach by anyone without limit. With the importance of media information at this time, because through the medium of information the public can find out information or exchanging information to other communities as well as interacting with each other especially in the field of education, namely SMK ( Vocational High School). His research methods, namely: Data collection, Analysis, Problem analysis method of design Media, and Production method of the concept of Mavib (KPM) which include: PreProduction, Production, Multimedia Design, planning of Audio, Visual, and Perecanaan PostProduction. The results of his research in the form of promotional video CMS Ariya Metta Tangerang contained item images, motion, sound and music. So that through the promotion done, SMK Ariya Metta can be easily recognized community and was able to increase the number of registrants registrants.
Osservazioni in tema di riforma della gestione “collettiva” dei diritti d’autore

The research is about the liberalization of the collective administration of copyright, now no longer reserved for the SIAE monopoly but open to competition from other organizations (OGC and EGI). In particular, the question arises the difference in the ways in wich copyright are managed among the different organizations, in wich way the choice option can be virtuously developed, and what role can be played by administrative controls (AGCOM and AGCM).
Open Source Face Recognition Performance Evaluation Package

Biometrics-related research has been accelerated significantly by deep learning technology. However, there are limited open-source resources to help researchers evaluate their deep learning-based biometrics algorithms efficiently, especially for the face recognition tasks. In this work, we design and implement a light-weight, maintainable, scalable, generalizable, and extendable face recognition evaluation toolbox named FaRE that supports both online and offline evaluation to provide feedback to algorithm development and accelerate biometrics-related research. FaRE consists of a set of evaluation metric functions and provides various APIs for commonly-used face recognition datasets including LFW, CFP, UHDB31, and IJB-series datasets, which can be easily extended to include other customized datasets. The package and the pre-trained baseline models will be released for public academic research use after obtaining university approval.
Low-Tech Steganography for Covert Operations

Text steganography, the art of concealing a secret text inside another innocuous text called the cover, is usually performed by insertion of whitespace, punctuation marks, misspelling words, or by arbitrarily capitalizing words or inserting synonyms, changing font-sizes & colors, etc.All of these have the disadvantage that they either arouse suspicion or are easily noticeable; and even lost if manually copied, i.e. handwritten.Furthermore, they are easily detectable by automated checkers.Still there are other methods which require a stego-key in order to decrypt the message.In covert intelligence operations, transmission of the stego-key may not be possible at all, more so when the message is urgent.Digital communications and Internet connectivity may also be lacking in certain situations, and the only mode of message passing available may be the exchange of handwritten text on paper; which effectively rules out text modifications like font-changes, whitespace insertion, etc. or any form of digital steganography like image/audio steganography.Finally, in almost all textsteganographic techniques, there is no provision to for the receiver to detect whether or not there is indeed any message embedded.This is very important in intelligence operations where a number of decoy text need to be sent with only one concealing the actual message.In this paper, we propose a new tool called STEGASSIST that can help the sender in generating the stego-text manually.It is a low-tech form of steganography that is especially suited to covert operations like espionage or under-cover journalism.In this method, the generated cover and the stego-text are identical, or in other words, there is no cover-text.Moreover, decryption does not require a stego-key, and the stego-text may be printed or even hand-written and sent via unreliable messengers, or published, without arousing any suspicion.Finally, the received stego-text can be checked by the receiver to detect whether or not there is any actual message embedded in it.
Achieving Trust-Based and Privacy-Preserving Customer Selection in Ubiquitous Computing

The recent proliferation of smart devices has given rise to ubiquitous computing, an emerging computing paradigm which allows anytime &amp; anywhere computing possible. In such a ubiquitous computing environment, customers release different computing or sensing tasks, and people, also known as data processors, participate in these tasks and get paid for providing their idle computing and communication resources. Thus, how to select an appropriate and reliable customer while not disclosing processors' privacy has become an interesting problem. In this article, we present a trust-based and privacy-preserving customer selection scheme in ubiquitous computing, called TPCS, to enable potential processors select the customers with good reputation. The basic concept of TPCS is that each data processor holds a trust value, and the reputation score of the customer is calculated based on processors' trust values and feedbacks via a truth discovery process. To preserve processors' privacy, pseudonyms and Paillier cryptosystem are applied to conceal each processor's real identity. In addition, three authentication protocols are designed to ensure that only the valid data processors (i.e., the processors registering in the system, holding the truthful trust values, and joining the computing tasks) can pass the authentication. A comprehensive security analysis is conducted to prove that our proposed TPCS scheme is secure and can defend against several sophisticated attacks. Moreover, extensive simulations are conducted to demonstrate the correctness and effectiveness of the proposed scheme.
Curvature-Exploiting Acceleration of Elastic Net Computations

This paper introduces an efficient second-order method for solving the elastic net problem. Its key innovation is a computationally efficient technique for injecting curvature information in the optimization process which admits a strong theoretical performance guarantee. In particular, we show improved run time over popular first-order methods and quantify the speed-up in terms of statistical measures of the data matrix. The improved time complexity is the result of an extensive exploitation of the problem structure and a careful combination of second-order information, variance reduction techniques, and momentum acceleration. Beside theoretical speed-up, experimental results demonstrate great practical performance benefits of curvature information, especially for ill-conditioned data sets.
Representation Learning for Heterogeneous Information Networks via Embedding Events

Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackle this issue, this paper proposes a new NRL framework, called Event2vec, for HINs to consider both quantities and properties of relations during the representation learning process. Specifically, an event (i.e., a complete semantic unit) is used to represent the relation among multiple objects, and both event-driven first-order and second-order proximities are defined to measure the object relevance according to the quantities and properties of relations. We theoretically prove how event-driven proximities can be preserved in the embedding space by Event2vec, which utilizes event embeddings to facilitate learning the object embeddings. Experimental studies demonstrate the advantages of Event2vec over state-of-the-art algorithms on four real-world datasets and three network analysis tasks (including network reconstruction, link prediction, and node classification).
Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability

In many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensemble detectors based on label feedback allows us to quickly discover true anomalies. This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also present empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.
Close-reading of Linked Data: a case study in regards to the quality of online authority files

More and more cultural institutions use Linked Data principles to share and connect their collection metadata. In the archival field, initiatives emerge to exploit data contained in archival descriptions and adapt encoding standards to the semantic web. In this context, online authority files can be used to enrich metadata. However, relying on a decentralized network of knowledge bases such as Wikidata, DBpedia or even Viaf has its own difficulties. This paper aims to offer a critical view of these linked authority files by adopting a close-reading approach. Through a practical case study, we intend to identify and illustrate the possibilities and limits of RDF triples compared to institutions' less structured metadata.
Generative Cloud of Points SLAM (G-SLAM)

Mobile robots need an environmental perception ability in order to interact with the surrounding environment. In this paper we present the G-SLAM method, where the map is consisted of a cloud of scattered points in the continuous space and each point is accompanied by an obstacle existence probability. On the other hand the robot's pose (and trajectory) is estimated by an particle filters while the cloud of points is estimated by an adaptive recursive algorithm which is presented. Main feature of the presented method is the adaptive repositioning of the scattered points and their convergence around obstacles. In addition to our previous publications, In this paper a mathematical derivation of the method is presented and real case scenarios are discussed, presented and compared with other methods.
Implicit Diversity in Image Summarization

Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes. This skew goes beyond that which already exists in the world - e.g., Kay et al. showed that although 28% of CEOs in US are women, only 10% of the top 100 results for CEO in Google Image Search are women. Most existing approaches to correct for this kind of bias assume that the images of people include socially salient attribute labels. However, such labels are often unknown. Further, using automated techniques to infer these labels may often not be possible within acceptable accuracy ranges, and may not be desirable due to the additional biases this process could incur. We develop a novel approach that takes as input a visibly diverse control set of images and uses this set to select a set of images of people in response to a query. The goal is to have a resulting set that is more visibly diverse in a manner that emulates the diversity depicted in the control set. Importantly, this approach does not require images to be labelled at any point; effectively, it gives a way to implicitly diversify the set of images selected. We provide two variants of our approach: the first is a modification of the MMR algorithm to incorporate the diversity scores, and second is a more efficient variant that does not consider within-list redundancy. We evaluate these approaches empirically on two datasets 1) a new dataset containing top Google image results for 96 occupations, for which we evaluate gender and skin-tone diversity with respect to occupations and 2) the CelebA dataset for which we evaluate gender diversity with respect to facial features. Our approaches produce image sets that significantly improve the visible diversity of the results, compared to current Google search and other diverse image summarization algorithms, at a minimal cost to accuracy.
Learning Transformation Synchronization

Reconstructing the 3D model of a physical object typically requires us to align the depth scans obtained from different camera poses into the same coordinate system. Solutions to this global alignment problem usually proceed in two steps. The first step estimates relative transformations between pairs of scans using an off-the-shelf technique. Due to limited information presented between pairs of scans, the resulting relative transformations are generally noisy. The second step then jointly optimizes the relative transformations among all input depth scans. A natural constraint used in this step is the cycle-consistency constraint, which allows us to prune incorrect relative transformations by detecting inconsistent cycles. The performance of such approaches, however, heavily relies on the quality of the input relative transformations. Instead of merely using the relative transformations as the input to perform transformation synchronization, we propose to use a neural network to learn the weights associated with each relative transformation. Our approach alternates between transformation synchronization using weighted relative transformations and predicting new weights of the input relative transformations using a neural network. We demonstrate the usefulness of this approach across a wide range of datasets.
ANALISIS STABILITAS HASIL GENOTIPE JAGUNG MENGGUNAKAN METODE FIXED AMMI

Additive Main Effect and Multiplicative Interaction (AMMI) is a method that is used in research to study interaction between genotype and location. The aim of this research is to apply fixed AMMI in examining the production of corn genotype data and to explore yield stability of its based on biplot picture and AMMI Stability Value (ASV). This research uses six corn genotypes, eight trial locations, and three repetitions. The Interaction Principal Component Analysis (IPCA) that are significant to entered in the model based on analysis of variance fixed AMMI are IPCA1, IPCA2, and IPCA3 with total diversity interaction as much as 92,16%. The biplot picture and ASV should the stable genotype in all location are genotype KUI Carotenoid Syn FS. 17-3-2-B-B T01 and genotype CML 305-B-B T01. In addition, corns that are able to adapt only in certain location is: genotype KUI Carotenoid Syn FS. 5-1-5-B-B T01, genotype KUI Carotenoid Syn FS. 25-3-2-B-B T01, genotype KUI Carotenoid Syn FS. 17-3-1-B T01, and genotype CML 130-B-B T01.
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning

We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the other agent, the objective is to collect further evidence to help discriminate potential threats. The main technical challenges are the partial observability of the agent intent, the adversary modeling, and the corresponding uncertainty modeling. Note that an adversary agent may act to mislead the autonomous agent by using a deceptive strategy that is learned from past experiences. We propose an approach that combines belief space planning, generative adversary modeling, and maximum entropy reinforcement learning to obtain a stochastic belief space policy. By accounting for various adversarial behaviors in the simulation framework and minimizing the predictability of the autonomous agent's action, the resulting policy is more robust to unmodeled adversarial strategies. This improved robustness is empirically shown against an adversary that adapts to and exploits the autonomous agent's policy when compared with a standard Chance-Constraint Partially Observable Markov Decision Process robust approach.
Enhancing Fault Tolerance of Neural Networks for Security-Critical Applications

Neural Networks (NN) have recently emerged as backbone of several sensitive applications like automobile, medical image, security, etc. NNs inherently offer Partial Fault Tolerance (PFT) in their architecture; however, the biased PFT of NNs can lead to severe consequences in applications like cryptography and security critical scenarios. In this paper, we propose a revised implementation which enhances the PFT property of NN significantly with detailed mathematical analysis. We evaluated the performance of revised NN considering both software and FPGA implementation for a cryptographic primitive like AES SBox. The results show that the PFT of NNs can be significantly increased with the proposed methodology.
Were student's tweets covered by conduct code?

The plaintiff, a University of Kansas student, began dating fellow student A.W. in 2012.
Electryo, In-person Voting with Transparent Voter Verifiability and Eligibility Verifiability

Selene is an e-voting protocol that allows voters to directly check their individual vote, in cleartext, in the final tally via a tracker system, while providing good coercion mitigation. This is in contrast to conventional, end-to-end verifiable schemes in which the voter verifies the presence of an encryption of her vote on the bulletin board. The Selene mechanism can be applied to many e-voting schemes, but here we present an application to the polling station context, resulting in a voter-verifiable electronic tally with a paper audit trail. The system uses a smartcard-based public key system to provide the individual verification and universal eligibility verifiability. The paper record contains an encrypted link to the voter's identity, requiring stronger assumptions on ballot privacy than normal paper voting, but with the benefit of providing good auditability and dispute resolution as well as supporting (comparison) risk limiting audits.
Which Is the Tallest Building in Europe? Representing and Reasoning About Knowledge

The need for representing knowledge is ubiquitous in applications; for example, Google needs to represent knowledge about the location and height of building in order to answer questions such as “which is the tallest building in Europe”. Google uses a graph to represent such knowledge, and so-called knowledge graphs are becoming increasingly popular as a knowledge representation formalism. Adding some form of rules greatly increases the power and utility of knowledge graphs, but can also lead to theoretical and/or practical tractability problems. In this papers we will briefly survey the relevant issues and possible solutions, and show that rule enhanced knowledge graphs are extremely powerful, can be given a formal logic-based semantics, and are highly scalable in practice.