Reviewerly/exHarmony huggingface dataset:
ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks

With the increased usage of AI accelerators on mobile and edge devices, on-device machine learning (ML) is gaining popularity. Thousands of proprietary ML models are being deployed today on billions of untrusted devices. This raises serious security concerns about model privacy. However, protecting model privacy without losing access to the untrusted AI accelerators is a challenging problem. In this paper, we present a novel on-device model inference system, ShadowNet. ShadowNet protects the model privacy with Trusted Execution Environment (TEE) while securely outsourcing the heavy linear layers of the model to the untrusted hardware accelerators. ShadowNet achieves this by transforming the weights of the linear layers before outsourcing them and restoring the results inside the TEE. The non-linear layers are also kept secure inside the TEE. ShadowNet's design ensures efficient transformation of the weights and the subsequent restoration of the results. We build a ShadowNet prototype based on TensorFlow Lite and evaluate it on five popular CNNs, namely, MobileNet, ResNet-44, MiniVGG, ResNet-404, and YOLOv4-tiny. Our evaluation shows that ShadowNet achieves strong security guarantees with reasonable performance, offering a practical solution for secure on-device model inference.
„Free reading”: the content of the concept and its interpretation in the scientific literature

The article considers the interpretation of the definition of „free reading” in humanitarian research (sociology of reading, psychology of reading, pedagogy, methodology of teaching literature, library science, cultural studies). The aim of the article is to differentiate the concept of „free reading” from other concepts. In order to achieve the result different interpretations of its content are analyzed and the type variety of reading is considered. The interrelation of the concepts of „free reading”, „leisure reading”, „independent reading”, „extra-curricular reading”, „reading for oneself” is traced, and the features (characteristics) inherent to free reading are revealed.
Efficient Sensor Management for Multitarget Tracking in Passive Sensor Networks via Cauchy-Schwarz Divergence

This paper presents an efficient sensor management approach for multi-target tracking in passive sensor networks. Compared with active sensor networks, passive sensor networks have larger uncertainty due to the nature of passive sensing. Multi-target tracking in passive sensor networks is challenging because the multi-sensor multi-target fusion problem is difficult and sensor management is necessary to achieve good trade-offs between tracking accuracy and energy consumption or other costs. To address this problem, we present an efficient information-theoretic approach to manage the sensors for better tracking of the unknown and time-varying number of targets. This is accomplished with two main technical innovations. The first is a tractable information-based multi-sensor selection solution via a partially observed Markov decision process framework. The Cauchy-Schwarz divergence is used as the criterion to select informative sensors sequentially from the candidates. The second is a novel dual-stage fusion strategy based on the iterated-corrector multi-sensor generalized labeled multi-Bernoulli filter. Since the performance of the iterated-corrector scheme is greatly influenced by the order of sensor updates, the selected sensors are first ranked in order of their abilities to detect targets according to the Cauchy-Schwarz divergence, followed the iterated-corrector update. The computation costs of ranking the sensors are negligible, since the Cauchy-Schwarz divergence has been computed in the multi-sensor selection procedure. Simulation results validate the effectiveness and efficiency of the proposed approach.
Identifying interception possibilities for WhatsApp communication

On a daily basis, law enforcement officers struggle with suspects using mobile communication applications for criminal activities. These mobile applications replaced SMS-messaging and evolved the last few years from plain-text data transmission and storage to an encrypted version. Regardless of the benefits for all law abiding citizens, this is considered to be the downside for criminal investigations. Normal smartphone, computer or network investigations do no longer provide the contents of the communication in real-time when suspects are using apps like WhatsApp, Signal or Telegram. Among them, WhatsApp is one of the most common smartphone applications for communication, both criminal as well as legal activities. Early 2016 WhatsApp introduced end-to-end encryption for all users, immediately keeping law enforcement officers around the world in the dark. Existing research to recuperate the position of law enforcement is limited to a single field of investigation and often limited to post mortem research on smartphone or computer while wiretapping is limited to metadata information. Therefore, it provides only historical data or metadata while law enforcement officers want a continuous stream of live and substantive information. This paper identified that gap in available scenarios for law enforcement investigations and identified a gap in methods available for forensic acquiring and processing these scenarios. In this paper, we propose a forensic approach to create real-time insight in the WhatsApp communication. Our approach is based on the wiretapping, decrypting WhatsApp databases, open source intelligence and WhatsApp Web communication analysis. We also evaluate our method with different scenarios in WhatsApp forensics to prove its feasibility and efficiency.
Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates

This work introduces the StoMADS-PB algorithm for constrained stochastic blackbox optimization, which is an extension of the mesh adaptive direct-search (MADS) method originally developed for deterministic blackbox optimization under general constraints. The values of the objective and constraint functions are provided by a noisy blackbox, i.e., they can only be computed with random noise whose distribution is unknown. As in MADS, constraint violations are aggregated into a single constraint violation function. Since all function values are numerically unavailable, StoMADS-PB uses estimates and introduces probabilistic bounds for the violation. Such estimates and bounds obtained from stochastic observations are required to be accurate and reliable with high, but fixed, probabilities. The proposed method, which allows intermediate infeasible solutions, accepts new points using sufficient decrease conditions and imposing a threshold on the probabilistic bounds. Using Clarke nonsmooth calculus and martingale theory, Clarke stationarity convergence results for the objective and the violation function are derived with probability one.
Learning to Predict the 3D Layout of a Scene

While 2D object detection has improved significantly over the past, real world applications of computer vision often require an understanding of the 3D layout of a scene. Many recent approaches to 3D detection use LiDAR point clouds for prediction. We propose a method that only uses a single RGB image, thus enabling applications in devices or vehicles that do not have LiDAR sensors. By using an RGB image, we can leverage the maturity and success of recent 2D object detectors, by extending a 2D detector with a 3D detection head. In this paper we discuss different approaches and experiments, including both regression and classification methods, for designing this 3D detection head. Furthermore, we evaluate how subproblems and implementation details impact the overall prediction result. We use the KITTI dataset for training, which consists of street traffic scenes with class labels, 2D bounding boxes and 3D annotations with seven degrees of freedom. Our final architecture is based on Faster R-CNN. The outputs of the convolutional backbone are fixed sized feature maps for every region of interest. Fully connected layers within the network head then propose an object class and perform 2D bounding box regression. We extend the network head by a 3D detection head, which predicts every degree of freedom of a 3D bounding box via classification. We achieve a mean average precision of 47.3% for moderately difficult data, measured at a 3D intersection over union threshold of 70%, as required by the official KITTI benchmark; outperforming previous state-of-the-art single RGB only methods by a large margin.
KMI-Panlingua-IITKGP @SIGTYP2020: Exploring rules and hybrid systems for automatic prediction of typological features

This paper enumerates SigTyP 2020 Shared Task on the prediction of typological features as performed by the KMI-Panlingua-IITKGP team.The task entailed the prediction of missing values in a particular language, provided, the name of the language family, its genus, location (in terms of latitude and longitude coordinates and name of the country where it is spoken) and a set of feature-value pair are available.As part of fulfillment of the aforementioned task, the team submitted 3 kinds of system -2 rule-based and one hybrid system.Of these 3, one rule-based system generated the best performance on the test set.All the systems were 'constrained' in the sense that no additional dataset or information, other than those provided by the organisers, was used for developing the systems.
Quasi-Linear SVM with Local Offsets for High-dimensional Imbalanced Data Classification

Imbalanced problems often occur in the classification problem. A special case is within-class imbalance, which worsen the imbalance distribution problem and increase the learning concept complexity. Most methods for solving imbalanced data classification focus on finding a globe boundary to solve between-class imbalance problem. My thesis proposes a effective quasi-linear network with local offsets adjustment for imbalanced classification problems. First, we proposed a gated piecewise linear network, an autoencoder-based partitioning method is modified for imbalanced datasets to divide input space into multiple linearly separable partitions along the potential separation boundary. Construct a quasi-linear SVM based on the gated signal that obtained by autoencoder partitioning information. Then training a neural network that let F-score as loss function to generate the local offsets on each local cluster. Finally a quasi-linear SVM classifier with local offsets is constructed for the imbalanced datasets. Our proposed method avoids calculating Euclidean distance, so it can be applied to high dimensional datasets. Simulation results on different real world datasets that our method is effective for imbalanced data classification especially in high-dimensional data.
Rechazo y discurso de odio en Twitter: análisis de contenido de los tuits sobre migrantes y refugiados en español

espanolSe analiza el rechazo verbal al extranjero como potencial detector de discurso de odio a traves de dos analisis de contenido de tuits en espanol recogidos con la API de Twitter: el primero, manual, a 1.469 tuits; el segundo, automatico, analiza otros 337.116 tuits utilizando tecnicas de big data. El rechazo fue predominante en el primer analisis y minoritario en el segundo, mostrando la fluctuacion que experimentan estas expresiones en funcion del contexto mediatico. En ambos casos el rechazo hacia los migrantes fue significativamente mayor que hacia los refugiados, como se habia observado ya en contextos internacionales. El trabajo tambien genero un corpus de entrenamiento sobre rechazo al extranjero y observo los aspectos negativos asociados mas frecuentemente al rechazo, asi como la relacion entre este y la condicion informativa u opinativa del tuit. EnglishWe use Twitter to study the verbal rejection towards migrants and refugees as a potential hate speech predictor with two content analysis of tweets in Spanish collected with Twitter’s API: the first analysis, manual, with 1,469 tweets; the second, automatic, uses big data techniques to study 337,116 new tweets. In the first one rejection was predominant over acceptance and neutrality. Rejection was smaller in the second one, showing how fluctuant these expressions are depending the media context. In both cases rejection toward migrants was significantly bigger than over refugees, as it had already been observed in international contexts. This work also created a training corpus about immigrant rejection, valid for future studies, andobserved the negative aspects most frequently associated to rejection of immigrants, as well as the relationship existing between this and the fact of tweets being information or opinion.
Pengaruh Kompleksitas Sistem, Kompleksitas Tugas dan Komunikasi Pemakai dan Pengembang Terhadap Kepuasan Pemakai Sistem Informasi Akuntansi pada Bank Syariah di Bandar Lampung

As advances in accounting information systems continue to be applied, proper application of the various information systems must be able to provide satisfaction with the service provided to customers. The purpose of this study was to examine the influence of the complexity of the system, the complexity of the task and communication to users and developers of user satisfaction in Accounting Information Systems Islamic Bank in Bandar Lampung. Partial results of analysis there is a significant effect between system complexity (X1), the complexity of the task (X2), and communication users and developers (X3) on user satisfaction SIA (Y) at the Islamic Bank in Bandar Lampung. Simultaneously there is influence between system complexity, task complexity, and communication to users and developers of user satisfaction SIA (Y) at the Islamic Bank in Bandar Lampung (F value 16.886> Ftabel2, 84 or sig. 0.000 <0.05). Retrieved Rsquare 0.535 means 53.5% user satisfaction accounting information system is described by the variable X and the remaining 46.5% is explained by other causes. Keywords: System Complexity, Task Complexity, User Communication and Development, and User Satisfaction System
Pelatihan E-Learning Google Classroom di SMAIT Asy-Syukriyyah Cipondoh

SMAIT Asy-Syukriyyah is a high school located in Cipondoh, about 12 km from STTPLN. This high school has started to implement the distance learning method via the Internet, or known as E-Learning, as a complement to the existing teaching and learning process. One of the obstacles faced by the school in organizing E-Learning is the lack of human resources. The ability of teachers and the number of teachers who use E-Learning are still limited, as well as the students abilities. Therefore, the PKM Team from STT PLN intends to hold training for new students so that these students know the benefits of E-Learning and get to know the basics of using Google Classroom, as an application for E-Learning chosen by the school. The training is divided into several classes, each of which contains approximately 20 students. Each student is given the opportunity to practice directly through a computer or smartphone. The total number of participants who took part in the training was 82 students. And from the questionnaire given, all stated that the training provided was useful ..
Quantum XYZ Product Codes

We study a three-fold variant of the hypergraph product code construction, differing from the standard homological product of three classical codes. When instantiated with 3 classical LDPC codes, this "XYZ product" yields a non CSS quantum LDPC code which might display a large minimum distance. The simplest instance of this construction, corresponding to the product of 3 repetition codes, is a non CSS variant of the 3-dimensional toric code known as the Chamon code. The general construction was introduced in Denise Maurice's PhD thesis, but has remained poorly understood so far. The reason is that while hypergraph product codes can be analyzed with combinatorial tools, the XYZ product codes also depend crucially on the algebraic properties of the parity-check matrices of the three classical codes, making their analysis much more involved. Our main motivation for studying XYZ product codes is that the natural representatives of logical operators are two-dimensional objects. This contrasts with standard hypergraph product codes in 3 dimensions which always admit one-dimensional logical operators. In particular, specific instances of XYZ product codes with constant rate might display a minimum distance as large as $\Theta(N^{2/3})$. While we do not prove this result here, we obtain the dimension of a large class of XYZ product codes, and when restricting to codes with dimension 1, we reduce the problem of computing the minimum distance to a more elementary combinatorial problem involving binary 3-tensors. We also discuss in detail some families of XYZ product codes that can be embedded in three dimensions with local interaction. Some of these codes seem to share properties with Haah's cubic codes and might be interesting candidates for self-correcting quantum memories with a logarithmic energy barrier.
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter

End-to-end models are favored in automatic speech recognition (ASR) because of its simplified system structure and superior performance. Among these models, recurrent neural network transducer (RNN-T) has achieved significant progress in streaming on-device speech recognition because of its high-accuracy and low-latency. RNN-T adopts a prediction network to enhance language information, but its language modeling ability is limited because it still needs paired speech-text data to train. Further strengthening the language modeling ability through extra text data, such as shallow fusion with an external language model, only brings a small performance gain. In view of the fact that Mandarin Chinese is a character-based language and each character is pronounced as a tonal syllable, this paper proposes a novel cascade RNN-T approach to improve the language modeling ability of RNN-T. Our approach firstly uses an RNN-T to transform acoustic feature into syllable sequence, and then converts the syllable sequence into character sequence through an RNN-T-based syllable-to-character converter. Thus a rich text repository can be easily used to strengthen the language model ability. By introducing several important tricks, the cascade RNN-T approach surpasses the character-based RNN-T by a large margin on several Mandarin test sets, with much higher recognition quality and similar latency.
Example Applications of CogTool+

In this chapter, we demonstrate and evaluate the effectiveness and usefulness of the developed software prototype CogTool+. In particular, we present our work of using CogTool+ to model three existing systems. The first system is an observer-resistant password system called Undercover; the second one is a 6-digit PIN entry system, and the third one is data entry interfaces (part of this chapter will appear in a full paper which is currently under review. (Yuan, H., Li, S., Rusconi, P., “CogTool+: Modeling human performance at large scale”.))
Autonomy, Efficiency and Effectiveness—Opportunities for Higher Education: A Pilot Study

Abstract Autonomy and efficiency are among the key topics that dominate the current higher education agenda and will shape the future of the European higher education landscape in the next decade. The capacity of higher education institutions and systems at large to respond to the rapidly changing needs of the society and economy will largely depend on what they can deliver and how autonomous, effective and efficient they are. The paper presents an analytical framework that connects the concepts of institutional autonomy, efficiency and effectiveness and explores the links between efficiency in university management, autonomy and accountability. It builds on (i) EUA’s work on institutional autonomy and the University Autonomy Scorecard, assessing the main components of institutional autonomy, and (ii) the higher education efficiency framework developed by EUA in the framework of the USTREAM project. This paper explores the following questions: (i) What mechanisms connect regulatory frameworks to efficiency in university management? (ii) How can autonomy be converted into efficiency and effectiveness at universities? (iii) How can efficiency support accountability? Methodologically, this paper will follow the four-pillar structure of the Autonomy Scorecard (organisational, financial, staffing and academic autonomy) and support its argumentation with several case studies.
Students’ Strategies in Writing Narrative Text at Grade XI IPS 3 at SMAN 9 Padang

Writing task is a difficult task for students.In order to face this problem, thestudens must have strategies.There are some strategies in writing narrative text.They are: pre-writing strategies, whilstwriting strategies and post-writing strategies.These strategies can help students in making a good essay.This study aims to observing the students' strategies in writing narrative text at the second year of SMAN 9 Padang.Of the three classes, XI IPS 3 was chosen as the sasmple.The method used in this study was desriptive research.The data were collected through observation, questionaire, and interview.It found that, most of the students didn't use the straegies in writing narrative text.Based on he research, only 26,49% the students always used strategies in pre-writing strategies, 26,4% in whilst-wiritng straegises, and 25, 48% in post writing strategies.It can be concluded that, most of them did't always use the strategies in pre-writing, whilst-writing or postwriting strategies in writing narrative text.So that, most of them got a bad mark in writing narrative text.
Deep multi-modal networks for book genre classification based on its cover

Book covers are usually the very first impression to its readers and they often convey important information about the content of the book. Book genre classification based on its cover would be utterly beneficial to many modern retrieval systems, considering that the complete digitization of books is an extremely expensive task. At the same time, it is also an extremely challenging task due to the following reasons: First, there exists a wide variety of book genres, many of which are not concretely defined. Second, book covers, as graphic designs, vary in many different ways such as colors, styles, textual information, etc, even for books of the same genre. Third, book cover designs may vary due to many external factors such as country, culture, target reader populations, etc. With the growing competitiveness in the book industry, the book cover designers and typographers push the cover designs to its limit in the hope of attracting sales. The cover-based book classification systems become a particularly exciting research topic in recent years. In this paper, we propose a multi-modal deep learning framework to solve this problem. The contribution of this paper is four-fold. First, our method adds an extra modality by extracting texts automatically from the book covers. Second, image-based and text-based, state-of-the-art models are evaluated thoroughly for the task of book cover classification. Third, we develop an efficient and salable multi-modal framework based on the images and texts shown on the covers only. Fourth, a thorough analysis of the experimental results is given and future works to improve the performance is suggested. The results show that the multi-modal framework significantly outperforms the current state-of-the-art image-based models. However, more efforts and resources are needed for this classification task in order to reach a satisfactory level.