Reviewerly/exHarmony huggingface dataset:
Whose tweets are surveilled for the police: an audit of a social-media monitoring tool via log files

Social media monitoring by law enforcement is becoming commonplace, but little is known about what software packages for it do. Through public records requests, we obtained log files from the Corvallis (Oregon) Police Department's use of social media monitoring software called DigitalStakeout. These log files include the results of proprietary searches by DigitalStakeout that were running over a period of 13 months and include 7240 social media posts. In this paper, we focus on the Tweets logged in this data and consider the racial and ethnic identity (through manual coding) of the users that are therein flagged by DigitalStakeout. We observe differences in the demographics of the users whose Tweets are flagged by DigitalStakeout compared to the demographics of the Twitter users in the region, however, our sample size is too small to determine significance. Further, the demographics of the Twitter users in the region do not seem to reflect that of the residents of the region, with an apparent higher representation of Black and Hispanic people. We also reconstruct the keywords related to a Narcotics report set up by DigitalStakeout for the Corvallis Police Department and find that these keywords flag Tweets unrelated to narcotics or flag Tweets related to marijuana, a drug that is legal for recreational use in Oregon. Almost all of the keywords have a common meaning unrelated to narcotics (e.g. broken, snow, hop, high) that call into question the utility that such a keyword based search could have to law enforcement. As social media monitoring is increasingly used for law enforcement purposes, racial biases in surveillance may contribute to existing racial disparities in law enforcement practices. We are hopeful that log files obtainable through public records request will shed light on the operation of these surveillance tools. There are challenges in auditing these tools: public records requests may go unfulfilled even if the data is available, social media platforms may not provide comparable data for comparison with surveillance data, demographics can be difficult to ascertain from social media and Institutional Review Boards may not understand how to weigh the ethical considerations involved in this type of research. We include in this paper a discussion of our experience in navigating these issues.
Learning to Correct Overexposed and Underexposed Photos

Capturing photographs with wrong exposures remains a major source of errorsin camera-based imaging. Exposure problems are categorized as either: (i)overexposed, where the camera exposure was too long, resulting in bright andwashed-out image regions, or (ii) underexposed, where the exposure was tooshort, resulting in dark regions. Both under- and overexposure greatly reducethe contrast and visual appeal of an image. Prior work mainly focuses onunderexposed images or general image enhancement. In contrast, our proposedmethod targets both over- and underexposure errors in photographs. We formulatethe exposure correction problem as two main sub-problems: (i) color enhancementand (ii) detail enhancement. Accordingly, we propose a coarse-to-fine deepneural network (DNN) model, trainable in an end-to-end manner, that addresseseach sub-problem separately. A key aspect of our solution is a new dataset ofover 24,000 images exhibiting a range of exposure values with a correspondingproperly exposed image. Our method achieves results on par with existingstate-of-the-art methods on underexposed images and yields significantimprovements for images suffering from overexposure errors.
Improving Transferability of Deep Neural Networks

Learning from small amounts of labeled data is a challenge in the area of deep learningDeep learning. This is currently addressed by Transfer LearningTransfer learning, where one learns the small dataset as a transfer task from a larger source dataset. Transfer LearningTransfer learning can deliver higher accuracy if the hyperparameters and source dataset are chosen well. One of the important parameters is the learning rate for the layers of the neural network. We show through experiments on the ImageNet22k and Oxford Flowers datasets that improvements in accuracy in range of 127% can be obtained by proper choice of learning rates. We also show that the images/label parameter for a dataset can potentially be used to determine optimal learning rates for the layers to get the best overall accuracy. We additionally validate this method on a sample of real-world imageImage classification classificationClassification tasks from a public visual recognition API.
Distributed Learning of Neural Networks with One Round of Communication

The optimal weighted average (OWA) is an algorithm for distributed learning of linear models. It achieves statistically optimal theoretical guarantees with only a single round of communication [3]. This paper introduces the non-linear OWA (NOWA) algorithm, which extends the linear OWA into the non-linear setting of neural networks. Due to the difficulty of proving theoretical results in this more complex setting, NOWA loses the theoretical guarantees of the OWA algorithm. Nevertheless, we show that NOWA works well empirically. We follow an evaluation procedure introduced by McMahan et. al. [16] for federated learning and show significantly improved results on a simple MNIST baseline task.
Data Center Resource Provisioning Using Particle Swarm Optimization and Cuckoo Search: A Performance Comparison

The major concerns of cloud providers is resource management. A wide range of approaches and tools have been proposed such as meta-heuristics. Among the most recent suggested meta-heuristics are: Cuckoo Search (CS) and Particle Swarm Optimization (PSO). The main contribution of this paper is to compare the performance of CS and PSO on the resource allocation in a data center. Extensive simulation, using a dataset varying from the range of 200 to 1000 demands, demonstrates that PSO converges most rapidly than CS. Moreover, the results show an enhancement of as much as 1% to 10% of energy consumption and 1% to 5% of resource utilization on average.
The existence of traditional Balinese architecture on Puri and Umah

Abstract Balinese architecture, basically, is seeking for the harmony between The Creator, the human and the environment which is stated in the local religious concept Tri Hita Karana . Nowadays Balinese architecture style has grown rapidly with influences of the externals. The research aims to find out the existence of Balinese architecture as cultural tourism potency of Denpasar as the main gate of Bali, especially in the traditional Balinese building namely puri and umah . This research uses a qualitative method that starts with data mining of respondent’s views descriptively, in-depth interviews, and the interpretation of both respondents and researcher as the found concept. This qualitative research proceeds inductively begin with obtaining detailed data, categorizing data, abstracting, and determining the concepts as a finding. The sampling technique is purposive sampling. Data collected by observation, interview, and documentation. From the theory of style and theory of decorative architecture, the existence of Balinese architecture in the traditional puri and umah , seen significantly developed in the zone of pawongan and palemahan without ignoring the ergonomic factor. Similarly, the traditional puri and umah used the ornament that not carved. In the parahyangan area, there are insignificant changes in the materials and colours.
Development of Distance Teaching System for College Professional Courses Based on 5G Network

Abstract 5G (the Fifth Generation Network) refers to the fifth generation mobile communication network. 5G has three important advantages: large bandwidth, low latency, and large connections. At present, 5G is being implemented in 4K / 8K coding and other technologies in remote consultation, remote monitoring, emergency rescue, remote surgery, and remote teaching scenarios, and has achieved good application results. We can foresee that the future of professional course teaching will be closely integrated with 5G technology, and the informationization of medical course teaching will also continue to increase. To further promote the construction of medical education and information technology. This paper mainly studies the development of distance education system for professional courses based on 5G network. In this paper, the system test in the student results query, class results query, time answer query, student information query and teacher information query test, the connection failure times were 22, 11, 9, 6, 9 respectively, and the successful connection success rate was 99.78%, 99.89%, 99.91%, 99.94%, 99.91%, the database connection success rate is high, and the system can be used.
Quantum Science and Quantum Technology: Progress and Challenges

Quantum computing has the potential to offer new capabilities and quantum computers will achieve breakthroughs in finance, cybersecurity, medical science and healthcare, defense and military missions, etc. Progress in quantum science and quantum technology are introduced in this paper that specifically include quantum computers, quantum devices, quantum processing units (QPU), quantum artificial intelligence (QAI), quantum machine learning (QML), quantum cloud computing, quantum network and communication, quantum teleportation, quantum experiment satellite, quantum cryptography, and quantum key distribution (QKD). Some challenges are also presented.
Pattern‐based software process modeling for dependability

Abstract Traditional process modeling focuses on modeling activities for functional requirements. For dependability requirements, a knowledge‐based aspect‐oriented software process modeling approach is proposed. First, we extend the pattern and context to the knowledge graph triplet structure to describe dependability‐oriented knowledge patterns. By applying the patterns, dependability requirements can be organized into dependability‐related activities that are integrated into the software process. Then, aspect‐oriented modeling patterns based on Petri nets are introduced to support the integration of these dependability‐related activities and model dependability‐oriented software processes. Finally, the modeling performance and the subjective usability of the patterns are evaluated by 110 students with different degrees. The results indicate that these two indexes are on the positive track. Hence, the patterns may be the backbone of dependability‐oriented software process modeling.
Research on Cost Control Method of Construction Engineering Based on Dea Model

In order to improve the cost control ability of steel frame structure construction engineering and reduce the operation and maintenance cost of the project, the cost control control model of steel frame structure construction project based on DEA model is put forward, the constraint parameter model of steel frame structure construction project cost control is established, and the full sample panel data analysis method is used to analyze the cost sequence characteristics of steel frame structure construction engineering operation control. The statistical feature sequence distribution set of engineering cost control is extracted, and the operation and maintenance reliability analysis of steel frame structure construction cost control is carried out by using fuzzy information feature mining method. Combined with the method of regression analysis and statistical inspection, the engineering control cost of steel frame structure is optimized and predicted, and the quantitative analysis and adaptive prediction of engineering control cost are carried out by using DEA information analysis technology. The cost optimization control of steel frame structure construction is realized. The simulation results show that the reliability of the method is good, the cost adaptive control ability is strong, and the prediction accuracy is high.
Security Assessment of White-Box Design Submissions of the CHES 2017 CTF Challenge

In 2017, the first CHES Capture the Flag Challenge was organized in an effort to promote good design candidates for white-box cryptography. In particular, the challenge assessed the security of the designs with regard to key extraction attacks. A total of 94 candidate programs were submitted, and all of them were broken eventually. Even though most candidates were broken within a few hours, some candidates remained robust against key extraction attacks for several days, and even weeks. In this paper, we perform a qualitative analysis on all candidates submitted to the CHES 2017 Capture the Flag Challenge. We test the robustness of each challenge against different types of attacks, such as automated attacks, extensions thereof and reverse engineering attacks. We are able to classify each challenge depending on their robustness against these attacks, highlighting how challenges vulnerable to automated attacks can be broken in a very short amount of time, while more robust challenges demand for big reverse engineering efforts and therefore for more time from the adversaries. Besides classifying the robustness of each challenge, we also give data regarding their size and efficiency and explain how some of the more robust challenges could actually provide acceptable levels of security for some real-life applications.
Peer Analysis of “Sanguj” with Other Sanskrit Morphological Analyzers

In linguistics, morphology is a study regarding word, word formation, its analysis, and generation. A morphological analyzer is a tool to understand grammatical characteristics and constituent’s part-of-speech information. A morphological analyzer is a useful tool in many NLP implementations such as syntactic parser, spell checker, information retrieval, and machine translation. Here, 328 Sanskrit words are tested through four morphological analyzers namely—Samsaadhanii, morphological analyzers by JNU and TDIL, both of which are available online and locally developed and installed Sanguj morphological analyzer. There is a negligible divergence in the reflected results.
A RSA-Biometric Based User Authentication Scheme for Smart Homes Using Smartphones

In the recent years, the Internet of Things (IoT) has become one of the leading technologies that allow the mainstreaming of smart homes and smart cities by creating a communication system for physical objects over the Internet. In a smart home, devices are not necessarily homogeneous in terms of topology, security protocols, computational power and communication. This nature of the devices causes some incompatibilities with conventional authentication methods and the security requirements of IoT standards. This paper proposes a RSA-Biometric based user Authentication Scheme for Smart Homes using Smartphones (denoted RSA-B-ASH-S). A formal security analysis of the proposed scheme using the Burrows-Abadi-Needham (BAN) logic is provided, showing that it achieves perfect forward secrecy by utilizing a fresh encryption key for each session. The performance evaluation of the proposed scheme in terms of computational time, storage requirements, and communication overhead, is also presented.
A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers

Fine-grained classifiers collect information about inter-class variations to best use the underlying minute and subtle differences. The task is challenging due to the minor differences between the colors, viewpoints, and structure in the same class entities. The classification becomes difficult and challenging due to the similarities between the differences in viewpoint with other classes and its own. This work investigates the performance of landmark traditional CNN classifiers, presenting top-notch results on large-scale classification datasets and comparing them against state-of-the-art fine-grained classifiers. This paper poses three specific questions. (i) Do the traditional CNN classifiers achieve comparable results to fine-grained classifiers? (ii) Do traditional CNN classifiers require any specific information to improve fine-grained ones? (iii) Do current traditional state-of-the-art CNN classifiers improve the fine-grained classification while utilized as a backbone? Therefore, we train the general CNN classifiers throughout this work without introducing any aspect specific to fine-grained datasets. We show an extensive evaluation on six datasets to determine whether the fine-grained classifier can elevate the baseline in their experiments. We provide ablation studies regarding efficiency, number of parameters, flops, and performance.
Deep Neural Network Pretrained by a Support Vector Machine

Recently, deep neural networks (DNNs) have shown strong performance in many applications. Some models achieve state of the art in a wide range of fields, such as natural language processing and image processing. Support vector machines (SVMs) have also been a popular approach thanks to their performance. Their criterion selects effective variables in a dataset, and kernel methods help the models extract useful features for prediction. In this paper, we propose a unique method to apply parameters of a pretrained SVM to a DNN. Our proposed method initializes the first layer of a DNN to behave as the pretrained SVM or to extract powerful features from input variables. As a result, the DNN is successfully tuned during the training, because it inherits the advantages of SVMs. To show performance of our proposed method in the experiments, we apply it to classification of a toy dataset and to a sentiment analysis of movie reviews. The results show that the pretrained parameters have a significant effect on the optimization of DNNs.
The Tandem Duplication Distance Is NP-Hard

In computational biology, tandem duplication is an important biological phenomenon which can occur either at the genome or at the DNA level. A tandem duplication takes a copy of a genome segment and inserts it right after the segment - this can be represented as the string operation AXB ⇒ AXXB. Tandem exon duplications have been found in many species such as human, fly or worm, and have been largely studied in computational biology. The Tandem Duplication (TD) distance problem we investigate in this paper is defined as follows: given two strings S and T over the same alphabet, compute the smallest sequence of tandem duplications required to convert S to T. The natural question of whether the TD distance can be computed in polynomial time was posed in 2004 by Leupold et al. and had remained open, despite the fact that tandem duplications have received much attention ever since. In this paper, we prove that this problem is NP-hard, settling the 16-year old open problem. We further show that this hardness holds even if all characters of S are distinct. This is known as the exemplar TD distance, which is of special relevance in bioinformatics. One of the tools we develop for the reduction is a new problem called the Cost-Effective Subgraph, for which we obtain W[1]-hardness results that might be of independent interest. We finally show that computing the exemplar TD distance between S and T is fixed-parameter tractable. Our results open the door to many other questions, and we conclude with several open problems.
Making a storyboard with Unreal® Engine

In this section we explore how to purchase an Unreal® Game Engine set from the Unreal® Marketplace and what to look for in the specifications. A set is selected, and a very elementary tutorial is provided about how to bring a camera into the Unreal® set to pre-plan camera locations, and also how to bring lights into the set and how to aesthetically light the environment. A method is described that shows how to spot and screen grab camera shots and shoot stills of performers posing in front of green screen to create a rough digital storyboard using Photoshop® to previz a Reality™ shoot.
Handheld pocket-size Fourier transform profilometry using projector-enabled smartphone

Surface Fourier transform profilometry (FTP) is a technique used for reconstructing three-dimensional (3-D) surface topography using light. It has been widely used in machine vision for biomedical and biometric automations, providing solutions beyond conventional 2-D imaging. This paper proposes an implementation of the handheld pocket-size FTP for 3-D surface profile imaging using a projector-enabled Samsung Galaxy Beam smartphone. In the implementation, a crossed-optical-axes geometry of the FTP is formed by using a mirror positioned over the phone’s projector via an adjustable tilt mounting bracket. Experimental proof-of-concept of the proposed profilometry is done by implementing conventional and non-phase shifting FTPs with different diffuse test objects. The experimental results obtained by using the non-phase shifting technique are in good agreement with those of the direct contact measurement. Besides having superiority of compactness, the proposed profilometry paves the way for the development of real-time 3-D profiling and printing through internet or Bluetooth interconnection.
OWN EXPECTATIONS OF FUTURE TEACHERS ABOUT TEACHING STYLE AND BEHAVIOR AND INSTRUCTIONAL MANAGEMENT IN PRIMARY SCHOOL

The aims of the study is to highlight the espectations of students (N = 75) from Pedagogy of Primary and Preschool Education Program, from first and third years of study, related to how they want, as future teachers, to develop pupils’ learning skills in classroom by adopting a certainbehavioral and instructional management in primary school and also a certain teaching style.The working tools used are the Behavior and Instructional Management Scale, developed by Martin & Sass (2010) and The Teaching for a Successful Intelligence Questionnaire (TSI-Q) developed by Paloș&Maricuțoiu (2013). The comparative analysis of the results obtained according to the year of study, captures the existence of statistically significant differences in the aspect of the management of the behavior in the classroom, during the lessons, with higher averages for the students of 3rd year. Students from both groups would prefer instructional management based on the use of student-centered teaching strategies. At the same time, the preferred teaching style is creative, practical and less preferred is the reproductive one. The results show an association relation between the preferred style of instructional management and the creative, analytical and practical teaching styles, but the effect size is small. Considering the results obtained, it was concluded that students from both groups are considering the student-centered teaching strategies but also that the students in the final year have a higher level of expectations for implementing the rules in the class, compared to the students of the 1styear, which draws attention to the knowledge of the factors involved in changing the expectations of the studentsduring their formation to have a greater control over the conduct of the pupils in classroom.