Reviewerly/exHarmony huggingface dataset:
EVOLVABLE ADAS: H-GQM S.M.A.R.T.E.S.T. APPROACH

The introduction to the mass market of vehicles with an ADAS 3+ level of automation is expectedin the early 2020s. Currently, the vast majority of automakers conduct research in thisfield, a fairly large number of prototypes, pre-production and production systems have alreadybeen demonstrated. ADAS (advanced driver assistance systems) are complex hardware &amp; softwaresystems, the feature of which is that the core hardware platform remains unchanged for one oreven several generations of vehicles (5–7 years). At the same time, the system should be able totransform and evolve to correct errors and expand functionality, especially due to active developmentof sensory peripheral systems and software algorithms. The GQM methodology and its modificationsare used to support the development process of complex systems and evaluate them.However, these methodologies are limited exclusively to software products. Also, authors of thesemethodologies are not addressing explicitly the issues of applying the GQM methodology for analyzingand tracking the process of evolution of complex technical systems. This paper presents HGQM(Hardware GQM) methodology for controllable evolution of complex automotive hardware&amp; software systems. The H-GQM methodology is based on GQM and is aimed at hardwaresoftwaresystems with a monolithic hardware core, a modifiable software core and atomic peripherals.Entity harmonization process is described to prove the applicability of the GQM for software-and-hardware systems analysis. S.M.A.R.T.E.S.T goal-setting concept is proposed for choiceof evolutionary goals. This concept is based on S.M.A.R.T. criteria for the setting objectives ofbusiness processes and extended with harmonization and evolvability restrictions. The formulationof the H-GQM plan framework is provided using ADAS as an example. Within the framework ofthe proposed methodology, an ADAS-specific scalable target template has been formed.
Private Set Intersection in the Internet Setting From Lightweight Oblivious PRF.

We present a new protocol for two-party private set intersection (PSI) with semi-honest security in the plain model and one-sided malicious security in the random oracle model. Our protocol achieves a better balance between computation and communication than existing PSI protocols. Specifically, our protocol is the fastest in networks with moderate bandwidth (e.g., 30–100 Mbps). Considering the monetary cost (proposed by Pinkas et al. in CRYPTO 2019) to run the protocol on a cloud computing service, our protocol also compares favorably.
Mabee Learning Center: Current Library Services: Home

This guide provides information regarding the library's remote services such as links to online resources and research assistance tools.
GALILEO@UGA Subject Guides: Using Electronic Library Resources Remotely: Electronic Library Resources

Tips for using electronic library resources from remote locations and common solutions for access errors.
A handheld, EOIR, synthetic aperture, beyond-diffraction-limited, 3D imaging prototype

A handheld, eye safe, EOIR, synthetic aperture, beyond-diffraction-limited, 3D imaging, prototype system has been demonstrated. The prototype uses a series of snapshot, simultaneous, multichannel, holographic images to derive all the necessary information to form synthetic aperture imagery. Each multi-channel, holographic image contains all the information necessary to form a snapshot, high-dynamic-range-depth 3-D image. The snapshot, 3-D imaging capability enables the prototype to be used as a handheld nondestructive evaluation tool for the inspection of visual surface defects on aircraft and other vehicles. The prototype’s snapshot, high-dynamic-range-depth specifications are approximately: 100mm<sup>3</sup> 3-D field of view, 1mm3 voxel, with 0.1mm depth accuracy. These specifications are for given range to target of approximately 500mm and scale linearly with distance. The prototype operates at a continuum of ranges from about 100 to 1,000mm. The prototype’s synthetic aperture ability enables an increase in resolution and has been demonstrated to deliver an approximate 6x resolution increase as a result of random handheld motion and a 10x increase due to prescribed motion. All elements of the prototype are scalable, e.g., resolution, range to target, and dynamic range. The wavelength band is selectable to accommodate requirements such as eye safety or target material properties. The prototype represents a baseline capability for either handheld, snapshot, 3-D imaging, or under certain conditions, handheld, synthetic aperture, EOIR, beyond diffraction limited, 3-D imaging.
Analyzing Social Behaviour Using NLP

One of the major reasons behind mental ill health in recent times has been depression. Depression also contributes towards the rising number of suicide rates all over the world and also leads to the impairment of daily life. Researchers and doctors have been trying to find out ways to detect depression in people in its early stages itself so that the person can be prevented from the more dangerous outcomes of depression in the later stages such as the danger of committing suicide and harming himself. In recent times social media has come to the rescue by emerging as a platform to annunciate information online. These online networks are used by legions of social media influencers to convey their personal experiences, thoughts and social ideals. Therefore, through this research paper we take a look at the future of social media to forecast, even prior to onset, depression in online personas. People suffering from depression have shown a tendency to communicate via online platforms, where they are not accountable to anyone for what they tweet or post, rather than discussing their problems with their families and friends. Also, previous studies have shown that depression also affects the language usage. In this paper, Naive-Bayes classifier has been used on twitter feeds for conducting emotion analysis focussing on depression. The results have been presented using the primary classification metrics including F1-score, accuracy and confusion matrix.
Organization of media training intended for teachers in the United States and Canada

In the time of digital technologies and transformation of teachers’ role, the problem of the lack of teachers’ media education support and their insufficient level of readiness to implement media technologies in educational process has become of vital importance. The analysis of American scientists’ ideas has resulted in the conclusion about irreversible consequences of an insufficient level of teachers’ media education that may lead to the loss of democracy in society. In the article, the author stresses the idea about the interconnection between the teachers’ level of training to incorporate media education in the process of learning and its efficiency. It is underlined that this understanding shown by state educational establishments, administrative staff, scientists and teachers is not widely accepted, which is proved by the quantity of media courses, character of training in universities and the time of such training appearance in the curriculum of pedagogical educational establishments. Until recently, in spite of inclusion of media education knowledge in curricular, teachers have been left without proper training in such an activity in the system of formal education. On the example of a number of American and Canadian universities (Indiana University Bloomington, University of Massachusetts, Manitoba University, Vancouver University), the author has distinguished the ways of future teacher media training in formal education (as special training in the area of communication, as a separate course, as components of every course, taught at university). The main peculiarity of the future teacher media training is the encouraging Centres for Online and Digital Learning and libraries which offer necessary support in conducting lessons with digital media tools, media services, consultations on doing media tasks and incorporating media in educational process in schools.
Computing disparity map using minimum sum belief propagation for stereo pair images

Stereo matching between two images is done by computing disparity of all points on the object. The process involves identifying corresponding points in stereo image and finding the horizontal shift. Presently, there is no method that finds the shift in the corresponding points between left and right images; this is due to non-availability of procedure to identify the group of pixel in the right and left image of the same object. The available local methods either use window or feature to find shift in a stereo image. In these methods, 'finalising size of the window' or 'deciding the correct feature' remains an unresolved issue. On the other hand, global methods use graph theory and probability theory to find the shift efficiently. The belief propagation algorithm is one of the global method devised to offer computationally efficient approach with good results. This paper has applied 'minimum sum belief propagation' method for message updates with linear 'quadratic function' for computation of horizontal shift in stereo image. The results with the computational estimations are presented hereby and based on these results, suggestive comments on effectiveness of update which indicate strategy versus type of the image are also mentioned.
Library Research Guides: GIMMS Guide to the United States Congress: Home

What kinds of documents does the U.S. Congress produce and how can I access them at Indiana University? Find out here!
Knowledge management and innovation performance in knowledge intensive organisations - the role of HR practices

Knowledge intensive organisations survive and sustain through effective knowledge management practices. The present study attempts to understand the interplay of knowledge management, human resource practices and innovation performance in knowledge-intensive organisations. In-depth interviews were carried out with five subject matter experts using semi-structured interview schedule for finalising the conceptual model. The model was empirically tested using a questionnaire on a sample of 152 respondents and the results are discussed. Organisation memory was found to be the most significant predictor of innovation performance followed by knowledge acquisition and knowledge integration. Separate parallel mediation analysis was carried out to see the influence of each KM practice on innovation performance via the four HR practices. Human resource acquisition was found to be the only HR practice that mediates the relationship between each of the KM practices and innovation performance.
English Language Learning in Kazakhstan: the Formation of Intercultural Communicative Competence based on the Digital Educational Resources

This paper is devoted to form of Intercultural Communicative Competence (ICC) of future teachers of English as a foreign language (EFL) based on the Digital Educational Resources (DER) in Kazakhstan’s Higher Education System. The data collection was obtained from a total of 250 higher education students and teachers. Research methods are determined by its purpose, objectives &hypothesis. To solve the listed problems and prove the correctness of the hypothesis put forward, in the course of the study, such methods as: theoretical, empirical, diagnostic, statistical, descriptive were used. The pedagogical experiment was organized and students also completed a questionnaire. The results prove the reliability and accuracy of the scale. The implications of these findings are discussed.
Robust End to End Acoustic Model Based on Deep Similarity Network

Automatic speech recognition has shown tremendous progress over the pass decade and many methods have been proposed for speech recognition under noise-free environment. However, these algorithms suffer from performance degradation in the presence of acoustic interference. In this paper, a new robust speech recognition model is proposed to tackle this problem. To improve the generalization capability of the model, the idea of parameter sharing between clean speeches and noisy ones is suggested. Furthermore, the similarity loss term is also proposed to enclose the feature results generated from clean and noisy samples for further recognition. Meanwhile, the Connectionist Temporal Classification approach is employed to label unsegmented sequences, making the model feasible for robust end-to-end recognition. Several types of noises from different speech databases are employed to evaluate the recognition performance, and the experimental results show that the proposed model is less sensitivity to acoustic inference and more robust to noises.
Macdonald-Kelce Library: Cybersecurity: Websites

Resources to assist with the successful completion of a Cybersecurity degree at UT Find organizations and community pages that deal with cybersecurity issues
A benchmark of data stream classification for human activity recognition on connected objects

This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of HAR. We measure both classification performance and resource consumption (runtime, memory, and power) of five usual stream classification algorithms, implemented in a consistent library, and applied to two real human activity datasets and to three synthetic datasets. Regarding classification performance, results show an overall superiority of the HT, the MF, and the NB classifiers over the FNN and the Micro Cluster Nearest Neighbor (MCNN) classifiers on 4 datasets out of 6, including the real ones. In addition, the HT, and to some extent MCNN, are the only classifiers that can recover from a concept drift. Overall, the three leading classifiers still perform substantially lower than an offline classifier on the real datasets. Regarding resource consumption, the HT and the MF are the most memory intensive and have the longest runtime, however, no difference in power consumption is found between classifiers. We conclude that stream learning for HAR on connected objects is challenged by two factors which could lead to interesting future work: a high memory consumption and low F1 scores overall.
Literature Learning in the Students of Class-X at SMA Muhammadiyah Pangkep

This research aims at decribing the process of literature learning at a senior high school in the academic year of 2019/2020. This study is a descriptive qualitative research with the subjects are a teacher of literature and 15 students of the tenth grade at SMA Muhammadiyah Pangkep. The instruments for collecting data are observation result, interview result, and documentation. The research results showed that the goals of literature learning were well-achieved, indicated by the students' capability in appreciating literary works and expressing the appreciation in the form of poems and short stories. The success in learning literature was marked by the increase of the students' reading interest, their frequently browsing of articles on both literary and non-literary materials from various websites, and their work products in the form of poetry. The achievement of those students was caused by the effort applied by the teacher, namely supporting students in developing their creativity on literary activities, either inside or outside school hours, such as short story writing contest, poetry writing contest, poetry reading contest, and drama performance.
New Constructions of Hinting PRGs, OWFs with Encryption, and more.

Over the last few years, there has been a surge of new cryptographic results, including laconic oblivious transfer [13, 16], (anonymous/ hierarchical) identity-based encryption [9], trapdoor functions [19, 20], chosen-ciphertext security transformations [32, 33], designated-verifier zero-knowledge proofs [30, 34, 37], due to a beautiful framework recently introduced in the works of Cho et al. [13], and Dottling and Garg [14]. The primitive of one-way function with encryption (OWFE) [19, 20] and its relatives (chameleon encryption, one-time signatures with encryption, hinting PRGs, trapdoor hash encryption, batch encryption) [9, 14, 16, 17, 33] have been a centerpiece in all these results.
Library: ANTH Remote Instruction Resources: MLA Format

This guide contains various instruction modules that teachers can use for remote library instruction
Black-box use of One-way Functions is Useless for Optimal Fair Coin-Tossing.

A two-party fair coin-tossing protocol guarantees output delivery to the honest party even when the other party aborts during the protocol execution. Cleve (STOC–1986) demonstrated that a computationally bounded fail-stop adversary could alter the output distribution of the honest party by (roughly) 1/r (in the statistical distance) in an r-message coin-tossing protocol. An optimal fair coin-tossing protocol ensures that no adversary can alter the output distribution beyond 1/r.
Coherence non-activating measurement

We define the coherence non-activating measurement as the positive operator-valued measurement which gives the same result whether or not the coherence in a quantum state is destroyed. A connection is built between the coherence activating ability of a measurement and its ability to steer quantum states when coherence non-activating measurements are allowed. Then, we study the quantum discord based on coherence non-activating measurement and its behavior under local incoherent operations. Our results contribute to the study of resource non-activating condition, which is a complement to the well-studied resource non-generating condition.
HIDING FINGERPRINT IN IRIS IMAGE BASED ON TEXTURE FEATURES

In this paper fingerprint hiding in iris image system using the LSB technique .the LSB data hiding technique does not affect the visible properties of the image. Steganography is art and science of hiding the fact that communication is taking place. Secrets can be hidden in all types of medium: text, audio, video and images. Steganography is an important area of research in recent years, involving a number of applications., as It is the science of embedding information into the cover image viz., text, video, and image without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of t ransferring the embedded information to the destination without being detected. This paper deals with hiding fingerprint in iris image using the Least Significant Bit (LSB) technique. The LSB algorithm is implemented in a spatial domain in which the payload bits are embedded into the least significant bits of the cover image to derive the stego-image.
Logo Detection Based on Convolutional Neural Networks

Logo detection processing is a popular application in images. However, due to sample variety in illumination, occlusion, rotation, and scale, the appearance of logos varies greatly, which is challenging for logo detection. In this paper, we propose a novel logo detection method based on the global pyramid text attention module. The global pyramid text attention module (GPTAM) contains the Global Text Attention Module(GTAM) and the Pyramid Text Attention Module (PTAM). GTAM can take use of the context information of texts in logos to reduce detection error, and PTAM can handle logos with multiple scales. We use non-maximum suppression(NMS) method in the postprocessing to obtain the final detection. The experiments on FlickrLogos-32 and TopLogos-10 databases demonstrate that our proposed outperforms the state-of-the-art methods. Furthermore, our method also achieves better or comparable performance on ICDAR2013 and ICDAR2015 datasets for scene text detection.
Chosen Ciphertext Security from Injective Trapdoor Functions.

We provide a construction of chosen ciphertext secure public-key encryption from (injective) trapdoor functions. Our construction is black box and assumes no special properties (e.g. “lossy”, “correlated product secure”) of the trapdoor function.
Real time Robust Invisible Hyperlinks in Physical Photographs Based on Embodied AI Platform

Quick Response (QR) Code plays an important role in connecting the physical world where we live in and the digital world that contains information. However, the pictures made up of black and white color blocks really affect people's moods. This paper designs a visual appealing two-dimensional code to help people access information from offline to online. A Deep Neural Network (DNN) based encoder hides information into natural images and a DNN based decoder recovers it. A novel finder pattern is designed to help the decoding device to locate our codes quickly. We design a decoding device. It consists of an AI chip, camera, and LCD screen. The information is invisible in our generated hidden images but detectable by our device. With the help of powerful edge computing, the device can recover the hidden information from the pictures in real time.
THE EFFECTIVENESS OF KNOW-WANTLEARNED (KWL) STRATEGY IN IMPROVING READING COMPREHENSION OF THE EIGHT GRADE STUDENTS OF SMP NEGERI 14 PALU

The problem focus of this research is to improve students reading comprehension by using Know-Want-Learned (KWL), to make the students comprehend the text, And how the students make their own conclusion about the text. The object of this research was the eighth grade students of SMP Negeri 14 Palu that were divided into two group that was controlled group and experimental group. The aim was to know their improvement after the treatment of KnowWant-Learned (KWL) technique. This research was quantitative research, and the procedure of collected the data were observation, interview, Pre test, treatment, and Post Test. The data analysis used statistical analysis that was descriptive analysis. Statistical analysis is used to test the hypothesis. And the formula used to test the hypothesis was t-test with the level of significance 0,05. The result showed that the application of Know-Want-Learned (KWL) strategy was successful in improving students’ reading comprehension. It can be proven with the result oftest of significance (t-test) was 0,2496. It means the result of t-test is more than level of significance (0,05). Based on the conclusion above, suggested to the teacher especially English teacher is to use Know-Want-Learned (KWL) strategy or find out the other method to make thestudents interest to the material.