Reviewerly/exHarmony huggingface dataset:
Almost Tight Security in Lattices with Polynomial Moduli – PRF, IBE, All-but-many LTF, and More

Achieving tight security is a fundamental task in cryptography. While one of the most important purposes of this task is to improve the overall efficiency of a construction (by allowing smaller security parameters), many current lattice-based instantiations do not completely achieve the goal. Particularly, a super-polynomial modulus seems to be necessary in all prior work for (almost) tight schemes that allow the adversary to conduct queries, such as PRF, IBE, and Signatures. As the super-polynomial modulus would affect the noise-to-modulus ratio and thus increase the parameters, this might cancel out the advantages (in efficiency) brought from the tighter analysis. To determine the full power of tight security/analysis in lattices, it is necessary to determine whether the super-polynomial modulus restriction is inherent. In this work, we remove the super-polynomial modulus restriction for many important primitives – PRF, IBE, All-but-many Lossy Trapdoor Functions, and Signatures. The crux relies on an improvement over the framework of Boyen and Li (Asiacrypt 16), and an almost tight reduction from LWE to LWR, which improves prior work by Alwen et al. (Crypto 13), Bogdanov et al. (TCC 16), and Bai et al. (Asiacrypt 15). By combining these two advances, we are able to derive these almost tight schemes under LWE with a polynomial modulus.
Low Complexity Control Policy Synthesis for Embodied Computation in Synthetic Cells

As robots become more capable, they also become more complicated—either in terms of their physical bodies or their control architecture, or both. An iterative algorithm is introduced to compute feasible control policies that achieve a desired objective while maintaining a low level of design complexity (quantified using a measure of graph entropy) and a high level of task embodiment (evaluated by analyzing the Kullback-Leibler divergence between physical executions of the robot and those of an idealized system). When the resulting control policy is sufficiently capable, it is projected onto a set of sensor states. The result is a simple, physically-realizable design that is representative of both the control policy and the physical body. This method is demonstrated by computationally optimizing a simulated synthetic cell.
The evaluation video quality in social clouds

Social clouds are nowadays very popular among users around the world, and users post videos and images on their wall to share with family and friends. Social cloud automatically compressed the videos during the upload process for fast load in webpages and save storage, which decreases the Quality of Experience (QoE) of the end-user. Previously subjective QoE of social cloud videos was conducted to measure the user satisfaction level of social cloud's compression, but objective QoE is never considered in research. Subjective QoE based on user perception, but the user did not know how much noise is added or blurred videos during compress and unable to provide accurate ratings. In this regards, technical details such as noise level, blurring, blocking and data frame metric (DFM) are required to know the comparison of original videos and compressed videos of social clouds. This paper is based on objective QoE of assessment of video quality of original and compressed videos of social clouds, such as Facebook, Qzone, and Tumblr. The results show Facebook adds more noise, blurring, blocking and DFM effects as compared to Qzone and Tumblr social clouds.
Optimized Light-Weight Convolutional Neural Networks for Histopathologic Cancer Detection

Cancer is one of the leading causes of death across the world, and early detection of cancer will greatly increase the chances of successful treatment. Histopathology diagnosis of cancer is crucial because most diagnoses of malignancy are made by pathological confirmation. In Japan, lack of pathologist has become a serious problem. The lack of pathologists to diagnose the huge amounts of pathological images has resulted the late of medical diagnosis thus delaying the necessary treatment for the patients. Recently, there has been extensive research work on how to incorporate artificial intelligence in the diagnosis process. For example, using convolutional neural networks (CNN) for histopathologic cancer detection can not only speed up diagnosis but also can increase the diagnosis accuracy. In this study, we consider EfficientNet-B6 CNN model for histopathologic cancer detection and apply different activation functions as well as gradient descent optimization algorithms, to analyze its effects on diagnosis accuracy. EfficientNet-B6 is a light-weighted model proposed by Google, an easy-to-deploy-model and works efficiently in classification and detection tasks. Patch-Camelyon benchmark dataset that consists of non-cancerous and cancerous histopathologic images is used in this study. It was found that the top accuracy of this method is 97.94% when it is trained by EfficientNetB6 with Rectified Adam as the optimizer and Mish as the activation function.
Assessment of Bangla Descriptive Answer Script Digitally

Answer script evaluation is an essential part of the student evaluation process in the education system. In an exam, students need to answer subjective and objective questions. In educational institutes, instructors need to evaluate the answer script manually to evaluate the students. In Bangladesh, the number of students and institutes are increasing day by day. For this reason, it is becoming hard to evaluate the answer script in a perfect way by the instructors. So it is necessary to find a way to evaluate the answer script automatically. Many techniques are proposed for the English language, but we didn't find any for Bangla language. Our paper proposed a way to evaluate Bangla subjective answer scrips automatically by keyword matching and linguistic analysis. Using our proposed model we have tested on answer scripts of 20 questions and found the minimum relative error of 1.8%. 15 teachers and 10 students volunteered to evaluate the answer scripts.
An Annotated Bangla Sentiment Analysis Corpus

This paper presents a Bangla corpus specifically targeted for sentiment analysis and made available to researchers under an open-source licensing scheme <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . We have collected and manually annotated over 10,000 sentences with sentiment polarity. We then moved to the Word domain and annotated over 15,000 words derived from these sentences with sentiment polarity. Each entry is the corpus has been cross-annotated by at least two and sometimes three annotators for ensuring quality. Also as a pre-requisite of creating a high quality sentiment analysis corpus, we had to build a secondary corpus for Bangla word stemming, which is also been cross-validated by at least two and sometimes three annotators for ensuring quality.
Neural vs Statistical Machine Translation: Revisiting the Bangla-English Language Pair

Machine translation systems facilitate our communication and access to information, taking down language barriers. It is a well-researched area of Natural Language Processing (NLP), especially for resource-rich languages (e.g., language pairs in Europarl Parallel corpus). Besides these languages, there is also work on other language pairs including the Bangla-English language pair. In the current study, we aim to revisit both Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) approaches using well-known, publicly available corpora for the Bangla-English (Bangla to English) language pair. We reported how the performance of the models differ based on the data and modeling techniques; consequently, we also compared the results obtained with Google's machine translation system. Our findings, across different corpora, indicates that NMT based approaches outperform SMT systems. Our results also outperform existing baselines by a large margin.
Deep Semisupervised Class- and Correlation-Collapsed Cross-View Learning

In many computer vision applications, an object can be represented by multiple different views. Due to the heterogeneous gap triggered by the different views' inconsistent distributions, it is challenging to exploit these multiview data for cross-view retrieval and classification. Motivated by the fact that both labeled and unlabeled data can enhance the relations among different views, this article proposes a deep cross-view learning framework called deep semisupervised classes- and correlation-collapsed cross-view learning (DSC3L) for cross-view retrieval and classification. Different from the existing methods which focus on the two-view problems, the proposed method learns U (generally U ≥ 2 ) view-specific deep transformations to gradually project U different views into a shared space in which the projection embraces the supervised learning and the unsupervised learning. We propose collapsing the instances of the same class from all views into the same point, with the instances of different classes into distinct points simultaneously. Second, to exploit the abundant unlabeled U -wise multiview data, we propose to collapse-correlated data into the same point, with the uncorrelated data into distinct points. Specifically, these two processes are formulated to minimize the two Kullback-Leibler (KL) divergences between the conditional distribution and a desirable one, for each instance. Finally, the two KL divergences are integrated into a joint optimization to learn a discriminative shared space. The experimental results on five widely used public datasets demonstrate the effectiveness of the proposed method.
Cognitive Load during Eye-typing

In this paper, we have measured cognitive load during an interactive eye-tracking task. Eye-typing was chosen as the task, because of its familiarity, ubiquitousness and ease. Experiments with 18 participants, where they memorized and eye-typed easy and difficult sentences over four days, were used to compare the difficulty levels of the tasks using subjective scores and eye-metrics like blink duration, frequency and interval and pupil dilation were explored, in addition to performance measures like typing speed, error rate and attended but not selected rate. Typing performance lowered with increased task difficulty, while blink frequency, duration and interval were higher for the difficult tasks. Pupil dilation indicated the memorization process, but did not demonstrate a difference between easy and difficult tasks.
Exploiting the Earth’s Spherical Geometry to Geolocate Images

Existing methods for geolocating images use standard classification or image retrieval techniques. These methods have poor theoretical properties because they do not take advantage of the earth's spherical geometry. In some cases, they require training data sets that grow exponentially with the number of feature dimensions. This paper introduces the Mixture of von-Mises Fisher (MvMF) loss function, which is the first loss function that exploits the earth's spherical geometry to improve geolocation accuracy. We prove that this loss requires only a dataset of size linear in the number of feature dimensions, and empirical results show that our method outperforms previous methods with orders of magnitude less training data and computation.
Balancing Unpredictability and Coverage in Adversarial Patrolling Settings

We present a novel strategy for a patroller defending a set of heterogeneous assets from the attacks carried by an attacker that through repeated observations attempts to learn the strategy followed by the patroller. Implemented through a Markov chain whose stationary distribution is a function of the values of the assets being defended and the topology of the environment, the strategy is biased towards providing more protection to valuable assets, yet is provably hard to learn for an opponent. After having studied its properties, we show that our proposed method outperforms strategies commonly used for this type of problems.
A Qualitative Study of the Background, Skill Acquisition, and Learning Preferences of Software Testers

Context: There is an indisputable industrial need for highly skilled individuals in the role of software testers. However, little is known about the educational background of these professionals, their first contact with the role, their preferences in acquiring skills, the impediments they face, and their perception of the software testing role. Objective: In the current paper, we report on the background, skills, learning preferences, and role profiles as described by professionals in software testing, spanning over a significant number of industries, countries, and software development models. Method: We conducted 19 in-depth, semi-structured interviews with software testing practitioners, across eight industries. We performed a content and thematic analysis of the collected data. Results: The practitioners in software testing had diverse educational backgrounds, and their first contact with the testing role was accidental. Exploratory testing was the preferred testing technique, while curiosity was identified as the most important feature in their skill set. Our respondents collaborated extensively with the developers, whom they perceived as a learning source and symbiotic work partner. Conclusion: The professionals in software testing described their skills as a rather undefined heap of knowledge, increasing with each work task. They used mainly informal and hands-on learning approaches. They found it necessary for education providers to present information on software testing. Generally, companies assisted them well in their skill development but need to allocate sufficient time for the learning. We identified five specialties of the role: product owner in testing, UX tester, DevOps tester, test-script automator, and test-process coordinator.
Medical Image Data Upscaling with Generative Adversarial Networks

Super-resolution is one of the frequently investigated methods of image processing. The quality of the results is a constant problem in the methods used to obtain high resolution images. Interpolation-based methods have blurry output problems, while non-interpolation methods require a lot of training data and high computing power. In this paper, we present a supervised generative adversarial network system that accurately generates high resolution images from a low resolution input while maintaining pathological invariance. The proposed solution is optimized for small sets of input data. Compared to existing models, our network also provides faster learning. Another advantage of our approach is its versatility for various types of medical imaging methods. We used peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as the output image quality evaluation method. The results of our test show an improvement of 5.76% compared to optimizer Adam used in the original paper [10]. For faster training of the neural network model, calculations on the graphic card with the CUDA architecture were used.
Towards inferring cognitive state changes from pupil size variations in real world conditions

The ability to infer cognitive state from pupil size provides an opportunity to reduce friction in human-computer interaction. For example, the computer could automatically turn off notifications when it detects, using pupil size, that the user is deeply focused on a task. However, our ability to do so has been limited. A principal reason for this is that pupil size varies with multiple factors (e.g., luminance and vergence), so isolating variations due to cognitive processes is challenging. In particular, rigorous benchmarks to detect cognitively-driven pupillary event from continuous stream of data in real-world settings have not been well-established. Motivated by these challenges, we first performed visual search experiments at room scale, with natural indoor conditions with real stimuli where the timing of the detection event was user-controlled. In spite of the natural experimental conditions, we found that the mean pupil dilation response to a cognitive state change (i.e., search target detected) was qualitatively similar and consistent with more controlled laboratory studies. Next, to address the challenge of detecting state changes from continuous data, we fit discriminant models using Support Vector Machine (SVM) computed on short epochs of 1-2 seconds extracted using rolling windows. We tested three different features (descriptive statistics, baseline corrected pupil size, and local Z-score) with our models. We obtained best performance using local Z-score as a feature (mean Area under the Curve (AUC) of 0.6). Our naturalistic experiments and modeling results provide a baseline for future research aimed at leveraging pupillometry for real-world applications.
Neural Machine Translation from Jordanian Dialect to Modern Standard Arabic

The development of cultures and societies all over the world was the first reason for the emergence of many different languages and dialects that differ from each other based on the geographical location of these communities, whether in the Arab countries or Western or other parts of the world. Due to these differences, there is a need to translate these dialects between each other to facilitate their understanding and handling by people who will use them from other communities. The tremendous technological advancement and the flourishing of the era of Deep Learning, has led to the emergence of so-called neural machine translation (NMT), which has significantly facilitated the translation process compared to other methods. In this paper, we present a framework to translate the Jordanian dialect into Modern Standard Arabic (MSA) using Deep Learning, in particular, the RNN encoder-decoder model, which provided good results on the level of our manually created dataset. The conducted experiments using this model were divided into two parts: word level and sentence level, and the results were as follows: loss equals 0.8 and accuracy equals 91.3% when using the model for word to word translation; and loss value equals 3.33 and accuracy equals 63.2% when using the model for sentence translation. These are very encouraging results in this largely unexplored topic.
Socio-psychological Program for the Selection of Students in the Adapters Public Institute

This work describes a unique institute of adapters that operates on the basis of the multidisciplinary Peter the Great St. Petersburg Polytechnic University. Prosocial behavior, or voluntary actions that intentionally benefit others, is often associated with desirable developmental outcomes, such as acceptance into a group, gaining public recognition, and forming stable social connections. In University, this type of behavior becomes the basis for the formation of specialized communication institutions between the University, young leaders and first-year students. Adapters is a student association where activists support freshmen in academic and social adaptation to the university environment. The main departments and directions of the institute’s work (the direction of international activity, the direction of university social infrastructure development, the direction of career guidance, the Cultural Department, the Information Department (PR campaign department)) are identified, the main problems at their work are revealed, and development prospects are outlined. The 4-step selection program aimed at improving the efficiency and effectiveness of the work of adapter students (receiving applications/interviews, approving adapters, training, working on projects) is presented.
Some Practical Aspects of Digital Image Correlation Technique to Evaluate Anisotropy Coefficient and Its Comparison with Traditional Method

Abstract Digital Image Correlation (DIC) is a robust method that is used to evaluate full-field displacements. In this technique, images taken from the specimen are compared before and after deformation; this method provides an accurate displacement field as well as strain values. The objective of the present work is to examine the DIC method using the traditional method of calculating the plastic anisotropy coefficients. In this research, the DIC method has been applied on one side of the specimen, and the conventional circle grid analysis technique is performed on the other side. Results showed that the fully automated 2-D DIC method in MATLAB (MathWorks, Natick, MA) was verified and that it could replace the traditional technique of circle grid analysis, which is time consuming and incurs a lot of human errors.
Either Give Me a Reason to Stand or an Opportunity to Sit in VR

In this position paper, we want to point, with a little bit of provocation and maybe a pinch of fun, to some grievances of and also chances for today’s pool of (consumer) VR applications concerning the chosen user posture. In our opinion, the user is considered standing or required to stand in too many cases, and transitions between postures are usually entirely unsupported.
Martial Arts Training in Virtual Reality with Full-body Tracking and Physically Simulated Opponents

Virtual reality (VR) training is an increasingly popular topic in sports science [1]. We introduce a VR application for martial arts training, which utilizes physics-based full-body interaction that extends our previous work on the subject [2]. Our use of full-body tracking and user-worn VR equipment without external tethering enables training of techniques that employ lower body: e.g., kicking, grappling, and leg sweeps. Hence a wide variety of martial art styles —char63 from stand-up fighting to ground fighting —char63 can be trained. The training application features virtual opponents and a motion tracked user avatar, which is implemented using RUIS toolkit [3]. The avatar —char63s and the virtual opponents —char63 body segments are part of physics simulation that determine their final motion. This enables dynamic hand-to-hand combat, where the user —char63s punches, takedowns, holds, and other techniques affect the opponents in a convincing manner. The user can engage any number of virtual opponents at full power in no-holds-barred matches without risk of injury to fellow practitioners. In addition to partner training, sparring, and virtual target mitts, the application also includes novel VR training features: performance playback, rhythm game, and controls for adjusting virtual opponent speed, strength, and knockout resistance. User performed techniques can be recorded and then replayed dynamically by the virtual opponent using the physics simulation. Thus, the user can be their own training partner, and even fight himself in the virtual arena
How to Hide the Real Receiver Under the Cover Receiver: CP-ABE With Policy Deniability

Attribute-based encryption (ABE) is a useful tool for sharing an encrypted data to a target group. In a ciphertext-policy ABE (CP-ABE) scheme, a ciphertext includes a policy to indicate its receivers and only those receivers can correctly decrypt the ciphertext. Since this design leaks the receiver identity, it may raise a new security issue about user privacy. Some hidden-policy ABE schemes, where the policy is secretly protected, are proposed to keep user privacy. However, these hidden-policy ABE schemes rely on the user trying all possibilities to decide if it belongs to the wanted receiver group. The decryption costs too much and every potential receiver will run the decryption process in vain since it does not know the policy. In this work, we apply the deniability concept to solve this problem. The encryption scheme allows the sender to claim the ciphertext is for some receiver group while actually it is for another receiver group. Both receiver groups can correctly decrypt the ciphertext except that the real group can get the real message and the cover group will get the cover message. While coercion, the sender can definitely claim the ciphertext is for the cover group and the real group is kept confidential.