Reviewerly/exHarmony huggingface dataset:
Consistent Optimization for Single-Shot Object Detection.

We present consistent optimization for single stage object detection. Previous works of single stage object detectors usually rely on the regular, dense sampled anchors to generate hypothesis for the optimization of the model. Through an examination of the behavior of the detector, we observe that the misalignment between the optimization target and inference configurations has hindered the performance improvement. We propose to bride this gap by consistent optimization, which is an extension of the traditional single stage detector's optimization strategy. Consistent optimization focuses on matching the training hypotheses and the inference quality by utilizing of the refined anchors during training. To evaluate its effectiveness, we conduct various design choices based on the state-of-the-art RetinaNet detector. We demonstrate it is the consistent optimization, not the architecture design, that yields the performance boosts. Consistent optimization is nearly cost-free, and achieves stable performance gains independent of the model capacities or input scales. Specifically, utilizing consistent optimization improves RetinaNet from 39.1 AP to 40.1 AP on COCO dataset without any bells or whistles, which surpasses the accuracy of all existing state-of-the-art one-stage detectors when adopting ResNet-101 as backbone. The code will be made available.
Subjective Answer Grader System Based on Machine Learning

According to experts, a good test paper should have a combination of objective and subjective questions. But the current online examinations mainly consist of only objective questions. This is because an accurate computerized grading is possible for such questions. But achieving an accurate computerized grading for the subjective questions is still a matter of concern. To address this problem, in this paper, we have designed and implemented a machine learning-based subjective answer grader system (SAGS) using two algorithms, namely latent semantic analysis (LSA) and information gain (IG) for the generation of grades. We have proposed the enhancement of these algorithms through synonym replacement using WordNet, and the accuracy of these algorithms is measured by comparing the generated scores with the scores given by human evaluators.
Dense Depth Posterior (DDP) from Single Image and Sparse Range

We present a deep learning system to infer the posterior distribution of a dense depth map associated with an image, by exploiting sparse range measurements, for instance from a lidar. While the lidar may provide a depth value for a small percentage of the pixels, we exploit regularities reflected in the training set to complete the map so as to have a probability over depth for each pixel in the image. We exploit a Conditional Prior Network, that allows associating a probability to each depth value given an image, and combine it with a likelihood term that uses the sparse measurements. Optionally we can also exploit the availability of stereo during training, but in any case only require a single image and a sparse point cloud at run-time. We test our approach on both unsupervised and supervised depth completion using the KITTI benchmark, and improve the state-of-the-art in both.
Advance Intelligent Video Surveillance System (AIVSS): A Future Aspect

Over the last few decades, remarkable infrastructure growths have been noticed in security-related issues throughout the world. So, with increased demand for Security, Video-based Surveillance has become an important area for the research. An Intelligent Video Surveillance system basically censored the performance, happenings, or changing information usually in terms of human beings, vehicles or any other objects from a distance by means of some electronic equipment (usually digital camera). The scopes like prevention, detection, and intervention which have led to the development of real and consistent video surveillance systems are capable of intelligent video processing competencies. In broad terms, advanced video-based surveillance could be described as an intelligent video processing technique designed to assist security personnel’s by providing reliable real-time alerts and to support efficient video analysis for forensic investigations. This chapter deals with the various requirements for designing a robust and reliable video surveillance system. Also, it is discussed the different types of cameras required in different environmental conditions such as indoor and outdoor surveillance. Different modeling schemes are required for designing of efficient surveillance system under various illumination conditions.
Incremental Transfer Learning in Two-pass Information Bottleneck Based Speaker Diarization System for Meetings

The two-pass information bottleneck (TPIB) based speaker diarization system operates independently on different conversational recordings. TPIB system does not consider previously learned speaker discriminative information while diarizing new conversations. Hence, the real time factor (RTF) of TPIB system is high owing to the training time required for the artificial neural network (ANN). This paper attempts to improve the RTF of the TPIB system using an incremental transfer learning approach where the parameters learned by the ANN from other conversations are updated using current conversation rather than learning parameters from scratch. This reduces the RTF significantly. The effectiveness of the proposed approach compared to the baseline IB and the TPIB systems is demonstrated on standard NIST and AMI conversational meeting datasets. With a minor degradation in performance, the proposed system shows a significant improvement of 33.07% and 24.45% in RTF with respect to TPIB system on the NIST RT-04Eval and AMI-1 datasets, respectively.
k-Reciprocal Harmonious Attention Network for Video-Based Person Re-Identification

Video-based person re-identification aims to retrieve video sequences of the same person in the multi-camera system. In this paper, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -reciprocal harmonious attention network (KHAN) to jointly learn discriminative spatiotemporal features and the similarity metrics. In KHAN, the harmonious attention module adaptively calibrates response at each spatial position and each channel by explicitly inspecting position-wise and channel-wise interactions over feature maps. Besides, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -reciprocal attention module attends key features from all frame-level features with a discriminative feature selection algorithm; thus, useful temporal information within contextualized key features can be assimilated to produce more robust clip-level representation. Compared with commonly used local-context based approaches, the proposed KHAN captures long dependency of different spatial regions and visual patterns while incorporating informative context at each time-step in a non-parametric manner. The extensive experiments on three public benchmark datasets show that the performance of our proposed approach outperforms the state-of-the-art methods.
Der Genitiv in der „Leichten Sprache“ – das Für und Wider aus theoretischer und empirischer Sicht

Abstract Rulebooks of Easy-to-read (ETR) German recommend to strictly avoid the genitive and to replace it by “von”-phrases with dative. This is backed by the assumption that for the target groups of ETR German genitive-phrases are generally harder to understand than “von”-phrases. However, there has been no empirical foundation for this assumption. The test reported in this paper addressed this problem and wanted to find out whether genitive is really harder to understand than “von”-phrases for two target groups: 17 adults with intellectual disability and 16 functional illiterates, that is people, who learned to read but, due to multiple reasons, do not have reading skills (anymore) that are sufficient for everyday life. The test was designed as multiple-choice test and compares the intelligibility of common forms of the attributive genitive (i.a. genitivus obiectivus, genitivus partitivus) with their “von”-paraphrases. The participants had to read and understand sentences and subsequently choose between statements that applied to the sentences or not. The overall results show that the tested forms of the attributive genitive were easy to understand. Comparing genitive-phrases and “von”-phrases there were no significant differences. That means, both forms were easy to understand for the participants. Some genitive forms were even easier to understand than the “von”-paraphrases. The results lead to the conclusion that the general prohibition of genitive in ETR German has to be revised.
Characterization of a high-brightness, laser-cooled Li+ ion source

Ion sources based on laser cooling have recently provided new pathways to high-resolution microscopy, ion milling, and ion implantation. Here, we present the design and detailed characterization of a 7Li magneto-optical trap ion source (MOTIS) with a peak brightness of (1.2 ± 0.2) × 105 A m-2 sr-1 eV-1 and a maximum continuous current over 1 nA. These values significantly surpass previous Li MOTIS performance benchmarks. Using simple models, we discuss how the performance of this system relates to fundamental operating limits. This source will support a range of projects using lithium ion beams for surface microscopy and nanostructure characterization, including Li+ implantation for studies of ionic transport in energy storage materials.
Machine Vision System Measuring the Trajectory of Upper Limb Motion Applying the Matlab Software

Abstract This paper discusses the problem of possibilities for applying a machine vision in the measurement of the trajectory of the upper limb movement in rehabilitation exercises. The fundamental presumption of designed system was to get the image from the camera’s CCD processor, possibilities of measuring and its processing. As a result of the application system, it is possible to dynamically determine the radius between the shoulder and forearm, and also the angle between the shoulder and the chest of the man, as function of the limb motion. The created system gives the possibility to use a non-invasive method of measurement, allows visualization and full analysis of the rehabilitation progress and also allows keeping electronic records of patients.
A hybrid fuzzy framework for face detection and recognition using behavioral traits

Face recognition integrated with emotions and behavior can be a salient feature for identification of antisocial/criminal people. In this paper, a hybrid Fuzzy based Behavior Prognostic System for Disparate traits (FBPSDs) has been proposed. FBPSDs can be used in an emergency situation to identify and extract mischievous person(s) involved in any swindling act. The technique uses face recognition to identify the contextual faces. Further, fuzzy rules have been formulated to extricate more features for the culprit identification. The proposed framework has three stages: face detection, feature extraction and behavior analysis. Face detection for the proposed FBPSDs is further implemented using three techniques Haar Cascade, Skin color based and HOG methods on Labeled Faces in the Wild (LFW) and Facial dataset (Face 94, Face 95 and Face 96). Testing accuracy of 91%, 50%, 92% (LFW) and 91%, 60%, 91.5% (facial dataset) is achieved. Result analysis and evaluation prove that HOG method is the most acceptable and appropriate method for face detection.
Visual Analytics of Multidimensional Projections for Constructing Classifier Decision Boundary Maps

Visualizing decision boundaries of modern machine learning classifiers can notably help in classifier design, testing, and fine-tuning. Dense maps are a very recent method that overcomes the key sparsity-related limitation of scatterplots for this task. However, the trustworthiness of dense maps heavily depends on the underlying dimensionality-reduction (DR) techniques they use. We design and perform a detailed study aimed at finding the best DR techniques to use when creating trustworthy dense maps, by studying a large collection of 28 DR algorithms, 4 classifiers, and 2 datasets from a real-world challenging classification problem. Our results show how one can pick suitable DR algorithms to create dense maps that help understanding classifier behavior.
Higher School: Look Beyond the Horizon

The authors concentrate their attention on the problem of the future of Russian education in connection with the changes in the classifier of professions. The paper analyzes the prospects for Russian higher education in personnel training in the context of growing technological innovations. In this regard, attention is drawn to two aspects. The first aspect covers the question of how sensitive Russian higher school is to the existing and future changes in the requirements of the employer and the labor market, and in general to the training of graduates. The second aspect is an attempt to connect the impending deformations, social and humanitarian, of professional reproduction with the mission of higher education. The key question reads as follows: can a university change its mission towards solving the social task of overcoming the crisis of “unemployed young people” that is looming along with the growth of technological innovations and the robotization of labor?
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes

We propose a novel method for computing exact pointwise robustness of deep neural networks for all convex $\ell_p$ norms. Our algorithm, GeoCert, finds the largest $\ell_p$ ball centered at an input point $x_0$, within which the output class of a given neural network with ReLU nonlinearities remains unchanged. We relate the problem of computing pointwise robustness of these networks to that of computing the maximum norm ball with a fixed center that can be contained in a non-convex polytope. This is a challenging problem in general, however we show that there exists an efficient algorithm to compute this for polyhedral complices. Further we show that piecewise linear neural networks partition the input space into a polyhedral complex. Our algorithm has the ability to almost immediately output a nontrivial lower bound to the pointwise robustness which is iteratively improved until it ultimately becomes tight. We empirically show that our approach generates distance lower bounds that are tighter compared to prior work, under moderate time constraints.
A Note on movement in logical grammar

In this article we make remarks on overt and covert movement inlogical grammar. With respect to the latter (e.g. quantification) weobserve how a treatment in terms of displacement calculus interactswith normal modalities for intensionality to allow a coding in logicalgrammar of the distinction between weak and strong quantifiers(i.e. those that may or may not not scope nonlocally such as'a' and 'every' respectively). With respect to overt movement (e.g.relativisation) we observe how displacement calculus supportsa coding of a filler-gap dependency similar to that employed in lambdagrammars, but we argue that this general approach does not extend toparasitic gaps, for which we propose exponentials.
Machine translation using natural language processing

Machine Translation is the translation of text or speech by a computer with no human involvement. It is a popular topic in research with different methods being created, like rule-based, statistical and examplebased machine translation. Neural networks have made a leap forward to machine translation. This paper discusses the building of a deep neural network that functions as a part of end-to-end translation pipeline. The completed pipeline would accept English text as input and return the French Translation. The project has three main parts which are preprocessing, creation of models and Running the model on English Text.
A robust registration algorithm of point clouds based on adaptive distance function for surface inspection

One key issue in the optical measurement of free-form or complex surfaces is the point cloud registration procedure, which aligns the measurement data to the part model for a robust, fast and accurate inspection process. Therefore, a robust registration method for surface inspection is proposed based on an adaptive distance function (ADF) and the M-estimation method. The ADF as the basis error metric can accurately describe the shortest point-surface distance, and the M-estimation method is used to eliminate outliers and enhance the robustness of the registration performance. The registration problem using the M-estimation method can be interpreted as an iterative reweighted least squares (IRLS) minimization. Then, a nonlinear optimization model called IRLS-ADF is established to obtain the transformation parameters. The convergence of the proposed method is also analysed. Moreover, compared to the previous algorithms, the experiments confirm that the proposed method can achieve a combination of good robustness, fast convergence speed and high accuracy.
Prevalence of Acute and Chronic Forms of Leukemia in Various Regions of Khyber Pakhtunkhwa, Pakistan: Needs Much More to be done!

Background and objective: Leukemia is one of the fatal diseases and their morbidity and mortality rates increases day by day all over the world. This piece of research study was designed in order to find out the prevalence of different types of leukemia in Khyber Pakhtunkhwa, Pakistan during January 2015 to December 2016.&#x0D; Material and Method: The retrospective research study was carried out at Institute of Radiotherapy and Nuclear Medicine (IRNUM) Peshawar. A data of 400 admitted patients of leukemia were evaluated.&#x0D; Result: It was observed that acute leukemia (80%) was more prevalent than chronic leukemia (20%). Amongst types of leukemia, Acute Lymphocytic Leukemia (ALL) 49.5% (n=198) was more prevalent than Acute Myelogenous Leukemia (AML) 31.25% (n=125), Chronic Myelogenous Leukemia (CML) 10% (n=40) and Chronic Lymphocytic Leukemia (CLL) 9.25% (n=37) was less prevalent in this study. It was also found that leukemia was more prevalent in male patients 64.5% (n=258) as compared to females 35.5% (n=142) and male to female ratio was 1.8:1. Most of the patients were under the age of 20 years.&#x0D; Conclusion: Acute leukemia was more prevalent than chronic leukemia during this study in this part of the country and needs to be address.&#x0D; Bangladesh Journal of Medical Science Vol.18(2) 2019 p.222-227
An Improved Particle Swarm Optimization Algorithm with Chi-Square Mutation Strategy

Particle Swarm Optimization (PSO) algorithm is a population-based strong stochastic search strategy empowered from the inherent way of the bee swarm or animal herds for seeking their foods.Consequently, flexibility for the numerical experimentation, PSO has been used to resolve diverse kind of optimization problems.PSO is much of the time caught in local optima in the meantime taking care of the complex real-world problems.Considering this, a novel modified PSO is introduced by proposing a chi square mutation method.The main functionality of mutation operator in PSO is quick convergence and escapes from the local minima.Population initialization plays a critical role in meta-heuristic algorithm.Moreover, in this work, to improve the convergence, rather applying random distribution for initialization, two quasi random sequences Halton and Sobol have been applied and properly joined with chi-square mutated PSO (Chi-Square PSO) algorithm.The promising experimental result suggests the superiority of the proposed technique.The results present foresight that how the proposed mutation operator influences on the value of cost function and divergence.The proposed mutated strategy is applied for eight (8) benchmark functions extensively used in the literature.The simulation results verify that Chi-Square PSO provide efficient results over other tested algorithms implemented for the function optimization.
On the impact of the cutoff time on the performance of algorithm configurators

Algorithm configurators are automated methods to optimise the parameters of an algorithm for a class of problems. We evaluate the performance of a simple random local search configurator (ParamRLS) for tuning the neighbourhood size k of the RLSk algorithm. We measure performance as the expected number of configuration evaluations required to identify the optimal value for the parameter. We analyse the impact of the cutoff time κ (the time spent evaluating a configuration for a problem instance) on the expected number of configuration evaluations required to find the optimal parameter value, where we compare configurations using either best found fitness values (ParamRLS-F) or optimisation times (ParamRLS-T). We consider tuning RLSk for a variant of the Ridge function class (Ridge*), where the performance of each parameter value does not change during the run, and for the OneMax function class, where longer runs favour smaller k. We rigorously prove that ParamRLS-F efficiently tunes RLSk for Ridge* for any κ while ParamRLS-T requires at least quadratic κ. For OneMax ParamRLS-F identifies k = 1 as optimal with linear κ while ParamRLS-T requires a κ of at least ω(n log n). For smaller κ ParamRLS-F identifies that k > 1 performs better while ParamRLS-T returns k chosen uniformly at random.
Semi-device-independent characterization of quantum measurements under a minimum overlap assumption

Recently, a novel framework for semi-device-independent quantum prepare-and-measure protocols has been proposed, based on the assumption of a limited distinguishability between the prepared quantum states. Here, we discuss the problem of characterizing an unknown quantum measurement device in this setting. We present several methods to attack the problem. Considering the simplest scenario of two preparations with lower bounded overlap, we show that genuine three-outcome positive-operator valued measures (POVMs) can be certified, even in the presence of noise. Moreover, we show that the optimal POVM for performing unambiguous state discrimination can be self-tested.
From GAN to WGAN

This paper explains the math behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs' training by adopting a smooth metric for measuring the distance between two probability distributions.
Compositionality of Rewriting Rules with Conditions

We extend the notion of compositional associative rewriting as recently studied in the rule algebra framework literature to the setting of rewriting rules with conditions. Our methodology is category-theoretical in nature, where the definition of rule composition operations encodes the non-deterministic sequential concurrent application of rules in Double-Pushout (DPO) and Sesqui-Pushout (SqPO) rewriting with application conditions based upon M-adhesive categories. We uncover an intricate interplay between the category-theoretical concepts of conditions on rules and morphisms, the compositionality and compatibility of certain shift and transport constructions for conditions, and thirdly the property of associativity of the composition of rules.