Reviewerly/exHarmony huggingface dataset:
ACE in Chains: How Risky Is CBC Encryption of Binary Executable Files?

We present malleability attacks against encrypted binary executable files when they are encrypted by CBC mode of operation. While the CBC malleability is classic and has been used to attack on various real-world applications, the risk of encrypting binary executable via CBC mode on common OSs has not been widely recognized. We showed that, with a certain non-negligible probability, it is possible to manipulate the CBC-encrypted binary files so that the decryption result allows an arbitrary code execution (ACE), which is one of the most powerful exploits, even without the knowledge of plaintext binary. More specifically, for both 32- and 64-bit Linux and Windows OS, we performed a thorough analysis on the binary executable format to evaluate the practical impact of ACE on CBC encryption, and showed that the attack is possible if the adversary is able to correctly guess 13 to 25 bits of the address to inject code. In principle, our attack affects a wide range of storage/file encryption systems that adopt CBC encryption. In addition, a manual file encryption using OpenSSL API (AES-256-CBC) is affected, which is presumed to be frequently used in practice for file encryption. We provide Proof-of-Concept implementations for Linux and Windows. We have notified our findings to the appropriate institution as an act of responsible disclosure.
OBJECT DETECTION WITH THE HIGH-FREQUENCY CHANGE OF OBJECTS CLASSES

Abstract. With the development of deep learning, object detection has a significantly improvement. But most of algorithms only focus on the detection accuracy and speed, they do not consider the difficulty of making training datasets and the time consumption of training detection models, which will have a bad influence on the performance of detection model when the class of objects change in high frequency. This paper proposes a method named double network detection (DN detection), it can improve the efficiency of making training datasets and shorten the time of training model. At the same time, the experiment shows that the DN detection have a good performance in accuracy and speed.
An Effective and Efficient Machine-Learning-Based Framework for Supporting Event Detection and Analysis in Complex Environments

In this paper we describe a falls detection and classification algorithm for discriminating falls from daily life activities using a MEMS accelerometer.The algorithm is based on a shallow Neural Network with three hidden layers, used as fall/non fally classifier, trained with daily life activities features and fall features.The novelty of this algorithm is that synthetic falls are generated as multivariate random Gaussian features, so only real daily life features must be collected during some day of normal living.Moreover, the features related to synthetic fall events are generated as complement of normal features.First of all, the features acquired during daily life are clustered by Principal Component Analysis and no Fall activities shall be recorded.The complement set of the normal features is found and used as a mask for Monte Carlo generation of synthetic fall.The two feature sets, namely the features recorded from daily life activities and those artificially generated are used to train the Neural Network.This approach is suitable for a practical utilization of a Neural Network based fall detection characterized by high Recall-Precision rate.
IMPROVING SMARTPHONE POSITION AND ATTITUDE FOR GEOSPATIAL AUGMENTED REALITY APPLICATIONS

Abstract. Augmented reality is a technology that visualizes and synthesizes virtual objects and information in the real world. Augmented reality can provide useful information to the real world realistically. As the investment and demand for augmented reality increase, many researches have been conducted to visualize objects based on ground coordinate system such as cadastral information and underground facilities using augmented reality. In order to visualize objects based on the ground coordinate system on a smartphone using augmented reality, high accuracy of the position and attitude of the smartphone is essential. Accordingly, this study proposed position and attitude correction method for the first image using reference images and single photo resection. Then, the absolute position of the entire image was estimated using the first calibrated image and the tracking algorithm. When the reference image-based correction method was used, the accuracy was 0.74m, 0.94m. And when the single photo resection correction method was used, the accuracy was 3.13m, 1.24m. The method presented in this study showed higher accuracy when only the sensor was used, but it was confirmed that errors in position and attitude occur as the tracking error accumulates.
Rényi Entropy Bounds on the Active Learning Cost-Performance Tradeoff

Semi-supervised classification, one of the most prominent fields in machine learning, studies how to combine the statistical knowledge of the often abundant unlabeled data with the often limited labeled data in order to maximize overall classification accuracy. In this context, the process of actively choosing the data to be labeled is referred to as active learning. In this paper, we initiate the non-asymptotic analysis of the optimal policy for semi-supervised classification with actively obtained labeled data. Considering a general Bayesian classification model, we provide the first characterization of the jointly optimal active learning and semi-supervised classification policy, in terms of the cost-performance tradeoff driven by the label query budget (number of data items to be labeled) and overall classification accuracy. Leveraging recent results on the Rényi Entropy, we derive tight information-theoretic bounds on such active learning cost-performance tradeoff.
Machine-learning based fast unsupervised variational image decomposition for fringe pattern analysis

The unsupervised variational image decomposition (uVID) algorithm developed in our group allows for automatic, accurate and robust preprocessing of diverse fringe patterns. Classical VID was initially used for image denoising. Its tailoring for fringe pattern preprocessing was justified by clear advantage over other methods (e.g. Wiener or Gauss filters) in maintaining sharp edges and details of the image. Historically first fringe pattern dedicated three-component variational image decomposition model assumed the use of the shearlet algorithm to separate the information component (fringes) and noise and the Chambolle projection algorithm to separate the fringes and background. We noticed that this model is computationally complicated and the result strongly depends on the values of the algorithm’s internal parameters, to be set manually. The uVID automatically introduces the parameters and stopping criterion for Chambolle’s iterative projection algorithm. Nevertheless, determining the stopping criterion in each iteration is a severely time-consuming process, which is particularly important given the fact that in many cases thousands of iterations have to be calculated in order to obtain a satisfactory fringe pattern decomposition result. Therefore, the idea of using machine learning algorithms to classify fringe patterns according to the required number of Chambolle projection iterations has emerged. Thus, it is no longer required to determine the value of the stopping criterion in every iteration, but only in the area of the predetermined number of iterations. We showed that the calculation time is reduced on average by half by employing the machine-learning based acceleration. This way we made a progress in developing uVID algorithm features for real-time studies of dynamic phenomena, i.e., biological cell development investigated by fringe-based bio-interferometry methods.
Slim LSTMs: parameter-reductions within gating signals

Long Short-Term Memory (LSTM) Recurrent Neural networks (RNNs) rely on gating signals, each driven by a function of a weighted sum of at least 3 components: (i) one of an adaptive weight matrix multiplied by the incoming external input vector sequence, (ii) one adaptive weight matrix multiplied by the previous memory/state vector, and (iii) one adaptive bias vector. In effect, they augment the simple Recurrent Neural Networks (sRNNs) structure with the addition of a memory cell and the incorporation of at most 3 gating signals.The standard LSTM structure and components encompass redundancy and overly increased parameterization. In this paper, we systemically introduce variants of the LSTM RNNs, referred to as Slim LSTMs. These variants express aggressively reduced parameterizations in the gates to achieve computational saving and/or speedup in (training) performance—while necessarily retaining (validation accuracy) performance comparable to the standard LSTM RNN.
The best tech for contact tracing?

column The best tech for contact tracing?: systems designed for healthcare workers Share on Authors: Margaret Bourdeaux Harvard Kennedy School Harvard Kennedy SchoolView Profile , Mary L. Gray Microsoft Research Microsoft ResearchView Profile , Barbara Grosz Harvard University Harvard UniversityView Profile Authors Info & Claims InteractionsVolume 27Issue 4July - August 2020 pp 90–93https://doi.org/10.1145/3406775Online:09 July 2020Publication History 0citation388DownloadsMetricsTotal Citations0Total Downloads388Last 12 Months269Last 6 weeks22 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Sistem Pakar Untuk Mendiagnosa Penyakit Syaraf Terjepit Pada Tulang Belakang (HNP) Menerapkan Metode Case Based Reasoning

Nucleus hernia (HNP) is a disease that often causes pain in the vertebrae. HNP occurs due to the presence of nucleus pulpus (a filling material in the form of a springy substance such as gell) that comes out of the discus vertebral (spinal joints). Early symptoms of this disease are often not realized by sufferers. As a result of this lack of awareness can actually cause activities to be inhibited. This is because HNP can cause pain in the spine that can affect the pain in the thighs, calves and legs and can even cause spinal deformities. To reduce the risk of this HNP disease, we need an expert system that can help both sufferers and experts in diagnosing the initial symptoms of this disease so that prevention of harmful effects can be done more quickly. In this study, an expert system was made that functions to diagnose the early symptoms of HNP with a case-based reasoning method as an expert system logic that was made. This solution was made because with the help of web technology, patients can easily diagnose the symptoms of HNP anytime and anywhere
An Implementation of Hybrid method towards the Privacy of HealthCare Record

This paper explains privacy preservation technique in data mining. Date mining is one of the most interesting field of research and discovering the pattern and relationship among the data. Data mining play a important role in various field in which one of the important area is healthcare. In healthcare dataset contain sensitive information about the patient. In this paper we hide the sensitive data of the patients using hybrid approach. This hybrid technique uses FP Growth algorithm. This paper also contains the review of previous work done in this field. This paper intended towards the privacy preservation in healthcare to hide sensitive information of patients.
Two Stage Fuzzy Inference Systems for Autonomous Lighting in 3D Animated Movie Scene

Lighting is one of the very important things in cinematography.Lighting in cinematography is used not only to illuminate the objects, but it has many important roles such as to make the object looks sharper, has tone, depth and separation, and so on.There are some parameters that affect the lighting result, such as light source type that is used, its colour, intensity, position, and direction.Unfortunately, lighting has so many permutations and variations and there is more than one right way to light the scene.In this research, a designer designs some scenes that will be used as inputs.In this study, the authors found that lighting set is strongly influenced by five important things, which are: the situation of the scene, Point-of-View, the intensity of the attached light sources, camera position, and emotion of the characters, that we called as the SPICE system that is the abbreviation from situation, PoV, intensity, camera, and emotion.some additional light sources will be added into the scene automatically according to the scenario and director's behaviour using the SPICE system that we propose.The SPICE framework has two fuzzy inference systems in calculating the intensity of light that hits an object and some additional light sources that are added to the scene automatically to make it looks much better.This research will make a calculation to gain the best scenes as an output.There are 20 scenes used in this research.As the final result, designers assess 80% outputs have been as expected.
Rancang Bangun Aplikasi PHP dalam Mendeteksi Penyakit Kelinci Menggunakan Metode Case-Based Reasoning (CBR)

The case-based reasoning method is one of the methods for building a system with the decision-making of a new case based on solutions from previous cases. This method provides a space on experts in giving similar or similarity value to the revealed knowledge. The disease in rabbit animals often makes the owner confused by the lack of owner knowledge about the animal's disease. Most of the diseases that occur in the pet rabbit are often underestimated, and if the disease is not immediately treated it will become more severe and serious in handling. Therefore, it is necessary a tool or a system that has a doctor's ability to diagnose diseases. The system is an expert system that seeks to adopt human knowledge to the computer in order to solve problems such as those commonly done by experts. The results showed that the case-based reasoning method can be used as a way to diagnose the type of disease suffered in rabbits
Deep learning-based human CT brain segmentation using MR derived labels

Computed tomography (CT) is a widely available, low-cost neuroimaging modality primarily used as a brain examination tool for visual assessment of structural brain integrity in neurodegenerative diseases such as dementia disorders. In this study, we developed a deep learning model to expand the applications of CT to morphological brain segmentation and volumetric extraction. We trained densely connected 3D convoluted neural network variants called U-Nets to segment intracranial volume (ICV), grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF). Dice similarity scores and volumetric Pearson correlation were the evaluation metrics incorporated. Our pilot study created a model that enables automated segmentation in CT with results comparable to magnetic resonance imaging.
On-Line Recognition of Fragments of Standard Images Distorted by Non-linear Devices and with a Presence of an Additive Impulse Interference

On the base of the first-order disproportion functions, an algorithm recognizing fragments of standard images under conditions when the analyzed signal contains these fragments in a distorted form due to passing through a nonlinear device, the static characteristic of which can be represented by a polynomial with unknown coefficients, is developed. Both, continuous signals and those, described by discrete pixel brightness values of a video image, are considered with the presence of additive impulse noises.
Perancangan Aplikasi Optimalisasi Jumlah Persediaan Bumbu Penyedap Masakan Menggunakan Metode Simpleks

Optimization is a measure that causes the achievement of objectives, whereas when viewed from the point of view of optimization efforts are efforts to maximize activities so as to realize the desired or desired benefits. PT. Ajinomoto Sales Indonesia always calculates the inventory of seasoning dishes, the obstacle faced by the company by using the current system is that there are often inaccuracies in the calculation of the amount of food seasoning inventory that must be provided by PT. Ajinomoto Sales Indonesia so as not to experience a shortage of product inventories or excess product inventory each month. The application is a computer application that is able to provide information on the optimal amount of food seasoning that must be provided by PT. Ajinomoto Sales Indonesia every month in the form of numbers, with this application, hopefully can be useful in determining the optimal amount of food seasoning and able to stabilize as well as carefulness in determining the amount of food seasoning stock that must be provided
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The purpose of this study is to conduct Project Based Learning (PBL) in collaboration with industry experts to operate practical subjects in an...
The Role of Conditional Propositions

This chapter explains why the practice of using conditionals assessed by the primary (suppositional) and secondary (testimonial) heuristics is best understood on a material, truth-functional semantics. The aptness of the testimony-based heuristic supports a semantics on which the semantic contribution of ‘if’ is context-insensitive; the aptness of the supposition-based heuristic supports a semantics on which the probability of a conditional is not overestimated by being treated like the conditional probability, and compatibly with that underestimates it as little as possible. A formal result shows that in almost all cases these desiderata mandate the truth-functional interpretation. But that does not imply that the truth-functional interpretation will be transparent to speakers: it does not grant them epistemic access to the truth-conditional equivalence of ‘If A, C’ and ‘Not A or C’. This non-transparent ‘synonymy’ is compared to our understanding in cases of vagueness.
A Different Study of Various Methods based on Underwater image Restoration: A Survey

Due to the dissipated lighting and light assimilated conditions, the underwater pictures frequently experienced the low difference just as the color immersion. Since underwater pictures with similar tones of shading are recorded under various light conditions, it is hard to reestablish and improve those pictures. There are various ordinary techniques answered to improve the nature of submerged pictures, however they perform inadequately constantly. The underwater image can be prepared in the form of a perfect picture and a mixture of background light, in which the relative volume of each camera is deeply fixed. The researches mainly founded on improving the value of picture and also focused on reducing the noise. In the past few years, many approaches were put forwarded to progress the visibility of underwater images (UI) by removing haze and some color correction techniques are introduced to improve the perception of UIs. This paper is a review on various approaches for underwater image restoration over the last few years.
Психолого-педагогические проблемы адаптивной физической культуры и спорта и геронтогогика

The relevance of the research carried out is connected with the attempt of a holistic understanding of the problems of adaptive physical culture and sports in the context of developing and implementing technologies and programs and accompanying the elderly and old people in the context of geragogics (gerontogogics).The purpose of the study is to explore the possibilities and problems of adaptive physical culture and sports in working with older and old people in the context of geragogy.
Pola Pencarian Data Investigasi di Media Online

Abstract: In investigative journalism work, there are three general forms of investigative methods, namely, related to the work of investigating documents, as well as investigations of individual subjects related to the problem. This general form of investigative reportage is termed a paper trail, people trail, and money trail. Tempo Magazine is a media that is considered to have competence and credibility for investigative coverage. Tempo magazine is a pioneer of investigative journalism in the period after 1990. In addition, the internet media has opened up information expansion based on a multidimensional network. A number of platforms on the internet have emerged that can attract people's attention. Like it or not, the print media must also adapt their work to the needs of the public. In the end, the print media also experienced convergence. In the world of journalism, online journalism was born. This study aims to determine why and how investigative data search patterns in online media. This study uses a qualitative method with a case study approach. In this case the researcher tried to find out the pattern of investigative data collection in the online media through investigative news entitled Pesta Terakhir Aktivis Ambon conducted by Tempo.co and investigative news entitled Emas Hitam Mahakam dalam Bidikan KPK conducted by Katadata.co.id . The result of this research is that investigative journalism work in online media is basically the same as investigative journalism work in print media. The only difference is that there are more readers in online media. Especially in reporting done on online media, the writer can add digital content that can help explain this information Keyword: Investigative Journalism, Online Media, Investigation Material.
Face Liveness Detection

Face recognition systems are becoming more prevalent than ever. From face recognition on your iPhone/smartphone, to face recognition for mass surveillance, face recognition systems are being utilized everywhere. However, face recognition systems are easily fooled by “spoofing” and “non-real” faces. Face recognition systems can be circumvented simply by holding up a photo of a person (whether printed, on a smartphone, etc.) to the face recognition camera. In order to make face recognition systems more secure, we need to be able to detect such fake/non-real faces — liveness detection is the term used to refer to such algorithms. In order to create the liveness detector, we’ll be training a deep neural network capable of distinguishing between real versus fake faces and then create a Python + OpenCV script capable of taking our trained liveness detector model and apply it to real-time video.
Persepsi Guru tentang Pemahaman Indikator Standar Pembiayaan Sekolah Menengah Atas

This research was conducted to determine the extent of teachers' perceptions of the standard indicators of financing for high school education at the National Education Standards (SNP). This study used descriptive qualitative method. The population was 10 respondents, divided into 5 Islamic education teacher respondents and 5 Non-Islamic Islamic education teacher respondents from several schools in Purwakarta Regency. The instrument used was to use a questionnaire about understanding the standard of financing, which amounted to 16 indicators, then given triangulation through direct interviews to find out the causes and solutions to problems related to the achievement of financing standards. The results of this study indicate that the achievement of indicators is at an average of 82% in the Non-Islamic Education group and 79% in the Islamic Education group. Several weaknesses were found regarding the standard indicators of financing, among others in the aspect of the realization of the cost of periodic maintenance and repairs and the aspect of reporting and delivering financial accountability. Based on the findings in the field, the researchers have recommendations for schools and related parties to pay more attention to the stages of education financing budgeting, especially during the planning, realization, and reporting stages and submission of financing reports.