Reviewerly/exHarmony huggingface dataset:
Historical corpora meet the digital humanities: the Jerusalem Corpus of Emergent Modern Hebrew

The paper describes the creation of the first open access multi-genre historical corpus of Emergent Modern Hebrew, made possible by implementation of digital humanities methods in the process of corpus curation, encoding, and dissemination. Corpus contents originate in the Ben-Yehuda Project, an open access repository of Hebrew literature online, and in digital images curated from the collections of the National Library of Israel, a selection of which have been transcribed through a dedicated crowdsourcing task that feeds back into the library’s online catalog. Texts in the corpus are encoded following best practices in the digital humanities, including markup of metadata that enables time-sensitive research, linguistic and other, of the corpus. Evaluation of morphological analysis based on Modern Hebrew language models is shown to distinguish between genres in the historical variety, highlighting the importance of ephemeral materials for linguistic research and for potential collaboration with libraries and cultural institutions in the process of corpus creation. We demonstrate the use of the corpus in diachronic linguistic research and suggest ways in which the association it provides between digital images and texts can be used to support automatic language processing and to enhance resources in the digital humanities.
Knowledge Construction Through Semantic Interpretation of Visual Information

The skills required by machines in the last decade have grown exponentially. Recent efforts made by the scientific community have shown amazing results in the field of research related to artificial intelligence and robotics. Recent studies show that machines may be superior to humans in carrying out certain tasks. However, in many approaches they still fail to achieve high-level skills required to support humans and interact with them. Furthermore, the “intelligence” exhibited is hardly ever the result of a real autonomous decision. In this article we propose a novel approach, with the aim of providing a machine with the ability to evolve and build its own knowledge by combining both semantic and visual information. The proposed concept, its implementation and experimental results are shown and discussed.
A New Approach Towards the Combined Algorithm Selection and Hyper-parameter Optimization Problem

Machine learning algorithms often have many hyper-parameters that can be tuned to improve empirical performance. However, manually exploring the complex search spaces is tedious and cannot guarantee to find satisfactory outcomes. Recently, the Efficient Global Optimization (EGO) algorithm for solving the hyper-parameter optimization problem have shown substantial improvements. However, these algorithms cannot easily be expanded to include the selection of an algorithm. Automatically determining and optimizing an algorithm is known as the combined algorithm selection and hyper-parameter optimization (CASH) problem. In this paper, a novel mixed integer efficient global optimization algorithm and its variants are proposed to solve the CASH problem efficiently. The proposed algorithm is compared with a wide set of state-of-the-art algorithms in both a Black Box Optimization and a CASH problem setting. For the CASH problem setup, seven machine learning algorithms are optimized for a large set of classification t asks. Results show that the proposed algorithms outperform on the black box optimization task and can also outperform alternative state-of-the-art approaches on the CASH problem for specific instances.
Agent-based cooperative animation for box-manipulation using reinforcement learning

This paper presents an approach to assist the generation of agent-based cooperative animation using reinforcement learning. We focus on manipulation skills for box-shaped objects, including pushing, pulling, and moving objects in a relay way. There are a learning process and an animation process. In the learning process, different kinds of agents are trained using reinforcement learning. Policies are learned to control the agents to perform specific tasks. A physics simulator is adopted to simulate the interaction among objects. In the animation process, users animate agents with the learned policies. We propose several tools to intuitively create cooperative animations. We applied our method to generate several animations in which agents work together to finish tasks. A user study indicates that by using our tools, diverse cooperative animations can be easily created.
A Co-model for Research Through Co-design

The ‘collaborative model’ (co-model) is a modeling of the Research Through Co-design (RTC) that is based on a new methodology that uses the control system theory as an intuitive language for defining RTC as an essential key. Closed-loop control system theory is discussed and used to identify the most complex parts of the RTC system such as the human-collaborative approaches and processes during the development of the research question. This collaborative model improves the state-of-art in RTC ensuring quality and efficiency for the knowledge acquisition. This allows practitioners, researchers, human factor experts doing RTC using a collaborative model and co-logical aids to find the desirable research answer.
Path Planning Based on ADFA* Algorithm for Quadruped Robot

At present, the path-planning algorithm based on the grid map is generally adopted in the field of quadruped robot and the obtained environmental information is represented by a standardized grid map.In this paper, the ADFA * algorithm introduces a dilation factor based on the DFA * to solve the path planning problem under the constraint of computing time and provide a path search result related to the time limit.Path-planning algorithms based on raster maps often equate robots with particles, causing problems, such as path blocking.The FA * algorithm adds raster tolerance to expand obstacles.DFA * uses a path-splitting approach that, such as the DA * algorithm, has better dynamic environment processing capabilities than the FA * .However, during the actual operation of the robot, the environmental information acquired is extremely frequent due to its instability.The robot itself is often in a relatively static state.Therefore, compared with obtaining the shortest path, it is more practical to improve the path search efficiency under dynamic map environment.ADFA * will gradually optimize the path and eventually get the optimal solution when time is sufficient.When the time is limited, ADFA * will search for the current optimal solution under the specified search time but may not be able to obtain the shortest path, which is called the second best solution.
Applying Answer Set Programming for Knowledge-Based Link Prediction on Social Interaction Networks

Link prediction targets the prediction of possible future links in a social network, i.e., we aim to predict the next most likely links of the network given the current state. However, predicting the future solely based on (scarce) historic data is often challenging. In this paper, we investigate, if we can make use of additional (domain) knowledge to tackle this problem. For this purpose, we apply answer set programming (ASP) for formalizing the domain knowledge for social network (and graph) analysis. In particular, we investigate link prediction via ASP based on node proximity and its enhancement with background knowledge, in order to test intuitions that common features, e.g., a common educational background of students, imply common interests. In addition, then the applied ASP formalism enables explanation-aware prediction approaches.
A natural language-inspired multilabel video streaming source identification method based on deep neural networks

This paper presents a deep-learning based traffic classification method for identifying multiple streaming video sources at the same time within an encrypted tunnel. The work defines a novel feature inspired by Natural Language Processing (NLP) that allows existing NLP techniques to help the traffic classification. The feature extraction method is described, and a large dataset containing video streaming and web traffic is created to verify its effectiveness. Results are obtained by applying several NLP methods to show that the proposed method performs well on both binary and multilabel traffic classification problems. We also show the ability to achieve zero-shot learning with the proposed method.
Review on Lossless Compression Techniques

Abstract In the present world data compression is used in every field. Through data compression the bits required to represent a message will be reduced. By compressing the given data, we can save the storage capacity, files are transferred at high speed, storage hardware is decreased so that its cost is also decreased, and storage bandwidth is decreased. There are many methods to compress the data. But in this paper, we are discussing about Huffman coding and Arithmetic coding. For various input streams we are comparing adaptive Huffman coding and arithmetic coding and we will observe which technique will be more efficient to compress the data.
Deep Residual Network-Based Recognition of Finger Wrinkles Using Smartphone Camera

Iris, fingerprint, and three-dimensional face recognition technologies used in mobile devices face obstacles owing to price and size restrictions by additional cameras, lighting, and sensors. As an alternative, two-dimensional face recognition based on the built-in visible-light camera of mobile devices has been widely used. However, face recognition performance is greatly influenced by the factors, such as facial expression, illumination, and pose changes. Considering these limitations, researchers have studied palmprint, touchless fingerprint, and finger-knuckle-print recognition using the built-in visible light camera. However, these techniques reduce user convenience because of the difficulty in positioning a palm or fingers on the camera. To consider these issues, we propose a biometric system based on a finger-wrinkle image acquired by the visible-light camera of a smartphone. A deep residual network is used to address the degradation of recognition performance caused by misalignment and illumination variation occurring during image acquisition. Owing to the unavailability of the finger-wrinkle open database obtained by smartphone camera, we built the Dongguk finger-wrinkle database, including the images from 33 people. The results show that the recognition performance by our method exceeds in those of conventional methods.
Timbre as Harmony—Harmony as Timbre

Abstract While timbre is typically understood as a property of a single musical note or event, many contemporary musical practices depend on the combination of multiple events—each with their own pitch, dynamic, and sound color—into unified composites with their own emergent timbres. Such composites are essential for composers of spectral music such as Gérard Grisey and Tristan Murail, but also can be heard in works by composers from Arnold Schoenberg to James Tenney. Viewed from the perspective of music psychology, these composites are “chimeric” percepts, thwarting the usual parsing of sonic input into separate sources through auditory scene analysis. When timbre is redefined to include composite events, it overlaps significantly with the discipline of harmony; many musical effects—textural sound-masses, synthesized acoustic spectra, virtual ring modulation, etc.—blur the lines between timbre and harmony, opening up a hybrid space between the two domains.
A Deep Learning Based Fast-Flux and CDN Domain Names Recognition Method

Fast-Flux(FF) service network is one of the common technical means for network malicious behavior (such as phishing websites, spam, botnets, etc.) to avoid attack. Through the rapid change of answered IP addresses of the required domain name, Fast-Flux can avoid being isolated by IP blacklist and improve the availability of its services. Due to the use of Content Distribution Network (CDN) and circular DNS technology, some normal network services have similar characteristics. Differentiation between FF domain name and CDN domain name is one of the key problems in FF domain name detection. For the problems that manual extraction of FF features is complex and is easy to be deliberately bypass by the FF designer, this paper proposes a method called Fast-flux and CDN Domains Recognizer (FCDR) to self-learning FF and CDN features and recognition them based on LSTM network. By learning the domain names' resolved results collected at different times in different areas, the FCDR can classify and identify Fast Flux domain name, CDN domain name, other malicious domain names and common non-CDN domain name more accurately.
Enhanced Data Encryption Standard (DES) Algorithm based on Filtering and Striding Techniques

Data Encryption Standard (DES) is the most widely used symmetric-key algorithm for encryption algorithm. Different attacks like brute force and cryptanalysis attack can be used to break DES due to its small key size and simple and uniform distribution XOR operation. With the issues mentioned above, this paper proposes modification to enhance DES against these various attacks. The proposed enhancement used f-function by incorporating striding techniques instead of just performing the XOR operation between 56- bit sub key and the plaintext. The security against the known exhaustive attack and cryptanalysis attack was enhanced. The enhanced DES have been evaluated and compared against original DES using avalanche effect. Enhanced DES resulted to an average of avalanche effect of 55% due to one-bit vibration in plaintext.
Gecko: A Resilient Dispersal Scheme for Multi-Cloud Storage

We have entered an era where copious amounts of sensitive data are being stored in the cloud. To meet the rising privacy, reliability, and verifiability needs, we propose Gecko, a multi-cloud dispersal scheme where: (a) the key used to encrypt the data file is the secret in a Latin-square-autotopism secret-sharing scheme, (b) data files and encryption keys are dispersed separately to multiple clouds, and (c) a blockchain-based integrity-check protocol is devised to pinpoint faulty data. Gecko enables fast and thorough key renewal: when a portion of the key (the secret) is leaked, we replace all shares of the partially-leaked secret without replacing the secret itself; this immediately resists targeted attack to certain file without re-encrypting the data file itself. Key renewal is further accelerated by the blockchain-based integrity check. We evaluate Gecko theoretically and experimentally against the traditional AONT-RS dispersal scheme, drawing two conclusions: 1) Gecko admits powerful key renewal and identification of damaged data, with a minor transfer overhead; and 2) Gecko performs key renewal three to five times faster than AONT-RS hybrid-slice renewal (the closest thing AONT-RS has to key renewal).
Hybrid Encryption using Confused and Stream Cipher to Improved Medical Images Security

Abstract The medical image is an image that needs to be protected because it is private and confidential. The more modern technology enables tele surgery, tele diagnosis, and others to demand safe and confidential medical images. This research proposes cryptography method in a medical image by using the combination algorithm of Arnold chaotic map (ACM) and Rivest Cipher 4 (RC4). ACM is a chaotic cryptographic technique superior to attack deferential attack and brute force. The way chaotic cryptography works is to confuse the pixel location. While RC4 is a stream cipher technique that has resistance to statistical attacks and fasts in computing. The purpose of merging these two algorithms is to get stronger encryption techniques, have good performance, and can be computed quickly. Based on the experimental results proved that the use of a combination of these two algorithms can produce satisfactory quality encryption. This is evidenced by several measuring tools such as SSIM, Correlation Coefficient, Entropy, NPCR, UACI, and histogram analysis. Computation time required is also relatively very fast ie less than one second, where computational time is calculated by tic toc function.
Behind the Scenes

Youth content creators have been under-researched on video platforms such as Snapchat, YouTube, Instagram, etc. In order to better understand the ways that technology could support youth as content creators we conducted a 30-week series of workshops with an intergenerational design team including pre-teen youth and undergraduate designers-in-training. Qualitative analysis of workshop videos and notes revealed underlying challenges in video creation related to the social dynamics of creating videos in groups. In line with previous work, we note that a primary motivation for using social media is to spend time with friends. We found that existing video technology reveals opportunities and challenges to support the collaboration and interaction that motivates youth creators. We offer illustrative designs of potential collaborations in video sharing technology and propose that some of the concerns of youth social media use could be addressed by incorporating more opportunities for social creation.
Larping (Live Action Role Playing) as an Embodied Design Research Method

Embodied design methods are gaining popularity among design researchers. They leverage the physical and situated experience of designers to access and better understand present and future situations, humans, and design opportunities. Here, we propose a workshop to learn about, engage with, and discuss larping (live action role playing) as an embodied design research method, in particular as: i) a sensitizing activity prior to design; and ii) a test-bed to investigate and further iterate design concepts and prototypes. The workshop is organized by design research experts in embodied design methods and larps, and it is aimed at those interested in embodied design methods, with or without experience with larps. Insights from the workshop will be captured in a joint article extending current embodied design methods.
Smart Assistance Library System for the Disabled : An IOT based User-Friendly Wheelchair

The smart wheelchair is designed to aid people with infirmity, comprehensive impairment have a better quality, and a more self-supporting lifestyle. The aim of this paper is to increase a typical wheelchair attached with sensors to an audio combination that interpret commands, and a mobility-control software as an outcome of the wheelchair's movement. Automated chair studies the arrangement of surrounding area (school, infirmary, healing center, houses, shopping mall etc.,) through a recited, directed journey that a person enters. Eventually, the chair will move to any formerly-updated position underneath the voice instruction (e.g., "Take ME to the cafeteria"). This automation is acceptable to those that have quality due to brain injury or the loss of limbs. The technology may enhance safety for users who use standard joystick-controlled high-powered wheelchairs, by preventing collisions with walls, mounted objects, article of furniture and people. We envision that a voice-commendable wheelchair could improve the quality of life and safety of tens of thousands of users. Moreover, considerable health improvements.
Ensemble with estimation: seeking for optimization in class noisy data

Class noise, as know as the mislabeled data in training set, can lead to poor accuracy in classification no matter what machine learning methods are used. A reasonable estimation of class noise has a significant impact on the performance of learning methods. However, the error in existing estimation is inevitable theoretically and infer the performance of optimal classifier trained on noisy data. Instead of seeking a single optimal classifier on noisy data, in this work, we use a set of weak classifiers, which are caused by negative impacts of noisy data, to learn an ensemble strong classifier which is based on the training error and estimation of class noise. By this strategy, the proposed ensemble with estimation method overcomes the gap between the estimation and true distribution of class noise. Our proposed method does not require any a priori knowledge about class noises. We prove that the optimal ensemble classifier on the noisy distribution can approximate the optimal classifier on the clean distribution when the training set grows. Comparisons with existing algorithms show that our methods outperform state-of-the-art approaches on a large number of benchmark datasets in different domains. Both the theoretical analysis and the experimental result reveal that our method can improve the performance, works well on clean data and is robust on the algorithm parameter.
Simulating the Behaviour of Choquet-Like (pre) Aggregation Functions for Image Resizing in the Pooling Layer of Deep Learning Networks

The data volume expansion has generated the need to develop efficient knowledge extraction techniques. Most problems that are processed by these techniques have complex information to be identified and use different machine learning methods, such as Convolutional and Deep Learning Network. These networks may use a variety of aggregation functions to resize images in the pooling layer. This paper presents a study of the application of aggregation functions based on the generalizations of the Choquet integral, namely, the novel Choquet-like (pre) aggregation functions, in image dimensional reduction, simulating the pooling layer of a Deep Learning Networks. This paper is the natural evolution of the initial study where only the standard Choquet integral was applied. We compare the behaviour of such functions with the usual ones used in the literature, namely, the maximum and the arithmetic mean. A quantitative evaluation is done over an image dataset by using different image quality measures to compare the results.
Exploring An Easy Way for Imbalanced Data Sets in Semantic Image Segmentation

Abstract In recent years, deep convolutional neural networks have gradually become the preferred method for image processing. After the development of Classification, Detection and Segmentation, a large variety of state-of-the-art models and algorithms have emerged in the field. However, for some specific data sets or tasks, not all methods are applicable, which is inconvenient to researchers. This paper took the data set provided in the airbus ship detection challenge in Kaggle as an example to explore an easy and effective method for segmentation tasks of data sets with class imbalance. This paper used U-Net with a pre-trained ResNets model, and tried different methods to explore the feature of the set. In the process of training ResNets, this paper proposed a new convolutional block structure which is inspired by Fibonacci sequence, but the effect is not good. In the end, the mF2 values of the models this paper trained achieved good results, which is better than the model of the combined training of ResNets and ordinary U-Net34. Moreover, the training parameters are less than that. This paper believe that this simple and effective training method will bring convenience to researchers in related fields.
HOT SWAP

HOT SWAP is a game platform based on unique physical controllers that are designed to activate the physical and social space around players. The controllers require players to keep track of and utilize multiple physical input components that must be exchanged as a crucial gameplay component. In this paper, we reflect on the design process of two iterations of HOT SWAP games and our initial observations on how they facilitate embodied interaction. The games presented in this paper are designed to probe how game interfaces can be more social, tangible, and customizable for embodied experiences.