Reviewerly/exHarmony huggingface dataset:
Rameau and the Sciences: The Impact of Scientific Discoveries of the Lumières on Rameau’s Theory of Harmony

The current Anglophone theorists criticize Jean-Philippe Rameau for the imperfection of mathematical apparatus, the problem of natural interpretation of a minor triad, and unartistic treatment of the bass line. However, if to look at this with an unbiased eye, one can notice that Rameau lived in the center of the emerging scientific culture of unprecedented magnitude and depth. Rameau’s contemporary mathematics, physics, geometry, language theory and general scientific categories should have influenced him. The method used in this presentation takes into consideration the possibility for Rameau of absorbing these ideas without directly referencing them. It is difficult to explain, otherwise, the discovery of tonal-harmonic functions by Rameau without the analogy with the introduction of the term function by Gottfried Leibniz and Johann Bernoulli. It is hard to imagine that Rameau himself put all chords and their modifications into classification in a form of magnificent double hierarchy without the innovations of Carolus Linnaeus. The order of chords in the fundamental bass progression could not have seen the light without the universal syntax of Antoine Arnauld and Pierre Nicole. The concept of physical or quasi-physical motion in music, established by Rameau, depends on discoveries of differential calculus by Leibniz and Newton. The infinite approximation of the leading tone to tonic is the manifestation of the idea of infinitesimal (inverted infinity) of Leibniz. It is time to revisit Rameau, to place him in the real context of his time and, perhaps, reevaluate the significance of his contribution to the theory and art of music..
A comparative recapitulating study of recent sentiment analysis approaches

In accordance with the convention, sentiments are defined as a thought, judgement or attitude prompted by feelings or a specific notion. With explosion of social networks, every individual/user actively manifests and share their ideas or emotions in disparate life aspects. Past few years have shown Sentiment Analysis as a pivotal activity in various systems and thus become a center of attention for the researchers. Considerable amount of research has already been done to inspect and examine user's sentiments, which are extracted either from web pages or social media, through lexicon, machine learning or deep learning techniques. This paper presents a vital contribution in this field of research through comprehensive exploration of most recent articles/studies, extremely aiming to highlight the applicability of sentiment analysis in software engineering field as well as through bio-inspired algorithms along with other modi operandi, and thereby helping practitioners to get detailed insights of some of the lately exercised feature extraction tactics, methodologies and datasets considered for the analysis of sentiment-specific studies.
Tribalism and Finance

We assess the correlations between tribalism and financial development in 60 countries using data averages from 2000-2010. The tribalism index is used to measure tribalism whereas financial development is measured from perspectives of financial intermediary and stock market developments. The long term finance variable is stock market capitalisation while the short run variable is private and domestic credit. We find that tribalism is negatively correlated with financial development and the magnitude of negativity is higher for financial intermediary development relative to stock market development. The findings are particularly relevant to African and Middle Eastern countries where the scourge of tribalism is most pronounced.
Projector and Camera Shade Detection Based on Stripe Pattern Sequence for High Quality Structured Light Depth Imaging

Structured light depth imaging offers a high precision of 3D measurement for applications to industrial modeling, inspection, reproduction and archiving. Despite recent progress, however, structured light depth imaging has yet to settle the trade-off between the number of captured 3D points and that of unwanted outliers, in particular, when a low reflectance surface is mixed with projector and camera shades. This paper presents an approach to solving such trade-off by accurately mapping projector and camera shades under the presence of a low reflectance surface. Unlike conventional approaches relying mostly on pattern intensity for detecting shades, the proposed approach first identifies stripe boundaries legitimate in terms of the projected pattern sequence. Based on the list of legitimate boundaries identified, then, the gaps in pixel and boundary addresses are analyzed to define the respective projector and camera shades, possibly, in the presence of address juxtaposition. Experimental results show that the proposed approach shows superior performance in shade detection as well as outlier removal compared to the state-of-the-art approaches, maximizing the number of captured 3D points while minimizing that of unwanted outliers.
Pivot-based Candidate Retrieval for Cross-lingual Entity Linking

Entity candidate retrieval plays a critical role in cross-lingual entity linking (XEL). In XEL, entity candidate retrieval needs to retrieve a list of plausible candidate entities from a large knowledge graph in a target language given a piece of text in a sentence or question, namely a mention, in a source language. Existing works mainly fall into two categories: lexicon-based and semantic-based approaches. The lexicon-based approach usually creates cross-lingual and mention-entity lexicons, which is effective but relies heavily on bilingual resources (e.g. inter-language links in Wikipedia). The semantic-based approach maps mentions and entities in different languages to a unified embedding space, which reduces dependence on large-scale bilingual dictionaries. However, its effectiveness is limited by the representation capacity of fixed-length vectors. In this paper, we propose a pivot-based approach which inherits the advantages of the aforementioned two approaches while avoiding their limitations. It takes an intermediary set of plausible target-language mentions as pivots to bridge the two types of gaps: cross-lingual gap and mention-entity gap. Specifically, it first converts mentions in the source language into an intermediary set of plausible mentions in the target language by cross-lingual semantic retrieval and a selective mechanism, and then retrieves candidate entities based on the generated mentions by lexical retrieval. The proposed approach only relies on a small bilingual word dictionary, and fully exploits the benefits of both lexical and semantic matching. Experimental results on two challenging cross-lingual entity linking datasets spanning over 11 languages show that the pivot-based approach outperforms both the lexicon-based and semantic-based approach by a large margin.
Numerical approach for the tuning of tubes in musical instruments

Abstract Musical instrument design is still a relatively complex task. It frequently relies on empirical and oversimplified analytical approaches. The current computation era may be coupled with these processes to generate easy using tools to aid the design and creation of musical instrument. This study uses numerical routines, based on the modal analysis of simple geometries employed in musical instruments, as a tool to help the intended instrument design. Aluminum tubes, with flexural vibrations designed to resonate in a C4 register were first tested by experimental modal analysis and these results were used as reference to validate the referred numerical approach. Overall, it is shown that the numerical results highly replicate the experimental results and may be further optimized to display an even better correlation. It is, therefore, concluded that this type of computational method may be a fast and easy tool for the tuning of musical instruments.
Dynamic Access Control Solution for Cross-Tenancy in a Cloud Environment

Data sharing in an interoperable environment enhances the productivity and quality of services in the respective organizations. The organizations these days have been moving their data and resources over the cloud. The primary reason is that the cloud provides flexibility, resource sharing, and to a limit most apt solution for Disaster Recovery (DR). When two or more organizations agree to share their cloud resources to an extent, there is a huge risk of accessing the data beyond the extent of the agreed privileges. All tenants may have different access control models implemented at their place. Practically, further the rules are generated for access control which is decided on the organizational hierarchy. In this paper, dynamic system is proposed where cross-tenancy in access control models in an interoperable environment, i.e., it is able to address the dynamic nature of the cloud access control environment in an effective manner.
Game-Theoretic Decision-Making Analysis on Antivirus

Information security decision making is important to an organization. Moreover, the methodology of decision making about information security is insufficient. In this paper, we propose game model to analyze antivirus decision making in information security. We conduct empirical analysis using an antivirus report with real data. In order to proceed with game theory, we assume a two-player game and set up the payoffs to calculate the expected payoffs and the performance of antivirus. Finally, we find an equilibrium and the optimal proportion for each information security strategy. With an optimal strategy for antivirus selection game, companies can effectively plan information security investments within a limited budget.
Assessment of Manifold Unfolding in Trained Deep Neural Network Classifiers

Research on explainable artificial intelligence has progressed remarkably in the last years. In the subfield of deep learning, considerable effort has been invested to the understanding of deep classifiers that have proven successful in case of various benchmark datasets. Within the methods focusing on geometry-based understanding of the trained models, an interesting, manifold disentanglement hypothesis has been proposed. This hypothesis, supported by quantitative evidence, suggests that the class distributions become gradually reorganized over the hidden layers towards lower inherent dimensionality and hence easier separability. In this work, we extend our results, concerning four datasets of low and medium complexity, and using three different assessment methods that provide robust consistent support for manifold untangling. In particular, our quantitative analysis supports the hypothesis that the data manifold becomes flattened, and the class distributions become better separable towards higher layers.
Increased IT security by model-based vulnerability analysis of IT/I&amp;C systems and proof of ISO conformity

This paper shows the extension of an entity model developed in cooperation with project partners by an “Organizational Entity” in order to detect potential weaknesses in IT security as well as to prove the standard conformity (IT security level according to IEC 62443) of the examined systems. This allows suitable security measures to be taken systematically and IT security to be optimized in the overall system. The first application of this progressive approach on a subsystem, the distillation, of the model factory ET of the University of Applied Sciences Magdeburg-Stendal is promising.
Detecting Post-Edited References and Their Effect on Human Evaluation

This paper provides a quick overview of possible methods how to detect that reference translations were actually created by post-editing an MT system. Two methods based on automatic metrics are presented: BLEU difference between the suspected MT and some other good MT and BLEU difference using additional references. These two methods revealed a suspicion that the WMT 2020 Czech reference is based on MT. The suspicion was confirmed in a manual analysis by finding concrete proofs of the post-editing procedure in particular sentences. Finally, a typology of post-editing changes is presented where typical errors or changes made by the post-editor or errors adopted from the MT are classified.
Indifferentiable hash functions in the standard model

Indifferentiability of iterated hash functions is seen as evidence that there are no structural flaws in the iteration structure of the algorithm. However, it is often overlooked that such considerations only hold in the random oracle model and do not give any guarantee in the standard model. In this article, we show the following separation result: there is a hash function that is indifferentiable from a random oracle, but is totally insecure in the standard model. In particular, we show that it does not satisfy collision or multicollision-resistance, second preimage-resistance or preimage-resistance for any family of compression functions. Therefore, at least in theory, hash function indifferentiability does not guarantee the structural integrity of the hash algorithm in the standard model. Results in the random oracle model are not affected.
Deep Reinforcement Learning Based on Spatial-Temporal Context for IoT Video Sensors Object Tracking

The Internet of Things (IoT) is the upcoming one of the major networking technologies. Using the IoT, different items or devices can be allowed to continuously generate, obtain, and exchange information. The new video sensor network has gradually become a research hotspot in the field of wireless sensor network, and its rich perceptual information is more conducive to the realization of target positioning and tracking function. This paper presents a novel model for IoT video sensors object tracking via deep Reinforcement Learning (RL) algorithm and spatial-temporal context learning algorithm, which provides a tracking solution to directly predict the bounding box locations of the target at every successive frame in video surveillance. Crucially, this task is tackled in an end-to-end approach. Considering the tracking task can be processed as a sequential decision-making process and historical semantic coding that is highly relevant to future decision-making information. So a recurrent convolutional neural network is adopted acting as an agent in this model, with the important insight that it can interact with the video overtime. In order to maximize tracking performance and make a great use the continuous, inter-frame correlation in the long term, this paper harnesses the power of deep reinforcement learning (RL) algorithm. Specifically, Spatial-Temporal Context learning (STC) algorithm is added into our model to achieve its tracking performance more efficiently. The tracking model proposed above demonstrates good performance in an existing tracking benchmark.
Cross-Gender and Age Speech Conversion Using Hidden Markov Model Based on Cepstral Coefficients Conversion

Animation movies often use children’s characters and they need children aged 5–10 to do a dubbing. For cost efficiency, a speech conversion can be done to support dubbing a children’s speech. To deal with it, in this research we propose the method to converting an adult’s speech to children’s speech. The contribution of this study is to design a signal processing algorithm to perform the conversion. In this study we propose a conversion method using the Hidden Markov Model (HMM) based on Cepstral Coefficients Conversion. The input is the speech of source speakers and the target speakers that using similar sentences. Features extraction, which is used is by extracted pitch (f0) and cepstral in conversion process, and the modeling method is HMM. System output is converted speech signals that has similar characteristics with target speech signal. From the testing results, the most optimal HMM parameter is using 4-state. The highest increase of cepstral Root Mean Square Error (RMSE) before conversion and after conversion is equal to 32.35% and an average 25.83% which obtained from 400 samples. Mean Opinion Score (MOS) on a scale from 1 (converted speech is very dissimilar with the target speech) to 5 (converted speech is very similar with the target speech). It resulted an average value of 2.505 in terms of similarities and has an average value of 2.805 in terms of quality which obtained from 30 respondents. The proposed method is expected to be used in the animation film industry in order to simplify and make efficient the dubbing process.
HAVS: Hardware-accelerated Shared-memory-based VPP Network Stack

The number of requests to transfer large files is increasing rapidly in web server and remote-storage scenarios, and this increase requires a higher processing capacity from the network stack. However, to fully decouple from applications, many latest userspace network stacks, such as VPP (vector packet processing) and snap, adopt a shared-memory-based solution to communicate with upper applications. During this communication, the application or network stack needs to copy data to or from shared memory queues. In our verification experiment, these multiple copy operations incur more than 50% CPU consumption and severe performance degradation when the transferred file is larger than 32 KB. This paper adopts a hardware-accelerated solution and proposes HAVS which integrates Intel I/O Acceleration Technology into the VPP network stack to achieve high-performance memory copy offloading. An asynchronous copy architecture is introduced in HAVS to free up CPU resources. Moreover, an abstract memcpy accelerator layer is constructed in HAVS to ease the use of different types of hardware accelerators and sustain high availability with a fault-tolerance mechanism. The comprehensive evaluation shows that HAVS can provide an average 50%-60% throughput improvement over the original VPP stack when accelerating the nginx and SPDK iSCSI target application.
CFILT IIT Bombay@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion using Multilingual Representation fromTransformers

With the internet becoming part and parcel of our lives, engagement in social media has increased a lot. Identifying and eliminating offensive content from social media has become of utmost priority to prevent any kind of violence. However, detecting encouraging, supportive and positive content is equally important to prevent misuse of censorship targeted to attack freedom of speech. This paper presents our system for the shared task Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI, EACL 2021. The data for this shared task is provided in English, Tamil, and Malayalam which was collected from YouTube comments. It is a multiclass classification problem where each data instance is categorized into one of the three classes: ‘Hope speech’, ‘Not hope speech’, and ‘Not in intended language’. We propose a system that employs multilingual transformer models to obtain the representation of text and classifies it into one of the three classes. We explored the use of multilingual models trained specifically for Indian languages along with generic multilingual models. Our system was ranked 2nd for English, 2nd for Malayalam, and 7th for the Tamil language in the final leader board published by organizers and obtained a weighted F1-score of 0.92, 0.84, 0.55 respectively on the hidden test dataset used for the competition. We have made our system publicly available at GitHub.
TTS-driven Embodied Conversation Avatar for UMB-SmartTV

When human-TV interaction is performed by remote controller and mobile devices only, the interactions tend to be mechanical, dreary and uninformative. To achieve more advanced interaction, and more human-human like, we introduce the virtual agent technology as a feedback interface. Verbal and co-verbal gestures are linked through complex mental processes, and although they represent different sides of the same mental process, the formulations of both are quite different. Namely, verbal information is bound by rules and grammar, whereas gestures are influenced by emotions, personality etc. In this paper a TTS-driven behavior generation system is proposed for more advanced interface used for smart IPTV platforms. The system is implemented as a distributive non-IPTV service and integrated into UMB-SmartTV in a service-oriented fashion. The behavior generation system fuses speech and gesture production models by using FSMs and HRG structures. Features for selecting the shape and alignment of co-verbal movement are based on linguistic features (that can be extracted from arbitrary input text), and prosodic features (as predicted within several processing steps in the TTS engine). At the end, the generated speech and co-verbal behavior are animated by an embodied conversational agent (ECA) engine and represented to the user within the UMB-SmarTV user interface.
Retrieving images through Content Based Fuzzy Information Retrieval-A Survey

Due to the excessive increase in visual data set on the web it has led to the fuzziness of not able to search mage based on ranking and inconsistency. The Content Based Fuzzy Information Retrieval has led to the sustainability of achieving better results through visual identification and neglecting noise. The semantic gap between similar images and tags attached to it has led to challenges that are encountered in the present world. The proposed algorithm to quantify and evaluate algorithm are studied and surveyed in this paper.
The Effect of Pulchritudinous Commitment: The Key to Promoting Global Prosperity

Commitment is an incredible instrument to success. Presently, people are highly committed to various things. Therefore, this research discovered that contemporary society can be categorized into two. The first group is the people that are appropriately committed to deserving things. The second category is those that are committed to unnecessary things. Hence, commitment to unnecessary things cannot lead to victory. It might be a waste of time. For instance, a person who only spends hours and hours watching television every day and he watches more than one should. So, this person is dedicated to inappropriately. However, the greatest commitment that creates success is to be committed usefully to relevant things. This is because all of the successful people find their destiny based on the relevant things that they are committed to. For example, the Google Chief Executive Officer Sundar Pichai said that he had got his mobile phone at the age of 12, since that time he was so fascinated and interested in technology. He also said he used to commit himself in reading about Silicon Valley. The place where many technologies were invented. Specifically, this research mentioned three logical principles on commitment: what is the meaning of pulchritudinous commitment? How to have a pulchritudinous commitment? And what is the importance of pulchritudinous commitment to promoting global prosperity? Finally, the research found that beautiful commitment helps to build intrinsic motivation, maintain positive engagement, optimize performance, uphold accomplishment and promote global prosperity.