Reviewerly/exHarmony huggingface dataset:
How Deep is Your Encoder: An Analysis of Features Descriptors for an Autoencoder-Based Audio-Visual Quality Metric

The development of audio-visual quality assessment models poses a number of challenges in order to obtain accurate predictions. One of these challenges is the modelling of the complex interaction that audio and visual stimuli have and how this interaction is interpreted by human users. The No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder (NAViDAd) deals with this problem from a machine learning perspective. The metric receives two sets of audio and video features descriptors and produces a low-dimensional set of features used to predict the audio-visual quality. A basic implementation of NAViDAd was able to produce accurate predictions tested with a range of different audio-visual databases. The current work performs an ablation study on the base architecture of the metric. Several modules are removed or re-trained using different configurations to have a better understanding of the metric functionality. The results presented in this study provided important feedback that allows us to understand the real capacity of the metric's architecture and eventually develop a much better audio-visual quality metric.
Hate Speech Detection: A Bird’s-Eye View

In recent years, a lot of data is being poured on social media. Due to the penetration of social media among people, a lot of people have started posting their sentiments, ideas, etc., on social media. These posts can be facts or personal emotions. In this paper, we introduce the concept of hate speech and discuss how it differs from non-hate speeches. The concept of hate speech is very old; however, posting them on social media needs special attention. We have reviewed several techniques and approaches to identify hate speech from textual data with a focus on micro-blogs. Since the notion of hate speech is quite personal, we feel that better IR systems are required to identify hate speech and delete build the systems that are capable to delete the content automatically from social media.
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems

The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and racial or ethnic populations. This varying degree of performance could impact users’ trust in the system and may pose legal and ethical issues in domains where fairness and equity are critical concerns, like job recommendation. In this paper, we investigate several potential factors that could be associated with discriminatory performance of a recommendation algorithm for women versus men. We specifically study several characteristics of user profiles and analyze their possible associations with disparate behavior of the system towards different genders. These characteristics include the anomaly in rating behavior, the entropy of users’ profiles, and the users’ profile size. Our experimental results on a public dataset using four recommendation algorithms show that, based on all the three mentioned factors, women get less accurate recommendations than men indicating an unfair nature of recommendation algorithms across genders.
Agents are Dead. Long live Agents

In recent years, the future of agent research has often been dis-cussed. Most prominent is the issue whether agents should be seen as a conceptual framework or as a software development paradigm.At the same time, developments on AI seem to have taken the field into a new direction. In this paper we argue that in order for agents research to create added value for actual, real problems in the world we need to reconsider possible agent architectures and their strengths and weaknesses, their overlaps and commonalities. Finally we present a first sketch of an architecture for such agents.
Neighborhood Growth Determines Geometric Priors for Relational Representation Learning

The problem of identifying geometric structure in heterogeneous, high-dimensional data is a cornerstone of representation learning. While there exists a large body of literature on the embeddability of canonical graphs, such as lattices or trees, the heterogeneity of the relational data typically encountered in practice limits the applicability of these classical methods. In this paper, we propose a combinatorial approach to evaluating embeddability, i.e., to decide whether a data set is best represented in Euclidean, Hyperbolic or Spherical space. Our method analyzes nearest-neighbor structures and local neighborhood growth rates to identify the geometric priors of suitable embedding spaces. For canonical graphs, the algorithm's prediction provably matches classical results. As for large, heterogeneous graphs, we introduce an efficiently computable statistic that approximates the algorithm's decision rule. We validate our method over a range of benchmark data sets and compare with recently published optimization-based embeddability methods.
AN ANALYSIS OF STUDENTS’ GRAMMATICAL ERRORS IN WRITING ENGLISH TEXT IN THE SECOND GRADE STUDENTS OF SMK-SMTI BANDA ACEH

Writing has been a challenging skill for EFL or ESL Students to cope with. It needs not only the ability to compose a good piece of writing but also goes on a deep layer called grammatical aspect. Understanding grammar is vital to construct a good sentence. Therefore, this study aims at finding out the types of grammatical errors produced by students while composing the English text. Ten students (10) in the second grade of SMK-SMTI Banda Aceh were selected as participants in this study. They were given a task to compose English texts. Then those 10 students writing sheets were analyzed to find out the grammatical errors made. The results revealed that there were 11 categories of grammatical error types; error in using verb, tense, singular/plural noun, adjectives, preposition, to be, articles, modal/auxiliary, noun, objective pronoun, and phrasal verb with the total of grammatical made are 48 total numbers of errors that students produced in their writing sheet. It can be concluded that these 10 students still cannot compose a good sentence because they still made errors in using the grammatical rule.
Evald Ilyenkov's Marxian Critique of Epistemology and Education

Based on the concepts of “activity” and the “ideal,” Ilyenkov's critique of epistemology follows the Marxian conceptualization of knowledge as the product of the metabolic relation between human and social nature. Accordingly, one specificity of the human being is her “object-oriented activity,” which in essence means changing the world. Ilyenkov criticizes views that reduce knowledge to a system of propositions. This conceptualization of knowledge produces not critical thinkers but automatons, and is determined by the logic and needs of capital. “Critical pedagogy,” e.g., that of Paulo Freire, fails to provide a genuine alternative to capitalist education, as it considers knowledge a mental entity, ignoring the fact that the education system is an integral part of capitalist relations of production. Communist reorganization of the education system should aim at providing every individual with access to the totality of human cultural wealth, artefacts, and means, along with the capability to deploy them.
Fast and effective techniques for T-count reduction via spider nest identities

In fault-tolerant quantum computing systems, realising (approximately) universal quantum computation is usually described in terms of realising Clifford+T operations, which is to say a circuit of CNOT, Hadamard, and $\pi/2$-phase rotations, together with T operations ($\pi/4$-phase rotations). For many error correcting codes, fault-tolerant realisations of Clifford operations are significantly less resource-intensive than those of T gates, which motivates finding ways to realise the same transformation involving T-count (the number of T gates involved) which is as low as possible. Investigations into this problem [arXiv:1206.0758, 1303.2042, 1308.4134, 1601.07363, 1606.01904, 1701.00140] has led to observations that this problem is closely related to NP-hard tensor decomposition problems [arXiv:1712.01557] and is tantamount to the difficult problem of decoding exponentially long Reed-Muller codes [arXiv:1601.07363]. This problem then presents itself as one for which must be content in practise with approximate optimisation, in which one develops an array of tactics to be deployed through some pragmatic strategy. In this vein, we describe techniques to reduce the T-count, based on the effective application of spider nest identities: easily recognised products of parity-phase operations which are equivalent to the identity operation. We demonstrate the effectiveness of such techniques by obtaining improvements in the T-counts of a number of circuits, in run-times which are typically less than the time required to make a fresh cup of coffee.
Time-Space Tradeoffs for Finding a Long Common Substring

We consider the problem of finding, given two documents of total length $n$, a longest string occurring as a substring of both documents. This problem, known as the Longest Common Substring (LCS) problem, has a classic $O(n)$-time solution dating back to the discovery of suffix trees (Weiner, 1973) and their efficient construction for integer alphabets (Farach-Colton, 1997). However, these solutions require $\Theta(n)$ space, which is prohibitive in many applications. To address this issue, Starikovskaya and Vildhoj (CPM 2013) showed that for $n^{2/3} \le s \le n^{1-o(1)}$, the LCS problem can be solved in $O(s)$ space and $O(\frac{n^2}{s})$ time. Kociumaka et al. (ESA 2014) generalized this tradeoff to $1 \leq s \leq n$, thus providing a smooth time-space tradeoff from constant to linear space. In this paper, we obtain a significant speed-up for instances where the length $L$ of the sought LCS is large. For $1 \leq s \leq n$, we show that the LCS problem can be solved in $O(s)$ space and $\tilde{O}(\frac{n^2}{L\cdot s}+n)$ time. The result is based on techniques originating from the LCS with Mismatches problem (Flouri et al., 2015; Charalampopoulos et al., CPM 2018), on space-efficient locally consistent parsing (Birenzwige et al., SODA 2020), and on the structure of maximal repetitions (runs) in the input documents.
Robust Malicious Domain Detection

Malicious domains are increasingly common and pose a severe cybersecurity threat. Specifically, many types of current cyber attacks use URLs for attack communications (e.g., C&C, phishing, and spear-phishing). Despite the continuous progress in detecting these attacks, many alarming problems remain open, such as the weak spots of the defense mechanisms. Because ML has become one of the most prominent methods of malware detection, we propose a robust feature selection mechanism that results in malicious domain detection models that are resistant to black-box evasion attacks. This paper makes two main contributions. Our mechanism exhibits high performance based on data collected from ~5000 benign active URLs and ~1350 malicious active (attacks) URLs. We also provide an analysis of robust feature selection based on widely used features in the literature. Note that even though we cut the feature set dimensional space in half (from nine to four features), we still improve the performance of the classifier (an increase in the model’s F1-score from 92.92% to 95.81%). The fact that our models are robust to malicious perturbations but are also useful for clean data demonstrates the effectiveness of constructing a model that is solely trained on robust features.
Anomaly Detection in Database using BAT algorithm

Intrusion detection is a very effective mechanism to deal with threats and challenges in database security. The rapid development of the usage of the Internet, E-commerce and cashless transactions has raised the need for high-security mechanism such as Smart Intrusion detection system as traditional detection system not always handle and compete with newly intelligent attacks. Thus, the application of machine learning/ data mining comes to play to identify such unusual attacks in an efficient manner. In this paper, we propose a database intrusion detection system based on the concept of outlier detection which is a derived concept of data mining. We deploy Bat algorithm, a swarm intelligence technique in order to build the intrusion detection model. The performance of the approach is then measured by feeding with an biometric dataset and achieved promising results.
Model-Targeted Poisoning Attacks: Provable Convergence and Certified Bounds.

Machine learning systems that rely on training data collected from untrusted sources are vulnerable to poisoning attacks, in which adversaries controlling some of the collected data are able to induce a corrupted model. In this paper, we consider poisoning attacks where there is an adversary who has a particular target classifier in mind and hopes to induce a classifier close to that target by adding as few poisoning points as possible. We propose an efficient poisoning attack based on online convex optimization. Unlike previous model-targeted poisoning attacks, our attack comes with provable convergence to any achievable target classifier. The distance from the induced classifier to the target classifier is inversely proportional to the square root of the number of poisoning points. We also provide a certified lower bound on the minimum number of poisoning points needed to achieve a given target classifier. We report on experiments showing our attack has performance that is similar to or better than the state-of-the-art attacks in terms of attack success rate and distance to the target model, while providing the advantages of provable convergence, and the efficiency benefits associated with being an online attack that can determine near-optimal poisoning points incrementally.
A New Non-Full Rank Algorithm for the IMC-Derived d-Step MIMO Structures in the Pole-Free State Space

In this paper, the powerful issue of a non-full rank inverse model control investigation is provided. It is broadly known, that the inverse-based methodology is associated with the full-rank control systems only. However, following the recently obtained authors' results in this matter, it should be concluded, that for single-delayed non-full rank state-space plants, the inverse model control-related perfect control expression can also be established. It is shown here, that for all right- and left-oriented multivariable LTI non-full rank systems, including the square ones, governed by the discrete-time state-space domain arranging the zero-reference value, the maximum-speed pole-free instance of such inverse model control strategy can be achieved. Thus, the new non-full rank algorithm does not coincide with the z-transfer-function scenario, which additionally sounds the intriguing peculiarity. The innovative content of the manuscript, that has never been seen before, is strongly supported by the numerical examples. Henceforth, the presented methodology covers the entire set of LTI MIMO state-space-oriented plants in the discrete-time domain, which is also a consequence of conducted research investigation in the past.
Handling governance of out of school children in Parereja village in supporting back to school movement

Abstract The large number of out-of-school children in Brebes district is handled by Back to School Movement (GKB) program. The program is implemented as a participation program for all stakeholders, district government, regional education organization, school principals, village heads, community organizations, and other society elements. How is Parereja village’s role in supporting GKB program? This study is a case study, which researchers were as instruments and directly involved in the respondents’ activities. The role of Parereja village in succeeding GKB was indicated by the description of series of activities in preparing it returning out of school children in their areas. Under the coordination of village head, Education Care Community Forum (FMPP) was having collaboration with existing community organizations. The series of activities included verifying and validating data for out-of-school children, continuing advocacy to out-of-school children and their families carried out by children’s forum with a peer approach. In conclusion, the role of Parereja village with village FMPP together with partners was the key to the success of GKB program in Brebes district. Out-of-school children of Parereja village that joined PKBM were more than in formal schools. There were 76% successive additions and 35% from the previous year.
Personalized Recommender Systems with Multi-source Data

Pervasive applications of personalized recommendation models aim to seek a targeted advertising strategy for business development and to provide customers with personalized suggestions for products or services based on their personal experience. Conventional approaches to recommender systems, such as Collaborative Filtering (CF), use direct user ratings without considering latent features. To overcome such a limitation, we develop a recommendation strategy based on the so-called heterogeneous information networks. This method can combine two or multiple sources datasets and thus can reveal more latent associations/features between items. Compared with the well-known ‘k Nearest Neighborhood’ model and ‘Singular Value Decomposition’ approach, the new method produces a substantial higher accuracy under the commonly used measurement which is mean absolute deviation.
Joint Unsupervised Learning of Depth, Pose, Ground Normal Vector and Ground Segmentation by a Monocular Camera Sensor

We propose a completely unsupervised approach to simultaneously estimate scene depth, ego-pose, ground segmentation and ground normal vector from only monocular RGB video sequences. In our approach, estimation for different scene structures can mutually benefit each other by the joint optimization. Specifically, we use the mutual information loss to pre-train the ground segmentation network and before adding the corresponding self-learning label obtained by a geometric method. By using the static nature of the ground and its normal vector, the scene depth and ego-motion can be efficiently learned by the self-supervised learning procedure. Extensive experimental results on both Cityscapes and KITTI benchmark demonstrate the significant improvement on the estimation accuracy for both scene depth and ego-pose by our approach. We also achieve an average error of about 3 ∘ for estimated ground normal vectors. By deploying our proposed geometric constraints, the IOU accuracy of unsupervised ground segmentation is increased by 35% on the Cityscapes dataset.
Probabilistic Approach to Physical Object Disentangling

Physically disentangling entangled objects from each other is a problem encountered in waste segregation or in any task that requires disassembly of structures. Often there are no object models, and especially with cluttered irregularly shaped objects, the robot cannot create a model of the scene due to occlusion. One of our key insights is that based on previous sensory input we are only interested in moving an object out of the disentanglement around obstacles. That is, we only need to know where the robot can successfully move in order to plan the disentangling. Due to the uncertainty we integrate information about blocked movements into a probability map. The map defines the probability of the robot successfully moving to a specific configuration. Using as cost the failure probability of a sequence of movements we can then plan and execute disentangling iteratively. Since our approach circumvents only previously encountered obstacles, new movements will yield information about unknown obstacles that block movement until the robot has learned to circumvent all obstacles and disentangling succeeds. In the experiments, we use a special probabilistic version of the Rapidly exploring Random Tree (RRT) algorithm for planning and demonstrate successful disentanglement of objects both in 2-D and 3-D simulation, and, on a KUKA LBR 7-DOF robot. Moreover, our approach outperforms baseline methods.
Designing augmented reality-based mathematics mobile apps for outdoor mathematics learning

Abstract Augmented reality, one of the current technologies, can make a virtual object on the real-life environment, and it is suitable for the space geometry learning. The purpose of this research is to design an Augmented Reality-based mathematics mobile phone application for outdoor mathematics learning about space geometry. This study used a design research method consisting of the design and implementation stages. The focus of this paper was on the designing stage. The Preliminary analysis in school showed that mathematics learning used instructional media frequently, and the school environment had some real-life objects similar to the space geometry objects. The app design of the user interface was made in order to make it easy to use, the 3D model was designed according to the scientific learning to help students to illustrate mathematically the real problems faced. The outdoor mathematics learning was designed by making some exercises that used the objects around the school. The results of the feasibility test of the product indicated that the application was suitable for the use in outdoor mathematics learning in schools.
Design of smart laboratory management system based on cloud computing and internet of things technology

Abstract In view of the backward and low efficiency problems of laboratory instruments and equipment management and laboratory environmental monitoring in Colleges and universities, by analyzing the traditional management mode and method of the laboratory, based on cloud computing platform of Sichuan University of Science &amp; Engineering and radio frequency identification, wireless sensor network, Internet and other technologies, the construction scheme of the laboratory instrument and equipment management platform was designed. The system architecture, system topology, software functions have been designed. The system is applied to the management of scientific research and teaching laboratories in Colleges and universities, improving the efficiency of supervision and utilization of equipment, and precisely maintaining the life cycle of equipment.
An Analysis on the Experience of Fisheries and Maritime High School Teachers on NCS-based Curriculum

The purpose of this study is to provide an in-depth and comprehensive understanding of various experiences related to the instruction of NCS-based curriculum by Fisheries and Maritime high school teachers and to help prepare field-oriented improvements and institutional complements to establish an effective NCS-based curriculum for fisheries and maritime high school. The data were collected through in-depth interview of 5 teachers and data analysis was done using NVivo12 program. As a result, 303 significant statements were derived, which were grouped 51 subcategories and 15 upper categories, and then according to the flow of teachers’ experience, it was categorized into three themes: application, problem recognition, and teachers’ efforts to improve. Based on the results of the study, suggestions were made through comparison and analysis with related prior studies. The results of this study can be a toehold to help prepare for field-oriented improvement and institutional supplementation for effective NCS-based curriculum of fisheries and maritime high schools.