Reviewerly/exHarmony huggingface dataset:
Multi-Feature Graph Attention Network for Cross-Modal Video-Text Retrieval

Cross-modal retrieval between videos and texts has attracted growing attention due to the rapid growth of user-generated videos on the web. To solve this problem, most approaches try to learn a joint embedding space to measure the cross-modal similarities, while paying little attention to the representation of each modality. Video is more complicated than the commonly used visual feature, since the audio and caption on the screen also contain rich information. Recently, the aggregations of multiple features in videos boost the benchmark of the video-text retrieval system. However, they usually handle each feature independently, which ignores the interchange of high-level semantic relations among these multiple features. Moreover, despite the inter-modal ranking constraint where semantically-similar texts and videos should stay closer, the modality-specific requirement, i.e. two similar videos/texts should have similar representations, is also significant. In this paper, we propose a novel Multi-Feature Graph ATtention Network (MFGATN) for cross-modal video-text retrieval. Specifically, we introduce a multi-feature graph attention module, which enriches the representation of each feature in videos with the interchange of high-level semantic information among them. Moreover, we elaborately design a novel Dual Constraint Ranking Loss (DCRL), which simultaneously considers the inter-modal ranking constraint and the intra-modal structure constraint to preserve both the cross-modal semantic similarity and the modality-specific consistency in the embedding space. Experiments on two datasets, i.e. MSR-VTT and MSVD, demonstrate that our method achieves significant performance gain compared with the state-of-the-arts.
Deep Understanding of Technical Documents: An Enhancement on Diagrams Understanding

Humans are capable of understanding the knowledge that is included in technical documents automatically by consciously combining the information that is presented in the document’s individual modalities. These modalities are mathematical formulas, charts, tables, diagram images and etc. In this paper, we significantly enhance a previously presented technical document understanding methodology 3 that emulates the way that humans also perceive information. More specifically, we make the original diagram understanding methodology adaptive to larger architectures with more complex structures and modules. The overall understanding methodology results in the generation of a Stochastic Petri-net (SPN) graph that describes the system’s functionality. Finally, we conclude with the introduction of the hierarchical association of different diagram images from the same technical document. This processing step aims to provide a holistic understanding of all illustrated diagram information.
KAJIAN JEJAK KARBON (CARBON FOOTPRINT) DI FMIPA UNIVERSITAS NEGERI SEMARANG

Human activities that produce carbon dioxide emissions and are called carbon footprints or carbon footprints have a big role in global warming. As part of its contribution to climate change mitigation efforts and environmental preservation, Faculty of Mathematics and Natural Science Universitas Negeri Semarang (FMIPA UNNES) has been actively developing conservation policies. The programs developed as an effort to mitigate climate change have been running for a long time. However, so far it is not certain how much CO2 emissions are produced and minimized by FMIPA UNNES. The purpose of this study was to analyze the amount of CO2 emissions produced in the FMIPA environment, to map the carbon footprint (CO2) produced from activities on the Unnes campus, to identify what factors influence CO2 emissions resulting from activities in FMIPA. The research was conducted from May to October 2020. The data obtained were obtained from primary data and secondary data. Primary data were obtained through field surveys, while secondary data were obtained from existing data. The carbon footprint that will be taken is obtained from electronic measurements, waste and transportation. The data instrument was converted into carbon footprint units (grams CO2-ek) using a carbon footprint calculator (Ver 2) developed by IESR. The results showed that the carbon footprint generated from campus activities at FMIPA in 2020 before the Covid-19 pandemic outbreak was 10,670.25 ton CO2-eq / month. This carbon footprint decreased during the pandemic to 4,312.27 ton CO2-eq / month. This total carbon footprint consists of scope one in the form of transportation activities by operational vehicles and use of LPG, scope two of electricity spending activities, scope three in the form of transportation activities by lecturers, staff, and students, use of paper, and waste disposal.
DatAgent

In this demonstration, we present DatAgent , an intelligent data assistant system that allows users to ask queries in natural language, and can respond in natural language as well. Moreover, the system actively guides the user using different types of recommendations and hints, and learns from user actions. We will demonstrate different exploration scenarios that show how the system and the user engage in a human-like interaction inspired by the interaction paradigm of chatbots and virtual assistants.
Practical Security Testing Of Electronic Commerce Web Applications

The availability of the internet and cheaper data tariffs made the effective use of Electronic Commerce (ecommerce) applications by the people for purchasing the daily needs and regular household items.The success of the e-commerce platforms is based on the trust and security that they maintain regarding users personal and payment data.However, the poor design and development, unnoticed mistakes in coding of the E-commerce websites and applications lead to many vulnerabilities and thereby becomes the simple target for the hackers.Along with conventional security testing methods, application dependent methods need to be applied on the ecommerce web applications which are built using various programming environments.To this end, this paper presents various possible practical security methods followed by penetration testers along with countermeasures that can be applicable for avoiding vulnerabilities in e-commerce websites.
Effects of the free evolution in the Arthurs–Kelly model of simultaneous measurement and in the retrodictive predictions of the Heisenberg uncertainty relations

The simultaneous measurement approach of Arthurs and Kelly has been a significant tool for the better understanding of the measurement process in quantum mechanics. This model considers a strong interaction Hamiltonian by discarding the free evolution part. In this work, we study the effect of the full dynamics -- taking into account the free Hamiltonian -- on the optimal limits of retrodictive and predictive accuracy of the simultaneous measurement process of position and momentum observables. To do that, we consider a minimum uncertainty Gaussian state as the system under inspection, which allows to carry out an optimal simultaneous measurement. We show that the inclusion of the free Hamiltonian induces a spreading on the probability density of the measurement setting, which increases the value of the product of the variances of the so-called retrodictive and predictive error operators, this is equivalent to a reduction in the accuracy of the measurement.
Dynamic optimization of hessian determinant image pyramid for memory‐efficient and high performance keypoint detection in SURF

With increasing demand for scale-invariant and fast object recognition, speeded up robust features (SURF) have emerged and become a widely used feature extraction algorithm in computer vision. Nevertheless, SURF still requires high memory usage and heavy computations caused by the keypoint detection procedure that produces huge image pyramid composed of many hessian determinant (HD) data for supporting the scale-invariance. Therefore, in this paper, dynamic optimization schemes of the HD image pyramid are proposed for completely removing the redundancies of the keypoint detection with keeping the original functionality of SURF without any loss. The proposed approach has shown to reduce memory usage by 45–51%, the execution time by 37–50% and the power consumption by 18–42% when compared with the keypoint detection in the original SURF algorithm.
Computer forensics and Cyber Crimes: COVID-19 perspective

Civilization, as we probably aware, is encountering the most exceedingly awful pandemic of this era. The coronavirus COVID-19 outbreak had an enormous effect on the planet and has crushed a few nations to a stop as of now. This pandemic renders people and society incredibly defenseless in all regards. There is a proof that malicious persons are misusing those vulnerabilities to further their own potential benefit. During this emergency, we as a whole depend like never before on computers, mobile phones, and Internet for working, transmission, sharing, shopping, and getting informed and in any case alleviate the effect of social distancing. During this critical time cyber security is of considerably more significance, as nature is perfect for Cyber Criminals to strike. Cyber Criminals are exploiting the dread and vulnerability encompassing the coronavirus outbreak. COVID-19 keeps spreading over the globe and Cyber Crime graph has been ascending with it. Attackers are utilizing COVID 19 as impulse to imitate the phishing attacks, mail-spams and ransomware attacks. Cyber Crime is a developing issue, yet the legal systems to investigate and effectively prosecute criminals are unclear. Legal authorities need to collaborate fully to identify, detect, examine, investigate, and prosecute the offenders and punish those who exploit the coronavirus outbreak for their personal criminal purposes. This study talks about Cyber Crime, explanations behind leading a computer forensic investigation, different features of computer forensics in detail, potential source of advanced proof, standard working strategy for cyber evidence, and the role of computer forensics in this COVID-19 pandemic.
Perfect Codes Over Induced Subgraphs of Unit Graphs of Ring of Integers Modulo n

The induced subgraph of a unit graph with vertex set as the idempotent elements of a ring R is a graph which is obtained by deleting all non idempotent elements of R. Let C be a subset of the vertex set in a graph Γ. Then C is called a perfect code if for any x, y ∈ C the union of the closed neighbourhoods of x and y gives the the vertex set and the intersection of the closed neighbourhoods of x and y gives the empty set. In this paper, the perfect codes in induced subgraphs of the unit graphs associated with the ring of integer modulo n, Zn that has the vertex set as idempotent elements of Zn are determined. The rings of integer modulo n are classified according to their induced subgraphs of the unit graphs that accept a subset of a ring Zn of different sizes as the perfect codes
Imperceptible adversarial attacks against traffic scene recognition

Adversarial examples have begun to receive widespread attention owning to their potential destructions to the most popular DNNs. They are crafted from original images by embedding well-calculated perturbations. In some cases, the perturbations are so slight that neither human eyes nor detection algorithms can notice them, and this imperceptibility makes them more covert and dangerous. For the sake of investigating the invisible dangers in the applications of traffic DNNs, we focus on imperceptible adversarial attacks on different traffic vision tasks, including traffic sign classification, lane detection and street scene recognition. We propose a universal logits map-based attack architecture against image semantic segmentation and design two targeted attack approaches on it. All the attack algorithms generate the micro-noise adversarial examples by the iterative method of C&W optimization and achieve 100% attack rate with very low distortion, among which, our experimental results indicate that the MAE (mean absolute error) of perturbation noise based on traffic sign classifier attack is as low as 0.562, and the other two algorithms based on semantic segmentation are only 1.503 and 1.574. We believe that our research on imperceptible adversarial attacks has a certain reference value to the security of DNNs applications.
Faktor dan Dampak Keterlambatan Berbicara (Speech Delay) Terhadap Perilaku Anak Studi Kasus Anak Usia 3-5 Tahun: Kajian Psikolinguistik

The purpose of this study is to reveal what factors cause children aged 3-5 years to experience a speech delay and to describe the impact of children experiencing speech delay. The method used in this research is the method of observation, naturalistic observation, and accompanied by an interview method to obtain more accurate data. Sources of data in this study include children aged 3-5 years. The results of this study indicate that the factors that influence the delay in speaking are (1) the number of languages spoken by the child, so that the child is unable to capture and classify language variations. (2) economic factors and a large number of siblings, and (3) technological factors. Furthermore, the impact that occurs due to children experiencing speech delays, namely, (1) academic achievement tends to deteriorate, (2) it is difficult to socialize, and (3) children tend to be passive.&#x0D; Keywords: Factors, Impact, Speech Delay, Psycholinguistics
New type i binary [72, 36, 12] self-dual codes from composite matrices and &lt;i&gt;R&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt; lifts

In this work, we define three composite matrices derived from group rings.We employ these composite matrices to create generator matrices of the form [In | Ω(v)], where In is the identity matrix and Ω(v) is a composite matrix and search for binary self-dual codes with parameters [36, 18, 6 or 8].We next lift these codes over the ring R 1 = F 2 + uF 2 to obtain codes whose binary images are self-dual codes with parameters [72, 36,12].Many of these codes turn out to have weight enumerators with parameters that were not known in the literature before.In particular, we find 30 new Type I binary self-dual codes with parameters [72, 36,12].
Aligning Standards Communities: Sustainable Darwin Core MIxS Interoperability

Biodiversity is increasingly being assessed using omic technologies (e.g. metagenomics or metatranscriptomics); however, the metadata generated by omic investigations is not fully harmonised with that of the broader biodiversity community. There are two major communities developing metadata standards specifications relevant to omic biodiversity data: TDWG, through its Darwin Core (DwC) standard, and the Genomic Standard Consortium (GSC), through its Minimum Information about any (x) Sequence (MIxS) checklists. To prevent these specifications leading to silos between the communities using them (e.g. INSDC: an internationally mandated database collaboration for nucleotide sequencing data [from health, biodiversity, microbiology, etc.] using the MIxS checklists; OBIS and GBIF: global biodiversity data networks using the DwC standard), there is a need to harmonise them at the level of the standards organisations themselves. To this end, we have brought together representatives from these standardisation bodies, along with representatives from established biodiversity data infrastructures, domain experts, data generators, and publishers to develop sustainable interoperability between the two specifications. Together, we have: generated a semantic mapping between the terminology used in each specification, and syntactic mapping of their associated values following the Simple Standard for Sharing Ontology Mappings (SSSOM), and created an example MIxS-DwC extension showing the incorporation of unmapped MIxS terms into a DwC-Archive. generated a semantic mapping between the terminology used in each specification, and syntactic mapping of their associated values following the Simple Standard for Sharing Ontology Mappings (SSSOM), and created an example MIxS-DwC extension showing the incorporation of unmapped MIxS terms into a DwC-Archive. To sustain these mechanisms of interoperability, we have proposed a Memorandum of Understanding between the GSC and TDWG. During our work, we also noted a number of key challenges that currently preclude interoperation between these two specifications. In this talk, we will outline the major steps we took to get here, as well as the future activities we recommend based on our outputs.
SE4SA: a deep syntactical contextualized text representation learning approach for sentiment analysis

Recently, many pre-trained text embedding models have been applied to effectively extract latent features from texts and achieve remarkable performance in various downstream tasks of sentiment analysis domain. However, these pre-trained text embedding models also encounter limitations related to the capability preserving the syntactical structure as well as the global long-range dependent relationships of words. Thus, they might fail to recognize the relevant syntactical features of words as valuable evidences for analyzing sentiment aspects. To overcome these limitations, we proposed a novel deep semantic contextual embedding technique for sentiment analysis, called as: SE4SA. Our proposed SE4SA is a multi-level text embedding model which enables to jointly exploit the long-range syntactical and sequential representations of texts. Then, these achieved rich semantic textual representations can support to have a better understanding on the sentiment aspects of the given text corpus, thereby resulting the better performance on sentiment analysis task. Extensive experiments in several benchmark datasets demonstrate the effectiveness or our proposed SE4SA model in comparing with recent state-of-the-art model.
Instantaneous Approach for Evaluating the Initial Centers in the Agricultural Databases Using K-Means Clustering Algorithm

Clustering algorithms are most probably and widely used analysis method for grouping agricultural data with high similarity. For example, one of the most widely used approaches in previous study is K-means, which is simpler, more versatile, and easier to understand and formulate. The only disadvantage of the K-means algorithm has always been that the predetermined set of cluster centres must be prepared ahead of time and provided as feedback. This paper addresses the issue of estimating cluster random centres for data segmentation and proposes a new method for locating appropriate random centres based on the frequency of attribute values. As a consequence of calculating cluster random centres, the number of iterations required to achieve optimum clusters in K-means will be reduced, as will the time required to shape the final clusters. The experimental findings show that our approach is efficient at estimating the right random cluster centres that indicate a fair separation of objects in the given database. The technique observation and comparative test results showed that the new strategy does not use present manual cluster centres, is more efficient in determining the original cluster centres, and therefore more successful in terms of time to converge the actual clusters especially in agricultural data bases.
AuthGuide: Analyzing Security, Privacy and Usability Trade-Offs in Multi-factor Authentication

Multi-factor authentication (MFA) reduces the risk of compromised credentials. However, selecting, configuring and combining different authentication factors is a challenge for both security administrators and end-users, as the configuration possibilities are large and the implications of choices on security, privacy and usability are not always well understood. This concern is further aggravated when the security administrator grants the end-user some flexibility for the selection of authentication factors, or when the latter are combined in a risk-adaptive manner. In this work, we present AuthGuide, an authentication knowledge and configuration framework that increases the awareness about these trade-offs. Additionally, it raises the level of abstraction to configure MFA for a given identity and access management (IAM) platform through a series of questions by mapping the responses onto the IAM’s workflow of authentication steps for registration and login. We implemented AuthGuide, validated it on top of the open source Keycloak IAM, and evaluated the effectiveness of our framework to analyze the security, privacy and usability trade-offs.
Restricted Unification in the DL $$\mathcal {FL}_0$$

Unification in the Description Logic (DL) \(\mathcal {FL}_0\) is known to be ExpTime-complete and of unification type zero. We investigate whether a lower complexity of the unification problem can be achieved by either syntactically restricting the role depth of concepts or semantically restricting the length of role paths in interpretations. We show that the answer to this question depends on whether the number formulating such a restriction is encoded in unary or binary: for unary coding, the complexity drops from ExpTime to PSpace. As an auxiliary result, we prove a PSpace-completeness result for a depth-restricted version of the intersection emptiness problem for deterministic root-to-frontier tree automata. Finally, we show that the unification type of \(\mathcal {FL}_0\) improves from type zero to unitary (finitary) for unification without (with) constants in the restricted setting.
Customer emotions when making an online purchase decision: Results of neuromarketing experiments

The primary task of marketing is to establish consumers' emotional connection with a product and awake emotions that motivate them to make a purchase.Deciphering buyers' emotional state when making a purchase decision allows designing effective and responsive interfaces to market and promote products online.The study aims to identify the emotions acting as predictors of consumer choice when ordering ready-made meals online.Methodologically, the research rests on marketing and neuromarketing theories of studying consumer behavior that make it possible to determine sensory modalities at different stages of the consumer-product interaction.In accordance with the methodology, marketing (cognitive assessment of events) and neuromarketing (experiments using an eye tracker, face reader and computer polygraph) research methods were applied.Data processing was carried out using SPSS 22.0 software, and the following statistical methods were utilized: the Kolmogorov-Smirnov test, the nonparametric Mann-Whitney U test and Kruskal-Wallis H test, the parametric Student's t-test, as well as correlation, variance and regression analysis.The information base includes a series of controlled neuromarketing experiments using an eye tracker, an emotion classifier and a computer polygraph, as well as the authors' algorithms and calculations.The experimental results demonstrate that virtual shelf placement and on-shelf visibility have no effect on product choice.At the stage of purchase, the prevailing stereotypical relationship to the product is of high importance.The research results show that customer choice is usually accompanied by a change in the emotional valence from disgust, fear and sadness to surprise and enjoyment.Statistically significant differences in the emotional states of men and women when making online choices were found in relation to such emotions as surprise, enjoyment, disgust and anger, and were not found in relation to sadness and fear.Positive mood, if evoked by a particular product, leads to customers preferring this product to broaden the positive experience, whereas negative emotions on the contrary encourage people to choose products incompatible with the current level of emotional arousal.