Balancing Soft and Hard Law for Business and Human Rights

In the wake of increasing corporate disasters, there has been an urgent need to address the impact of business on human rights. Yet business responsibilities for human rights are mainly voluntary and best understood as ‘soft law’. Recently, however, States have begun negotiations for an internationally binding treaty in this area, suggesting that there is a need to turn to ‘hard law’ to increase the efficacy of business and human rights (BHR) initiatives. This article argues that because soft and hard law concepts are not dichotomous, BHR governance need not become ‘hard law’ to be effective. Rather ‘hardened’ soft law instruments can be equally effective

Pairwise Discriminative Speaker Verification in the I-Vector Space

This work presents a new and efficient approach to discriminative speaker verification in the i-vector space. We illustrate the development of a linear discriminative classifier that is trained to discriminate between the hypothesis that a pair of feature vectors in a trial belong to the same speaker or to different speakers. This approach is alternative to the usual discriminative setup that discriminates between a speaker and all the other speakers. We use a discriminative classifier based on a Support Vector Machine (SVM) that is trained to estimate the parameters of a symmetric quadratic function approximating a log-likelihood ratio score without explicit modeling of the i-vector distributions as in the generative Probabilistic Linear Discriminant Analysis (PLDA) models. Training these models is feasible because it is not necessary to expand the i-vector pairs, which would be expensive or even impossible even for medium sized training sets. The results of experiments performed on the tel-tel extended core condition of the NIST 2010 Speaker Recognition Evaluation are competitive with the ones obtained by generative models, in terms of normalized Detection Cost Function and Equal Error Rate. Moreover, we show that it is possible to train a gender- independent discriminative model that achieves state-of-the-art accuracy, comparable to the one of a gender-dependent system, saving memory and execution time both in training and in testin

Investigations on vinylene carbonate. V. Immobilization of alkaline phosphatase onto LDPE films cografted with vinylene carbonate and N-vinyl-N-methylacetamide

Low-density polyethylene (LDPE) films cografted with vinylene carbonate (VCA) and N-vinyl-N-methylacetamide (VIMA) were studied as a matrix for the immobilization of the enzyme alkaline phosphatase (ALP) either by direct fixation or by inserting spacers. When water-soluble alkyldiamines such as diaminoethylene, diaminobutane, diethylenetriamine, and diaminohexane were used as spacers between the matrix and the enzyme, the surface concentration (SC) of the active ALP coupled on the matrix was increased, whereas the effect of the spacer on the SC was dependent on the length of the spacer. Bovine serum albumin (BSA) was preimmobilized onto the LDPE films to provide a better simulation of the biological environment for the enzyme, and the SC of ALP on the matrix was significantly increased by coupling ALP onto the BSA preimmobilized surfaces. Compared to native ALP, some physicochemical properties of ALP could be improved by the covalent immobilization

The Domain Mismatch Problem in the Broadcast Speaker Attribution Task

The demand of high-quality metadata for the available multimedia content requires the development of new techniques able to correctly identify more and more information, including the speaker information. The task known as speaker attribution aims at identifying all or part of the speakers in the audio under analysis. In this work, we carry out a study of the speaker attribution problem in the broadcast domain. Through our experiments, we illustrate the positive impact of diarization on the final performance. Additionally, we show the influence of the variability present in broadcast data, depicting the broadcast domain as a collection of subdomains with particular characteristics. Taking these two factors into account, we also propose alternative approximations robust against domain mismatch. These approximations include a semisupervised alternative as well as a totally unsupervised new hybrid solution fusing diarization and speaker assignment. Thanks to these two approximations, our performance is boosted around a relative 50%. The analysis has been carried out using the corpus for the Albayzín 2020 challenge, a diarization and speaker attribution evaluation working with broadcast data. These data, provided by Radio Televisión Española (RTVE), the Spanish public Radio and TV Corporation, include multiple shows and genres to analyze the impact of new speech technologies in real-world scenarios

Dissonance in Global Financial Law

This article explores whether the post-GFC global financial architecture is likely to provide efficient regulation capable of preventing a future crisis from occurring. The article starts with a brief overview of the emergence in the 1970s of global financial architecture. A thorough descriptive analysis of the post-crisis architecture follows, raising serious doubts regarding the current architecture’s ability to accomplish its goal. This analysis is performed in two stages, taking first an outsider’s perspective on the changes the architecture underwent after the crisis and moving then to the inside — the structure and contents of the architecture. Using macro-prudential methodological tools, the establishment of the Financial Stability Board is reviewed, along with three cutting edge regulations: the Basel III framework for banking, the IOSCO’s recommendation for money market funds, and the FSB’s recommendations regarding repurchase agreements. Pointing out the architecture’s perceived failure to provide stability due to severe regulatory arbitrage, the article then widens the lens to explore the implications of the above regulation. The article suggests that the current architecture encourages ‘financialisation’ and pushes the financial system and the real economy further apart. Consequently, the article raises normative concerns regarding the legal foundations of the global financial architecture, and its legitimacy

Racism, anti-racist practice and social work: articulating the teaching and learning experiences of Black social workers

In the mid 1990s a Black practice teacher programme was established in Manchester and Merseyside with the primary aim to increase the number of Black practice teachers in social work organisations, and in turn provide a supportive and encouraging learning environment for Black student social workers whilst on placement. In the north‐west of England research has been undertaken, to establish the quality of the practice teaching and student learning taking place with Black practice teachers and students. This paper is an exploration of the ideas generated within the placement process that particularly focused on the discourse of racism and ant‐racist practice. Black students and practice teachers explain their understanding of racism and anti‐racist practice within social work. From the research, the paper will critique some of the ideas concerning anti‐racism. In particular, it will question whether anti‐racist social work practice needs to be re‐evaluated in the light of a context with new migrants, asylum seekers and refugees. It will concluded, by arguing that whilst the terms anti‐racism, Black and Minority Ethnic have resonance as a form of political strategic essentialism, it is important to develop more positive representations in the future

On the Use of Deep Feedforward Neural Networks for Automatic Language Identification

In this work, we present a comprehensive study on the use of deep neural networks (DNNs) for automatic language identification (LID). Motivated by the recent success of using DNNs in acoustic modeling for speech recognition, we adapt DNNs to the problem of identifying the language in a given utterance from its short-term acoustic features. We propose two different DNN- based approaches. In the first one, the DNN acts as an end-to-end LID classifier, receiving as input the speech features and providing as output the estimated probabilities of the target languages. In the second approach, the DNN is used to extract bottleneck features that are then used as inputs for a state-of-the-art i-vector system. Experiments are conducted in two different scenarios: the complete NIST Language Recognition Evaluation dataset 2009 (LRE’09) and a subset of the Voice of America (VOA) data from LRE’09, in which all languages have the same amount of training data. Results for both datasets demonstrate that the DNN-based systems significantly outperform a state-of-art i-vector system when dealing with short-duration utterances. Furthermore, the combination of the DNN-based and the classical i-vector system leads to additional performance improvements (up to 45% of relative improvement in both EER and Cavg on 3s and 10s conditions, respectively)